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[ "<title>Introduction</title>", "<p>The occurrence of instrument separation during endodontic therapy poses a challenging situation, with reported incidences ranging from 2% to 6% in investigated cases [##REF##11199757##1##]. The presence of a distinct instrument within the root canal obstructs access to the root apex during nonsurgical root canal therapy. These instruments commonly encompass a variety of types, including files, reamers, peeso reamers, Gates-Glidden drills, thermomechanical compactors for gutta-percha compaction, Lentulo spirals, or the tips of specific hand instruments such as gutta-percha spreaders or explorers [##REF##33072211##2##].</p>", "<p>Common etiologies of file separation include incorrect use, restrictions in its physical properties, insufficient access, aberrant anatomy of the root canal, and possible manufacturing flaws [##REF##33072211##2##]. If the broken piece protrudes from the root apex, it may irritate the periapex or obstruct comprehensive root canal shaping and cleaning treatments apical to the point of its separation. This is significant in endodontic therapy, impacting the final treatment outcome [##UREF##0##3##]. Therefore, it is crucial to attempt instrument retrieval or bypass before considering obturation to the level of separation or resorting to surgical intervention.</p>", "<p>The Masserann approach is unique among the many ways to remove foreign items from the root canal [##REF##17055902##4##]. With success rates as high as 55%, this method is very useful for extracting intracanal silver points, broken fragments of files, and posts [##REF##8143573##5##]. The armamentarium employed includes long, crown-cutting diamonds (Shofu Preparation Kit, Kyoto, Japan), Gates-Glidden drills (Mani Inc., Tochigi, Japan), a slow-speed, contra-angle handpiece (NSK, Japan), and the Masserann kit (Micro Mega, Besançon, France) [##REF##20142883##6##]. By severing the surrounding radicular dentin, the Masserann kit trephine burs, which are end-cutting and color-coded, progressing in size, rotate anticlockwise to liberate space in the periphery of the separated instrument’s coronal end. The extractor resembles a tube and has a stylet or plunger rod. It seals the fragment's exposed coronal end just short of the extractor's end against an internal dent when screwed within. After that, the fragment can be eliminated by rotating anticlockwise [##REF##20142883##6##]. This case report describes the successful extraction of a separated file firmly lodged in the root canal dentin of a first premolar on the right mandible.</p>" ]

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[ "<title>Discussion</title>", "<p>A significant obstacle to root canal cleaning and shaping is the separation of instruments within the canal, which prevents access to the apex. As a result, there is a danger to the endodontic treatment outcome and a decreased likelihood of successful retreatment [##REF##8143573##5##,##UREF##1##7##]. Some of the factors that influence the prognosis in these kinds of cases are the state of the root canal (vital or nonvital), the tooth's status (symptomatic or asymptomatic, with or without periapical pathology), the level of cleaning and shaping at the time of separation, and the location of the separation within the canal. Generally speaking, the prognosis is worse than with conventional endodontic therapy [##REF##11199757##1##].</p>", "<p>As a result, every attempt should be taken to retrieve or avoid using the detached instrument. The root canal’s length, curvature, and diameter of its cross-section; thickness of dentin and root morphology; the instrument's content and cutting action (counterclockwise or clockwise); and the location, length, and degree of binding of the instrument within the canal are among the variables that affect orthograde retrieval [##REF##8143573##5##].</p>", "<p>Three instrument retrieval strategies exist, including chemical, mechanical, and surgical techniques [##UREF##1##7##]. Since surgery does not necessitate a crucial amount of dentin removal, it should be considered first when the separated fragment is mostly or entirely outside the root canal [##UREF##1##7##]. Chemical methods that corrode the fractured metallic instrument with solvents such as nitric acid, sulfuric acid, iodine trichloride, hydrochloric acid, and iodine crystals [##REF##8143573##5##] or dissolve the instrument electrochemically using electrolyzed solutions of sodium chloride or fluoride [##REF##20307750##8##] are inefficient for retrieving instruments because they take a significant amount of time to dissolve the metallic instrument completely. Furthermore, because these chemical solvents are limited to the shattered instrument surface in the canal, they are regarded as unpredictable and may harm the nearby soft and hard tissues [##UREF##1##7##].</p>", "<p>There are two steps in every mechanical technique for retrieving instruments. The initial step in preparing the root canal is using ultrasonic or rotary instruments to release the broken instrument. The next step is to try to retrieve the broken instrument using ultrasonics or special equipment [##UREF##1##7##]. Mechanical methods for retrieving instruments can be broadly divided into two categories: those that use trephine bursts to penetrate the separated instrument's periphery during the preparation phase, followed by attempts to remove the instrument using equipment, and those that use ultrasonics or particular files to create a tiny space only on the fractured instrument's side during the preparation phase, followed by attempts to remove the instrument using devices or ultrasonics. Particular files and loops are included with these devices to remove the broken instruments [##UREF##1##7##]. The Canal Finder system, the EndoPuls system (EndoTechnic, San Diego, CA), and small-diameter ultrasonic tips, such as ET25 and TFRK-S, are examples of systems employing ultrasonics or specific files [##UREF##1##7##]. These devices provide a vertical movement using a handpiece and specific files, which helps bypass the separated instrument [##REF##2237106##9##]. The needle-sleeve technique, Masserann kit, Endo Extractor (Brasseler Inc., Savannah, GA), Cancellier Extractor Kit (SybronEndo, Orange, CA), and Micro-Retrieve and Repair System (Superline NIC Dental, China) are mechanical devices using trephine burs in endodontic procedures. In the preparation process, these technologies uncover the coronal region of the separated instrument using a hollow tube with a cutting end and a diameter of 0.7-2.4 mm [##UREF##1##7##].</p>", "<p>As in our case the separated instrument was present in the straight portion of the posterior teeth and most of it was present within the canal, we employed the nonsurgical mechanical method and used the Masserann kit for its retrieval. With over 30 years of experience, the Masserann kit has been used to remove damaged tools from teeth. Success rates for anterior and posterior teeth are 73% and 44%, respectively [##REF##12877265##10##]. However, because using relatively large and rigid trephines can result in removing considerable quantities of root dentin, weakening the tooth, or increasing the risk of perforation, it requires regular radiographic monitoring. It may be less effective in teeth having thin or curved roots or in apically fractured fragments [##UREF##0##3##].</p>", "<p>Notwithstanding these drawbacks, the Masserann kit works incredibly well to remove metal fillings from front teeth with robust, straight roots. The extractor's locking mechanism provides significant retention for grasping and removing firmly wedged impediments in the canal. A direct path to the fragment makes it easier for the trephine to center over it, releasing the coronal end circumferentially and safely removing the surrounding dentin. This makes it easier to grasp the fragment firmly and makes it easier to retrieve it along the root's long axis, allowing for regular retreatment [##REF##20142883##6##].</p>", "<p>Our successful attempts to remove detached files in posterior teeth using the Masserann kit challenge the literature's suggestion that employing the Masserann approach for posterior teeth can be challenging [##REF##11853240##11##]. Every operation was carried out under rubber dam isolation. In one instance, the clamp's wing obscured the separated segment's appearance on the radiograph, requiring the clamp to be removed to obtain a clean image. In another instance, where the tooth was severely damaged, wedges were used to hold the rubber dam in place rather than clamps. The optimum course of action is prevention, and in situations where instruments separate, the significance of safe retrieval or bypassing is emphasized [##UREF##2##12##]. Although the Masserann procedure is time-consuming and technique-sensitive [##REF##15088037##13##], detached files from maxillary lateral incisors and maxillary and mandibular molars were successfully recovered through strategic use within clinical constraints and operator expertise. On the other hand, in some situations, using ultrasonics and a dental operating microscope can increase efficacy [##UREF##3##14##].</p>" ]

[ "<title>Conclusions</title>", "<p>Instrument separation during endodontic therapy poses a significant challenge, affecting the success of the treatment. The presented case report demonstrates the successful removal of a tightly wedged file in a mandibular first premolar using this technique. Despite its limitations, such as the need for frequent radiographic monitoring and restricted application in certain tooth types, the Masserann kit proves effective in many cases. The importance of instrument retrieval before surgery is emphasized, underlining the technique's clinical relevance and the positive outcomes in challenging cases.</p>" ]

[ "<p>Instrument separation during endodontic therapy is a complication occurring in 2% to 6% of cases. Focusing on the Masserann technique, the study presents a success rate of 55% in retrieving separated instruments. The technique's effectiveness is demonstrated through a case involving retrieving an instrument from the mandibular first premolar. The technique utilizes various tools, including trephine burs and an extractor, providing a reliable means to dislodge tightly wedged fragments. Despite limitations in specific tooth types and the necessity for frequent radiographic monitoring, the Masserann kit proves effective and underscores the importance of attempting retrieval before considering surgical interventions. The presented case exemplifies the technique's clinical applicability and positive outcomes in intricate scenarios, emphasizing its significance in endodontic practice.</p>" ]

[ "<title>Case presentation</title>", "<p>A 50-year-old male patient visited the department with a complaint of pain in the lower right back region of his jaw that had been there for three days. The pain was localized, of moderate intensity, characterized as a dull ache, and continuous. It exacerbated while lying down and during mastication. Upon clinical examination, temporary restorations were observed on teeth 44 and 45. Radiographic examination revealed a white radiopaque shadow in the root canal, indicating the presence of a separated instrument. The fragment was situated in the coronal third of the root of tooth 44, extending approximately 5 mm beyond the apex, with a length of 13.6 mm. Periapical radiolucency was evident around the separated fragment, as shown in Figure ##FIG##0##1##.</p>", "<p>Considering the single-rooted nature of the tooth, the absence of root canal curvature, and the fragment's location in the coronal third of the root, attempts to bypass the fragment proved unsuccessful. Therefore, the decision was made to use the Masserann technique for fragment removal. Gates-Glidden drills were used one after the other to straighten the root canal to allow radicular access to the fragment's coronal end. A contra-angle handpiece was used to attach a pre-selected 1.2 mm trephine, which was then revolved in a counter-clockwise orientation to form a trench surrounding the fragment's coronal end and remove dentin. The trephine was centered correctly over the fragment, and radiographic confirmation was obtained. The piece was then sleeved by sliding an extractor tube with a 1.2 mm diameter into the trough. The extraction tube's plunger rod was manually rotated clockwise to grasp the fragment against its wall upon radiological confirmation of its placement, as shown in Figure ##FIG##1##2##.</p>", "<p>The entire assemblage was spun counterclockwise to release the instrument from the dentin and enable removal as soon as the tightest hold was detected, as shown in Figure ##FIG##2##3##.</p>", "<p>The working length was determined using an apex locator, with tooth 44 having a length of 14 mm. Biomechanical preparation was carried out on tooth 44 using Dentsply hand protaper files until reaching file F3. Calcium hydroxide intracanal medicament was placed, and a temporary dressing was applied to tooth 44. The patient was recalled after seven days. During the subsequent visit, the patient was asymptomatic. The temporary dressing was removed, and the canal was thoroughly irrigated with normal saline and 5.25% sodium hypochlorite. Mastercone fit was evaluated, and the canal was obturated, followed by post-endodontic restoration with teeth 44 and 45, as shown in Figure ##FIG##3##4##.</p>", "<p>The patient was then scheduled for a follow-up after one month, during which he remained completely asymptomatic.</p>" ]

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[ "<fig position=\"anchor\" fig-type=\"figure\" id=\"FIG1\"><label>Figure 1</label><caption><title>Preoperative radiograph showing 13.6 mm fractured fragment.</title><p>(A) Temporary restoration seen with teeth 44 and 45, and fractured fragment seen with tooth 44.</p><p>(B) A 13.6 mm fractured fragment was seen with tooth 44.</p></caption></fig>", "<fig position=\"anchor\" fig-type=\"figure\" id=\"FIG2\"><label>Figure 2</label><caption><title>Radiograph showing the extractor tube placed over the fractured fragment.</title></caption></fig>", "<fig position=\"anchor\" fig-type=\"figure\" id=\"FIG3\"><label>Figure 3</label><caption><title>Postoperative images.</title><p>(A) Image showing the extractor tube and retrieved fractured fragment.</p><p>(B) Postoperative radiograph after retrieval of the fractured fragment.</p></caption></fig>", "<fig position=\"anchor\" fig-type=\"figure\" id=\"FIG4\"><label>Figure 4</label><caption><title>Postoperative radiograph showing obturated root canal with tooth 44 and post-endodontic restoration with teeth 44 and 45.</title></caption></fig>" ]

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[ "<fn-group content-type=\"other\"><title>Author Contributions</title><fn fn-type=\"other\"><p><bold>Concept and design:</bold>  Paridhi Agrawal, Manoj Chandak, Jay Bhopatkar, Nikhil Mankar, Swayangprabha Sarangi</p><p><bold>Acquisition, analysis, or interpretation of data:</bold>  Paridhi Agrawal, Manoj Chandak, Jay Bhopatkar, Nikhil Mankar, Swayangprabha Sarangi</p><p><bold>Drafting of the manuscript:</bold>  Paridhi Agrawal, Manoj Chandak, Jay Bhopatkar, Nikhil Mankar, Swayangprabha Sarangi</p><p><bold>Critical review of the manuscript for important intellectual content:</bold>  Paridhi Agrawal, Manoj Chandak, Jay Bhopatkar, Nikhil Mankar, Swayangprabha Sarangi</p></fn></fn-group>", "<fn-group content-type=\"other\"><title>Human Ethics</title><fn fn-type=\"other\"><p>Consent was obtained or waived by all participants in this study</p></fn></fn-group>", "<fn-group content-type=\"competing-interests\"><fn fn-type=\"COI-statement\"><p>The authors have declared that no competing interests exist.</p></fn></fn-group>" ]

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[ "<title>Introduction</title>", "<p>Novel oncological therapies are a major area of new drug development and comprise a substantial portion of new drug approvals in the United States and Europe [##UREF##0##1##,##UREF##1##2##], driven by modern advances in targeted molecular therapies [##UREF##2##3##]. However, newer anti-cancer therapies are increasingly expensive, and the therapeutic benefit provided is often modest in comparison to prices [##REF##32480184##4##]. National regulatory agencies such as the Medicines and Healthcare Products Regulatory Agency (MHRA) in the United Kingdom (UK) assess whether medicines provide an appropriate benefit to the risk profile of patients to licence them for marketing but do not take economic aspects into account. Independent organisations such as the National Institute for Health and Care Excellence (NICE) in England and the Scottish Medicines Consortium (SMC) in Scotland are responsible for pharmacoeconomic appraisals to assess the cost-effectiveness of medications. These organisations publish guidance on whether the National Health Service (NHS), the public healthcare provider in the UK, should reimburse a treatment after assessing the clinical benefits of the treatment against the financial costs of the treatment. Cost-effectiveness is commonly defined through the quality-adjusted life year (QALY), equal to one year of life in perfect health [##REF##21037243##5##], as a measure incorporating both lifespan and quality of life. The incremental cost per additional QALY added by a novel therapy compared to existing therapies is assessed as a metric of the health utility provided by a medicine. The SMC does not have a formal QALY threshold for funding, whilst NICE commonly utilises an incremental threshold of £20,000 to £30,000 per QALY added to guide funding decisions [##UREF##3##6##, ####UREF##4##7##, ##UREF##5##8####5##8##].</p>", "<p>Although both organisations share the use of the QALY as a cost-effectiveness measure, they use different criteria, methodologies and implementation processes for appraising medicines. Despite this, the two organisations generally align on appraisal outcomes, although the SMC is noted to take longer to publish appraisals for oncology indications [##UREF##6##9##]. Whilst the health technology assessment (HTA) process provides another protection for the health service and taxpayers, the process can be lengthy and delay public access to medications. Common criticisms include ambiguity about decision-making, inappropriate funding decisions, lack of consistency, delays and lack of access for patients to treatments, particularly for cancer and orphan indications [##REF##11934781##10##, ####UREF##7##11##, ##UREF##8##12##, ##UREF##9##13##, ##REF##25145802##14####25145802##14##]. Healthcare providers in England have a statutory responsibility to make funding available for a medicine within 90 calendar days after guidance recommending its use is published by NICE. In Scotland, NHS providers are expected to reach a decision on the provision of an SMC-accepted medicine within 90 days of the SMC issuing advice to the health board that the medicine is recommended. Thus, the guidance of bodies such as NICE and SMC is integral to ensuring that novel oncological therapies are cost-effective and appropriate for funding, and their decisions and timelines can have significant impacts on the availability of therapeutic options for cancer patients in the UK.</p>" ]

[ "<title>Materials and methods</title>", "<p>Single health technology assessments (HTA) of oncological therapies published by NICE from January 1, 2017, to December 31, 2022, were identified on the NICE website. All appraisals of single technologies for oncology indications published by SMC between January 1, 2017, and December 31, 2022, were identified from published documents on the SMC website. The time from marketing authorisation (MA) until publication of HTA guidance was the primary outcome measure. The UK MA approval dates for the relevant indication or dates of label extension were obtained from the European Medicines Agency (EMA) or MHRA websites or the UK Summary of Product Characteristics for the product. If this data was not publicly available using the aforementioned sources, then it was obtained via an information request from the medical information department of the MA holder. Re-appraisals of technologies where HTA guidance was previously published were excluded. Assessments that were terminated due to non-submission by the MA holder were excluded. Differences in time from the date of the UK MA to publication of HTA between SMC and NICE were compared using a two-sided Mann-Whitney U test. P-values of &lt;0.05 were considered statistically significant, and there was no control for multiplicity. Data collation and analysis were carried out in Microsoft Excel (Microsoft Corporation, Washington, USA) and Minitab version 21 (Minitab, LLC., Pennsylvania, USA).</p>" ]

[ "<title>Results</title>", "<p>Two hundred and six single HTAs were published by the SMC, and 253 were published by NICE between January 1, 2017, and December 31, 2022, for oncology therapies. Following exclusions, we included 148 HTAs for SMC and 161 HTAs for NICE, of which 111 technologies had HTA guidance published by both agencies during the study period. Figure ##FIG##0##1## shows a flow diagram of the selection process.</p>", "<p>Overall median time from MA to publication of guidance was not significantly different between organisations: 291 days (IQR 222-406) for SMC and 257 days (IQR 167-448) for NICE (p=0.054, not significant) (Table ##TAB##0##1##). A similar amount of technologies were recommended in guidance by SMC and NICE (90.5% and 89.4%, respectively), with a similar proportion recommended with restrictions on their use (29.7% SMC and 26.1% NICE). The majority of HTAs were for solid organ cancers (70% for both organisations) (Table ##TAB##0##1##). The median time from MA to publication of HTA guidance for solid organ cancers was significantly lower for NICE at 231.5 days (IQR 148-392.25) compared to SMC at 273 days (IQR 202-378) (p=0.039) (Table ##TAB##0##1##). There was no significant difference in time from MA to publication of HTA guidance for haematological malignancy between SMC and NICE (p=0.597) (Table ##TAB##0##1##). The most common tumour types for technologies were lung and breast for both organisations (SMC: breast 23 and lung 23 appraisals, NICE: breast 20 and lung 32 appraisals) (Table ##TAB##1##2##). The median time to publication of HTA guidance after MA was significantly shorter for solid organ cancer therapies than haematological malignancy for NICE (231.5 days (IQR 148-392.25) vs. 339 days (IQR 206-623), p=0.010) and SMC (273 days (IQR 202-378) vs. 327 days (IQR 258.5-780), p=0.012).</p>", "<p>There were 111 technologies with published guidance by both agencies between January 1, 2017, and December 31, 2022 (Table ##TAB##2##3##). For these technologies, the median time from MA to HTA publication date was significantly longer for SMC with 287 days (IQR 217-362) than NICE with 233 days (IQR 144-358) (p=0.005) (Table ##TAB##2##3##). A similar number of technologies were recommended in guidance for SMC and NICE (90.1% and 91%, respectively) (Table ##TAB##2##3##), and similar proportions were recommended with restrictions (28.8% SMC and 26.1% NICE). All technologies not recommended were for solid organ cancer. The median time from MA to publication of HTA guidance was significantly shorter for NICE than SMC for solid organ cancer (NICE 225 days (IQR 135-313), SMC 272 days (IQR 220.5-354.75) (p=0.006)) but not for haematological malignancy (NICE 293 days (IQR 179-489), SMC 318 days (266-400), p=0.338). There were 14 technologies assessed by both agencies where there was discordance in the final guidance recommendation (Table ##TAB##3##4##). Six technologies were rejected by NICE and approved by SMC, and eight technologies were rejected by SMC and approved by NICE (Table ##TAB##3##4##).</p>" ]

[ "<title>Discussion</title>", "<p>In this study, we assessed 161 single HTAs published by NICE and 148 by SMC for oncological therapies. A similar proportion of technologies were recommended for use by both agencies (SMC 90.5%, NICE 89.4%). The number of technologies that were recommended with restrictions on their use was also similar between agencies. An overall recommendation rate of 85% is reported by NICE for data from 2000 to 2023 [##UREF##10##15##], although this also covers appraisals for non-oncology therapies. A 2012 study by Ford et al. reported an 80% recommendation rate for NICE and a 71.4% recommendation for SMC for cancer therapies [##UREF##6##9##]. This study reported a median time from MA to publication of single HTA guidance for cancer drugs of 25.2 months for NICE and 8.0 months for SMC. That study looked at data from different decades (the 2000s vs. 2010s/2020s), where NICE and SMC processes were different and the method of data extraction for SMC was different (from annual appraisal summary documents rather than single published guidance documents), which may explain the discrepancies seen. More recent studies looking at HTA final guidance documents between 2014 and 2016 for oncology therapies found recommendation rates of 79% for NICE and 75% for SMC [##REF##28854927##16##,##REF##29855313##17##].</p>", "<p>In our study, there was disagreement between SMC and NICE on recommendation or non-recommendation for a minority of technologies (12.6%). This is consistent with previous work that has shown a high degree of agreement between SMC and NICE for cancer therapies compared to other European countries [##REF##31488229##18##,##REF##32538341##19##]. We found that NICE published faster guidance for solid organ therapies but not haematological malignancy therapies than SMC, although the reasons for this are unclear. NICE was significantly faster at publishing guidance than SMC for technologies that were assessed by both agencies during the study period, which was driven by faster times for solid organ cancer therapies, with no difference in timelines seen in therapies for haematological malignancy. This likely reflects the fact that technologies appraised by both agencies in the study period were likely to be completely novel therapies in high-impact areas or areas of unmet need, which may have led to an acceleration of timelines for both agencies, although this appears to have been more pronounced for NICE than SMC.</p>", "<p>The Cancer Drugs Fund (CDF) was set up in England in 2010 to improve access to novel cancer therapies. The CDF provided reimbursement for therapies that were pending appraisal by NICE or have been rejected by NICE due to a lack of cost-effectiveness or immature data for health economic models [##UREF##11##20##]. In 2016, the CDF was reformed due to multiple consecutive years of spending outside of its allotted budget and re-aligned with NICE [##UREF##11##20##,##REF##28453615##21##]. These reforms included changes to the NICE appraisal process, such that the process began earlier, with initial submissions and reviews occurring prior to the drug receiving MA approval, thereby reducing delays to patient access after MA approval by the MHRA [##UREF##11##20##]. Previous work has indicated that 2014 reforms at the SMC for the approval of end-of-life and orphan indications had led to increased access to therapies for advanced cancer, although this only addressed approval rates and not timelines to approval [##REF##28854927##16##,##UREF##12##22##]. The difference in processes between NICE and the CDF may provide some explanation as to why NICE continues to publish oncology HTA guidance faster than the SMC. Other factors that can account for discrepancies between the two agencies that have previously been published in the literature include the method of dealing with uncertainties about cost-effectiveness, comparator choice, clinical benefits [##REF##17579932##23##,##REF##28292476##24##], negotiation of patient access schemes and market entry agreements [##REF##31488229##18##], consideration of indirect benefits of treatment and the innovative nature of treatments [##REF##26723201##25##,##REF##27624559##26##]. The increased recommendation rate observed in our studies compared to previous work likely reflects the ongoing consequences of reforms made in the 2010s at both organisations and further refinements in the approach of manufacturers and agencies to increase access to oncological therapies.</p>", "<p>Delays in publication of HTA guidance can occur for a variety of reasons. The type of technology appraised is relevant, as both therapies for oncology and orphan status are known to extend the appraisal process duration [##REF##33985575##27##,##REF##30617953##28##]. Initial draft guidance by NICE is negative in 60% of cases [##UREF##13##29##] and leads to significant delays in the issuance of final guidance [##REF##30617953##28##]. The manufacturer's response to the draft guidance is also important in terms of the final outcome. In one study, 38% of preliminary negative decisions could receive recommendations in final guidance after the introduction or enhancement of a patient access scheme discount, which would be at the discretion of the manufacturer [##REF##30617953##28##]. Drugs for advanced cancers are also known to require more committee meetings prior to the decision and a longer time from the first meeting until publication of final guidance than non-oncology therapies for both SMC and NICE [##UREF##6##9##,##REF##30617953##28##].</p>", "<p>Limitations</p>", "<p>This study has a number of limitations. We assessed only the time until publication of final guidance and did not take into account where draft guidance rejecting the drug was issued before the company resubmitted with final approval (which may come with substantial time delays). Additionally, the time to publication of results may be affected due to a lack of, or delay in, submission of data by the MA holder as well as delays in the SMC or NICE process, which our study does not distinguish between. We also assumed that the decision-making processes of NICE and SMC are independent; however, companies may change the contents of the submitted dossier, including their proposed pharmacoeconomic models, on the basis of feedback or interactions with the other agency. Furthermore, the initial models submitted by the manufacturer to the respective agencies may be different, which can lead to increased meetings and time until publication of the guidance or differences in the outcome of the HTA process. Finally, we only looked at decisions within the UK, although evidence suggests there is significant variation in HTA assessment and recommendations between G7 countries [##REF##37269843##30##].</p>" ]

[ "<title>Conclusions</title>", "<p>Recommendation rates are similar for single health technology assessments of oncological therapies for both NICE and SMC (89.4% and 90.5%), with only a minority of therapies (12.6%) having discordance in recommendation outcomes between the two organisations. NICE published guidance significantly faster than the SMC for solid organ cancer therapies, but timelines were similar for haematological malignancies. This is most likely due to differences in process and methodology between the two agencies, in particular the role of the CDF in England.</p>" ]

[ "<p>Background and aims</p>", "<p>Pharmacoeconomic assessment of novel oncological therapies is an increasingly important factor in determining patient access to therapies. Organisations such as the National Institute for Health and Care Excellence (NICE) in England and the Scottish Medicines Consortium (SMC) in Scotland assess medications for their cost-effectiveness through health technology assessments (HTA) and provide guidance on whether the public health service should fund a therapy. We assessed six years of data to determine if there were any differences in timescales and decisions between NICE and SMC for new oncological therapies.</p>", "<p>Methods and results</p>", "<p>Time (days) from marketing authorisation (MA) to publication of final HTA guidance was calculated for single technology appraisals published by NICE and SMC between January 1, 2017, and December 31, 2022, for oncological therapies. We assessed 161 HTAs by NICE and 148 HTAs by SMC published in the study period. The median time from MA to publication of HTA guidance was 291 days (IQR 222-406) for SMC and 257 days (IQR 167-448) for NICE (p=0.054). For solid organ cancer therapies, NICE was significantly faster in publishing guidance, with a median of 231.5 days (IQR 148-392.25), compared to SMC, which took 273 days (IQR 202-378) (p=0.039). Overall recommendation of technologies was similar between the SMC and NICE (90.5% and 89.4%, respectively), with discordance in a minority of cases (12.6%).</p>", "<p>Conclusions</p>", "<p>Recommendation rates for single HTAs are similar between NICE and SMC for oncological therapies with discordance in a minority of cases. The time from MA to publication of HTA guidance was similar overall, but NICE was faster in publishing HTA guidance for solid organ cancer indications. Differences in methodology and process between the two organisations, in particular the presence of the Cancer Drugs Fund in England, may explain this difference in publication times.</p>" ]

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[ "<fig position=\"anchor\" fig-type=\"figure\" id=\"FIG1\"><label>Figure 1</label><caption><title>Flow diagram of the selection process</title><p>Shows the flow diagram of the selection of published HTA guidance decisions with numbers and exclusions</p><p>NICE: National Institute for Health and Care Excellence, SMC: Scottish Medicines Consortium, MTA: multiple technology assessment, HTAs: health technology assessments</p></caption></fig>" ]

[ "<table-wrap position=\"float\" id=\"TAB1\"><label>Table 1</label><caption><title>Technologies assessed by either agency</title><p>Shows the overview of HTA guidance published by either agency during the study period</p><p>SMC: Scottish Medicines Consortium, NICE: National Institute for Health and Care Excellence, MA: marketing authorisation, IQR: interquartile range, HTAs: health technology assessments</p><p>p-values of &lt;0.05 are considered statistically significant</p></caption><table frame=\"hsides\" rules=\"groups\"><tbody><tr style=\"background-color:#ccc\"><td rowspan=\"1\" colspan=\"1\"> </td><td rowspan=\"1\" colspan=\"1\">SMC</td><td rowspan=\"1\" colspan=\"1\">NICE</td><td rowspan=\"1\" colspan=\"1\">p-value</td></tr><tr><td rowspan=\"1\" colspan=\"1\">Number of HTAs included</td><td rowspan=\"1\" colspan=\"1\">148</td><td rowspan=\"1\" colspan=\"1\">161</td><td rowspan=\"1\" colspan=\"1\"> </td></tr><tr style=\"background-color:#ccc\"><td rowspan=\"1\" colspan=\"1\">Technology recommended (no restrictions) (%)</td><td rowspan=\"1\" colspan=\"1\">90/148 (60.8%)</td><td rowspan=\"1\" colspan=\"1\">102/161 (63.3%)</td><td rowspan=\"1\" colspan=\"1\"> </td></tr><tr><td rowspan=\"1\" colspan=\"1\">Technology recommended (with restrictions) (%)</td><td rowspan=\"1\" colspan=\"1\">44/148 (29.7%)</td><td rowspan=\"1\" colspan=\"1\">42/161 (26.1%)</td><td rowspan=\"1\" colspan=\"1\"> </td></tr><tr style=\"background-color:#ccc\"><td rowspan=\"1\" colspan=\"1\">Technology not recommended (%)</td><td rowspan=\"1\" colspan=\"1\">14/148 (9.5%)</td><td rowspan=\"1\" colspan=\"1\">17/161 (10.6%)</td><td rowspan=\"1\" colspan=\"1\"> </td></tr><tr><td rowspan=\"1\" colspan=\"1\">Overall median time from MA to publication of guidance (days) (IQR)</td><td rowspan=\"1\" colspan=\"1\">291 (222-406)</td><td rowspan=\"1\" colspan=\"1\">257 (167-448)</td><td rowspan=\"1\" colspan=\"1\">0.054</td></tr><tr style=\"background-color:#ccc\"><td rowspan=\"1\" colspan=\"1\">Solid organ cancer HTAs (%)</td><td rowspan=\"1\" colspan=\"1\">105/148 (70.9%)</td><td rowspan=\"1\" colspan=\"1\">114/161 (70.8%)</td><td rowspan=\"1\" colspan=\"1\"> </td></tr><tr><td rowspan=\"1\" colspan=\"1\">Solid organ cancer technology not recommended (%)</td><td rowspan=\"1\" colspan=\"1\">12/105 (11.4%)</td><td rowspan=\"1\" colspan=\"1\">16/114 (14.0%)</td><td rowspan=\"1\" colspan=\"1\"> </td></tr><tr style=\"background-color:#ccc\"><td rowspan=\"1\" colspan=\"1\">Median time from MA to publication of guidance for solid organ cancer technology (days) (IQR)</td><td rowspan=\"1\" colspan=\"1\">273 (202-378)</td><td rowspan=\"1\" colspan=\"1\">231.5 (148-392.25)</td><td rowspan=\"1\" colspan=\"1\">0.039</td></tr><tr><td rowspan=\"1\" colspan=\"1\">Haematological malignancy HTAs (%)</td><td rowspan=\"1\" colspan=\"1\">43/148 (29.1%)</td><td rowspan=\"1\" colspan=\"1\">47/161 (29.2%)</td><td rowspan=\"1\" colspan=\"1\"> </td></tr><tr style=\"background-color:#ccc\"><td rowspan=\"1\" colspan=\"1\">Haematological malignancy technology not recommended (%)</td><td rowspan=\"1\" colspan=\"1\">2/43 (4.7%)</td><td rowspan=\"1\" colspan=\"1\">1/47 (2.1%)</td><td rowspan=\"1\" colspan=\"1\"> </td></tr><tr><td rowspan=\"1\" colspan=\"1\">Median time from MA to publication of guidance for haematological malignancy technology (days) (IQR)</td><td rowspan=\"1\" colspan=\"1\">327 (258.5-780)</td><td rowspan=\"1\" colspan=\"1\">339 (206-623)</td><td rowspan=\"1\" colspan=\"1\">0.597</td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"TAB2\"><label>Table 2</label><caption><title>HTA guidance published by tumour type</title><p>Shows the overview of HTA guidance published by tumour type</p><p>SMC: Scottish Medicines Consortium, NICE: National Institute for Health and Care Excellence, GI: gastrointestinal</p></caption><table frame=\"hsides\" rules=\"groups\"><tbody><tr style=\"background-color:#ccc\"><td rowspan=\"1\" colspan=\"1\">Tumour type</td><td rowspan=\"1\" colspan=\"1\">SMC</td><td rowspan=\"1\" colspan=\"1\">NICE</td></tr><tr><td rowspan=\"1\" colspan=\"1\">Myeloma (%)</td><td rowspan=\"1\" colspan=\"1\">10 (6.7%)</td><td rowspan=\"1\" colspan=\"1\">7 (4.3%)</td></tr><tr style=\"background-color:#ccc\"><td rowspan=\"1\" colspan=\"1\">Leukaemia (%)</td><td rowspan=\"1\" colspan=\"1\">15 (10.1%)</td><td rowspan=\"1\" colspan=\"1\">17 (10.6%)</td></tr><tr><td rowspan=\"1\" colspan=\"1\">Myeloproliferative disorder (%)</td><td rowspan=\"1\" colspan=\"1\">4 (2.7%)</td><td rowspan=\"1\" colspan=\"1\">4 (2.5%)</td></tr><tr style=\"background-color:#ccc\"><td rowspan=\"1\" colspan=\"1\">Lymphoma (%)</td><td rowspan=\"1\" colspan=\"1\">14 (9.5%)</td><td rowspan=\"1\" colspan=\"1\">20 (12.4%)</td></tr><tr><td rowspan=\"1\" colspan=\"1\">Lung (%)</td><td rowspan=\"1\" colspan=\"1\">23 (15.5%)</td><td rowspan=\"1\" colspan=\"1\">32 (19.9%)</td></tr><tr style=\"background-color:#ccc\"><td rowspan=\"1\" colspan=\"1\">Breast (%)</td><td rowspan=\"1\" colspan=\"1\">23 (15.5%)</td><td rowspan=\"1\" colspan=\"1\">20 (12.4%)</td></tr><tr><td rowspan=\"1\" colspan=\"1\">Prostate (%)</td><td rowspan=\"1\" colspan=\"1\">6 (4.1%)</td><td rowspan=\"1\" colspan=\"1\">8 (5.0%)</td></tr><tr style=\"background-color:#ccc\"><td rowspan=\"1\" colspan=\"1\">Hepatobiliary (%)</td><td rowspan=\"1\" colspan=\"1\">4 (2.7%)</td><td rowspan=\"1\" colspan=\"1\">5 (3.1%)</td></tr><tr><td rowspan=\"1\" colspan=\"1\">Upper GI (%)</td><td rowspan=\"1\" colspan=\"1\">5 (3.4%)</td><td rowspan=\"1\" colspan=\"1\">4 (2.5%)</td></tr><tr style=\"background-color:#ccc\"><td rowspan=\"1\" colspan=\"1\">Lower GI (%)</td><td rowspan=\"1\" colspan=\"1\">4 (2.7%)</td><td rowspan=\"1\" colspan=\"1\">3 (1.9%)</td></tr><tr><td rowspan=\"1\" colspan=\"1\">Melanoma (%)</td><td rowspan=\"1\" colspan=\"1\">5 (3.4%)</td><td rowspan=\"1\" colspan=\"1\">5 (3.1%)</td></tr><tr style=\"background-color:#ccc\"><td rowspan=\"1\" colspan=\"1\">Gynaecological</td><td rowspan=\"1\" colspan=\"1\">7 (4.7%)</td><td rowspan=\"1\" colspan=\"1\">6 (3.7%)</td></tr><tr><td rowspan=\"1\" colspan=\"1\">Urological (%)</td><td rowspan=\"1\" colspan=\"1\">17 (11.5%)</td><td rowspan=\"1\" colspan=\"1\">15 (9.3%)</td></tr><tr style=\"background-color:#ccc\"><td rowspan=\"1\" colspan=\"1\">Other (%)</td><td rowspan=\"1\" colspan=\"1\">11 (7.4%)</td><td rowspan=\"1\" colspan=\"1\">15 (9.3%)</td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"TAB3\"><label>Table 3</label><caption><title>Technologies assessed by both agencies</title><p>Shows the overview of HTA guidance published by both agencies for the same agency during the study period</p><p>SMC: Scottish Medicines Consortium, NICE: National Institute for Health and Care Excellence, MA: marketing authorisation, IQR: interquartile range, HTAs: health technology assessments</p><p>p-values of &lt;0.05 are considered statistically significant</p></caption><table frame=\"hsides\" rules=\"groups\"><tbody><tr style=\"background-color:#ccc\"><td rowspan=\"1\" colspan=\"1\"> </td><td rowspan=\"1\" colspan=\"1\">SMC</td><td rowspan=\"1\" colspan=\"1\">NICE</td><td rowspan=\"1\" colspan=\"1\">p-value</td></tr><tr><td rowspan=\"1\" colspan=\"1\">Number of HTAs</td><td rowspan=\"1\" colspan=\"1\">111</td><td rowspan=\"1\" colspan=\"1\">111</td><td rowspan=\"1\" colspan=\"1\"> </td></tr><tr style=\"background-color:#ccc\"><td rowspan=\"1\" colspan=\"1\">Technology recommended (no restrictions) (%)</td><td rowspan=\"1\" colspan=\"1\">68/111 (61.3%)</td><td rowspan=\"1\" colspan=\"1\">72/111 (64.9%)</td><td rowspan=\"1\" colspan=\"1\"> </td></tr><tr><td rowspan=\"1\" colspan=\"1\">Technology recommended (with restrictions) (%)</td><td rowspan=\"1\" colspan=\"1\">32/111 (28.8%)</td><td rowspan=\"1\" colspan=\"1\">29/111 (26.1%)</td><td rowspan=\"1\" colspan=\"1\"> </td></tr><tr style=\"background-color:#ccc\"><td rowspan=\"1\" colspan=\"1\">Technology not recommended (%)</td><td rowspan=\"1\" colspan=\"1\">11/111 (9.9%)</td><td rowspan=\"1\" colspan=\"1\">10/111 (9.0%)</td><td rowspan=\"1\" colspan=\"1\"> </td></tr><tr><td rowspan=\"1\" colspan=\"1\">Overall median time from MA to publication of guidance (days) (IQR)</td><td rowspan=\"1\" colspan=\"1\">287 (217-362)</td><td rowspan=\"1\" colspan=\"1\">233 (144-358)</td><td rowspan=\"1\" colspan=\"1\">0.005</td></tr><tr style=\"background-color:#ccc\"><td rowspan=\"1\" colspan=\"1\">Solid organ cancer HTAs (%)</td><td rowspan=\"1\" colspan=\"1\">82 (73.9%)</td><td rowspan=\"1\" colspan=\"1\">82 (73.9%)</td><td rowspan=\"1\" colspan=\"1\"> </td></tr><tr><td rowspan=\"1\" colspan=\"1\">Solid organ cancer technology not recommended (%)</td><td rowspan=\"1\" colspan=\"1\">11/82 (13.4%)</td><td rowspan=\"1\" colspan=\"1\">9/82 (11.0%)</td><td rowspan=\"1\" colspan=\"1\"> </td></tr><tr style=\"background-color:#ccc\"><td rowspan=\"1\" colspan=\"1\">Median time from MA to publication of guidance for solid organ cancer technology (days) (IQR)</td><td rowspan=\"1\" colspan=\"1\">272 (220.5-354.75)</td><td rowspan=\"1\" colspan=\"1\">225 (135-313)</td><td rowspan=\"1\" colspan=\"1\">0.006</td></tr><tr><td rowspan=\"1\" colspan=\"1\">Haematological malignancy HTAs (%)</td><td rowspan=\"1\" colspan=\"1\">29 (26.1%)</td><td rowspan=\"1\" colspan=\"1\">29 (26.1%)</td><td rowspan=\"1\" colspan=\"1\"> </td></tr><tr style=\"background-color:#ccc\"><td rowspan=\"1\" colspan=\"1\">Haematological malignancy technology not recommended (%)</td><td rowspan=\"1\" colspan=\"1\">0 (0%)</td><td rowspan=\"1\" colspan=\"1\">0 (0%)</td><td rowspan=\"1\" colspan=\"1\"> </td></tr><tr><td rowspan=\"1\" colspan=\"1\">Median time from MA to publication of guidance for haematological malignancy technology (days) (IQR)</td><td rowspan=\"1\" colspan=\"1\">318 (266-400)</td><td rowspan=\"1\" colspan=\"1\">293 (179-489)</td><td rowspan=\"1\" colspan=\"1\">0.338</td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"TAB4\"><label>Table 4</label><caption><title>Technologies with discordance between agencies</title><p>Shows technologies where NICE and SMC differed in overall recommendation guidance outcome (recommended vs. not recommended)</p><p>CDF: Cancer Drugs Fund, identifier: identifier signature of HTA guidance document published on the agency website, SMC: Scottish Medicines Consortium, NICE: National Institute for Health and Care Excellence, GI: gastrointestinal</p></caption><table frame=\"hsides\" rules=\"groups\"><tbody><tr style=\"background-color:#ccc\"><td rowspan=\"1\" colspan=\"1\">Technology</td><td rowspan=\"1\" colspan=\"1\">SMC guidance</td><td rowspan=\"1\" colspan=\"1\">SMC identifier</td><td rowspan=\"1\" colspan=\"1\">NICE guidance</td><td rowspan=\"1\" colspan=\"1\">NICE identifier</td><td rowspan=\"1\" colspan=\"1\">Tumour type</td></tr><tr><td rowspan=\"1\" colspan=\"1\">Alpelisib</td><td rowspan=\"1\" colspan=\"1\">Not recommended</td><td rowspan=\"1\" colspan=\"1\">SMC2481</td><td rowspan=\"1\" colspan=\"1\">Recommended (optimised)</td><td rowspan=\"1\" colspan=\"1\">NICE TA816</td><td rowspan=\"1\" colspan=\"1\">Breast</td></tr><tr style=\"background-color:#ccc\"><td rowspan=\"1\" colspan=\"1\">Ixekizumab</td><td rowspan=\"1\" colspan=\"1\">Not recommended</td><td rowspan=\"1\" colspan=\"1\">SMC2440</td><td rowspan=\"1\" colspan=\"1\">Recommended (optimised)</td><td rowspan=\"1\" colspan=\"1\">NICE TA718</td><td rowspan=\"1\" colspan=\"1\">Myeloma</td></tr><tr><td rowspan=\"1\" colspan=\"1\">Selpercatinib</td><td rowspan=\"1\" colspan=\"1\">Not recommended</td><td rowspan=\"1\" colspan=\"1\">SMC2371</td><td rowspan=\"1\" colspan=\"1\">Recommended (CDF)</td><td rowspan=\"1\" colspan=\"1\">NICE TA760</td><td rowspan=\"1\" colspan=\"1\">Lung</td></tr><tr style=\"background-color:#ccc\"><td rowspan=\"1\" colspan=\"1\">Olaparib</td><td rowspan=\"1\" colspan=\"1\">Recommended</td><td rowspan=\"1\" colspan=\"1\">SMC2366</td><td rowspan=\"1\" colspan=\"1\">Not recommended</td><td rowspan=\"1\" colspan=\"1\">NICE TA831</td><td rowspan=\"1\" colspan=\"1\">Prostate</td></tr><tr><td rowspan=\"1\" colspan=\"1\">Trifluridine/tipiracil (Lonsurf®)</td><td rowspan=\"1\" colspan=\"1\">Recommended (restricted)</td><td rowspan=\"1\" colspan=\"1\">SMC2329</td><td rowspan=\"1\" colspan=\"1\">Not recommended</td><td rowspan=\"1\" colspan=\"1\">NICE TA669</td><td rowspan=\"1\" colspan=\"1\">Upper GI</td></tr><tr style=\"background-color:#ccc\"><td rowspan=\"1\" colspan=\"1\">Pembrolizumab</td><td rowspan=\"1\" colspan=\"1\">Recommended (restricted)</td><td rowspan=\"1\" colspan=\"1\">SMC2247</td><td rowspan=\"1\" colspan=\"1\">Not recommended</td><td rowspan=\"1\" colspan=\"1\">NICE TA650</td><td rowspan=\"1\" colspan=\"1\">Urological</td></tr><tr><td rowspan=\"1\" colspan=\"1\">Abiraterone</td><td rowspan=\"1\" colspan=\"1\">Recommended</td><td rowspan=\"1\" colspan=\"1\">SMC2215</td><td rowspan=\"1\" colspan=\"1\">Not recommended</td><td rowspan=\"1\" colspan=\"1\">NICE TA721</td><td rowspan=\"1\" colspan=\"1\">Prostate</td></tr><tr style=\"background-color:#ccc\"><td rowspan=\"1\" colspan=\"1\">Atezolizumab</td><td rowspan=\"1\" colspan=\"1\">Not recommended</td><td rowspan=\"1\" colspan=\"1\">SMC2208</td><td rowspan=\"1\" colspan=\"1\">Recommended</td><td rowspan=\"1\" colspan=\"1\">NICE TA584</td><td rowspan=\"1\" colspan=\"1\">Lung</td></tr><tr><td rowspan=\"1\" colspan=\"1\">Cabozantinib</td><td rowspan=\"1\" colspan=\"1\">Not recommended</td><td rowspan=\"1\" colspan=\"1\">SMC2316</td><td rowspan=\"1\" colspan=\"1\">Recommended</td><td rowspan=\"1\" colspan=\"1\">NICE TA542</td><td rowspan=\"1\" colspan=\"1\">Urological</td></tr><tr style=\"background-color:#ccc\"><td rowspan=\"1\" colspan=\"1\">Pembrolizumab</td><td rowspan=\"1\" colspan=\"1\">Not recommended</td><td rowspan=\"1\" colspan=\"1\">1339/18</td><td rowspan=\"1\" colspan=\"1\">Recommended (CDF)</td><td rowspan=\"1\" colspan=\"1\">NICE TA522</td><td rowspan=\"1\" colspan=\"1\">Urological</td></tr><tr><td rowspan=\"1\" colspan=\"1\">Obinutuzumab</td><td rowspan=\"1\" colspan=\"1\">Not recommended</td><td rowspan=\"1\" colspan=\"1\">SMC2015</td><td rowspan=\"1\" colspan=\"1\">Recommended</td><td rowspan=\"1\" colspan=\"1\">NICE TA513</td><td rowspan=\"1\" colspan=\"1\">Lymphoma</td></tr><tr style=\"background-color:#ccc\"><td rowspan=\"1\" colspan=\"1\">Regorafenib</td><td rowspan=\"1\" colspan=\"1\">Recommended</td><td rowspan=\"1\" colspan=\"1\">1316/18</td><td rowspan=\"1\" colspan=\"1\">Not recommended</td><td rowspan=\"1\" colspan=\"1\">NICE TA514</td><td rowspan=\"1\" colspan=\"1\">Hepatobiliary</td></tr><tr><td rowspan=\"1\" colspan=\"1\">Atezolizumab</td><td rowspan=\"1\" colspan=\"1\">Not recommended</td><td rowspan=\"1\" colspan=\"1\">1297/18</td><td rowspan=\"1\" colspan=\"1\">Recommended</td><td rowspan=\"1\" colspan=\"1\">NICE TA525</td><td rowspan=\"1\" colspan=\"1\">Urological</td></tr><tr style=\"background-color:#ccc\"><td rowspan=\"1\" colspan=\"1\">Osimertinib</td><td rowspan=\"1\" colspan=\"1\">Recommended (restricted)</td><td rowspan=\"1\" colspan=\"1\">SMC2382</td><td rowspan=\"1\" colspan=\"1\">Not recommended</td><td rowspan=\"1\" colspan=\"1\">NICE TA621</td><td rowspan=\"1\" colspan=\"1\">Lung</td></tr></tbody></table></table-wrap>" ]

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[ "<fn-group content-type=\"other\"><title>Author Contributions</title><fn fn-type=\"other\"><p><bold>Concept and design:</bold>  Rory Taylor</p><p><bold>Acquisition, analysis, or interpretation of data:</bold>  Rory Taylor</p><p><bold>Drafting of the manuscript:</bold>  Rory Taylor</p><p><bold>Critical review of the manuscript for important intellectual content:</bold>  Rory Taylor</p></fn></fn-group>", "<fn-group content-type=\"other\"><title>Human Ethics</title><fn fn-type=\"other\"><p>Consent was obtained or waived by all participants in this study</p></fn></fn-group>", "<fn-group content-type=\"other\"><title>Animal Ethics</title><fn fn-type=\"other\"><p><bold>Animal subjects:</bold> All authors have confirmed that this study did not involve animal subjects or tissue.</p></fn></fn-group>", "<fn-group content-type=\"competing-interests\"><fn fn-type=\"COI-statement\"><p>The authors have declared that no competing interests exist.</p></fn></fn-group>" ]

[ "<graphic xlink:href=\"cureus-0015-00000050560-i01\" position=\"float\"/>" ]

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{ "acronym": [], "definition": [] }

[ "<title>Introduction</title>", "<p>Prostate cancer is the most common malignancy among men and is the second-leading cause of cancer death in men [##UREF##0##1##]. In contrast to other cancers that commonly spread to brain, it is unusual for prostate carcinoma to metastasize to the central nervous system (CNS), making it the subject of case reports due to the uncommon event of neurologic side effects.</p>", "<p>We report a case of a 72-year-old male who presented to the hospital with a chief complaint of diplopia in the setting of a recent onset of urinary incontinence and right-sided back pain and was subsequently diagnosed with prostate cancer, notably metastasizing to the right sphenoid bone, causing impingement of the oculomotor nerve. Unlike previously few reported cases of oculomotor nerve palsy due to prostate cancer with non-adenocarcinoma pathology, our case biopsy uncovered neuroendocrine and adenocarcinoma histology, which is a rare phenomenon, and the patient received palliative orbital radiotherapy.</p>", "<p>This case report highlights the significance of employing a multidisciplinary diagnostic approach and emphasizes the pivotal role of palliative radiotherapy in alleviating symptoms related to rare skeletal metastases. Additionally, it underscores the significance of utilizing advanced imaging techniques for the early detection of such rare instances. Increased awareness of these atypical manifestations can contribute to prompt intervention and enhance outcomes in similar cases.</p>" ]

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[ "<title>Discussion</title>", "<p>Prostate cancer is the second most common malignancy among men, with a range of clinical presentations, often urinary symptoms or bone pain due to metastasis [##REF##31912902##2##]. Diagnostic confirmation involves prostate biopsy displaying adenocarcinoma, small cell carcinoma, and/or neuroendocrine phenotypes with grading based on the Gleason scoring system [##REF##31068988##3##]. Metastasis is usually to the bone (84%), and much less commonly to distant lymph nodes (10.6%), liver (10.2%), thorax (9.1%), and brain (3.1%) [##UREF##1##4##].</p>", "<p>Our case is unique in that the patient’s initial presentation of prostate cancer was oculomotor nerve palsy with subsequent histologic analysis of the primary tumor showing both small cell neuroendocrine carcinoma along with adenocarcinoma with intermediate-to-high risk Gleason scores. Although intracranial metastasis from prostate adenocarcinoma is extremely rare, brain metastasis from other types of prostate tumors is much higher; however, in our case, FDG-PET shows that this is likely from adenocarcinoma and not from neuroendocrine prostate cancer (NEPC)/small cell. The utility of FDG-PET in identifying brain metastases has often been scrutinized in the literature, revealing limitations due to its low sensitivity. According to some reports, PET could only detect 61-68% of metastatic lesions compared to those identified by MRI. This highlights the superior diagnostic capability of MRI in the context of brain metastasis detection, especially in adenocarcinomas [##REF##22174509##5##]. Prostate adenocarcinoma is characterized by morphological features resembling luminal prostate cells, is androgen-driven, and is typically associated with elevated serum prostate-specific antigen (PSA). On the other hand, neuroendocrine prostate cancer (NEPC) is an aggressive subtype that can emerge spontaneously or develop in advanced stages of prostate cancer, or often as a result of treatment resistance. Patients with pathologically confirmed NEPC commonly exhibit visceral metastases, low PSA levels, and frequent loss of the RB1 and TP53 genes [##REF##24132735##6##].</p>", "<p>Two previous case reports have reported oculomotor nerve palsies as the presenting symptom of prostate cancer, but neither describes adenocarcinoma histology or the use of palliative radiotherapy [##REF##22052183##7##,##REF##18846004##8##]. Among 27 previously diagnosed prostate cancer patients who received bone scintigraphy, only 1 (3.1%) had skull metastasis [##UREF##2##9##]. A recent retrospective study reported that patients who had skull metastases had significantly higher biopsy Gleason scores, higher clinical T-stage, and shorter overall survival [##REF##36721133##10##]. While the patient in this case did not have brain metastasis, it has been noted that the tumor histology impacts the likelihood of brain metastasis with small cell and primary transitional cell carcinomas more likely to do so than adenocarcinoma [##REF##10590371##11##]. Also, prostate cancer with brain metastasis often involves mutations of homologous recombination repair genes, including BRCA1, BRCA2, and many others [##REF##35504881##12##]. Though cerebrovascular accident (CVA), trauma, myasthenia gravis, granulomatous lesions, and multiple sclerosis were differential diagnoses for his diplopia, the patient’s age, urinary symptoms, musculoskeletal pain, and initial diagnostic workup prompted further evaluation of prostate cancer. This patient’s imaging showed compressive effects on the oculomotor nerve at the right sphenoid bone. In addition, palliative radiation therapy has been successful in alleviating the immediate symptoms.</p>", "<p>Since 1940's androgen deprivation therapy (ADT) alone has been the standard of care for many years in men with metastatic prostate cancer. Due to the limited survival under this monotherapy, many new treatment options have been developed in recent years. Especially for hormone-sensitive prostate cancer, combination therapies of two or three agents of ADT, androgen receptor signaling inhibitors (ARSIs), and chemotherapy have proven effective, resulting in a substantial improvement in overall survival [##REF##37442702##13##]. The latest findings from cohort 6 in the COSMIC-021 study have reignited enthusiasm in the field of immunotherapy for metastatic prostate cancer. The study revealed a notable 32% response rate and an impressive 80% disease control rate when utilizing the combination of chemotherapy and immunotherapy. These promising results underscore the potential of immunotherapeutic in addressing this complicated disease [##REF##36672410##14##]. GnRH agonists such as leuprolide and anti-androgens such as bicalutamide can be used [##REF##16014598##15##]. Chemotherapy regimens often include docetaxel, cabazitaxel, and corticosteroid [##REF##29045523##16##]. Palliative radiotherapy offers a speedy, economical, and compelling approach to decreasing large numbers of focal symptoms, especially in advanced cancer. Numerous studies so far have proven significant pain control and improvement in quality of life in about 50-60% of patients after receiving palliative radiotherapy, like in our case [##REF##30120817##17##]. Typically, adenocarcinoma is androgen-sensitive and treated by ADT, while NEPC is rare, not androgen- or hormone-sensitive, more aggressive, and treated by chemotherapy. As mentioned earlier in neuroendocrine tumors, PSA levels are low due to a lack of expression of androgen receptors; hence, serum PSA does not correlate with disease burden. In our case, elevated PSA gave a clear diagnostic cue towards prostate as a sight of primary; however, this correlation is more prominent in patients with adenocarcinoma and not NEPC. In the event that the patient with isolated NEPC with metastasis, further testing and workup will be required.</p>", "<p>The attribution of prostate cancer metastasis as the cause of this patient’s oculomotor nerve palsy occurred after extensive evaluation for other causes, and imaging of the pelvis was only completed after realizing the patient had cervical spine metastasis. It is also interesting to note that despite the presence of seemingly aggressive disease from NEPC, which had metastasized to the bone, our patient’s symptoms were discovered through metastasis from relatively less aggressive adenocarcinoma, which was widespread.</p>" ]

[ "<title>Conclusions</title>", "<p>Ptosis itself is a rare presenting feature of prostate cancer. Other case reports describe ptosis as a presentation, but they do not describe the neuroendocrine histology. In addition, routine stroke protocol MRI and CTA missed the lesion, while gadolinium-enhanced targeted MRI revealed lesions in both the spine and the orbit. This case emphasizes the need for contrast-enhanced, as well as focused imaging in patients presenting with diplopia with negative initial workup for stroke. Ptosis can be a sign of metastasis from other cancers, including breast cancer, head and neck cancer, thyroid cancer, lymphoid or neuroblastoma, and it is important to have a broad differential including metastatic disease in patients presenting with similar symptoms and negative workup who may otherwise be at risk of cancer. The case illustrates the importance of physical examination, diagnostic evaluation, and radiologic imaging in providing appropriate care to a patient. </p>", "<p>As a final note, this case report suggests that a more extensive study encompassing more patient samples could further enhance our understanding of the clinical nuances associated with ptosis as a presentation of prostate cancer. Such studies would aid in refining diagnostic and therapeutic strategies for similar presentations in the future.</p>" ]

[ "<p>We report a case of a 72-year-old male who presented to the hospital with a chief complaint of diplopia in the setting of a recent onset of urinary incontinence and right-sided back pain. He was subsequently diagnosed with prostate cancer, notably metastasizing to the right sphenoid bone, causing impingement of the oculomotor nerve. Our case is unique in that the patient’s initial presentation of prostate cancer was oculomotor nerve palsy with subsequent histologic analysis of the primary tumor showing both small cell neuroendocrine carcinoma along with adenocarcinoma. Also, the initial routine stroke protocol MRI and computed tomography angiography (CTA) missed the lesion, while gadolinium-enhanced targeted MRI revealed lesions in both the spine and the orbit. This case emphasizes the need for enhanced contrast as well as focused imaging in patients presenting with diplopia with a negative initial workup for stroke. Ptosis can be a sign of metastasis from other cancers and it is important to have a broad differential including metastatic disease in patients' presenting with similar symptoms and negative initial workup who may otherwise be at risk of cancer.</p>" ]

[ "<title>Case presentation</title>", "<p>A 72-year-old male with medical history of hypertension, hyperlipidemia, prediabetes, Lyme disease, coronary artery disease, hepatitis C, 50-pack-year smoking history, and diverticulitis was admitted for four days of worsening diplopia and right-sided ptosis. His physical examination revealed ptosis with asymmetric pupils (right 2.5 mm, left 3 mm), bilaterally reactive to light and accommodation. The right eye was depressed and abducted with a disconjugate gaze. On rightward gaze, diplopia improved but on leftward gaze, the right eye did not adduct, diplopia worsened, and nystagmus was noted. Cranial nerves I, II, and IV-XII were intact, and no other neurological deficits were observed. Initial lab workup was unremarkable in explaining his symptoms. </p>", "<p>Workup for stroke, including CT angiography of the head and neck (Figures ##FIG##0##1A##, ##FIG##0##1B##) and stroke protocol MRI brain without contrast (Figures ##FIG##0##1C##, ##FIG##0##1D##), were essentially within normal limits. Autoimmune workup revealed antinuclear antibody (ANA) titer 1:640 but negative for anti-SCL-70, anti-Smith, anti-dsDNA, anti-Ro/SSA and anti-La/SSB, c-ANCA, p-ANCA, rheumatoid factor, anti-sm/RNP, and acetylcholine receptor-blocking antibodies. The viral hepatitis panel was positive for hepatitis C antibodies but undetectable for hepatitis C virus (HCV) RNA. A CT scan of the chest with contrast showed bilateral subtle pulmonary nodules (the largest being 6 mm in the right upper lobe).</p>", "<p>Later, during his hospital stay, he complained of severe bilateral back pain, more in the mid-scapular region. He endorsed having had similar complaints intermittently for the last two weeks. MRI of the cervical spine (Figure ##FIG##1##2##) revealed multiple vertebral lesions and abnormal marrow signals concerning diffuse metastatic disease. On detailed review, he endorsed worsening urinary urgency, hesitancy, incomplete emptying, and nocturia despite taking tamsulosin for the last three months. He was supposed to follow up with the urologist for the urinary symptoms and check prostate-specific antigen (PSA) levels, but unfortunately, due to some reasons, it was delayed. His serum PSA levels during the hospitalization were found to be significantly elevated (226.3 ng/ml).</p>", "<p>Contrast MRI of the orbits revealed right sphenoid metastasis with extraosseous extension into the right cavernous sinus, causing compression of the third cranial nerve. CT abdomen and pelvis with contrast showed right prostate apex mass with lymph node involvement, liver metastasis, as well as extensive skeletal metastasis.</p>", "<p>He underwent ultrasound-guided prostatic biopsy, which showed small cell neuroendocrine carcinoma (3/12 cores) and adenocarcinoma (Gleason 7 8/12 cores, Gleason 9 1/12 cores). Immunohistochemistry was positive for TTF-1, synaptophysin, and chromogranin. 18 F-fluorodeoxyglucose-positron emission tomography (FDG-PET) showed increased FDG uptake in the apical and lateral areas, consistent with the area showing a small cell neuroendocrine tumor on prostate biopsy, while adenocarcinoma, which was seen to be extensively present throughout the prostate, did not demonstrate increased FDG uptake (Figure ##FIG##2##3A##). Areas of orbital as well as epidural and vertebral metastasis did not demonstrate increased FDG uptake (Figures ##FIG##2##3B##, ##FIG##2##3C##), while areas of liver metastasis did demonstrate increased FDG uptake (Figure ##FIG##2##3D##).</p>", "<p>He was treated with dexamethasone and urgent palliative radiotherapy to the skull base (3000 cGy in 10 fractions) and C5-T1 (2000 cGy in five fractions) using a 3D conformal approach. His acute complaint of diplopia was eventually alleviated. He followed up with medical oncology outpatient, he was initiated on chemotherapy along with immunotherapy, which included carboplatin intravenous (I.V.) infusion on day 1, etoposide I.V. infusion on days one, two, and three, and atezolizumab I.V. infusion on day one, followed by maintenance therapy with atezolizumab I.V. infusion once every 21 days. He was also started on darolutamide (anti-androgen medication) 300 mg twice a day daily along with Lupron (hormone-modulating drug) every three months. Later on, lurbinectedin 3.2 mg (an alkylating agent) every three weeks was added to the chemotherapy regimen. In view of preventing skeletal-related events, he was started on bone anti-resorptive therapy called Xgeva and Percocet for pain. The patient's serum creatinine returned to baseline, and PSA decreased to 6.87 ng/mL. Re-staging CT after a couple of months showed a treatment response. He also received intermittent palliative radiation therapy, which considerably improved his pain, and he has been ambulating more steadily.</p>" ]

[ "<p>Mahvish Renzu and Ishaan Jay Bhatt are the first co-authors and equally contributed to the work.</p>" ]

[ "<fig position=\"anchor\" fig-type=\"figure\" id=\"FIG1\"><label>Figure 1</label><caption><title>CTA and MRI of head and neck.</title><p>(A) and (B): Computed tomography angiography (CTA) head and neck without any large vessel occlusion. (C) and (D): MRI brain with stroke protocol DWI and Flair without any evidence of stroke or metastasis. (E) and (F): T1 and T2 images of dedicated MRI orbits with contrast showing metastasis in the right orbit and cavernous sinus.</p></caption></fig>", "<fig position=\"anchor\" fig-type=\"figure\" id=\"FIG2\"><label>Figure 2</label><caption><title>MRI cervical spine.</title><p>(A) and (B) MRI cervical spine T1 and T2 with an abnormal marrow signal. (C) and (D) Diffusion-weighted signal imaging of (C) spine showing extensive signals from vertebral bodies suspicious of metastasis. (E) and (F) Enhancement on the vertebral body of C7 with enhancement behind it representing epidural spread and displacement of cord similar to seen on (A). </p></caption></fig>", "<fig position=\"anchor\" fig-type=\"figure\" id=\"FIG3\"><label>Figure 3</label><caption><title> FDG-PET CT.</title><p>(A) Increased uptake seen in apical-lateral prostate similar to area showing small cell neuroendocrine growth. (B) Skull not demonstrating enhanced growth near the area of orbital metastasis. (C) Spine not showing significant FDG enhancement. (D) Liver metastasis demonstrating increased uptake to FDG. FDG-PET: 18 F-fluorodeoxyglucose-positron emission tomography.</p></caption></fig>" ]

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[ "<fn-group content-type=\"other\"><title>Author Contributions</title><fn fn-type=\"other\"><p><bold>Concept and design:</bold>  Mahvish Renzu, Saad Ahmed, Akhil Jain, Ishaan J. Bhatt, Oleg M. Teytelboym, Gregory C. Stachelek, Rajesh Thirumaran</p><p><bold>Acquisition, analysis, or interpretation of data:</bold>  Mahvish Renzu, Saad Ahmed, Akhil Jain, Ishaan J. Bhatt, Oleg M. Teytelboym, Gregory C. Stachelek, Rajesh Thirumaran</p><p><bold>Drafting of the manuscript:</bold>  Mahvish Renzu, Saad Ahmed, Akhil Jain, Ishaan J. Bhatt, Oleg M. Teytelboym, Gregory C. Stachelek, Rajesh Thirumaran</p><p><bold>Critical review of the manuscript for important intellectual content:</bold>  Mahvish Renzu, Saad Ahmed, Akhil Jain, Ishaan J. Bhatt, Oleg M. Teytelboym, Gregory C. Stachelek, Rajesh Thirumaran</p><p><bold>Supervision:</bold>  Akhil Jain, Oleg M. Teytelboym, Gregory C. Stachelek, Rajesh Thirumaran</p></fn></fn-group>", "<fn-group content-type=\"other\"><title>Human Ethics</title><fn fn-type=\"other\"><p>Consent was obtained or waived by all participants in this study</p></fn></fn-group>", "<fn-group content-type=\"competing-interests\"><fn fn-type=\"COI-statement\"><p>The authors have declared that no competing interests exist.</p></fn></fn-group>" ]

[ "<graphic xlink:href=\"cureus-0015-00000050566-i01\" position=\"float\"/>", "<graphic xlink:href=\"cureus-0015-00000050566-i02\" position=\"float\"/>", "<graphic xlink:href=\"cureus-0015-00000050566-i03\" position=\"float\"/>" ]

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{ "acronym": [], "definition": [] }

[ "<title>Introduction</title>", "<p><italic>Mycobacterium tuberculosis</italic> complex (MTBC) causes tuberculosis. When a person coughs, sneezes, talks, or sings, droplet nuclei are produced, which spread from person to person through the air (##UREF##0##1##, ##REF##28074128##2##). Coughing for more than 2 weeks, fever, weight loss, and sputum production can occur in conjunction with hemoptysis, loss of appetite, night sweats, and fatigue, which are expressive clinical signs in patients positive for pulmonary tuberculosis (##UREF##1##3##).</p>", "<p>In 2021, approximately 1.6 million people died and 10.6 million people contracted tuberculosis (TB) worldwide. Low-and middle-income countries accounted for 80% of cases and deaths, with 23% of new cases in the World Health Organization (WHO) Africa region. TB is the second biggest killer among infectious diseases and the 13th leading cause of death worldwide (##UREF##0##1##).</p>", "<p>Globally, there were approximately 11 million people imprisoned in 2018. An increment of approximately 24% was observed between 2000 and 2018 globally. The imprisoned population in Africa has increased by 29% in recent years, and the tuberculosis burden in this region is the highest compared to other WHO regions (##UREF##0##1##). The prison system is a potential area for transmitting communicable diseases such as tuberculosis due to overcrowding, poor ventilation, inadequate lighting, illicit drug use, difficulty accessing health services, lack of or precarious basic sanitation housing infrastructure, and malnutrition (##UREF##0##1##, ##UREF##1##3–5##).</p>", "<p>In developing countries, TB is more common in prisons than in the general population, and prisons in SSA are riskier due to the high number of incarcerated people per cell block, ventilation systems, nutrition-related issues, and high prevalence of human immunodeficiency virus (HIV) (##REF##25809766##6–9##). TB prevalence among prisoners from 24 SSA countries ranges from 0.4 to 16.3% (##REF##30274489##10##).</p>", "<p>Prison staff are at risk of contracting tuberculosis due to their interaction with their inmates, which leads to the spread of the disease to their families and communities. This suggests that tuberculosis in prison is a concern for society as a whole, not just for prisoners (##UREF##4##7##). Therefore, compared to other regions in the world, SSA is one of the regions with a high burden of TB; therefore, this systematic review and meta-analysis helps to update the prevalence of tuberculosis among prisoners, inform policymakers, and improve approaches to prisoners.</p>" ]

[ "<title>Materials and methods</title>", "<title>Reporting</title>", "<p>The results were reported using the Preferred Reporting Items for Systematic Reviews and Meta-Analyzes (PRISMA) statement (##REF##19622511##11##). The article screening was based on the PRISMA 2009 statement, and the selection process has been shown using a PRISMA-P flow diagram. This review is registered in PROSPERO with registration number CRD42023428933.</p>", "<title>Search methods and strategies</title>", "<p>To identify potentially relevant articles, we performed exhaustive searches of electronic databases with no date limits on Google Scholar, Web of Science, PubMed/MEDLINE, Science Direct, PubMed/MEDLINE, and EMBASE. All searches were limited to articles written in English and human studies. We conducted a manual search for additional relevant studies using references from retrieved articles and related systematic reviews to identify original articles that may have been overlooked. The following keywords were used to generate search strings or terms: prevalence, magnitude, Tuberculosis, Pulmonary Tuberculosis, Mycobacterium infections, Prisoners, and sub-Saharan Africa. Advanced search databases were built with the above-mentioned terms in mind, using “Medical Subject Headings (MeSH) [((((“Prevalence”) OR “Burden” OR “Magnitude”) AND “tuberculosis” AND “prisoners”)) AND sub-Saharan Africa].</p>", "<title>Inclusion and exclusion criteria</title>", "<p>All studies on the prevalence of tuberculosis among prisoners in sub-Saharan Africa were included. Furthermore, this systematic review and meta-analysis included all cross-sectional studies on prisoners published in English and conducted in sub-Saharan Africa. Review papers, case series, case reports, abstracts, and qualitative studies were also barred from consideration.</p>", "<title>Outcome measurement</title>", "<p>One major finding that emerged from this systematic review and meta-analysis is the estimation of the pooled prevalence of tuberculosis among prisoners in sub-Saharan Africa. A tuberculosis-positive patient has a <italic>Mycobacterium tuberculosis</italic> complex found in a clinical specimen, whether by smear, culture, or WHO-recommended rapid diagnosis (such as Xpert MTB/RIF).</p>", "<title>Data extraction and quality assessment</title>", "<p>Endnote citation manager software version X9 for Windows was used to import retrieved studies from the databases, and manual removal was performed for duplicated articles. All articles were screened by three independent reviewers for predefined inclusion and exclusion criteria (abstract and title), followed by a full-text review. If disagreements regarding the inclusion of studies could not be resolved, a fourth investigator was invited to reach an agreement. Excel spreadsheet software was used to extract the data from the included studies. The spreadsheet included the first author’s name, publication year, study design, country, sample size, diagnostic methods, and number of cases (##TAB##0##Table 1##).</p>", "<title>Statistical analysis</title>", "<p>The analysis was carried out using the statistical software STATA Version 14.1 (StataCorp, College Station, Texas, United States), and heterogeneity was checked across studies by computing the I2 statistical test. If the I² values were 0, 25, 50, and 75%, we assumed no, low, medium, and high heterogeneity across studies. A meta-analysis using a fixed-effects model with 95% confidence intervals (CI) was performed to analyze the pooled prevalence of tuberculosis among prisoners (I²,16.3% <italic>p</italic> = 0.188). A visual inspection of the funnel plot was performed to check for evidence of publication bias, followed by Begg’s rank and Egger’s tests, with a value of p of less than 0.05 used as a cut-off point. Leave-one-out sensitivity analysis was also performed to assess the impact of a small study. The analysis was carried out step-by-step, excluding the study, to assess the effect of each study on the pooled prevalence of tuberculosis. A forest plot was used to estimate pooled prevalence.</p>" ]

[ "<title>Results</title>", "<title>Selection of studies</title>", "<p>A search of the biomedical electronic databases yielded 352 published and unpublished studies. Although 244 duplicate articles were identified and removed, 108 were included in the screening. After 64 studies were removed based on title and abstract screening, 44 studies remained. Finally, 40 studies that met the eligibility criteria were included in the final analysis to estimate the pooled prevalence of tuberculosis among sub-Saharan African prisoners. The full selection process is illustrated in ##FIG##0##Figure 1##.</p>", "<title>Included studies characteristics</title>", "<p>Among the 40 included studies, there were 19 studies were from Ethiopia; 3 from South Africa; 3 from Nigeria, Malawi, and Zambia; 2 from the Democratic Republic of Congo (DRC), Uganda, and Ghana; and 1 from Cameroon, Tanzania, and Cote d’Ivoire. The included study sample size ranged from 84 (2) to 31,843 (3), with 80,608 prisoners. Observational and interventional studies published between 1997 and 2020 were included. All 40 articles had a cross-sectional design. All included studies were facility-based (##TAB##0##Table 1## illustrates the included studies’ baseline characteristics).</p>", "<title>The pooled prevalence of tuberculosis among prisoners in sub-Saharan Africa</title>", "<p>The pooled prevalence of tuberculosis among sub-Saharan African prisoners was 4.02% (95% CI: 2.68–5.36). The forest plot shows that statistical heterogeneity was low (I² = 16.3%; <italic>p</italic> 0.188). As a result, we used a fixed effects model to estimate the pooled prevalence of tuberculosis (##FIG##1##Figure 2##).</p>", "<title>Sub-group analysis</title>", "<p>A sub-group analysis based on diagnostic methods and country setting was performed to identify potential sources of heterogeneity. It shows the highest detection of tuberculosis was by Gene Xpert, which was 4.97% (95% CI: 2.22–7.73); sputum smear microscopy was 3.53% (95% CI: 1.92–5.13) and culture was 2.88% (95% CI: 2.40–8.16) (##FIG##2##Figure 3##). Thus, we observed country variation in the prevalence of tuberculosis in this study. The prevalence of TB was found to range between 7.10 (95% CI: 4.58–9.62) in Ethiopia and 1.37 (95% CI:-1.17–3.91) in Malawi (##FIG##3##Figure 4##).</p>", "<title>Meta-regression</title>", "<p>Meta-regression was used to identify factors associated with the pooled prevalence of tuberculosis among prisoners while keeping continuous variables in mind. For the meta-regression, publication year and sample size were considered. Meta-regression analysis revealed no statistically significant relationship between the pooled prevalence of tuberculosis among prisoners and publication year or sample size (##TAB##1##Table 2##).</p>", "<title>Publication bias</title>", "<p>To assess possible publication bias, a visually inspected funnel plot was used, which was statistically supported by Egger’s and Begg’s rank regression tests. The symmetrical distribution of the included studies in a large inverted funnel demonstrated the absence of a publication bias. With <italic>p</italic>-values of (<italic>p</italic> = 0.26) and (<italic>p</italic> = 0.15), respectively, the Egger and Begg rank tests revealed no publication bias among the included articles to estimate the pooled prevalence of tuberculosis among prisoners in sub-Saharan Africa (##FIG##4##Figure 5##).</p>", "<title>Sensitivity analysis</title>", "<p>By excluding each study one at a time, a leave-out-one sensitivity analysis was used to determine the effect of a single study on the pooled prevalence of tuberculosis among prisoners in sub-Saharan Africa. According to the findings, no single study had a significant impact on the pooled estimate of tuberculosis among prisoners in sub-Saharan Africa (##TAB##2##Table 3##).</p>", "<title>Trends of TB prevalence</title>", "<p>The trend analysis indicated that despite efforts to eradicate TB, the disease burden among prisoners in sub-Saharan Africa continued to rise from 1997 to 2020 (##FIG##5##Figure 6##).</p>" ]

[ "<title>Discussion</title>", "<p>There is evidence that the number of people developing tuberculosis is increasing in many low-and middle-income countries, and between 2019 and 2021, the number of deaths from tuberculosis also increased (##UREF##0##1##). In prisons, infectious diseases such as tuberculosis may spread more easily because segregation criteria are based on criminal characteristics rather than on public health concerns (##REF##36425906##52##). As a result, the goal of this systematic review and meta-analysis was to report the most recent estimated pooled prevalence of tuberculosis among prisoners in sub-Saharan Africa.</p>", "<p>The prevalence of tuberculosis among household contacts was 3.29% (95% CI: 2.35–4.23) (##REF##36425906##52##). A recent systematic review has documented a 3- to 1,000-fold increase in the prevalence of TB in prisons compared to the general population (##REF##25203007##22##). In SSA, it is estimated to be 6–30 times higher than that in the general population (##UREF##17##53##). According to the results of the current systematic review and meta-analysis, the pooled prevalence of tuberculosis among prisoners in sub-Saharan Africa was 4.02% (95% CI: 2.68–5.36). This finding is consistent with findings from Tajikistan 4.5% (##REF##24465861##54##), South Africa 2.7% (##UREF##16##51##), and Ethiopia (4.0%) (##REF##31687207##55##). This could be attributed to the similarity in tuberculosis diagnostic methods used in the incarcerated population.</p>", "<p>However, the pooled prevalence of tuberculosis in this systematic review and meta-analysis was lower than that in an Ethiopian systematic review and meta-analysis (8.33%) (2). Furthermore, it was lower than that in studies conducted in Brazil 27.8% (##REF##26459530##56##), Malaysia 7.7% (##REF##27197601##57##), Nepal 10% (##REF##31687207##55##), Iran 7.9% (##UREF##18##58##), South Africa (8.8%) (##REF##19622511##11##), and Zambia (6.4%) (##REF##27038898##26##). The lower prevalence found in this study could be attributed to differences in geographical location and the number of rooms with prisoners with poor ventilation.</p>", "<p>The pooled prevalence of tuberculosis among prisoners in the current meta-analysis was higher than that in studies conducted in Brazil (1.89%) (##REF##30236184##59##), Thailand (2.1%) (##REF##30236189##60##), and Peru (2.5%) (##UREF##19##61##). The higher prevalence of tuberculosis in our study might be due to overcrowding and the difference in the incarcerated years of inmates.</p>", "<p>Sub-group analysis of the pooled prevalence of tuberculosis among prisoners in sub-Saharan Africa showed no statistically significant difference (<italic>p</italic> = 0.188). Using diagnostic methods, tuberculosis was detected by Gene Xpert (4.97%), sputum smear microscopy (3.53%), and culture (2.88%). Xpert MTB/RIF’s suitability and feasibility as an MTB diagnostic method are attributed to its suitability and feasibility as a quick, reliable, controllable, simple, and cost-effective test (##REF##29912907##62##). Gene Xpert uses DNA PCR technology to detect MTB and rifampicin resistance mutations simultaneously (##REF##33386072##63##).</p>", "<p>The sub-group analysis of this review also showed that the prevalence of tuberculosis among prisoners was higher in Ethiopia (7.10%) compared to other countries in sub-Saharan Africa. The variation in the prevalence of pulmonary TB within countries in prisons could be due to differences in diagnostic techniques, screening methods, overcrowding, and sociocultural and socioeconomic factors among the study participants.</p>", "<p>An ongoing intervention for Tuberculosis (TB) in sub-Saharan Africa is the implementation of active case-finding and treatment programs within prisons. This involves screening all inmates for TB, providing treatment for those who test positive, and implementing infection control measures to prevent the spread of the disease within the prison environment. Additionally, TB preventive therapy is provided to high-risk inmates, such as those with HIV or other underlying health conditions, which helps to reduce the overall burden of TB within the prison population (##REF##21251881##64##).</p>", "<title>Strengths and limitations of the study</title>", "<p>The strength of this review is that it follows the recommended PRISMA guidelines. We also rigorously searched the literature in different databases and identified eligible studies. Moreover, in the present review, the heterogeneity among studies was low. While interpreting the results of this systematic review and meta-analysis, we considered the limitations of this review. We were forced to compare our findings with those of primary studies in some parts of the discussion because of a lack of adequate systematic reviews and meta-analyzes. The other limitation of this review is that we only considered articles written in the English language, which may result in the exclusion of other articles. Last but not least, we found studies conducted in 13 SSA countries, which may not represent prisoners throughout the whole region.</p>" ]

[ "<title>Conclusion</title>", "<p>The pooled prevalence of tuberculosis among prisoners in sub-Saharan Africa was prominently high based on this systematic review and meta-analysis. Therefore, to reach the end of the global TB epidemic, improvement in the prison setting is important. Screening on entry to the prison, periodical TB symptom screening, TB prevention training and information dissemination among the health staff in the prison and the inmates, and immediate treatment of diseased prisoners are important these measure to be put in place. Finally, this will help with the early identification and diagnosis of tuberculosis, which will reduce multidrug-resistant tuberculosis occurrence.</p>" ]

[ "<p>Edited by: Belaineh Girma Belaineh, International Training and Education Center for Health (I-TECH), United States</p>", "<p>Reviewed by: Raymond Salanga Dankoli, World Health Organisation, Ukraine; Andargachew Kumsa Erena, Ministry of Health, Ethiopia</p>", "<title>Background</title>", "<p>Tuberculosis (TB) is a key community health problem in numerous settings, predominantly in sub-Saharan Africa (SSA). TB is the second most lethal infectious disease worldwide. Around 1.6 million people died from TB in 2021. TB prevention and control strategies are difficult to implement in prison, especially in sub-Saharan Africa, owing to overcrowding and poor ventilation. Thus, this systematic review and meta-analysis aimed to synthesize the estimated pooled prevalence of tuberculosis among prisoners in sub-Saharan Africa.</p>", "<title>Materials and methods</title>", "<p>Electronic biomedical databases such as Google Scholar, Web of Science, PubMed/Medline, EMBASE, and Science Direct were used to systematically explore candidate studies published until December 2022. Data extraction was performed using a Microsoft Excel spreadsheet. The estimated pooled prevalence of tuberculosis was determined using a fixed-effects model. Cochrane Q-test and I² statistics were used to check heterogeneity statistically across different studies. Begg’s rank and Egger’s tests were performed to assess evidence of possible publication bias.</p>", "<title>Results</title>", "<p>A total of 40 articles involving 59,300 prisoners were included in this systematic review and meta-analysis. The pooled prevalence of tuberculosis was 4.02% (95% CI: 2.68–5.36). We found the highest prevalence using Gene X pert as a diagnostic method, which was 4.97 (95% CI: 2.22–7.73). There is no evidence of publication bias.</p>", "<title>Conclusion</title>", "<p>The outcome of this review revealed a high prevalence of tuberculosis among prisoners in sub-Saharan Africa. To reach the “End Tuberculosis strategy” by 2030, early identification of cases through screening on entry and periodical active case finding is important. Moreover, prevention and prompt treatment after diagnosis must be implemented to limit transmission to the general population.</p>", "<title>Systematic review registration</title>", "<p><ext-link xlink:href=\"https://www.crd.york.ac.uk/prospero/#searchadvanced\" ext-link-type=\"uri\">https://www.crd.york.ac.uk/prospero/#searchadvanced</ext-link>, identifier (CRD42023428933).</p>" ]

[ "<title>Data availability statement</title>", "<p>The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.</p>", "<title>Author contributions</title>", "<p>The study was conceptualized and developed by YS and TM, who also conducted data analysis and interpretation and wrote the first draft. GA and YS built the search strategy, extracted the data, and assessed the quality of the studies included. The writing was reviewed and edited by YS and TM. All authors contributed to the article and approved the submitted version.</p>" ]

[ "<p>We would like to thank the primary study authors who contributed to this systematic review and meta-analysis.</p>", "<title>Conflict of interest</title>", "<p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p>", "<title>Publisher’s note</title>", "<p>All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.</p>" ]

[ "<fig position=\"float\" id=\"fig1\"><label>Figure 1</label><caption><p>PRISMA flow diagram of articles screened and the selection process on tuberculosis among prisons in sub-Saharan, 2023.</p></caption></fig>", "<fig position=\"float\" id=\"fig2\"><label>Figure 2</label><caption><p>Forest plot showing pooled prevalence estimate of tuberculosis infection among prisoners in sub-Saharan Africa, 2023.</p></caption></fig>", "<fig position=\"float\" id=\"fig3\"><label>Figure 3</label><caption><p>Forest plot displaying subgroup analysis on the pooled prevalence of tuberculosis by diagnostic method among prisoners in sub-Saharan Africa, 2023.</p></caption></fig>", "<fig position=\"float\" id=\"fig4\"><label>Figure 4</label><caption><p>Forest plot displaying subgroup analysis on the pooled prevalence of tuberculosis by country among prisoners in sub-Saharan Africa, 2023.</p></caption></fig>", "<fig position=\"float\" id=\"fig5\"><label>Figure 5</label><caption><p>Funnel plot showing publication bias of studies reporting the pooled prevalence of tuberculosis among prisoners in sub-Saharan Africa, 2023.</p></caption></fig>", "<fig position=\"float\" id=\"fig6\"><label>Figure 6</label><caption><p>Time trend of TB prevalence among prisoners in sub-Saharan Africa from 1997 to 2020.</p></caption></fig>" ]

[ "<table-wrap position=\"float\" id=\"tab1\"><label>Table 1</label><caption><p>The baseline characteristics of the included studies, 2023.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Authors</th><th align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Year</th><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Country</th><th align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Sample size</th><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Diagnostic methods</th><th align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">No_of cases</th><th align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Prevalence (%)</th></tr></thead><tbody><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Abebe et al. (##REF##21756520##12##)</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2011</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Ethiopia</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">382</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Sputum smear microscopy</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">33</td><td align=\"char\" valign=\"top\" char=\".\" rowspan=\"1\" colspan=\"1\">8.9</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Adane et al. (##REF##30824364##13##)</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2019</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Ethiopia</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">1,124</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Sputum smear microscopy</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">34</td><td align=\"char\" valign=\"top\" char=\".\" rowspan=\"1\" colspan=\"1\">3</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Adane et al. (##REF##26914770##14##)</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2016</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Ethiopia</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">809</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Sputum smear microscopy</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">74</td><td align=\"char\" valign=\"top\" char=\".\" rowspan=\"1\" colspan=\"1\">5.88</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Addis et al. (##REF##25902026##15##)</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2015</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Ethiopia</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">384</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Sputum smear microscopy</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">33</td><td align=\"char\" valign=\"top\" char=\".\" rowspan=\"1\" colspan=\"1\">8.59</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Adesokan et al. (##REF##26689165##16##)</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2014</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Nigeria</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">164</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Culture</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2</td><td align=\"char\" valign=\"top\" char=\".\" rowspan=\"1\" colspan=\"1\">1.2</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Agajie et al. (##UREF##5##17##)</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2018</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Ethiopia</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">84</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">GeneXpert</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">8</td><td align=\"char\" valign=\"top\" char=\".\" rowspan=\"1\" colspan=\"1\">9.5</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Ali et al. (##REF##26641654##18##)</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2015</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Ethiopia</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">765</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Sputum smear microscopy</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">71</td><td align=\"char\" valign=\"top\" char=\".\" rowspan=\"1\" colspan=\"1\">9.2</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Banda et al. (##REF##19919776##19##)</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2009</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Malawi</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">7,661</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Sputum smear microscopy</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">54</td><td align=\"char\" valign=\"top\" char=\".\" rowspan=\"1\" colspan=\"1\">0.7</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Bayu et al. (##UREF##6##20##)</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2016</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Ethiopia</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">302</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Sputum smear microscopy</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">17</td><td align=\"char\" valign=\"top\" char=\".\" rowspan=\"1\" colspan=\"1\">5.57</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Berihun et al. (##REF##29983535##21##)</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2018</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Ethiopia</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">162</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Sputum smear microscopy</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">32</td><td align=\"char\" valign=\"top\" char=\".\" rowspan=\"1\" colspan=\"1\">19.6</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Biyadgilign et al. (##REF##25203007##22##)</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2014</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Ethiopia</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">200</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">GeneXpert</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">16</td><td align=\"char\" valign=\"top\" char=\".\" rowspan=\"1\" colspan=\"1\">8</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Chigbuand Iroegbu (##REF##20824975##23##)</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2010</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Nigeria</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">168</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Interferon Gamma Release Assay</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">22</td><td align=\"char\" valign=\"top\" char=\".\" rowspan=\"1\" colspan=\"1\">13</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Chekesa et al. (##REF##32428042##24##)</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2020</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Ethiopia</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">352</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Sputum smear microscopy</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">180</td><td align=\"char\" valign=\"top\" char=\".\" rowspan=\"1\" colspan=\"1\">51.2</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Dibissa et al. (##UREF##7##25##)</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2019</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Ethiopia</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">249</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">GeneXpert</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">15</td><td align=\"char\" valign=\"top\" char=\".\" rowspan=\"1\" colspan=\"1\">6</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Fuge et al. (##REF##27038898##26##)</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2016</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Ethiopia</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">164</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Sputum smear microscopy</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">3</td><td align=\"char\" valign=\"top\" char=\".\" rowspan=\"1\" colspan=\"1\">1.8</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Gebrecherkos et al. (##REF##27756279##27##)</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2016</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Ethiopia</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">282</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">GeneXpert</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">15</td><td align=\"char\" valign=\"top\" char=\".\" rowspan=\"1\" colspan=\"1\">5.3</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Gizachew et al. (##REF##29226216##28##)</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2017</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Ethiopia</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">265</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">GeneXpert</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">9</td><td align=\"char\" valign=\"top\" char=\".\" rowspan=\"1\" colspan=\"1\">3.4</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Habeenzu et al. (##REF##17958984##29##)</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2007</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Zambian</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">1,080</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Culture</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">245</td><td align=\"char\" valign=\"top\" char=\".\" rowspan=\"1\" colspan=\"1\">22.7</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Henostriza et al. (##REF##23967048##30##)</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2013</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Zambian</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2,323</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Sputum smear microscopy</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">88</td><td align=\"char\" valign=\"top\" char=\".\" rowspan=\"1\" colspan=\"1\">3.8</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Jordan et al. (##REF##31718756##31##)</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2019</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">South Africa</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">31,843</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">GeneXpert</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">859</td><td align=\"char\" valign=\"top\" char=\".\" rowspan=\"1\" colspan=\"1\">2.7</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Kalonji et al. (##REF##27672349##32##)</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2016</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">DRC</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">733</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Sputum smear microscopy</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">130</td><td align=\"char\" valign=\"top\" char=\".\" rowspan=\"1\" colspan=\"1\">17.7</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Kanyerere et al. (##REF##26392938##33##)</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2012</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Malawi</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2,217</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Sputum smear microscopy</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">44</td><td align=\"char\" valign=\"top\" char=\".\" rowspan=\"1\" colspan=\"1\">2</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Kwabla et al. (##UREF##8##34##)</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2015</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Ghana</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">151</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">GeneXpert</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"char\" valign=\"top\" char=\".\" rowspan=\"1\" colspan=\"1\">0.9</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Keyomo et al. (##REF##30334730##35##)</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2018</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">DRC</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">918</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Sputum smear microscopy</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">27</td><td align=\"char\" valign=\"top\" char=\".\" rowspan=\"1\" colspan=\"1\">2.9</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Lawal et al. (##UREF##9##36##)</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2009</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Nigerian</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2002</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Sputum smear microscopy</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">48</td><td align=\"char\" valign=\"top\" char=\".\" rowspan=\"1\" colspan=\"1\">2.4</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Maggard et al. (##REF##25883402##37##)</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2015</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Zambian</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">7,638</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Culture</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">306</td><td align=\"char\" valign=\"top\" char=\".\" rowspan=\"1\" colspan=\"1\">4</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Merid et al. (##REF##29663957##38##)</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2018</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Ethiopia</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">372</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">GeneXpert</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">34</td><td align=\"char\" valign=\"top\" char=\".\" rowspan=\"1\" colspan=\"1\">9.13</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Mohammed et al. (##UREF##10##39##)</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2017</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Ethiopia</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">765</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">GeneXpert</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">23</td><td align=\"char\" valign=\"top\" char=\".\" rowspan=\"1\" colspan=\"1\">3</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Moges et al. (##REF##22214291##40##)</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2012</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Ethiopia</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">250</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Sputum smear microscopy</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">26</td><td align=\"char\" valign=\"top\" char=\".\" rowspan=\"1\" colspan=\"1\">10.4</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Mmbaga et al. (##UREF##11##41##)</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2013</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Tanzania</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">448</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Sputum smear microscopy</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">22</td><td align=\"char\" valign=\"top\" char=\".\" rowspan=\"1\" colspan=\"1\">5</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Mpeirwe et al. (##UREF##12##42##)</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2016</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Uganda</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">140</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Sputum smear microscopy</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">11</td><td align=\"char\" valign=\"top\" char=\".\" rowspan=\"1\" colspan=\"1\">8</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Noeske et al. (##REF##16642747##43##)</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2006</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Cameroon</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2,474</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Sputum smear microscopy</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">87</td><td align=\"char\" valign=\"top\" char=\".\" rowspan=\"1\" colspan=\"1\">3.5</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Nyangulu et al. (##REF##9357408##44##)</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">1997</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Malawi</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">914</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Sputum smear microscopy</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">47</td><td align=\"char\" valign=\"top\" char=\".\" rowspan=\"1\" colspan=\"1\">5.1</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Owokuhaisa et al. (##REF##26949722##45##)</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2014</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Uganda</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">248</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Sputum smear microscopy</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2</td><td align=\"char\" valign=\"top\" char=\".\" rowspan=\"1\" colspan=\"1\">1.2</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Sesay et al. (##UREF##13##46##)</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2016</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Ghana</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">148</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">GeneXpert</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">12</td><td align=\"char\" valign=\"top\" char=\".\" rowspan=\"1\" colspan=\"1\">8</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Seri et al. (##REF##28759620##47##)</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2017</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Cote d’ivoire</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">943</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Sputum smear microscopy</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">89</td><td align=\"char\" valign=\"top\" char=\".\" rowspan=\"1\" colspan=\"1\">9.3</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Telisinghe et al. (##REF##24498059##48##)</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2014</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">South Africa</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">981</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Sputum smear microscopy</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">34</td><td align=\"char\" valign=\"top\" char=\".\" rowspan=\"1\" colspan=\"1\">3.4</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Winsa et al. (##UREF##14##49##)</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2015</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Ethiopia</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">196</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Sputum smear microscopy</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">43</td><td align=\"char\" valign=\"top\" char=\".\" rowspan=\"1\" colspan=\"1\">21.9</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Zerdo et al. (##UREF##15##50##)</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2014</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Ethiopia</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">3,817</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">GeneXpert</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">24</td><td align=\"char\" valign=\"top\" char=\".\" rowspan=\"1\" colspan=\"1\">19.35</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Zishiri et al. (##UREF##16##51##)</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2015</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">South Africa</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">4,945</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">GeneXpert</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">445</td><td align=\"char\" valign=\"top\" char=\".\" rowspan=\"1\" colspan=\"1\">9</td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"tab2\"><label>Table 2</label><caption><p>Meta-regression to identify the source of heterogeneity for the pooled prevalence of tuberculosis among prisoners in sub-Saharan Africa, 2023.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Prevalence</th><th align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Coefficient</th><th align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">[95% Conf. Interval]</th><th align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\"><italic>p</italic>-value</th></tr></thead><tbody><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Publication year</td><td align=\"char\" valign=\"top\" char=\".\" rowspan=\"1\" colspan=\"1\">0.227</td><td align=\"char\" valign=\"top\" char=\".\" rowspan=\"1\" colspan=\"1\">(−0.120—0.575)</td><td align=\"char\" valign=\"top\" char=\".\" rowspan=\"1\" colspan=\"1\">0.194</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Sample size</td><td align=\"char\" valign=\"top\" char=\".\" rowspan=\"1\" colspan=\"1\">0.038</td><td align=\"char\" valign=\"top\" char=\".\" rowspan=\"1\" colspan=\"1\">(−0.120—0.197)</td><td align=\"char\" valign=\"top\" char=\".\" rowspan=\"1\" colspan=\"1\">0.626</td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"tab3\"><label>Table 3</label><caption><p>Sensitivity analysis for the pooled prevalence of tuberculosis among prisoners in sub-Saharan Africa, 2023.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Study omitted</th><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Estimate</th><th align=\"left\" valign=\"top\" colspan=\"2\" rowspan=\"1\">[95% Confidence Interval]</th></tr></thead><tbody><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Abebe et al. (##REF##21756520##12##)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">3.9517572</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2.6016049</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">5.3019094</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Adane et al. (##REF##26914770##14##)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">3.9698453</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2.610373</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">5.3293176</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Adane et al. (##REF##30824364##13##)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">4.0264149</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2.6838734</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">5.3689561</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Addis et al. (##REF##25902026##15##)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">3.9512789</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2.6003962</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">5.3021617</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Adesokan et al. (##REF##26689165##16##)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">4.1032853</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2.7440047</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">5.4625654</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Agajie et al. (##UREF##5##17##)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">3.9199085</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2.5669565</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">5.2728605</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Ali et al. (##REF##26641654##18##)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">3.9316785</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2.5795114</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">5.2838459</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Banda et al. (##REF##19919776##19##)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">4.9349384</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">3.4217978</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">6.4480796</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Bayu et al. (##UREF##6##20##)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">3.9951077</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2.6426461</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">5.3475695</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Berihun et al. (##REF##29983535##21##)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">3.9953275</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2.6537507</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">5.3369045</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Biyadgilign et al. (##REF##25203007##22##)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">3.9976826</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2.6530216</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">5.3423433</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Chekesa et al. (##REF##32428042##24##)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">3.8280344</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2.484884</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">5.1711845</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Chigbu and Iroegbu (##REF##20824975##23##)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">3.9933197</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2.6507154</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">5.3359241</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Dibissa et al. (##UREF##7##25##)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">3.9965336</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2.6471703</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">5.3458967</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Fuge et al. (##REF##27038898##26##)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">4.0650678</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2.7120979</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">5.4180374</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Gebrecherkos et al. (##REF##27756279##27##)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">4.0001011</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2.6476939</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">5.3525081</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Gizachew et al. (##REF##29226216##28##)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">4.0395069</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2.6816096</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">5.3974042</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Habeenzu et al. (##REF##17958984##29##)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">4.018136</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2.6775756</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">5.3586965</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Henostriza et al. (##REF##23967048##30##)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">4.0268888</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2.6752195</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">5.3785586</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Jordan et al. (##REF##31718756##31##)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">4.074502</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2.7083395</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">5.4406643</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Kalonji et al. (##REF##27672349##32##)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">3.8945763</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2.5479105</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">5.2412419</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Kenyerere et al. (##REF##26392938##33##)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">4.0827537</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2.722759</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">5.4427481</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Keyomo et al. (##REF##30334730##35##)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">4.0945396</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2.7121942</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">5.4768853</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Kwabla et al. (##UREF##9##36##)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">4.1072049</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2.748898</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">5.4655113</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Lawal et al. (##UREF##9##36##)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">4.0595646</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2.7042162</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">5.4149127</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Maggard et al. (##REF##25883402##37##)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">4.0239921</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2.6584303</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">5.3895535</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Merid et al. (##REF##29663957##38##)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">3.9560311</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2.6068742</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">5.3051877</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Mmbaga et al. (##UREF##11##41##)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">3.9964359</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2.6378741</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">5.3549976</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Moges et al. (##REF##22214291##40##)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">3.9682374</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2.6221004</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">5.3143744</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Mohammed et al. (##UREF##10##39##)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">4.0767627</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2.7016883</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">5.4518375</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Mpeirwe et al. (##UREF##12##42##)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">4.0155196</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2.6738584</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">5.3571806</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Noeske et al. (##REF##16642747##43##)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">4.0334573</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2.6798909</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">5.3870234</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Nyangulu et al. (##REF##9357408##44##)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">3.9854186</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2.6217782</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">5.3490591</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Owokuhaisa et al. (##REF##26949722##45##)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">4.2006702</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2.8187807</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">5.5825596</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Seri et al. (##REF##28759620##47##)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">3.9125924</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2.5582464</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">5.2669387</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Sesay et al. (##UREF##13##46##)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">4.0129809</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2.6708927</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">5.3550696</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Telisinghe et al. (##REF##24498059##48##)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">4.0587621</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2.6805737</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">5.4369502</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Winsa et al. (##UREF##14##49##)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">3.9790123</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2.6369779</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">5.3210464</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Zerdo et al. (##UREF##15##50##)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">3.9120765</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2.566848</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">5.2573047</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Zishiri et al. (##UREF##16##51##)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">3.9422443</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2.5910163</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">5.2934723</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Combined</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">4.0231154</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2.6827336</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">5.3634972</td></tr></tbody></table></table-wrap>" ]

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[ "<fn-group><title>Abbreviations</title><fn fn-type=\"abbr\"><p>CI, Confidence Interval; HIV/AIDS, Human immune Virus/Acquired immune deficiency syndrome; PRISMA, Preferred Reporting Items for Systematic Reviews and Meta-Analyzes; PTB, Pulmonary Tuberculosis; SSA, sub-Sahara Africa; WHO, World Health Organization.</p></fn></fn-group>" ]

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{ "acronym": [], "definition": [] }

[ "<title>Background</title>", "<p id=\"Par14\">Cervical cancer is the fourth most common female cancer globally after breast, colorectal, and lung cancers, and is the second most common female cancer in Low- and Middle-Income Countries (LMICs) after breast cancer [##REF##33538338##1##]. In 2020, it was estimated that approximately 604,127 women were diagnosed with cervical cancer worldwide, with an estimated 341,831 deaths attributable to the disease [##REF##36528031##2##]. The majority (90%) of cervical cancer-related deaths in 2020 occurred in LMICs among women aged 15–49 years, largely due to a lack of access to screening programs, lack of availability of the Human Papillomavirus (HPV) vaccine, poor health infrastructure, and delays in diagnosis [##REF##36528031##2##, ##UREF##0##3##].</p>", "<p id=\"Par15\">Several risk factors are attributed to the development of cervical cancer, including HPV infection, low socioeconomic status, cigarette smoking, long-term use of oral contraceptives, marriage before the age of 18 years, early sexual intercourse, multiple sexual partners, and multiparity [##UREF##1##4##, ##REF##19819687##5##]. Nonetheless, HPV infection (primarily genotypes 16 and 18) is considered to be the central etiological risk factor for cervical cancer [##UREF##2##6##]. According to data sourced from 900 invasive cervical cancer cases originating from 22 countries, 93% of biopsies have HPV DNA [##UREF##1##4##]. Thus, cervical cancer can be primarily prevented by HPV vaccination and routine Pap smear screening [##UREF##1##4##, ##REF##28988647##7##].</p>", "<p id=\"Par16\">While many countries have introduced national HPV vaccination programs over the last several decades, such programs remain rare in LMICs [##REF##28988647##7##]. Furthermore, the implementation of Pap smear screening in LMICs is challenging due to poor attendance and lack of awareness of the importance of this type of screening in the prevention and early detection of cervical cancer [##UREF##3##8##]. Previous studies conducted in LMICs have indicated that women may encounter several barriers to cervical cancer screening, including poor knowledge regarding the importance of the HPV vaccine or Pap smear test, negative attitudes towards cervical cancer-related risk factors (particularly HPV infection), fear of the results, and concern regarding the gender of the doctor performing the screening test [##REF##24289628##9##–##REF##33110645##11##]. In Oman, the HPV vaccine is not yet included in the Expanded National Immunization Program and is therefore not systematically provided by government-funded health institutes under the national Ministry of Health [##UREF##4##12##, ##UREF##5##13##].</p>", "<p id=\"Par17\">In Oman, the incidence of cancer has increased over the past 20 years, a finding which may be attributed to population aging, rapid socioeconomic changes, and the increased prevalence of unhealthy lifestyle practices (e.g., tobacco use, physical inactivity, and unhealthy dietary habits), as well as advances in diagnostic and treatment modalities [##REF##31360330##14##]. Among the population of 1.17 million women aged 15 years and older in Oman, there is a notable risk of developing cervical cancer; current estimates suggest that approximately 88 women are diagnosed with cervical cancer each year, resulting in 50 deaths [##UREF##6##15##]. Moreover, cervical cancer ranks as the fourth most common cancer among women of all ages in Oman and the third most common among women aged 15–44 years [##REF##31360330##14##, ##UREF##6##15##].</p>", "<p id=\"Par18\">Unfortunately, there are currently no available data on the prevalence of HPV in the general population of Oman. However, in Western Asia, the region to which Oman belongs, it is estimated that about 2.5% of women in the general population are affected by cervical HPV infection at any given time [##UREF##7##16##]. Furthermore, 72.4% of invasive cervical cancers in this region are attributed to HPV types 16 or 18 [##UREF##7##16##]. Additionally, a recent study from 2020 indicated that the prevalence of HPV infection remains high in Oman at 17.8% [##REF##31935539##17##].</p>", "<p id=\"Par19\">In Oman, the absence of a well-structured national screening program for cervical cancer and limited availability of Pap smear testing at the primary care level are significant concerns. This lack of accessibility and awareness regarding the availability of the Pap smear test are believed to be the main reasons for the high incidence of cervical cancer in the Middle East region [##REF##24289628##9##, ##REF##29058371##18##]. A previous study indicated that while most Omani women attending a tertiary teaching institute had heard of cervical cancer, they lacked specific knowledge regarding cervical cancer signs and symptoms, risk factors, and Pap smear testing [##REF##28030906##19##]. Therefore, the present study aimed to assess knowledge, attitudes, and practices regarding cervical cancer and Pap smear screening among Omani women attending an outpatient clinic at a tertiary teaching hospital in Muscat, Oman. The study also aimed to establish correlations with various sociodemographic factors. These findings may be useful in informing future health promotion activities that aim to improve cervical cancer awareness in the general public and promote utilization of screening services in Oman.</p>" ]

[ "<title>Methods</title>", "<title>Study design and setting</title>", "<p id=\"Par20\">An observational, cross-sectional study was carried out from October 2019 to February 2020 at the outpatient clinic of the Department of Obstetrics and Gynecology at the Sultan Qaboos University Hospital (SQUH), Muscat, Oman. As a tertiary hospital, SQUH receives patients from all over the country.</p>", "<title>Study subjects and recruitment strategy</title>", "<p id=\"Par21\">A total of 380 Omani women aged 18–50 years old and attending the clinic for various reasons during the study period were recruited via a systematic random sampling strategy in which every second women registered with the clinic was selected for participation.</p>", "<title>Exclusion and inclusion criteria</title>", "<p id=\"Par22\">The inclusion criteria for the participants included all women of Omani nationality between 15 and 50 years of age attending the outpatient clinic for various reasons. However, those with learning difficulties, those who did not speak Arabic or English, those with emergency conditions or who were very sick, and those with no time to participate in the survey were excluded.</p>", "<title>Sample size</title>", "<p id=\"Par23\">The necessary sample size was calculated to be 374 women, based on an anticipated level of knowledge regarding cervical cancer and its screening (50%), with a 5% margin of error, 95% confidence level, 5% alpha error, and a design effect of 2.</p>", "<title>Survey instrument</title>", "<p id=\"Par24\">A validated, pre-tested, Arabic-language questionnaire was used for data collection purposes. The questionnaire had been previously used in similar studies performed in Oman [##REF##28030906##19##–##REF##33773541##22##]. This four-part questionnaire was self-administered and took approximately 15–20 minutes to complete. The first section covered the participants’ sociodemographic characteristics, including their age, education level, employment status, marital status, number of marriages, number of previous pregnancies, their husbands’ education level, and monthly family income. The second section assessed the presence of known risk factors for cervical cancer, such as smoking status, personal history of cervical cancer, and family history of cervical cancer.</p>", "<p id=\"Par25\">The third part of the questionnaire assessed the participants’ knowledge regarding cervical cancer, related risk factors, and appropriate screening. This section covered whether the participants had ever heard of cervical cancer and whether they believed that cervical cancer can be prevented, has a latent and asymptomatic period, can be detected in its early stages, is curable when detected early, is a genetic disease, is more likely if a family member has it, can be prevented by maintaining healthy sexual hygiene, if postmenopausal women and HPV-positive women are at risk of getting cervical cancer, whether cytological examination is the main screening method in the early stages of the disease, and whether the disease is caused by a specific HPV genotype. In addition, this section explored knowledge of cervical cancer-related risk factors, including HPV infection, early sexual activity, multiple sexual partners, multiparity, and smoking status. All questions in the second and third sections of the questionnaire were designed to elicit yes/no responses.</p>", "<p id=\"Par26\">The last part of the questionnaire evaluated the participants’ knowledge, attitudes, and practices related to cervical cancer screening and Pap smear testing. Participants were asked if they had ever heard of Pap smear screening, if they had previously undergone Pap smear testing, and whether they would be willing to undergo such testing. In turn, those who had never undertaken Pap smear testing were asked about their reasons for not doing so and their willingness to undertake such screening in the future. Awareness of the actual screening procedure was assessed, with participants being asked about the aim, usefulness, and importance of screening, the appropriate time for testing, the site of the test, whether one needed to be symptomatic to get the test, and when to stop the test. Most of the questions in this section also required yes/no responses; however, there were exceptions for certain questions, such as reasons for not taking action, screening aims, appropriate time for testing, site of testing, and when to stop the test. For these questions, respondents were provided with multiple-choice options to choose from.</p>", "<title>Scoring</title>", "<p id=\"Par27\">All knowledge-related items in the questionnaire were compiled, and a scoring system was created. Each correct response received a score of 1, resulting in a total score range of 0–30. Patients were then divided into two categories based on their total scores: not knowledgeable (scores of ≤15) and knowledgeable (scores of 16–30).</p>", "<title>Ethics</title>", "<p id=\"Par28\">Ethical approval for this study was obtained from the Medical Research and Ethics Committee of the College of Medicine and Health Sciences, Sultan Qaboos University (#SQU-EC/214/19, #MREC2013). All participants were briefed regarding the objectives of the study and were informed that their participation was voluntary in nature and that they had the right to withdraw at any time. Written informed consent was received from all of the women prior to their participation in the study. The participants’ anonymity and confidentiality were ensured at all times, with each participant assigned a unique identification number for the purposes of data analysis.</p>", "<title>Statistical analysis</title>", "<p id=\"Par29\">The data analysis was carried out using the Statistical Package for the Social Sciences (SPSS), version 23 (IBM Corp., Armonk, NY). Descriptive statistics were used to report the sample’s characteristics. For categorical variables, frequencies and percentages were reported, whereas means and standard deviations were used to present continuous variables. Crude and adjusted Odds Ratios (ORs) and corresponding 95% Confidence Intervals (CIs) were used to test correlations, and a <italic>p</italic> value of ≤0.05 was considered statistically significant.</p>" ]

[ "<title>Results</title>", "<title>Sociodemographic characteristics</title>", "<p id=\"Par30\">The mean age was 32.1 ± 7.6 years (range: 18–50 years), with a similar proportion of participants aged 18–30 years (<italic>n</italic> = 182; 48%) and 31–50 years (<italic>n</italic> = 198; 52%). More than half of the participants had an undergraduate-level education or higher (<italic>n</italic> = 247; 65%). Most were unemployed (<italic>n</italic> = 222, 58%) and had been married once (<italic>n</italic> = 290; 76%), with few having been married more than once (<italic>n</italic> = 7; 2%). Of the participants who had been married at least once, the majority of their husbands had secondary school diplomas (<italic>n</italic> = 149; 51%) or undergraduate degrees (<italic>n</italic> = 141; 49%). In terms of family income, 209 (55%) reported earning &lt; 1000 Omani Riyals (OMR) per month (equivalent to &lt;$2598 USD). The vast majority were non-smokers (<italic>n</italic> = 377; 99%). Only two participants (1%) had a personal history of cervical cancer, while only one (&lt; 1%) was aware of a family history of cervical cancer (Table ##TAB##0##1##).\n</p>", "<title>Knowledge of cervical cancer and pap smear testing</title>", "<p id=\"Par31\">In terms of cervical cancer-related knowledge, most participants (<italic>n</italic> = 325; 86%) had previously heard of cervical cancer. Over half (<italic>n</italic> = 224; 59%) believed that cervical cancer can be prevented and 251 (66%) thought it a genetic disease. The most frequently identified cervical cancer-related risk factors included multiple sexual partners (<italic>n</italic> = 162; 43%), smoking (<italic>n</italic> = 145; 38%), and HPV infection (<italic>n</italic> = 91; 24%). Early sexual activity (<italic>n</italic> = 41; 11%) and having three or more children (<italic>n</italic> = 30; 8%) were the least frequently reported risk factors.</p>", "<p id=\"Par32\">With regards to knowledge, attitudes, and practices related to Pap smear testing, 209 women (55%) had previously heard of this screening method, although the majority (<italic>n</italic> = 302; 80%) admitted that they did not undertake such testing on a regular basis. Only 78 women (21%) had themselves previously undergone Pap smear testing, although 283 (75%) reported a willingness to undergo such testing in the future. Various reasons were reported for not having previously undertaken Pap smear testing, including concern regarding being examined by a male doctor (<italic>n</italic> = 287; 76%), feeling embarrassed (<italic>n</italic> = 183; 48%), fear of the test results (<italic>n</italic> = 172; 45%), fear of pain (<italic>n</italic> = 139; 37%), being healthy (<italic>n</italic> = 136; 36%), being busy (<italic>n</italic> = 115; 30%), fear of the test itself (<italic>n</italic> = 98; 26%), the unavailability of nearby health services (<italic>n</italic> = 89; 23%), being discouraged by their partner (<italic>n</italic> = 88; 23%), privacy concerns (<italic>n</italic> = 87; 23%), fear of bleeding (<italic>n</italic> = 69; 18%), being unaware of where to get the test (<italic>n</italic> = 50; 13%), having no time (<italic>n</italic> = 49; 13%), the expense of being tested (<italic>n</italic> = 45; 12%), religious reasons (<italic>n</italic> = 44; 12%), and being too old to be tested (<italic>n</italic> = 34; 9%).</p>", "<title>Associations with sociodemographic characteristics</title>", "<p id=\"Par33\">Overall, the vast majority of participants (<italic>n</italic> = 281; 74%) were not knowledgeable with regards to cervical cancer and Pap smear testing, receiving total scores of &lt; 16 for all knowledge-related items in the questionnaire. Only 99 (26%) women were considered knowledgeable on these topics, with total scores of ≥16. Knowledge scores were significantly associated with several sociodemographic factors, including marital status (OR 2.84, 95% CI 1.42–5.67) and a previous awareness of cervical cancer (OR 4.82, 95% CI 1.68–13.83), with married women, those who had had one or two pregnancies, and those who had previously heard of cervical cancer being more knowledgeable compared to their respective counterparts. No significant associations were observed between knowledge scores and other sociodemographic characteristics of the participants, including age, educational and employment status, monthly income level, and number of previous pregnancies (<italic>p</italic> &gt; 0.05) (Table ##TAB##1##2##).\n</p>", "<p id=\"Par34\">Furthermore, significant associations were noted between Pap smear practices and several factors, including age (OR 2.13, 95% CI 1.18–3.84), marital status (OR 21.76,, 95% CI 2.91–162.63), and a previous awareness of cervical cancer (OR 2.44, 95% CI 0.90–6.54). Specifically, older women, those who were married, and those who had previously heard of cervical cancer more frequently reported having previously undertaken Pap smear testing compared to those who were younger, those unmarried, and those who had not heard of cervical cancer before (Table ##TAB##2##3##). Moreover, married women (OR 4.56, 95% CI 2.52–8.25) and those who had heard of cervical cancer before (OR 2.42, 95% CI 1.29–4.52) more frequently reported a willingness to undertake such testing in the future (Table ##TAB##3##4##).\n</p>" ]

[ "<title>Discussion</title>", "<p id=\"Par35\">Although cervical cancer is the one of the most common cancers among Omani women, there is as yet no established cervical cancer screening program in the country [##UREF##4##12##–##UREF##6##15##]. Moreover, Pap smear testing is often only provided to married women and is unavailable at the primary healthcare level, performed solely in secondary and tertiary care facilities for diagnostic purposes. Combined with the lack of a national HPV vaccination program, such concerns are critical because the onus for cervical cancer screening and Pap smear testing therefore rests on the individual patient. As such, public knowledge and awareness of the importance of such screening and cervical cancer risk factors is crucial for early identification and prevention purposes. Thus, the present study was conducted in order to assess knowledge, attitudes, and practices regarding cervical cancer and Pap smear screening and to establish correlations with various sociodemographic factors among a cohort of Omani women attending an outpatient clinic at a tertiary teaching hospital in Muscat, Oman. The findings indicated that cervical cancer knowledge was limited, with 86% having heard of it, but only 59% believing in its preventability. Regarding Pap smear testing, 55% were aware, but 80% did not undergo such testing regularly. Sociodemographic factors such as marital status and previous awareness of cervical cancer were associated with higher knowledge scores and increased likelihood of participating in Pap smear practices.</p>", "<p id=\"Par36\">In our study, although the vast majority of participants (86%) had previously heard of cervical cancer, only 55% had heard of Pap smear testing. A prior survey conducted in Oman similarly reported that 80% of participants had heard of cervical cancer [##REF##28030906##19##]. This level of public awareness is high, possibly because such studies were conducted among patients, students, and employees at a tertiary care institute that routinely provides this service. According to Shrestha et al., only 18% of women who visited a tertiary care facility in Nepal knew of Pap smear testing, despite 66% having previously heard of cervical cancer [##UREF##8##23##]. In contrast, much higher rates of awareness (85 and 76%, respectively) were recorded among women accessing primary healthcare facilities in Qatar [##REF##22276494##24##]. In Kuwait, 77% of married women visiting polyclinics had heard of Pap smear testing; similarly, 74% of Vietnamese American women surveyed in the USA had heard of the Pap smear test, although 90% were aware of cervical cancer [##REF##19060489##25##, ##REF##12350454##26##]. Differences in knowledge across different countries or regions may be due to cultural or healthcare system factors. For example, although Qatar is also a Middle Eastern country with a similar sociocultural milieu to Oman, the researchers noted that Pap smear testing is available at most primary health centers due to the country’s well-woman clinic program [##REF##22276494##24##].</p>", "<p id=\"Par37\">In the current study, various sociodemographic factors were found to have a significant impact on knowledge of cervical cancer and Pap smear testing, including marital status and a previous awareness of cervical cancer. Similar findings have been reported in studies conducted in Qatar, Kuwait, and the USA [##REF##22276494##24##–##REF##12350454##26##]. In particular, married women were significantly more knowledgeable compared to their counterparts, as well as those who had previously heard of cervical cancer. Marital status likely influences cervical cancer-related knowledge in Oman due to family planning practices among married women, which would involve regular consultations with an obstetrician or gynecologist. These healthcare professionals often provide guidance on fertility, prenatal care, and childbirth preparation, possibly encompassing discussions about women’s general reproductive health, including cervical cancer. Indeed, this would tie in with the finding that knowledge scores were significantly associated with number of previous pregnancies. Studies from Kazakhstan have similarly shown that number of births is associated with increased awareness of HPV and cervical cancer screening practices [##REF##34898639##27##, ##REF##33784210##28##]. Indeed, the impact of marital status on cervical cancer-related knowledge may be more pronounced in conservative, religious countries like Oman where premarital sexual relations are strictly prohibited. Alternatively, as previously described, Pap smear testing is often offered only to married women in Oman; this might also explain why this group were more likely to demonstrate knowledge of cervical cancer and Pap smear testing. However, previous studies from Oman have also reported associations with other factors, such as education level, employment status, and income [##REF##28030906##19##–##REF##33773541##22##]. These variations in findings could be due to differences in the research population and study design, as well as in the availability and accessibility of cervical cancer screening services in different healthcare settings and regions.</p>", "<p id=\"Par38\">In terms of knowledge regarding cervical cancer, our study reported results in line with previous studies performed in Qatar and Kuwait [##REF##36407716##21##, ##REF##33773541##22##]. However, given that the majority of the participants (74%) had inadequate knowledge regarding cervical cancer and Pap smear testing, regardless of education level, this may indicate that the Omani public’s overall exposure to cervical cancer-related education is lacking. Unfortunately, only 24% of participants in the current study were aware of HPV infection as a risk factor for cervical cancer. This is concerning as previous research has identified a link between awareness of HPV as a major cause of cervical cancer and participation in Pap smear practices [##REF##34898639##27##, ##REF##33784210##28##]. Only 21% of the women in our study revealed that they had previously undergone Pap smear testing themselves. Despite this, the majority (75%) reported that they would be willing to undergo such testing in the future,</p>", "<p id=\"Par39\">One of the limitations of this study includes the length of the questionnaire and the fact that the questionnaires were completed by participants while in the clinic’s waiting area, which can often be noisy and crowded. Moreover, some of the questions sought information on the participants’ past experiences and were related to potentially sensitive topics which could have resulted in recall or response bias. Secondly, the study was conducted in a single institution located in the capital city of Muscat; accordingly, the findings cannot be generalized to other institutions or regions of Oman, particularly more remote regions. Nonetheless, a strength of the study was that the participants involved in the study originated from all over the country as SQUH is one of the few tertiary-level institutions in Oman.</p>", "<p id=\"Par40\">Overall, the present study identified inadequate awareness on matters related to cervical cancer, its risk factors, and screening methods among a sample of Omani women attending a tertiary outpatient clinic. Accordingly, more efforts need to be made to increase general awareness of cervical cancer and its screening methods and risk factors through various channels, including school-level interventions and health promotion activities. In particular, healthcare professionals should be encouraged to deliver cervical cancer-related information to female patients of appropriate age groups as they would be able to immediately and accurately respond to any inquiries and correct any misconceptions. Commonly reported barriers to taking part in cervical cancer screening in Oman include fear of being examined by a male doctor, being embarrassed, fear of the results, fear of pain, being healthy, being busy, and fear of the procedure itself [##REF##28030906##19##–##REF##33773541##22##]. It is likely that these women would demonstrate more positive attitudes concerning Pap smear testing if they were to be reassured and given more detailed information concerning the test procedure itself. Much of this responsibility is likely to fall on the attending physician who should provide detailed information to female patients regarding cervical cancer screening, including its indications, ideal frequency, and possible complications. Moreover, healthcare professionals—whether at the primary, secondary, or tertiary care level—should seek to maintain appropriate channels of communication throughout the entire consultation and follow-up process in order to build up a trusting relationship with their patients, particularly given that such topics may be considered sensitive or embarrassing, especially in very conservative, religious societies.</p>" ]

[ "<title>Conclusions</title>", "<p id=\"Par41\">The burden imposed by cervical cancer on health systems remains considerable, especially in LMICs such as Oman which lack adequate HPV vaccination and cervical cancer screening coverage. As such, public awareness of these issues is paramount to ensure early identification and prevention. Overall, the present study identified suboptimal awareness on matters related to cervical cancer, its risk factors, and screening methods among a cohort of Omani women attending a tertiary outpatient clinic. Despite this, most participants were generally accepting of the idea of undergoing Pap smear testing in future. Thus, it is strongly recommended that the Omani Ministry of Health consider implementing a national screening program in order to lower the incidence of cervical cancer. Moreover, awareness and education campaigns are also urgently needed to effectively inform the public on the importance of and indications for cervical cancer screening. Such campaigns might be implemented utilizing a multimedia approach; in addition, collaboration with other government sectors, such the Ministry of Education, would be helpful in order to reach adolescents and younger women and to incorporate knowledge about cervical cancer warning signs and risk factors into national school curricula. To support the execution of such initiatives, further research is recommended, particularly studies exploring ways to reduce barriers to Pap smear screening and the impact of religious and cultural factors on cervical cancer awareness and screening practices. In addition, comprehensive research is needed to determine the prevalence of HPV infection in the general population of Oman, with a focus on identifying specific genotypes. This could inform decisions related to the inclusion of the HPV vaccine in national immunization strategies.</p>" ]

[ "<title>Objectives</title>", "<p id=\"Par1\">To assess knowledge, attitudes, and practices regarding cervical cancer and Pap smear screening among Omani women attending a tertiary clinic in Muscat, Oman, and to establish correlations with selected sociodemographic factors.</p>", "<title>Methods</title>", "<p id=\"Par2\">An observational, cross-sectional study was carried out among Omani women aged 18–50 years old attending the outpatient clinic of the Department of Obstetrics and Gynecology, Sultan Qaboos University Hospital, from October 2019 to February 2020. A validated Arabic-language questionnaire was utilized to collect data regarding the participants’ sociodemographic characteristics, their knowledge of cervical cancer and related risk factors, and their knowledge, attitudes, and practices related to cervical cancer screening and Pap smear testing.</p>", "<title>Results</title>", "<p id=\"Par3\">Of the 380 respondents, 86 and 55% had previously heard of cervical cancer and Pap smear testing, respectively; however, only 26% were knowledgeable concerning these topics. Knowledge scores were significantly associated with various sociodemographic factors, including marital status and a previous awareness of cervical cancer (odds ratio: &gt; 1, <italic>p</italic> &lt; 0.05). Only 21% had themselves previously undergone Pap smear testing; however, 75% reported being willing to undergo such screening in future.</p>", "<title>Conclusions</title>", "<p id=\"Par4\">Knowledge regarding cervical cancer-related risk factors and Pap smear screening was poor among a cohort of Omani women attending a tertiary clinic in Muscat, Oman. This may play a role in the increased frequency of cervical cancer cases observed in Oman over recent years. As such, a well-structured public education program is recommended to raise awareness of this issue.</p>", "<title>Keywords</title>" ]

[]

[ "<title>Acknowledgements</title>", "<p>The authors thank all of the women who participated in this study. They are also grateful to the staff at the Department of Obstetrics and Gynecology at SQUH for their cooperation with the data collection procedures.</p>", "<title>Authors’ contributions</title>", "<p>T.M., K.M, and M.A conceived the presented research idea and went through literature review. T.M, and K.M under the supervision of M.A, M.K and R.K. designed the research methodology and the questionnaire format. T.M, and K.M were involved in the data collection and date entry. T.M, K.M, H.S and R.K analyzed and interpreted the results. T.M, R.K and H.S were a major contributor in writing the manuscript in consultation with M.A. and M.K. R.K, M.K and M.A were the research supervisors who guided T.M and K.M throughout the project. All authors read and approved the final manuscript.</p>", "<title>Funding</title>", "<p>No funding was received for this work.</p>", "<title>Availability of data and materials</title>", "<p>The raw datasets used and/or analyzed during this study are available from the corresponding author upon reasonable request.</p>", "<title>Declarations</title>", "<title>Ethics approval and consent to participate</title>", "<p id=\"Par42\">The study received ethical approval from the Medical Research and Ethics Committee of the College of Medicine and Health Sciences, Sultan Qaboos University, Oman (#SQU-EC/214/19, #MREC2013). Written informed consent was obtained from all participants prior to their inclusion in the study. The participants’ anonymity and confidentiality were ensured at all times, with each participant assigned a unique identification number for the purposes of data analysis.</p>", "<title>Consent for publication</title>", "<p id=\"Par43\">Not applicable as no details regarding individual patients have been reported in the manuscript.</p>", "<title>Competing interests</title>", "<p id=\"Par44\">The authors declare no competing interests.</p>" ]

[]

[ "<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Selected sociodemographic characteristics of Omani women attending a tertiary clinic in Muscat, Oman (<italic>N</italic> = 380)</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th>Characteristic</th><th>n (%)</th></tr></thead><tbody><tr><td><bold>Age (years)</bold></td><td/></tr><tr><td> 18–30</td><td>182 (47.9)</td></tr><tr><td> 31–50</td><td>198 (52.1)</td></tr><tr><td><bold>Education level</bold></td><td/></tr><tr><td> Secondary</td><td>133 (35.0)</td></tr><tr><td> Undergraduate or higher</td><td>247 (65.0)</td></tr><tr><td><bold>Employment status</bold></td><td/></tr><tr><td> Unemployed</td><td>222 (58.4)</td></tr><tr><td> Employed</td><td>158 (41.6)</td></tr><tr><td><bold>Marital status</bold></td><td/></tr><tr><td> Never married</td><td>90 (23.7)</td></tr><tr><td> Married once</td><td>283 (74.5)</td></tr><tr><td> Married more than once</td><td>7 (1.8)</td></tr><tr><td><bold>Monthly income (OMR)</bold></td><td/></tr><tr><td> &lt; 1000^a</td><td>209 (55.0)</td></tr><tr><td> ≥ 1000</td><td>171 (45.0)</td></tr><tr><td><bold>Number of previous pregnancies</bold></td><td/></tr><tr><td> 0</td><td>112 (29.7)</td></tr><tr><td> 1–2</td><td>91 (23.9)</td></tr><tr><td> ≥ 3</td><td>176 (46.3)</td></tr><tr><td><bold>Previously heard of cervical cancer</bold></td><td/></tr><tr><td> Yes</td><td>325 (85.5)</td></tr><tr><td> No</td><td>55 (14.5)</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab2\"><label>Table 2</label><caption><p>Associations between sociodemographic characteristics and knowledge, attitudes, and practices related to cervical cancer and Pap smear testing among Omani women attending a tertiary clinic in Muscat, Oman (<italic>N</italic> = 380)</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th rowspan=\"2\">Characteristics</th><th colspan=\"2\">Knowledge of cervical cancer^a</th><th rowspan=\"2\">Adjusted Odds Ratio (95% CI)</th></tr><tr><th>Good</th><th>Poor</th></tr></thead><tbody><tr><td><bold>Age (years)</bold></td><td/><td/><td rowspan=\"3\">0.71 (0.43–1.18)^b</td></tr><tr><td> 18–30</td><td>44 (44.4)</td><td>138 (49.1)</td></tr><tr><td> 31–50</td><td>55 (55.6)</td><td>143 (50.9)</td></tr><tr><td><bold>Education level</bold></td><td/><td/><td rowspan=\"3\">1.22 (0.74–2.02)^d</td></tr><tr><td> Secondary</td><td>30 (31.3)</td><td>103 (36.3)</td></tr><tr><td> Undergraduate or higher</td><td>66 (68.8)</td><td>181 (63.7)</td></tr><tr><td><bold>Employment status</bold></td><td/><td/><td rowspan=\"3\">0.91 (0.56–1.48)^d</td></tr><tr><td> Unemployed</td><td>56 (58.3)</td><td>166 (58.5)</td></tr><tr><td> Employed</td><td>40 (41.7)</td><td>118 (41.5)</td></tr><tr><td><bold>Marital status</bold></td><td/><td/><td rowspan=\"3\">2.84 (1.42–5.67)^c</td></tr><tr><td> Never married</td><td>13 (13.5)</td><td>77 (27.1)</td></tr><tr><td> Married once or more</td><td>83 (86.5)</td><td>207 (72.9)</td></tr><tr><td><bold>Monthly income (OMR)</bold></td><td/><td/><td rowspan=\"3\">0.90 (0.56–1.45)^d</td></tr><tr><td> &lt; 1000</td><td>56 (58.3)</td><td>153 (53.9</td></tr><tr><td> ≥ 1000</td><td>40 (41.7)</td><td>131 (46.1)</td></tr><tr><td><bold>Number of prior pregnancies</bold></td><td/><td/><td rowspan=\"3\">0.95 (0.38–2.42)^d</td></tr><tr><td> 0</td><td>20 (20.8)</td><td>93 (32.7)</td></tr><tr><td> ≥ 1</td><td>76 (79.2)</td><td>191 (67.3)</td></tr><tr><td><bold>Previously heard of cervical cancer</bold></td><td/><td/><td rowspan=\"3\">4.82 (1.68–13.83)^d</td></tr><tr><td> No</td><td>3 (3.0)</td><td>52 (18.5)</td></tr><tr><td> Yes</td><td>96 (97.0)</td><td>229 (81.5)</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab3\"><label>Table 3</label><caption><p>Associations between sociodemographic characteristics and practice related to cervical cancer and Pap smear testing among Omani women attending a tertiary clinic in Muscat, Oman (<italic>N</italic> = 380)</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th rowspan=\"2\">Characteristic</th><th colspan=\"2\">Previously undergone Pap smear testing</th><th rowspan=\"2\">Adjusted Odds Ratio (95% CI)</th></tr><tr><th>Yes</th><th>No</th></tr></thead><tbody><tr><td><bold>Age (years)</bold></td><td/><td/><td rowspan=\"3\"><bold>2.13 (1.18–3.84)</bold>^{<bold>a</bold>}</td></tr><tr><td> 18–30</td><td>19 (24.4)</td><td>163 (54.0)</td></tr><tr><td> 31–50</td><td>59 (75.6)</td><td>139 (46.0)</td></tr><tr><td><bold>Education level</bold></td><td/><td/><td rowspan=\"3\">1.03 (0.59–1.77)^c</td></tr><tr><td> Secondary</td><td>61 (29.2)</td><td>72 (42.1)</td></tr><tr><td> Undergraduate or higher</td><td>148 (70.8)</td><td>99 (57.9)</td></tr><tr><td><bold>Employment status</bold></td><td/><td/><td rowspan=\"3\">1.16 (0.69–1.96)^c</td></tr><tr><td> Unemployed</td><td>117 (56.0)</td><td>105 (61.4)</td></tr><tr><td> Employed</td><td>92 (44.0)</td><td>66 (38.6)</td></tr><tr><td><bold>Marital status</bold></td><td/><td/><td rowspan=\"3\"><bold>21.76 (2.91–162.63)</bold>^{<bold>b</bold>}</td></tr><tr><td> Never married</td><td>27 (12.9)</td><td>63 (36.8)</td></tr><tr><td> Married once or more</td><td>182 (87.1)</td><td>108 (63.2)</td></tr><tr><td><bold>Monthly income (OMR)</bold></td><td/><td/><td rowspan=\"3\">1.09 (0.64–1.85)^c</td></tr><tr><td> &lt; 1000</td><td>113 (54.1)</td><td>96 (56.1)</td></tr><tr><td> ≥ 1000</td><td>96 (45.9)</td><td>75 (43.9)</td></tr><tr><td><bold>Number of prior pregnancies</bold></td><td/><td/><td rowspan=\"3\">0.58 (0.23–1.46)^c</td></tr><tr><td> 0</td><td>9 (11.5)</td><td>104 (34.4)</td></tr><tr><td> ≥ 1</td><td>69 (88.5)</td><td>198 (65.6)</td></tr><tr><td><bold>Previously heard of cervical cancer</bold></td><td/><td/><td rowspan=\"3\"><bold>2.44 (0.90–6.54)</bold>^{<bold>c</bold>}</td></tr><tr><td> Yes</td><td>73 (93.6)</td><td>50 (16.6)</td></tr><tr><td> No</td><td>5 (6.4)</td><td>252 (83.4)</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab4\"><label>Table 4</label><caption><p>Associations between sociodemographic characteristics and attitude related to cervical cancer and Pap smear testing among Omani women attending a tertiary clinic in Muscat, Oman (<italic>N</italic> = 380)</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th rowspan=\"2\">Characteristic</th><th colspan=\"2\">Willingness to undertake Pap smear testing in future</th><th rowspan=\"2\">Adjusted Odds Ratio (95% CI)</th></tr><tr><th>Yes</th><th>No</th></tr></thead><tbody><tr><td><bold>Age (years)</bold></td><td/><td/><td rowspan=\"3\">0.89 (0.51–1.56)^a</td></tr><tr><td> 18–30</td><td>16 (26.2)</td><td>166 (52.0)</td></tr><tr><td> 31–50</td><td>45 (73.8)</td><td>153 (48.0)</td></tr><tr><td><bold>Education level</bold></td><td/><td/><td rowspan=\"3\">1.49 (0.90–2.47)^c</td></tr><tr><td> Secondary</td><td>93 (32.9)</td><td>40 (41.2)</td></tr><tr><td> Undergraduate or higher</td><td>190 (67.1)</td><td>57 (58.8)</td></tr><tr><td><bold>Employment status</bold></td><td/><td/><td rowspan=\"3\">1.07 (0.65–1.78)^c</td></tr><tr><td> Unemployed</td><td>160 (56.5)</td><td>62 (63.9)</td></tr><tr><td> Employed</td><td>123 (43.5)</td><td>35 (36.1)</td></tr><tr><td><bold>Marital status</bold></td><td/><td/><td rowspan=\"3\"><bold>4.56 (2.52–8.25)</bold>^{<bold>b</bold>}</td></tr><tr><td> Never married</td><td>46 (16.3)</td><td>44 (45.4)</td></tr><tr><td> Married once or more</td><td>237 (83.7)</td><td>53 (54.6)</td></tr><tr><td><bold>Monthly income (OMR)</bold></td><td/><td/><td rowspan=\"3\">1.20 (0.74–1.97)^c</td></tr><tr><td> &lt; 1000</td><td>154 (54.4)</td><td>55 (56.7)</td></tr><tr><td> ≥ 1000</td><td>129 (45.6)</td><td>42 (43.3)</td></tr><tr><td><bold>Number of prior pregnancies</bold></td><td/><td/><td rowspan=\"3\">0.19 (0.52–1.62)^c</td></tr><tr><td> 0</td><td>7 (11.5)</td><td>106 (33.2)</td></tr><tr><td> ≥ 1</td><td>54 (88.5)</td><td>213 (66.8)</td></tr><tr><td><bold>Previously heard of cervical cancer</bold></td><td/><td/><td rowspan=\"3\"><bold>2.42 (1.29–4.52)</bold>^{<bold>c</bold>}</td></tr><tr><td> Yes</td><td>57 (93.4)</td><td>268 (84.0)</td></tr><tr><td> No</td><td>4 (6.6)</td><td>51 (16.0)</td></tr></tbody></table></table-wrap>" ]

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[ "<table-wrap-foot><p><italic>OMR</italic> Omani Riyals. ^aEquivalent to &lt;$2598 USD</p></table-wrap-foot>", "<table-wrap-foot><p>^aAssessed using a validated, pre-tested, Arabic-language questionnaire [##REF##31935539##17##–##REF##35814043##20##]. Total scores of ≤15 and 16–30 were considered to indicate poor and good knowledge of cervical cancer, respectively. ^badjusted for marital status only; ^cadjusted for age only; ^dadjusted for age and marital status</p></table-wrap-foot>", "<table-wrap-foot><p>^aAdjusted for marital status only; ^bAdjusted for age only; ^cAdjusted for age and marital status</p></table-wrap-foot>", "<table-wrap-foot><p>^aAdjusted for marital status only; ^badjusted for age only; ^cAdjusted for age and marital status</p></table-wrap-foot>", "<fn-group><fn><p><bold>Publisher’s Note</bold></p><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn></fn-group>" ]

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{ "acronym": [ "HPV", "LMICs", "OMR", "Pap", "SQUH", "SPSS", "OR", "CI", "USA" ], "definition": [ "Human Papillomavirus", "Low- and Middle-Income Countries", "Omani Riyals", "Papanicolaou", "Sultan Qaboos University Hospital", "Statistical Package for the Social Sciences", "Odds Ratio", "Confidence Interval", "United States of America" ] }

[ "<title>Introduction</title>", "<p id=\"Par5\">Bruxism refers to the phenomenon of involuntary contraction of the masticatory muscles under non-physiological conditions, resulting in intermittent masticatory movements [##UREF##0##1##]. It is divided into awake bruxism and sleep bruxism. Awake bruxism occurs in a conscious state and is usually associated with emotions such as mental tension, anxiety, stress, anger, or depression. However, sleep bruxism occurs at night and is usually caused by sleep apnea or related to sleep disorders. Research have shown that bruxism is common in all age groups and has become an important factor for dental health. It can lead to rapid tooth wear, resulting in pulpitis, narrowing of the occlusal surface, temporomandibular joint disease, nervous system disease, and muscle pain [##UREF##1##2##]. The prevalence of sleep bruxism in adults ranges from 8 to 16%, whereas in children, it can be as high as 40% [##UREF##2##3##, ##UREF##3##4##]. Unfortunately, the etiology of bruxism is complex and the pathogenesis is unclear [##UREF##4##5##]. Therefore, it is of great significance to detect sleep bruxism as early as possible in order to select the most appropriate treatment method.</p>", "<p id=\"Par6\">Polysomnography (PSG) is considered the gold standard for diagnosing bruxism, but it requires many sensors that increases the complexity causing discomfort to patients. However, electroencephalogram (EEG) provides information related to brain activities that helps to understand relationship between bruxism and brain function. In literature, various physiological signals such as electrocardiogram (ECG), Electromyography (EMG), and EEG have been used in the detection of sleep bruxism. Based on ECG, heart rate variability was used to assess sympathetic cardiac activity in patients with bruxism. Research indicates that patients with bruxism exhibit higher sympathetic cardiac activity compared to healthy controls [##UREF##5##6##, ##UREF##6##7##]. Facial EMG can be used to record the potential activity of the patient’s masseter and temporal muscles at night, and the potential value and activity of the EMG can be used to determine whether bruxism occurs [##UREF##7##8##, ##UREF##8##9##]. Research has shown that combining ECG and EMG to achieve classification of nocturnal bruxism has also achieved good results [##UREF##9##10##]. In addition, video capture can also be used to detect the occurrence of bruxism [##UREF##10##11##], or magnetic resonance imaging for bruxism examination [##REF##37048653##12##]. In addition, EEG is a non-invasive signal, which can be easily obtained from the electrode. It can record neural activity in sleep through different frequency bands. This technology has been widely used as a standard to quantify potential neural activity in sleep research [##UREF##11##13##, ##UREF##12##14##]. Researchers have found that most patients with bruxism have significant signs of increased electrical activity in the cerebral cortex during tooth grinding [##UREF##0##1##]. Sleep bruxism detection has also been conducted by analyzing EEG signals [##UREF##13##15##–##UREF##16##18##]. The research of Dakun Lai et al. [##UREF##9##10##]. shows that the power Spectral density (PSD) of EEG channels in patients with bruxism is significantly higher than that in normal people during rapid eye movement (REM) and awake sleep. Their research also shows that on the basis of the power spectrum characteristics of EEG signals, the fusion of EMG1 and ECG channel signals can achieve better results in the recognition of sleep bruxism patients. Bin Heyat et al. also demonstrate the effectiveness of EEG in detecting sleep bruxism [##UREF##14##16##]. In addition, the theta activity is also believed to be associated with the occurrence of bruxism [##UREF##16##18##].</p>", "<p id=\"Par7\">Although the effectiveness of EEG signals has been proven in previous studies, there are problems obtaining EEG signals with too many electrodes causing difficulty in installation and makes patients discomfort. Therefore, identifying the neural correlates of bruxism via single-channel EEG may be of high clinical significance in managing the adverse consequences of sleep bruxism.</p>", "<p id=\"Par8\">The goal of this study is to fully extract the PSD features of EEG in patients with sleep bruxism, thereby attempting to obtain the most effective single channel EEG for identifying bruxism. We propose a new data processing algorithm based on the fusion of multiple EEG frequency band signal features. The algorithm first extracts the PSD values of different EEG frequency bands in REM sleep stage, and then extracts 28 features of these frequency bands in time domain, frequency domain and nonlinear. On the basis of fully extracting the features of EEG signals, the machine learning algorithm is used to identify patients with bruxism. After integrating these features, the classifier can obtain sufficient reference information, providing a reliable basis for the training and accurate classification using machine learning algorithms. The remaining sections of the paper are structured as follows: Section II covers data preparation, data processing, feature extraction, and statistical analysis. Subsequently, machine learning algorithm is applied to classify patients with bruxism. Section III presents the results from data analysis. In Section IV, the research results are thoroughly discussed and compared with existing methods and findings. Section V presents conclusions and prospects for future work.</p>" ]

[ "<title>Materials &amp; methods</title>", "<p id=\"Par9\">To accurately describe the classification process of bruxism, we designed a data processing flow as shown in Fig. ##FIG##0##1##. Data processing flow description:</p>", "<title>Data processing and feature extraction</title>", "<p id=\"Par10\">EEG Signal Reading: Read the EEG signals from the REM sleep stages of the subjects. Wavelet Decomposition: Utilize the DB5 wavelet to decompose the EEG signals into delta, theta, alpha, and beta frequency bands. Relative PSD Calculation: Calculate the relative PSD for each frequency band, capturing time-domain, frequency-domain, and non-linear features.</p>", "<title>Statistical analysis</title>", "<p id=\"Par11\">Normality Test: Perform a normality test on the extracted features to ensure their distribution is suitable for statistical analysis. Statistical Test Selection: Determine the appropriate statistical test (e.g., t-test, Mann-Whitney Rank-Sum test) based on the distribution. P-value Calculation and Significance Analysis: Carry out the selected statistical test, calculate the p-value, and determine the significance of the features.</p>", "<title>Classification and result statistics</title>", "<p id=\"Par12\">Classifier Selection: Choose an appropriate classification algorithm based on the features. Classification and Result Collection: Utilize the selected classifier to classify the data and collect the classification results. Optimization and Validation: Iterate over multiple rounds of experimentation and comparison to identify the optimal EEG channel and classify the data effectively.</p>", "<p id=\"Par13\">Through these three key processing steps, we aim to extract significant features from the EEG signals, perform statistical analysis to identify significant features, and utilize these features for effective classification of sleep bruxism. The end result would be the identification of the optimal EEG channel for classification and the resulting classification outcomes.</p>", "<p id=\"Par14\">\n\n</p>", "<title>Experimental protocol</title>", "<p id=\"Par15\">The data was acquired from cyclic alternating pattern (CAP) sleep database of PhysioNet [##REF##14592270##19##, ##UREF##17##20##]. It provides representative PSG records of 108 participants with various pathophysiological backgrounds, including 16 controls, 2 bruxism patients, 9 Insomnia, 5 Narcolepsy, 40 Nocturnal frontal lobe epilepsy, 10 Periodic leg movements, 22 REM behavior orders, and 4 Sleep disordered breathing. Each record includes three or more EEG signals, as well as EMG, airflow, respiratory effort, SaO₂, and ECG signals, and each record has been carefully reviewed by expert neurologists for sleep stage and CAP annotations. The healthy controls who participated in the study exhibited no neurological disorders and were not taking any medications that could affect the central nervous system. All the bipolar EEG channels were sampled at 512 Hz and placed according to 10–20 international electrode placement system [##UREF##18##21##].</p>", "<p id=\"Par16\">Since the EEG channels collected by each subject in the database are different, we can only select data from 4 health controls (participants: n3, n5, n10, and n11) and 2 bruxism patients (participants: brux1 and brux2) with the same EEG channels (F4C4, C4P4, Fp1F3, F3C3, and C4A1) in this study. According to the annotations made by neurologists, the length of each REM sleep segment was determined to be 30 s. In this study, the total number of REM sleep events is 1295 segments (duration is 38,850 s), including 276 segments of data from bruxism patients. Demographic information for the participants, along with REM sleep events, is presented in Table ##TAB##0##1##.</p>", "<p id=\"Par17\">\n\n</p>", "<title>Data processing</title>", "<p id=\"Par18\">First, the “edfread” function is used to read each polysomnographic record, which helps to obtain the identification and specific data of each channel, providing a foundation for subsequent analysis. Then, the “ScoringReader” function is executed to obtain the identification code and duration of each sleep stage. This information is crucial for accurately determining an individual’s sleep state. Finally, EEG data from the REM sleep stage are extracted, which prepare for further in-depth research. After preparing the REM segment of the EEG signal, the next step is to calculate its PSD. First, the REM segment of the EEG signal is decomposed into different frequency bands using the DB5 wavelet, including (δ, 1–4 Hz), theta (θ, 4–8 Hz), alpha (α, 8–13 Hz), and beta (β, 13–30 Hz). Next, the PSD was calculated using Welch estimate with Hamming window size of 128 samples with 50% overlap and 256 discrete Fourier transform points for each frequency bands. Finally, the absolute PSD (APSD) were calculated.</p>", "<p id=\"Par1834\">\n\n</p>", "<p id=\"Par19\">The relative PSD on each frequency band was obtained from the ratio of absolute power of each four frequency bands to the total power within the spectrum of 1–30 Hz [##UREF##11##13##]. The delta band is chosen beyond 1 Hz to minimize low frequency head movement and ocular artifacts below 1 Hz. The PSD analysis within the frequency band 1–30 Hz is chosen since EEG information relating to sleep relies within this spectrum.</p>", "<p id=\"Par20\">In order to conduct subsequent statistical analysis and obtain significant feature indicators for characterizing bruxism, we performed subsequent processing. Various features were extracted by time domain, frequency domain, and sample entropy (SampEn) for each REM sleep epochs from healthy controls and bruxism patients. For time domain features, Mean value, Standard Deviation (SD), and Root Mean Square (RMS) were calculated for each of the frequency bands [##UREF##19##22##]. For frequency domain features, the relative power spectral density (RPSD) including RPSD (δ), RPSD (θ), RPSD (α), RPSD (β), and ratios including (θ + α)/β, α/δ, α/θ, and α/β were computed as the additional features [##UREF##20##23##]. For nonlinear features, the Shannon entropy (SampEn) was calculated with default parameters (the maximum template length is 5 and the matching threshold is 0.2). Finally, a total of 28 features including 12 time domain features, 12 frequency domain features, and 4 non-linear analysis features were extracted.</p>", "<title>Statistical analysis</title>", "<p id=\"Par21\">The statistical analysis is performed to compare differences in EEG features from five EEG bipolar electrodes (channels) among healthy controls and bruxism patients. A Shapiro-Wilk test is used to examine the normality of data and a suitable parametric or non-parametric test is adopted based on the data distribution [##UREF##21##24##]. Since the data samples are independent, either two sample t-test as a parametric test for normalized data or Mann-Whitney Rank-Sum test as a non-parametric test for non-normalized data can be performed to obtain statistical comparison among two groups: healthy controls and bruxism patients.</p>", "<title>Classification and cross-validation</title>", "<p id=\"Par22\">The systematic empirical evaluation of various machine learning algorithms shows that Decision tree (Fine Tree) algorithm is the optimal algorithm for the classification of bruxism in CAP database. Therefore, unless stated otherwise, all the data discussed in this paper is the output of the Fine Tree classifier.</p>", "<p id=\"Par23\">Decision tree is a basic method of classification and regression. It achieves classification by dividing input features into different subsets layer by layer. The core idea of decision tree classifier is to determine the decision rules for classification by systematically dividing features, thus facilitating data classification. Due to its resemblance to the branches of a tree, this decision graph is called a decision tree. During the decision tree classification process, instances are segmented based on features and assigned to distinct categories. The main advantages of this method are model readability, easy to understand, fast classification, fast modeling, and prediction [##UREF##22##25##].</p>" ]

[ "<title>Result</title>", "<p id=\"Par24\">The normality test in the data have exhibited mixed behavior and the samples are independent between the two groups. Therefore, Mann-Whitney Rank-Sum test is used to test the significant difference. The significance level is set at the alpha criterion α = 0.05. The relative PSD in five EEG channels during REM sleep stage among healthy controls and bruxism patients are compared and their trends (mean ± SE) are illustrated in Fig. ##FIG##1##2##.</p>", "<p id=\"Par25\">\n\n</p>", "<p id=\"Par26\">The statistical analysis comparing healthy controls and bruxism patients revealed a significantly lower relative delta power (<italic>p</italic> &lt; 0.05) in three channels (F4C4, Fp1F3, and F3C3), as well as a significant decrease in relative theta power across all five channels. In contrast, the relative alpha and beta power exhibited a significant increase (<italic>p</italic> &lt; 0.05) in all five channels for bruxism patients. The overall results depicted decrease in low frequency band power (delta and theta bands) and increase in high frequency band power (alpha and beta bands) for bruxism patients. In low frequency bands, the profound lower relative power was observed in theta band and more dominant in C4A1 and C4P4 channels whereas, the profound higher in relative power was observed in beta band for high frequency bands and more dominant in Fp1F3 and F4C4 channels. Among four frequency bands, relative beta power had most significant differences for sleep bruxism patients.</p>", "<p id=\"Par27\">The significance of 28 features from each channel was compared between healthy controls and individuals with bruxism. As an instance, Tables ##TAB##1##2##, ##TAB##2##3##, ##TAB##3##4## and ##TAB##4##5## compare all the features of participants under healthy controls and bruxsim for C4P4 channel. The Shapiro-Wilk test was used to observe the normality of data distribution and the result showed mixed behavior due to limited sample size. Therefore, Wilcoxon rank sum test was used for statistical analysis [##UREF##19##22##]. The test result at <italic>p</italic> ≤ 0.05 was considered significant. The results showed that SD (α), SD (β), RMS (α), RMS (β), SampEn (α), SampEn (β), RPSD (δ), RPSD (θ), RPSD (α), (θ + α)/β, α/δ, α/θ, α/β, and APSD (α) had significant differences between healthy controls and bruxism patients, demonstrating that these features are effective for classification. Most of the features used for the classification task were found to be significant.</p>", "<p id=\"Par28\">\n\n</p>", "<p id=\"Par29\">\n\n</p>", "<p id=\"Par30\">\n\n</p>", "<p id=\"Par31\">\n\n</p>", "<p id=\"Par32\">To visually illustrate the behavior of each feature across different channels, Fig. ##FIG##2##3## displays all 28 features of participants from both healthy controls and bruxism patients for the C4P4 channel, as an example. Upon observing Fig. ##FIG##2##3##, it can be inferred that, in comparison to other frequency bands, the delta frequency band exhibits significant amplitude values on all features, except for a smaller amplitude on the SampEn feature. There were significant differences in SD, RMS, SampEn and four ratio PSD characteristics between healthy controls and bruxism patients. Features related to the theta, alpha, and beta frequency bands exhibited distinct behavioral patterns, with some of them also demonstrating statistical significance.</p>", "<p id=\"Par33\">\n\n</p>", "<p id=\"Par34\">All the above mentioned 28 features (see Tables ##TAB##1##2##, ##TAB##2##3##, ##TAB##3##4## and ##TAB##4##5## for detail) can provide basis for correct identification of bruxism patients from different aspects. By utilizing these features, we evaluated various machine learning algorithms and ultimately concluded that Fine Tree is the optimal algorithm for bruxism classification in the CAP database. Fine Tree classifier with default parameters, in MATLAB (Mathworks Inc., MA, USA), was used for classification. Table ##TAB##5##6## lists the sensitivity, specificity, accuracy, and positive predictive value (PPV) obtained from all the five channels in the parietal region using five-folds cross validation. It showed the C4P4 channel achieves the highest classification performance with sensitivity (95.59%), specificity (98.44%), accuracy (97.84%), and PPV (94.20%) in C4P4 channel.</p>", "<p id=\"Par35\">\n\n</p>" ]

[ "<title>Discussion</title>", "<p id=\"Par36\">The purpose of this study was to comprehensively investigate the utility of single EEG channel for classification of bruxism patients from healthy controls in REM sleep. Literature relies primarily on PSD changes in the EEG frequency bands, including delta, theta, alpha, and beta to estimate the characteristics of neural activities in the brain [##UREF##6##7##, ##UREF##9##10##, ##UREF##13##15##]. However, in this study, in order to obtain sufficient signal features, we combined time domain, frequency domain, and nonlinear features from the EEG signals.</p>", "<p id=\"Par37\">The artifacts in EEG signals are highly complex and can appear in any frequency bands. The effective identification and filtering of artifacts such as swallowing, yawning, jaw alignment, snoring, or apnea is challenging that requires further in-depth research. However, in our database, the recordings were annotated by expert neurologists with information on sleep stages (wakefulness, S1-S4 sleep stages, REM, and body movements), body position (left, right, prone, or supine), and duration (seconds). Among all these annotated EEG signals, we only extracted the REM segment of interest, and this data segment already excluded body movements and body positions. Further, to remove slow frequency motion artifacts, we used DB5 wavelet transform to decompose the single-channel EEG signals into multi-frequency bands and then obtained the EEG signals at selected frequency bands within 1–30 Hz. This further filter out slow frequency artifacts below 1 Hz and higher frequency artifacts above 30 Hz.</p>", "<p id=\"Par38\">Since EEG signals can directly reflect the neural activities of the human brain and more directly reflect the influence and characteristics of bruxism at the neural level, it is of great importance in the analysis of bruxism. In the present study, we used statistical methods to analyze EEG frequency bands to compare the PSD dynamics from EEG among bruxism patients and healthy controls during REM sleep. Since there are many EEG channels, it will be beneficial if the channels and frequency bands reflecting the occurrence of bruxism are selective that simplifies the design of EEG acquisition instrument having improved classification accuracy. Therefore, in this paper, we analyzed five different EEG channels and revealed the most effective frequency band of EEG channel in characterizing sleep bruxism.</p>", "<p id=\"Par39\">Tang Jin Cheng et al. [##UREF##23##26##] made important achievements in the research of brain-computer interface by extracting the mean, standard deviation, root mean square value and other time-domain features of EEG signals, revealing that these time-domain features are helpful to understand the data distribution characteristics of different EEG frequency bands. By conducting experiments on multiple channels of EEG signals, it was found that the standard deviation and variance features were the most significant [##UREF##23##26##]. Hayat [##UREF##14##16##] and Lai [##UREF##9##10##] analyzed the average, maximum, and minimum values of the average normalized power spectrum using two EEG channels, revealing the effectiveness of power spectrum in the detection of sleep bruxism. Among time-domain features, mean, SD, and RMS were typical approaches to measure the amplitude of EEG [##UREF##24##27##–##UREF##26##29##]. Therefore, we also attempted to extract the relevant time-domain features of EEG signals and evaluate their role in the identification of bruxism. In this study, we conducted statistical analysis on the time-domain features of PSD (mean, SD, and RMS) in each frequency bands of EEG and found that SD(δ), SD(θ), SD(α), SD(β), RMS(δ), RMS(θ), RMS(α), and RMS(β) had significant differences (<italic>p &lt;</italic> 0.001) between bruxism patients and healthy controls.</p>", "<p id=\"Par40\">The frequency domain features can represent the proportion of components in different frequency bands, thus representing the activity of cranial nerves [##UREF##27##30##, ##UREF##28##31##]. Studies have also found that some ratios of different bands, which includes following derived indices (θ + α)/β and α/β [##UREF##29##32##], and (θ + α)/(α + β) and θ/β [##UREF##20##23##] are useful for EEG features analysis. In this study, the RPSD including RPSD (δ), RPSD (θ), RPSD (α), RPSD (β), and ratios including (θ + α)/β, α/δ, α/θ, and α/β were computed. We found that many frequency domain features have significant differences (<italic>p &lt;</italic> 0.001) that includes RPSD(θ), RPSD(α), RPSD(β), α/δ, α/θ, α/β, APSD(δ), APSD(θ), and APSD(α).</p>", "<p id=\"Par41\">The nonlinear feature (Shannon entropy, SampEn) is a measure of the complexity of a system, and it is very effective for the analysis of short-length time series. The SampEn of EEG signal can be used to reveal the potential regularity and periodicity of data, which has been proven to accurately distinguish patients diagnosed with depression from the control group, which can serve as a highly sensitive and clinically relevant marker [##UREF##30##33##]. Mahshid Dastgoshadeh et al. also showed that the SampEn feature of EEG signals is a good tool for detecting epilepsy [##UREF##31##34##]. Richman et al. have revealed that SampEn is more suitable for the study of biological time series signals [##UREF##32##35##]. In this study, we found that the behavior of SampEn (δ), and SampEn (β) had significant differences (<italic>p &lt;</italic> 0.001) and SampEn(α) also had significant differences (<italic>p &lt;</italic> 0.05) between bruxism patients and healthy controls.</p>", "<p id=\"Par42\">In order to maximize the extraction of EEG frequency band features, we extracted 28 time-domain, frequency-domain, and nonlinear features (SampEn). Then statistical methods were used to compare the ability of each feature in classifying bruxism. The statistical results show that most of the features are effective to identify bruxism patients from healthy controls, especially in the high frequency bands (theta, alpha, and beta) that vary significantly. This variation in the high frequency bands might be due to the activation of neural activities involved in clenching teeth [##UREF##0##1##]. Mastication is controlled by central nervous system of brain [##UREF##4##5##, ##UREF##33##36##] and the cause of involuntary clenching during sleep bruxism is mostly unknown. The relative increase in high power around the frontal and central EEG channels in our results indicates high-frequency neural oscillations in the fronto-central brain regions, contributing to involuntary teeth grinding during sleep bruxism. Certainly, further analysis with larger sample size needs to be performed to support the claim with higher confidence level.</p>", "<p id=\"Par43\">Table ##TAB##6##7## shows the comparison between the classification performance of this study and previous studies. In the literature, among the detection methods of bruxism patients, EMG, ECG and EEG have been used by researchers. The accuracy of identifying bruxism patient based on EMG can reach 82.8% [##UREF##8##9##]. However, after combining EEG, EMG and ECG signals, the bruxism recognition accuracy reached 97.21% [##UREF##9##10##]. This method needs to collect EEG, EMG and ECG signals at the same time that increases the difficulty in signal acquisition and signal processing, and is not suitable for using portable devices to identify bruxism patients. Therefore, researchers began to study the strategy of only using EEG to identify bruxism, but the recognition rate of bruxism was not ideal. When utilizing only time-domain features (maximum, minimum, and mean) of PSD, even with the fusion of features from two EEG channels, the accuracy of bruxism classification can only achieve 81.25% [##UREF##14##16##]. Due to the correlation between EEG channels, even using channel fusion does not effectively improve classification accuracy. However, if the features of a single channel EEG signal can be fully extracted, it may help to improve classification accuracy. Based on this assumption, we used the same database as in literature [##UREF##9##10##, ##UREF##14##16##, ##UREF##15##17##] for our study. Our experimental results showed that when using 28 time domain, frequency domain, and nonlinear features, we could achieve higher accuracy (97.84%) from single EEG channel (C4P4). Our results demonstrated bruxism detection using single channel EEG.</p>", "<p id=\"Par44\">\n\n</p>" ]

[ "<title>Conclusion</title>", "<p id=\"Par45\">While previous studies have put several schemes for bruxism recognition, the utilization of multi-channel data acquisition has rendered the detection system more complex and unsuitable for portable devices. Some studies still have insufficient feature extraction and low recognition rates. Our research findings will help explore the potential of designing a portable sleep bruxism detection system based on a single channel EEG. We propose a classification method for sleep bruxism using a single EEG channel combined with time domain, frequency domain, and nonlinear features. Experimental results showed that there are significant differences (<italic>p</italic> &lt; 0.05) in all the 28 features except for Mean(δ), Mean(θ), Mean(α), Mean(β), RPSD(δ), APSD(β), SampEn(θ) between bruxism patients and healthy controls. Investigation on classification had also confirmed that these features were useful for classification.</p>", "<p id=\"Par46\">Multi-channel EEG devices are inconvenient to place electrodes, while using a single channel EEG acquisition can effectively reduce the complexity of the detection device, facilitate the installation of the device, and reduce the discomfort caused by data acquisition. The results of our study also showed that the classification performance of different channels of the brain were different. Among the channels, the C4P4 channel had the best classification results (the accuracy can reach 97.84%). In the 10–20 international electrode placement system, C4P4 is located on the right side of the Parietal and Central of scalp. Our experimental results suggest that placing electrode in this area is conducive to the development of a single channel portable bruxism detection system.</p>", "<p id=\"Par47\">Admittedly, our sample size is limited to 2 bruxism patients and 4 healthy controls. A larger sample size and more uniform population distribution (age, sex, and body weight) will give people more confidence in extending the results to the prediction and monitoring of patients with bruxism. Although the CAP sleep database of PhysioNet only has fewer patients with bruxism, it has a long record time for each subject, and expert neurologists annotate the data, making it authoritative. The database has important academic research value and has been used by multiple groups for research on bruxism. In addition, EEG has been used to characterize sleep bruxism in this study, we have extracted more effective features, and achieved higher classification performance using single channel EEG. This finding not only further supports the previously reported findings, but also extends the ability of single channel EEG to recognize bruxism.</p>", "<p id=\"Par48\">Indeed, the classification of bruxism in clinical research is a continuously evolving process that requires the combination of various assessment methods to obtain more reliable results. Simply relying on database analysis is not sufficient for accurate classification of bruxism. Attempts have been made in the literature to use EMG, ECG, or EEG signals to characterize the features of bruxism occurrence [##UREF##8##9##, ##UREF##9##10##, ##UREF##13##15##]– [##UREF##16##18##]. Although these methods cannot fully replace the accurate judgments of doctors, they can serve as medical auxiliary equipment to provide monitoring and warnings to potential bruxism patients or assist dentists in diagnosis. These data and analysis methods still have certain reference value for the diagnosis of “bruxism”.</p>", "<p id=\"Par49\">Additionally, we also need to clarify that the proposed method is only suitable for sleep bruxism recognition and is not a general bruxism classification system. Therefore, it is very important to comprehensively consider various data and analysis methods in the research and diagnosis of bruxism to improve the accuracy and reliability of classification. In the future, additional physiological signals such as ECG, EMG, and SaO₂ will be analyzed across all five sleep stages. The study will also be expanded to assess the recognition ability of bruxism in different sleep stages.</p>" ]

[ "<title>Background</title>", "<p id=\"Par1\">In the classification of bruxism patients based on electroencephalogram (EEG), feature extraction is essential. The method of using multi-channel EEG fusing electrocardiogram (ECG) and Electromyography (EMG) signal features has been proved to have good performance in bruxism classification, but the classification performance based on single channel EEG signal is still understudied. We investigate the efficacy of single EEG channel in bruxism classification.</p>", "<title>Methods</title>", "<p id=\"Par2\">We have extracted time-domain, frequency-domain, and nonlinear features from single EEG channel to classify bruxism. Five common bipolar EEG recordings from 2 bruxism patients and 4 healthy controls during REM sleep were analyzed. The time domain (mean, standard deviation, root mean squared value), frequency domain (absolute, relative and ratios power spectral density (PSD)), and non-linear features (sample entropy) of different EEG frequency bands were analyzed from five EEG channels of each participant. Fine tree algorithm was trained and tested for classifying sleep bruxism with healthy controls using five-fold cross-validation.</p>", "<title>Results</title>", "<p id=\"Par3\">Our results demonstrate that the C4P4 EEG channel was most effective for classification of sleep bruxism that yielded 95.59% sensitivity, 98.44% specificity, 97.84% accuracy, and 94.20% positive predictive value (PPV).</p>", "<title>Conclusions</title>", "<p id=\"Par4\">Our results illustrate the feasibility of sleep bruxism classification using single EEG channel and provides an experimental foundation for the development of a future portable automatic sleep bruxism detection system.</p>", "<title>Keywords</title>" ]

[]

[ "<title>Author contributions</title>", "<p>CW participated in data acquisition, post processing, algorithm development, data analysis, drafting, and writing of the manuscript. BG and CL made contributions in data analysis, feature extraction, and classification, while AKV and XX provided constructive suggestions for the revision of the manuscript. All authors read and approved the final manuscript.</p>", "<title>Funding</title>", "<p>This work was supported in part by the National Natural Science Foundation of China under Grant 82160347, Chaozhou Science and Technology Plan Project under Grant 202201GY01, and Scientific Research Fund of Hanshan Normal University under Grant XY202106, QD202321, and QD2021218. Special Project of Guangdong Province in Key Fields of Ordinary Colleges and Universities under Grant 2023ZDZX2038. Innovation Teams of Ordinary Universities in Guangdong Province under Grant 2023KCXTD022.</p>", "<title>Data availability</title>", "<p>The data that support the findings of this study are available from PhysioNet [##REF##14592270##19##, ##UREF##17##20##], which are publicly available. PhysioNet is a repository of freely available medical research data, managed by the MIT Laboratory.</p>", "<title>Availability of data and material</title>", "<p>All data generated or analyzed during this case are included in this published article.</p>", "<title>Declarations</title>", "<title>Ethics approval and consent to participate</title>", "<p id=\"Par51\">Not applicable. All data were publicly available in PhysioNet.</p>", "<title>Consent for publication</title>", "<p id=\"Par52\">Not applicable.</p>", "<title>Competing interests</title>", "<p id=\"Par50\">The authors declare no competing interests.</p>" ]

[ "<fig id=\"Fig1\"><label>Fig. 1</label><caption><p>The block diagram of this study</p></caption></fig>", "<fig id=\"Fig2\"><label>Fig. 2</label><caption><p>The relative spectral power distributed (mean ± SE) in four EEG frequency bands between two groups: healthy controls and bruxism patients with five EEG bipolar channels: F4C4, C4P4, Fp1F3, F3C3, and C4A1 during REM sleep. The ‘*’ represents alpha levels: <italic>p</italic> &lt; 0.05 correspond to significant differences</p></caption></fig>", "<fig id=\"Fig3\"><label>Fig. 3</label><caption><p>The 28 features distributed (mean ± SD) in four EEG frequency bands: δ, θ, α, and β between two groups: healthy controls and bruxism patients with channel C4P4 during REM sleep. ‘*’ represent the alpha level (<italic>p</italic> &lt; 0.05) for significant difference</p></caption></fig>" ]

[ "<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>The participant’s demographic information with the period of rem sleep events obtained from hypnogram. (Note: brux stands for bruxism patients and n stands for normal controls. The number at the suffix corresponds to the participant in the Physionet dataset label.)</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Participants</th><th align=\"left\">Age in Years</th><th align=\"left\">Gender</th><th align=\"left\">REM Sleep Events</th><th align=\"left\">Sleep epoch (sec)</th><th align=\"left\">Total time (sec)</th></tr></thead><tbody><tr><td align=\"left\">brux1</td><td align=\"left\">34</td><td align=\"left\">M</td><td char=\".\" align=\"char\">67</td><td char=\".\" align=\"char\">30</td><td char=\".\" align=\"char\">2010</td></tr><tr><td align=\"left\">brux2</td><td align=\"left\">23</td><td align=\"left\">M</td><td char=\".\" align=\"char\">209</td><td char=\".\" align=\"char\">30</td><td char=\".\" align=\"char\">6270</td></tr><tr><td align=\"left\">n3</td><td align=\"left\">35</td><td align=\"left\">F</td><td char=\".\" align=\"char\">188</td><td char=\".\" align=\"char\">30</td><td char=\".\" align=\"char\">5640</td></tr><tr><td align=\"left\">n5</td><td align=\"left\">35</td><td align=\"left\">F</td><td char=\".\" align=\"char\">232</td><td char=\".\" align=\"char\">30</td><td char=\".\" align=\"char\">6960</td></tr><tr><td align=\"left\">n10</td><td align=\"left\">23</td><td align=\"left\">M</td><td char=\".\" align=\"char\">218</td><td char=\".\" align=\"char\">30</td><td char=\".\" align=\"char\">6540</td></tr><tr><td align=\"left\">n11</td><td align=\"left\">28</td><td align=\"left\">F</td><td char=\".\" align=\"char\">381</td><td char=\".\" align=\"char\">30</td><td char=\".\" align=\"char\">11,430</td></tr><tr><td align=\"left\"/><td align=\"left\">\n<bold>29.7 ± 5.3</bold>\n</td><td align=\"left\"/><td char=\".\" align=\"char\">\n<bold>1295</bold>\n</td><td char=\".\" align=\"char\">\n<bold>180</bold>\n</td><td char=\".\" align=\"char\">\n<bold>38,850</bold>\n</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab2\"><label>Table 2</label><caption><p>The comparison of p-values from 6 participants with 12 time domain features under two psychological states (healthy controls and bruxsim) for C4P4 channel</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">SN</th><th align=\"left\">Features</th><th align=\"left\">Healthy controls (mean ± SD) (mV)</th><th align=\"left\">Bruxism (mean ± SD) (mV)</th><th align=\"left\">p-value</th></tr></thead><tbody><tr><td align=\"left\">1</td><td align=\"left\">Mean(δ)</td><td align=\"left\">0.018 ± 0.006</td><td align=\"left\">0.064 ± 0.045</td><td char=\".\" align=\"char\">0.566</td></tr><tr><td align=\"left\">2</td><td align=\"left\">Mean(θ)</td><td align=\"left\">-1.021E-5 ± 1.092E-5</td><td align=\"left\">-2.336E-±5.055E-5</td><td char=\".\" align=\"char\">0.989</td></tr><tr><td align=\"left\">3</td><td align=\"left\">Mean(α)</td><td align=\"left\">2.955E-6 ± 1.991E-6</td><td align=\"left\">-2.198E-6 ± 1.238E-5</td><td char=\".\" align=\"char\">0.842</td></tr><tr><td align=\"left\">4</td><td align=\"left\">Mean(β)</td><td align=\"left\">1.106E-6 ± 7.875E-7</td><td align=\"left\">5.211E-7 ± 5.211E-6</td><td char=\".\" align=\"char\">0.959</td></tr><tr><td align=\"left\">5</td><td align=\"left\">SD(δ)</td><td align=\"left\">8.280 ± 0.233</td><td align=\"left\">6.976 ± 0.708</td><td char=\".\" align=\"char\">&lt;=0.001**</td></tr><tr><td align=\"left\">6</td><td align=\"left\">SD(θ)</td><td align=\"left\">0.944 ± 0.009</td><td align=\"left\">1.576 ± 0.063</td><td char=\".\" align=\"char\">&lt;=0.001**</td></tr><tr><td align=\"left\">7</td><td align=\"left\">SD(α)</td><td align=\"left\">0.309 ± 0.005</td><td align=\"left\">0.835 ± 0.033</td><td char=\".\" align=\"char\">&lt;=0.001**</td></tr><tr><td align=\"left\">8</td><td align=\"left\">SD(β)</td><td align=\"left\">0.135 ± 0.004</td><td align=\"left\">0.608 ± 0.052</td><td char=\".\" align=\"char\">&lt;=0.001**</td></tr><tr><td align=\"left\">9</td><td align=\"left\">RMS(δ)</td><td align=\"left\">8.281 ± 0.233</td><td align=\"left\">6.981 ± 0.710</td><td char=\".\" align=\"char\">&lt;=0.001**</td></tr><tr><td align=\"left\">10</td><td align=\"left\">RMS(θ)</td><td align=\"left\">0.944 ± 0.009</td><td align=\"left\">1.576 ± 0.063</td><td char=\".\" align=\"char\">&lt;=0.001**</td></tr><tr><td align=\"left\">11</td><td align=\"left\">RMS(α)</td><td align=\"left\">0.309 ± 0.005</td><td align=\"left\">0.835 ± 0.033</td><td char=\".\" align=\"char\">&lt;=0.001**</td></tr><tr><td align=\"left\">12</td><td align=\"left\">RMS(β)</td><td align=\"left\">0.135 ± 0.004</td><td align=\"left\">0.608 ± 0.052</td><td char=\".\" align=\"char\">&lt;=0.001**</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab3\"><label>Table 3</label><caption><p>The comparison of p-values for 6 participants in frequency domain features in (RPSD and ratios) under two psychological states (healthy controls and bruxsim) for C4P4 channel</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">SN</th><th align=\"left\">Features</th><th align=\"left\">Healthy controls (mean ± SD)(%)</th><th align=\"left\">Bruxsim<break/>(mean ± SD)(%)</th><th align=\"left\">p-value</th></tr></thead><tbody><tr><td align=\"left\">1</td><td align=\"left\">RPSD(δ)</td><td char=\"?\" align=\"char\">65.26 ± 0.422</td><td char=\"?\" align=\"char\">65.302 ± 0.597</td><td char=\".\" align=\"char\">0.677</td></tr><tr><td align=\"left\">2</td><td align=\"left\">RPSD(θ)</td><td char=\"?\" align=\"char\">20.619 ± 0.158</td><td char=\"?\" align=\"char\">17.480 ± 0.317</td><td char=\".\" align=\"char\">&lt;=0.001**</td></tr><tr><td align=\"left\">3</td><td align=\"left\">RPSD(α)</td><td char=\"?\" align=\"char\">8.424 ± 0.185</td><td char=\"?\" align=\"char\">9.931 ± 0.348</td><td char=\".\" align=\"char\">&lt;=0.001**</td></tr><tr><td align=\"left\">4</td><td align=\"left\">RPSD(β)</td><td char=\"?\" align=\"char\">5.696 ± 0.140</td><td char=\"?\" align=\"char\">7.287 ± 0.197</td><td char=\".\" align=\"char\">&lt;=0.001**</td></tr><tr><td align=\"left\">5</td><td align=\"left\">(θ + α)/β</td><td char=\"?\" align=\"char\">8.246 ± 0.289</td><td char=\"?\" align=\"char\">4.721 ± 0.199</td><td char=\".\" align=\"char\">&lt;=0.001**</td></tr><tr><td align=\"left\">6</td><td align=\"left\">α/δ</td><td char=\"?\" align=\"char\">0.159 ± 0.005</td><td char=\"?\" align=\"char\">0.169 ± 0.009</td><td char=\".\" align=\"char\">&lt;=0.001**</td></tr><tr><td align=\"left\">7</td><td align=\"left\">α/θ</td><td char=\"?\" align=\"char\">0.382 ± 0.008</td><td char=\"?\" align=\"char\">0.633 ± 0.029</td><td char=\".\" align=\"char\">&lt;=0.001**</td></tr><tr><td align=\"left\">8</td><td align=\"left\">α/β</td><td char=\"?\" align=\"char\">1.601 ± 0.017</td><td char=\"?\" align=\"char\">1.434 ± 0.032</td><td char=\".\" align=\"char\">&lt;=0.001**</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab4\"><label>Table 4</label><caption><p>The comparison of p-values for 6 participants in frequency domain features (APSD) under two psychological states (healthy controls and bruxsim) for C4P4 channel</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">SN</th><th align=\"left\">Features</th><th align=\"left\">healthy controls (mean ± SD)<break/>(mV2/Hz)</th><th align=\"left\">Bruxsim<break/>(mean ± SD)<break/>(mV2/Hz)</th><th align=\"left\">p-value</th></tr></thead><tbody><tr><td align=\"left\">1</td><td align=\"left\">APSD(δ)</td><td char=\"?\" align=\"char\">83.048 ± 16.083</td><td char=\"?\" align=\"char\">129.720 ± 65.344</td><td char=\".\" align=\"char\">&lt;=0.001**</td></tr><tr><td align=\"left\">2</td><td align=\"left\">APSD(θ)</td><td char=\"?\" align=\"char\">16.698 ± 2.191</td><td char=\"?\" align=\"char\">17.517 ± 5.569</td><td char=\".\" align=\"char\">&lt;=0.001**</td></tr><tr><td align=\"left\">3</td><td align=\"left\">APSD(α)</td><td char=\"?\" align=\"char\">3.885 ± 0.111</td><td char=\"?\" align=\"char\">7.135 ± 2.170</td><td char=\".\" align=\"char\">&lt;=0.001**</td></tr><tr><td align=\"left\">4</td><td align=\"left\">APSD(β)</td><td char=\"?\" align=\"char\">2.367 ± 0.059</td><td char=\"?\" align=\"char\">4.292 ± 1.203</td><td char=\".\" align=\"char\">0.188</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab5\"><label>Table 5</label><caption><p>The comparison of p-values for 6 participants with 4 sample entropy features under two psychological states (healthy controls and bruxsim) for C4P4 channel</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">SN</th><th align=\"left\">Features</th><th align=\"left\">healthy controls (mean ± SD)</th><th align=\"left\">Bruxsim (mean ± SD)</th><th align=\"left\">p-value</th></tr></thead><tbody><tr><td align=\"left\">1</td><td align=\"left\">SampEn(δ)</td><td char=\"?\" align=\"char\">0.530 ± 0.002</td><td char=\"?\" align=\"char\">0.572 ± 0.006</td><td char=\".\" align=\"char\">&lt;=0.001**</td></tr><tr><td align=\"left\">2</td><td align=\"left\">SampEn(θ)</td><td char=\"?\" align=\"char\">0.822 ± 0.001</td><td char=\"?\" align=\"char\">0.810 ± 0.005</td><td char=\".\" align=\"char\">0.695</td></tr><tr><td align=\"left\">3</td><td align=\"left\">SampEn(α)</td><td char=\"?\" align=\"char\">0.977 ± 0.003</td><td char=\"?\" align=\"char\">0.957 ± 0.007</td><td char=\".\" align=\"char\">0.01*</td></tr><tr><td align=\"left\">4</td><td align=\"left\">SampEn(β)</td><td char=\"?\" align=\"char\">1.378 ± 0.006</td><td char=\"?\" align=\"char\">1.303 ± 0.013</td><td char=\".\" align=\"char\">&lt;=0.001**</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab6\"><label>Table 6</label><caption><p>Comparison of bruxism classification with five-folds cross-validation for respective EEG channels</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Channel</th><th align=\"left\">Sensitivity (%)</th><th align=\"left\">Specificity (%)</th><th align=\"left\">Accuracy (%)</th><th align=\"left\">PPV(%)</th></tr></thead><tbody><tr><td align=\"left\">F4C4</td><td char=\".\" align=\"char\">86.11</td><td char=\".\" align=\"char\">97.22</td><td char=\".\" align=\"char\">94.75</td><td char=\".\" align=\"char\">89.86</td></tr><tr><td align=\"left\">\n<bold>C4P4</bold>\n</td><td char=\".\" align=\"char\">\n<bold>95.59</bold>\n</td><td char=\".\" align=\"char\">\n<bold>98.44</bold>\n</td><td char=\".\" align=\"char\">\n<bold>97.84</bold>\n</td><td char=\".\" align=\"char\">\n<bold>94.20</bold>\n</td></tr><tr><td align=\"left\">Fp1F3</td><td char=\".\" align=\"char\">91.70</td><td char=\".\" align=\"char\">96.80</td><td char=\".\" align=\"char\">95.75</td><td char=\".\" align=\"char\">88.04</td></tr><tr><td align=\"left\">F3C3</td><td char=\".\" align=\"char\">91.82</td><td char=\".\" align=\"char\">97.17</td><td char=\".\" align=\"char\">96.06</td><td char=\".\" align=\"char\">89.49</td></tr><tr><td align=\"left\">C4A1</td><td char=\".\" align=\"char\">88.64</td><td char=\".\" align=\"char\">96.67</td><td char=\".\" align=\"char\">94.98</td><td char=\".\" align=\"char\">87.68</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab7\"><label>Table 7</label><caption><p>Comparison classification performance between the proposed and previous works</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Authors</th><th align=\"left\">Signal</th><th align=\"left\">Method</th><th align=\"left\">Channel</th><th align=\"left\">Sleep stage</th><th align=\"left\">Accuracy (%)</th></tr></thead><tbody><tr><td align=\"left\">E. O’Hare et al. [##UREF##8##9##]</td><td align=\"left\">EMG</td><td align=\"left\">Linear discriminant analysis</td><td align=\"left\">EMG</td><td align=\"left\">Awake</td><td align=\"left\">82.8%</td></tr><tr><td align=\"left\">Bin Heyat et al. [##UREF##14##16##]</td><td align=\"left\">EEG</td><td align=\"left\">Decision tree</td><td align=\"left\">C4P4,C4A1</td><td align=\"left\">REM</td><td align=\"left\">81.25%</td></tr><tr><td align=\"left\">Bin Heyat et al. [##UREF##15##17##]</td><td align=\"left\">EEG,EMG,ECG</td><td align=\"left\">Hybrid Machine Learning Classifier</td><td align=\"left\">ECG1,ECG2,C4P4,C4A1</td><td align=\"left\">REM</td><td align=\"left\">97%</td></tr><tr><td align=\"left\">D. Lai et al. [##UREF##9##10##]</td><td align=\"left\">EEG,EMG,ECG</td><td align=\"left\">Decision tree</td><td align=\"left\">EEG,EMG,ECG1</td><td align=\"left\">REM</td><td align=\"left\">97.21%</td></tr><tr><td align=\"left\">\n<bold>Present</bold>\n</td><td align=\"left\">\n<bold>EEG</bold>\n</td><td align=\"left\">\n<bold>Decision tree (Fine Tree classifier)</bold>\n</td><td align=\"left\">\n<bold>C4P4</bold>\n</td><td align=\"left\">\n<bold>REM</bold>\n</td><td align=\"left\">\n<bold>97.84%</bold>\n</td></tr></tbody></table></table-wrap>" ]

[ "<disp-formula id=\"Equ1\"><label>1</label><alternatives><tex-math id=\"M1\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$RPS{D_n} = \\frac{{PS{D_n}}}{{PS{D_\\delta } + PS{D_\\theta } + PS{D_\\alpha } + PS{D_\\beta }}} \\times 100\\% \\,,\\,(n \\in [\\delta ,\\theta ,\\alpha ,\\beta ])$$\\end{document}</tex-math></alternatives></disp-formula>" ]

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[ "<table-wrap-foot><p>The ‘*’ and ‘**’ represent alpha levels: <italic>p</italic> = 0.05, and <italic>p</italic> = 0.001 correspond to significant differences, respectively</p></table-wrap-foot>", "<table-wrap-foot><p>The ‘*’ and ‘**’ represent alpha levels: <italic>p</italic> = 0.05, and <italic>p</italic> = 0.001 correspond to significant differences, respectively</p></table-wrap-foot>", "<table-wrap-foot><p>The ‘*’ and ‘**’ represent alpha levels: <italic>p</italic> = 0.05, and <italic>p</italic> = 0.001 correspond to significant differences, respectively</p></table-wrap-foot>", "<table-wrap-foot><p>The ‘*’ and ‘**’ represent alpha levels: <italic>p</italic> = 0.05, and <italic>p</italic> = 0.001 correspond to significant differences, respectively</p></table-wrap-foot>", "<fn-group><fn><p><bold>Publisher’s Note</bold></p><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn></fn-group>" ]

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[ "<title>Background</title>", "<p id=\"Par5\">For decades, insecticide-treated nets (ITNs) have played an important role in preventing malaria cases and deaths in malaria-endemic countries. Between 2000 and 2015, the malaria case incidence declined by 27% owing mostly to mass distribution campaigns of ITNs in malaria-risk countries [##UREF##0##1##, ##UREF##1##2##]. The malaria case incidence slightly increased due to disruption caused by the Coronavirus disease (COVID-19) pandemic in 2020. However, while ITNs are still being widely distributed in endemic countries every three years, recent malaria case incidence has not substantially reduced [##UREF##2##3##].</p>", "<p id=\"Par6\">Various explanations may be posited to explain the lack of a downward trend in malaria case incidence, but one contributory factor could be related to the lack of physical durability provided by modern ITN products. Clearly, if nets are insufficiently robust and quickly accumulate holes that undermine their ability to provide physical protection, they can no longer perform their basic function. Field studies have repeatedly reported loss of ITN physical integrity within two years post-distribution, with many ITNs being so badly damaged, they are discarded [##REF##29132421##4##].</p>", "<p id=\"Par7\">Over the same period there has been a lack of performance standards in product specifications relating to the physical durability of nets, meaning manufacturers have not been given appropriate targets to address. This has created a disconnect between the engineering design of ITN products and their expected performance in the field. Meanwhile, the weighted average price for ITNs sharply decreased from USD 4.20 per ITN in 2011, down to USD 1.88 per ITN in 2019 due to greater cost transparency, volume, industry consultation and synchronised demand [##UREF##3##5##]. This raises the question as to whether the physical durability of ITNs has also been impacted by product design changes, in response to price pressures. With so much emphasis being placed on long-term insecticidal functionality, the critical role the net textile plays in providing physical protection has been given insufficient attention. If the textile forming the net is too weak to withstand forces it is exposed to during use, large holes can be expected to form rapidly, compromising the ITN’s ability to provide physical protection, regardless of the insecticide’s bioefficacy. The physical durability of an ITN therefore depends on its inherent strength and ability to resist damage, which is governed by textile properties, as well as how carefully it is used following distribution.</p>", "<p id=\"Par8\">The inherent Resistance to Damage (RD) of any ITN can be measured before it is distributed. It depends on the mechanical properties of the textile used to manufacture the net and can be determined by a suite of four laboratory textile tests, in which fabric properties linked to damage mechanisms observed in the field are measured under controlled conditions. Each of these properties is a measure of the ability of the net to withstand the mechanical forces it will be exposed to during normal household use.</p>", "<p id=\"Par9\">The RD methodology introduced by Wheldrake et al. [##REF##33468114##6##–##UREF##4##9##] has also highlighted marked differences in the performance of existing WHO prequalified ITNs. The RD score characterises the inherent resistance to damage of an ITN, and is measured on a 0–100 scale, where a RD score of 100 indicates the highest performance. Knowing the RD score of an ITN product before it is distributed is, therefore, a useful indicator of its inherent robustness and ability to retain physical integrity during normal use. A link between the RD performance and survivorship has been confirmed by Kilian et al. who reported a correlation between RD scores and actual ITN performance in the field, where RD scores measured in the laboratory of &gt; 50, resulted in substantially longer service life in the field [##REF##33413383##10##].</p>", "<p id=\"Par10\">Given the ongoing concerns about the physical durability of ITNs, the purpose of this study was to determine if there have been any changes in the inherent resistance to damage of ITNs from 2013 to 2020. Test results for WHOPES-recommended (2013) and WHO-prequalified (2020) branded ITNs sampled in 2013 and 2020, respectively, were compared based on the textile testing methodology of Wheldrake et al<italic>.</italic> [##REF##33468114##6##–##UREF##4##9##]. The study involved many different branded ITNs, including WHO-PQ qualified nets available in 2020, and aimed to identify any trends or changes in performance over the seven-year period.</p>" ]

[ "<title>Methods</title>", "<title>ITN samples</title>", "<p id=\"Par11\">Existing data for nine WHOPES-recommended ITNs measured in 2013 by Wheldrake et al<italic>.</italic> [##REF##33468152##7##] was compared with primary data for twenty-four WHO-prequalified ITNs sampled in 2020. This included ITN products that were available both in 2013 and 2020, as well as newer products that were not available in 2013. The purpose of including both was not just to compare performance changes in the same products, but also to determine the extent to which product development has led to performance improvements across all available ITNs. The ITNs were made by different suppliers: Vestergaard Frandsen; Tianjin Yorkool International Trading Co., Ltd; Bayer industry Co., Ltd; V.K.A Polymers Pvt Ltd; Sumitomo chemical; BASF Agro B.V. Arnhem; Disease Control Technologies; Tana netting FZ; Shobikaa Impex Private Ltd. and Moon Netting.</p>", "<p id=\"Par12\">Comparison was possible between several of the ITNs available in 2020 and 2013 because in most cases they were produced by the same supplier and were identically branded. Herein, the ITNs are anonymously labelled, and general product specifications are given in Table ##TAB##0##1## and Fig. ##FIG##0##1##, including their principal polymer compositions (as indicated on the product packaging), abbreviated as follows: high density polyethylene (HDPE); polyethylene terephthalate (PET) and polyethylene (PE). The area densities of the ITNs ranged from 27 g m⁻² to 52 g m⁻². Ownership of the Net U product transferred to another company between 2013 and 2020 so this lineage was reflected in the coding, even though the ITN specification also changed.</p>", "<title>Laboratory testing of ITNs</title>", "<p id=\"Par13\">All ITN samples were tested in accordance with RD methodology, which involves four textile tests each of which reflects different mechanisms of damage that nets are exposed to in the field [##REF##33468114##6##]. For each ITN, the sample preparation methods, test procedures and number of replicates recommended by Wheldrake et al<italic>.</italic> [##REF##33468152##7##, ##UREF##4##9##] were followed to obtain data for the bursting strength, snag strength, abrasion resistance and resistance to hole enlargement. For each ITN, three separate net samples were analysed with five measurements per sample. The sampling method also accounts for those products with different fabrics forming the roof and side panels.</p>", "<title>Resistance to Damage (RD) scores</title>", "<p id=\"Par14\">The RD methodology developed by Wheldrake et al<italic>.</italic> [##REF##33468151##8##] enables a single performance metric to be obtained by aggregating the textile testing data for snag strength, abrasion resistance, bursting strength and hole enlargement resistance, together with human factors (to obtain aspirational performance values). The RD score characterises the inherent resistance to damage of the ITN on a RD = 0–100 scale, where RD = 100 represents the highest performance as shown in Eq. ##FORMU##0##1##[##FORMU##0##8##].where:</p>", "<p id=\"Par15\">RD = Resistance to damage.</p>", "<p id=\"Par16\">= Actual bursting strength (kPa).</p>", "<p id=\"Par17\"> = Aspirational bursting strength (kPa) </p>", "<p id=\"Par18\"> = Actual snag strength (N).</p>", "<p id=\"Par19\"> = Aspirational snag strength (N).</p>", "<p id=\"Par20\">= Actual abrasion resistance strength (number of rubs).</p>", "<p id=\"Par21\"> = Aspirational abrasion resistance (number of rubs).</p>", "<p id=\"Par22\">σ_H = Hole enlargement resistance.</p>" ]

[ "<title>Results</title>", "<p id=\"Par23\">To enable comparison between 2013 and 2020, the mean data for each physical property associated with the RD methodology is reported, together with the aggregated RD scores for all ITNs.</p>", "<title>Bursting strength</title>", "<p id=\"Par24\">Although bursting strength alone is a poor predictor of physical integrity and resistance to hole formation [##UREF##4##9##], it is a useful measure when aggregated with other textile mechanical property data, and measurements form part of the RD score. Figure ##FIG##1##2## reveals that in 2013 the mean bursting strength of ITNs ranged from 299 kPa to 626 kPa, and in 2020, from 281 kPa to 578 kPa. Except for Net N and Net L which performed significantly better (p &lt; 0.05) (+ 37% and + 42%, respectively), the ITNs in 2020 performed similarly, or worse compared to 2013. Nets B, C, H and U had significantly lower bursting strengths (p &lt; 0.05) in 2020 compared to 2013 (− 8%, − 7%, − 17%, − 22% respectively) whereas Nets Q, W and X exhibited similar bursting strength in 2020 and in 2013 (p &gt; 0.05). Note that all ITNs passed the long-standing bursting strength requirement set by WHO of 250 kPa, with most achieving values &gt; 400 kPa in 2020. This reflects the fact that each ITN product has its own bursting strength requirements and specifications, which can be substantially higher than 250 kPa.</p>", "<title>Snag strength</title>", "<p id=\"Par25\">Snagging is the most frequently encountered form of mechanical damage in ITNs retrieved from the field and is an inherent weakness of lightweight knitted fabrics [##REF##25476877##11##]. Figure ##FIG##2##3## illustrates marked differences in the mean snag strengths across ITNs (2013 and 2020) from as low as 25N to 56N. Nets L and N performed significantly better in 2020 (p &lt; 0.05) than in 2013 (+ 56% and + 17% respectively), while the snag strength of Net B, H and U significantly decreased by approximately − 9, − 10 and − 22%, respectively in 2020 compared to 2013 (p &lt; 0.05). In respect of Net U (2020), this is likely to be attributable to a decrease in the yarn linear density and the area density of the fabric compared to 2013.</p>", "<title>Abrasion resistance</title>", "<p id=\"Par26\">Figure ##FIG##3##4## compares the abrasion resistance of ITNs in 2013 and 2020, specifically the rate at which nets failed as the number of rubs (abrasion cycles) applied to the net increased. Except for Net U, the abrasion resistance of the 2020 ITN samples was similar, or worse than the 2013 performance.</p>", "<title>Hole enlargement resistance</title>", "<p id=\"Par27\">A comparison of the hole enlargement resistance of ITNs in 2013 and 2020 is shown in Fig. ##FIG##4##5##. A significant decrease in the performance of Net L, Net U and Net W was observed between 2013 and 2020 (p &lt; 0.05) with an increase in the end hole size of + 63%, + 154% and + 47% respectively. However, Net N and Net Q showed significantly improved performance in 2020 (p &lt; 0.05) with reductions in the end hole size of -8% -19% respectively. Large differences in hole enlargement resistance were evident between different ITN products, with the worst performing ITNs generally being made from polyethylene (PE) monofilament warp knitted tulle fabrics. Across all nets, end hole sizes ranged from only 9.4 mm for Net N (very resistant to hole enlargement), to 73 mm for Net J, which is so large it is likely to completely undermine the ITN’s ability to provide long-term physical protection.</p>", "<title>Resistance to damage (RD) scores for ITNs</title>", "<p id=\"Par28\">A comparison of RD scores for ITNs in 2013 and 2020 are reported in Fig. ##FIG##5##6##. These scores highlight marked differences in the performance of different products. According to RD methodology, the performance of ITNs should be achieving RD &gt; 50, and ideally approaching RD = 100. Comparing the results from 2013 and 2020, a reduction in the RD value was observed for Net B, Net C net Q, Net H, Net U, Net W and Net X of -12%, -7%, -6%, -33%, -35%, -14% and -34%, respectively. Only Net N and Net L showed improved RD values between 2013 and 2020 of + 5 and + 47%, respectively.</p>", "<p id=\"Par29\">In 2020, only Net B and Net T achieved RD scores &gt; 50. Apart from Net N and Net L, the RD scores of ITNs in 2020 were lower than in 2013. Concerningly, in 2020, six out of twenty-four WHO-recommended ITNs (25%) produced very low RD scores of &lt; 30, making them highly susceptible to mechanical damage in the field and compromising their ability to provide long-term physical durability in the field.</p>", "<p id=\"Par30\">The average RD score of all comparable ITNs brands (excluding Net U, which was subject to a known specification change in the intervening years) decreased from 40 in 2013 to 36 in 2020. Figure ##FIG##6##7## compares the average RD scores of nets in 2013 and 2020 and indicates reductions from 57 to 44 (for HDPE) and from 31 to 24 (for PE). However, the average RD scores for PET nets did not follow a similar trend. Note that there are also differences in the type of yarn construction (mono and multifilament) as well as knitting patterns, which means caution is needed in making generalised conclusions about the performance of nets made from different polymer types. Certainly, it is evident that there has been no major improvement in average RD scores in the seven-year period for any of the nets grouped by polymer type.</p>" ]

[ "<title>Discussion</title>", "<p id=\"Par31\">Over the last decade, field study evaluations of ITNs have repeatedly reported unsatisfactory performance in terms of physical integrity and survivorship, due to nets becoming too torn or damaged to provide long-term physical protection for at least three years. Whilst progress has been made in developing new insecticidal formulations, relatively little attention has been paid to the importance of the net textile itself, even though it plays a vital role in providing physical barrier protection and substantial physical damage could result in the net being disposed of [##UREF##1##2##]. Apart from bursting strength, no pre-assessment of the inherent ability of an ITN to resist damage is normally required prior to its approval and distribution, even though holes and tears are generally regarded as inevitable.</p>", "<p id=\"Par32\">ITNs with inherently high resistance to damage can be expected to last longer in the field, promoting improved survivorship, and this basic hypothesis has been confirmed by Kilian et al. [##REF##33413383##10##] who demonstrated that existing ITNs achieving RD scores &gt; 50, improved survivorship in the field by approximately seven months. However, as evidenced in for example Fig. ##FIG##4##5## (hole enlargement), some ITNs resist damage so poorly that their ability to provide long-term physical protection is highly questionable. The fact that existing ITN products are not ‘the same’ in terms of their resistance to damage may also partly explain why the physical durability of ITNs generally appears to be so variable.</p>", "<p id=\"Par33\">The disconnect between the performance standards ITNs need to meet in the lab, and their required performance in the field, is in stark contrast to the insecticide, which is subject to thorough laboratory testing, as well as semi- and full field testing to ensure bioefficacy before use. Elsewhere, textiles used to make products for protecting people from potential harm, such as personal protective equipment (PPE) are subject to much stricter performance standards in the laboratory than ITNs, to ensure they are fit for purpose. ITN textiles need to be more thoroughly tested in the laboratory, based on appropriate performance standards, measuring properties that are relevant to the required performance in the field. In this regard, it is positive that WHO PQ-Vector Control team has made constructive steps to consider adoption of additional textile performance standards to drive improvements in the physical durability of ITNs.</p>", "<p id=\"Par34\">The pressing need for better ITN performance standards related to physical durability is highlighted by comparing the inherent resistance to damage data for ITNs sampled in 2013 and 2020, which reveals that the performance of many ITN products has not improved in seven years (Figs. ##FIG##1##2##, ##FIG##2##3##, ##FIG##3##4##, ##FIG##4##5## and ##FIG##5##6##). In 2020, only two of the twenty-four ITNs examined achieved an RD score &gt; 50, while 25% of the ITN products available achieved a very low RD score of &lt; 30 (Fig. ##FIG##5##6##). The ITNs with RD scores &gt; 50 (Net B and T) were made from fabrics having relatively high area densities, mesh counts and linear density.</p>", "<p id=\"Par35\">By contrast in 2013, six of the sixteen WHOPES-recommended ITNs available, achieved RD values of &gt; 50 [##REF##33468152##7##]. The generally lower RD values observed in 2020 compared to 2013 is also highlighted when comparing ITNs in terms of their polymer composition (Fig. ##FIG##6##7##), although comparisons between nets made from HDPE, PE and PET are not straightforward because the knitting pattern is not consistent between products.</p>", "<p id=\"Par36\">Low RD values are likely to increase the vulnerability of ITNs to physical damage in the field, such that they will be prone to accumulating holes more quickly, limiting their long-term service life. Given the inherent weakness of current ITNs, particular focus is needed on improving abrasion resistance (Fig. ##FIG##3##4##), snag strength (Fig. ##FIG##2##3##) and hole enlargement resistance (Fig. ##FIG##4##5##), to markedly increase overall resistance to damage. This is likely to require major upgrades to the specifications of textile fabrics used to manufacture ITNs, including but not limited to polymer grades, filament linear density, knitting pattern, as well as basis weight (g/m²). Of course, this will have cost implications, but these should be balanced against resulting increases in the reliability and value of ITNs in providing longer service life. At the same time, user perceptions and experience are key to ensuring on-going product acceptance and so the design of more durable ITNs should also consider factors such as the aesthetics and thermophysical comfort of products.</p>", "<p id=\"Par37\">The availability of robust, more damage resistant ITNs is not only a technical issue, but one of market dynamics. The design of more physically durable, longer-lasting ITN products is feasible, but there is little incentive to pursue improvements or innovate given that prices and margins are so low. As evidenced in this study, even though existing WHO Prequalified ITNs are not ‘the same’ in terms of their inherent resistance to damage, there is no recognition of this in procurement policies. Progressive price reductions in ITNs over many years combined with specifications that do not reward quality in terms of product performance standards, are disincentivising innovation and risk stagnating product innovation. Price pressures also incentivise cost-cutting, which is likely to negatively impact product performance. Evidence of changes to product specifications since 2013 (Table ##TAB##0##1##) leading to a fall in ITN performance are apparent in the present data. For example, the drop in bursting strength (Fig. ##FIG##1##2##), abrasion resistance (Fig. ##FIG##3##4##) and snag strength of Net U from 2013 to 2020 is likely to be attributable to the reduced fabric basis weight (-12%), together with decrease in both the mesh count and filament linear density of approximately, -36% and -20%, respectively.</p>", "<p id=\"Par38\">Raising the bar by upgrading textile performance standards for ITNs, combined with better pricing policies could act as a positive incentive for innovation, and drive major improvements in physical durability in the field. Upgraded textile performance standards would also ensure that better performance is built in to ITN products prior to use, instead of physical durability issues being highlighted when it is too late, or after lengthy field studies. Practically, this means implementing additional textile testing requirements and quantitative performance standards for snag strength, abrasion resistance and hole enlargement resistance, alongside bursting strength. It is the implementation of the individual textile testing methods that is key. Aggregating the resulting data to determine RD would be an optional step. Such an approach is also likely to lead to a more cost and time-efficient process for ITN development and evaluation, ensuring ITNs become longer-lasting more quickly, and truly fit for purpose.</p>" ]

[ "<title>Conclusion</title>", "<p id=\"Par39\">The inherent resistance to damage (RD) of ITNs has not markedly improved in the seven-year period from 2013 to 2020, and the performance of some ITN products has declined. This is likely to be a contributory factor in the high rates of ITN attrition and poor survivorship that have been repeatedly reported in field studies over the last decade. Of the WHO pre-qualified ITNs tested in 2020, only two achieved an RD score &gt; 50, and six scored &lt; 30, indicating a high degree of inherent weakness amongst currently available products. There are also large differences in the performance of existing ITNs, such that they cannot all be considered ‘the same’. More rigorous textile testing of ITN products is required, to provide new performance standards for snag strength, hole enlargement resistance and abrasion resistance to encourage development of more physically durable, longer-lasting ITN products, capable of protecting users more effectively. The resulting metrics would also assist procurers and countries to make informed choices.</p>" ]

[ "<title>Background</title>", "<p id=\"Par1\">For at least a decade, concerns have been raised about the physical durability of insecticide-treated nets (ITNs) and their ability to remain in good condition for at least three years. To discover if the resistance to damage (RD) of ITNs has improved or not, the RD scores of ITNs sampled in 2013 and 2020 were compared.</p>", "<title>Methods</title>", "<p id=\"Par2\">The RD scores and disaggregated textile performance data for nine ITNs recommended by the WHO pesticide evaluation scheme (WHOPES) measured in 2013 were compared with WHO-prequalified ITNs sampled in 2020. This included assessment of newer ITNs not available in 2013, to determine the extent to which product development has led to performance improvements across all available ITNs in the intervening years.</p>", "<title>Results</title>", "<p id=\"Par3\">The resistance to damage of ITNs has not generally improved from 2013 to 2020, and in some cases performance is worse. The average RD score of comparable ITNs brands decreased from 40 in 2013 to 36 in 2020. Of the nets available in 2020, only two of the twenty-four ITN products tested achieved an RD score of &gt; 50, while six ITNs had very low RD scores of &lt; 30, highlighting a serious inherent, and literal weakness in many WHO-prequalified ITNs.</p>", "<title>Conclusions</title>", "<p id=\"Par4\">The long-term physical durability of ITN products cannot be expected to improve while their resistance to damage remains so low, and major upgrades to the performance standards of textile materials used to make ITNs, as well as incentives to develop stronger ones are urgently required.</p>", "<title>Keywords</title>" ]

[]

[ "<title>Acknowledgements</title>", "<p>We gratefully acknowledge the generous support of the Bill &amp; Melinda Gates Foundation in the development of this work. We also wish to thank Dr. Angus Spiers and Dr. Edward Thomsen of Innovation to Impact (i2i) for their helpful, insightful comments and suggestions, which greatly assisted preparation of the manuscript. Thanks, are also due to Manoj Rathod, Rachel Douglas-Phillips, Dr. Fadi Junaid, Nicole Mitchell and Jack Street for their assistance with laboratory textile testing.</p>", "<title>Author contributions</title>", "<p>AW, EG and SJR developed the methodology and contributed to the data analysis and preparation of the manuscript. EG, HA, EZ and VC contributed to the data analysis and preparation of the manuscript. All authors read and approved the final manuscript.</p>", "<title>Funding</title>", "<p>This work was supported, in whole or in part, by the Bill &amp; Melinda Gates Foundation [INV-003160]. Under the grant conditions of the Foundation, a Creative Commons Attribution 4.0 Generic License has already been assigned to the Author Accepted Manuscript version that might arise from this submission.</p>", "<title>Availability of data and materials</title>", "<p>Datasets are available on reasonable request to NIRI, UK.</p>", "<title>Declarations</title>", "<title>Competing interests</title>", "<p id=\"Par40\">The authors declare that they have no competing interests.</p>" ]

[ "<fig id=\"Fig1\"><label>Fig. 1</label><caption><p>Tulle (<bold>A</bold>) and Traverse (<bold>B</bold>) knitting patterns</p></caption></fig>", "<fig id=\"Fig2\"><label>Fig. 2</label><caption><p>Mean bursting strength values for ITNs in 2013 and 2020. Error bars correspond to standard deviation (n = 15)−−250 kPa threshold is the bursting strength requirement set by WHO</p></caption></fig>", "<fig id=\"Fig3\"><label>Fig. 3</label><caption><p>Mean snag strength values for ITNs in 2013 and 2020. Error bars correspond to standard deviation (n = 30)</p></caption></fig>", "<fig id=\"Fig4\"><label>Fig. 4</label><caption><p>Abrasion resistance of ITNs in 2013 (dotted line) and 2020 (solid line)</p></caption></fig>", "<fig id=\"Fig5\"><label>Fig. 5</label><caption><p>Mean hole enlargement (end hole size) for ITNs in 2013 and 2020. Error bars correspond to standard deviation (n = 15)</p></caption></fig>", "<fig id=\"Fig6\"><label>Fig. 6</label><caption><p>Resistance to Damage (RD) scores for ITNs in 2013 and 2020</p></caption></fig>", "<fig id=\"Fig7\"><label>Fig. 7</label><caption><p>Resistance to Damage (RD) scores for ITNs in 2013 and 2020 grouped by yarn material (polymer) and year. Where, HDPE is high density polyethylene; PE is polyethylene, and PET is polyethylene terephthalate. Data points overlapped on the graph to show variability</p></caption></fig>" ]

[ "<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>ITN products sampled and tested in 2013 and 2020</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Net</th><th align=\"left\">Filament type</th><th align=\"left\">Knitting pattern</th><th align=\"left\">Year</th><th align=\"left\">Area density (gˑm⁻²)</th><th align=\"left\">Mesh count (holesˑin⁻²)</th><th align=\"left\">Linear density (Denier)</th></tr></thead><tbody><tr><td align=\"left\">Net A</td><td align=\"left\">HDPE monofilament</td><td align=\"left\">Tulle</td><td align=\"left\">2020</td><td align=\"left\">37</td><td char=\".\" align=\"char\">108</td><td char=\".\" align=\"char\">130</td></tr><tr><td align=\"left\"><bold>Net B</bold></td><td align=\"left\"><bold>HDPE monofilament</bold></td><td align=\"left\"><bold>Tulle</bold></td><td align=\"left\"><p><bold>2013</bold></p><p><bold>2020</bold></p></td><td align=\"left\"><p><bold>50</bold></p><p><bold>50</bold></p></td><td char=\".\" align=\"char\"><p><bold>164</bold></p><p><bold>164</bold></p></td><td char=\".\" align=\"char\"><p><bold>150</bold></p><p><bold>150</bold></p></td></tr><tr><td align=\"left\"><bold>Net C</bold></td><td align=\"left\"><bold>PET multifilament</bold></td><td align=\"left\"><bold>Traverse</bold></td><td align=\"left\"><p><bold>2013</bold></p><p><bold>2020</bold></p></td><td align=\"left\"><p><bold>42</bold></p><p><bold>43</bold></p></td><td char=\".\" align=\"char\"><p><bold>156</bold></p><p><bold>164</bold></p></td><td char=\".\" align=\"char\"><p><bold>100</bold></p><p><bold>100</bold></p></td></tr><tr><td align=\"left\">Net D</td><td align=\"left\">PET multifilament</td><td align=\"left\">Traverse</td><td align=\"left\">2020</td><td align=\"left\">42</td><td char=\".\" align=\"char\">155</td><td char=\".\" align=\"char\">100</td></tr><tr><td align=\"left\">Net E</td><td align=\"left\">PE monofilament</td><td align=\"left\">Tulle</td><td align=\"left\">2020</td><td align=\"left\">47</td><td char=\".\" align=\"char\">138</td><td char=\".\" align=\"char\">120</td></tr><tr><td align=\"left\">Net F</td><td align=\"left\">PE monofilament</td><td align=\"left\">Tulle</td><td align=\"left\">2020</td><td align=\"left\">36</td><td char=\".\" align=\"char\">130</td><td char=\".\" align=\"char\">120</td></tr><tr><td align=\"left\">Net G</td><td align=\"left\">PE monofilament</td><td align=\"left\">Tulle</td><td align=\"left\">2020</td><td align=\"left\">41</td><td char=\".\" align=\"char\">76</td><td char=\".\" align=\"char\">130</td></tr><tr><td align=\"left\"><bold>Net H</bold></td><td align=\"left\"><bold>HDPE monofilament</bold></td><td align=\"left\"><bold>Tulle</bold></td><td align=\"left\"><p><bold>2013</bold></p><p><bold>2020</bold></p></td><td align=\"left\"><p><bold>50</bold></p><p><bold>50</bold></p></td><td char=\".\" align=\"char\"><p><bold>132</bold></p><p><bold>148</bold></p></td><td char=\".\" align=\"char\"><p><bold>150</bold></p><p><bold>150</bold></p></td></tr><tr><td align=\"left\">Net I</td><td align=\"left\">PE monofilament</td><td align=\"left\">Tulle</td><td align=\"left\">2020</td><td align=\"left\">39</td><td char=\".\" align=\"char\">128</td><td char=\".\" align=\"char\">120</td></tr><tr><td align=\"left\">Net J</td><td align=\"left\">PET multifilament</td><td align=\"left\">Traverse</td><td align=\"left\">2020</td><td align=\"left\">27</td><td char=\".\" align=\"char\">142</td><td char=\".\" align=\"char\">75</td></tr><tr><td align=\"left\">Net K</td><td align=\"left\">PET multifilament</td><td align=\"left\">Traverse</td><td align=\"left\">2020</td><td align=\"left\">43</td><td char=\".\" align=\"char\">162</td><td char=\".\" align=\"char\">100</td></tr><tr><td align=\"left\"><bold>Net L</bold></td><td align=\"left\"><bold>PET multifilament</bold></td><td align=\"left\"><bold>Traverse</bold></td><td align=\"left\"><p><bold>2013</bold></p><p><bold>2020</bold></p></td><td align=\"left\"><p><bold>42</bold></p><p><bold>39</bold></p></td><td char=\".\" align=\"char\"><p><bold>156</bold></p><p><bold>165</bold></p></td><td char=\".\" align=\"char\"><p><bold>100</bold></p><p><bold>100</bold></p></td></tr><tr><td align=\"left\">Net M</td><td align=\"left\">PET multifilament</td><td align=\"left\">Traverse</td><td align=\"left\">2020</td><td align=\"left\">43</td><td char=\".\" align=\"char\">96</td><td char=\".\" align=\"char\">150</td></tr><tr><td align=\"left\"><bold>Net N</bold></td><td align=\"left\"><bold>PET multifilament</bold></td><td align=\"left\"><bold>Traverse</bold></td><td align=\"left\"><p><bold>2013</bold></p><p><bold>2020</bold></p></td><td align=\"left\"><p><bold>41</bold></p><p><bold>41</bold></p></td><td char=\".\" align=\"char\"><p><bold>156</bold></p><p><bold>176</bold></p></td><td char=\".\" align=\"char\"><p><bold>100</bold></p><p><bold>100</bold></p></td></tr><tr><td align=\"left\">Net O</td><td align=\"left\">PET multifilament</td><td align=\"left\">Traverse</td><td align=\"left\">2020</td><td align=\"left\">32</td><td char=\".\" align=\"char\">157</td><td char=\".\" align=\"char\">75</td></tr><tr><td align=\"left\">Net P</td><td align=\"left\">PET multifilament</td><td align=\"left\">Traverse</td><td align=\"left\">2020</td><td align=\"left\">42</td><td char=\".\" align=\"char\">168</td><td char=\".\" align=\"char\">100</td></tr><tr><td align=\"left\"><bold>Net Q</bold></td><td align=\"left\"><bold>PET multifilament</bold></td><td align=\"left\"><bold>Traverse</bold></td><td align=\"left\"><p><bold>2013</bold></p><p><bold>2020</bold></p></td><td align=\"left\"><p><bold>42</bold></p><p><bold>44</bold></p></td><td char=\".\" align=\"char\"><p><bold>156</bold></p><p><bold>186</bold></p></td><td char=\".\" align=\"char\"><p><bold>100</bold></p><p><bold>100</bold></p></td></tr><tr><td align=\"left\">Net R</td><td align=\"left\">PET multifilament</td><td align=\"left\">Traverse</td><td align=\"left\">2020</td><td align=\"left\">28</td><td char=\".\" align=\"char\">154</td><td char=\".\" align=\"char\">75</td></tr><tr><td align=\"left\">Net S</td><td align=\"left\">PET multifilament</td><td align=\"left\">Traverse</td><td align=\"left\">2020</td><td align=\"left\">45</td><td char=\".\" align=\"char\">96</td><td char=\".\" align=\"char\">150</td></tr><tr><td align=\"left\">Net T</td><td align=\"left\">PE monofilament</td><td align=\"left\">Tulle</td><td align=\"left\">2020</td><td align=\"left\">52</td><td char=\".\" align=\"char\">145</td><td char=\".\" align=\"char\">150</td></tr><tr><td align=\"left\"><bold>Net U</bold></td><td align=\"left\"><p><bold>PET multifilament</bold></p><p><bold>PE monofilament</bold></p></td><td align=\"left\"><p><bold>Traverse</bold></p><p><bold>Tulle</bold></p></td><td align=\"left\"><p><bold>2013</bold></p><p><bold>2020</bold></p></td><td align=\"left\"><p><bold>42</bold></p><p><bold>37</bold></p></td><td char=\".\" align=\"char\"><p><bold>156</bold></p><p><bold>100</bold></p></td><td char=\".\" align=\"char\"><p><bold>150</bold></p><p><bold>120</bold></p></td></tr><tr><td align=\"left\">Net V</td><td align=\"left\">PE monofilament</td><td align=\"left\">Tulle</td><td align=\"left\">2020</td><td align=\"left\">32</td><td char=\".\" align=\"char\">132</td><td char=\".\" align=\"char\">130</td></tr><tr><td align=\"left\"><bold>Net W</bold></td><td align=\"left\"><bold>PE monofilament</bold></td><td align=\"left\"><bold>Tulle</bold></td><td align=\"left\"><p><bold>2013</bold></p><p><bold>2020</bold></p></td><td align=\"left\"><p><bold>43</bold></p><p><bold>29</bold></p></td><td char=\".\" align=\"char\"><p><bold>75</bold></p><p><bold>72</bold></p></td><td char=\".\" align=\"char\"><p><bold>150</bold></p><p><bold>150</bold></p></td></tr><tr><td align=\"left\"><bold>Net X</bold></td><td align=\"left\"><bold>PE monofilament</bold></td><td align=\"left\"><bold>Tulle</bold></td><td align=\"left\"><p><bold>2013</bold></p><p><bold>2020</bold></p></td><td align=\"left\"><p><bold>43</bold></p><p><bold>33</bold></p></td><td char=\".\" align=\"char\"><p><bold>80</bold></p><p><bold>111</bold></p></td><td char=\".\" align=\"char\"><p><bold>150</bold></p><p><bold>135</bold></p></td></tr></tbody></table></table-wrap>" ]

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[ "<table-wrap-foot><p>Nets highlighted in bold were compared in 2013 and 2020</p></table-wrap-foot>", "<fn-group><fn><p><bold>Publisher's Note</bold></p><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn></fn-group>" ]

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[ "<title>Background</title>", "<p id=\"Par5\">The incidence of thyroid mass has rapidly increased in the past few decades [##UREF##0##1##, ##UREF##1##2##]. Papillary thyroid carcinoma (PTC), the most common type of thyroid malignancy, is prone to lymph node metastasis (LNM). As it is acknowledged that PTC has quite a low mortality rate and LNM has a less significant factor influencing survival rate [##UREF##2##3##], several recent studies found that LNM still negatively affected long-term recurrence [##UREF##3##4##–##UREF##5##6##]. The most common site of LNM in PTC is the central compartment, followed by the lateral compartment.</p>", "<p id=\"Par6\">According to the latest American Thyroid Association (ATA) guidelines and National Comprehensive Cancer Network (NCCN) guidelines, lateral neck dissection (LND) is only recommended for patients who were diagnosed with lateral lymph node metastasis (LLNM) before or during the operation. Prophylactic LND is not suggested. However, the sensitivity of evaluations, including preoperative imaging examination and intraoperative frozen pathology, is limited [##UREF##6##7##, ##UREF##7##8##], and occult LNM may lead to relapse and secondary operation. Previous studies reported that the incidence of occult LLNM in PTC patients could reach 18.6-64% [##REF##27188295##9##–##UREF##9##11##]. Thus, constructing a precise predictive model for LLNM seems essential in improving the efficacy of operations.</p>", "<p id=\"Par7\">It is proved that age is one of the most important prognostic factors of overall survival and disease-specific survival in PTC [##UREF##10##12##–##UREF##12##14##]. Several studies have also reported that the risk of LLNM decreased as age increased simultaneously [##UREF##13##15##–##UREF##15##17##]. However, the exact tendency of the risk ratio of LLNM as age changes is still unclear. Therefore, the cut point of patient age to stratify the risk of LLNM has not been well established yet. In addition, studies focusing on investigating distinct predictors of LLNM according to patient age were limited, leading to a lack of evidence to support more specific surgical strategies for PTC patients of different ages. As the risk of LLNM changes with age, a novel prediction model based on age stratification might be more accurate and benefit clinical practice.</p>", "<p id=\"Par8\">Our study aimed to identify the differences in clinicopathological characteristics, especially age, between patients with or without LLNM, to determine the optimal breakpoint of age for a more precise prediction model of LLNM, and to reveal differences in risk factors between patients of distinct age stages.</p>" ]

[ "<title>Materials and methods</title>", "<title>Patients</title>", "<p id=\"Par9\">This institutional ethics committee-approved retrospective study was conducted by searching the Department of Head and Neck Surgery, Cancer Hospital of Chinese Academy of Medical Sciences, and Peking Union Medical College databases from February 2016 to January 2020. Patients were included if they met all the following inclusion criteria: (i) primary thyroid lesion was confirmed PTC by pathology; (ii) patients had undergone thyroid lobectomy or total thyroidectomy and LND by the same experienced clinician; (iii) patients had no history of neck surgery, or radioactive treatment; (iv) patients had no history of other systematic malignant tumors. Clinical data were collected on demographics (age, gender, etc.) and tumor characteristics (tumor size, tumor location, multifocality, etc.). Informed consent was obtained from all participants. We explained intraoperative and postoperative risks to all patients in detail before surgery to ensure they fully understood the disease and surgical methods.</p>", "<title>Surgery</title>", "<p id=\"Par10\">We performed total thyroidectomy on patients with bilateral PTC. For unilateral PTC patients, total thyroidectomy or lobectomy plus isthmusectomy were performed depending on patients’ wishes. When unilateral PTC patients met one of the following conditions, total thyroidectomy was recommended: tumor size &gt; 4 cm, multifocality in one lobe, contralateral benign nodules, or distant metastasis (according to guidelines of the Chinese Thyroid Association).</p>", "<p id=\"Par11\">All patients received central lymph node dissection. LND of level II-IV or II-V was performed on patients pathologically confirmed to have LLNM by fine-needle aspiration or intraoperative frozen biopsy in the lateral compartments. Additionally, when LNM in the central compartment was found before or during surgery, we performed LND of level III and IV based on the original thyroid collar incision. After surgeries, all specimens had pathologically examinations for LNM diagnosing.</p>", "<title>Statistical analysis</title>", "<p id=\"Par12\">All statistical analyses were performed using SPSS version 26.0 (IBM Inc, Armonk, NY, USA) and R version 4.2.1(<ext-link ext-link-type=\"uri\" xlink:href=\"http://www.r-project.org\">www.r-project.org</ext-link>). Measurement data were expressed in mean ± standard deviation, and independent samples t-test was used for comparative analysis; categorical variables were expressed as numbers [percent (%)] and were compared by the chi-square test. Variables with <italic>p</italic> value less than 0.05 in univariate analysis were included in multivariate logistic regression analysis to identify independent risk factors. <italic>P</italic> &lt; 0.05 was considered statistically significant.</p>", "<p id=\"Par13\">The impact of age on LLNM was evaluated by logistic regression analysis. A locally weighted scatterplot smoothing (LOWESS) curve was used to fit and visualize the tendency of the OR of LLNM according to age. The structural break point of the fitting curves was considered as the optimal cutoff point and was determined by ‘changepoint’ package using R.</p>" ]

[ "<title>Results</title>", "<title>Patient characteristics</title>", "<p id=\"Par14\">A total of 499 patients were enrolled in this study. Among them, 396 (88.2%) were confirmed to have LLNM, and 103(22.9%) were absent from LLNM. A total of 163 patients (32.7%) undergone prophylactic lateral neck dissection of level III and IV as LNM in the central compartment was found, and 36.8% of these patients were confirmed to have occult LLNM. The details of characteristics were presented in Table ##TAB##0##1##, which showed statistical differences between the two groups with regard to age(<italic>p</italic> = 0.003), gender(<italic>p</italic> = 0.005), tumor size(<italic>p</italic> = 0.009), tumor location(<italic>p</italic> = 0.011), multifocality(<italic>p</italic> &lt; 0.001), bilaterality(<italic>p</italic> = 0.002), extra-thyroidal extension(<italic>p</italic> &lt; 0.001), extra-nodal extension(<italic>p</italic> &lt; 0.001), and central lymph node metastasis (CLNM) (<italic>p</italic> &lt; 0.001). No statistically significant difference between the two groups was observed in thyroiditis.</p>", "<p id=\"Par15\">\n\n</p>", "<title>Predictive value of age for LLNM</title>", "<p id=\"Par16\">A logistic regression analysis was performed to identify further the relationship between age and LLNM (Table ##TAB##1##2##). The results indicated that age was significantly related to LLNM (<italic>p</italic> = 0.043). As shown in Fig. ##FIG##0##1##, the LOWESS curve fitting the trend of odds ratio as above demonstrated that the risk of LLNM decreased as age grew. Correspondingly, structural breakpoints of the fitting curve identified by R using the ‘changepoint’ package confirmed that optimal age cut points were 30 and 45.</p>", "<p id=\"Par17\">\n\n</p>", "<p id=\"Par18\">\n\n</p>", "<p id=\"Par19\">Patients were divided into three groups based on cut points of 30 and 45 years old to verify the predictive value of age. The likelihood of LLNM was significantly different between the three groups, and older patients tended to have a lower risk of LLNM. After multivariate analysis, the risk of LLNM in the patient groups divided using the identified cut points remained distinctly different (<italic>p</italic> = 0.020) (Table ##TAB##2##3##). P for interaction was also calculated between age group and other risk factors of LLNM, apart from extra-thyroidal extension, which showed an interaction p-value of 0.011 with age, there was no statistically significant interaction between age group and other risk factors.</p>", "<p id=\"Par20\">\n\n</p>", "<title>Distinct risk factors of LLNM according to age</title>", "<p id=\"Par21\">As age played a significant role in LLNM, we further investigated and compared independent predictors of patients of different ages. For patients younger than 30 years old, the univariate analyses revealed that sex(<italic>p</italic> = 0.039), tumor size(<italic>p</italic> = 0.046), tumor location(<italic>p</italic> = 0.047), multifocality(<italic>p</italic> = 0.017), extra-thyroidal extension(<italic>p</italic> = 0.010), extra-nodal extension(<italic>p</italic> = 0.011), and CLNM(<italic>p</italic> = 0.003) were related to LLNM. Further multivariate analyses showed that sex(<italic>p</italic> = 0.033), tumor size(<italic>p</italic> = 0.027), tumor location(<italic>p</italic> = 0.020), and CLNM(<italic>p</italic> = 0.019) were independent predictors for LLNM. Surprisingly, apart from CLNM, which had an 11.011(95%CI: 1.475–82.214) times the OR of LLNM compared to those without CLNM, tumors located in the upper lobe of glands had a 25.780(95% CI: 1.651-402.522) times the OR of LLNM (Table ##TAB##3##4##). The c-index of the prediction model was 0.811 (95%CI: 0.730–0.892).</p>", "<p id=\"Par22\">\n\n</p>", "<p id=\"Par23\">As for patients between 30 and 45 years old, tumor size(<italic>p</italic> = 0.049), multifocality(<italic>p</italic> &lt; 0.001), bilaterality(<italic>p</italic> &lt; 0.001), extra-nodal extension(<italic>p</italic> = 0.015), and CLNM(<italic>p</italic> &lt; 0.001) were found to be related to LLNM. After including all factors above in multivariate analyses, CLNM(<italic>p</italic> = 0.007) was identified as the only predictor of LLNM and showed a 2.990(95% CI: 1.344–6.648) times the OR (Table ##TAB##4##5##). CLNM had a c-index of 0.626 (95%CI: 0.547–0.705) in predicting LLNM.</p>", "<p id=\"Par24\">\n\n</p>", "<p id=\"Par25\">For patients over 45 years old, tumor location(<italic>p</italic> = 0.026), extra-thyroidal extension(<italic>p</italic> &lt; 0.001), extra-nodal extension(<italic>p</italic> = 0.001), and CLNM(<italic>p</italic> &lt; 0.001) were significant in univariate analyses. Further multivariate analyses confirmed that tumor location(<italic>p</italic> = 0.013), extra-thyroidal extension(<italic>p</italic> &lt; 0.001), extra-nodal extension(<italic>p</italic> = 0.042), and CLNM(<italic>p</italic> &lt; 0.001) were all independent risk factors to LLNM. And extra-thyroidal extension showed the highest OR of LLNM of 13.005(95%CI: 3.202–11.371) times (Table ##TAB##5##6##). The c-index of the prediction model was 0.852 (95%CI: 0.781–0.923).</p>", "<p id=\"Par26\">\n\n</p>" ]

[ "<title>Discussion</title>", "<p id=\"Par27\">Previous studies have reported many risk factors related to LLNM in PTC, including age, sex, tumor size, tumor location, extra-thyroidal invasion, multifocality, and CLNM [##UREF##16##18##, ##UREF##17##19##]. As for age, younger patients tend to be more likely to have LLNM than older ones [##UREF##13##15##–##UREF##15##17##]. This is similar to our findings. In our study, the figure that demonstrated the trend of the OR of LLNM decreased rapidly as age grew, especially after the specific age threshold. This suggested that a separate prediction system of LLNM should be constructed for patients of different ages to achieve better sensitivity and specificity.</p>", "<p id=\"Par28\">It was generally agreed that younger patients were more likely to have LNM. Still, the exact relationship between the risk of LNM and a decrease in age was shown in one large population research based on the SEER database, which aimed to investigate the impact of age on the risk of LNM; patients were divided into five subgroups by age. The result showed that younger patients had an increased predisposition for LNM [##UREF##18##20##]. However, the comparison groups were divided according to the cut points of age defined by authors and failed to demonstrate the difference in risk of LNM between groups. We provided a similar result that younger patients were more likely to have LLNM. Apart from that, our division of patients based on age was more accurate and reliable, since the cut points of age were determined based on analyzing the risk of LLNM in each patient subgroup stratified by age in 5-year intervals. The structural breakpoint of the OR fitting curve was identified using the R package, and it found that 30 and 45 were the optimal cut points for the possibility of LLNM.</p>", "<p id=\"Par29\">Many studies have found that CLNM was related to LLNM, and one Meta-analysis showed that the risk of LLNM of CLNM was 7.64 times more than those patients without CLNM [##UREF##19##21##]. Some researchers suggested that when the number of CLNM &gt; 3, LND should be more actively performed [##UREF##20##22##, ##UREF##21##23##]. Additionally, our previous study showed that the risk of occult metastasis in the lateral compartment was much higher for patients with CLNM [##UREF##22##24##]. Tumor located in the upper pole was another risk factor for LLNM due to an abundant blood supply and direct lymphatic vessels between the upper lobe of the thyroid and lateral neck [##UREF##23##25##]. Extra-thyroidal extension was an independent predictor for LLNM revealed by several studies as an extension may indicate the tumor’s aggressiveness and lead to metastasis.</p>", "<p id=\"Par30\">For patients with occult LLNM, insufficient treatment may lead to residual tumor or relapse, and secondary operation carries a higher risk of surgical complication and leads a psychological and economic burden to patients. Therefore, our study could be interpreted as an implication for a change in surgical management. In the condition of CLNM being observed before or during surgery, detection of the lateral compartment should be considered. For patients younger than 30 years old, male gender and large tumor size, especially tumor located in the upper lobe, were risk factors that required more attention and appropriate expansion of the extent of surgery when needed. The sufficient surgical extent was more critical for patients older than 45 years old with a poorer prognosis. When finding tumors with extra-thyroidal extension during operation, including gross and minimal, a careful evaluation of potential LLNM was highly recommended.</p>", "<p id=\"Par31\">Despite these findings, we acknowledge that there were still some limitations in this study. First, there is an inherent bias in all single-center and retrospective studies. Second, our sample size was relatively small, especially the group of patients without LLNM, which could reduce the statistical power of this research. Some merits of this study should also be noted. First, the preoperative evaluation of suspicious lymph nodes was evaluated through high-resolution US, which the same experienced experts performed. This avoided bias of diagnosis due to different experienced physicians to the greatest extent. Second, our dissection of levels III and IV was based on the original thyroid incision and did not involve the accessory nerve, and this would not increase the risk of skin numbness and shoulder dysfunction. Third, a group of pathologists described and documented the characteristics of PTC that may be related to LNM.</p>" ]

[ "<title>Conclusion</title>", "<p id=\"Par32\">This study indicated that the optimal age cut points to stratify the risk of LLNM were 30 and 45 years old. CLNM was a prominent risk factor for further LNM in all patients, and LND should be more actively performed when CLNM was confirmed. We also revealed other distinct risk factors of LLNM in patients with different age stages. Especially for younger patients with tumors in the upper lobe and older patients with extra-thyroidal extension tumors, more aggressive detection of the lateral neck and more intensive surgical treatment should be considered.</p>" ]

[ "<title>Introduction</title>", "<p id=\"Par1\">Studies have revealed that age is associated with the risk of lateral lymph node metastasis (LLNM) in papillary thyroid cancer (PTC). This study aimed to identify the optimal cut point of age for a more precise prediction model of LLNM and to reveal differences in risk factors between patients of distinct age stages.</p>", "<title>Methods</title>", "<p id=\"Par2\">A total of 499 patients who had undergone thyroidectomy and lateral neck dissection (LND) for PTC were enrolled. The locally weighted scatterplot smoothing (LOWESS) curve and the ‘changepoint’ package were used to identify the optimal age cut point using R. Multivariate logistic regression analysis was performed to identify independent risk factors of LLNM in each group divided by age.</p>", "<title>Results</title>", "<p id=\"Par3\">Younger patients were more likely to have LLNM, and the optimal cut points of age to stratify the risk of LLNM were 30 and 45 years old. Central lymph node metastasis (CLNM) was a prominent risk factor for further LNM in all patients. Apart from CLNM, sex(<italic>p</italic> = 0.033), tumor size(<italic>p</italic> = 0.027), and tumor location(<italic>p</italic> = 0.020) were independent predictors for patients younger than 30 years old; tumor location(<italic>p</italic> = 0.013), extra-thyroidal extension(<italic>p</italic> &lt; 0.001), and extra-nodal extension(<italic>p</italic> = 0.042) were independent risk factors for patients older than 45 years old.</p>", "<title>Conclusions</title>", "<p id=\"Par4\">Our study could be interpreted as an implication for a change in surgical management. LND should be more actively performed when CLNM is confirmed; for younger patients with tumors in the upper lobe and older patients with extra-thyroidal extension tumors, more aggressive detection of the lateral neck might be considered.</p>", "<title>Keywords</title>" ]

[]

[ "<title>Author contributions</title>", "<p>H.Z. designed the study and wrote the manuscript. L.Z designed the study and analyzed the data. Z.H., S.W., and P.S. collected data. Y.D. prepared the manuscript. L.N. performed all ultrasound examinations. Z.L. designed the study and edited the manuscript. All authors reviewed the manuscript.</p>", "<title>Funding</title>", "<p>This work was supported by a grant provided by the Capital Clinical Features Application Research (Z171100001017211). This work was supported by a grant provided by National Natural Science Foundation of China (82373439).</p>", "<title>Data availability</title>", "<p>The data that support the findings of this study are available on request from corresponding authors.</p>", "<title>Declarations</title>", "<title>Competing interests</title>", "<p id=\"Par33\">The authors declare no competing interests.</p>", "<title>Ethical approval and consent to participate</title>", "<p id=\"Par34\">This study was approved by the ethics committee of the Cancer Hospital of Chinese Academy of Medical Sciences (reference number: NCC2016ST-23). We explained intraoperative and postoperative risks to all patients in detail before surgery to ensure they fully understood the disease and surgical methods. Informed consent was obtained from all participants.</p>", "<title>Consent for publication</title>", "<p id=\"Par35\">Not Applicable (NA).</p>" ]

[ "<fig id=\"Fig1\"><label>Fig. 1</label><caption><p>Associations of age with odds ratio for lateral lymph node metastasis. The fitting curve was determined by the locally weighted scatterplot smoothing (LOWESS), and structural breakpoints were identified by the ‘changepoint’ package in R</p></caption></fig>" ]

[ "<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Clinicopathologic characteristics of patients</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\"/><th align=\"left\"/><th align=\"left\" colspan=\"2\">LLNM</th><th align=\"left\"/></tr><tr><th align=\"left\">Characteristic</th><th align=\"left\">Overall(<italic>n</italic> = 499)</th><th align=\"left\">Present(<italic>n</italic> = 396)</th><th align=\"left\">Absent(<italic>n</italic> = 103)</th><th align=\"left\"><italic>p</italic> value</th></tr></thead><tbody><tr><td align=\"left\">\n<bold>Age(years)</bold>\n</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\">\n<bold>0.003</bold>\n</td></tr><tr><td align=\"left\">Mean (SD)</td><td align=\"left\">39.0(11.7)</td><td align=\"left\">38.2(11.4)</td><td align=\"left\">42.1(12.4)</td><td align=\"left\"/></tr><tr><td align=\"left\">\n<bold>Gender,n(%)</bold>\n</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\">\n<bold>0.005</bold>\n</td></tr><tr><td align=\"left\">Male</td><td align=\"left\">159(31.9)</td><td align=\"left\">138(34.8)</td><td align=\"left\">21(20.4)</td><td align=\"left\"/></tr><tr><td align=\"left\">Female</td><td align=\"left\">340(68.1)</td><td align=\"left\">258(65.2)</td><td align=\"left\">82(79.6)</td><td align=\"left\"/></tr><tr><td align=\"left\" colspan=\"2\">\n<bold>Tumor size,n(%)</bold>\n</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\">\n<bold>0.009</bold>\n</td></tr><tr><td align=\"left\">≥ 20 mm</td><td align=\"left\">105(21.0)</td><td align=\"left\">93(23.5)</td><td align=\"left\">12(11.7)</td><td align=\"left\"/></tr><tr><td align=\"left\">&lt; 20 mm</td><td align=\"left\">394(79.0)</td><td align=\"left\">303(76.5)</td><td align=\"left\">91(88.3)</td><td align=\"left\"/></tr><tr><td align=\"left\">\n<bold>Location,n(%)</bold>\n</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\">\n<bold>0.011</bold>\n</td></tr><tr><td align=\"left\">Upper lobe</td><td align=\"left\">142(28.5)</td><td align=\"left\">123(31.1)</td><td align=\"left\">19(18.4)</td><td align=\"left\"/></tr><tr><td align=\"left\">Others</td><td align=\"left\">357(71.5)</td><td align=\"left\">273(68.9)</td><td align=\"left\">84(81.6)</td><td align=\"left\"/></tr><tr><td align=\"left\" colspan=\"2\">\n<bold>Thyroiditis,n(%)</bold>\n</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\">0.521</td></tr><tr><td align=\"left\">Present</td><td align=\"left\">209(41.9)</td><td align=\"left\">163(41.2)</td><td align=\"left\">46(44.7)</td><td align=\"left\"/></tr><tr><td align=\"left\">Absent</td><td align=\"left\">290(58.1)</td><td align=\"left\">233(58.8)</td><td align=\"left\">57(55.3)</td><td align=\"left\"/></tr><tr><td align=\"left\" colspan=\"2\">\n<bold>Multifocality,n(%)</bold>\n</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\">\n<bold>&lt; 0.001</bold>\n</td></tr><tr><td align=\"left\">Present</td><td align=\"left\">305(61.1)</td><td align=\"left\">262(66.2)</td><td align=\"left\">43(41.7)</td><td align=\"left\"/></tr><tr><td align=\"left\">Absent</td><td align=\"left\">194(38.9)</td><td align=\"left\">134(33.8)</td><td align=\"left\">60(58.3)</td><td align=\"left\"/></tr><tr><td align=\"left\" colspan=\"2\">\n<bold>Bilaterality,n(%)</bold>\n</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\">\n<bold>0.002</bold>\n</td></tr><tr><td align=\"left\">Present</td><td align=\"left\">232(46.5)</td><td align=\"left\">198(50.0)</td><td align=\"left\">34(33.0)</td><td align=\"left\"/></tr><tr><td align=\"left\">Absent</td><td align=\"left\">267(53.5)</td><td align=\"left\">198(50.0)</td><td align=\"left\">69(67.0)</td><td align=\"left\"/></tr><tr><td align=\"left\" colspan=\"2\">\n<bold>Extra-thyroidal extension,n(%)</bold>\n</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\">\n<bold>&lt; 0.001</bold>\n</td></tr><tr><td align=\"left\">Present</td><td align=\"left\">391(78.4)</td><td align=\"left\">325(82.1)</td><td align=\"left\">66(64.1)</td><td align=\"left\"/></tr><tr><td align=\"left\">Absent</td><td align=\"left\">108(21.6)</td><td align=\"left\">71(17.9)</td><td align=\"left\">37(35.9)</td><td align=\"left\"/></tr><tr><td align=\"left\" colspan=\"2\">\n<bold>Extra-nodal extension,n(%)</bold>\n</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\">\n<bold>&lt; 0.001</bold>\n</td></tr><tr><td align=\"left\">Present</td><td align=\"left\">156(31.3)</td><td align=\"left\">144(36.4)</td><td align=\"left\">12(11.7)</td><td align=\"left\"/></tr><tr><td align=\"left\">Absent</td><td align=\"left\">343(68.7)</td><td align=\"left\">252(63.6)</td><td align=\"left\">91(88.3)</td><td align=\"left\"/></tr><tr><td align=\"left\">\n<bold>CLNM,n(%)</bold>\n</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\">\n<bold>&lt; 0.001</bold>\n</td></tr><tr><td align=\"left\">Present</td><td align=\"left\">392(78.6)</td><td align=\"left\">339(85.6)</td><td align=\"left\">53(51.5)</td><td align=\"left\"/></tr><tr><td align=\"left\">Absent</td><td align=\"left\">107(21.4)</td><td align=\"left\">57(14.4)</td><td align=\"left\">50(48.5)</td><td align=\"left\"/></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab2\"><label>Table 2</label><caption><p>Odds ratio of lateral lymph node metastasis of different age</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" rowspan=\"2\"/><th align=\"left\" rowspan=\"2\">OR</th><th align=\"left\" colspan=\"2\">95%CI</th><th align=\"left\"><italic>p</italic> value</th></tr><tr><th align=\"left\">Lower</th><th align=\"left\">Upper</th><th align=\"left\"/></tr></thead><tbody><tr><td align=\"left\">\n<bold>Age(years)</bold>\n</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td char=\".\" align=\"char\">\n<bold>0.043</bold>\n</td></tr><tr><td align=\"left\">≤ 25</td><td char=\".\" align=\"char\">1.000</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\">25–30</td><td char=\".\" align=\"char\">0.464</td><td align=\"left\">0.165</td><td align=\"left\">1.305</td><td char=\".\" align=\"char\">0.146</td></tr><tr><td align=\"left\">30–35</td><td char=\".\" align=\"char\">0.559</td><td align=\"left\">0.206</td><td align=\"left\">1.517</td><td char=\".\" align=\"char\">0.253</td></tr><tr><td align=\"left\">35–40</td><td char=\".\" align=\"char\">0.509</td><td align=\"left\">0.182</td><td align=\"left\">1.426</td><td char=\".\" align=\"char\">0.199</td></tr><tr><td align=\"left\">40–45</td><td char=\".\" align=\"char\">0.716</td><td align=\"left\">0.247</td><td align=\"left\">2.073</td><td char=\".\" align=\"char\">0.538</td></tr><tr><td align=\"left\">45–50</td><td char=\".\" align=\"char\">0.488</td><td align=\"left\">0.163</td><td align=\"left\">1.460</td><td char=\".\" align=\"char\">0.199</td></tr><tr><td align=\"left\">50–55</td><td char=\".\" align=\"char\">0.295</td><td align=\"left\">0.103</td><td align=\"left\">0.845</td><td char=\".\" align=\"char\">0.023</td></tr><tr><td align=\"left\">&gt; 55</td><td char=\".\" align=\"char\">0.206</td><td align=\"left\">0.073</td><td align=\"left\">0.583</td><td char=\".\" align=\"char\">0.003</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab3\"><label>Table 3</label><caption><p>Univariate and multivariate logistic regression analysis of characteristics associated with lateral lymph node metastasis</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\"/><th align=\"left\" colspan=\"2\">Univariate analysis</th><th align=\"left\"/><th align=\"left\"/><th align=\"left\" colspan=\"2\">Multivariate analysis</th><th align=\"left\"/></tr><tr><th align=\"left\">Characteristic</th><th align=\"left\">OR</th><th align=\"left\">95% CI</th><th align=\"left\"><italic>p</italic> value</th><th align=\"left\"/><th align=\"left\">OR</th><th align=\"left\">95% CI</th><th align=\"left\"><italic>p</italic> value</th></tr></thead><tbody><tr><td align=\"left\">\n<bold>Age(years)</bold>\n</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\">\n<bold>0.015</bold>\n</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\">\n<bold>0.020</bold>\n</td></tr><tr><td align=\"left\">&gt; 45</td><td align=\"left\">0.488</td><td align=\"left\">0.267–0.891</td><td align=\"left\">0.019</td><td align=\"left\"/><td align=\"left\">0.030</td><td align=\"left\">0.206–0.921</td><td align=\"left\">0.030</td></tr><tr><td align=\"left\">30–45</td><td align=\"left\">0.933</td><td align=\"left\">0.520–1.674</td><td align=\"left\">0.817</td><td align=\"left\"/><td align=\"left\">0.999</td><td align=\"left\">0.511–1.952</td><td align=\"left\">0.998</td></tr><tr><td align=\"left\">≤ 30</td><td align=\"left\">1.000</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\">1.000</td><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\">\n<bold>Gender</bold>\n</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\">\n<bold>0.006</bold>\n</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\">\n<bold>0.030</bold>\n</td></tr><tr><td align=\"left\">Male</td><td align=\"left\">2.089</td><td align=\"left\">1.239–3.520</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\">1.916</td><td align=\"left\">1.064–3.452</td><td align=\"left\"/></tr><tr><td align=\"left\">Female</td><td align=\"left\">1.000</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\">1.000</td><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\">\n<bold>Tumor size</bold>\n</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\">\n<bold>0.010</bold>\n</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\">\n<bold>0.002</bold>\n</td></tr><tr><td align=\"left\">≥ 20 mm</td><td align=\"left\">2.328</td><td align=\"left\">1.221–4.437</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\">3.212</td><td align=\"left\">1.535–6.822</td><td align=\"left\"/></tr><tr><td align=\"left\">&lt; 20 mm</td><td align=\"left\">1.000</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\">1.000</td><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\">\n<bold>Location</bold>\n</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\">\n<bold>0.013</bold>\n</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\">\n<bold>&lt; 0.001</bold>\n</td></tr><tr><td align=\"left\">Upper lobe</td><td align=\"left\">1.992</td><td align=\"left\">1.159–3.423</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\">3.874</td><td align=\"left\">1.997–7.517</td><td align=\"left\"/></tr><tr><td align=\"left\">Others</td><td align=\"left\">1.000</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\">1.000</td><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\" colspan=\"2\">\n<bold>Multifocality</bold>\n</td><td align=\"left\"/><td align=\"left\">\n<bold>&lt; 0.001</bold>\n</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\">\n<bold>0.006</bold>\n</td></tr><tr><td align=\"left\">Present</td><td align=\"left\">2.728</td><td align=\"left\">1.751–4.251</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\">3.108</td><td align=\"left\">1.378–7.009</td><td align=\"left\"/></tr><tr><td align=\"left\">Absent</td><td align=\"left\">1.000</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\">1.000</td><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\">\n<bold>Bilaterality</bold>\n</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\">\n<bold>0.002</bold>\n</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\">0.991</td></tr><tr><td align=\"left\">Present</td><td align=\"left\">2.029</td><td align=\"left\">1.287-3.200</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\">1.005</td><td align=\"left\">0.429–2.353</td><td align=\"left\"/></tr><tr><td align=\"left\">Absent</td><td align=\"left\">1.000</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\">1.000</td><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\" colspan=\"3\">\n<bold>Extra-thyroidal extension</bold>\n</td><td align=\"left\">\n<bold>&lt; 0.001</bold>\n</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\">\n<bold>0.022</bold>\n</td></tr><tr><td align=\"left\">Present</td><td align=\"left\">2.566</td><td align=\"left\">1.592–4.137</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\">1.962</td><td align=\"left\">1.104–3.487</td><td align=\"left\"/></tr><tr><td align=\"left\">Absent</td><td align=\"left\">1.000</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\">1.000</td><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\" colspan=\"3\">\n<bold>Extra-nodal extension</bold>\n</td><td align=\"left\">\n<bold>&lt; 0.001</bold>\n</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\">\n<bold>0.024</bold>\n</td></tr><tr><td align=\"left\">Present</td><td align=\"left\">4.333</td><td align=\"left\">2.295–8.184</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\">2.227</td><td align=\"left\">1.111–4.464</td><td align=\"left\"/></tr><tr><td align=\"left\">Absent</td><td align=\"left\">1.000</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\">1.000</td><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\">\n<bold>CLNM</bold>\n</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\">\n<bold>&lt; 0.001</bold>\n</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\">\n<bold>&lt; 0.001</bold>\n</td></tr><tr><td align=\"left\">Present</td><td align=\"left\">5.611</td><td align=\"left\">3.480–9.045</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\">5.299</td><td align=\"left\">2.948–9.527</td><td align=\"left\"/></tr><tr><td align=\"left\">Absent</td><td align=\"left\">1.000</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\">1.000</td><td align=\"left\"/><td align=\"left\"/></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab4\"><label>Table 4</label><caption><p>Risk factors of lateral lymph node metastasis of patients younger than 30 years old</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\"/><th align=\"left\" colspan=\"2\">LLNM</th><th align=\"left\"/><th align=\"left\" colspan=\"2\">Univariate analysis</th><th align=\"left\"/><th align=\"left\" colspan=\"2\">Multivariate analysis</th></tr><tr><th align=\"left\">Variables</th><th align=\"left\">Present(<italic>n</italic> = 100)</th><th align=\"left\">Absent(<italic>n</italic> = 20)</th><th align=\"left\"/><th align=\"left\">OR(95% CI)</th><th align=\"left\"><italic>p</italic> value</th><th align=\"left\"/><th align=\"left\">OR(95% CI)</th><th align=\"left\"><italic>p</italic> value</th></tr></thead><tbody><tr><td align=\"left\">Male</td><td align=\"left\">33(33.0%)</td><td align=\"left\">2(10.0%)</td><td align=\"left\"/><td align=\"left\">4.433(0.970-20.251)</td><td align=\"left\">\n<bold>0.039</bold>\n</td><td align=\"left\"/><td align=\"left\">6.242(1.163–33.497)</td><td align=\"left\">\n<bold>0.033</bold>\n</td></tr><tr><td align=\"left\">Tumor size ≥ 20 mm</td><td align=\"left\">32(32.0%)</td><td align=\"left\">2(10.0%)</td><td align=\"left\"/><td align=\"left\">4.235(0.926–19.367)</td><td align=\"left\">\n<bold>0.046</bold>\n</td><td align=\"left\"/><td align=\"left\">7.753(1.259–47.753)</td><td align=\"left\">\n<bold>0.027</bold>\n</td></tr><tr><td align=\"left\">Upper lobe</td><td align=\"left\">25(25.0%)</td><td align=\"left\">1(5.0%)</td><td align=\"left\"/><td align=\"left\">6.333(0.806–49.750)</td><td align=\"left\">\n<bold>0.047</bold>\n</td><td align=\"left\"/><td align=\"left\">25.780(1.651-402.522)</td><td align=\"left\">\n<bold>0.020</bold>\n</td></tr><tr><td align=\"left\">Multifocality</td><td align=\"left\">59(59.0%)</td><td align=\"left\">6(30.0%)</td><td align=\"left\"/><td align=\"left\">3.358(1.191–9.462)</td><td align=\"left\">\n<bold>0.017</bold>\n</td><td align=\"left\"/><td align=\"left\">3.293(0.820-13.225)</td><td align=\"left\">0.093</td></tr><tr><td align=\"left\">Bilaterality</td><td align=\"left\">36(36.0%)</td><td align=\"left\">6(30.0%)</td><td align=\"left\"/><td align=\"left\">1.313(0.464–3.713)</td><td align=\"left\">0.608</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\">Extra-thyroidal extension</td><td align=\"left\">78(78.0%)</td><td align=\"left\">10(50.0%)</td><td align=\"left\"/><td align=\"left\">6.682(1.309-9.600)</td><td align=\"left\">\n<bold>0.010</bold>\n</td><td align=\"left\"/><td align=\"left\">1.989(0.557–7.110)</td><td align=\"left\">0.290</td></tr><tr><td align=\"left\">Extra-nodal extension</td><td align=\"left\">33(33.0%)</td><td align=\"left\">1(5.0%)</td><td align=\"left\"/><td align=\"left\">9.358(1.200-72.958)</td><td align=\"left\">\n<bold>0.011</bold>\n</td><td align=\"left\"/><td align=\"left\">5.321(0.598–47.355)</td><td align=\"left\">0.134</td></tr><tr><td align=\"left\">CLNM</td><td align=\"left\">95(95.0%)</td><td align=\"left\">15(75.0%)</td><td align=\"left\"/><td align=\"left\">6.333(1.635–24.526)</td><td align=\"left\">\n<bold>0.003</bold>\n</td><td align=\"left\"/><td align=\"left\">11.011(1.475–82.214)</td><td align=\"left\">\n<bold>0.019</bold>\n</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab5\"><label>Table 5</label><caption><p>Risk factors of lateral lymph node metastasis of patients between 30 and 45 years old</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\"/><th align=\"left\" colspan=\"2\">LLNM</th><th align=\"left\"/><th align=\"left\" colspan=\"2\">Univariate analysis</th><th align=\"left\"/><th align=\"left\" colspan=\"2\">Multivariate analysis</th></tr><tr><th align=\"left\">Variables</th><th align=\"left\">Present(<italic>n</italic> = 196)</th><th align=\"left\">Absent(<italic>n</italic> = 42)</th><th align=\"left\"/><th align=\"left\">OR(95% CI)</th><th align=\"left\"><italic>p</italic> value</th><th align=\"left\"/><th align=\"left\">OR(95% CI)</th><th align=\"left\"><italic>p</italic> value</th></tr></thead><tbody><tr><td align=\"left\">Male</td><td align=\"left\">71(36.2%)</td><td align=\"left\">11(26.2%)</td><td align=\"left\"/><td align=\"left\">1.601(0.758–3.379)</td><td align=\"left\">0.214</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\">Tumor size ≥ 20 mm</td><td align=\"left\">39(19.9%)</td><td align=\"left\">3(7.1%)</td><td align=\"left\"/><td align=\"left\">3.229(0.948–10.999)</td><td align=\"left\">\n<bold>0.049</bold>\n</td><td align=\"left\"/><td align=\"left\">2.580(0.719–9.251)</td><td align=\"left\">0.146</td></tr><tr><td align=\"left\">Upper lobe</td><td align=\"left\">59(30.1%)</td><td align=\"left\">10(23.8%)</td><td align=\"left\"/><td align=\"left\">1.378(0.636–2.985)</td><td align=\"left\">0.415</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\">Multifocality</td><td align=\"left\">128(65.3%)</td><td align=\"left\">13(31.0%)</td><td align=\"left\"/><td align=\"left\">4.199(2.050–8.603)</td><td align=\"left\">\n<bold>&lt; 0.001</bold>\n</td><td align=\"left\"/><td align=\"left\">2.384(0.835–6.810)</td><td align=\"left\">0.105</td></tr><tr><td align=\"left\">Bilaterality</td><td align=\"left\">99(50.5%)</td><td align=\"left\">8(19.0%)</td><td align=\"left\"/><td align=\"left\">4.338(1.911–9.844)</td><td align=\"left\">\n<bold>&lt; 0.001</bold>\n</td><td align=\"left\"/><td align=\"left\">1.921(0.582–6.338)</td><td align=\"left\">0.284</td></tr><tr><td align=\"left\">Extra-thyroidal extension</td><td align=\"left\">151(77.0%)</td><td align=\"left\">29(69.0%)</td><td align=\"left\"/><td align=\"left\">1.504(0.722–3.134)</td><td align=\"left\">0.274</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\">Extra-nodal extension</td><td align=\"left\">76(38.8%)</td><td align=\"left\">8(19.0%)</td><td align=\"left\"/><td align=\"left\">2.692(1.183–6.124)</td><td align=\"left\">\n<bold>0.015</bold>\n</td><td align=\"left\"/><td align=\"left\">1.522(0.621–3.728)</td><td align=\"left\">0.358</td></tr><tr><td align=\"left\">CLNM</td><td align=\"left\">166(84.7%)</td><td align=\"left\">25(59.5%)</td><td align=\"left\"/><td align=\"left\">3.763(1.816–7.797)</td><td align=\"left\">\n<bold>&lt; 0.001</bold>\n</td><td align=\"left\"/><td align=\"left\">2.990(1.344–6.648)</td><td align=\"left\">\n<bold>0.007</bold>\n</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab6\"><label>Table 6</label><caption><p>Risk factors of lateral lymph node metastasis of patients older than 45 years old</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\"/><th align=\"left\" colspan=\"2\">LLNM</th><th align=\"left\"/><th align=\"left\" colspan=\"2\">Univariate analysis</th><th align=\"left\"/><th align=\"left\" colspan=\"2\">Multivariate analysis</th></tr><tr><th align=\"left\">Variables</th><th align=\"left\">Present(<italic>n</italic> = 100)</th><th align=\"left\">Absent(<italic>n</italic> = 41)</th><th align=\"left\"/><th align=\"left\">OR(95% CI)</th><th align=\"left\"><italic>p</italic> value</th><th align=\"left\"/><th align=\"left\">OR(95% CI)</th><th align=\"left\"><italic>p</italic> value</th></tr></thead><tbody><tr><td align=\"left\">Male</td><td align=\"left\">34(33.0%)</td><td align=\"left\">8(19.5%)</td><td align=\"left\"/><td align=\"left\">2.125(0.885–5.104)</td><td align=\"left\">0.088</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\">Tumor size ≥ 20 mm</td><td align=\"left\">22(22.0%)</td><td align=\"left\">7(17.1%)</td><td align=\"left\"/><td align=\"left\">1.370(0.535–3.511)</td><td align=\"left\">0.511</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\">Upper lobe</td><td align=\"left\">39(39.0%)</td><td align=\"left\">8(19.5%)</td><td align=\"left\"/><td align=\"left\">2.637(1.104–6.299)</td><td align=\"left\">\n<bold>0.026</bold>\n</td><td align=\"left\"/><td align=\"left\">3.897(1.336–11.371)</td><td align=\"left\">\n<bold>0.013</bold>\n</td></tr><tr><td align=\"left\">Multifocality</td><td align=\"left\">75(75.0%)</td><td align=\"left\">24(58.5%)</td><td align=\"left\"/><td align=\"left\">2.125(0.985–4.584)</td><td align=\"left\">0.052</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\">Bilaterality</td><td align=\"left\">63(53.0%)</td><td align=\"left\">20(48.8%)</td><td align=\"left\"/><td align=\"left\">1.788(0.858–3.727)</td><td align=\"left\">0.119</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\">Extra-thyroidal extension</td><td align=\"left\">96(96.0%)</td><td align=\"left\">27(65.9%)</td><td align=\"left\"/><td align=\"left\">12.444(3.784–40.922)</td><td align=\"left\">\n<bold>&lt; 0.001</bold>\n</td><td align=\"left\"/><td align=\"left\">13.005(3.202–52.822)</td><td align=\"left\">\n<bold>&lt; 0.001</bold>\n</td></tr><tr><td align=\"left\">Extra-nodal extension</td><td align=\"left\">35(35.0%)</td><td align=\"left\">3(7.3%)</td><td align=\"left\"/><td align=\"left\">6.821(1.964–23.691)</td><td align=\"left\">\n<bold>0.001</bold>\n</td><td align=\"left\"/><td align=\"left\">4.309(1.057–17.569)</td><td align=\"left\">\n<bold>0.042</bold>\n</td></tr><tr><td align=\"left\">CLNM</td><td align=\"left\">78(78.0%)</td><td align=\"left\">13(31.7%)</td><td align=\"left\"/><td align=\"left\">7.636(3.396–17.171)</td><td align=\"left\">\n<bold>&lt; 0.001</bold>\n</td><td align=\"left\"/><td align=\"left\">8.600(3.217–22.992)</td><td align=\"left\">\n<bold>&lt; 0.001</bold>\n</td></tr></tbody></table></table-wrap>" ]

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[ "<fn-group><fn><p><bold>Publisher’s Note</bold></p><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn><fn><p>Huizhu Cai and Lingdun Zhuge contributed equally.</p></fn></fn-group>" ]

[ "<graphic xlink:href=\"12893_2024_2309_Fig1_HTML\" id=\"d32e889\"/>" ]

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{ "acronym": [], "definition": [] }

[ "<title>Introduction</title>", "<p id=\"Par5\">The temporomandibular joint (TMJ) is classified as a bicondylar diarthrosis. The joint is composed of the mandibular condyle and the glenoid fossa [##REF##31530110##1##–##REF##31709922##4##]. The TMJ is vital in guiding jaw movement and managing daily activities such as swallowing, chewing, and speaking [##REF##31726945##5##–##REF##31900091##7##]. Temporomandibular disorders (TMD) are a range of both musculoskeletal and degenerative conditions [##REF##38041596##8##–##UREF##2##12##]. The leading causes of TMD are an altered position of the intra-articular disc or abnormal muscle hyperactivity. The main symptoms include joint clicks, limitation of movement, and facial muscle tension, known as orofacial pain [##REF##33948541##13##, ##REF##36615298##14##]. It is estimated that 25% of the population shows signs of TMD, while only a tiny percentage shows the need for treatment. Furthermore, the prevalence of symptoms is much higher in women than men [##REF##28029069##15##]. The average age of patients with symptoms varies between 20 and 50 years. More than 70% of TMD patients show joint disc malposition as the cause. This condition is called an internal disorder. The progression of the disease is poorly understood; however, most affected individuals show a degenerative condition known as osteoarthritis or osteoarthrosis, depending on whether there is an inflammatory state. A clinical study on TMD patients with symptoms at the opening and closing of the mouth, on palpation, showed osteoarthritis phenomena at the joint level. Several studies have demonstrated by the magnetic resonance that the articular discs of asymptomatic patients in their normal anatomical position during movements have morphological change processes at the condyle and eminence level due to adaptive procedures [##REF##33534890##16##, ##REF##29574810##17##].</p>", "<p id=\"Par6\">While in symptomatic patients, there is a critical alteration also at the bone level. The observed changes are abrasion and deterioration of the articular cartilage and bone remodeling. Treatment options vary according to the joint's internal imbalance severity and osteodegenerative phenomena. There are non-invasive or minimally invasive treatments for patients in the early stages of internal disorders [##UREF##3##18##–##UREF##6##21##]. While in advanced or complex cases, patients require joint replacements or invasive therapies [##REF##28600812##22##]. The first phase of the pathology passes through the remodeling of the articular disc and bone heads. This process is one of adaptation and distributes excessive stress loads better. This is an adaptive process and therefore remains physiological. When the adaptive and remodeling capacity of the disc is exceeded, osteoarthritis phenomena occur. Significant alterations include alteration of bone components such as flattening of the joint eminence, decreased glenoid fossa, and flattening of the articular disc [##REF##29861806##23##, ##REF##30017771##24##].</p>", "<p id=\"Par7\">Degenerative arthritis can result from a lack of adaptation or excessive or prolonged stress. All the alterations of the TMJ start from an imbalance and an alteration of the articular disc. The various pathological transitions from internal disorders to osteoarthritis are not understood; however, there is a correlation between the two conditions. The progression and onset of TMD are poorly understood; however, Wilkes has divided the passage into 5 phases. In stage 1, there is a painless click at the beginning of the opening and the end of the closing. There is a displacement of the disc forward and an inability of the disc to return to its original position. The bone bases appear unaltered. In stage 2, click symptoms and orofacial pain are present. The magnetic resonance shows a slight disc deformation and displacement forward; however, the disc in the maximum opening returns to the physiological position [##REF##21419546##25##, ##REF##17803388##26##].</p>", "<p id=\"Par8\">Stage 3 is associated with frequent orofacial pain; jaw blocking during movement is more common.</p>", "<p id=\"Par9\">There is a thickening of the articular disc. At the beginning of phase 3, the disc is recaptured but not entirely and in response, the disc deforms under the thrust of the condyle forward. During stage 4, symptoms are more persistent and include chronic pain and limitation in movement. The disc appears very thickened and is not recaptured during the maximum opening. There are also osteoarthritis phenomena. Stage 5 is the most advanced. Patients have persistent chronic pain, crackles, and movement restrictions. Non-invasive treatment of TMDs is the therapy of choice [##REF##11458263##27##]. There are several non-invasive therapies available for the treatment of TMD. One of these therapies is drug treatment. TMDs affect a substantial portion of the global population, with prevalence rates ranging from 5 to 12%. The diverse nature of TMDs, which may involve myofascial pain, arthralgia, disc displacement, and osteoarthritis, contributes to the complexity of their clinical presentation. Patients often experience symptoms such as pain during mastication, restricted jaw movement, joint noises, and referred pain to the head and neck region. Moreover, TMDs can lead to additional comorbidities, including headaches, sleep disturbances, and psychological distress, underscoring the need for effective therapeutic approaches. Pharmacological interventions in the management of TMDs target the underlying pathophysiological mechanisms, aiming to alleviate pain, improve joint function, and enhance overall patient well-being. Non-steroidal anti-inflammatory drugs (NSAIDs) remain a cornerstone of pharmacotherapy, offering analgesic and anti-inflammatory effects through inhibition of cyclooxygenase enzymes. Muscle relaxants, such as benzodiazepines and cyclobenzaprine, act by reducing muscle hyperactivity and relieving muscle-related symptoms.</p>", "<p id=\"Par10\">Tricyclic antidepressants (TCAs) and selective serotonin-norepinephrine reuptake inhibitors (SNRIs) have demonstrated efficacy in managing TMD-related pain, likely through their modulation of central pain pathways and neurotransmitter balance. Botulinum toxin injections, a relatively novel approach, target muscle hyperactivity and have shown promise in reducing pain associated with TMDs. Pharmacological interventions represent a vital component of the multifaceted management approach for TMDs. These interventions aim to alleviate pain, restore joint function, and enhance patients' quality of life. As our understanding of the pathophysiology of TMDs continues to evolve, it is imperative to critically evaluate the efficacy, safety, and long-term outcomes of various pharmacological agents.</p>", "<p id=\"Par11\">This review aims to consider the different drugs used in the treatment of TMD [##UREF##7##28##]. In addition, a meta-analysis was conducted regarding the different pharmacological treatments of temporomandibular pain. The purpose of this systematic literature review with meta-analysis is to evaluate the main pharmacological treatments of pain caused by TMD. The purpose of this systematic literature review with meta-analysis is to evaluate the main pharmacological treatments of pain caused by TMD. In fact, the purpose of the meta-analysis is to evaluate which is the best pharmacological treatment of pain caused by temporomandibular disorders.</p>" ]

[ "<title>Materials and methods</title>", "<title>Eligibility criteria</title>", "<p id=\"Par12\">The following population (including animal species), Exposure, Comparator, and Outcomes (PECO) were used to determine the eligibility of all documents:<list list-type=\"bullet\"><list-item><p id=\"Par13\">P) Participants are patients with TMD</p></list-item><list-item><p id=\"Par14\">E) Exposure consisted of patients with TMD treated with different types of drugs, nonsteroidal anti-inflammatory drugs, corticosteroids, antidepressant drugs, centrally acting muscle relaxants, anticonvulsants, benzodiazepines and to whom pain was assessed by VAS scale.</p></list-item><list-item><p id=\"Par15\">C) Comparisons are patients with TMD and treated with placebo.</p></list-item><list-item><p id=\"Par16\">O) Outcome is to evaluate the effectiveness of different types of drugs on TMD pain. As a secondary outcome is to evaluate the effectiveness on TMD treated with the different drugs compared with placebo.</p></list-item></list></p>", "<p id=\"Par17\">Only papers providing data at the end of the intervention were included. Exclusion criteria were: 1) history of Temporomandibular joint (TMJ) trauma; 2) patients suffering from any inflammatory disorders or rheumatic diseases (e.g., rheumatoid arthritis, psoriatic arthritis); 3) patients with fibromyalgia; 4) patients with headache/migraine; 5) patients with a congenital abnormality or neoplastic conditions in TMJ region; 6) cross-over study design; 7) studies written in a language different from English; 8) full-text unavailability (i.e., posters and conference abstracts); 9) studies involving animal: 10) review article; 11) case report. Inclusion criteria are: patients with TMD; RCTs; observational studies; clinical trial.</p>", "<title>Search strategy</title>", "<p id=\"Par18\">The study made use of major scholarly databases (PUBMED, WEB of SCIENCE, LILACS). The time period taken into account for the electronic search was from January 3, 2000, to January 2, 2023. Following the method outlined in Table ##TAB##0##1##, papers were systematically searched for in the databases of PubMed, Web of Science, and Lilacs. In addition, a manual search of earlier systematic reviews on the same subject was also done. The connector \"AND\" was used to unite the words \"drug\" and \"temporomandibular disorders\". MESH was utilized to assist with the web search (Medical Subjects Headings).\n</p>", "<p id=\"Par19\">This systematic review was conducted according to Preferred Reporting Items for Systematic Reviews (PRISMA) guidelines and the Cochrane Handbook for Systematic Reviews of Interventions. The systematic review protocol has been registered on the International Prospective Register of Systematic Reviews (PROSPERO) with the following number CRD 42022316112 [##REF##33789819##29##].</p>", "<title>Data extraction</title>", "<p id=\"Par20\">Two reviewers (M.G.; R.F.) independently extracted data from the included studies using a customized data extraction on a Microsoft Excel sheet. In case of disagreement, a consensus was reached through a third reviewer.</p>", "<p id=\"Par21\">The following data were extracted: 1) First author; 2) Type of study; 3) Sample; 4) Type of drugs; 5) Data used for meta-analysis; 6) Results.</p>", "<title>Quality assessment</title>", "<p id=\"Par22\">The risk of bias in papers was assessed by two reviewers using Version 2 of the Cochrane risk-of-bias tool for randomized trials (RoB 2). Any disagreement was discussed until a consensus was reached with a third reviewer [##REF##15327804##30##].</p>", "<title>Statistical analysis</title>", "<p id=\"Par23\">The following factors were analyzed: VAS scale pre and after therapy. The mean values and standard deviation (SD) for each variable in each cohort were extracted. A meta-analysis was carried out when it was feasible. A 95% confidence interval (CI) for the mean difference was determined. Plots depicting forests were made to show the findings. Because of the high heterogeneity of the studies, the random effects model was used. Heterogeneity and the I² statistic describes the percentage total variation across studies that is due to heterogeneity rather than change. The suggested interpretation of I² is as follows: 0%–40% may represent low heterogeneity, 30%–60% may represent moderate heterogeneity, 50%–90% may represent substantial heterogeneity and 75%–100% considerable heterogeneity.</p>", "<p id=\"Par24\">The pooled analyses were performed using the software SPSS version 27 (IBM Corp. (2020). IBM SPSS Statistics for Windows (Version 27.0) [Computer software]. IBM Corp). In this meta-analysis, used Pre-Calculated Effect Sizes models was used in order to assess the variances between the VAS scale before and after therapy with different drug classes. he reported summary statistics were calculated as random-effects models based on the assumption of heterogeneity between studies. Pooling was done according to the DerSimonian and Laird method, using inverse variance.</p>", "<p id=\"Par25\">The <italic>p</italic>- value was set at 0.05 The Risk ratio between the two groups was measured. The Ta et Dionne study was divided into Ta et Dionne in which it evaluated the effects of naproxen and Ta et Dionne 1 in which the effects of celecoxib were evaluated.</p>" ]

[ "<title>Results</title>", "<title>Study characteristics</title>", "<p id=\"Par26\">At the end of the research, 1698 studies were identified. During the first screening phase, 190 articles were eliminated because they were not in English, precisely 130 from PubMed, 15 from Web of Science, and 45 from Lilacs. In addition, 668 items were excluded because they were duplicates. Ultimately, clinical trials and randomized trials were selected through special filtering. Therefore, 671 articles were excluded. In the final stage, 178 articles were selected, and abstracts were read. In the final stage of selection, 170 articles were excluded. One hundred fifty-eight did not meet the PECO, and 20 articles because they were off-topic (they treated oral-facial pain or did not meet the inclusion criteria) (Fig. ##FIG##0##1##). The selected studies come from various parts of the world and are heterogeneous regarding drug use. They consider a heterogeneous population of age. In the studies reviewed, adults were considered apart from Arabshahi's study, which evaluated children. In this systematic review, 531 patients were assessed; the population is heterogeneous regarding the type of TMD.</p>", "<title>Main findings</title>", "<p id=\"Par27\">Ta and Dionne studied the pharmacological efficacy of NSAIDs. They performed a double-blind, randomized, placebo-controlled study in which they administered one group of celecoxib (100 mg twice daily), the second group of naproxen (500 mg twice daily) and the placebo control group for six weeks. This study showed that naproxen effectively reduces orofacial pain symptoms due to TMD compared to celecoxib. The study Singer [##REF##10332320##31##] evaluated the efficacy of different drugs on gold facial myogenic pain thanks to a double-blind, randomized and controlled study. Thirty-nine subjects were recruited, including 35 women and four men, with orofacial pain due to TMD for at least three months and marked distress on palpation of the chewing muscles. Patients were treated for pain with one of the drugs to be tested: placebo, diazepam, ibuprofen, or a combination of diazepam and ibuprofen. Muscle pain, maximal opening and limitation of the movement were assessed at times 0 at two weeks and four weeks, respectively. Pain, as measured by a visual analogue scale, was significantly decreased in the diazepam and diazepam plus ibuprofen groups but not in the ibuprofen or placebo groups. Analysis of variance showed a significant pharmacological effect for diazepam but not ibuprofen, indicating that pain relief was attributable to diazepam. No significant changes were observed in muscle tenderness, interincisal opening or plasma beta-endorphin level [##REF##10332320##31##, ##REF##11731064##32##].</p>", "<p id=\"Par28\">The study by List [##REF##11731064##32##] evaluated the effectiveness of injecting an intra-articular dose of morphine. Fifty-three patients with joint pain were recruited. This randomized, double-blind study evaluated patients before treatment and a follow-up one week after treatment. The pain intensity was recorded using the VAS scale at the mouth's maximum opening and resting position. Intra articulaar injection was made into one TMJ containing either 1.0 mg morphine-HCl, 0.1 mg morphine-HCl, or saline (placebo). The pain was recorded in a diary three days before surgery and five days after. Pain assessed by VAS was significantly reduced 1–10 h after injection. The VAS score was lower in the 0.1 mg morphine group than in the 1.0 mg morphine group (<italic>P</italic> &lt; 0.043) and in the placebo group (<italic>P</italic> &lt; 0.021) [##REF##16255045##33##]. Arabshahi's study evaluated the effects of corticosteroid injection into the temporomandibular joint in children with idiopathic arthritis and TMJ inflammation. Twenty-three children aged 4 to 16 years were recruited and received intra-articular corticosteroid injections (triamcinolone acetonide [<italic>n</italic> = 16] or triamcinolone hexacetonide [<italic>n</italic> = 7]). Pain and maximum incisal opening before and after treatment were evaluated as benchmarks. From the follow-up results of 13 patients with joint pain, 10 had complete resolution of symptoms with a significance of <italic>P</italic> &lt; 0.05. All patients showed a reduction in mouth opening. After injection, the maximum aperture improved by 0.5 mm in 10 patients with a significance of <italic>p</italic> = 0.0017. Post-injection oedema occurred in only two patients [##REF##8669234##34##]. In the Alstergren research, 22 patients (29 joints) with specific or nonspecific temporomandibular joint (TMJ) arthritis received a single intra-articular glucocorticoid (GC) injection. At follow-up appointments, 2–3 or 4–6 weeks following therapy, the impact on subjective symptoms, clinical signs in the craniomandibular system, and joint aspirate concentration of neuropeptide Y-like immunoreactivity (NPY-LI) were assessed. The medication improved the symptoms and clinical indicators in patients with the particular inflammatory joint condition, and 2–3 weeks later, the TMJ level of NPY-LI decreased. After 2–3 weeks, there was also a variable clinical improvement and NPY-LI level decrease in the patients with unspecific inflammatory joint illness, but not statistically significantly [##REF##12889679##35##]. Several studies show the analgesic effect of tricyclic antidepressants in chronic pain. The study of Rizzatti-Barbosa showed that amitriptyline in the dose of 25 mg daily decreased symptoms in patients with chronic orofacial pain. While increasing the amount to 50–75 mg/day showed no substantial analgesic effects. The quantities of tricyclic antidepressants used to treat pain are much lower than those used to treat depression [##UREF##8##36##]. Another class of antidepressants used are SSRIs. They were introduced in the 1980s and have become the most widely used drugs for treating depression. This study of Kimos was to assess gabapentin's analgesic effects on myalgia. Fifty participants were randomly assigned to one of two research groups in this 12-week randomized controlled clinical trial: 25 received gabapentin, and 25 received a placebo. Palpation Index, VAS-measured pain, and a VAS-measured impact of myalgia on daily functioning were the outcome measures used (VAS-function). Thirty-six subjects completed the trial. Clinically and statistically, gabapentin was more effective than a placebo in reducing patient-reported pain, masticatory muscle hyperalgesia, and the impact of myalgia on daily functioning (Gabapentin = 51.04%; placebo = 24.30%; <italic>P</italic> = 0.037; gabapentin = 67.03%; placebo = 14.37%; <italic>P</italic> = 0.001; gabapentin = 57.70%; placebo = 16.92%; <italic>P</italic> = 0.022) [##UREF##9##37##].</p>", "<p id=\"Par29\">The study of Gilron evaluates the administration of pregabalin in the wider variety of neuropathic pain etiologies in this multicenter experiment. Pregabalin was administered to 256 participants in this enriched enrolment randomized withdrawal trial in a single-blind, flexible-dose for four weeks when stable concomitant analgesics were permitted. A total of 157 patients were randomized and treated, double-blind, to receive either pregabalin (<italic>n</italic> = 80) or a placebo (<italic>n</italic> = 77) for five weeks, and 135 of them (65%) reported a pain improvement of at least 30%. Of the single-blind responders who were randomly assigned, 81% received a placebo, and 86% received pregabalin. At the double-blind endpoint, the pregabalin group's mean (SD) pain scores were 2.9 (1.9), and the placebo groups were 3.5 (1.7) (<italic>P</italic> = 0.002). These small but significant pregabalin-placebo differences were seen in each patient category with a diabetic peripheral neuropathy or postherpetic neuralgia diagnosis (<italic>P</italic> = 0.03) and those with other diagnoses (<italic>P</italic> = 0.02). Sleep disruption, Hospital Anxiety and Depression Scale Anxiety and Depression subscales, and other secondary measures showed significant variations. 28 out of 80 (35.0%) pregabalin users and 28 out of 77 (36.4%) placebo users discontinued the double-blind phase due to a noticeable increase in pain. In the single-blind stage, adverse events were consistent with the pregabalin tolerability profile. They resulted in the discontinuation of 9 patients, five in the placebo group and two in the pregabalin group [##REF##1820833##38##] (Table ##TAB##1##2##).\n</p>", "<title>Quality assessment and risk of bias</title>", "<p id=\"Par30\">Using RoB 2, the risk of bias was estimated and reported in Fig. ##FIG##1##2##. Regarding the randomization process, 88% of the studies ensured a low risk of bias. However, 16% of the studies excluded a performance bias, but 84% reported all outcome data, 89% of the included studies adequately excluded bias in the selection of reported outcomes, and 50% excluded bias in self-reported outcomes. Overall, 6 of the eight studies were shown to have a low risk of incurring bias<bold>.</bold></p>", "<title>Meta-analysis</title>", "<p id=\"Par31\">The meta-analysis was conducted by SPSS software. Continuous Outcomes with Pre-Calculated Effect Sizes was performed because are continuous variables, using the p value at 0.05. In this meta-analysis, in order to statistically analyse the data, only studies with a control group and taking into account the VAS before and after therapy were taken into account, therefore the studies taken into account for statistics are 4. In order to conduct this meta-analysis, we evaluated and considered the change in VAS 6 weeks later in order to assess which is the best pharmacological treatment for TMD pain. The overall effect, reported in the forest plot (Fig. ##FIG##2##3##). The Forrest plot found no significant variation in pain symptomatology assessed by the VAS scale among the different therapies analyzed (Overall:33.47; C.I. 5.79–61.79). In Ta et Dionne we analyzed naproxen and celecoxib. In the study by Rizzatti Barbosa et al. we evaluated the effects of amytriptyline on the VAS scale. In the study by Gilron et Al. the effects of pregabalin were evaluated. The Meta-Analysis with Continuous Outcomes with Pre-Calculated Effect Sizes resulted in the rejection that there is intergroup variability (p.0.02). Effect Size Estimates for Individual Studies was reported in Fig. ##FIG##2##3##.</p>" ]

[ "<title>Discussion</title>", "<p id=\"Par32\">In light of the results of the meta-analysis and literature review, this article aims to evaluate which pharmacological treatment is most effective for the treatment of TMD pain. Therefore, we evaluated the main drugs used and compared their change in pain at 6-week follow-up. This meta-analysis showed no signigicative differences. Therefore, the clinician's choice on the type of drug treatment should be based on a number of variables including the patient's overall assessment, the presence of comorbidities. The following part will evaluate all the advantages and disadvantages of each drug, and then we can arrive at choosing a drug that can create fewer interactions and fewer side effects for the TMD patient.</p>", "<p id=\"Par33\">NSAIDs inhibit cyclooxygenases and thus prevent the formation of prostaglandins. They were the most prescribed drugs for orofacial pain. NSAIDs treat patients with mild to acute TMJ inflammation, especially in disc dislocation cases, without reduction or acute trauma. These drugs must be taken for a minimum of 2 weeks. Several NSAIDs are used extensively in the dental field, such as ibuprofen, naproxen, diflunisal and ketorolac. The superiority of any NSAID over the others has not been demonstrated. The significant side effect is on the gastrointestinal level. NSAIDs can cause ulcers and bleeding in the gastric tract. Studies in the USA found that about 16,000 people die from gastrointestinal side effects [##UREF##10##39##–##UREF##17##47##].</p>", "<p id=\"Par34\">Therefore, administering an NSAID to a patient with active gastrointestinal problems is not recommended. Naproxen and Ibruprofen appear to be the safest at the cardiovascular level. Ibuprofen also has fewer gastrointestinal effects. Another pharmacological possibility for patients at risk of gastric bleeding is using COX-2 inhibitors; however, patients should not have cardiovascular or cerebrovascular risk factors. NSAIDs can also interact with other types of drugs. For example, the clearance of Lithium decreased following the intake of NSAIDs. Therefore, there may be an increase in the serum concentration of Lithium and an increase in toxicity [##UREF##18##48##]. Combining NSAIDs with angiotensin conversion inhibitors or loop diuretics can cause acute kidney injury. NSAIDs reduce renal blood flow and the excretion of these drugs. Therefore, if NSAIDs are taken for more than five days, the effects of antihypertensive medications such as diuretics may be enhanced [##UREF##19##49##]. NSAIDs have an antiplatelet effect by inhibiting the synthesis of thromboxane. Therefore, they should be used cautiously in patients on anticoagulant therapy (e.g., warfarin) [##UREF##20##50##].</p>", "<p id=\"Par35\">Widely used drugs are opioids, and their action against moderate pain is very effective. In the treatment of TMD, their use is mild to severe pain. The most widely used opioid drugs are codeine, oxycodone, and hydromorphone for severe pain. If the oral route is not preferred, one can opt for the fentanyl patch. The study did not show a variation between injectable and oral opioids [##REF##30008340##51##].</p>", "<p id=\"Par36\">Corticosteroid drugs have a chemical similarity to cortisol which is produced by the adrenal glands. These drugs are used to treat moderate to severe TMD. They block phospholipase A2, decreasing the production of prostaglandins and leukotrienes. Corticosteroids can be injected directly into the joint capsule or orally. Usually, intra-articular cortisone solutions are diluted with a local anaesthetic.</p>", "<p id=\"Par37\">Other drugs used in the treatment of TMD are centrally-acting muscle relaxants. They are used and administered in patients with chronic pain. These drugs cause drowsiness and are therefore taken before going to bed. The most common muscle relaxants are carisoprodol, cyclobenzaprine, metaxalone and methocarbamol [##REF##33584275##52##]. Antidepressive drugs are widely used for the management of TMD pain. Tricyclic antidepressant drugs and selective serotonin reuptake inhibitors appear to be the most important. Several studies evaluate the efficacy of tricyclic antidepressants in the management and control of chronic pain. Furthermore, patients with chronic pain also suffer from depression and sleep disturbances. The exact mechanism of action is not fully known; however, it is probably given by inhibiting the serotonin reuptake and noradrenaline at the synaptic level of the central nervous system. By blocking the reuptake at the back of the horn, there is an increase in the availability of these neurotransmitters, which block the transmission of pain. The drugs used are amitriptyline, nortriptyline and desipramine [##REF##24703543##53##].</p>", "<p id=\"Par38\">Adverse events of SSRI drugs are sweating, dizziness, blurred vision, and constipation. With an increase in the bioavailability of endogenous catecholamines, the administration of epinephrine can cause an overdose reaction. In addition, monoamine oxidase inhibitors, given together with tricyclic antidepressants, can lead to serotonin syndrome with fever, ataxia and severe hypertension. These drugs cause gastrointestinal problems, headaches, sexual dysfunction, dry mouth and sweating. These drugs should be used with caution. These patients need combined management with their treating physician. Anticonvulsant medications are often used for neuropathic pain. Their analgesic mechanism remains unclear. These drugs inhibit excessive neuronal activation. The sites of action are the voltage-gated ion channels. Gabapentin and pregabalin are used for orofacial pain. They have a chemical structure like GABA, the primary inhibitory neurotransmitter. However, none of these drugs acts on the GABA receptor [##REF##29513209##54##].</p>", "<p id=\"Par39\">Benzodiazepines are drugs used to treat sleep disorders and muscle disorders. These drugs are associated with tolerance and dependence; therefore, their long-term use is not recommended. These act on GABA receptors which mediate inhibitory transmission in the central nervous system. These drugs act on the chloride receptor, opening it and thus promoting neuronal hyperpolarization. They are mainly used as anxiolytics, but they also have their use as muscle relaxants. Their use for the treatment of epilepsy is also well established. Several clinical trials have evaluated the efficacy of these drugs in treating TMD compared to a placebo. These drugs have been discouraged due to adverse reactions such as sleepiness. Furthermore, these drugs show tolerance and dependence whereby the sudden discontinuation leads to symptoms including anxiety, agitation, restlessness, insomnia and convulsions. They should not be used in patients with myasthenia gravis and glaucoma. These drugs are degraded by the CYP 4503A4 and therefore show numerous interactions with other medications. Also, some agonist drugs of this cytochrome, such as grapefruit juice, can reduce the metabolism of benzodiazepines and thus increase their bioavailability. They are delicate drugs that experienced medical personnel must administer [##REF##25822556##55##].</p>", "<p id=\"Par40\">The main side effects of opioids are sedation, dizziness, nausea, vomiting, constipation, physical addiction, tolerance and respiratory depression. All these symptoms are accentuated in geriatric patients. The use of opioids with other central nervous system depressants, such as benzodiazepines, may have additive effects and cause increased sedation. Opioids are not recommended due to their tolerance and dependence. Therefore, the prescription of this category of drugs should be limited. Furthermore, no clinical evidence exists that long-term opioid therapy is more effective than other treatments [##UREF##21##56##, ##UREF##22##57##].</p>", "<p id=\"Par41\">The structure of cyclobenzaprine is like tricyclic antidepressants. This drug is contraindicated in patients with hyperthyroidism, congestive heart failure, arrhythmias, and recent heart attacks. Low doses of cyclobenzaprine have positive effects on pain. Therefore, no more than 10 mg before bedtime, cyclobenzaprine is prescribed in low doses. The treatment plan should be 30 days, followed by a 2-week off period during which the patient's symptoms are evaluated. However, chronic therapies with cyclobenzaprine should be managed with the treating physician.</p>", "<p id=\"Par42\">Therefore having evaluated all pharmacological mechanisms and side effects we were able to assess that as an effect on pain all drugs are effective. Moreover, given the side effects, the clinician should well categorize the pain and start through NSAIDs which are the drugs of first choice and with less side effects and then vary the therapy in case of failure.</p>", "<title>Limitations of this meta-analysis</title>", "<p id=\"Par43\">The limitation of this meta-analysis is that we analyzed the different types of drugs together as we wanted to analyze which treatment was the most effective. However, we did not take into consideration the duration of treatment of each study. The limitation of this study lies in having evaluated different drug therapies for the treatment of TMDs.</p>" ]

[ "<title>Conclusions</title>", "<p id=\"Par44\">The study includes different types of pharmacological treatment for TMD and therefore we cannot state that there is a first choice drug for the treatment of pain. We can state that NSAIDs are the most widely used drugs. However, we can conclude from the review and the meta-analysis that NSAIDs are undoubtedly very effective drugs in the treatment of acute pain and are undoubtedly the safest drug class. Opiods are the substitute drugs for NSAIDs in the case of patients with previous gastrointestinal bleeding or in the case of acute moderate/severe TMJ pain Corticosteroids are always used for the treatment of acute moderate/severe pain, however, the first choice is an intra-articular injection. Myorelaxants are the drugs of choice either for acute contractions and/or contractures or are used to treat chronic pain. Another drug class used are antidepressants; they are used for chronic pain and in patients refractory to bite therapy. Anticonvulsants are drugs to treat neuropathic pain and thus chronic TMJ pain. Benzodiazepines are still drugs used in the treatment of chronic myofascial pain, however, they are drugs that need careful use and their usefulness also lies in alleviating sleep disturbances. In fact, the clinician should help himself with criteria such as DC/TMD for diagnosis and diagnostic classification.</p>", "<p id=\"Par45\">Therefore, in conclusion, the clinician's skill lies in identifying the type of dysfunction and knowing how to choose drugs also on the basis of the patient's other comorbidities. Therefore, the gnathological framing of the type of dysfunction is fundamental and helps the clinician to choose the appropriate drug therapy. In addition, pharmacological treatment must be supported by functional therapy, physiotherapy and behavioural therapy.</p>" ]

[ "<title>Background</title>", "<p id=\"Par1\">Temporomandibular disorders (TMD) are manifested by soreness in the jaw joint area and jaw muscles, clicks or creaks when opening or closing the mouth. All these symptoms can be disabling and occur during chewing and when the patient yawns or speaks. Several classes of drugs are used to treat symptoms. This review aims to assess which drug suits the different signs.</p>", "<title>Methods</title>", "<p id=\"Par2\">Pubmed, Web of Science and Lilacs were systematically searched until 01/02/2023. Clinical trials were selected that dealt with drugs used in temporomandibular dysfunction</p>", "<title>Results</title>", "<p id=\"Par3\">Out of 830 papers, eight studies were included. The Meta-Analysis with Continuous Outcomes with Pre-Calculated Effect Sizes resulted in the rejection that there is intergroup variability (p.0.74).</p>", "<title>Conclusions</title>", "<p id=\"Par4\">Treatment of orofacial pain is still a significant challenge for dentistry. We can conclude that there is no drug of first choice in the treatment of temporomandibular pain. However, the clinician must distinguish the type of pain and the aetioloic cause of the pain so that the patient can be treated and managed pharmacologically.</p>", "<title>Keywords</title>" ]

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[ "<title>Acknowledgements</title>", "<p>Not applicable.</p>", "<title>Authors’ contributions</title>", "<p>Conceptualization, GM, RF, VR; methodology, GM, RF, and SC; software, GM, RF, and MDB; validation, GM, RF, and VR; formal analysis GM, RF, and GC; investigation, AB; data curation, GM, RF; writing—original draft preparation, GM, RF, and MC; writing—review and editing, GM, RF; visualization, MC, AB, GC; supervision, MC, AB, GC, and GM; All au-thors have read and agreed to the published version of the manuscript.</p>", "<title>Funding</title>", "<p>This research received no external funding.</p>", "<title>Availability of data and materials</title>", "<p>Data generated and analysed during study will be available from Rocco Franco upon reasonable request.</p>", "<title>Declarations</title>", "<title>Ethics approval and consent to participate</title>", "<p id=\"Par46\">Not applicable.</p>", "<title>Consent for publication</title>", "<p id=\"Par47\">Not applicable.</p>", "<title>Competing interests</title>", "<p id=\"Par48\">The authors declare no competing interests.</p>" ]

[ "<fig id=\"Fig1\"><label>Fig. 1</label><caption><p>Prisma flowchart</p></caption></fig>", "<fig id=\"Fig2\"><label>Fig. 2</label><caption><p>Risk of bias domains of the included studies</p></caption></fig>", "<fig id=\"Fig3\"><label>Fig. 3</label><caption><p>Forest plots of the analyzed studies showing correlation on different types of drug treatment for TMJ treatment. The study by Ta et Dionne evaluates the effect of naproxen before and after 6-week therapy; the study by Ta et Dionne 1 evaluates the effect of celecoxib; the study by Rizzatti et Al. evaluates the effect of gabapentin; the study by Gilron et Al. evaluates the effect of pregabalin. The <italic>p</italic> value was set at 0.5</p></caption></fig>" ]

[ "<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Search strategy</p></caption><table frame=\"hsides\" rules=\"groups\"><tbody><tr><td align=\"left\"><p><bold><italic>PubMed</italic></bold></p><p><italic>\"Temporomandibular disorders\" AND \"drugs\"</italic></p></td></tr><tr><td align=\"left\"><p><bold><italic>Web of Science</italic></bold></p><p><italic>(ALL = (temporomandibular disorders) AND ALL = (drugs)</italic></p></td></tr><tr><td align=\"left\"><p><bold><italic>Lilacs</italic></bold></p><p><italic>temporomandibular disorders [Palavras] and drugs [Palavras]</italic></p></td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab2\"><label>Table 2</label><caption><p>Main characteristics of the studies included in the present systematic review</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">First Author</th><th align=\"left\">Type of study</th><th align=\"left\">Sample</th><th align=\"left\">Type of drugs</th><th align=\"left\">Data used for meta-analysis</th><th align=\"left\">Results</th></tr></thead><tbody><tr><td align=\"left\">Ta et Dionne,</td><td align=\"left\">Double blind, randomized</td><td align=\"left\">68 subjects with joint displacement</td><td align=\"left\">Naproxen,colecoxib, placebo</td><td align=\"left\">P:0.27; P:0.54</td><td align=\"left\">Two drugs are equally effective in reducing pain</td></tr><tr><td align=\"left\">Singer et al.,</td><td align=\"left\">Double blind, randomized</td><td align=\"left\">39 subjects</td><td align=\"left\">Placebo,diazepam, ibuprofen or a comination of the two drugs</td><td align=\"left\"/><td align=\"left\">Diazepam and the combination of the two drugs are more effective than ibuprofen</td></tr><tr><td align=\"left\">List et al.</td><td align=\"left\">Double blind, randomized</td><td align=\"left\">53 subjects</td><td align=\"left\">Intracapsular injection of morphine compared to placebo</td><td align=\"left\"/><td align=\"left\">the pain was significantly lessened</td></tr><tr><td align=\"left\">Arabshahi et al.</td><td align=\"left\">Randomized</td><td align=\"left\">23 children</td><td align=\"left\">Intracapsular injection of corticosteroid</td><td align=\"left\"/><td align=\"left\">Maximum opening and pain significantly decreased</td></tr><tr><td align=\"left\">Alstergren et al.</td><td align=\"left\">Randomized</td><td align=\"left\">22 patients</td><td align=\"left\">Intracapsular injection of corticosteroid</td><td align=\"left\"/><td align=\"left\">symptom improvement not statistically significant</td></tr><tr><td align=\"left\">Rizzatti-Barbosa et al.</td><td align=\"left\">Randomized</td><td align=\"left\">20 patients (female)</td><td align=\"left\">Administration of amitryptiline</td><td align=\"left\">P:0.72</td><td align=\"left\">There were no statistically significant effects</td></tr><tr><td align=\"left\">Kimos et al.</td><td align=\"left\">Double blind randomized</td><td align=\"left\">50 patients</td><td align=\"left\">Administration of gabapentin</td><td align=\"left\"/><td align=\"left\">gabapentin has a statistically significant pain relief effect</td></tr><tr><td align=\"left\">Gilron et al.</td><td align=\"left\">Randomized</td><td align=\"left\">256 patients</td><td align=\"left\">Administration of pregabalin</td><td align=\"left\">P:0.01</td><td align=\"left\">Pregabalin had an efficacy on the reduction of pain</td></tr></tbody></table></table-wrap>" ]

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[ "<title>Background</title>", "<p id=\"Par5\">Since laparoscopic pancreatoduodenectomy (LPD) was first reported in 1993 [##REF##7915434##1##], LPD has been increasingly adopted worldwide for the treatment of benign and malignant tumors surrounding the duodenum, ampulla, lower common bile duct and head of the pancreas [##REF##29771723##2##]. However, bleeding, abdominal infection, and even potentially life-threatening pancreatic fistula remain as existing challenges [##REF##31077200##3##]. Several studies have demonstrated that the postoperative pancreatic fistula (POPF) rate remains high, with a related mortality rate of 3–8%; thus, POPF is known as the Achilles’ heel of the Whipple procedure [##REF##31562793##4##–##UREF##0##6##]. A variety of approaches to reduce the incidence of POPF due to transanastomotic stenting, fibrin glue use, pancreaticogastrostomy and pancreaticojejunostomy (PJ) have been developed by pancreatic surgeons [##REF##32093527##7##–##REF##25392839##9##]. However, none of these approaches can completely avoid POPF.</p>", "<p id=\"Par6\">Recently, it has been reported that Blumgart anastomosis, a well-accepted procedure among pancreatic surgeons, reduced the incidence of POPF by enhancing the adhesion between the pancreatic parenchyma and intestine [##REF##27804043##10##, ##REF##27017162##11##]. However, precise needle handling and prevention of suture tangling during LPD are still needed. We herein introduce our modified Blumgart approach to secure these structures with adjustable adhesions using a homemade crochet needle in LPD.</p>" ]

[ "<title>Methods</title>", "<title>Study population</title>", "<p id=\"Par7\">We performed the modified Blumgart PJ technique using a homemade crochet needle for LPD in February 2019 at the Department of Hepatobiliary and Pancreatic Surgery, The Second Affiliated Hospital, Zhejiang University School of Medicine. The inclusion criteria were resectable benign and malignant tumors surrounding the duodenum, ampulla, lower common bile duct and head of the pancreas. The exclusion criteria were malignant tumors with distant metastases and invading the superior mesenteric vessels on preoperative radiologic evaluation. We applied the enhanced recovery after surgery (ERAS) pathway in the LPD from December 2017, whereas the standard perioperative care protocol was used before. We retrospectively collected clinicopathologic variables (sex, age, body mass index (BMI), pancreatic texture, pancreatic duct diameter, and histopathological findings), operative details (total operating time, time needed for PJ, and intraoperative bleeding volume), and postoperative hospitalization data (amylase level on postoperative days 3 and 5, incidence of postoperative complications, incidence of clinically relevant postoperative pancreatic fistula (CR-POPF including grades B and C pancreatic fistula), hospitalization length, and 90-day mortality rate). All laparoscopic procedures were performed by the same surgeons following the same criteria and using the same anastomosis technique. This study was approved by the Ethics Review Committee of the Second Affiliated Hospital of Zhejiang University School of Medicine.</p>", "<title>Surgical procedures</title>", "<title>Patient position and trocar distribution</title>", "<p id=\"Par8\">A supine position was adopted for the patient, with his or her legs spread and head elevated above the feet (at a 30-degree incline). The resection was performed with five trocars (Fig. ##FIG##0##1##): two 12-mm trocars (right and left upper quadrants), two 5-mm trocars (the right and left flank patterns) and one 10-mm trocar (umbilical).</p>", "<p id=\"Par9\">\n\n</p>", "<title>Self-made crochet needle</title>", "<p id=\"Par10\">Our homemade crochet needle consists of 4 − 0 prolene (Ethicon Inc, Somerville, NJ, USA), an injection needle with a diameter of 0.9 mm and a length of 80 mm (Zhejiang Kindly Medical Equipment Co., Ltd., China) and a frame constructed with 3 M Tegaderm transparent film (3 M company, USA). First, the two ends of the prolene thread were passed through the injection needle and fixed at the tail of the injection needle using a 3 M Tegaderm transparent film dressing. Finally, a closed circle with a circumference of approximately 3 cm was formed on the tip of the injection needle (Fig. ##FIG##1##2##). During our operation, the posterior wall of the two pancreaticojejunostomy U-shapes was fixed outside the body using a homemade crochet needle to adjust the tension at any time and reduce interference from the threads under laparoscopy, especially in obese patients.</p>", "<p id=\"Par11\">\n\n</p>", "<title>PJ procedure</title>", "<title>Preparation of the pancreatic stump and jejunal loop</title>", "<p id=\"Par12\">Typically, dissociation of 1–2 cm of the pancreatic stump borders was needed to perform PJ later (Fig. ##FIG##2##3##A). The main pancreatic duct was verified by either performing careful visual inspection for thick ducts or using a slender tube for narrow ducts. The closer end of the jejunum loop was closed. The jejunal limb was moved to the right of the middle colic vessels in a retrocolic fashion, while the blind end was placed close to the pancreatic remnant.</p>", "<p id=\"Par13\">\n\n</p>", "<title>Pancreaticojejunal anastomosis</title>", "<p id=\"Par14\">A large 4 − 0 prolene suturing needle was used to vertically enter the pancreas stump 1 cm from the ventral side, extending out from the dorsal side of the edge of the pancreas (Fig. ##FIG##2##3##B). After the needle has been inserted horizontally along the long axis of the jejunum, it is advanced 1 cm within the seromuscular layers of the jejunum. Next, the needle protrudes 1 cm from the pancreatic dorsal side to the ventral side. A U-shaped suture was created, and the needle was cut. Then, both ends of the thread were lifted out of the body with our homemade crochet needle, and the two threads were fixed with a vascular clamp to facilitate adjusting the tension between the pancreas and jejunum (Fig. ##FIG##2##3##C). Furthermore, a similar second U-shaped suture encompassing the main pancreatic duct that extended between the pancreatic parenchyma and the jejunal seromuscular layer was created (Fig. ##FIG##2##3##D), and both ends of the thread were also lifted out of the body with our homemade crochet needle (Fig. ##FIG##3##4##). Finally, a third U-shaped suture was created with both ends of the thread and fixed with a hemolock (Fig. ##FIG##2##3##G). This maneuver has an advantage over the classical technique because at this point, the posterior faces of both the jejunum and pancreas are not yet sutured so the duct-to-mucosa (DTM) anastomosis can be made.</p>", "<p id=\"Par15\">\n\n</p>", "<p id=\"Par16\">The posterior semicircle sutures of the DTM were placed at the 4, 6, and 8 o’clock positions using 5 − 0 PDS II (Ethicon Inc, Somerville, NJ, USA) (Fig. ##FIG##2##3##E). With traction on the externalized transpancreatic stitches, the jejunal loop can be reinserted into the pancreatic posterior face, while the stitches on the posterior face of the TMD served as anchors for the loop. It is usually necessary to place a stent into the pancreatic duct. Later, stitches were placed on the anterior face at the 10, 12, and 2 o’clock positions of the DTM in the same manner (Fig. ##FIG##2##3##F). Once the DTM anastomosis was completed, the three U-shaped sutures were knotted sequentially. Then, a single layer of continuous sutures was made between the pancreatic stump and the anterior seromuscular layer of the jejunum using the 3/0 barbed suture Stratafix (Ethicon Inc, Somerville, NJ, USA) (Fig. ##FIG##2##3##H and I).</p>", "<p id=\"Par17\">After PJ was completed, biliary and gastric reconstructions were sequentially performed. Two external drainage tubes were routinely placed around the hepaticojejunostomy and PJ.</p>", "<title>Postoperative management</title>", "<p id=\"Par18\">The drain output was recorded each day after the operation. The amylase level in the drainage fluid was measured on postoperative days 3 and 5 and at any time when POPF was suspected. One abdominal CT scan was routinely conducted on postoperative day 5. To prevent infection after surgery, broad-spectrum antibiotics and anti-anaerobic drugs were used for 72 h postoperatively. All patients received octreotide after the operation to decrease the volume of pancreatic external secretion. POPF was diagnosed and graded according to the definition from the International Study Group on Pancreatic Fistula (ISGPF) (2016 version). If the amylase content of any measurable drainage on or after postoperative day 3 was greater than 3 times the upper normal serum value or the drains were either left in place for &gt; 3 weeks or repositioned endoscopically or percutaneously, a grade B POPF was considered. On the other hand, grade C POPFs were those that needed reoperation or led to single/multiple organ failure and/or mortality. The postoperative complications included delayed gastric emptying, abdominal hemorrhage, gastrointestinal anastomotic hemorrhage, bile leakage, infection, chylous fistula and mortality. On postoperative days 5, if the amylase content of any measurable drainage is lower than 3 times the upper limit of normal serum amylase and follow-up abdominal CT shows no sign of abdominal fluid in the operative region, the drainage tube can be removed.</p>", "<title>Statistical analysis</title>", "<p id=\"Par19\">Student’s t test was performed to compare continuous variables represented as mean ± standard deviation (SD). Categorical data are described as numbers (percentages) and were compared using the chi-square test or Fisher’s exact test. Furthermore, the differences between non-POPF group and POPF group were evaluated by t tests in the case of normally distributed variables or by chi-square test in the case of categorical data. Statistical significance was determined by a P value of 0.05. All analyses were conducted with SPSS 22.0 software (SPSS, Inc., Chicago, IL, USA).</p>" ]

[ "<title>Results</title>", "<title>Demographic and clinicopathological features of patients</title>", "<p id=\"Par20\">There were 96 patients who underwent LPD with the new technique, including 54 men and 42 women with a mean age of 63.38 ± 10.41 years (Table ##TAB##0##1##). The average BMI of the patients was 22.52 kg/m². The leading indication for LPD was pancreatic ductal adenocarcinoma (n = 48, 50%), followed by duodenal papillary carcinoma (n = 21, 21.9%), distal cholangiocarcinoma (n = 12, 12.5%), ampullary carcinoma (n = 7, 7.3%), intraductal papillary mucinous neoplasm (n = 4, 4.2%), solid pseudopapillary tumor (n = 2, 2.1%), pancreatic neuroendocrine tumor (n = 1, 1.0%), and duodenal adenoma (n = 1, 1.0%). The average total operative time, intraoperative blood loss and postoperative hospital stay were 445.30 min, 198.43 mL and 13.68 days, respectively. The mean operation time for PJ was 66.28 min. Grade B POPF occurred in 13 patients (13.5%), while 1 grade C POPF was observed. One patient with postoperative abdominal hemorrhage was cured after reoperation to achieve homeostasis. Six patients (6.3%) suffered from chylous fistula, 4 patients (4.2%) suffered from delayed gastric emptying, 3 patients (3.1%) suffered from pneumonia, 2 patients (2.1%) suffered from bile leakage, 2 patients (2.1%) suffered from abdominal infection, and 1 patient (1.0%) suffered from gastrointestinal anastomotic hemorrhage, which were all treated with conservative therapy. There were no operative or in-hospital deaths.</p>", "<p id=\"Par21\">\n\n</p>", "<title>Comparisons between the non-POPF and POPF subtypes</title>", "<p id=\"Par22\">When the patients in these subgroups were compared (Table ##TAB##1##2##), the incidence of soft pancreas was higher in the POPF group than in the non-POPF group (P = 0.016). Furthermore, the incidence of pancreatic ductal adenocarcinoma or pancreatitis was lower in the POPF group than in the non-POPF group (P = 0.013). However, no significant differences were observed in age (P = 0.094), BMI (P = 0.575), operative time (P = 0.419), intraoperative bleeding (P = 0.610) or pancreatic duct diameter (P = 0.270). The fistula risk score of non-POPF group was significant lower than that of POPF group (3.18 ± 1.69 vs. 5.50 ± 1.34, P &lt; 0.001).</p>", "<p id=\"Par23\">\n\n</p>" ]

[ "<title>Discussion</title>", "<p id=\"Par24\">In recent decades, LPD has been adopted in many medical centers for the radical treatment of both benign and malignant pancreatic and periampullary disease. To date, the mortality and morbidity of LPD have significantly declined, and the data show that patients who underwent LPD in high-volume centers achieved a better prognosis than those treated in low-volume centers [##REF##26275542##12##–##REF##30672792##15##]. However, the overall postoperative morbidity rate remains high, and the most fatal complication is POPF. The POPF rate in recent literature ranges from 10 to 29% [##REF##29438817##16##, ##REF##31282769##17##], and this complication can also prolong patient hospitalization, increase mortality and increase costs. Some studies have revealed various risk factors for POPF, such as the PJ technique, pancreatic texture, and duct diameter. Moreover, many efforts have been made to decrease POPF incidence. In fact, many improvements to the PJ technique have been developed to minimize the rate of POPF.</p>", "<p id=\"Par25\">End-to-end and end-to-side approaches are the main approaches to PJ after pancreatoduodenectomy (PD) in most medical centers. Peng et al. reported a study that consisted of 150 patients who underwent PJ, with a 0% rate of POPF [##REF##14592664##18##]. Although this method achieved the lowest POPF rate, it has not been repeated in subsequent foreign studies. Maggiori et al. reported a POPF rate of 36% using Peng’s technique [##REF##20577828##19##]. Furthermore, duct-to-mucosa anastomosis has been improved in various ways. Karavias et al. reported a POPF rate of 7.9% after using their method called true duct-to-mucosa anastomosis. Although it was not mentioned in the report, the PJ time seemed prolonged because mucosal eversion was performed [##REF##25472029##20##]. Interestingly, triple-layer duct-to-mucosa PJ was introduced by Su et al. with a POPF rate of 4%. The three layers included the pancreatic duct to jejunal mucosa, the pancreatic capsular parenchyma to the jejunal seromuscular and the pancreatic capsular parenchyma to the jejunal serosa [##REF##24095023##21##]. However, despite the numerous PJ techniques, no prospective randomized controlled trials (RCTs) have been carried out to determine the best approach.</p>", "<p id=\"Par26\">Recently, Blumgart anastomosis, a new DTM anastomosis procedure well accepted among pancreatic surgeons, was reported to reduce the incidence rate of POPF [##REF##27804043##10##, ##REF##27017162##11##]. However, this pancreatic anastomosis procedure has disadvantages that mainly limit the extent of LPD. First, multiple small sutures must be left untied, causing confusion in the field of vision. Second, it is difficult to create the posterior face of the DTM anastomosis when the posterior face’s capsular stitches have been previously tied [##REF##25125092##22##–##REF##29748825##24##]. Therefore, we applied a modified Blumgart method using a homemade crochet needle to facilitate PJ in LPD. With our proposed modification, the pancreatic duct and jejunal orifice are aligned correctly during DTM after the application of external traction through the homemade crochet needle. The space between the posterior wall of pancreatic remnant and jejunal loop can be exposed by adjusting the tension of the external threads, which can facilitate DTM. Moreover, there are few small sutures left untied, which makes the surgical field clearer. Finally, the homemade crochet needle does not leave scars after puncture and can be used for puncture at many places on the abdominal wall. In the present study, the CR-POPF rate (grade B-C) was only 14.6%, and 1 grade C POPF was observed using our PJ procedure; these rates are lower than those in most reported studies. Only one patient with postoperative abdominal hemorrhage was cured after reoperation, thereby achieving homeostasis. There were no operative or in-hospital deaths. Thus, this new PJ procedure is feasible for achieving a safe LPD.</p>", "<p id=\"Par27\">This study has several potential limitations due to its retrospective design. It is necessary to perform a prospective, randomized study that includes more patients and centers in order to validate the rate of pancreatic fistula following this type of anastomosis.</p>" ]

[ "<title>Conclusions</title>", "<p id=\"Par28\">We demonstrate the feasibility and safety of a modified Blumgart method using a homemade crochet needle to facilitate PJ in LPD. However, randomized controlled trials are needed to further verify the feasibility of the present PJ technique in LPD.</p>" ]

[ "<title>Background</title>", "<p id=\"Par1\">Among the safest procedures for anastomosis in pancreaticoduodenectomy, Blumgart pancreaticojejunostomy is associated with low rates of postoperative pancreatic fistula (POPF) and postoperative complications. However, this technique is difficult to perform during laparoscopic pancreaticoduodenectomy (LPD). This study presents a modified Blumgart method using a homemade crochet needle to facilitate laparoscopic pancreaticojejunostomy and evaluates its safety and reliability.</p>", "<title>Methods</title>", "<p id=\"Par2\">From February 2019 to October 2022, 96 LPD surgeries with the new technique were performed by the same surgeons in the Second Affiliated Hospital of Zhejiang University School of Medicine. The operative details (operative time, pancreaticojejunostomy time, POPF rate, postoperative complication rate, mortality rate) were analyzed along with clinical and pathological indicators (pancreatic duct diameter, pancreatic texture, and histopathological findings).</p>", "<title>Results</title>", "<p id=\"Par3\">There were 54 men and 42 women with a mean age of 63.38 ± 10.41 years. The intraoperative bleeding volume, operative time and postoperative length of hospital stay were 198.43 ± 132.97 mL, 445.30 ± 87.05 min and 13.68 ± 4.02 days, respectively. The operation time of pancreaticojejunostomy was 66.28 ± 10.17 min. Clinically relevant POPFs (grades B and C) occurred in 14.6% of patients. Only one patient had postoperative abdominal hemorrhage and was cured after reoperation. There were no operative or in-hospital deaths. With our proposed modification, the pancreatic duct and jejunal orifice are aligned correctly during duct-to-mucosa (DTM) after the application of external traction through the homemade crochet needle. The space between the posterior wall of pancreatic remnant and jejunal loop can be exposed by adjusting the tension of the external threads, which can facilitate DTM.</p>", "<title>Conclusions</title>", "<p id=\"Par4\">A modified Blumgart method using a homemade crochet needle could be technically feasible and safe during LPD. A randomized control trial is needed to confirm these findings.</p>", "<title>Keywords</title>" ]

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[ "<title>Acknowledgements</title>", "<p>Not applicable.</p>", "<title>Author contributions</title>", "<p>BZ contributed to the conceptualization and design of the study and drafting of the manuscript. ZG and YT contributed to the collection and analysis of the data. SY helped supervise and interpret the data. BZ and SY conceived and supervised the whole project. All authors read and approved the final manuscript.</p>", "<title>Funding</title>", "<p>This work was supported by grants from the Zhejiang Province Medical and Health Science and Technology Program (No. 2023KY109) and the Zhejiang Province Basic Public Welfare Research Project (No. 2018C37115).</p>", "<title>Data Availability</title>", "<p>The datasets analyzed during the current study are available from the corresponding author on reasonable request.</p>", "<title>Declarations</title>", "<title>Ethics approval and consent to participate</title>", "<p id=\"Par30\">Our study was approved by the Ethics Review Committee of the Second Affiliated Hospital of Zhejiang University School of Medicine. Written informed consent was obtained from all subjects in our study.</p>", "<title>Consent for publication</title>", "<p id=\"Par31\">Not applicable.</p>", "<title>Competing interests</title>", "<p id=\"Par29\">The authors declare no competing interests.</p>" ]

[ "<fig id=\"Fig1\"><label>Fig. 1</label><caption><p>Placement of the trocars for LPD.</p></caption></fig>", "<fig id=\"Fig2\"><label>Fig. 2</label><caption><p>The details of the homemade crochet needle. The components of the homemade crochet needle (<bold>A</bold> and <bold>B</bold>). A U-shaped suture was created to encompass the posterior wall of the pancreatic parenchyma and the jejunal seromuscular layer and was fixed outside the body by a homemade crochet needle (<bold>C</bold>)</p></caption></fig>", "<fig id=\"Fig3\"><label>Fig. 3</label><caption><p>Intraoperative images of the modified Blumgart pancreaticojejunostomy procedure. (<bold>A</bold>) The specimen was removed first, and then pancreaticojejunostomy was performed. (<bold>B</bold>) A large 4 − 0 needle penetrated the pancreas 1 cm from the edge of the pancreatic stump. (<bold>C</bold> and <bold>D</bold>) A U-shaped suture was created to encompass the posterior wall of the pancreatic parenchyma and the jejunal seromuscular layer and was fixed outside the body by a homemade crochet needle. (<bold>E</bold> and <bold>F</bold>) The pancreatic duct and jejunal mucosa were sutured together with an internal pancreatic stent. (<bold>G</bold>) A third U-shape suture was placed between the pancreatic parenchyma and the jejunal seromuscular layer. (<bold>H</bold>) A single layer of continuous sutures was placed between the pancreatic stump and the anterior seromuscular layer of the jejunum using the 3/0 barbed suture Stratafix. (<bold>I</bold>) Final image after pancreaticojejunostomy</p></caption></fig>", "<fig id=\"Fig4\"><label>Fig. 4</label><caption><p>Two U-shaped transpancreatic stitches (one on side of the pancreatic duct and another encompassing the main pancreatic duct) were externalized with our homemade crochet needle, and the two threads were fixed with a vascular clamp to facilitate adjusting the tension between the pancreas and jejunum</p></caption></fig>" ]

[ "<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Baseline characteristics and results of patients who underwent LPD</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\"/><th align=\"left\">LPD (n = 96)</th></tr></thead><tbody><tr><td align=\"left\">Age (years)</td><td align=\"left\">63.38 ± 10.41</td></tr><tr><td align=\"left\">Sex (M/F)</td><td align=\"left\">54/42</td></tr><tr><td align=\"left\">BMI (kg/m²)</td><td align=\"left\">22.52 ± 2.87</td></tr><tr><td align=\"left\">Pathology</td><td align=\"left\"/></tr><tr><td align=\"left\"> Ampullary carcinoma</td><td align=\"left\">7 (7.3%)</td></tr><tr><td align=\"left\"> Distal cholangiocarcinoma</td><td align=\"left\">12 (12.5%)</td></tr><tr><td align=\"left\"> Pancreatic ductal adenocarcinoma</td><td align=\"left\">48 (50%)</td></tr><tr><td align=\"left\"> Duodenal papillary carcinoma</td><td align=\"left\">21 (21.9%)</td></tr><tr><td align=\"left\"> Duodenal adenoma</td><td align=\"left\">1 (1.0%)</td></tr><tr><td align=\"left\"> Neuroendocrine tumor</td><td align=\"left\">1 (1.0%)</td></tr><tr><td align=\"left\"> Pancreatic duct stones or pancreatitis</td><td align=\"left\">0</td></tr><tr><td align=\"left\"> Intraductal papillary mucinous neoplasms</td><td align=\"left\">4 (4.2%)</td></tr><tr><td align=\"left\"> Solid pseudopapillary tumor</td><td align=\"left\">2 (2.1%)</td></tr><tr><td align=\"left\">Operative time (min)</td><td align=\"left\">445.30 ± 87.05</td></tr><tr><td align=\"left\">Pancreaticojejunostomy time (min)</td><td align=\"left\">66.28 ± 10.17</td></tr><tr><td align=\"left\">Vascular resection (portal vein reconstruction)</td><td align=\"left\">1 (1.0%)</td></tr><tr><td align=\"left\">Blood loss (mL)</td><td align=\"left\">198.43 ± 132.97</td></tr><tr><td align=\"left\">Pancreatic parenchymal texture</td><td align=\"left\"/></tr><tr><td align=\"left\"> Soft</td><td align=\"left\">51</td></tr><tr><td align=\"left\"> Hard</td><td align=\"left\">45</td></tr><tr><td align=\"left\">Pancreatic duct diameter</td><td align=\"left\"/></tr><tr><td align=\"left\"> &gt; 3 mm</td><td align=\"left\">35</td></tr><tr><td align=\"left\"> ≤ 3 mm</td><td align=\"left\">61</td></tr><tr><td align=\"left\">Grade B POPF</td><td align=\"left\">13 (13.5%)</td></tr><tr><td align=\"left\">Grade C POPF</td><td align=\"left\">1 (1.0%)</td></tr><tr><td align=\"left\">Reoperation</td><td align=\"left\">1 (1.0%)</td></tr><tr><td align=\"left\">Delayed gastric emptying</td><td align=\"left\">4 (4.2%)</td></tr><tr><td align=\"left\">Bile leakage</td><td align=\"left\">2 (2.1%)</td></tr><tr><td align=\"left\">Abdominal hemorrhage</td><td align=\"left\">1 (1.0%)</td></tr><tr><td align=\"left\">Gastrointestinal anastomotic hemorrhage</td><td align=\"left\">1 (1.0%)</td></tr><tr><td align=\"left\">Abdominal infection</td><td align=\"left\">2 (2.1%)</td></tr><tr><td align=\"left\">Pneumonia</td><td align=\"left\">3 (3.1%)</td></tr><tr><td align=\"left\">Chylous fistula</td><td align=\"left\">6 (6.3%)</td></tr><tr><td align=\"left\">Postoperative hospital stay (day)</td><td align=\"left\">13.68 ± 4.02</td></tr><tr><td align=\"left\">Mortality &lt; 90 days</td><td align=\"left\">0</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab2\"><label>Table 2</label><caption><p>Comparisons between the non-POPF and POPF subtypes following LPD</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\"/><th align=\"left\">POPF (n = 14)</th><th align=\"left\">None POPF (n = 82)</th><th align=\"left\">P value</th></tr></thead><tbody><tr><td align=\"left\">Age (years)</td><td align=\"left\">68.60 ± 7.34</td><td align=\"left\">62.78 ± 10.57</td><td char=\".\" align=\"char\">0.094</td></tr><tr><td align=\"left\">BMI (kg/m²)</td><td align=\"left\">22.04 ± 2.68</td><td align=\"left\">22.58 ± 2.90</td><td char=\".\" align=\"char\">0.575</td></tr><tr><td align=\"left\">Operative time (min)</td><td align=\"left\">466.50 ± 71.53</td><td align=\"left\">442.84 ± 88.70</td><td char=\".\" align=\"char\">0.419</td></tr><tr><td align=\"left\">Blood loss (mL)</td><td align=\"left\">225.00 ± 211.15</td><td align=\"left\">195.35 ± 169.19</td><td char=\".\" align=\"char\">0.610</td></tr><tr><td align=\"left\">Pancreatic parenchymal texture</td><td align=\"left\"/><td align=\"left\"/><td char=\".\" align=\"char\">\n<bold>0.016</bold>\n</td></tr><tr><td align=\"left\"> Soft</td><td align=\"left\">12</td><td align=\"left\">42</td><td align=\"left\"/></tr><tr><td align=\"left\"> Hard</td><td align=\"left\">2</td><td align=\"left\">40</td><td align=\"left\"/></tr><tr><td align=\"left\">Pancreatic duct diameter</td><td align=\"left\"/><td align=\"left\"/><td char=\".\" align=\"char\">0.270</td></tr><tr><td align=\"left\"> &gt; 3 mm</td><td align=\"left\">3</td><td align=\"left\">30</td><td align=\"left\"/></tr><tr><td align=\"left\"> ≤ 3 mm</td><td align=\"left\">11</td><td align=\"left\">52</td><td align=\"left\"/></tr><tr><td align=\"left\">Pathology</td><td align=\"left\"/><td align=\"left\"/><td char=\".\" align=\"char\">\n<bold>0.013</bold>\n</td></tr><tr><td align=\"left\"> Pancreatic ductal adenocarcinoma or pancreatitis</td><td align=\"left\">3</td><td align=\"left\">47</td><td align=\"left\"/></tr><tr><td align=\"left\"> Others</td><td align=\"left\">11</td><td align=\"left\">35</td><td align=\"left\"/></tr></tbody></table></table-wrap>" ]

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[ "<table-wrap-foot><p>LPD, laparoscopic pancreaticoduodenectomy; BMI, body mass index; POPF, postoperative pancreatic fistula</p></table-wrap-foot>", "<table-wrap-foot><p>LPD, laparoscopic pancreaticoduodenectomy; BMI, body mass index; POPF, postoperative pancreatic fistula. The bold values in the table denote P values less than 0.05 (indicating a significant difference)</p></table-wrap-foot>", "<fn-group><fn><p><bold>Publisher’s Note</bold></p><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn></fn-group>" ]

[ "<graphic xlink:href=\"12893_2023_2308_Fig1_HTML\" id=\"d32e236\"/>", "<graphic xlink:href=\"12893_2023_2308_Fig2_HTML\" id=\"d32e263\"/>", "<graphic xlink:href=\"12893_2023_2308_Fig3_HTML\" id=\"d32e311\"/>", "<graphic xlink:href=\"12893_2023_2308_Fig4_HTML\" id=\"d32e340\"/>" ]

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{ "acronym": [ "LPD", "POPF", "PJ", "BMI", "CR-POPF", "DTM", "SD" ], "definition": [ "Laparoscopic pancreaticoduodenectomy", "Postoperative pancreatic fistula", "Pancreaticojejunostomy", "Body mass index", "Clinically relevant postoperative pancreatic fistula", "Duct-to-mucosa", "Standard deviation" ] }

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[ "<p id=\"Par1\">\n<bold>BMC Urology (2023) 23:209</bold>\n</p>", "<p id=\"Par2\">\n10.1186/s12894-023-01378-4\n</p>", "<p id=\"Par3\">The author name “Yin Guonan” was incorrectly written as “Yin Gounan” and the same has been updated.</p>", "<p id=\"Par4\">The original article has been corrected.</p>" ]

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[ "<fn-group><fn><p>The online version of the original article can be found at 10.1186/s12894-023-01378-4.</p></fn><fn><p><bold>Publisher’s Note</bold></p><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn></fn-group>" ]

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{ "acronym": [], "definition": [] }

[ "<title>Introduction</title>", "<p id=\"Par4\">Dirofilariasis is a parasitic zoonosis caused by nematodes of the genus <italic>Dirofilaria</italic>. There are over 20 dirofiliaris species, which mostly infect dogs, cats, and wild carnivores and are being transmitted by multiple mosquito species [##REF##25384527##1##]. In these mosquito vectors, microfilariae mature into infectious larvae after which transmission takes place when the mosquito takes a blood meal. Canidae are the main reservoir but incidentally the nematodes can be transmitted to humans. In human beings, complete sexual maturation of the microfilaria cannot occur due to the host defense, preventing the expression of larvae in the blood stream. Therefore, no further transmission to other hosts takes place [##REF##29291748##2##]. Most cases have been described of the species <italic>Dirofilaria repens</italic> and <italic>Dirofilaria immitis</italic> regarding human infections [##REF##22763636##3##]. <italic>D. immitis</italic> causes deep organ infections, mostly in the pulmonary arteries and right ventricle of the heart, and <italic>D. repens</italic> mainly causes subcutaneous and ocular infection [##REF##22763636##3##].</p>", "<p id=\"Par5\">Most human infections by <italic>D. repens</italic> occur in the Mediterranean region and other subtropic and tropic places in Europe, Asia, and Africa [##REF##29291748##2##, ##REF##22763636##3##]. Sri Lanka is the most afflicted country in Asia, where 30–60% of the dog population is infected with <italic>D. repens</italic> in some parts of the country [##REF##9802095##4##]. The most reported symptoms of subcutaneous dirofilariasis are migrating subcutaneous lesions, intermittent painful erythema, and itching [##UREF##0##5##]. Subcutaneous dirofilariasis should be treated with complete surgical extirpation of the lesion. Antihelminthic drugs are usually only required when immunodeficiency is present, since a normal functioning immune system is able to prevent reproduction and transmission [##REF##29291748##2##, ##UREF##0##5##].</p>", "<p id=\"Par6\">On ultrasound, subcutaneous dirofilariasis presents as a hypoechoic nodular lesion with an internal serpiginous structure with internal parallel echogenic walls and an anechoic center. Sometimes active motion of microfilariae is visible, similar to the “filarial dance sign” seen in lymphatic filariasis caused by filarial nematodes of the species <italic>Wuchereria bancrofti</italic>, <italic>Brugia malayi</italic>, and <italic>Brugia timori</italic> [##REF##33278570##6##].</p>", "<p id=\"Par7\">Human subcutaneous dirofilariasis by <italic>D. repens</italic> is scarcely reported in nonendemic areas such as Belgium, and in almost all cases the diagnosis is made after excisional biopsy, often with initial misdiagnosis, significant treatment delay, and medication error.</p>", "<p id=\"Par8\">To the best of our knowledge, this case is the first well-reported human subcutaneous dirofilariasis preoperatively diagnosed on ultrasound in Western Europe.</p>" ]

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[ "<title>Discussion</title>", "<p id=\"Par10\">Human subcutaneous dirofilariasis is a zoonosis caused by infection due to nematodes of the genus <italic>Dirofilaria</italic>, with the majority caused by <italic>D. repens</italic>. They are transmitted by mosquito vectors and Canidae are the main reservoir. Incidentally, transmission to humans are dead ends in <italic>Dirofilaria</italic> infestation due to the fact that the worms do not attain maturity and are unable to express microfilaria in the blood stream. Therefore, serological tests are usually not useful and systemic therapy is not indicated. Eosinophilia is an inconsistent finding depending on immune response [##REF##31378402##7##]. Subcutaneous dirofilariasis should be considered if a patient presents with a migrating subcutaneous nodule, also in nonendemic areas.</p>", "<p id=\"Par11\">Ultrasonography is the first-line imaging technique with high specificity showing a cystic nodule with an internal serpiginous structure consisting of parallel echogenic lines. Active twirling movement of the serpiginous structure can be seen, as described in lymphatic filariasis as the “filarial dance sign.” Magetic resonance imaging (MRI) can rarely be valuable if extension to the muscles or joints is expected [##REF##31378402##7##, ##REF##10094355##8##].</p>", "<p id=\"Par12\">The subcutaneous lesion should be treated with total surgical extirpation of the lesion. Anthelmintic treatment (e.g., albendazole) is not recommended in most cases but can be useful for immunocompromised patients or migratory lesions, especially in the face, because these drugs promote fixation, after which the lesion can be surgically removed [##REF##29291748##2##, ##UREF##0##5##].</p>" ]

[ "<title>Conclusion</title>", "<p id=\"Par13\">Despite the characteristic imaging features of subcutaneous dirofilariasis on ultrasound, these lesions are usually removed surgically with significant delay and without preoperative ultrasonographic investigation, especially in nonendemic areas. Early and correct diagnosis prevents significant patient distress and prevents medication error (e.g., inappropriate antibiotic use).</p>", "<p id=\"Par14\">Hopefully, this case report will create more knowledge of the characteristic ultrasound appearance of subcutaneous dirofilariasis and more awareness of the disease in general, leading to better patient care with early and correct diagnosis and treatment.</p>" ]

[ "<title>Background</title>", "<p id=\"Par1\">Subcutaneous dirofilariasis is a parasitic zoonosis commonly described in Canidae but rarely seen in humans. Most physicians are unfamiliar with this disease, especially in nonendemic areas, which can lead to medication error and diagnostic and treatment delay. To the best of our knowledge, no previous case of subcutaneous dirofilariasis preoperatively diagnosed on ultrasound has been described in Western Europe.</p>", "<title>Case presentation</title>", "<p id=\"Par2\">A 25-year-old Belgian male patient presented with a subcutaneous nodule in the epigastric region. Ultrasound investigation showed a typical cystic lesion with an internal serpiginous structure with echogenic lines, and there was active twirling movement of this serpentine structure during investigation, pathognomonic for subcutaneous dirofilariasis. Surgical extirpation was performed, and the diagnosis was histopathologically confirmed.</p>", "<title>Conclusion</title>", "<p id=\"Par3\">Subcutaneous dirofilariasis has a characteristic appearance on ultrasound but is not well known in nonendemic areas, often leading to diagnostic delay and initial incorrect treatment. More knowledge of this disease and of its characteristic ultrasound appearance will hopefully lead to better patient care.</p>", "<title>Keywords</title>" ]

[ "<title>Case presentation</title>", "<p id=\"Par9\">A 25-year-old native male patient from Flanders (Belgium) with no relevant medical history presented to the general practitioner (GP) with an infrasternal, clinically palpable nodule in the epigastric region with localized irritation and itching. A cyst or benign lymphadenopathy was suspected and initially no treatment was started but at the patient’s request, an ultrasound examination was conducted to exclude other pathology. Approximately 6 months before symptom onset, the patient had been in France and central Italy. The leukocyte count and eosinophil count was within normal limits. Symptoms started about 2 weeks before ultrasonography. On ultrasound, there was a cystic structure with a diameter of circa 8 mm with an internal tubular serpiginous structure with parallel echogenic lines (Fig. ##FIG##0##1##A). During the investigation, there was active twirling movement of the serpentine structure, similar to the “filarial dance sign” seen in lymphatic filariasis. The lesion showed no vascularization during Doppler investigation, and there was no penetration through the fascia with an intact appearance of the underlying musculature (Fig. ##FIG##0##1##B). The preoperative diagnosis of a subcutaneous dirofilariasis was made and surgical extirpation was performed (Fig. ##FIG##1##2##). The excisional specimen was send to the Institute of Tropical Medicine, where histopathological assessment confirmed a subcutaneous dirofilariasis caused by <italic>D. repens</italic>.</p>" ]

[ "<title>Acknowledgements</title>", "<p>We thank our patient for consenting for publication and providing detailed information regarding the case.</p>", "<title>Author contributions</title>", "<p>CV performed the ultrasound investigation of the patient and SK collected the data and wrote the manuscript. CV supervised the manuscript. Both authors read and approved the final manuscript.</p>", "<title>Funding</title>", "<p>No funding has been received for this article.</p>", "<title>Availability of data and materials</title>", "<p>All data analyzed during this study are included in this published article.</p>", "<title>Declarations</title>", "<title>Ethics approval and consent to participate</title>", "<p id=\"Par15\">Individual approval and consent to participate was obtained.</p>", "<title>Consent for publication</title>", "<p id=\"Par16\">Written informed consent was obtained from the patient for publication of this case report and any accompanying images. A copy of the written consent is available for review by the Editor-in-Chief of this journal.</p>", "<title>Competing interests</title>", "<p id=\"Par17\">The authors declare no conflicts of interests.</p>" ]

[ "<fig id=\"Fig1\"><label>Fig. 1</label><caption><p><bold>A</bold> Ultrasound shows a cystic structure with a diameter of circa 8 mm and an internal tubular serpiginous structure with parallel echogenic lines (arrow). During the investigation, there was active twirling movement of the serpentine structure. <bold>B</bold> Doppler ultrasonography showed no significant vascularization of the lesion</p></caption></fig>", "<fig id=\"Fig2\"><label>Fig. 2</label><caption><p>The excisional biopsy specimen contains a lobulated subcutaneous mass on the left side and the worm on the right side, which was later histopathologically confirmed as <italic>Dirofilaria repens</italic></p></caption></fig>" ]

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[ "<fn-group><fn><p><bold>Publisher’s Note</bold></p><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn></fn-group>" ]

[ "<graphic xlink:href=\"13256_2023_4242_Fig1_HTML\" id=\"MO1\"/>", "<graphic xlink:href=\"13256_2023_4242_Fig2_HTML\" id=\"MO2\"/>" ]

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[ "<title>Background</title>", "<p id=\"Par5\">Acute appendicitis is a common abdominal emergency that requires prompt diagnosis and treatment. For over a century, open appendectomy was the only standard treatment for appendicitis. However, recent studies have challenged the necessity of surgery in uncomplicated cases of appendicitis, and nonoperative management (NOM) with antibiotics alone has emerged as a promising alternative [##REF##35434736##1##–##REF##7749676##4##]. Although appendectomy has long been considered the gold standard operative management (OM) for acute appendicitis, there is growing interest in NOM with antibiotics in both adults and children [##REF##29637158##5##].</p>", "<p id=\"Par6\">While nonoperative management may offer certain advantages over appendectomy, such as decreased morbidity and shorter recovery time, there are concerns regarding the efficacy and safety of this approach. For instance, nonoperative management may be associated with a higher rate of recurrent appendicitis and an increase in the duration of hospital stay [##REF##35895073##6##]. Thus, it is important to evaluate the efficacy and safety of nonoperative management compared to appendectomy in uncomplicated cases of appendicitis.</p>", "<p id=\"Par7\">Despite years of experience performing surgery to treat uncomplicated appendicitis, there is still a shortage of data that can be used to compare NOM and OM, making the choice between the two more challenging. This systematic review and meta-analysis of RCTs purpose was to compare NOM and OM in terms of efficacy, costs, length of hospital stay, quality of life and complications in a population of adults.</p>" ]

[ "<title>Material and methods</title>", "<p id=\"Par8\">A systematic literature review was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and as outlined in a predefined protocol (PROSPERO 2023: CRD42023413780) [##UREF##2##7##].</p>", "<title>Literature search strategy</title>", "<p id=\"Par9\">The PubMed, Scopus, and Cochrane Library databases and ClinicalTrials.gov, Google Scholar were screened without time restrictions up to November 23rd, 2023 using the Mesh major topic “appendicitis” and “surgery” and Mesh terms “appendectomy” and “conservative treatment”. The search query is available in the Additional file ##SUPPL##0##1##. Articles without free full text availability were searched through the University of Milan digital library in order to realize a complete research. The bibliographies of potentially relevant studies that were identified were manually searched for additional studies. Additionally, all studies that cited the primary studies were screened for inclusion on Google Scholar. We did not apply language or publication status restrictions.</p>", "<title>Eligibility criteria</title>", "<p id=\"Par10\">The study selection criteria encompassed randomized controlled trials (RCTs) that investigated the comparison between antibiotic treatment and appendectomy in adult participants, presenting with uncomplicated acute appendicitis diagnosed either clinically or radiologically. Exclusion criteria consisted of non-randomized studies and studies that included patients with complicated appendicitis or children.</p>", "<title>Study selection</title>", "<p id=\"Par11\">Two investigators (FB, GB) performed the literature search independently with the aid of Rayyan systematic review software [##REF##27919275##8##]. Cases of disagreement were resolved by a third investigator (LC). In cases where multiple reports were found for the same study, data from all reports were utilized as necessary, while ensuring that there was no duplication of study participants.</p>", "<title>Data extraction</title>", "<p id=\"Par12\">Data extraction was performed independently by two authors (F.B. and G.B.), with any discrepancies resolved through consultation with a third senior author (L.C.). Data were gathered and recorded in a digital database, including information on the baseline characteristics of the studies, including characteristics of patients as follows: exam blood test, Alvarado score [##REF##3963537##9##], LOS, recurrence at 1 year, and efficacy of the treatment performed.</p>", "<title>Outcome measures</title>", "<title>Primary outcome measures</title>", "<p id=\"Par13\">\n<list list-type=\"order\"><list-item><p id=\"Par14\">Complication-free treatment success: the success of the initial treatment (nonoperative management or operative management) was evaluated based on an uncomplicated course, with no occurrence of postoperative complications (complications or recurrences for NOM; postoperative complications for surgical intervention)</p></list-item><list-item><p id=\"Par15\">Treatment efficacy based on 1-year follow-up: the efficacy of nonoperative management (NOM) was defined as achieving a definitive improvement without the need for surgery within a median follow-up of 1 year. Lack of efficacy in the NOM group included two scenarios: the persistence of acute appendicitis during hospitalization (referred to as index admission NOM failure, characterized by non-resolving appendicitis with persistent or worsening symptoms during the primary hospital stay) and recurrence of acute appendicitis. For OM, efficacy is defined as the resolution of symptoms following surgical treatment.</p></list-item><list-item><p id=\"Par16\">Postoperative complications: the analysis involved evaluating the number and rates of various postoperative complications.</p></list-item></list></p>", "<title>Secondary outcome measures</title>", "<p id=\"Par17\">\n<list list-type=\"order\"><list-item><p id=\"Par18\">The study analyzed the number and rates of patients treated with a laparoscopic approach in both groups.</p></list-item><list-item><p id=\"Par19\">Total costs: This encompassed the overall medical and surgical costs associated with the primary hospital stay.</p></list-item><list-item><p id=\"Par20\">Length of primary hospital stay: This refers to the number of days of inpatient admission during the initial hospitalization.</p></list-item><list-item><p id=\"Par21\">Quality of life following antibiotic therapy (AT) and surgical therapy (ST) was assessed.</p></list-item></list></p>", "<title>Assessment of risk of bias</title>", "<p id=\"Par22\">To assess any potential bias in the studies included in the analysis, the researchers (F.B. and G.B.) utilized the risk of bias tool developed by the Cochrane Collaboration [##REF##22008217##10##]. The studies were evaluated based on criteria such as selection bias, performance bias, detection bias, and attrition bias. A total risk of bias score was then determined based on these domains, with the levels categorized as low risk of bias, high risk of bias, or unclear risk of bias.</p>", "<title>Statistical analysis</title>", "<p id=\"Par23\">Data from the individual eligible studies were entered into a spreadsheet for further analysis. Review Manager (RevMan) (Version 5.4.1. Copenhagen: The Nordic Cochrane Center, the Cochrane Collaboration, 2011). Risk Ratio (RR) was calculated for discrete variables with 95% confidence intervals (c.i.) calculated using a Mantel–Haenszel random-effects model. Mean Difference (MD) were calculated for continuous variables with 95% c.i. using an inverse-variance random-effects model. Statistical significance was taken at <italic>P</italic> &lt; 0.05 using two-tailed testing. Heterogeneity among the trials was determined by means of the Cochrane Q value and quantified using the <italic>I</italic>² inconsistency test [##REF##22008217##10##].</p>", "<title>Trial sequential analysis</title>", "<p id=\"Par24\">Cumulative meta-analyses of trials face a susceptibility to stochastic errors due to inadequate data and repetitive testing as the data accumulates [##REF##18411040##11##, ##REF##18083463##12##]. Trial sequential analysis (TSA) was employed, for primary outcome measures, to evaluate the statistical robustness of the data in a cumulative meta-analysis. TSA served as a means to gauge whether the existing evidence was sufficiently conclusive. The adjusted required information size (RIS) was computed using a significance level (alpha) of 0.05 (two sided) and a power (1—beta) of 0.20 (corresponding to 80% power). This calculation involved a control group proportion derived from the outcomes of our meta-analysis for binary outcomes. The decision to seek additional evidence from additional trials can be determined by assessing whether the cumulative Z-curve crosses trial sequential monitoring boundaries (TSMB) or the futility zone. Trial sequential analysis version 0.9 beta (<ext-link ext-link-type=\"uri\" xlink:href=\"http://www.ctu.dk/tsa\">http://www.ctu.dk/tsa</ext-link>) was used for all these analyses [##UREF##3##13##].</p>" ]

[ "<title>Results</title>", "<p id=\"Par25\">Figure ##FIG##0##1## displays the PRISMA flowchart. Eight RCT fulfilled the inclusion criteria and were included in the meta-analysis (publication dates 1995–2022). In total, 3213 patients were allocated to NOM (<italic>n</italic> = 1615) or OM (<italic>n</italic> = 1598). General characteristics of patients as reported in the studies are shown in Table ##TAB##0##1##.</p>", "<title>Study characteristics</title>", "<p id=\"Par26\">There was a significant amount of heterogeneity observed among the studies included in the analysis, particularly in terms of the diagnostic criteria used to define uncomplicated appendicitis. Additionally, there was substantial heterogeneity found in the type of antibiotics administered, the duration of administration, and the various outcomes that were evaluated.</p>", "<title>Risk of bias</title>", "<p id=\"Par27\">Figure ##FIG##1##2## shows the RoB (Risk of Bias) analysis, indicating the assessment of bias in the included studies. In terms of study quality assessment, the included RCTs exhibited varying levels of risk across different domains. Out of the 8 RCTs analyzed, 6 studies reported a low risk of selection bias as they adequately described random sequence generation and allocation concealment [##UREF##4##14##, ##REF##33534226##17##–##REF##16736333##19##, ##REF##33017106##21##, ##REF##21550483##22##]. However, the risk of selection bias remained unclear in two studies, where insufficient information was provided [##REF##7749676##4##, ##REF##19358184##15##].</p>", "<p id=\"Par28\">Concerning attrition bias, two studies were deemed to have a high risk due to inconsistencies in the reported numbers in tables and text [##UREF##4##14##, ##REF##19358184##15##]. Additionally, two studies were identified as having a high risk of selective reporting due to the lack of predefined endpoints [##REF##19358184##15##, ##REF##16736333##19##].</p>", "<p id=\"Par29\">The meta-analysis portrays a robust picture with most of the included studies exhibiting a low risk of bias across crucial domains. This underscores the reliability of our results, affirming the study's overall credibility.</p>", "<p id=\"Par30\">A graphical representation of the risk of bias assessment is provided in Additional file ##SUPPL##0##1## of the manuscript.</p>", "<p id=\"Par31\">In terms of potential publication bias, no significant indications were observed graphically, as evidenced by the funnel plots. For further details and visual representations, the funnel plots are available as Additional file ##SUPPL##0##1## accompanying this paper.</p>", "<p id=\"Par32\">The risk of language and geographic bias in this study is deemed low, as the nature of the research conducted, and the comprehensive analysis undertaken help mitigate any potential skew toward specific languages or regions.</p>", "<title>Complication-free treatment success (Fig. ##FIG##2##3##)</title>", "<p id=\"Par33\">All studies included in the analysis provided data to evaluate the effectiveness of the treatments [##REF##7749676##4##, ##UREF##4##14##, ##REF##19358184##15##, ##REF##33534226##17##–##REF##16736333##19##, ##REF##33017106##21##, ##REF##21550483##22##]. The results showed that antibiotic treatment had a significantly lower treatment efficacy rate (70.45%, 1066 of 1513) compared to appendectomy (84.49%, 1248 of 1477). The risk ratio (RR) was 0.80 (95% confidence interval 0.71 to 0.90, <italic>p</italic> &lt; 0.00001), indicating a statistically significant difference between the two treatment approaches. Furthermore, a substantial level of heterogeneity was observed in the meta-analysis, with an <italic>I</italic>-squared value of 87%, suggesting significant variation among the included studies. Trial sequential analysis of 8 trials comparing NOM vs. OM for overall treatment efficacy. The cumulative Z-curve crossed the conventional boundary for benefit and required information size but did not cross the trial sequential monitoring boundary for benefit, suggesting that the current evidence is statistically significant but does not support a superiority of OM and further trials will not change this conclusion. A diversity adjusted required information size of 2805 patients was calculated (Fig. ##FIG##3##4##). </p>", "<title>Treatment efficacy at 1-year follow-up (Fig. ##FIG##4##5##)</title>", "<p id=\"Par34\">Seven studies included in the analysis provided data to evaluate the effectiveness of the treatments at 1-year follow-up [##REF##7749676##4##, ##UREF##4##14##, ##REF##19358184##15##, ##REF##33534226##17##–##REF##16736333##19##, ##REF##21550483##22##]. The results showed that antibiotic treatment had a significantly lower treatment efficacy rate (64.51%, 540 of 837) compared to appendectomy (96.8%, 788 of 814). The risk ratio (RR) was 0.69 (95% confidence interval 0.61 to 0.77, <italic>p</italic> &lt; 0.00001), indicating a statistically significant difference between the two treatment approaches. Furthermore, a substantial level of heterogeneity was observed in the meta-analysis, with an <italic>I</italic>-squared value of 81%, suggesting significant variation among the included studies. Trial sequential analysis of 7 trials comparing NOM vs. OM for treatment efficacy at 1-year follow-up. The cumulative Z-curve crossed the conventional boundary for benefit, the trial sequential monitoring boundary for benefit and required information size, suggesting that the current evidence is conclusive and further trials will not change this conclusion. A diversity adjusted required information size of 611 patients was calculated (Fig. ##FIG##5##6##). </p>", "<title>Length of primary hospital stay (Fig. ##FIG##6##7##)</title>", "<p id=\"Par35\">All studies reported LOS at index hospital admission [##REF##7749676##4##, ##UREF##4##14##, ##REF##19358184##15##, ##REF##33534226##17##–##REF##16736333##19##, ##REF##33017106##21##, ##REF##21550483##22##]. The analysis showed that there was no statistically significant difference between antibiotic treatment and appendectomy in terms of their effect on the duration of hospital stay. The mean difference was − 0.58 days (95% confidence interval − 1.59 to 0.43, <italic>p</italic> = 0.26), indicating that the difference observed was not statistically significant. However, there was a high level of heterogeneity among the included studies, with an <italic>I</italic>-squared value of 99%, suggesting an important variability in the results across studies.</p>", "<title>Costs (Fig. ##FIG##7##8##)</title>", "<p id=\"Par36\">The pooled analysis of primary costs included 3 studies [##REF##19358184##15##, ##REF##33534226##17##, ##REF##27974169##20##].</p>", "<p id=\"Par37\">Overall, NOM resulted in significantly lower costs when compared to OM (sample size: 599; MD − 214.6; 95% CI − 218.51 − 210.69; <italic>P</italic> &lt; 0.00001, <italic>I</italic>² = 0%). There was a low level of heterogeneity among the included studies, with an <italic>I</italic>-squared value of 0%, suggesting a negligible variability in the results across studies.</p>", "<title>Postoperative complications (Fig. ##FIG##8##9##)</title>", "<p id=\"Par38\">Eight studies reported post-treatment complications [##REF##7749676##4##, ##UREF##4##14##, ##REF##19358184##15##, ##REF##33534226##17##–##REF##16736333##19##, ##REF##33017106##21##, ##REF##21550483##22##]. There was no statistically significant difference in the rate of post-treatment complications between participants treated with antibiotics (8.98%; 145 out of 1613) and those who underwent appendectomy (10.88%; 173 out of 1590). The risk ratio (RR) was 0.66 (95% confidence interval 0.41 to 1.04, <italic>p</italic> = 0.07), indicating that the difference observed was not statistically significant. However, there was a considerable level of heterogeneity among the included studies, with an <italic>I</italic>-squared value of 69%, suggesting some variability in the results across studies. Trial sequential analysis of 8 trials comparing NOM vs. OM for postoperative complications. The cumulative <italic>Z</italic>-curve did not cross both the conventional boundary and the trial sequential monitoring boundary but crossed the required information size, suggesting that there are no significant differences in terms of complications and further trials difficulty will change this conclusion. A diversity adjusted required information size of 787 patients was calculated (Fig. ##FIG##9##10##). </p>", "<title>Quality of life</title>", "<p id=\"Par39\">Three studies provided data regarding quality of life [##REF##33534226##17##, ##REF##27974169##20##, ##REF##33017106##21##]. However, a pooled analysis could not be done because of numerous scales utilized to evaluate the outcome.</p>", "<p id=\"Par40\">In the Talan et al. trial, NOM patients had higher physical SF-12v2 scores than OM patients at the 2-week and 1-month follow-up intervals (median 54 vs. 44). On the contrary, individuals who had OM both at 2 weeks (median 58 vs 55) and at 1 month follow-up (median 56 vs 55) had higher scores for the mental SF-12v2.</p>", "<p id=\"Par41\">The study “A Randomized Trial Comparing Antibiotics with Appendectomy for Appendicitis” (CODA trial), in a single time point of 30 days following randomization, reported QoL using the EQ-5DTM (EuroQoL Group, Rotterdam, The Netherlands), demonstrating no difference between antibiotic therapy and appendectomy (mean 0.92; SD 0.13 vs. mean 0.91; SD 0.13).</p>", "<p id=\"Par42\">O’Leary et al. assessed quality of life (QoL) using the same scale but at four different points in time (one week, one month, three months, and twelve months after randomization). However, data were reported with participants divided into three groups (appendectomy, antibiotic treatment, and failed antibiotic treatment with subsequent appendectomy). When compared to the group that underwent successful antibiotic therapy, the appendectomy group's mean QoL at 12 months was substantially higher (mean 0.976; CI 0.962 to 0.990 vs. mean 0.888; CI 0.856 to 0.920).</p>" ]

[ "<title>Discussion</title>", "<p id=\"Par43\">This study, including 3213 patients and 8 RCTs [##UREF##0##2##, ##REF##7749676##4##, ##UREF##4##14##, ##REF##19358184##15##, ##REF##33534226##17##–##REF##25072441##24##], is, to our knowledge, the largest meta-analysis of randomized controlled trials conducted thus far encompassing an adult population.</p>", "<p id=\"Par44\">The results demonstrate that antibiotic therapy as a first-line treatment has a failure rate of 29.5% during the initial hospitalization, 35.4% at 1-year follow-up, a non-statistically significant difference in terms of length of stay (LOS), a comparable rate of complications and significantly lower costs compared to surgical treatment.</p>", "<p id=\"Par45\">Several meta-analyses over the previous years have highlighted that surgical treatment is associated with an increased rate of complications, such as the study by Podda et al. [##REF##30720508##25##], published in 2019. On the contrary, two recent studies [##REF##35895073##6##, ##REF##35971796##26##] did not observe a lower rate of complications in the conservatively treated group. Our study aligns with these latter findings. This is likely attributed to the higher number of laparoscopic appendectomies performed more recently. As compared to open technique, laparoscopic appendectomy has been shown to significantly reduce wound infection rates [##REF##30484855##27##]. In our analysis the rate of laparoscopic appendectomies performed was 68.44%, as reported by 6 RCTs. Furthermore, recent trials included in our study predominantly analyzed laparoscopic appendectomies, with a percentage of 100% for a trial [##UREF##4##14##], 96% [##REF##33017106##21##], and 90% [##REF##33534226##17##], respectively. In contrast, previous studies, particularly the Antibiotic Therapy vs Appendectomy for Treatment of Uncomplicated Acute Appendicitis (APPAC) trial and the study conducted by Styrud et al., primarily consisted of open procedures.</p>", "<p id=\"Par46\">Another important factor that could influence these results is the presence of appendicoliths. In the CODA trial, participants who were randomized to antibiotic medication and had an appendicolith experienced problems with a rate of 14% compared to 2% in those who did not [##REF##33017106##21##]. This latter trial and the study by Vons et al. [##REF##21550483##22##] included patients with appendicoliths diagnosed by CT scan. The other trials had a heterogeneous diagnostic protocol, so several patients with appendicoliths may have remained unrecognized.</p>", "<p id=\"Par47\">In conclusion, we can affirm that NOM is safe, as it has a comparable rate of complications to laparoscopic appendectomy. However, there was heterogeneity in diagnostic assessment, antibiotic regimens and treatment duration among the various studies, which could impact the results.</p>", "<p id=\"Par48\">The higher number of laparoscopic appendectomies may have also influenced the outcome regarding LOS. It is well-established in the literature that LOS is shorter when the procedure is performed laparoscopically, leading to an equivalence in LOS with conservative treatment [##REF##30484855##27##]. It was not possible to perform a subgroup analysis due to lack of the necessary data. However, it would be important, in the future, to have RCTs that perform totally laparoscopic appendectomies, as Ceresoli et al. did, or that perform a subgroup analysis to explore the differences between laparoscopic and laparotomy appendectomies for this outcome.</p>", "<p id=\"Par49\">However, this result is certainly influenced by significant heterogeneity in the implementation and management of conservative therapy. Indeed, there is inconsistency among the studies regarding the type of antibiotic used, the duration of intravenous administration, and subsequent oral administration. For example, in the CODA trial [##REF##33017106##21##], an initial bolus, administered on the first day, was followed by oral therapy from the second to the tenth day. On the other hand, O’Leary et al. [##REF##33534226##17##] continued that the antibiotic therapy until a clear clinical improvement of the patient was achieved.</p>", "<p id=\"Par50\">Regarding the results concerning the complication-free treatment success during the initial hospitalization, they significantly favor surgical intervention. The conservative treatment has an efficacy rate of 71.84% in the index hospitalization.</p>", "<p id=\"Par51\">Undoubtedly, a clear advantage of appendectomy is the ability to remove the pathogenic cause with a negligible risk of stump appendicitis [##REF##21985727##28##]. Conversely, this is not possible with conservative treatment, which carries a significant risk of lifetime recurrence, estimated between 6.7% and 8.6% [##REF##2239906##29##].</p>", "<p id=\"Par52\">The treatment effectiveness assessed at one-year follow-up demonstrates a greater effectiveness of surgery compared to conservative treatment; this latter has an efficacy of 67.3% at one year compared to 97.4% for appendectomy.</p>", "<p id=\"Par53\">It is important, therefore, to determine whether a conservative treatment with a lower efficacy measured at one-year follow-up and with comparable rates of complications, can be considered acceptable and feasible as a first-line treatment. It is true that approximately one-third of patients experience a recurrence within the first year. However, according to the 5-year follow-up results of the APPAC trial, patients can be successfully treated again with antibiotic therapy, and if surgery is required, it does not appear to be associated with increased complications or technical difficulty.</p>", "<p id=\"Par54\">In fact, when Salminen et al. [##REF##30264120##30##] published the 5-year follow-up findings of the APPAC randomized clinical trial in 2018, they addressed the issue of the paucity of research on the long-term clinical efficacy of antibiotics, which had previously been seen as one of the most significant barriers to the widespread adoption of NOM for uncomplicated appendicitis. Only 2.3% of patients undergoing surgery for recurrent appendicitis were found to have complicated forms of the disease and the overall complication rate was significantly lower in the antibiotic group than in the appendectomy group (6.5% vs. 24.4%, <italic>P</italic> = 0.001) among patients who were initially treated with antibiotics for uncomplicated appendicitis.</p>", "<p id=\"Par55\">Recently, Pàtkovà et al. have published a cohort study regarding the long-term outcomes of NOM [##REF##37556160##31##]. This study drew patients from two RCTs included in this meta-analysis: Eriksson et al.'s study [##REF##7749676##4##] published in 1995 and Styrud et al.'s study [##REF##16736333##19##] published in 2006. The article concludes that over the course of two decades, more than half of the patients treated through NOM did not experience recurrences, and there is no evidence of long-term risks associated with NOM, except for the recurrence itself. The long-term follow-up confirmed the feasibility of NOM as a surgical alternative. It would be very important to have new RCTs that analyze the results of the comparison between NOM and OM in the long term, in order to draw more robust conclusions on the topic.</p>", "<p id=\"Par56\">Therefore, given these circumstances, an informed patient choice is crucial, in our opinion. In a study published by Hanson et al. [##REF##29322168##32##] in 2018, 9.4% of the surveyed population responded that they would opt for nonoperative management (NOM) in the case of appendicitis. This number increased to 14.5% when asked about choosing for their children. The study focused on discussing the failure rates of NOM, and indeed, the authors themselves speculate that different numbers would have been obtained if the success rates were presented to patients. A more recent study, published in 2021 by Bom et al., presents very different results. Approximately half of the participants in the average population sample expressed a preference for antibiotics as a treatment for uncomplicated appendicitis, even if it entailed a higher risk of recurrence, in order to avoid surgery initially. Additional rigorous qualitative research will be necessary to investigate the factors behind the strikingly different outcomes observed in these two studies and to gain a deeper understanding of patient preferences in various situations.</p>", "<p id=\"Par57\">We are faced with two therapies that are equivalent in terms of safety, with one being less expensive, less effective, and non-invasive, while the other is more expensive, more effective, and invasive. Beyond the decision of which therapy should be considered first-line, the outcome that could matter the most is the patient's quality of life.</p>", "<p id=\"Par58\">Regarding this latter outcome, the diversity of presented results highlights the need for more literature. To establish more reliable analyses, it is crucial to use homogeneous scales across various trials. It is interesting to notice, despite the limitations outlined above, that in the three studies examined in one case, there is no difference in QoL between NOM and OM, and in the remaining two, the surgery appears to be associated with higher QoL.</p>", "<p id=\"Par59\">Interpreting these results for clinical application requires consideration of several limitations. The significant heterogeneity limits confidence, variations in intervention expertise and the broad timespan of included RCTs may introduce confounding factors. Our study encompassed RCTs spanning a significant time frame from 1995 to 2022. Over this period, there were significant advancements in surgical techniques, diagnostic imaging, and antibiotic selection, resulting in noticeable variations in treatment protocols across the included studies. These variations were evident in the use of different antibiotics and the progression of surgical techniques from predominantly open appendectomies to primarily laparoscopic procedures throughout the chronology of the included RCTs. These variations have resulted in a high degree of heterogeneity among the studies, which constitutes a significant limitation, potentially biasing the analysis.</p>" ]

[ "<title>Conclusions</title>", "<p id=\"Par60\">This meta-analysis and trial sequential analysis provide evidence that NOM with antibiotics is safe and, in the majority of cases, successful. NOM is equivalent to surgery in terms of complications and LOS while also incurring lower costs. While NOM's efficacy is lower than surgery, it does not seem to increase long-term complications. In relation to the three primary outcomes examined in our study, the evidence gleaned from current literature can be regarded as conclusive. It is highly unlikely that new RCTs focusing on these outcomes will substantially alter the existing body of evidence available to date. Thus, offering NOM and discussing its risks and benefits with the patient is reasonable based on this data.</p>", "<p id=\"Par61\">Further scientific efforts should be directed toward the attempt to provide surgeons with tools that allow the early identification of those patients who might respond adequately to NOM.</p>" ]

[ "<title>Background</title>", "<p id=\"Par1\">The aim of this study is to provide a meta-analysis of randomized controlled trials (RCT) comparing conservative and surgical treatment in a population of adults with uncomplicated acute appendicitis.</p>", "<title>Methods</title>", "<p id=\"Par2\">A systematic literature review was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines<bold>.</bold> A comprehensive search was conducted in MEDLINE, Embase, and CENTRAL. We have exclusively incorporated randomized controlled trials (RCTs). Studies involving participants with complicated appendicitis or children were excluded. The variables considered are as follows: treatment complications, complication-free treatment success at index admission and at 1 year follow-up, length of hospital stay (LOS), quality of life (QoL) and costs.</p>", "<title>Results</title>", "<p id=\"Par3\">Eight RCTs involving 3213 participants (1615 antibiotics/1598 appendectomy) were included. There was no significant difference between the two treatments in terms of complication rates (RR = 0.66; 95% CI 0.61—1.04, <italic>P</italic> = 0.07, <italic>I</italic>² = 69%). Antibiotics had a reduced treatment efficacy compared with appendectomy (RR = 0.80; 95% CI 0.71 to 0.90, <italic>p</italic> &lt; 0.00001, <italic>I</italic>² = 87%) and at 1 year was successful in 540 out of 837 (64.6%, RR = 0.69, 95% confidence interval 0.61 to 0.77, <italic>p</italic> &lt; 0.00001, <italic>I</italic>² = 81%) participants. There was no difference in LOS (mean difference − 0.58 days 95% confidence interval − 1.59 to 0.43, <italic>p</italic> = 0.26, <italic>I</italic>² = 99%). The trial sequential analysis has revealed that, concerning the three primary outcomes, it is improbable that forthcoming RCTs will significantly alter the existing body of evidence.</p>", "<title>Conclusions</title>", "<p id=\"Par4\">As further large-scale trials have been conducted, antibiotic therapy proved to be safe, less expensive, but also less effective than surgical treatment. In order to ensure well-informed decisions, further research is needed to explore patient preferences and quality of life outcomes.</p>", "<title>Supplementary Information</title>", "<p>The online version contains supplementary material available at 10.1186/s13017-023-00531-6.</p>", "<title>Keywords</title>" ]

[ "<title>Supplementary Information</title>", "<p>\n</p>" ]

[ "<title>Acknowledgements</title>", "<p>The authors acknowledge support from the University of Milan through the APC initiative.</p>", "<title>Author contributions</title>", "<p>FB, GB and JV wrote the main manuscript text and LC, FD, PF and CF prepared Figures 1, 2 and 3. PD and LA reviewed the manuscript. All authors reviewed the manuscript.</p>", "<title>Funding</title>", "<p>None to declare.</p>", "<title>Availability of data and materials</title>", "<p>Data-sharing requests will be considered by the management group upon written request to the corresponding author. If agreed, deidentified participant data will be available, subject to a data-sharing agreement.</p>", "<title>Declarations</title>", "<title>Ethics approval and consent to participate</title>", "<p id=\"Par62\">Not applicable.</p>", "<title>Consent for publication</title>", "<p id=\"Par63\">Not applicable.</p>", "<title>Competing interests</title>", "<p id=\"Par64\">None to declare.</p>" ]

[ "<fig id=\"Fig1\"><label>Fig. 1</label><caption><p>Preferred reporting items for systematic reviews and meta-analysis flow diagram of included randomized control trials in the systematic review and meta-analysis</p></caption></fig>", "<fig id=\"Fig2\"><label>Fig. 2</label><caption><p>Risk of bias graph of the included studies</p></caption></fig>", "<fig id=\"Fig3\"><label>Fig. 3</label><caption><p>NOM success rate</p></caption></fig>", "<fig id=\"Fig4\"><label>Fig. 4</label><caption><p>Trial sequential analysis of 8 trials comparing NOM vs. OM for overall treatment efficacy. The cumulative Z-curve crossed the conventional boundary for benefit and required information size but did not cross the trial sequential monitoring boundary for benefit, suggesting that the current evidence is statistically significant but does not support a superiority of OM and further trials will not change this conclusion. A diversity adjusted required information size of 2805 patients was calculated using an alpha = 0.05 (two sided) and a beta = 0.20 (power 80%), and empirical estimation from TSA software</p></caption></fig>", "<fig id=\"Fig5\"><label>Fig. 5</label><caption><p>NOM success rate at 1-year follow-up</p></caption></fig>", "<fig id=\"Fig6\"><label>Fig. 6</label><caption><p>Trial sequential analysis of 7 trials comparing NOM vs. OM for treatment efficacy at 1-year follow-up. The cumulative Z-curve crossed the conventional boundary for benefit, the trial sequential monitoring boundary for benefit and required information size, suggesting that the current evidence is conclusive and further trials will not change this conclusion. A diversity adjusted required information size of 611 patients was calculated using an alpha = 0.05 (two sided) and a beta = 0.20 (power 80%), and empirical estimation from TSA software</p></caption></fig>", "<fig id=\"Fig7\"><label>Fig. 7</label><caption><p>Total length of stay</p></caption></fig>", "<fig id=\"Fig8\"><label>Fig. 8</label><caption><p>Costs</p></caption></fig>", "<fig id=\"Fig9\"><label>Fig. 9</label><caption><p>Complications rate</p></caption></fig>", "<fig id=\"Fig10\"><label>Fig. 10</label><caption><p>Trial sequential analysis of 8 trials comparing NOM vs. OM for postoperative complications. The cumulative Z-curve did not cross both the conventional boundary and the trial sequential monitoring boundary but crossed the required information size, suggesting that there are no significant differences in terms of complications and further trials difficulty will change this conclusion. A diversity adjusted required information size of 787 patients was calculated using an alpha = 0.05 (two sided) and a beta = 0.20 (power 80%), and empirical estimation from TSA software</p></caption></fig>" ]

[ "<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Brief information of the included studies</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Study</th><th align=\"left\">Study design</th><th align=\"left\">Participants</th><th align=\"left\">Intervention</th><th align=\"left\">Outcomes</th><th align=\"left\">Follow-up</th></tr></thead><tbody><tr><td align=\"left\">Ceresoli [##UREF##4##14##]</td><td align=\"left\">RCT, single center, 45 patients</td><td align=\"left\"><p>Participants aged 18–65 years were diagnosed by AIR score and adjunctive abdominal ultrasound in selected participants</p><p>Participants with intermediate probability of acute appendicitis from AIR score were examined with abdominal</p><p>ultrasound and were included in the study if ultrasound findings confirmed the clinical suspicion of acute appendicitis</p><p>Participants with high probability of acute appendicitis from AIR score without signs of perforation and with WCC of less</p><p>than 15 000/µl and CRP less than 5 mg/l were included for the randomization</p></td><td align=\"left\"><p>Days 1–3: IV ertapenem (1 g, q24h)</p><p>Days 4–8: PO amoxicillin plus clavulanic acid (1 g TID)\"</p></td><td align=\"left\"><p>Primary outcome: resolution of symptoms and inflammatory markers (WCC &lt; 10 000/µl and CRP &lt; 1 mg/l) within 2 weeks</p><p>after surgery in the surgical group or from the third dose of ertapenem without other treatments in the antibiotic group</p><p>Secondary outcomes: complications, negative appendicectomy, duration of hospital stay, work absence, long-term</p><p>negative outcomes within 1 year, including: bowel occlusion/intraperitoneal abscess leading to surgical re-operation,</p><p>bowel occlusion longer than 48 h, intraperitoneal abscess, incisional hernia or wound dehiscence in the surgical group</p><p>and recurrence of acute appendicitis in the antibiotic group</p></td><td align=\"left\"/></tr><tr><td align=\"left\">Eriksson [##REF##7749676##4##]</td><td align=\"left\">RCT, single center, 40 patients</td><td align=\"left\"><p>typical history and clinical signs, positive findings at ultrasound and either increased WCC and CRP</p><p>values or high CRP or WCC on two occasions within a 4 h interval</p></td><td align=\"left\">Days 1–2: IV cefotaxime (2 g, q12h) plus tinidazole (800 mg, q24h) Days 3–10: PO ofloxacin (200 mg BID) plus tinidazole (500 mg BID)</td><td align=\"left\"><p>Pain scores (every 6 h using a visual analogue scale), morphine consumption, WCC and temperature, positive diagnosis at</p><p>surgery, duration of hospital stay, wound infection and recurrent appendicitis</p></td><td align=\"left\">6, 10, and 30 days AD</td></tr><tr><td align=\"left\">Hansson [##REF##19358184##15##]</td><td align=\"left\">RCT, multicenter, 369 patients</td><td align=\"left\"><p>Participants with positive history, clinical signs, laboratory tests and in some cases, ultrasonography, CT and</p><p>gynecological examination</p></td><td align=\"left\"><p>Day 1: IV cefotaxime (1 g × 2 doses) plus metronidazole (1.5 g × 1 dose)</p><p>Days 2–11: PO ciprofloxacin (500 mg BID) plus Metronidazole (400 mg TID)</p></td><td align=\"left\"><p>Treatment efficacy, complications, recurrences and reoperations, duration of antibiotic therapy, abdominal pain after</p><p>discharge from hospital, duration of hospital stay and sick leave. The total costs for the primary hospital stay were</p><p>analyzed for each patient</p></td><td align=\"left\"><p>1 month and 1 year</p><p>AD</p></td></tr><tr><td align=\"left\">Khan [##UREF##5##16##]</td><td align=\"left\">RCT, single center, 130 patients</td><td align=\"left\">Participants aged 15–45 years old with positive history, clinical signs, laboratory tests and in some cases, ultrasonography, CT</td><td align=\"left\">Days 1–5: ciprofloxacin (250 mg TID) plus metronidazole (500 mg TID), route of administration not specified</td><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\">O'Leary [##REF##33534226##17##]</td><td align=\"left\">RCT, single center, 186 patients</td><td align=\"left\"><p>Participants aged 16 years and older admitted to the emergency department with right iliac fossae pain, raised WCC/CRP,</p><p>fluent in English (and negative β-HCG in females) were screened for inclusion</p><p>Participants without exclusion criteria would then proceed to radiological investigation with abdominal ultrasound with/without magnetic resonance imaging performed in those under 45 years; CT in participants above 45 years of age</p><p>was performed</p><p>Participants were randomized if acute uncomplicated appendicitis was evidenced from radiological investigation</p></td><td align=\"left\">Intravenous (IV) antibiotic (co-amoxiclav,1.2 g, 3 times daily). IV antibiotics were continued until there was a clinical improvement followed by 5 days of oral co-amoxiclav(625 mg 3 times a day orally for 5 days)</td><td align=\"left\"><p>Primary endpoint: success rate of antibiotic treatment at 1-year follow-up for the antibiotic group; successful</p><p>appendicectomy for the surgical group</p><p>Secondary endpoints: quality of life, cost and duration of hospital stay</p></td><td align=\"left\"/></tr><tr><td align=\"left\">Salminen [##REF##26080338##18##]</td><td align=\"left\">RCT, multicenter, 530 patients</td><td align=\"left\"><p>Participants aged 18–60 years admitted to the emergency department with clinical suspicion of acute uncomplicated</p><p>appendicitis confirmed by CT were considered. Acute appendicitis was considered present when the appendiceal</p><p>diameter exceeded 6 mm with wall thickening and at least one of the following: abnormal contrast enhancement of the</p><p>appendiceal wall, inflammatory oedema, or fluid collections around the appendix. Participants with complicated</p><p>appendicitis, defined as the presence of an appendicolith, perforation, abscess or suspicion of a tumor on the scan, were</p><p>excluded</p></td><td align=\"left\"><p>Days 1–3: IV ertapenem (1 g/day)</p><p>Days 4–10: PO levofloxacin (500 mg QD) and metronidazole (500 mg TID)</p></td><td align=\"left\"><p>The primary outcome measure in the antibiotic group was resolution of acute appendicitis, with discharge from</p><p>hospital without the requirement for surgical intervention and no recurrent appendicitis during the 1-year follow-up</p><p>Treatment success in the appendicectomy group was defined as the patient successfully undergoing an</p><p>appendicectomy</p><p>Secondary outcomes: post-intervention complications, late recurrence of appendicitis (more than 1 year), duration of</p><p>hospital stay, sick leave taken, pain scores on a visual analogue scale, and the use of analgesics</p></td><td align=\"left\"><p>1 week, 2 months, and</p><p>1 year after</p><p>intervention</p></td></tr><tr><td align=\"left\">Styrud [##REF##16736333##19##]</td><td align=\"left\">RCT, multicenter, 252 patients</td><td align=\"left\"><p>Men, 18–50 years of age, admitted to six different hospitals between 1996 and 1999. Participants with suspected</p><p>appendicitis with a CRP concentration above 10 mg/l and with no clinical signs of perforation</p></td><td align=\"left\">Days 1–2: V cefotaxime (2 g, q12h) plus tinidazole (800 mg, q24h) Days 3–12: PO ofloxacin (200 mg BID) plus tinidazole (500 mg BID)</td><td align=\"left\">Duration of hospital stay, sick leave, diagnosis at operation, recurrences and complications</td><td align=\"left\"><p>1 week, 6 weeks, and</p><p>1 year AD</p></td></tr><tr><td align=\"left\">Talan [##REF##27974169##20##]</td><td align=\"left\">RCT, single center, 30 patients</td><td align=\"left\"/><td align=\"left\"><p>Days 1–2: IV ertapenem (1 g/day)</p><p>Days 3–10: PO cefdinir and metronidazole</p><p>Dosing is dependent on age</p><p>Cefdinir: 13 + years, 300 mg BID; 5–12 years, 7 mg/kg BID, max 300 mg</p><p>Metronidazole: 13 + years, 500 mg tablets TID; 5–12 years, 10 mg/kg TID, max. 500 mg</p></td><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\">The CODA Collaborative [##REF##33017106##21##]</td><td align=\"left\">RCT, multicenter, 1552 patients</td><td align=\"left\"><p>Consecutive English-speaking or Spanish-speaking participants above 18 years of age were approached by the research</p><p>coordinator if imaging confirmed they had appendicitis. All participants with evidence of appendicolith from imaging</p><p>results were included in a prespecified subgroup before randomization. Evidence of perforation from the imaging result</p><p>was not an exclusion criterion</p></td><td align=\"left\">Day 1: IV metronidazole (+ ceftriaxone or levofloxacin), ertapenem, cefoxitin Days 2–10: PO metronidazole + ciprofloxacin or cefdinir</td><td align=\"left\"><p>Primary outcome: 30-day health status, assessed with EQ-5D™ questionnaires</p><p>Secondary outcomes: appendicectomy in the antibiotics group, patient-reported resolution of symptoms, and National</p><p>Surgical Quality Improvement Program-defined complications at the time of index treatment or during follow-up, visits</p><p>to the emergency department or hospital related to appendicitis symptoms, appendiceal neoplasms, treatment-related</p><p>complications, days of missed work for the participants and their career</p></td><td align=\"left\"/></tr><tr><td align=\"left\">Vons [##REF##21550483##22##]</td><td align=\"left\">RCT, multicenter, 239 patients</td><td align=\"left\"><p>All adults 18 years and older with suspected acute appendicitis. Eligible participants had CT diagnosis of uncomplicated</p><p>appendicitis, using defined radiological criteria and were randomized to appendicectomy or antibiotic therapy</p></td><td align=\"left\">IV amoxicillin plus clavulanic acid (3 g/day)</td><td align=\"left\"><p>Primary endpoint: occurrence of peritonitis within 30 days of initial treatment, diagnosed either at appendicectomy or</p><p>postoperatively by CT</p><p>Secondary endpoints: number of days with a post-intervention visual analogue scale pain score of 4 or higher, duration of</p><p>hospital stay and absence from work, incidence of complications other than peritonitis within 1 year and recurrence of</p><p>appendicitis after antibiotic treatment (appendicectomy performed between 30 days and 1 year follow-up, with a</p><p>confirmed diagnosis of appendicitis)</p></td><td align=\"left\"><p>15, 30, 90, 180, and</p><p>360 days AD</p></td></tr></tbody></table></table-wrap>" ]

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{ "acronym": [], "definition": [] }

[ "<title>Background</title>", "<p id=\"Par4\">After a chest trauma, patients often have a wide range of chest injuries. Therefore, injuries of the myocardium and valvular apparatus, if not clinically manifested immediately, can remain unrecognized for a long period of time. Traumatic tricuspid regurgitation is usually well tolerated in the acute phase, which is why surgical treatment of the tricuspid valve is performed much later than the onset of the injury. Transthoracic echocardiography (TTE) plays an important role in the diagnosis of valvular apparatus injuries, thus enabling early adequate treatment. A very small number of pericardial ruptures caused by chest wall trauma has been described in clinical practice, with various presentations. We present the case of a patient who was injured in a traffic accident, and manifested signs and symptoms of tricuspid valve and pericardial rupture with partial cardiac herniation 10 years later.</p>" ]

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[ "<title>Discussion and conclusions</title>", "<p id=\"Par15\">Although chest injuries in a car accident are common, injuries of valvular structures are very rare (less than 1%) and usually present late [##REF##17116624##1##–##REF##28255632##3##]. The right ventricle is located just behind the sternum and therefore prone to injury, caused by the pressure forces to the front or back of the chest. The mechanism of tricuspid valve injury is usually due to a deceleration force transmitted to the chest and heart, especially if the force acted during late diastole, thus leading to a rapid increase in right ventricular intracavitary pressure, which may lead to the rupture of the papillary muscle or tendinous chords [##REF##17116624##1##, ##REF##22678241##4##]. The mechanism of delayed rupture of the tricuspid valve is usually due to contusion of the papillary muscle, followed by haemorrhage, inflammation, and necrosis that can lead over time to rupture of the valvular apparatus [##REF##16488736##5##, ##REF##21519493##6##]. Rupture of the papillary muscle usually presents acutely and is therefore treated very quickly surgically [##REF##31388544##7##]. In contrast, rupture of tendinous chords has a much milder clinical course and often remains unrecognized after the injury [##REF##28539566##8##]. Therefore, wide time periods are described in the literature during which the rupture of tricuspid valve was detected and corrected [##REF##31388544##7##–##REF##31079895##10##]. Our patient belongs to the group of late ruptures, with significant tricuspid regurgitation and signs of right ventricular failure. We may assume that the rupture of tricuspid chords occurred earlier (before the patient reported symptoms), so that tricuspid regurgitation (and volume overload) lasted longer and caused both - degeneration of the anterior leaflet and RV dilatation and dysfunction, leading to RV-related heart failure. When these symptoms became prominent, together with symptoms caused by the compression of the edges of the ruptured pericardium on the coronary arteries, the patient presented to the emergency department. As the anterior leaflet of tricuspid valve suffered significant degeneration due to loss of support and huge motions in large blood stream, it became shortened and thickened.</p>", "<p id=\"Par16\">Myocardial injuries, in addition to rupture of the valvular apparatus, may include myocardial contusion, rupture of a free wall or septum, and pericardial effusion. The highest percentage of traumatic injuries to the valvular apparatus was observed on the aortic and mitral valves, due to higher pressures in the left heart [##UREF##0##11##].</p>", "<p id=\"Par17\">Echocardiography has a significant role, especially in patients with minimal clinical symptoms. This technique also serves to adequately describe anatomical disorders that occur after an injury, which is of great importance to the cardiac surgeon, in order to select an adequate surgical technique. A limitation of TTE is the fact that these patients usually have significant chest injuries, including haemothorax and pneumothorax, which makes their echocardiographic windows less adequate for interpretation compared to patients without chest injury. Prolonged haemodynamic instability of the patient prompts the physician to repeat the TTE examination or consider a TEE [##REF##31388544##7##]. Both TTE and TEE may not be ideal in some cases [##REF##16098328##9##]. In our patient, pericardial rupture with LV protrusion was not seen on echocardiography (probably because of the elastic forces of the LV wall), which indicates the necessity of other diagnostic procedures, such as chest computed tomography (CT) scan and cardiac magnetic resonance (CMR) imaging in symptomatic post-trauma patients, which is advised by other authors, as well [##REF##25414827##12##].</p>", "<p id=\"Par18\">The rupture of the pericardium in blunt chest trauma is also very rare [##UREF##1##13##]. Deceleration forces are usually responsible for the occurrence of pericardial defect, since the base of the heart is more fixed to the pulmonary vasculature and aorta, while the apex is more mobile, causing the rupture mostly on the lateral side of pericardium [##UREF##1##13##]. Pericardial rupture is seen in less than 0.5% of patients presenting after blunt trauma, and cardiac herniation through a pericardial defect is a potential complication of this injury [##REF##18841214##14##]. In some occasions, herniation of the heart can be asymptomatic and go unrecognized [##REF##7897718##15##]. On the other hand, major cardiac herniation can cause torsion of the great vessels, included inferior vena cava and strangulation of the herniated heart, causing cardiogenic shock and sudden death [##REF##18841214##14##, ##REF##32420444##16##, ##REF##19242311##17##]. Also, if pneumopericardium occurs, air within a limited potential space can result in cardiac tamponade and hemodynamic instability [##REF##19242311##17##].</p>", "<p id=\"Par19\">Pericardial rupture is difficult to diagnose by echocardiographic techniques because of tiny structure of pericardium. Some indirect signs such as pneumopericardium or hemopericardium might be of help but could not prove definite diagnosis [##REF##32420444##16##]. Chest CT scan enables timely recognition of pericardial rupture. The defect in the pericardium outlined by air may be directly visible on CT. If there is accompanying cardiac herniation, constriction by the pleuro-pericardial defect can be visible like a collar or waist [##REF##7897718##15##, ##REF##19242311##17##]. Also, cardiac tamponade can be seen, as compression of the heart chambers by the air in the pericardial space which results in a small heart size [##REF##29988807##18##].</p>", "<p id=\"Par20\">CMR imaging plays an important role in the assessment of pericardial injuries and cardiac herniation. The best way to visualize the pericardium is by using T1 weighted imaging during systole [##REF##22695953##19##, ##REF##34463084##20##]. This visualization method could make very good distinction between the pericardial and myocardial tissue. Besides that, CMR imaging is superior to CT because it generates motion pictures and can estimate regional wall motion abnormalities. These cine MR images could identify motions of the heart which is dislocated from the pericardial sac through the pericardial tear, indicating possible dynamic obstruction of the ventricles, as well as major blood vessels. However, even the CMR imaging has limitations. The parietal pericardium may be incompletely visualized, especially over left sided chambers, where pericardial rupture happens very often, because of scarcity of surrounding fat [##REF##23610095##21##]. The cardiac herniation visualised by the CMR imaging is often intermittent and limited by the changes in the decubital position of the patient [##REF##23610095##21##].</p>", "<p id=\"Par21\">The question of when to operate the patient with traumatic tricuspid regurgitation that occurred during a chest injury remains open. The best results were achieved with early use of surgical techniques in patients with severe tricuspid valve regurgitation [##REF##21095339##22##]. In contrast, in patients who were presented to a cardiac surgeon late, atrophy of the papillary muscle and chords and significantly increased amplitude of tricuspid valve leaflet movement were noted. Therefore, it is considered that surgical treatment of these patients, before the development of right ventricular failure, prevents further complications and maintains a stable sinus rhythm. Reparative techniques for treating traumatic tricuspid valve injuries today involve the use of synthetic materials to replace the ruptured chord or papillary muscle [##REF##21095339##22##–##UREF##2##24##].</p>", "<p id=\"Par22\">Traumatic injuries of the tricuspid valve and pericardium are often unrecognized in a timely manner, leading to late complications and right heart failure. Transthoracic and transoesophageal echocardiography play a crucial role in the recognition and proper treatment of these entities, though cardiac CMR may be needed in some cases. Early surgical treatment of unstable patients with severe tricuspid regurgitation prevents further complications and maintains a stable sinus rhythm.</p>" ]

[ "<title>Discussion and conclusions</title>", "<p id=\"Par15\">Although chest injuries in a car accident are common, injuries of valvular structures are very rare (less than 1%) and usually present late [##REF##17116624##1##–##REF##28255632##3##]. The right ventricle is located just behind the sternum and therefore prone to injury, caused by the pressure forces to the front or back of the chest. The mechanism of tricuspid valve injury is usually due to a deceleration force transmitted to the chest and heart, especially if the force acted during late diastole, thus leading to a rapid increase in right ventricular intracavitary pressure, which may lead to the rupture of the papillary muscle or tendinous chords [##REF##17116624##1##, ##REF##22678241##4##]. The mechanism of delayed rupture of the tricuspid valve is usually due to contusion of the papillary muscle, followed by haemorrhage, inflammation, and necrosis that can lead over time to rupture of the valvular apparatus [##REF##16488736##5##, ##REF##21519493##6##]. Rupture of the papillary muscle usually presents acutely and is therefore treated very quickly surgically [##REF##31388544##7##]. In contrast, rupture of tendinous chords has a much milder clinical course and often remains unrecognized after the injury [##REF##28539566##8##]. Therefore, wide time periods are described in the literature during which the rupture of tricuspid valve was detected and corrected [##REF##31388544##7##–##REF##31079895##10##]. Our patient belongs to the group of late ruptures, with significant tricuspid regurgitation and signs of right ventricular failure. We may assume that the rupture of tricuspid chords occurred earlier (before the patient reported symptoms), so that tricuspid regurgitation (and volume overload) lasted longer and caused both - degeneration of the anterior leaflet and RV dilatation and dysfunction, leading to RV-related heart failure. When these symptoms became prominent, together with symptoms caused by the compression of the edges of the ruptured pericardium on the coronary arteries, the patient presented to the emergency department. As the anterior leaflet of tricuspid valve suffered significant degeneration due to loss of support and huge motions in large blood stream, it became shortened and thickened.</p>", "<p id=\"Par16\">Myocardial injuries, in addition to rupture of the valvular apparatus, may include myocardial contusion, rupture of a free wall or septum, and pericardial effusion. The highest percentage of traumatic injuries to the valvular apparatus was observed on the aortic and mitral valves, due to higher pressures in the left heart [##UREF##0##11##].</p>", "<p id=\"Par17\">Echocardiography has a significant role, especially in patients with minimal clinical symptoms. This technique also serves to adequately describe anatomical disorders that occur after an injury, which is of great importance to the cardiac surgeon, in order to select an adequate surgical technique. A limitation of TTE is the fact that these patients usually have significant chest injuries, including haemothorax and pneumothorax, which makes their echocardiographic windows less adequate for interpretation compared to patients without chest injury. Prolonged haemodynamic instability of the patient prompts the physician to repeat the TTE examination or consider a TEE [##REF##31388544##7##]. Both TTE and TEE may not be ideal in some cases [##REF##16098328##9##]. In our patient, pericardial rupture with LV protrusion was not seen on echocardiography (probably because of the elastic forces of the LV wall), which indicates the necessity of other diagnostic procedures, such as chest computed tomography (CT) scan and cardiac magnetic resonance (CMR) imaging in symptomatic post-trauma patients, which is advised by other authors, as well [##REF##25414827##12##].</p>", "<p id=\"Par18\">The rupture of the pericardium in blunt chest trauma is also very rare [##UREF##1##13##]. Deceleration forces are usually responsible for the occurrence of pericardial defect, since the base of the heart is more fixed to the pulmonary vasculature and aorta, while the apex is more mobile, causing the rupture mostly on the lateral side of pericardium [##UREF##1##13##]. Pericardial rupture is seen in less than 0.5% of patients presenting after blunt trauma, and cardiac herniation through a pericardial defect is a potential complication of this injury [##REF##18841214##14##]. In some occasions, herniation of the heart can be asymptomatic and go unrecognized [##REF##7897718##15##]. On the other hand, major cardiac herniation can cause torsion of the great vessels, included inferior vena cava and strangulation of the herniated heart, causing cardiogenic shock and sudden death [##REF##18841214##14##, ##REF##32420444##16##, ##REF##19242311##17##]. Also, if pneumopericardium occurs, air within a limited potential space can result in cardiac tamponade and hemodynamic instability [##REF##19242311##17##].</p>", "<p id=\"Par19\">Pericardial rupture is difficult to diagnose by echocardiographic techniques because of tiny structure of pericardium. Some indirect signs such as pneumopericardium or hemopericardium might be of help but could not prove definite diagnosis [##REF##32420444##16##]. Chest CT scan enables timely recognition of pericardial rupture. The defect in the pericardium outlined by air may be directly visible on CT. If there is accompanying cardiac herniation, constriction by the pleuro-pericardial defect can be visible like a collar or waist [##REF##7897718##15##, ##REF##19242311##17##]. Also, cardiac tamponade can be seen, as compression of the heart chambers by the air in the pericardial space which results in a small heart size [##REF##29988807##18##].</p>", "<p id=\"Par20\">CMR imaging plays an important role in the assessment of pericardial injuries and cardiac herniation. The best way to visualize the pericardium is by using T1 weighted imaging during systole [##REF##22695953##19##, ##REF##34463084##20##]. This visualization method could make very good distinction between the pericardial and myocardial tissue. Besides that, CMR imaging is superior to CT because it generates motion pictures and can estimate regional wall motion abnormalities. These cine MR images could identify motions of the heart which is dislocated from the pericardial sac through the pericardial tear, indicating possible dynamic obstruction of the ventricles, as well as major blood vessels. However, even the CMR imaging has limitations. The parietal pericardium may be incompletely visualized, especially over left sided chambers, where pericardial rupture happens very often, because of scarcity of surrounding fat [##REF##23610095##21##]. The cardiac herniation visualised by the CMR imaging is often intermittent and limited by the changes in the decubital position of the patient [##REF##23610095##21##].</p>", "<p id=\"Par21\">The question of when to operate the patient with traumatic tricuspid regurgitation that occurred during a chest injury remains open. The best results were achieved with early use of surgical techniques in patients with severe tricuspid valve regurgitation [##REF##21095339##22##]. In contrast, in patients who were presented to a cardiac surgeon late, atrophy of the papillary muscle and chords and significantly increased amplitude of tricuspid valve leaflet movement were noted. Therefore, it is considered that surgical treatment of these patients, before the development of right ventricular failure, prevents further complications and maintains a stable sinus rhythm. Reparative techniques for treating traumatic tricuspid valve injuries today involve the use of synthetic materials to replace the ruptured chord or papillary muscle [##REF##21095339##22##–##UREF##2##24##].</p>", "<p id=\"Par22\">Traumatic injuries of the tricuspid valve and pericardium are often unrecognized in a timely manner, leading to late complications and right heart failure. Transthoracic and transoesophageal echocardiography play a crucial role in the recognition and proper treatment of these entities, though cardiac CMR may be needed in some cases. Early surgical treatment of unstable patients with severe tricuspid regurgitation prevents further complications and maintains a stable sinus rhythm.</p>" ]

[ "<title>Background</title>", "<p id=\"Par1\">Although chest trauma happens very often, accompanying tricuspid valve injuries occur rarely and may be manifested by scarce symptoms and signs. Pericardial rupture with cardiac herniation is even a bigger rarity. Transthoracic echocardiography plays a key role in the diagnosis of valve injuries but is of limited value in cardiac herniation.</p>", "<title>Case presentation</title>", "<p id=\"Par2\">We present the case of 58-year-old man who experienced severe chest trauma in a car accident. Symptoms of right heart failure occurred 10 years after the injury, due to the loss of tricuspid leaflet support caused by the rupture of tendinous chords with significant tricuspid regurgitation. Intraoperatively, old posttraumatic pericardial rupture into left pleura was also found, with partial cardiac herniation and pressure of the edge of pericardium on all left-sided coronary arteries simultaneously. The patient was successfully operated and is free of symptoms 4 years later.</p>", "<title>Conclusions</title>", "<p id=\"Par3\">This case emphasizes the importance of timely diagnosis and underlines a mechanism that leads to delayed rupture of the tricuspid valve apparatus. Repeated echocardiography in all patients who experienced chest trauma could be of great importance. Also, given the limited value of echocardiography in posttraumatic pericardial rupture and cardiac herniation, cardiac computed tomography should be performed.</p>", "<title>Supplementary Information</title>", "<p>The online version contains supplementary material available at 10.1186/s12872-024-03716-2.</p>", "<title>Keywords</title>" ]

[ "<title>Case presentation</title>", "<p id=\"Par5\">A 58-year-old patient was admitted to the hospital due to chest pain, dyspnea and fatigue on physical exertion. The symptoms started two weeks before admission. His previous history was unremarkable, except for a car accident 10 years ago, with chest trauma and fracture of two ribs. On physical examination, the patient was cyanotic, with signs of right ventricular failure and pansystolic murmur at the lower left sternal border; the blood pressure was 110/80 mmHg, heart rate was 110/min. The electrocardiogram (ECG) showed a right bundle branch block (Fig. ##FIG##0##1##). TTE revealed flail of the anterior leaflet of tricuspid valve due to chordal rupture, leaving the anterior half of the leaflet completely unsupported. Also, there was a moderate tethering of the septal and posterior leaflet. (Fig. ##FIG##1##2##, Supplementary material ##SUPPL##0##1## and ##SUPPL##1##2##). Massive tricuspid regurgitation, registered by Colour Doppler (Fig. ##FIG##2##3##, Supplementary material ##SUPPL##2##3##), was considered to be post-traumatic, based on the exclusion of other causes. Ebstein anomaly was excluded by echocardiography (the septal displacement index of the insertion of tricuspid to mitral leaflet was 0.76 cm/m2 (i.e. &lt; 0.8 cm/m2). Other causes were ruled out by normal values of laboratory blood tests, including inflammatory markers and blood cultures (infective endocarditis, carcinoid), as well as the absence of any gastrointestinal symptoms, invasive cardiac intervention or radiation therapy. Detailed TTE and transoesophageal echocardiography (TEE) examination also revealed enlarged right ventricle (RV diastolic diameter was 5,1 cm from the PLAX view, compared to LV diameter of 4,8 cm), moderate reduction of right ventricular function, and TV annular dilatation (4,4 cm) with reduced motion amplitude. Left ventricular shape and dimensions were within normal limits and no wall motion abnormalities were observed, apart from paradoxical movements of the septum, caused by right ventricular overload. There was no significant pericardial effusion. (Fig. ##FIG##3##4##, Supplementary material ##SUPPL##3##4##).</p>", "<p id=\"Par6\">\n\n</p>", "<p id=\"Par7\">\n\n</p>", "<p id=\"Par8\">\n\n</p>", "<p id=\"Par9\">\n\n</p>", "<p id=\"Par10\">After 10 days of intensive heart failure therapy, the patient was transferred to the cardiac surgery department for reconstruction of the tricuspid valve. Coronary angiography, performed prior to cardiac surgery, revealed the presence of an unusual finding of multiple dynamic stenotic lesions at the same level of all left-sided coronary vessels, predominantly on the first, second and third obtuse marginal branches of the circumflex coronary artery (Fig. ##FIG##4##5##, Supplementary material ##SUPPL##4##5##). After medial sternotomy, an old pericardial rupture with thick edge was observed (Fig. ##FIG##5##6##a). Pericardial opening was measuring 7 × 6 cm in size and the heart was herniated through the pericardial defect. The edge of the ruptured pericardium compressed the coronary arteries along the line, which was in accordance with the previous coronary angiographic findings. The heart was returned to normal position and the pericardial rupture was sutured. Intraoperatively, it was confirmed that the anterior leaflet of the tricuspid valve completely lost its support, due to the rupture of the main common chord at the level of papillary muscle (Fig. ##FIG##5##6##b). Based on the measures calculated by transesophageal echocardiography, the CorMatrix patch was constructed and a tube was formed for the reconstruction of the tricuspid valve. The leaflets were then excised and the CorMatrix patch was sutured in three places to the bases of the papillary muscles and proximally to the tricuspid annulus. The specific tricuspid surgery with Cor Matrix patch was performed because the leaflet tissue was insufficient in size to cover the valve area and to perform neochordal implantation.</p>", "<p id=\"Par11\">\n\n</p>", "<p id=\"Par12\">\n\n</p>", "<p id=\"Par13\">On control transthoracic echocardiography, a tube connected to the annulus and the base of the papillary muscles was confirmed (Fig. ##FIG##6##7##, Supplementary material ##SUPPL##5##6##). Mild residual tricuspid regurgitation persisted, with right ventricular systolic pressure 38 mmHg. After recovery, the patient was discharged home, without symptoms and signs of right heart failure.</p>", "<p id=\"Par14\">\n\n</p>", "<title>Electronic supplementary material</title>", "<p>Below is the link to the electronic supplementary material.</p>", "<p>\n\n</p>", "<p>\n\n</p>", "<p>\n\n</p>", "<p>\n\n</p>", "<p>\n\n</p>", "<p>\n\n</p>" ]

[ "<title>Acknowledgements</title>", "<p>Not applicable.</p>", "<title>Author contributions</title>", "<p>N.R. and M.P. participated in manuscript writing, literature search, data collection, critical revision. M.R.R. participated in data analysis, critical revision, literature search. I.B., O.P., N.L., E.K., A.D. and M.R. participated in literature search, data collection, and data analysis. D.M., a principal investigator of this study participated in manuscript writing, literature search, critical revision and gave final approval. All authors reviewed the manuscript.</p>", "<title>Funding</title>", "<p>There was no funding for this study.</p>", "<title>Data availability</title>", "<p>Please contact the corresponding author regarding data availability.</p>", "<title>Declarations</title>", "<title>Ethics approval and consent to participate</title>", "<p id=\"Par34\">Not applicable.</p>", "<title>Consent for publication</title>", "<p id=\"Par35\">Informed consent was obtained from all subjects for publication of identifying information/images in an online open-access publication.</p>", "<title>Competing interests</title>", "<p id=\"Par24\">The authors declare no competing interests.</p>" ]

[ "<fig id=\"Fig1\"><label>Fig. 1</label><caption><p>ECG on admission showing right bundle branch block</p></caption></fig>", "<fig id=\"Fig2\"><label>Fig. 2</label><caption><p>Transthoracic echocardiography showing flail anterior tricuspid leaflet (arrows): (<bold>a</bold>) parasternal short axis view and (<bold>b</bold>) modified parasternal long axis view</p></caption></fig>", "<fig id=\"Fig3\"><label>Fig. 3</label><caption><p>Massive tricuspid regurgitation, shown by transthoracic colour doppler (parasternal short axis view)</p></caption></fig>", "<fig id=\"Fig4\"><label>Fig. 4</label><caption><p>Transthoracic echocardiography shows left ventricle in parasternal short axis view with paradoxical movements of the septum</p></caption></fig>", "<fig id=\"Fig5\"><label>Fig. 5</label><caption><p>Coronary angiography showing multiple dynamic stenoses of left coronary arteries, caused by the thick edge of the ruptured pericardium: (<bold>a</bold>) obstructed flow; (<bold>b</bold>) resolved flow</p></caption></fig>", "<fig id=\"Fig6\"><label>Fig. 6</label><caption><p>Intraoperative finding: (<bold>a</bold>) pericardial rupture (arrow); (<bold>b</bold>) anterior leaflet of the tricuspid valve with the rupture of the main common chord (arrow)</p></caption></fig>", "<fig id=\"Fig7\"><label>Fig. 7</label><caption><p>Postoperative TTE showing reconstructed tricuspid valve (modified apical four chamber view)</p></caption></fig>" ]

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[ "<supplementary-material content-type=\"local-data\" id=\"MOESM1\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM2\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM3\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM4\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM5\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM6\"></supplementary-material>" ]

[ "<fn-group><fn><p><bold>Publisher’s Note</bold></p><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn></fn-group>" ]

[ "<graphic xlink:href=\"12872_2024_3716_Fig1_HTML\" id=\"d32e276\"/>", "<graphic xlink:href=\"12872_2024_3716_Fig2_HTML\" id=\"d32e293\"/>", "<graphic xlink:href=\"12872_2024_3716_Fig3_HTML\" id=\"d32e303\"/>", "<graphic xlink:href=\"12872_2024_3716_Fig4_HTML\" id=\"d32e313\"/>", "<graphic xlink:href=\"12872_2024_3716_Fig5_HTML\" id=\"d32e344\"/>", "<graphic xlink:href=\"12872_2024_3716_Fig6_HTML\" id=\"d32e361\"/>", "<graphic xlink:href=\"12872_2024_3716_Fig7_HTML\" id=\"d32e379\"/>" ]

[ "<media xlink:href=\"12872_2024_3716_MOESM1_ESM.mp4\"><caption><p><bold>Supplementary Material 1:</bold> Transthoracic echocardiography showing flail of the anterior leaflet of tricuspid valve (parasternal short axis view)</p></caption></media>", "<media xlink:href=\"12872_2024_3716_MOESM2_ESM.mp4\"><caption><p><bold>Supplementary Material 2:</bold> Transthoracic echocardiography showing flail of the anterior leaflet of tricuspid valve (modified parasternal long axis view)</p></caption></media>", "<media xlink:href=\"12872_2024_3716_MOESM3_ESM.mp4\"><caption><p><bold>Supplementary Material 3:</bold> Massive tricuspid regurgitation, shown by Transthoracic Colour Doppler (parasternal short axis view)</p></caption></media>", "<media xlink:href=\"12872_2024_3716_MOESM4_ESM.mp4\"><caption><p><bold>Supplementary Material 4:</bold> Transthoracic echocardiography showing left ventricle in short axis view</p></caption></media>", "<media xlink:href=\"12872_2024_3716_MOESM5_ESM.mp4\"><caption><p><bold>Supplementary Material 5:</bold> Coronary angiography showing dynamic stenoses of left coronary arteries caused by the thick edge of the ruptured pericardium with obstructed and resolved flow</p></caption></media>", "<media xlink:href=\"12872_2024_3716_MOESM6_ESM.mp4\"><caption><p><bold>Supplementary Material 6:</bold> Postoperative transthoracic echo showing reconstructed tricuspid valve (modified apical four chamber view)</p></caption></media>" ]

{ "acronym": [ "CMR", "CT", "ECG", "LV", "PLAX view", "RV", "TEE", "TV" ], "definition": [ "Cardiac magnetic resonance", "computed tomography", "Electrocardiogram", "left ventricle", "parasternal long axis view", "right ventricle", "Transthoracic echocardiography", "tricuspid valve" ] }

[ "<title>Background</title>", "<p id=\"Par5\">Migration is now a common reality for tens of millions of people around the world. Among people who have left their country of origin to settle in another country, some portion will engage in problematic drug use [##UREF##0##1##], whether initiating in their country of origin or in the host country. Data on drug use in migrant populations are scarce and inconclusive. For example, a review identified eight studies comparing drug use in first-generation migrants to drug use in the general population of which two found higher levels in migrants, four found lower levels in migrants and two found different results depending on the type of drugs [##UREF##1##2##]. However, these studies were highly heterogeneous in their methodologies and the type of populations and drugs they included. Although, generally, migrant groups seem to have lower rates of substance use than host populations, several risk factors make them particularly vulnerable to engaging in problematic drug use [##UREF##2##3##]. Persons who migrated often face challenges in the country they settled in, including limited job opportunities and acculturation difficulties driven, in part, through poverty (as they tend to occupy lower socio-economic positions in society), language barriers, mental health problems, and the consequences of trauma [##REF##27411086##4##–##UREF##3##6##]. The latter can stem from pre-migration traumatic experiences related to political conflict, war, or economic deprivation that prompted their migration, as well as the potentially traumatic migration journey itself [##REF##29447257##7##–##REF##30165756##9##].</p>", "<p id=\"Par6\">Although exact numbers on the burden of drug use among migrants are inconclusive, recent global events have likely impacted the number of migrants using drugs in the European Union (EU). As a result of the war with Russia, millions of people from Ukraine, one of the countries with the highest levels of injecting drug use globally, have been entering the EU [##UREF##5##10##]. In addition, globally, migrants’ and refugees’ self-reported use of alcohol and drugs increased by 20% during the COVID-19 pandemic [##UREF##6##11##]. In March 2023, the European Monitoring Centre for Drugs and Drug Addiction (EMCDDA) recognized migration as a “megatrend”—a long-term driving force that will likely have significant future influence—in its work addressing the increasingly complex and dynamic drug markets in Europe through 2030 [##UREF##7##12##]. However, the EMCDDA European drug report 2023 only made a limited mention of drug use among migrants [##UREF##8##13##]. In summary, migrants with problematic drug use are a neglected group and should be provided with adequate support.</p>" ]

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[ "<p id=\"Par1\">Each year, thousands of migrants enter the EU. Data on drug use in migrant populations are scarce and inconclusive. However, several risk factors make them particularly vulnerable to engaging in problematic drug use. In this perspective, we summarize the limited information that is available on migrants who use drugs and make a case as to why it is essential to improve access to health and social services, including harm reduction services, for this population. With this aim, we call for the co-creation of integrated services that better address the needs of migrants who use drugs in Europe.</p>", "<title>Keywords</title>" ]

[ "<title>Limited access to services for migrants who use drugs</title>", "<p id=\"Par7\">Providing adequate health and social services to migrants who use drugs (MWUD) is not only a matter of human dignity and sound public health, but also of their rights under European and international law [##UREF##9##14##]. However, MWUD face significant barriers when trying to access health and social services, including in European health systems. These barriers are often rooted in a range of factors, including cultural differences such as language and (mis)understanding of the health system [##UREF##10##15##], legal status [##UREF##10##15##, ##UREF##11##16##], and discrimination [##UREF##11##16##, ##REF##25873788##17##]. By denying migrants access to health and social services, irrespective of drug use, these fundamental rights are violated and perpetuate inequity, stigma, and discrimination in addition to contributing to poorer health outcomes.</p>", "<title>Public health benefits of improved healthcare for migrant who use drugs</title>", "<p id=\"Par8\">Improving access to health and social services, including harm reduction services, for migrants who use drugs is crucial for promoting public health. MWUD may be at a higher risk of infectious diseases, such as HIV and viral hepatitis, due to unsafe drug use practices [##UREF##12##18##–##REF##36996853##20##], poor living conditions and, in some cases, higher prevalence of these diseases in their country of origin. For example, in 2022, the prevalence of HIV in the EU increased, which has been largely attributed to refugees living with HIV that arrived from Ukraine [##UREF##14##21##]. By providing MWUD with access to harm reduction services, such as needle and syringe programs, opioid substitution therapy, and HIV and viral hepatitis B and C testing and treatment, the spread of infectious diseases will likely be reduced. Further, investment in harm reduction services is cost-effective in the promotion of public health [##REF##25873788##17##, ##REF##29660211##22##, ##REF##25727260##23##].</p>", "<title>Recommendations from civil society experts on improving health and social services</title>", "<p id=\"Par9\">MWUD may experience social marginalization or isolation due to their drug use and/or their migrant status [##REF##36561278##24##]. This can result in stigma, discrimination, and exclusion from social, economic, and political opportunities. Further, unaddressed health and social needs may lead to the development or exacerbation of serious mental health conditions. Inclusive practices for treating vulnerable and marginalized groups can help improve the social and mental health of MWUD [##REF##32265247##25##]. For example, including interpreters or cultural mediators in healthcare services improves the quality of care for patients [##REF##36816444##26##, ##REF##31898494##27##]. Civil society and health experts working with migrants who use drugs in the European Union recently published recommendations in four areas as part of an EU-funded project “Services for vulnerable migrants who use drugs in the EU (SEMID-EU)” [##UREF##12##18##] (Table ##TAB##0##1##).</p>", "<title>Including the voices of migrants who use drugs</title>", "<p id=\"Par10\">The civil society and health experts agreed that addiction services available in EU countries are often not sensitive to the specific needs of migrants [##UREF##12##18##]. Further, a literature search conducted in 2022 revealed no published studies on the self-reported needs of MWUD [##UREF##1##2##]. One study from Norway, later published in 2023, interviewed MWUD about their drug use and help seeking barriers; however, this study only included six participants [##REF##36593483##28##]. Despite the recommendations of many international organizations to involve people who use drugs in research and program and policy development, implementation of these efforts continues to stall [##UREF##15##29##]. Evidence shows that migrant involvement has a positive impact on research, service adaptations, policy dialogues, and the social and personal circumstances of migrants when they are involved [##REF##34169612##30##]. To this end, the SEMID-EU project team conducted a community-based participatory research to identify and explore the specific needs of MWUD in the EU aiming to improve availability of and access to services for this population. A report including the findings from 98 interviews with MWUD with 45 different nationalities living in Amsterdam, Athens, Berlin, or Paris provided a nuanced overview of the interrelatedness between problems these populations are facing related to their migration background, drug use, and additional factors such as homelessness. In addition, it described barriers and good practices for accessing healthcare and harm reduction services, including the differences between settings and different migrant populations [##UREF##16##31##].</p>", "<title>Call to co-create integrated services that better address the needs of MWUD</title>", "<p id=\"Par11\">Improving availability of and access to health and social services for all MWUD is essential for upholding human rights, promoting public health, and facilitating social integration and is especially urgent with the current number of migrants in the EU from Ukraine. Considering that the societal vulnerability of many migrants who use drugs leads to issues on multiple life domains, such as (mental) health problems, housing, and financial issues, an integrated, holistic approach is needed that offers support across these domains. To achieve these goals, policymakers must recognize the importance of providing MWUD with access to adequate services and work to eliminate the barriers that prevent them from accessing the care they need, while engaging migrants who use drugs at every step of the process.</p>" ]

[ "<title>Acknowledgements</title>", "<p>Authors acknowledge support to ISGlobal from the Spanish Ministry of Science, Innovation and Universities through the “Centro de Excelencia Severo Ochoa 2019–2023” Programme (CEX2018-000806-S) and from the Government of Catalonia through the CERCA Programme.</p>", "<title>Author contributions</title>", "<p>LvS and TMW wrote the first draft of this perspective and all co-authors provided feedback.</p>", "<title>Funding</title>", "<p>This paper was written as part of the project SErvices for vulnerable MIgrants who use Drugs in the EU (SEMID-EU) and funded by the European Union’s Justice Programme—Drugs Policy Initiatives (Grant number 101045837).</p>", "<title>Availability of data and materials</title>", "<p>Not applicable.</p>", "<title>Declarations</title>", "<title>Ethics approval and consent to participate</title>", "<p id=\"Par12\">Not applicable.</p>", "<title>Consent for publication</title>", "<p id=\"Par13\">Not applicable.</p>", "<title>Competing interests</title>", "<p id=\"Par14\">JVL reports research grants to his institution from AbbVie, Gilead Sciences and MSD, and speaker fees from AbbVie, Gilead Sciences, Intercept, Janssen, MSD, Novo Nordisk, and ViiV, and an advisory board fee from AbbVie and Novavax, all unrelated to this work. CAP participated in a podcast financed by Gilead Sciences, unrelated to this work.</p>" ]

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[ "<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Areas of recommendations by civil society and health experts working with migrants who use drugs in the European Union</p></caption><table frame=\"hsides\" rules=\"groups\"><tbody><tr><td align=\"left\">1. Increasing data availability and quality, to inform guidelines;</td><td align=\"left\"/></tr><tr><td align=\"left\">2. Increasing the availability of drug dependency services for migrants, including screening for mental health issues and involving migrants who use drugs in the development of services;</td><td align=\"left\"/></tr><tr><td align=\"left\">3. Eliminating country, and service level barriers for accessing these services, as well as providing migrants who use drugs with suitable information and combating stigma and discrimination; and</td><td align=\"left\"/></tr><tr><td align=\"left\">4. The need for increased collaboration among and within EU countries regarding healthcare for migrants who use drugs, on policy level as well as service level, including civil society organizations, peer navigation and multilingual cultural mediators.”</td><td align=\"left\"/></tr></tbody></table></table-wrap>" ]

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[ "<fn-group><fn><p><bold>Publisher's Note</bold></p><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn></fn-group>" ]

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{ "acronym": [ "EU", "MWUD", "SEMID-EU" ], "definition": [ "European Union", "Migrations who use drugs", "Services for vulnerable migrants who use drugs in the EU" ] }

[ "<title>Introduction</title>", "<p id=\"Par5\">Since the advent of echocardiography, researchers have focused on cardiac masses. These masses can be divided into non-neoplastic masses (such as thrombi, vegetations, calcifications, or other rare conditions), pseudotumors (lesions not originating from a neoplastic transformation of a specific cell type), benign tumors, or malignant tumors. Non-neoplastic masses account for 75% of all cases [##REF##33655300##1##, ##UREF##0##2##]. Although cardiac tumors are rare, primary cardiac tumors have a prevalence of 0.001–0.03%, while metastatic cardiac tumors occur 10–1,000 times more frequently (2.3–18.3%) [##REF##30813147##3##–##UREF##1##5##]. Primary cardiac tumors are classified based on histological characteristics into benign or malignant. A previous study indicated that the distribution of cardiac tumors was 34% in the left atrium, 26% in the right atrium, 6% in the left ventricle, 7% in the right ventricle, and 27% in other locations [##UREF##2##6##].</p>", "<p id=\"Par6\">Cardiac masses located in any chamber adjacent to large blood vessels or pericardium may require treatments, such as surgical removal or chemoradiotherapy, depending on the histopathological type, extent of invasion, and patient risk stratification [##UREF##3##7##]. Early detection and accurate differentiation of cardiac masses can lead to prolonged survival and improved quality of life for affected patients. Several imaging modalities are used to assess cardiac masses; these modalities include transthoracic echocardiography (TTE), transesophageal echocardiography (TEE), cardiac magnetic resonance (CMR), positron emission tomography (PET), computed tomography (CT) [##REF##34396236##8##], CT-PET [##REF##32563654##9##], etc. [##UREF##4##10##–##REF##33327646##12##]. However, no guidelines or consensus have been established on the best diagnostic approach due to the diversity of cardiac masses. A recent comprehensive review suggests that TTE is typically the first choice for cardiac mass examination, and CMR provides high-resolution imaging for further evaluation if a mass is suspected. PET can be useful for staging malignancies and guiding biopsy location [##UREF##5##13##].</p>", "<p id=\"Par7\">TTE is a valuable tool for determining the presence, size, shape, echogenicity, mobility, attachment point, and hemodynamic effects of cardiac masses and has a sensitivity of 93%. However, TTE may not be sufficient in some cases where image quality is suboptimal or the echoes are complex. Accurate differentiation between benign and malignant tumors using TTE can be challenging, with an accuracy of less than 70% [##REF##30813147##3##, ##REF##22159318##14##, ##UREF##6##15##]. To address these limitations, contrast echocardiography (CE) has emerged as a promising technology in recent years. Although published guidelines suggest that CE can improve image quality and aid in differentiating between benign and malignant lesions, most studies on CE diagnosis of cardiac masses are case reports or retrospective/small-sample-sized prospective cohorts [##UREF##6##15##–##REF##36218203##17##]. The present study aimed to evaluate the diagnostic accuracy of CE in patients with suspected cardiac masses and address the insufficient evidence for differential diagnosis using CE.</p>" ]

[ "<title>Materials and methods</title>", "<p id=\"Par8\">This prospective study was conducted in four tertiary hospitals in China including First Affiliated Hospital of Guangxi Medical University, Hunan Provincial People’s Hospital, First Affiliated Hospital of University of South China and Xiangyang No. 1 People’s Hospital, Hubei University of Medicine.</p>", "<title>Study participants</title>", "<p id=\"Par9\">Adult patients who underwent TTE between April 2018 and July 2022 and were suspected to have cardiac masses were included in this consecutive cohort study. Exclusion criteria were allergies to albumin, blood products, or ultrasound enhancing agents. Patients with severe heart failure (New York Heart Association Class IV), severe arrhythmia, respiratory failure, severe liver or kidney dysfunction, or mental illness or epilepsy were also excluded.</p>", "<title>Echocardiographic image acquisition</title>", "<p id=\"Par10\">Each patient underwent echocardiographic examinations in the left lateral position by using a Philips iE33 ultrasound system (Philips Medical Systems, Bothell, WA, USA) and a TTE probe (S5–1, 1–5 MHz) by an echocardiographist with over 10 years of TTE experience at each center. All images and measurements were obtained in accordance with the echocardiography guideline [##UREF##7##18##]. Following the TTE examination, all patients underwent CE according to the most recent published guidelines [##UREF##8##19##].</p>", "<title>CE protocol</title>", "<p id=\"Par11\">The study protocol was designed in accordance with the most recent guideline for CE [##UREF##9##20##]. Commercially available ultrasound enhancing agents (SonoVue; Bracco, Plan-Les-Ouates, Switzerland) were utilized during CE. The left ventricular opacification (LVO) mode was activated with a low mechanical index of 0.2 and 30-Hz frame rates. Subsequently, 0.8 mL of prepared ultrasound enhancing agents were rapidly injected via peripheral vein, followed by a slow (10–20 s) 3–5 mL saline flush as necessary to achieve optimal delineation of the left ventricular cavity and cardiac masses. Morphological and hemodynamic features of cardiac lesions were observed and digitally saved in this mode. The myocardial CE (MCE) mode was activated with a very low mechanical index of 0.08 and 30-Hz frame rates. After the left ventricle and myocardium were filled, the ultrasound enhancing agents were continuously infused with a dedicated Vueject R syringe pump (Bracco, Milano, Italy) at a rate of 1 mL/min. Intermittent flash technique (high mechanical index of 1.0) was employed to destroy the microbubbles. High mechanical index ultrasound impulse was transmitted between 5 and 10 frames to destroy the microbubbles. Perfusion was verified after contrast replenishment following the impulse to prevent false-positive readings caused by saturation artifact. The imaging results of the masses and adjacent normal myocardium before and after the flash were saved.</p>", "<title>Echocardiographic image analysis</title>", "<p id=\"Par12\">The study used qualitative and quantitative analyses. For patients with an echocardiographic suspicion of cardiac masses, qualitative analysis included observing echogenicity (uniform/non-uniform), boundary (well-demarcated/not well-demarcated), base morphology (narrow with peduncle/narrow with notch/broad), mass perfusion (no perfusion/mild perfusion/intense perfusion) [##UREF##10##21##], motility (absent/present), and pericardial effusion (absent/present) [##UREF##11##22##, ##REF##25129516##23##]. Two physicians with 10 years of experience in echocardiography jointly made a diagnosis based on the above qualitative indicators. Quantitative analysis was conducted using QLAB software (version 13.0; Philips Medical Systems, Andover, MA, USA). The area of the masses was measured when the long maximum diameter was visible, and the peak intensity of the masses and adjacent myocardium were measured as A1 and A2, respectively, with a ratio of A1 to A2 to differentiate between malignant and benign tumors (Figs. ##FIG##0##1##, ##FIG##1##2## and ##FIG##2##3##).</p>", "<p id=\"Par13\">\n\n</p>", "<p id=\"Par14\">\n\n</p>", "<p id=\"Par15\">\n\n</p>", "<title>Follow-up and validation</title>", "<p id=\"Par16\">All patients were prospectively followed up until March 1, 2022 to determine all-cause mortality by reviewing their medical records, conducting telephone interviews, and performing outpatient examinations every 6 months. Three types of cardiac masses were identified. (I) Pseudomass is defined as a variant or prominent normal structure, including Eustachian valve or Chiari network, Crista terminalis, and Coumadin ridge. The diagnosis was confirmed using CMR, and no morphological changes were observed in follow-up imaging [##UREF##12##24##]. (II) Thrombus is defined as a distinct mass of echoes visible throughout systole and diastole. The diagnosis was confirmed based on one of the following two criteria: (i) a significant reduction in size or complete resolution after anticoagulation therapy, with confirmation of thrombus upon follow-up TEE or CT; or (ii) pathological confirmation [##REF##7083502##25##]. (III) All tumors were confirmed by surgery or biopsy and classified as benign or malignant based on the 2015 World Health Organization classification of tumors of the heart and pericardium [##REF##26725181##26##].</p>", "<title>Statistical analysis</title>", "<p id=\"Par17\">Continuous parameters were presented as mean ± standard deviation, while non-normally distributed parameters were shown as median (interquartile range, IQR). Independent sample t-test was used to evaluate differences in continuous parameters among groups, while Mann–Whitney U test was used for non-normally distributed parameters. Pearson’s Chi-squared test or Fisher’s exact test was used to compare categorical parameters among groups. Receiver operating characteristic (ROC) analysis was used to determine the differentiating capacity of variables for cardiac masses. Youden’s J statistic was used to identify the optimal cut-off value. The area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated. The level of statistical significance was set at <italic>P</italic> &lt; 0.05.</p>" ]

[ "<title>Results</title>", "<title>Population characteristics</title>", "<p id=\"Par18\">Between April 2018 and July 2022, a total of 49,354 TTEs were performed at six departments, of which 153 (0.31%) examinations were conducted on patients with suspected cardiac masses. Eight patients with allergic constitution refused CE (Fig. ##FIG##3##4##). A total of 145 patients with a median age of 59.4 years (IQR: 51.2–63.9 years) and including 90 (62.0%) men were enrolled. Table ##TAB##0##1## summarizes the baseline demographic and clinical characteristics of all patients. Of the 145 patients, two did not have any cardiac masses, four had a cardiac pseudomass, 43 had a cardiac thrombus, 66 had a benign tumor, and 30 had a malignant tumor. These findings indicated that the history of previous cardiovascular disease and malignancy varied significantly among the four groups.</p>", "<p id=\"Par19\">\n\n</p>", "<p id=\"Par20\">\n\n</p>", "<p id=\"Par21\">Three cases of cardiac pseudomass were attributed to the hypertrophy of the interatrial septum, while one case was due to the hypertrophy of the papillary muscle. Anticoagulation therapy was administered to all patients diagnosed with a cardiac thrombus, and none underwent pathological analysis. Solitary thrombi were observed in all cases. Among the 43 patients with thrombi, 72.1% (31/43) experienced dissolution, and 27.9% (12/43) had a significant reduction in thrombus volume. Benign tumors were confirmed through surgery (60/66) and biopsy (6/66). Malignant tumors were confirmed through surgery (9/36) and biopsy (27/36).</p>", "<p id=\"Par22\">Following the administration of contrast enhancement (CE), two investigators diagnosed cardiac masses based on qualitative characteristics such as echogenicity, boundary, base, mass fusion, motility, and pericardial fusion. The results revealed that out of 145 cardiac masses, 140 were consistent with the final diagnosis, yielding a diagnostic accuracy of 96.6%. Of the 140 consistent diagnoses, 2 cases had no mass, 4 were pseudomasses, 43 were thrombi, 63 were benign tumors, and 28 were malignant tumors. However, instances of misdiagnosis were recorded. Three benign tumors erroneously identified as malignant tumors, and two malignant tumors were misclassified as benign tumors (Fig. ##FIG##3##4##).</p>", "<title>Comparison and differentiation of cardiac tumors from thrombi</title>", "<p id=\"Par23\">The tumor group exhibited a significantly larger area, higher rate of non-uniform echogenicity, wider base, higher perfusion intensity, and higher A1/A2 compared with the thrombus group (<italic>P</italic> &lt; 0.05, Table ##TAB##1##2##). When the cut-off value for A1/A2 was set to 0.499, the AUC for A1/A2 was 0.977 (95% CI: 0.947–1.000). The sensitivity, specificity, accuracy, PPV, and NPV were 0.979, 0.884, 0.957, 0.959, and 0.957, respectively (Fig. ##FIG##4##5##; Table ##TAB##2##3##).</p>", "<p id=\"Par24\">\n\n</p>", "<p id=\"Par25\">\n\n</p>", "<p id=\"Par26\">\n\n</p>", "<title>Comparison and differentiation of malignant tumors from benign tumors</title>", "<p id=\"Par27\">Compared with the benign group, the tumor group exhibited a larger area, a higher rate of non-uniform echogenicity, an indistinct boundary, a wider base, presence of motility, and higher A1/A2 (<italic>P</italic> &lt; 0.05, Table ##TAB##3##4##). The AUC for A1/A2 was 0.950 (95% CI: 0.894–1.000) when the cutoff value was set to 1.58. The sensitivity, specificity, accuracy, PPV, and NPV were 0.933, 0.939, 0.938, 0.875, and 0.969, respectively (Fig. ##FIG##5##6##; Table ##TAB##2##3##).</p>", "<p id=\"Par28\">\n\n</p>", "<p id=\"Par29\">\n\n</p>" ]

[ "<title>Discussion</title>", "<p id=\"Par30\">This study reports that the diagnostic performance of CE is notable in patients with suspected cardiac masses. CE exhibited high sensitivity and specificity in distinguishing cardiac tumors from non-neoplastic cardiac masses. It outperformed conventional TTE and showed comparable accuracy with pathological analysis in discriminating between malignant and benign tumors.</p>", "<p id=\"Par31\">Cardiac masses pose a significant threat to patients, and improving their diagnostic efficiency is an essential objective for radiologists and cardiologists. The management of the diagnostic approach is also important for clinicians [##REF##28436488##4##, ##REF##22159318##14##]. Currently, TTE, TEE, cardiac CT and CMR are commonly used in diagnostic procedures. In the approach to cardiac masses, some echocardiographic parameters could provide good diagnostic accuracy if integrated in weighted and not weighted scores [##REF##32563654##9##, ##REF##36357143##27##]. CMR is the subject of intense research and exhibits excellent accuracy in differentiating cardiac thrombi from tumors and distinguishing between benign and malignant neoplasms in various retrospective and prospective studies. Although prospective studies have highlighted useful imaging characteristics such as tumor size, invasiveness, irregular border, and late heterogeneous gadolinium enhancement, they have been limited to qualitative or semi-quantitative analysis. Therefore, a diagnostic imaging technique with quantitative parameters should be developed to ease the burden on CMR and reduce the workload of pathologists [##UREF##4##10##, ##REF##33327646##12##, ##REF##25129516##23##].</p>", "<p id=\"Par32\">TTE is still the primary imaging modality used to evaluate cardiac masses [##UREF##6##15##]. However, conventional TTE has limitations in accurately assessing the characteristics of cardiac tumors, particularly in differentiating benign from malignant tumors; it also heavily relies on the experience of the radiologist and cardiologists [##REF##27277840##28##]. To overcome this limitation, CE has become an essential part of echocardiography because it can improve the accuracy of left ventricular ejection fraction measurement and provide quantitative analysis of cardiac masses. Kirkpatrick et al. [##REF##15093876##29##] demonstrated the diagnostic utility of A1 and A2 values by using CE in cardiac masses in 2004; subsequent studies provided evidence for the differential diagnostic value of A1/A2. For example, Xia et al. [##REF##28096044##30##] found a significant difference in A1/A2 between malignant and benign tumors, while Mao et al. [##UREF##13##31##] revealed that A1/A2 &gt; 1 had a high diagnostic accuracy in differentiating benign masses from malignant metastatic tumors in a cohort study.</p>", "<title>Differentiation between cardiac tumors and thrombi</title>", "<p id=\"Par33\">In this study, it was discovered that CE demonstrated remarkable accuracy in diagnosing intracardiac thrombi. When diagnosing a thrombus, setting A1/A2 with a cut-off value of 0.499 exhibited a specificity of 97.9% and a sensitivity of 88.4%. The A1/A2 value for the majority of thrombi was close to zero. However, in three cases, the A1/A2 values were significantly higher (1.39, 1.62, and 1.47) possibly due to fresh thrombi. In previous research, the loose texture of fresh thrombi and the ability of ultrasound-enhancing agents to enter from the periphery during CE resulted in a higher A1/A2. By contrast, old thrombi have a dense texture, and microbubbles of the ultrasound-enhancing agents cannot penetrate; as such, the A1/A2 values are near zero. Differentiating between fresh and old thrombi is crucial because fresh thrombi are more easily removed and less attached to the left ventricular wall; this structure makes them more brittle due to their collagen-poor organization. As a result, careful evaluation of the risk of fresh thrombus shedding is necessary.</p>", "<p id=\"Par34\">Uenishi et al. [##UREF##14##32##] demonstrated another perfusion phenomenon, where ultrasound-enhancing agents did not penetrate the interior of the thrombus (81.8%, 27/33) or remained only at the periphery (12.1%, 4/33). They also found that the agents typically perfused the periphery (44.7%, 21/47) or even the entire cardiac tumor (48.9%, 23/47). However, additional samples are required to confirm the perfusion patterns observed in the present study and the findings of Uenishi et al. in the future.</p>", "<title>Differentiation between cardiac malignant tumors from benign tumors</title>", "<p id=\"Par35\">This study demonstrates that CE could effectively distinguish between most benign and malignant tumors through qualitative and quantitative diagnostic methods. CE can enhance image quality and assess blood supply within tumors. Malignant tumors typically have abundant blood supply, and benign tumors have a sparse blood supply [##UREF##15##33##]. In previous studies, an A1/A2 cut-off value of 1.0 was utilized to differentiate malignant from benign tumors [##REF##15093876##29##, ##UREF##16##34##, ##REF##26087885##35##]. However, some benign tumors may have an A1/A2 value that is close to or slightly higher than 1, such as 1.32 in hemangioma, 1.08 in rhabdomyoma, 0.84 in fibroma, and 0.92 in hemangioma, and 1.06–1.15 in myxoma. Some malignant tumors contain necrotic tissues, which can result in an A1/A2 value of less than 1, as seen in 3.6% of cases in the study of Mao et al. The present study suggests that a cut-off value of 1.58 is better than 1 for differentiating malignant from benign tumors by using A1/A2. For less experienced radiologists, using A1/A2 with a cut-off value of 1.58 would result in good diagnostic accuracy. The size of the tumor area is beneficial in distinguishing between malignant and benign tumors, consistent with prior research.</p>", "<p id=\"Par36\">This study possesses several strengths, including a novel diagnostic approach for distinguishing cardiac masses, a prospective study design, and a relatively large sample size. The use of a simple, rapid, and highly reproducible quantitative parameter (A1/A2) can greatly assist in clinical diagnosis, particularly for radiologists without extensive experience in using TTE to diagnose cardiac masses [##REF##30551963##36##].</p>", "<title>Other modalities</title>", "<p id=\"Par37\">In addition to TTE, transesophageal (TEE) echocardiography, cardiac CT, CMR, and 18Ffluorodeoxyglucose (18 F FDG)-PET have a complementary and mutually reinforcing role in assessing cardiac masses. TEE can serve as a valuable tool in diagnosing cardiac masses. Previous studies demonstrated that ultrasound-enhancing agents can enhance the diagnostic accuracy of cardiac thrombi during TEE for patients with atrial fibrillation [##UREF##2##6##]. Xia et al. also reported that combining TEE with CE can detect suspected cardiac masses and had an accuracy of 97.8–100%, particularly in distinguishing between benign and malignant lesions [##REF##28096044##30##].</p>", "<p id=\"Par38\">With the availability of various tissue characterization imaging sequences, CMR has distinctive advantages in noninvasively diagnosing cardiac masses. In a recent study involving 213 pediatric cardiac masses, CMR demonstrated the following diagnostic accuracies for cardiac tumors: 94% for fibromas, 71% for rhabdomyomas, and 50% for myxomas [##REF##34419404##37##].</p>", "<p id=\"Par39\">Cardiac CT may serve as an alternative to CMR, particularly in cases where other imaging techniques are non-diagnostic or contraindicated [##UREF##17##38##]. Cardiac CT is particularly useful for evaluating calcified masses compared with other imaging modalities. Previous studies demonstrated that the diagnostic accuracy of cardiac CT in predicting the malignant nature of cardiac masses could be more than 90% [##REF##32563654##9##]. However, the use of cardiac CT has some limitations, including radiation exposure, a low risk of contrast-induced nephropathy, and a restricted soft tissue and temporal resolution in comparison with magnetic resonance imaging. Studies have suggested that cardiac CT can distinguish between cardiac tumors and thrombi [##UREF##18##39##]; however, further research with a large sample size is required to confirm this finding.</p>", "<p id=\"Par40\">18 F-FDG PET/CT is confirmed as an extremely powerful tool to provide substantial information regarding the nature of cardiac masses. A recent study reported that the accuracy of 18 F-FDG PET/CT in predicting the benign or malignant nature of cardiac masses exceeds 91%. In particular, the study emphasized the value of PET in cases with inconclusive diagnoses following cardiac CT, specifically among patients exhibiting three or four abnormal CT findings. In these instances, the presence of all PET parameters below the specified cut-off values indicates a benign mass, while the identification of at least one abnormal PET characteristic reliably indicates malignancy [##REF##32563654##9##].</p>", "<title>Limitations</title>", "<p id=\"Par41\">This study has several limitations that need to be addressed. First, the participating hospitals were tertiary, which may have introduced selection bias because secondary hospitals typically treat thrombi with a well-demarcated boundary and low echocardiographic suspicion. Second, more cases of pseudomass should be included in future analyses because their low representation in the current study resulted in limited conclusions. Third, the recruitment period was short, and long-term follow-up may be necessary to determine whether A1/A2 can predict the prognosis for patients with cardiac tumors. Finally, the analysis did not explore the diagnostic performance of CE by less experienced radiologists, which may be an underlying confounder in this study.</p>" ]

[ "<title>Conclusions</title>", "<p id=\"Par42\">CE can be a promising tool in accurately differentiating cardiac masses by combining qualitative and quantitative analyses. However, additional studies with larger sample sizes are needed to validate these findings due to the limited sample size and the potential for underlying confounders.</p>" ]

[ "<title>Background</title>", "<p id=\"Par1\">Cardiac masses can encompass a variety of conditions, such as tumors, thrombi, vegetations, calcific lesions, and other rare diseases. Treatment and management of these types of cardiac masses differ considerably. Thus, accurately distinguishing among thrombi, benign tumors, and malignant tumors in the heart is of great importance. Contrast echocardiography (CE) has emerged as a promising technology. Although published guidelines suggest that CE can enhance image quality and assist in differentiating between benign and malignant lesions, most studies on CE diagnosis of cardiac masses are limited to case reports or retrospective/small-sample-sized prospective cohorts. This study aims to evaluate the diagnostic accuracy of CE in patients with suspected cardiac masses and address the insufficient evidence for differential diagnosis using CE.</p>", "<title>Methods</title>", "<p id=\"Par2\">Between April 2018 and July 2022, a prospective multicenter study was conducted, which included 145 consecutive patients suspected to have cardiac masses based on transthoracic echocardiography. All patients underwent CE examinations. The echocardiographic diagnosis relied on qualitative factors such as echogenicity, boundary, morphology of the base, mass perfusion, pericardial effusion, and motility as well as quantitative factors such as the area of the masses and the peak intensity ratio of the masses to adjacent myocardium (A1/A2).</p>", "<title>Results</title>", "<p id=\"Par3\">The final confirmed diagnoses were as follows: 2 patients had no cardiac mass, 4 patients had pseudomass, 43 patients had thrombus, 66 patients had benign tumors, and 30 patients had malignant tumors. The receiver operating characteristic (ROC) analysis indicated that an optimal A1/A2 cutoff value of 0.499 distinguished a cardiac tumor from a thrombus, with AUC, sensitivity, specificity, PPV, and NPV of 0.977, 97.9%, 90.7%, 95.9%, and 95.1%, respectively. The optimal A1/A2 cutoff value of 1.583 distinguished a cardiac tumor from a thrombus, with AUC, sensitivity, specificity, PPV, and NPV of 0.950, 93.3%, 93.9%, 87.5%, and 96.9%, respectively.</p>", "<title>Conclusions</title>", "<p id=\"Par4\">Combined with qualitative and quantitative analyses, CE has the potential to accurately differentiate among different types of cardiac masses.</p>", "<title>Keywords</title>" ]

[]

[ "<title>Acknowledgements</title>", "<p>The authors wish to thank Manying Xie, radiologist, for the ultrasound image analysis, Sufen Zhou and Ling Gan for the excellent technical assistance, and Caihong Chang and Hongzhi Guo for their cooperation in organizing cardiac mass data.</p>", "<title>Author contributions</title>", "<p>ZJQ and HZ designed this project, QTW wrote the first draft of this article, and completed Figs. ##FIG##0##1##, ##FIG##1##2## and ##FIG##2##3##. BW revised this article and completed Figs. ##FIG##3##4##, ##FIG##4##5## and ##FIG##5##6##. XFZ, XZ, and SC, as multicenter units, provided data on cardiac tumors according to operating standards. YJB, LJ and QQY jointly completed Tables 1, 2, 3 and 4 and assisted in data analysis.All authors reviewed the manuscript.</p>", "<title>Funding</title>", "<p>This work was supported by the Key R&amp;D foundation of Hubei Province and Science (2022BCE004), the Clinical Medical Technology Innovation Guidance Project of Hunan Provincial Department of Science and Technology (2021SK50921), and the Technology Foundation of Xiangyang (2021YL27 and 2022YL26A).</p>", "<title>Data availability</title>", "<p>The data used to support the findings of this study are available from the corresponding author upon request.</p>", "<title>Declarations</title>", "<title>Ethics statement and consent to participate</title>", "<p id=\"Par73\">This study was approved by the Ethics Committee of Xiangyang No. 1 People’s hospital (2018KYLX61). All methods were performed in accordance with the relevant guidelines and regulations compatible with the Declaration of Helsinki. Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article.</p>", "<title>Consent for publication</title>", "<p id=\"Par51\">Not applicable.</p>", "<title>Competing interests</title>", "<p id=\"Par50\">The authors declare no competing interests.</p>" ]

[ "<fig id=\"Fig1\"><label>Fig. 1</label><caption><p>(<bold>A</bold>) A slightly strong echogenic mass can be seen in the right atrium, and it almost fills the right atrium. The internal echo is uneven and appears to adhere to the anterior wall of the right atrium with a wide base, with little amplitude swing with the cardiac cycle. (<bold>B</bold>) Contrast-enhanced signal can be seen in the slightly stronger echogenic mass above the right atrium. (<bold>C, D</bold>) Immunohistochemistry: VIM (+), CD31 (+), FVM (+), FLI1 (+), D2-40 (-), Ki-67Li about 40%, final diagnosis through histological examination as angiosarcoma (right atrial)</p></caption></fig>", "<fig id=\"Fig2\"><label>Fig. 2</label><caption><p>(<bold>A, B</bold>) A solid-cystic mass can be seen outside the left coronary sinus of the aorta in echocardiography. (<bold>C</bold>) No blood flow in the isoechoic area of the mass. (<bold>D</bold>) The contrast agent enters the cystic cavity after continuous interruption, and the enhancement in the isoechoic area is not obvious; it is ultimately diagnosed as a huge coronary artery aneurysm and concurrent thrombosis</p></caption></fig>", "<fig id=\"Fig3\"><label>Fig. 3</label><caption><p>(<bold>A, B</bold>) Abnormal masses can be seen in the epicardial layer of the lateral wall of the left ventricle, with strong echoes and dark areas separated by radial ribbons. The boundary with normal myocardial echo is still clear, and the left ventricular normal myocardial hypertrophy is mild. (<bold>C</bold>) The left ventricular wall is significantly thickened, and the abnormal echogenicity of the left ventricular lateral wall appears to be accompanied by contrast agent echo. (<bold>D</bold>) MRI revealed uneven thickening of the myocardium, local nodular formation, and diffuse edema, ultimately confirmed by surgery as myocardial cavernous hemangioma</p></caption></fig>", "<fig id=\"Fig4\"><label>Fig. 4</label><caption><p>Diagnostic process based on qualitative characteristics such as echogenicity, boundary, base, mass fusion, motility, and pericardial fusion</p></caption></fig>", "<fig id=\"Fig5\"><label>Fig. 5</label><caption><p>ROC curve for identifying thrombi and cardiac tumors by using quantitative analysis values A1/A2 of cardiac CE</p></caption></fig>", "<fig id=\"Fig6\"><label>Fig. 6</label><caption><p>ROC curve for differentiating benign and malignant cardiac tumors using quantitative analysis values A1/A2 of cardiac CE.</p></caption></fig>" ]

[ "<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Characteristics of the population</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\"/><th align=\"left\">No cardiac mass (n = 2) and Pseudomass (n = 4)</th><th align=\"left\">Thrombus (n = 43)</th><th align=\"left\">Benign tumor (n = 66)</th><th align=\"left\">Malignant tumor (n = 30)</th><th align=\"left\"><italic>P</italic> value</th></tr></thead><tbody><tr><td align=\"left\">Age, mean (SD), years</td><td align=\"left\">59.2 (13.6)</td><td align=\"left\">54.6 (15.4)</td><td align=\"left\">53.8 (11.5)</td><td align=\"left\">64.3 (10.3)</td><td align=\"left\">0.163</td></tr><tr><td align=\"left\">Sex (Male/Female)</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\">0.322</td></tr><tr><td align=\"left\"> Male</td><td align=\"left\">4</td><td align=\"left\">23</td><td align=\"left\">46</td><td align=\"left\">17</td><td align=\"left\"/></tr><tr><td align=\"left\"> Female</td><td align=\"left\">2</td><td align=\"left\">20</td><td align=\"left\">20</td><td align=\"left\">13</td><td align=\"left\"/></tr><tr><td align=\"left\">Symptom</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\">0.993</td></tr><tr><td align=\"left\"> Asymptomatic</td><td align=\"left\">1</td><td align=\"left\">7</td><td align=\"left\">12</td><td align=\"left\">5</td><td align=\"left\"/></tr><tr><td align=\"left\"> Dyspnea</td><td align=\"left\">2</td><td align=\"left\">15</td><td align=\"left\">22</td><td align=\"left\">8</td><td align=\"left\"/></tr><tr><td align=\"left\"> Chest pain</td><td align=\"left\">2</td><td align=\"left\">9</td><td align=\"left\">13</td><td align=\"left\">7</td><td align=\"left\"/></tr><tr><td align=\"left\"> Palpitations</td><td align=\"left\">0</td><td align=\"left\">6</td><td align=\"left\">12</td><td align=\"left\">7</td><td align=\"left\"/></tr><tr><td align=\"left\"> Others</td><td align=\"left\">1</td><td align=\"left\">6</td><td align=\"left\">7</td><td align=\"left\">3</td><td align=\"left\"/></tr><tr><td align=\"left\">History of cardiovascular disease</td><td align=\"left\">5</td><td align=\"left\">38</td><td align=\"left\">31</td><td align=\"left\">17</td><td align=\"left\">&lt; 0.001</td></tr><tr><td align=\"left\">History of malignant disease</td><td align=\"left\">0</td><td align=\"left\">3</td><td align=\"left\">6</td><td align=\"left\">27</td><td align=\"left\">&lt; 0.001</td></tr><tr><td align=\"left\">Localization</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\">0.154</td></tr><tr><td align=\"left\"> Left ventricle</td><td align=\"left\">3</td><td align=\"left\">10</td><td align=\"left\">8</td><td align=\"left\">5</td><td align=\"left\"/></tr><tr><td align=\"left\"> Left atrium</td><td align=\"left\">1</td><td align=\"left\">15</td><td align=\"left\">26</td><td align=\"left\">4</td><td align=\"left\"/></tr><tr><td align=\"left\"> Right ventricle</td><td align=\"left\">1</td><td align=\"left\">6</td><td align=\"left\">10</td><td align=\"left\">9</td><td align=\"left\"/></tr><tr><td align=\"left\"> Right atrium</td><td align=\"left\">0</td><td align=\"left\">7</td><td align=\"left\">15</td><td align=\"left\">7</td><td align=\"left\"/></tr><tr><td align=\"left\"> Others</td><td align=\"left\">1</td><td align=\"left\">5</td><td align=\"left\">7</td><td align=\"left\">5</td><td align=\"left\"/></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab2\"><label>Table 2</label><caption><p>Comparison of echocardiographic parameters between thrombus and tumor</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\"/><th align=\"left\">Thrombus<break/>(<italic>n</italic> = 43)</th><th align=\"left\">Tumor<break/>(<italic>n</italic> = 96)</th><th align=\"left\">P value</th></tr></thead><tbody><tr><td align=\"left\">Area, mean (SD), mm²</td><td align=\"left\">917.6 (386.7)</td><td align=\"left\">1,513.2(794.2)</td><td align=\"left\">&lt; 0.001</td></tr><tr><td align=\"left\">Echogenicity</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\">0.002</td></tr><tr><td align=\"left\"> Uniform</td><td align=\"left\">29</td><td align=\"left\">37</td><td align=\"left\"/></tr><tr><td align=\"left\"> Non-uniform</td><td align=\"left\">14</td><td align=\"left\">59</td><td align=\"left\"/></tr><tr><td align=\"left\">Boundary</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\">0.254</td></tr><tr><td align=\"left\"> Well-demarcated</td><td align=\"left\">31</td><td align=\"left\">59</td><td align=\"left\"/></tr><tr><td align=\"left\"> Not well-demarcated</td><td align=\"left\">12</td><td align=\"left\">37</td><td align=\"left\"/></tr><tr><td align=\"left\">Base</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\">&lt; 0.001</td></tr><tr><td align=\"left\"> Narrow with peduncle</td><td align=\"left\">0</td><td align=\"left\">32</td><td align=\"left\"/></tr><tr><td align=\"left\"> Narrow with notch</td><td align=\"left\">39</td><td align=\"left\">23</td><td align=\"left\"/></tr><tr><td align=\"left\"> Broad</td><td align=\"left\">4</td><td align=\"left\">41</td><td align=\"left\"/></tr><tr><td align=\"left\">Mass perfusion</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\">&lt; 0.001</td></tr><tr><td align=\"left\"> No perfusion</td><td align=\"left\">27</td><td align=\"left\">0</td><td align=\"left\"/></tr><tr><td align=\"left\"> Mild perfusion</td><td align=\"left\">11</td><td align=\"left\">54</td><td align=\"left\"/></tr><tr><td align=\"left\"> Intense Perfusion</td><td align=\"left\">5</td><td align=\"left\">42</td><td align=\"left\"/></tr><tr><td align=\"left\">Motility</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\">0.172</td></tr><tr><td align=\"left\"> Absent</td><td align=\"left\">33</td><td align=\"left\">62</td><td align=\"left\"/></tr><tr><td align=\"left\"> Present</td><td align=\"left\">10</td><td align=\"left\">34</td><td align=\"left\"/></tr><tr><td align=\"left\">Pericardial effusion</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\">0.438</td></tr><tr><td align=\"left\"> Absent</td><td align=\"left\">31</td><td align=\"left\">61</td><td align=\"left\"/></tr><tr><td align=\"left\"> Present</td><td align=\"left\">12</td><td align=\"left\">35</td><td align=\"left\"/></tr><tr><td align=\"left\">Enhancement A1/A2,</td><td align=\"left\">0.39</td><td align=\"left\">1.20</td><td align=\"left\">&lt; 0.001</td></tr><tr><td align=\"left\">median (IQR)</td><td align=\"left\">(0.20–0.76)</td><td align=\"left\">(0.83–1.57)</td><td align=\"left\"/></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab3\"><label>Table 3</label><caption><p>Comparison of diagnostic performance in differentiating thrombus from cardiac tumor and Malignant tumor from benign tumor</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\"/><th align=\"left\"/><th align=\"left\">Sensitivity</th><th align=\"left\">Specificity</th><th align=\"left\">AUC</th><th align=\"left\">Accuracy</th><th align=\"left\">PPV</th><th align=\"left\">NPV</th></tr></thead><tbody><tr><td align=\"left\">Thrombus/cardiac tumor</td><td align=\"left\"><p>A1/A2 (Cutoff</p><p>value = 0.499)</p></td><td align=\"left\">0.979 (90.92-99.8%)</td><td align=\"left\">0.884 (74.3-96.1%)</td><td align=\"left\">0.977 (0.947-1.000)</td><td align=\"left\">0.9568 (0.936–0.994)</td><td align=\"left\">0.959 (0.943–0.981)</td><td align=\"left\"><p>0.951</p><p>(0.947-1.000)</p></td></tr><tr><td align=\"left\">Malignant tumor/benign tumor</td><td align=\"left\"><p>A1/A2 (Cutoff</p><p>value = 1.583)</p></td><td align=\"left\">0.933 (77.9-99.2%)</td><td align=\"left\">0.939 (85.2-98.3%)</td><td align=\"left\">0.950(0.894-1.000)</td><td align=\"left\"><p>0.9375</p><p>(0.921–0.984)</p></td><td align=\"left\"><p>0.875</p><p>(0.841–0.978)</p></td><td align=\"left\"><p>0.969</p><p>(0.962-1.000)</p></td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab4\"><label>Table 4</label><caption><p>Comparison of echocardiographic parameters between malignant tumor and benign tumor</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\"/><th align=\"left\">Benign tumor (<italic>n</italic> = 66)</th><th align=\"left\">Malignant tumor (<italic>n</italic> = 30)</th><th align=\"left\">P value</th></tr></thead><tbody><tr><td align=\"left\">Area, mean (SD), mm</td><td align=\"left\">1,253.26 (659.25)</td><td align=\"left\">1,812.43 (713.59)</td><td align=\"left\">&lt; 0.001</td></tr><tr><td align=\"left\">Echogenicity</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\"> Uniform</td><td align=\"left\">34</td><td align=\"left\">3</td><td align=\"left\">&lt; 0.001</td></tr><tr><td align=\"left\"> Non-uniform</td><td align=\"left\">32</td><td align=\"left\">27</td><td align=\"left\"/></tr><tr><td align=\"left\">Boundary</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\">&lt; 0.001</td></tr><tr><td align=\"left\"> Well-demarcated</td><td align=\"left\">49</td><td align=\"left\">10</td><td align=\"left\"/></tr><tr><td align=\"left\"> Not well-demarcated</td><td align=\"left\">17</td><td align=\"left\">20</td><td align=\"left\"/></tr><tr><td align=\"left\">Base</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\">&lt; 0.001</td></tr><tr><td align=\"left\"> Narrow with peduncle</td><td align=\"left\">28</td><td align=\"left\">4</td><td align=\"left\"/></tr><tr><td align=\"left\"> Narrow with notch</td><td align=\"left\">20</td><td align=\"left\">3</td><td align=\"left\"/></tr><tr><td align=\"left\"> Broad</td><td align=\"left\">18</td><td align=\"left\">23</td><td align=\"left\"/></tr><tr><td align=\"left\">Mass perfusion</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\">0.045</td></tr><tr><td align=\"left\"> Mild perfusion</td><td align=\"left\">42</td><td align=\"left\">12</td><td align=\"left\"/></tr><tr><td align=\"left\"> Intense Perfusion</td><td align=\"left\">24</td><td align=\"left\">18</td><td align=\"left\"/></tr><tr><td align=\"left\">Motility</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\">0.011</td></tr><tr><td align=\"left\"> Absent</td><td align=\"left\">37</td><td align=\"left\">25</td><td align=\"left\"/></tr><tr><td align=\"left\"> Present</td><td align=\"left\">29</td><td align=\"left\">5</td><td align=\"left\"/></tr><tr><td align=\"left\">Pericardial effusion</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\">0.253</td></tr><tr><td align=\"left\"> Absent</td><td align=\"left\">39</td><td align=\"left\">22</td><td align=\"left\"/></tr><tr><td align=\"left\"> Present</td><td align=\"left\">27</td><td align=\"left\">8</td><td align=\"left\"/></tr><tr><td align=\"left\">Enhancement A1/A2, median (IQR)</td><td align=\"left\">1.07 (0.64–1.17)</td><td align=\"left\">1.48 (0.77–2.08)</td><td align=\"left\">&lt; 0.001</td></tr></tbody></table></table-wrap>" ]

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[ "<table-wrap-foot><p>SD, standard deviation</p></table-wrap-foot>", "<table-wrap-foot><p>CI, confidence interval; IQR, interquartile range; OR, odds ratio; SD, standard deviation. * variables entered into the multivariate regression included area, echogenicity, base, massperfusion, and enhancement A1/A2</p></table-wrap-foot>", "<table-wrap-foot><p>AUC, the area under the receiver operating characteristic curve; NPV, negative predictive value; PPV, positive predictive value</p></table-wrap-foot>", "<table-wrap-foot><p>CI, confidence interval; IQR, interquartile range; OR, odds ratio; SD, standard deviation</p></table-wrap-foot>", "<fn-group><fn><p><bold>Publisher’s Note</bold></p><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn><fn><p>Qingtao Wang and Bing Wang contributed equally to this study.</p></fn></fn-group>" ]

[ "<graphic xlink:href=\"12872_2024_3708_Fig1_HTML\" id=\"d32e384\"/>", "<graphic xlink:href=\"12872_2024_3708_Fig2_HTML\" id=\"d32e404\"/>", "<graphic xlink:href=\"12872_2024_3708_Fig3_HTML\" id=\"d32e424\"/>", "<graphic xlink:href=\"12872_2024_3708_Fig4_HTML\" id=\"d32e466\"/>", "<graphic xlink:href=\"12872_2024_3708_Fig5_HTML\" id=\"d32e963\"/>", "<graphic xlink:href=\"12872_2024_3708_Fig6_HTML\" id=\"d32e1263\"/>" ]

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{ "acronym": [ "AUC", "CE", "CMR", "LVO", "MCE", "PPV", "NPV", "ROC", "TEE", "TTE" ], "definition": [ "Area Under Curve", "Contrast echocardiography", "Cardiac magnetic resonance", "Left ventricular opacification", "myocardial contrast echocardiography", "Positive predictive value", "Negative predictive value", "Receiver operating characteristic curve", "transesophageal echocardiography", "Transthoracic echocardiography" ] }

[ "<title>Introduction</title>", "<p id=\"Par9\">Critical obstetric hemorrhage is a significant cause of maternal mortality, currently accounting for 27% of maternal deaths worldwide and is the leading cause of maternal mortality in Japan, accounting for 22% of maternal deaths [##UREF##0##1##, ##REF##35715763##2##]. Because disseminated intravascular coagulation (DIC) can easily complicate even moderate amounts of blood loss, especially in cases of obstetric hemorrhage with underlying disease [##REF##35023983##3##], a decision must be made to perform a pregnancy-related hysterectomy at the appropriate time. In particular, the proportion of pregnant women with an underlying condition of placenta accreta spectrum (PAS) has continued to increase with the recent increase in cesarean section deliveries, and the associated number of PAS cesarean hysterectomies has also increased. These findings are evident in data from two recently published large multinational cohort studies [##REF##28755459##4##], in which PAS cesarean hysterectomies performed as the final step in a management protocol for massive hemorrhage associated with PAS disorders are associated with considerable maternal morbidity and mortality reportedly high. We need to be aware of this condition and the difficulties in its diagnosis and management and be prepared for the surgical procedure and its management.</p>", "<p id=\"Par10\">Despite the increasing trend, the occasions when PAS cesarean hysterectomy must be performed are extremely rare. Therefore, many surgical procedures and general management algorithms have been proposed by quality centers with a multidisciplinary approach, but unfortunately, not all of them are based on pathologically confirmed cases of PAS [##REF##31126811##5##]. Despite the high morbidity and mortality associated with hysterectomy, many studies unanimously suggest that it should be performed under multidisciplinary management. Recently, a multidisciplinary management algorithm has gained attention, proposing the involvement of gynecologic oncologists in the surgical management of PAS cesarean hysterectomies. The rationale behind this proposal is that the changes occurring in the female reproductive system during pregnancy add complexity to PAS cesarean hysterectomies [##REF##35781164##6##].</p>", "<p id=\"Par11\">The purpose of this study, which is a single-center, retrospective study, is to evaluate the diagnostic, surgical, and management of cases in which obstetricians and gynecologic oncologists performed multidisciplinary management and PAS cesarean hysterectomy, to determine the impact on maternal morbidity, and to help establish this troublesome treatment modality in the future.</p>" ]

[ "<title>Materials and methods</title>", "<title>Cases and surgical procedures</title>", "<p id=\"Par12\">A case series study of single, non-normal pregnancies pathologically confirmed as PAS in hysterectomized uteri between 2013 and 2022 at the University of Fukui. The primary endpoint was to assess intraoperative and postoperative complications associated with hysterectomy. The definition of complications was determined based on Clavien-Dindo classification [##REF##19638912##7##]. Secondary endpoints were to examine preoperative diagnostic ability and risk factors for PAS. In addition, we examined and evaluate the utility of management interventions implemented in the sequence of events leading to obstetric crisis hemorrhage and PAS cesarean hysterectomy. We examined whether these endpoints differed between patients who underwent hysterectomy with a Shock Index (S.I.) &gt; 1.5 at hysterectomy (Group I; S.I.&gt;1.5) and those who underwent total hysterectomy before reaching this state (Group II; S.I. ≤ 1.5) [##REF##24373705##8##, ##REF##25546050##9##].</p>", "<p id=\"Par13\">Our surgical procedure for PAS cesarean hysterectomy, while comparable to non-obstetric hysterectomy in general steps, exhibits distinctive characteristics that are tailored to the complexity of PAS. The operation is meticulously planned and executed by our multidisciplinary team, which is led by experienced gynecologic oncologists. The following outlines the full operative steps.</p>", "<title>Preoperative planning and team assembly</title>", "<p id=\"Par16\">A comprehensive preoperative plan is formulated, with a multidisciplinary team at the helm to ensure all necessary expertise is available. This team includes, but is not limited to, gynecologic oncologists, anesthesiologists, neonatologists, and urologists.</p>", "<title>Vascular access and monitoring</title>", "<p id=\"Par19\">Central venous access is established to facilitate rapid fluid administration and central venous pressure monitoring. An arterial line is placed for continuous blood pressure monitoring and regular blood gas analysis. The patient’s hemodynamic status is closely monitored throughout the procedure. Adjustments to medication regimens are made in real-time, based on ongoing assessments of blood loss, uterine tone, and the patient’s overall condition.</p>", "<title>Anesthetic management</title>", "<p id=\"Par22\">Anesthesia is initiated with either spinal or epidural anesthesia. In cases where a total hysterectomy is anticipated, the patient is transitioned to general anesthesia to ensure patient immobility and optimal pain control.</p>", "<title>Patient positioning</title>", "<p id=\"Par25\">The patient is positioned in the lithotomy position to provide the surgical team with adequate access to the operative field.</p>", "<title>Hemostatic measures preparation</title>", "<p id=\"Par28\">\n<list list-type=\"bullet\"><list-item><p id=\"Par29\">Intrauterine balloon catheters (such as the Atom Uterine Compression Balloon) are prepared for potential rapid deployment to control uterine bleeding.</p></list-item><list-item><p id=\"Par30\">Uterine compression sutures with blunt needles are on standby for immediate use if necessary.</p></list-item><list-item><p id=\"Par31\">Iliac artery balloon occlusion is prepared in collaboration with the radiology department. Toe SpO2 monitors are also attached to monitor peripheral perfusion. When employed, the balloon is expanded for 15 min with 5-min intervals, without heparinization to mitigate the risk of bleeding.</p></list-item></list>\n</p>", "<title>Blood products and transfusion management</title>", "<p id=\"Par34\">Transfusion preparations include the availability of autologous blood (4–8 units) and allogeneic blood with 10 units of red blood cells and 10 units of fresh frozen plasma, ensuring readiness for potential massive blood loss.</p>", "<title>Insertion of bilateral ureteral stents</title>", "<p id=\"Par37\">In all planned surgeries, bilateral ureteral stents are inserted preoperatively. This step is crucial for the identification and preservation of the ureters during the surgery.</p>", "<title>Uterine incision and fetal extraction</title>", "<p id=\"Par40\">The uterus is typically incised transversely, unless otherwise indicated by the placental position. The method of fetal extraction is determined by fetal position and placental location, with care taken to avoid placental disruption.</p>", "<title>Administration of ecbolics and other medications</title>", "<p id=\"Par43\">Immediately after the delivery of the fetus, ecbolics are administered to control bleeding. Intravenous oxytocin is administered as the first-line agent. A 10 units of bolus dose is given initially, followed by a continuous infusion to sustain uterine contractions. The dose is titrated based on the response of the uterus and the clinical judgment of the attending anesthesiologist and obstetrician. The use of additional ecbolics, such as methylergonovine or carboprost, is considered if the response to oxytocin is inadequate or if there is a contraindication to its use. The selection of these agents is tailored to the individual’s clinical status, including blood pressure and any pre-existing medical conditions.</p>", "<p id=\"Par44\">Simultaneously, the uterine incision wound is quickly sutured simply to hemostat and promote uterine contractions.</p>", "<title>Use of energy devices</title>", "<p id=\"Par47\">Energy devices such as Bipolar scissors (Ellman-Japan, Osaka, Japan) and HARMONIC FOCUS® (ETHICON, Bridgewater, NL, USA) are employed for cutting and coagulation, which minimizes blood loss and enhances precision in tissue dissection. The upper uterine ligament is fully ligated and severed. The ovaries are typically preserved.</p>", "<title>Bladder and uterine cavity preparation</title>", "<p id=\"Par50\">To facilitate a complete hysterectomy, the lateral cavity of the bladder is meticulously expanded, and the lateral cavity of the uterus is secured before dissection begins. This preparation is critical for safely accessing the surgical planes.</p>", "<title>Cystocele dissection and release of the uterus</title>", "<p id=\"Par53\">Following the separation of the bladder, the sacrouterine ligaments are transected, and the Douglas fossa peritoneum is incised to release the uterus posteriorly and laterally.</p>", "<title>The “holding-up uterus” method</title>", "<p id=\"Par56\">Our unique ‘holding-up uterus’ technique is then employed. The surgeon places one hand in the cysto-uterine fossa and the other in the Douglas fossa to grasp and lift the entire uterus. This method not only provides superior visualization but also creates necessary distance between the ureter and the cervix, facilitating the safe liberation of the ureter.</p>", "<title>Identification and division of uterine artery</title>", "<p id=\"Par59\">With the uterus lifted, the uterine artery can be clearly identified and safely divided. This step is critical to controlling the blood supply to the uterus and ensuring hemostasis.</p>", "<title>Completion of hysterectomy</title>", "<p id=\"Par62\">The cervix is amputated at the predetermined site, identified by inserting a finger into the posterior fornix and lifting the cervix. The uterus is then completely removed.</p>", "<title>Hemostasis verification</title>", "<p id=\"Par65\">A thorough examination of the surgical field is conducted to confirm complete hemostasis. If any bleeding points are identified, they are addressed immediately with additional sutures, electrocautery, or application of hemostatic agents as required. The bilateral uterine arteries and the site of the cervix, particularly, are inspected meticulously given their potential as primary sources of hemorrhage.</p>", "<title>Application of adhesion prevention agents</title>", "<p id=\"Par68\">Once hemostasis is confirmed, adhesion prevention agents are applied. These may include hyaluronic acid-based gels or oxidized regenerated cellulose, which are strategically placed over areas prone to adhesion formation, such as the raw surfaces created by dissection. This step is crucial for reducing the risk of postoperative adhesions, which can lead to chronic pain and ileus.</p>", "<p id=\"Par69\">Each step is conducted with utmost precision, keeping in mind the altered anatomy due to PAS. Our technique is notable for the emphasis on preoperative stenting of the ureters, the use of advanced energy devices, and the strategic ‘holding-up uterus’ maneuver, all of which are pivotal for the success of these complex surgical procedures. (Fig. ##FIG##0##1## and Additional file ##SUPPL##0##1##)</p>", "<p id=\"Par70\">\n\n</p>", "<title>Statistical analysis</title>", "<p id=\"Par71\">Continuous variables were described using mean ± standard deviation. To evaluate normally distributed data, the student t-test was utilized, and Mann-Whitney’s U test was used for between-group comparisons to evaluate data that were not normally distributed. PAS and diagnostic efficiency in ultrasonography and MRI examinations were performed with the binomial distribution test, and significance between the two was performed with Fisher’s exact definite test. The IBM Statistical Package for the Social Sciences (SPSS, version 22, SPSS Inc., Chicago, IL, USA) software was used in all analyses. P-values &lt; 0.05 were judged as significant.</p>" ]

[ "<title>Results</title>", "<p id=\"Par72\">Twelve patients who were managed conservatively for obstetric crisis hemorrhage at the University of Fukui Hospital from 2013 to 2023, but who ultimately underwent PAS caesarean hysterectomy. Pathology included four cases of simple adherent placenta (FIGO Grade 1), seven cases of invasive placenta (FIGO Grade 2) and one case of placental penetration (FIGO Grade 3) (Sup. Tables ##SUPPL##0##1##, ##SUPPL##2##2##) [##REF##31173360##10##].</p>", "<p id=\"Par73\">The group that underwent total hysterectomy with S.I. &gt; 1.5 (Group I) had 6 cases with a total blood loss of 5490 mL (± 1821 mL), and the group that underwent total hysterectomy by S.I. ≤ 1.5 (Group II) had a total blood loss of 1959 mL (± 909 mL). With regard to intraoperative and postoperative complications, there were no serious complications above Grade 3 or deaths in Group I, although significantly more complications occurred in Group I. The only intraoperative complication was a partial bladder injury: one in Group I and the other in Group II. Both were completely healed by surgical repair sutures at the same time (Table ##TAB##0##1##).</p>", "<p id=\"Par74\">\n\n</p>", "<p id=\"Par75\">None of the three cases in which PAS was suspected preoperatively and combined with uterine artery embolization (UAE) had any serious complications. In addition, a total hysterectomy with UAE performed immediately after cesarean section and additional prophylactic intravascular balloon catheter placed within 7 days after surgery was safe. Three out of three patients who underwent emergency common iliac artery balloon occlusion (CIABO) and then primary total hysterectomy with S.I. &gt; 1.5 developed postoperative Grade 2 thrombosis. Three patients in group I had preoperative suspicion of placenta accreta and underwent scheduled surgery, and three patients had no suspicion of placenta accreta and underwent emergency total hysterectomy. Three patients in group II also had preoperative suspicion of placenta accreta, while three had no suspicion of placenta accreta. Preoperative intervention for obstetric crisis hemorrhage due to PAS was more common in group I, and intervascular radiology was comparable in both groups (Table ##TAB##1##2##).</p>", "<p id=\"Par76\">\n\n</p>", "<p id=\"Par77\">At our institution, initial screening was performed with ultrasound, and MRI was the second imaging modality of choice when PAS was suspected. In the present study, there were 6 suspected cases on ultrasound and 4 suspected cases on MRI. The present study did not predict the diagnosis of PAS or cases with S.I. &gt; 1.5 on risk factor assessment or preoperative imaging evaluation. The diagnostic efficiency of ultrasound and MRI examinations performed at our institution with PAS was 0.5 (50%), with a binomial distribution binomial distribution test, the point estimate for preoperative diagnosis of PAS by ultrasound was 0.5 (50%), with a 95% confidence interval of 0.218 (21.8%) to 0.782 (78.2 (%). For MRI, the rate was 0.444 (44.4%), with 95% confidence intervals ranging from 0.121 (12.1%) to 0.767 (76.7%). There was no difference in diagnostic efficiency between the two, with a P value = 0.3306 in Fisher’s exact definite test.</p>", "<p id=\"Par78\">The history of the patients in the present study was characterized by the fact that one patient had undergone three cesarean sections and two patients had undergone one cesarean section. Six patients had total placenta previa. Other surgical procedures that caused surgical damage to the uterine wall, such as surgical hysteroscopy, and suction curettage were observed in three cases. Notable history includes one patient with a history of UAE, two patients with a uterine cavity length of less than 5 cm at non-pregnancy, and two patients with systemic lupus erythematosus [##REF##35780086##11##]. In vitro fertilization (using cryopreserved embryos) was performed in 7 cases. The only significant difference was that 2 assisted reproductive technology (ART) pregnancies with a pre-pregnancy uterine cavity length of less than 5 cm were observed in Group I (Table ##TAB##2##3##). There was no significant difference between the two groups in pre-operative management, but there was more pre-operative management in Group I (Table ##TAB##1##2##).</p>", "<p id=\"Par79\">\n\n</p>" ]

[ "<title>Discussion</title>", "<p id=\"Par80\">Our “Holding-up uterus” method during PAS cesarean hysterectomy, even in critical situations with S.I. &gt;1.5, facilitates ureteral emancipation, identification of the uterine artery, and facilitates the Pelosi method [##UREF##1##12##] in which the bladder is finally detached after cutting the vaginal wall, avoiding adhesions on the anterior bladder from the previous cesarean-section. In cases of severe adhesions, the posterior vaginal canal may be opened first, facilitating the treatment of the basal ligament and dissection of the bladder and anterior vaginal wall, which may be an extremely useful method for complete hysterectomy.</p>", "<p id=\"Par81\">PAS cesarean hysterectomy has become the gold standard as the final step in the management protocol for massive hemorrhage associated with PAS disorders. However, this primary radical surgical treatment is associated with a high incidence of maternal surgical-related adverse events, particularly massive hemorrhage, and damage to surrounding organs (40–50%) and maternal death (approximately 7%) [##UREF##2##13##, ##REF##23869630##14##]. Pregnancy-related hysterectomy for PAS is considerably more technically challenging than hysterectomy for uterine atony because of the higher risk of adjacent organ injury [##REF##23638753##15##]. Urinary tract injuries have been reported in 29% of surgeries, with lacerations of the bladder reported in 76%, ureteral injuries in 17%, and urogenital fistulas in 5% [##REF##23003574##16##].</p>", "<p id=\"Par82\">The procedure for pregnancy-related hysterectomy is identical to that for non-pregnancy hysterectomy [##REF##28268196##17##]. However, during the operation, one must be aware of the changes that occur in the female reproductive organs during pregnancy [##REF##28268196##17##]. As the uterus enlarges, it becomes more difficult to manipulate it and to visualize the entire pelvis. In addition, the ureters may become tortuous and dilated, resulting in significant hydroureteria. Tissue fragility and edema increase. Most importantly, uterine blood flow increases 10- to 30-fold in late pregnancy, and pregnant women with underlying diseases such as PAS are more prone to complications of DIC, even with moderate blood loss [##REF##35023983##3##]. The reason is that it has been reported to shorten operative time and decrease blood loss. We use energy devices during hysterectomy. The reason is that it has been reported to reduce operative time and blood loss [##REF##25813428##18##]. And supra-hysterectomy is often performed because of the short operating time required under conditions of critical bleeding, the increased risk of ureteral injury during emergency surgery, and, in the case of placenta previa, the fully dilated cervix, which makes identification of the transition from the cervix to the vagina difficult. However, FIGO recommends performing a supra-hysterectomy because of the potential risk of malignancy in the residual cervix and the consequent need for periodic cervical cytology, and because the residual cervix is a cause of postoperative bleeding (placenta previa PAS) [##REF##29405317##19##]. It has also recently been shown that identification and clamping of the bilateral uterine arteries and removal of the uterus as low as possible at the inferior margin of the placenta, avoiding the ureter, reduces maternal bleeding morbidity the most [##REF##30607590##20##].</p>", "<p id=\"Par83\">For this reason, we try to perform total hysterectomies using the “Holding-up uterus” technique even in emergency situations. In this context, our “Holding-up uterus” technique during PAS cesarean hysterectomy facilitates ureteral emancipation, identification of the uterine artery, and dissection of the adherent bladder. Currently, even in critical situations of Group I (S.I. &gt; 1.5), the operation can be performed in a short time. One case of bladder injury was observed in each Group I and II, but it could be easily repaired.</p>", "<p id=\"Par84\">Another proposed radical surgery is delayed hysterectomy. In this procedure, the uterus is closed after delivery, leaving the placenta in uterus, the mother’s abdomen is closed, and then a total hysterectomy is performed 3–12 weeks later. The rationale for this procedure is that uterine perfusion is reduced after delivery, even if the placenta is left in uterus, and the subsequent surgery is less risky for the woman due to uterine retraction and decreased vascularity [##REF##30260097##21##]. However, abdominal closure for the purpose of delayed total hysterectomy may be followed by massive bleeding due to partial abruption of the placenta.</p>", "<p id=\"Par85\">Prophylactic intravascular balloon catheters have been proposed to reduce intraoperative bleeding during hysterectomy. It improves maternal morbidity and allows the surgeon to operate in a “cleaner” and more visible operative field. However, the incidence of potential complications is high, including the risk of vessel rupture, thromboembolism development, and impaired blood supply to the lower extremity [##REF##33204176##22##]. In addition, PAS is associated with extensive abnormal neovascularization, and occlusion of some pelvic vessels may increase blood loss from collateral blood vessels [##REF##35781164##6##, ##REF##33204176##22##, ##REF##30849356##23##]. Furthermore, two RCTs comparing balloon catheter placement in the iliac artery versus no intervention at all found no difference in the number of packed red blood cells transfused to patients, and a recent RTC comparing bilateral internal iliac artery ligation versus control found no difference regarding intraoperative blood loss [##REF##35781164##6##, ##REF##33204176##22##, ##REF##30849356##23##]. In our study, in the critical situation of group I (S.I. &gt;1.5), total hysterectomy with intravascular balloon insertion was more likely to cause postoperative venous thrombosis.</p>", "<p id=\"Par86\">The most important risk factor for the development of PAS has been shown to be the number of previous cesarean Sect. [##REF##35781164##6##]. In the present study, one patient had had three cesarean sections and two had had one cesarean section. In vitro fertilization (using cryopreserved embryos), a risk factor that has received much attention recently, was used in seven cases. In terms of preoperative assessment factors, 67% of pregnancies in both groups were ART pregnancies. The only significant difference was that two ART pregnancies with hypoplastic uteri were observed in Group I. Although the definition of hypoplastic uterus is not known, both pregnancies were ART pregnancies with small pre-eclamptic uteri and a cervix to uterus size ratio of 1:1. The uterine cavity length was about 5 cm.</p>", "<p id=\"Par87\">There was no difference in diagnostic efficiency between PAS and diagnostic efficiency between ultrasound and MRI examinations performed at our hospital. This result did not differ significantly from the prenatal detection rate of PAS by ultrasound in two large population-based studies in the U.K. and U.S [##REF##29660186##24##, ##REF##23924326##25##]. Nearly half of PAS is diagnosed only at birth, and even among pregnant women who underwent prenatal MRI testing, over a quarter were detected at birth [##REF##28268196##17##]. The inclusion of imaging and clinical factors has been reported to improve the prenatal diagnosis of PAS [##REF##28268196##17##] and should be actively investigated in the future.</p>", "<p id=\"Par88\">“Holding-up uterus” method has shown several significant strengths in the management of PAS cesarean hysterectomy. Firstly, it has proven to be beneficial in providing improved operative visualization. By holding up the uterus, the technique allows for a clearer view of the ureters and uterine arteries, which is crucial in the context of PAS where normal anatomical landmarks may be distorted. This is of particular importance as it facilitates the Pelosi method and other critical steps such as ureteral emancipation. Moreover, the method has been associated with reduced operative time and blood loss, which are critical outcomes in PAS cesarean hysterectomy. This reduction is not only beneficial for the patient’s immediate surgical outcome but also has long-term implications on their recovery process. The facilitation of complex surgical steps such as the dissection of the adherent bladder and identification of the uterine artery is another advantage that cannot be overstated. This simplification is vital, especially in the backdrop of the high incidence of maternal surgical-related adverse events. The ability of the technique to be effectively utilized even in emergency situations where the shock index is high is a testament to its robustness. This is underscored by our findings that even in group I (S.I. &gt; 1.5), the operation can be executed in a short time frame, indicating that the method is adaptable to critical situations. Additionally, the practice of performing total hysterectomies aligns with recommendations to reduce potential risks associated with residual cervical tissue.</p>", "<p id=\"Par89\">However, the limitations of our technique are as important to consider as its strengths. Organ injury remains a substantial risk in PAS cesarean hysterectomy, and while our method aids in minimizing this risk, it does not eliminate it. The surgeon’s experience and the technique’s learning curve are additional factors that could affect the outcomes of the surgery. Our findings also suggest that the generalizability of the “Holding-up uterus” method might be limited by variations in surgical practices across different institutions. Furthermore, the comparative data on the “Holding-up uterus” method versus other techniques is not extensive. This lack of robust comparative data could be seen as a limitation as it does not allow for a conclusive argument for the superiority of our method. While we have observed benefits in our own practice, additional comparative studies are required to validate these findings further. Lastly, despite the method being designed to minimize complications, the potential for unforeseen surgical difficulties and postoperative complications remains. This highlights the necessity of vigilance and preparedness for managing such events should they occur.</p>", "<p id=\"Par90\">While our “Holding-up uterus” method demonstrates considerable promise, particularly in facilitating ureteral emancipation, identification of the uterine artery, and dissection of the adherent bladder, there is a need for a careful evaluation of the risks and benefits. Future studies should aim to provide a more comprehensive comparison with other techniques, evaluate the learning curve associated with the method, and explore strategies to mitigate the inherent risks of PAS cesarean hysterectomy.</p>" ]

[ "<title>Conclusions</title>", "<p id=\"Par91\">The primary surgical treatment for PAS is pregnancy-related hysterectomy, which is technically challenging due to the increased risk of adjacent organ injury. The “Holding-up uterus” technique during PAS cesarean hysterectomy facilitates ureteral emancipation, identification of the uterine artery, and dissection of the adherent bladder, making the total hysterectomy easier to perform even in critical situations. Prophylactic intravascular balloon catheters have been proposed to reduce intraoperative bleeding during hysterectomy, but their use may lead to potential complications. The most important risk factor for the development of PAS is the number of previous cesarean sections.</p>", "<p id=\"Par92\">Future research should focus on collecting high-quality data from well-designed prospective studies on a multidisciplinary team approach to diagnosis (prenatal imaging) and management strategies.</p>" ]

[ "<title>Background</title>", "<p id=\"Par1\">Placenta accreta spectrum (PAS) cesarean hysterectomy is performed under conditions of shock and can result in serious complications. This study aimed to evaluate the usefulness of the “Holding-up uterus” surgical technique with a shock index (S.I.) &gt; 1.5.</p>", "<title>Methods</title>", "<p id=\"Par2\">Twelve patients who underwent PAS cesarean hysterectomy were included in the study.</p>", "<title>Results</title>", "<p id=\"Par3\">Group I had S.I. &gt; 1.5, and group II had S.I. ≤ 1.5. Group I had more complications, but none were above Grade 3 or fatal. Preoperative scheduled uterine artery embolization did not result in serious complications, but three patients who had emergency common iliac artery balloon occlusion (CIABO) and a primary total hysterectomy with S.I. &gt; 1.5 had postoperative Grade 2 thrombosis. Two patients underwent manual ablation of the placenta under CIABO to preserve the uterus, both with S.I. &gt; 1.5.</p>", "<title>Conclusions</title>", "<p id=\"Par4\">The study found that the “Holding-up uterus” technique was safe, even in critical situations with S.I. &gt; 1.5. CIABO had no intervention effect. The study also identified assisted reproductive technology pregnancies with a uterine cavity length of less than 5 cm before conception as a critical factor.</p>", "<title>Supplementary Information</title>", "<p>The online version contains supplementary material available at 10.1186/s12893-024-02311-8.</p>", "<title>What does this study add to the clinical work</title>", "<p id=\"Par6\">\n<list list-type=\"bullet\"><list-item><p id=\"Par7\">•The holding-up uterus technique following periuterine cavity expanded enables safe placenta accreta spectrum (PAS) cesarean hysterectomy.</p></list-item><list-item><p id=\"Par8\">•PAS hysterectomy with holding-up uterus is effective even in situation of critical bleeding or shock.</p></list-item></list>\n</p>", "<title>Supplementary Information</title>", "<p>The online version contains supplementary material available at 10.1186/s12893-024-02311-8.</p>", "<title>Keywords</title>" ]

[ "<title>Electronic supplementary material</title>", "<p>Below is the link to the electronic supplementary material.</p>", "<p>\n\n</p>", "<p>\n\n</p>", "<p>\n\n</p>" ]

[ "<title>Author contributions</title>", "<p>Conceptualization, J.T. and Y.Y.; data acquisition, A.S. and J.T.; data analysis, interpretation, and statistical analysis, J.T., D.I. and Y.Y.; writing (original draft preparation), and review and editing, M.O., H.K., D.I., H.T., N.T., T.K. All authors have read and agree to the published version of the manuscript.</p>", "<title>Funding</title>", "<p>Not applicable.</p>", "<title>Data availability</title>", "<p>The data presented in this study are available in Tables ##TAB##0##1##, ##TAB##1##2## and ##TAB##2##3## and supplemental Tables ##SUPPL##0##1##, ##SUPPL##2##2##.</p>", "<title>Declarations</title>", "<title>Ethics approval and consent to participate</title>", "<p id=\"Par94\">The study was conducted in accordance with the Declaration of Helsinki, and approved by the Institutional Review Board of the University of Fukui Hospital (IRB Number: 20170100). Informed consent was obtained from all subjects involved in the study.</p>", "<title>Consent for publication</title>", "<p id=\"Par95\">Not applicable.</p>", "<title>Competing interests</title>", "<p id=\"Par93\">The authors declare no competing interests.</p>" ]

[ "<fig id=\"Fig1\"><label>Fig. 1</label><caption><p>(<bold>a</bold>) Shows the “Holding-up uterus” technique in the PAS cesarean hysterectomy. (<bold>b</bold>) is reproduced from Teiou sekkai no kyoukasyo by Yoshida et al. published in KANEHARA &amp; Co., LTD, pp. 163, 2017</p></caption></fig>" ]

[ "<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Intraoperative and postoperative complications associated with hysterectomy</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\"/><th align=\"left\">Group 1<break/>S.I. &gt; 1.5 (<italic>n</italic> = 6)</th><th align=\"left\" colspan=\"2\">Group 2<break/>S.I. ≤ 1.5 (<italic>n</italic> = 6)</th><th align=\"left\">P-value</th></tr></thead><tbody><tr><td align=\"left\">Total blood loss, including during hysterectomy (mL)*</td><td align=\"left\" colspan=\"2\">5490 ± 1821</td><td align=\"left\">1959 ± 909</td><td align=\"left\"/></tr><tr><td align=\"left\">Intraoperative Grade 2 or higher adverse events**</td><td align=\"left\" colspan=\"2\">1 (16)</td><td align=\"left\">1 (16)</td><td align=\"left\">0.104</td></tr><tr><td align=\"left\">Postoperative Grade 2 or higher adverse events**</td><td align=\"left\" colspan=\"2\">4 (16)</td><td align=\"left\">2 (33)</td><td align=\"left\">0.018</td></tr><tr><td align=\"left\">Hospitalization for more than 7 days after hysterectomy**</td><td align=\"left\" colspan=\"2\">6 (33)</td><td align=\"left\">1 (16)</td><td align=\"left\">0.001</td></tr><tr><td align=\"left\">FIGO stage**</td><td align=\"left\" colspan=\"2\"><p>Grade1; 2</p><p>Grade2; 3</p><p>Grade3; 1</p></td><td align=\"left\"><p>Grade1; 2</p><p>Grade2; 4</p></td><td align=\"left\">0.664</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab2\"><label>Table 2</label><caption><p>Interventions for preoperative treatment of obstetric crisis hemorrhage by placenta accreta spectrum</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\"/><th align=\"left\">Group 1<break/>S.I. &gt; 1.5 (<italic>n</italic> = 6)</th><th align=\"left\">Group 2<break/>S.I. ≤ 1.5 (<italic>n</italic> = 6)</th><th align=\"left\">P-value</th></tr><tr><th align=\"left\" colspan=\"4\">Medications (uterotonic agents)</th></tr></thead><tbody><tr><td align=\"left\">oxytocin*</td><td align=\"left\">5 (83)</td><td align=\"left\">3 (50)</td><td align=\"left\">0.339</td></tr><tr><td align=\"left\" colspan=\"4\">\n<bold>Surgical Intervention</bold>\n</td></tr><tr><td align=\"left\">Interventional radiology*</td><td align=\"left\">4 (66)</td><td align=\"left\">3 (50)</td><td align=\"left\">0.339</td></tr><tr><td align=\"left\">Intrauterine balloon tamponade*</td><td align=\"left\">3 (50)</td><td align=\"left\">0</td><td align=\"left\">0.082</td></tr><tr><td align=\"left\">Uterine compression suture*</td><td align=\"left\">4 (66)</td><td align=\"left\">0</td><td align=\"left\">0.066</td></tr><tr><td align=\"left\">Vaginal/uterine packing *</td><td align=\"left\">2 (33)</td><td align=\"left\">2 (33)</td><td align=\"left\">0.438</td></tr><tr><td align=\"left\">Placental bed suture*</td><td align=\"left\">3 (50)</td><td align=\"left\">0</td><td align=\"left\">0.082</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab3\"><label>Table 3</label><caption><p>Preoperative risk factors for placenta accreta spectrum</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\"/><th align=\"left\">Group 1<break/>S.I. &gt; 1.5 (<italic>n</italic> = 6)</th><th align=\"left\">Group 2<break/>S.I. ≤ 1.5 (<italic>n</italic> = 6)</th><th align=\"left\">P-value</th></tr></thead><tbody><tr><td align=\"left\">Age*</td><td align=\"left\">35.7 ± 7.14</td><td align=\"left\">36.0 ± 4.6</td><td align=\"left\"/></tr><tr><td align=\"left\">History of cesarean section**</td><td align=\"left\">1 (16)</td><td align=\"left\">2 (33)</td><td align=\"left\">0.809</td></tr><tr><td align=\"left\">Abortion**</td><td align=\"left\">2 (33)</td><td align=\"left\">1 (16)</td><td align=\"left\">0.191</td></tr><tr><td align=\"left\">History of intrauterine curettage**</td><td align=\"left\">2 (33)</td><td align=\"left\">1 (16)</td><td align=\"left\">0.191</td></tr><tr><td align=\"left\">ART**</td><td align=\"left\">4 (67)</td><td align=\"left\">4 (67)</td><td align=\"left\">0.438</td></tr><tr><td align=\"left\">History of placental delivery difficulty**</td><td align=\"left\">1 (16)</td><td align=\"left\">1 (16)</td><td align=\"left\">0.104</td></tr><tr><td align=\"left\">History of SLE**</td><td align=\"left\">1 (16)</td><td align=\"left\">1 (16)</td><td align=\"left\">0.104</td></tr><tr><td align=\"left\">Pre-pregnancy uterine cavity length is less than 5 cm**</td><td align=\"left\">2 (33)</td><td align=\"left\">0 (0)</td><td align=\"left\">0.039</td></tr></tbody></table></table-wrap>" ]

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[ "<supplementary-material content-type=\"local-data\" id=\"MOESM1\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM2\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM3\"></supplementary-material>" ]

[ "<table-wrap-foot><p>S.I.: Shock index * Data presented as Mean ± SD. **Data presented as n (%). Independent sample <italic>t</italic> test</p></table-wrap-foot>", "<table-wrap-foot><p>* Data presented as n (%). Independent sample <italic>t</italic> test</p></table-wrap-foot>", "<table-wrap-foot><p>ART: assisted reproductive technology; SLE: systemic lupus erythematosus; ARDS: Acute respiratory distress syndrome; NA: Not available</p><p>* Data presented as Mean ± SD. **Data presented as n (%). Independent sample <italic>t</italic> test</p></table-wrap-foot>", "<fn-group><fn><p><bold>Publisher’s Note</bold></p><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn></fn-group>" ]

[ "<graphic xlink:href=\"12893_2024_2311_Fig1_HTML\" id=\"d32e382\"/>" ]

[ "<media xlink:href=\"12893_2024_2311_MOESM1_ESM.docx\"><caption><p><bold>Supplementary Material 1: Supplementary Table1.</bold> Case series</p></caption></media>", "<media xlink:href=\"12893_2024_2311_MOESM2_ESM.mp4\"><caption><p>\n<bold>Supplementary Material 2</bold>\n</p></caption></media>", "<media xlink:href=\"12893_2024_2311_MOESM3_ESM.docx\"><caption><p><bold>Supplementary Material 3: Supplementary Table2.</bold> Case series</p></caption></media>" ]

{ "acronym": [], "definition": [] }

[ "<title>Introduction</title>", "<p id=\"Par5\">Stroke is a leading cause of long-term disability in the United States, affecting more than 800,000 people per year [##UREF##0##1##]. Unilateral paralysis (hemiparesis) affects up to 80% of stroke survivors, leaving many to struggle with activities of daily living (ADLs) including the ability to manipulate objects such as doors, utensils, and clothing due to decreased upper-extremity muscle coordination and weakness [##REF##19608100##2##]. Restoration of hand and arm function to improve independence and overall quality of life is a top priority for stroke survivors and caregivers [##REF##23227818##3##]. Intensive physical rehabilitation is the current gold standard for improving motor function after stroke. Unfortunately, 75% of stroke survivors, caregivers, and health care providers report that current upper extremity training practice is insufficient [##REF##30497993##4##]. The development of user-centric neurotechnologies to restore motor function in stroke survivors could address these unmet clinical needs through a range of different mechanisms, such as improving motivation, enhancing neuroplasticity in damaged sensorimotor networks, and enabling at-home therapy.</p>", "<p id=\"Par6\">Assistive technologies (AT) hold potential to restore hand function and independence to individuals with paralysis [##UREF##1##5##]. ATs, including exoskeletons and functional electrical stimulation (FES), can assist with opening the hand and also evoke grips strong enough to hold and manipulate objects [##REF##25621206##6##]. Additionally, these systems have been used therapeutically during rehabilitation to strengthen damaged neural connections to restore function [##REF##31900169##7##]. A wide variety of mechanisms to control ATs have been investigated including voice [##UREF##2##8##], switch [##REF##29752242##9##], position sensors [##UREF##3##10##], electroencephalography (EEG) [##REF##27555805##11##], electrocorticography (ECoG) [##REF##19381156##12##], intracortical microelectrode arrays (MEA) [##REF##28827605##13##], and electromyography (EMG) [##REF##33655789##14##]. Unfortunately, no single system has simultaneously delivered an intuitive, user-friendly system with a high degree-of-freedom (DoF) control for practical use in real-world settings [##REF##30497993##4##].</p>", "<p id=\"Par7\">Recent advances in portable, high-density EMG-based (HDEMG) systems have the potential to overcome several of these barriers and deliver an intuitive and entirely non-invasive AT control solution [##UREF##4##15##, ##REF##29023548##16##]. While various EMG-based ATs exist, including the commercially available MyoPro Orthosis [##UREF##4##15##], most of these systems use a small number of electrodes and rely on threshold-based triggering [##REF##33655789##14##]. Consequently, these systems have limited DoF control which constrains their practical use. Conversely, HDEMG systems consisting of dozens of electrodes and leveraging machine learning approaches to infer complex movement intention can provide high DoF control, significantly expanding functional use cases as well as increasing the proportion of the stroke population that could benefit from these technologies [##REF##29023548##16##–##REF##25389242##19##]. Currently, HDEMG systems are primarily research systems and are not optimized for usability, including being difficult to set up, requiring manual placement of electrodes, and being non-portable and bulky, which can hinder the successful translation of technologies [##REF##30497993##4##].</p>", "<p id=\"Par8\">To address these limitations, we developed the NeuroLife^® EMG System to decode complex forearm motor intention in chronic stroke survivors while simultaneously addressing end user needs. The EMG system was designed to be used as a control device for various end effectors, such as FES systems and exoskeletons. Additionally, the system was specifically designed to meet user needs in domains previously identified as high-value for stroke survivors: donning/doffing simplicity, device setup and initialization, portability, robustness, comfortability, size and weight, and intuitive usage [##REF##30497993##4##]. The sleeve is a wearable garment consisting of up to 150 embedded electrodes that measure muscle activity in the forearm to decode the user’s motor intention. A single zipper on one edge of the sleeve allows for a simplified and streamlined donning and doffing by the user and/or a caregiver. The sleeve design facilitates an intuitive setup process as embedded electrodes that span the entire forearm are consistently placed, eliminating the need for manual electrode placement on specific muscles. The lightweight stretchable fabric, similar to a compression sleeve, was chosen to enhance comfort for long-term use. The sleeve connects to backend Intan hardware housed in a lightweight, 8 × 10″ signal acquisition module appropriate for tabletop upper-extremity rehabilitation. Overall, these design features help address critical usability factors for ATs [##REF##30497993##4##].</p>", "<p id=\"Par9\">In this work, we demonstrate that our EMG system can extract task-specific myoelectric activity at high temporal and spatial resolution to resolve individual movements. Based on EMG data collected from seven individuals with upper extremity hemiparesis due to stroke, trained neural network machine learning models can accurately decode muscle activity in the forearm to infer movement intention, even in the absence of overt motion. We demonstrate the viability of this technique for online decoding, as two subjects used the system for closed-loop control of a virtual hand. This online demonstration is a promising step towards using HDEMG sleeves for high DoF control of ATs based on motor intention. Finally, we present usability data collected from study participants that highlight the user-centric design of the sleeve. These data will be used to inform future developments to deliver an effective EMG-based neural interface that meets end user needs.</p>" ]

[ "<title>Methods</title>", "<title>Subjects</title>", "<p id=\"Par10\">Seven individuals (3 female, 4 male; 60 ± 5 years) with a history of stroke participated in a study that recorded EMG using the NeuroLife EMG System while attempting various hand and wrist movements. Additionally, data were collected from seven able-bodied individuals (4 female, 3 male; 27 ± 1 years) to serve as a general comparison of EMG data and to benchmark decoding algorithms. Able-bodied subjects were employees of Battelle Memorial Institute, but none were authors of this work. Data were collected as part of an ongoing clinical study being conducted at Battelle Memorial Institute that was approved by the Battelle Memorial Institute Institutional Review Board. All participants provided written informed consent before participation, in accordance with the Declaration of Helsinki. Demographics of study subjects with stroke are provided in Table ##TAB##0##1## (data on able-bodied participants can be found in Additional file ##SUPPL##0##1##: Table ##TAB##0##1##). Eligibility criteria were set to recruit adult chronic stroke survivors with hemiparesis affecting the arm and hand that were able to follow 3-step commands and communicate verbally. Specific inclusion and exclusion criteria are listed in the Additional file ##SUPPL##0##1##: Methods.</p>", "<p id=\"Par11\">During the first session prior to EMG data collection, standardized clinical assessments were performed by a licensed occupational therapist in all subjects with stroke. These included the upper extremity section of the Fugl-Meyer (UE-FM) to assess upper extremity motor impairment, the Box and Blocks test to assess manual dexterity, and the Modified Ashworth test to assess spasticity of the finger, wrist, and elbow flexors. Based on predetermined exclusion criteria, an eighth subject was removed from data analysis due to hemispatial neglect affecting their ability to consistently follow movement cues.</p>", "<title>Experimental setup</title>", "<p id=\"Par12\">Subjects sat facing a computer monitor with their arms placed on a table, and the sleeve on the paretic arm for participants with stroke (Fig. ##FIG##0##1##). The sleeve was placed on the right arm for able-bodied subjects, regardless of handedness. The sleeve comprises a stretchable fabric with an embedded array of electrodes (Additional file ##SUPPL##0##1##: Fig. S1). Depending on the forearm size of the participant, a small, medium, or large sized sleeve was used containing 128 electrodes (64 channel pairs), 142 electrodes (71 channel pairs), or 150 electrodes (75 channel pairs), respectively. Each electrode is 12 mm diameter, spaced 25 mm apart, and wrap the forearm from elbow to wrist. With a flexible and lightweight nylon-Lycra hybrid material, the sleeve wears like a compression sleeve and weighs 180, 195, and 220 g for the small, medium, and large sleeves, respectively. A zipper on the ulnar edge of the sleeve allows for easy donning and doffing. Prior to donning, an electrode solution spray (Signaspray, Parker Laboratories, Fairfield, NJ) was applied to the subject’s forearm to improve signal quality. Bipolar EMG signals were sampled at 3 kHz with a gain of 192 V/V using an Intan Electrophysiology Amplifiers (Intan RHD2000, Intan Technologies, Los Angeles, CA) [##UREF##7##20##]. An embedded electrode in the sleeve near the elbow was used as a reference for all bipolar amplifiers. The sleeve was connected to a custom-built, 8 × 10″ footprint, EMG signal acquisition module, which then connected to a laptop computer (Fig. ##FIG##0##1## and Additional file ##SUPPL##0##1##: Figure S1a).</p>", "<p id=\"Par13\">The subjects were instructed to attempt a series of hand, wrist, and forearm movements. A series of images of the desired hand movement was presented on a computer monitor, and the subjects were instructed to attempt each movement shown to the best of their ability. Subjects were instructed to attempt the movement at 25–50% of their subjective maximal effort to minimize muscle fatigue and co-contractions throughout the session.</p>", "<p id=\"Par14\">The following movements were collected during the session: Hand Close (Power Grip), Hand Open, Index Extension, Thumb Flexion, Thumb Extension, Thumb Abduction, Forearm Supination, Forearm Pronation, Wrist Flexion, Wrist Extension, Two Point Pinch, and Key Pinch. These movements were identified by a licensed occupational therapist as highly relevant functional movements for dexterous hand use, and these movements have been used in similar studies [##REF##33256073##21##]. Recording blocks consisted of a single movement repeated 10 times (referred to as “single blocks”), or multiple movements repeated within a single recording block (referred to as “mixed blocks”). Every block began with an 8 s rest period, followed by alternating movement and rest periods. During mixed blocks, a collection of movements (e.g., Hand Close, Hand Open, Forearm Supination) were randomly presented to the subject with interleaved rest periods. Before beginning the block, subjects were shown the movement(s) in the upcoming block. For subjects with stroke, the time for each movement was randomly selected from a uniform distribution between 4 and 6 s, and rest time was randomly selected between 4 and 6 s. For able-bodied subjects, the movement and rest times were both set randomly between 2 and 3 s. The cue and rest times were shortened in able-bodied subjects due to faster movement times and the expectation of simpler decoding compared to the subjects with stroke. In the last recording session, a usability questionnaire assessing user needs (adapted from [##REF##30497993##4##]) was given to stroke subjects to evaluate the usability of the current sleeve design (responses from subjects are presented in Additional file ##SUPPL##0##1##: Table 5).</p>", "<p id=\"Par15\">We collected data from each stroke subject across 3–4 sessions lasting &lt; 2 h each. Data from all sessions were used to train the classifiers, with the last half of the data from the final session held out for testing. The sleeve was not doffed before collection of the test dataset in the final session. The total amount of training data per movement for subjects with stroke are shown in Additional file ##SUPPL##0##1##: Figure S3. For able-bodied experiments, data were collected in a single session with a total of 10 repetitions for each movement. The first 5 repetitions were used for training, and the last 5 repetitions were used for testing, without doffing the sleeve between. This structure was designed to simulate an envisioned use case in which a decoding algorithm would be calibrated for a rehabilitation session using both previous session data and data from a short same-day calibration protocol.</p>", "<p id=\"Par16\">To assess each subject’s ability to perform the movements without any assistance, each movement was scored by a licensed occupational therapist based on a scoring scheme adapted from the Action Research Arm Test (ARAT) [##UREF##6##18##]. The “observed movement score” was ranked using the following categories: 0 = no movement; 1 = incomplete range of motion; 2 = complete range of motion but impaired; 3 = normal.</p>", "<title>Pre-processing, windowing, and feature extraction</title>", "<p id=\"Par17\">The EMG data were bandpass filtered (20–400 Hz, 10th order Butterworth filter), and a 60 Hz notch filter was applied similar to previous studies [##REF##30606226##23##]. The root mean square (RMS) was extracted using consecutive 100 ms data windows with no overlap (Fig. ##FIG##1##2##B, ##FIG##1##C##). For decoding of movement intent during a given time window, the current window and three preceding windows were used, totaling 400 ms of RMS data used for each prediction. Next, the training data were normalized (mean = 0, variance = 1) and the testing data were normalized using the mean and variance from the training data.</p>", "<p id=\"Par18\">Classification of movement intention in stroke participants was performed in two different ways: (1) using the 2.5 s center window during a cue or rest period, or (2) on the continuous timeseries data. For the 2.5 s center window method, the middle 2.5 s of each cue and rest period during a block was extracted (Fig. ##FIG##2##3##A). This resulted in a total of 22 predictions of 100 ms binned RMS data per cue (2.5 s with the first three 100 ms bins removed for containing out-of-window data at the beginning of the cue). This method was applied to both the training and testing datasets to reduce noise from motion artifact and transient muscle activation by removing the transition periods, similar to previous studies [##REF##31570723##24##]. This dataset was used to evaluate different machine learning models for decoding the user’s movement intent. In able-bodied subjects, classification was performed as described above but with a 1.5 s center window during a cue or rest period, resulting in a total of 12 predictions. These data are presented in Fig. ##FIG##2##3## for participants with stroke and Additional file ##SUPPL##0##1##: Figure S5 for able-bodied subjects.</p>", "<p id=\"Par19\">For decoding of continuous timeseries data, we performed a dynamic cue shifting technique to account for the variability in the subject’s ability to respond to the onset and offset of cues. Latency between cue onset and the onset of EMG activity is a persistent problem within decoding that can lead to significant deficits in algorithm performance and is exacerbated in data recorded from subjects with neurological impairments such as stroke. Traditionally, these onset and offset variabilities are handled by shifting cues a predetermined amount of time based on reaction times [##REF##30250141##25##] or by assigning each cue manually [##REF##33256073##21##]. However, these methods still fail to capture the full distribution of onset and offset variability. Here, we use an automated approach to dynamically shift cue labels to match the EMG activity. The average EMG signal was aligned with the intended cue times, and residuals were calculated between the EMG signal and the signal mean for each cue segment. The transition point between segments was then iteratively optimized to minimize the sum of squared residuals (Additional file ##SUPPL##0##1##: Figure S4). Cue timings were shifted up to a maximum time of 2 s beyond the intended cue time.</p>", "<title>Classification</title>", "<p id=\"Par20\">Classification was performed using all recording blocks (single and mixed). Importantly, the testing consisted of the final 4 recording blocks of data collected for that subject. In other words, none of the training set occurred later in time than the testing set to prevent data leakage of time dependent signal fluctuations that could significantly influence decoding performance.</p>", "<p id=\"Par21\">Three classifiers were compared: a logistic regression (LR) model [##UREF##9##26##], a support vector machine (SVM) [##UREF##10##27##], and a neural network (NN). For the LR and SVM models, data were additionally preprocessed using principal component analysis for dimensionality reduction, keeping components that accounted for &gt; 95% of the variance. LR and SVM models were trained using the scikit-learn toolbox [##UREF##11##28##] in Python 3.8. To optimize hyperparameters for both LR and SVM, a grid search on the training data with fivefold cross validation was applied to tailor a specific model for each subject. Hyperparameter C was varied from 1e-4 to 1e4 for LR, and hyperparameters C and Gamma were varied from 1e-4 to 1e4 for SVM. The best performing model hyperparameter combinations for each were selected for evaluation.</p>", "<p id=\"Par22\">The NN was developed in Python 3.8 using the FastAI package [##UREF##12##29##]. FastAI defaults were used for training except where noted. The model architecture takes an input of a flattened N channels × 4 array from the N channels of the sleeve and 4, 100 ms windows of mean RMS signal. The input layer connects to two fully-connected dense layers, with size 1000 and 500 respectively, with batch normalization and the ReLU activation function between layers. The final layer had 13 classes corresponding to the 12 cued movements and rest. Finally, a Softmax activation function was applied to the model outputs to provide prediction probabilities for each of the movements. The predicted movement for a given time point was the movement with the greatest prediction probability. The training procedure used label smoothing cross entropy loss (p = 0.9) and the Adam optimizer. During training, dropout was applied to each layer with 20% probability to prevent overfitting. The learning rate was optimized using the FastAI learning rate finder tool [##UREF##12##29##]. Each model was trained for 400 epochs with early stopping criterion, using the one cycle training policy from FastAI.</p>", "<p id=\"Par23\">To simulate massed practice rehabilitation exercises, participants repeated movements with interleaved rest. We evaluated the decoding algorithms with two complementary metrics relevant to this use case and commonly used for similar applications. Accuracy was defined as the percentage of 100 ms time bins predicted by the classifier to be the same as ground truth similar to our group’s previous decoding study [##REF##34788156##30##]. Accuracy is a standard classification metric and provides a high temporal resolution metric of performance. Chance level accuracy was determined based on the percentage of labels equal to the majority class (Rest; 50.41%), which represents the accuracy of a naïve classifier. Since the majority class prediction yields the highest chance level accuracy of any random strategy in a 13-class problem (e.g. stratified, uniform, or majority), we chose to use this method to represent the naïve decoder option for all reported chance levels. When decoding a subset of the full 12 movement set (Fig. ##FIG##3##4##), the rest cues directly before each target movement were sampled to maintain rest at 50% of the sampled dataset to avoid biasing the results. We also present success rate as a decoding performance metric, similar to previous studies [##REF##30250141##25##]. A movement is considered successful if there is at least 1 s continuous period within a cue that is correctly decoded as the intended movement. The success rate is then calculated as the percentage of cues which are considered successful. This metric approximates an observer rating each cue as a binary success or failure and is more aligned with how a user would perceive performance.</p>", "<title>Real-time demonstration</title>", "<p id=\"Par24\">In separate sessions, we tested the performance of the decoder online in two stroke subjects (Subjects 13,762 and 30,458) to demonstrate the ability to predict a user’s motor intention in real-time. An occupational therapist identified a bottle pouring task as an appropriate massed practice therapy task shared for both of these subjects based on their personal abilities. The movements Hand Open, Hand Close, and Forearm Supination were further chosen by the therapist as movements for which the NeuroLife EMG system may be programmed to control FES or an exoskeleton to assist the subjects. We used a decoder trained to classify these movements and rest for this real-time evaluation of a simulated use case. The same NN architecture described above was used during this real-time demonstration. Data collected from this online decoding session is referred to as the real-time demonstration dataset. The NN decoder was built using two blocks of training data that was collected during the online decoding session. Each block contained 5 repeats of three movements (Hand Close, Hand Open, Forearm Supination) with interleaved rests. EMG data was filtered using identical filters as the offline method described above. RMS was extracted using consecutive 100 ms data windows with no overlap, and the current window and three preceding windows were used, totaling 400 ms of RMS data for each prediction. Training data were normalized (mean = 0, variance = 1) and the online data were normalized using the mean and variance from the training data. Cue labels were shifted by 300 ms to account for reaction time of the participant. To avoid unintentional flipping between states in the online system, the NN class output probabilities needed to exceed a threshold of 0.6 to change the decoder class prediction from the previous prediction. Additionally, a stable decoder output was required to change decoder state, therefore two consecutive samples of the same prediction class were required to update the final class prediction. An experimenter then prompted each subject with randomized cues with the online decoder running. A virtual hand on the computer screen reflected the real-time movement detection. An experimenter manually labeled cues provided to the participant and the decoding accuracy was calculated. Videos of these blocks for each subject can be found in Additional file ##SUPPL##0##1##: Media 1 and 2. Following the online session, NN models were re-trained on the same real-time demonstration dataset using the same methods described above for a comparison of online and offline performance. These data are presented in Fig. ##FIG##5##6##.</p>", "<title>Statistical analysis</title>", "<p id=\"Par25\">All comparisons were planned in the experimental design a priori<italic>.</italic> Normality of distributions were tested using Lilliefors tests. Significant differences were determined using paired t-tests (Fig. ##FIG##2##3##C) and unpaired t-tests (Figs. ##FIG##2##3##D, ##FIG##3##4##A) and where appropriate. Significant differences for multiple comparisons were determined using one-way ANOVAs followed by Tukey HSD tests (Fig. ##FIG##2##3##C, ##FIG##2##D##). Alpha of 0.05 was used for single comparisons. To correct for multiple comparisons, a Bonferroni-corrected alpha of 0.0167 was used for Fig. ##FIG##2##3##D and an alpha of 0.025 was used for Fig. ##FIG##4##5##A. The p-value for the correlations were determined using Wald Test with t-distribution of the test statistic (Additional file ##SUPPL##0##1##: Figure S10). Statistical tests for each comparison are noted in the text. Statistical analysis was performed in Python 3.8 using SciPy and Statsmodels. In all figures, * indicates p &lt; 0.05, ** indicates p &lt; 0.01, and *** indicates p &lt; 0.001. Error bars indicate mean ± SEM in all figures.</p>", "<title>Usability assessment</title>", "<p id=\"Par26\">Usability is a critical factor in the long-term adoption of an AT. Inconveniences of setup and comfort, as well as frustrations with reliability can often lead to eventual device abandonment. Therefore, in our final EMG recording session with each participant, we collected initial usability data of the NeuroLife Sleeve for use in chronic stroke survivors to help guide future development efforts. The questions posed to subjects here were adapted to investigate overarching themes mentioned by stroke survivors, caregivers, and HCPs for the use of an assistive technology [##REF##30497993##4##]. Subjects answered each question on a 1 to 5 scale, and questions were targeted at the following categories: simple to apply, comfort for long-term use, freedom of movement during use, functionality / lightweightness and portability, potential for clinical and home use, and overall aesthetic design of the device (Additional file ##SUPPL##0##1##: Table 5). Subjects were instructed to consider a use case in which the NeuroLife EMG System (sleeve and signal acquisition module) is used to control a FES or exoskeleton system when responding. For usability metrics with more than one question (e.g. simple to apply), the mean value was scored for that assessment.</p>" ]

[ "<title>Results</title>", "<title>Movement intention can be inferred from forearm EMG activity of subjects with stroke using the NeuroLife EMG system</title>", "<p id=\"Par27\">Removing the transition periods and focusing on periods of consistent activity yielded a standardized dataset to compare performance of various models (Fig. ##FIG##2##3##A). Heatmaps of EMG activity across the sleeve are shown for one subject with stroke (Fig. ##FIG##2##3##B). These heatmaps highlight the visual differences between forearm EMG activity across the various movements. In contrast to the heatmaps of able-bodied subjects (Additional file ##SUPPL##0##1##: Figure S2), EMG activity is less localized in the heatmaps of subjects with stroke. This trend is consistent across stroke severity, with more severely impaired subjects having less localized forearm EMG activity (Additional file ##SUPPL##0##1##: Figure S9). These results are consistent with previous reports of lack of independent muscle control following stroke [##REF##19135153##31##].</p>", "<p id=\"Par28\">To validate our decoding pipeline, we tested decoding performance in able-bodied subjects across all 12 movements with the expectation of highly accurate decoding using three different approaches. Overall, the NN obtained 96.8 ± 0.5% accuracy and outperformed LR and SVM models, which had 91.5 ± 0.8% and 90.8 ± 1.2% accuracy, respectively (Additional file ##SUPPL##0##1##: Figure S5; One-Way ANOVA: F[3, 10] = 4015, p = 9.02 × 10^–16; paired t-test NN vs. LR, p = 5.8 × 10^–5; NN vs. SVM, p = 1.6 × 10^–3). These decoding results are consistent in the dataset comprised of subjects with stroke attempting all 12 movements, where the NN obtained 77.1 ± 5.6% accuracy, and outperforms the LR (69.0 ± 5.4%) and SVM models (66.6 ± 6.9%) (Fig. ##FIG##2##3##C; One-Way ANOVA: F[4, 12] = 64.02, p = 5.41 × 10^–8; paired t-test NN vs. LR, p = 9.1 × 10^–4; NN vs. SVM, p = 9.3 × 10^–3). In summary, the NN outperforms the LR and SVM when decoding forearm EMG activity to infer movement intention. All subsequent analyses were performed in subjects with stroke using the NN for decoding.</p>", "<p id=\"Par29\">Next, we investigated the relationship between the subject’s ability to perform a movement unassisted and our ability to accurately decode that movement. Generally, decoding performance improved as the observed movement score increased (Fig. ##FIG##2##3##D; One-way ANOVA: F[3, 80] = 13.38, p = 3.7 × 10^–7). A comparison of decoding accuracy based on movement score was computed using a Tukey HSD test (Additional file ##SUPPL##0##1##: Table 3). For movements with visible motion (score ≥ 1), the overall decoding accuracy was 85.7 ± 3.2%, whereas for movements where the subject had no visible motion (score = 0) the accuracy dropped significantly to 27.3 ± 3.2% (Chance: 4.0%) (Movement Ability: Movement score = 0 vs. Movement score = 1–3: unpaired t-test, p = 3.9 × 10^–9). We also investigated the relationship between decoding accuracy and the assessed clinical metrics (Additional file ##SUPPL##0##1##: Figure S10). We observed moderate and significant correlations (Wald Test) between decoding performance and the UEFM Hand subset, and both the MAS wrist and fingers scores. In summary, these data suggest that EMG decoding performance decreases as impairment increases across a variety of clinical metrics assessing various aspects of dysfunction.</p>", "<p id=\"Par30\">Next, we investigated decoding performance of individual movements in subjects with stroke. The confusion matrix with individual movements for one subject is shown in Fig. ##FIG##2##3##E. The best performing movements across subjects were Wrist Flexion and Index Extension with an average accuracy of 68.7 ± 2.2%. On average across subjects, the worst performing movements were Forearm Supination and Thumb Abduction, with an average accuracy of 39.4 ± 9.9% (Additional file ##SUPPL##0##1##: Figure S6). The success rate per movement type for one subject is presented in the right column of the confusion matrix (Fig. ##FIG##2##3##E). The overall grand average success rate across all movements and rest achieved 75.9 ± 4.2%. The top movements quantified by successes/attempts were Index Extension: 23/30 Wrist Flexion: 20/30, and the bottom movements were Forearm Supination: 14/43 and Thumb Abduction: 17/43.</p>", "<title>Decoding movement subsets to achieve high performance in subjects with severe stroke impairments</title>", "<p id=\"Par31\">As the decoding performance of our algorithms was dependent on the presence of visible movement in our subjects, we next investigated the association of hand impairment severity based on the Upper Extremity Fugl-Meyer Hand Subscore (UEFM-HS) with observed movement scores and decoding performance (Fig. ##FIG##3##4##A). Both the observed movement score and decoding performance in subjects with severe hand impairment (UEFM-HS &lt; 3) were significantly different than in individuals with moderate or mild hand impairment (UEFM-HS ≥ 3) (Average movement score: unpaired t-test UEFM-HS &lt; 3 vs. UEFM-HS ≥ 3, p = 0.02; Decoding accuracy: unpaired t-test UEFM-HS &lt; 3 vs. UEFM-HS ≥ 3, p = 0.006). To better understand if a smaller subset of movements could be decoded in the presence of severe impairment, we assessed if sufficient signal was present to decode general muscle activity during cued movement periods compared to rest. Practically, this decoding scheme would enable an individual with severe hand impairment to control an AT with a single movement. We separated the problem into two classes (Rest vs. Move), where the “Move” class consists of the 12 different movements combined into one class (Fig. ##FIG##3##4##B). The NN decoder was able to achieve high performance in individuals with severe hand impairment with 86.7 ± 2.6% accuracy and 85.2 ± 3.6% success rate (Successes/Attempts; Rest: 164/185, Move: 151/185). These results indicate that the surface EMG collected from individuals with severe hand impairment is sufficient for binary scenarios.</p>", "<p id=\"Par32\">Encouraged by the binary decoder performance, we extended our analysis to include key functional movements for restoring grasp function, namely Rest, Hand Close, and Hand Open (Fig. ##FIG##3##4##C). In this 3-class scenario, the rest periods before each movement were downselected from the full 12 movement dataset to keep chance accuracy decoding at 50%. With these key movements in individuals with severe hand impairment, the decoding performance achieved 85.4 ± 6.4% accuracy and 88.0 ± 7.7% success rate (Successes/Attempts; Rest: 45/46, Hand Close: 22/23, Hand Open: 14/23). While decoding the movements to enable Hand Close and Hand Open is ideal for intuitive control of an AT, alternatively decoded movements with the greatest performance can be mapped to the most impactful functional movements. Thus, we tested decoding only the top performing movements for each subject (Fig. ##FIG##3##4##D). When comparing Rest and the top two movements for each individual, decoding performance achieved 91.0 ± 3.9% with a grand average success rate of 90.6 ± 4.2% (see Additional file ##SUPPL##0##1##: Table 4 for full details). This performance was comparable to the decoding performance of individuals with UEFM-HS ≥ 3 on 12 movements (87.6 ± 3.4%) and provides a reasonable alternative for subjects with more severe impairments.</p>", "<title>Decoding continuous forearm EMG data in real-time scenarios in chronic stroke survivors</title>", "<p id=\"Par33\">To demonstrate the utility of the NeuroLife EMG System to interpret muscle activity from the forearm to act as a control signal for assistive devices, we tested our decoding algorithms in a continuous dataset. Following a stroke, the ability to contract and relax muscle groups is slowed and highly variable [##REF##19357330##32##], which consequently makes automated labeling of cues using a static time shift (e.g., 800 ms) for training machine learning models imprecise. To account for this cue onset and offset variability, we first performed a dynamic cue shifting technique to automatically shift cue labels to match EMG activity (Additional file ##SUPPL##0##1##: Figure S4A). An average of 843 ± 95 ms of cue data per cue change or a grand average of 16.1 ± 1.0% of the full cue data stream across all subjects was shifted using this technique (Additional file ##SUPPL##0##1##: Figure S4B). To verify this method, we compared decoding performance with and without cue shifting. Dynamic cue shifting significantly improved decoding performance achieving 74.7 ± 5.0% overall with no cue shift achieving 62.5 ± 6.7% (Fig. ##FIG##4##5##A; Cue Shift: paired t-test Dynamic vs. None, p = 0.020). However, we found no significant difference in decoding accuracy between dynamic cue shifting and a static 800 ms cue shift (70.5 ± 5.4% decoding accuracy) representing an estimate of the average dynamic shift (Fig. ##FIG##4##5##A; Cue Shift: paired t-test Dynamic vs. Static 800 ms, p = 0.22). One subject (13,762) had an increase in decoding performance from a static shift, while the rest of the subjects experienced a decrease or no change in performance, suggesting that the dynamic cue shift was the most robust technique for our analyses.</p>", "<p id=\"Par34\">Using the dynamic cue shifting technique, we investigated decoding performance of individual movements in the continuous dataset. The confusion matrix with individual movements for a single subject is shown in Fig. ##FIG##4##5##B. The best performing movements across subjects were Wrist Flexion and Wrist Extension, with an average accuracy of 61.2 ± 5.0%. The worst performing movements across subjects were Forearm Supination and Thumb Abduction, with an average accuracy of 29.5 ± 9.0%. A continuous time series plot of all movement probabilities is shown in Fig. ##FIG##4##5##C Shaded regions indicate the cued movement with the probability of the movement type decoded based on motor intention.</p>", "<p id=\"Par35\">To assess whether the NN decoder could be used in real-time situations, inference testing was conducted using a Surface Book 2 with NVIDIA GeForce GTX 1060 GPU. The NN decoder was trained using cued movement data collected at the beginning of the session, totaling 1526 sample bins (2.54 min) for subject 13,762 and 3000 sample bins (5.0 min) for subject 30,458. The trained NN decoder was exported and loaded in using the Open Neural Network Exchange (ONNX) Runtime [##UREF##13##33##] for inference testing. NN forward model prediction times on average took less than 1 ms (307 ± 49 µs). Taking the entire preprocessing pipeline into consideration in addition to the NN forward prediction, the total inference time was 23.1 ± 4.4 ms. Since the resulting inference time is under 100 ms (time bin for RMS feature calculation), the NN model was deemed suitable for real-time inference.</p>", "<p id=\"Par36\">We next tested the decoder online to verify closed-loop control of a virtual hand on two stroke subjects (13,762 and 30,458) using the NN model. The confusion matrix with individual movements tested during the online testing for Subject 30,458 are shown in Fig. ##FIG##5##6##A. The best performing movement was Forearm Supination, with an overall accuracy of 97.1%. The continuous time series plot of movement probabilities for Subject 30,458’s online decoding session is shown in Fig. ##FIG##5##6##C. Finally, videos from the online sessions are shown in Additional file ##SUPPL##0##1##: Media 1 &amp; 2, with the user following along with movements cued from an experimenter, and the decoded motor intention controlling a virtual hand on the computer monitor. These videos demonstrate the online decoding accuracy and responsiveness of the system and highlight the utility of the NeuroLife EMG System for eventual closed-loop control of upper-extremity devices.</p>", "<title>The NeuroLife Sleeve meets usability needs of chronic stroke survivors</title>", "<p id=\"Par37\">Summary data from the usability questionnaire suggest that the NeuroLife Sleeve meets many user needs (Fig. ##FIG##6##7##). Subjects answered questions on a scale of 1 to 5 with higher values indicating stronger agreement. In general, subjects were optimistic that they could don and doff the NeuroLife Sleeve with the help of a caretaker in their home (3.60 ± 0.28). Concerns were generally centered around the pre-application of the conductive spray and relative positioning of the system, which we are actively addressing in our next design iteration. During sessions, subjects had the sleeve donned for &gt; 1.5 h, and all participants reported general satisfaction with the overall comfort of the device (4.57 ± 0.20). The sleeve was designed with a lightweight stretchable fabric, and participants were generally satisfied with the ability to move their arm while the sleeve was donned (4.07 ± 0.32). Subjects were highly confident (4.07 ± 0.22) that they could wear the sleeve during functional light activities around their home, suggesting that the sleeve is non-restrictive, lightweight, portable, and promising for home use. A commonly overlooked barrier to widespread adoption of assistive technologies is user acceptance of the overall look and feel of the device [##REF##30497993##4##]. All subjects were extremely satisfied with the overall design of the sleeve (4.36 ± 0.24). In general, they were all very excited for the opportunity to use the sleeve with the “general favorability” metric receiving the highest score of 4.79 ± 0.15. In summary, the usability results from the current study provide promising early data that the NeuroLife Sleeve can meet end user needs with directions on where to improve for future iterations.</p>" ]

[ "<title>Discussion</title>", "<p id=\"Par38\">In this study, we demonstrate decoding of motor intention using the NeuroLife EMG System in people with upper-extremity hemiparesis due to chronic stroke. Based on high-density surface EMG data collected from the forearm, 12 functional hand, wrist and forearm movements were classified with high accuracy. Overall decoding accuracy was associated with the subject’s ability to perform the movement (quantified here as observed movement score), with greater functional movement corresponding with higher decoding accuracy. Even in movements with little to no movement capacity (movement score ≤ 1), the system was able to accurately differentiate movement intent, albeit with some decrease in performance. Furthermore, we report that decoding performance was associated with a variety of different aspects of impairment such as overall motor impairment and spasticity. We also demonstrate online decoding of 3 task-relevant movements and rest for closed-loop control of a virtual hand, highlighting the decoding accuracy, speed and responsiveness of the system. Usability data demonstrated that the sleeve is comfortable and lightweight, allowing stroke survivors to wear the sleeve for extended periods of time without restricting their movement. In summary, this work demonstrates the NeuroLife EMG System’s utility as a wearable, user-friendly device to infer movement intention in stroke survivors with severe motor impairments.</p>", "<p id=\"Par39\">Previous studies have demonstrated decoding of motor intention using surface EMG in the upper extremity in chronic stroke survivors [##REF##33256073##21##, ##UREF##14##34##–##REF##22453603##36##]. In these studies, a range of machine learning techniques, impairment levels of the participants with stroke, and types of movements were investigated. The classification accuracy we measured was comparable to previous work with similar movement sets, although differences in study methodology restrict direct comparison. We found that a NN model outperformed the LR and SVM models in both able-bodied and stroke subjects across all movements. However, decoding accuracy decreased in stroke subjects with severe motor impairments. Specifically, we find our hardware and NN decoding techniques provide high performance in able-body (96.8% accuracy) and stroke (85.7% accuracy) participants if visible movement was observed. Included in the 85.7% accuracy are movements where participants had incomplete or impaired range of motion, indicating that we could consistently decode the subject’s intent to move despite their inability to properly complete the movement. These movements would be strong candidates for improvement with an EMG-controlled assistive device. Our complete 12-movement survey is helpful for understanding what movements may be decodable for each subject and may be appropriate for facilitating ATs in individuals with moderate or mild hand impairments. However, those with severe hand impairments are unlikely to be able to accurately control that many movements. Instead, it may be desirable to use only a subset of movements customized to the individual, that they can accurately control.</p>", "<p id=\"Par40\">A dynamic cue shifting technique may present a more robust and automated solution to account for differences among subjects. Improvement in decoding performance from using dynamic cue shifting is likely due to: (1) improved accuracy of the timing of cue onset and offsets in the training data which gives a better representation of each movement and thus better decoding performance, and (2) more accurate testing alignment and better testing parameters. These results suggest that cue labeling can substantially affect overall decoding performance in online decoders, and intelligent cue labeling can improve overall performance. Though we only briefly assessed our system’s online decoding capabilities, our initial results suggest that online EMG decoding of motor intention is possible, though more subjects and functional movements are needed to increase robustness.</p>", "<p id=\"Par41\">Recent studies have shown encouraging results using a limited set of manually placed electrodes, which may account for some performance differences compared to our results [##REF##33256073##21##, ##UREF##15##35##]. Moreover, localizing electrodes to muscle activity critical to grasp production can be an effective strategy to minimize system complexity. The optimization of electrode placement and reduction of hardware complexity is a planned future direction for the NeuroLife Sleeve. Additional studies have used similar numbers of channels as we have presented [##REF##33256073##21##, ##UREF##16##37##, ##UREF##17##38##]. However, the system presented here streamlines system setup with a single zipper closure aligned by easily recognizable anatomical landmarks, the elbow and radial styloid process. Usability around donning/doffing is a key concern for adoption of AT, and systems with extensive setup procedures risk poor acceptance in clinics, rehabilitation settings, and the home. Prior studies have also shown that time domain features, such as RMS, combined with NN approaches can outperform more classical statistical or machine learning approaches [##REF##33256073##21##]. Our results agree with these findings, further supporting that high density EMG recordings have sufficient complexity to leverage the recent developments in deep learning. We extend the findings of previous studies by presenting an easy-to-don and doff wearable device that removes the need for manual placement of electrodes. This reduces the necessary setup time and ensures consistent placement of recording electrodes across sessions. Additionally, we present data to support the real time performance of the decoding paradigm. This study provides evidence that the device can decode motor intention with high performance across a variety of subjects, and we demonstrate decoding speed that is fast enough to reliably perform real-time inference alongside data collection. Notably, processing in this context did not involve removing transition periods during training or online testing, indicating the system’s robustness to motion artifact. Finally, we present a viable, automated cue shifting method that removes the necessity for manual relabeling and improves system performance.</p>", "<p id=\"Par42\">Usability is an important factor for clinical technologies to assist with stroke rehabilitation by supporting motivation for consistent and active training. While existing AT solutions show promising results, these systems tend to focus on the technology and often fall short in the user-centric designs. Most clinical ATs involve manual placement of patch electrodes and long calibration procedures which limits the amount of practice that can be achieved within a given rehabilitation session. Furthermore, many systems are bulky and lack portability, which can limit patient adoption for use outside of rehabilitation training and into the home [##UREF##18##39##]. The system evaluated here uses a lightweight wearable and reusable sleeve connected with a ruggedized cable to an 8 × 10″ signal acquisition module. Further work is required to ensure this system’s reliability a variety of home and non-laboratory contexts, but here we demonstrate that the NeuroLife EMG System can address many usability concerns of current technologies while providing robust decoding of motor intention. In combination with soft exoskeletons or FES, the sleeve can drive intention-based training coupled with functional movements in a user-centric form factor.</p>", "<p id=\"Par43\">Based on user feedback from the current study, the sleeve design meets various end user needs. The design allows for use on either arm, and the stretchable, lightweight fabric design was reported by participants to be comfortable without limiting natural arm movements. Aesthetically, subjects were pleased with the sleeve design and advocated that they would use the system at home for rehabilitation and activities of daily living given the opportunity. Participants mostly agreed that the sleeve was straightforward to don and doff during the study with the help of the researchers and believed that they could apply the sleeve with the help of a caretaker. However, participants identified the simplicity to apply the sleeve as an area that is currently lacking, and participants were not confident in being able to apply the sleeve independently without assistance. This is an identified area for future development and will be the focus of next design iterations to enable at-home use. Despite this current usability limitation, participants indicated that not only would they feel comfortable performing rehabilitation therapy at home but are excited for the possibility of using the sleeve as a therapy tool indicated by the highest score for general favorability.</p>", "<p id=\"Par44\">This study expands the scope of previous EMG decoding studies by presenting the performance of a novel algorithm across a wider range of subjects, UEFM score of 7 to able-bodied, in offline and online contexts while highlighting the importance of usability. The data collection was designed to simulate a realistic use case in which EMG-controlled ATs are used to assist in tabletop task-oriented upper-extremity rehabilitation. This study indicates the practicality and usability of AT control using this EMG system and highlights the shortcomings of decoding in severely impaired subjects and low observed movement scores. These findings will inform future work for the field of EMG decoding and may inspire new approaches for EMG-controlled ATs in the space of rehabilitation suitable for severely impaired stroke survivors.</p>", "<p id=\"Par45\">The present study provides an initial demonstration of the NeuroLife EMG System to decode motor intention in chronic stroke survivors while simultaneously meeting needs, but some limitations merit consideration. We did not age match the able-bodied subjects to the stroke subjects, which may have affected comparisons between the two groups of subjects. Data was not collected from the non-paretic arm in the stroke subjects, although we do provide data from able-bodied subjects to demonstrate high-accuracy decoding to validate our approach. While the reported results indicate that the Neurolife EMG System can be used to decode motor intention in a package that meets end user needs, there is still room for improvement in various areas, including refinement of decoding algorithms, the sleeve design and related hardware, and eventual applications. Future work refining decoding algorithms will focus on overall improvements to decoding performance by leveraging many of the advancements made in recent years in the field of deep learning [##UREF##19##40##]. We will investigate the use of more sophisticated neural network models, including recurrent neural networks (RNNs), transformers optimized for time series modeling which could improve overall decoding accuracy, specifically for participants with limited movement capability [##REF##32098264##41##, ##UREF##20##42##]. Advanced neural network models may also aid in our ability to identify altered states of muscle activity, including spasticity and fatigue [##UREF##21##43##, ##REF##32523505##44##]. To better address inter-session variability, we will apply various machine learning techniques including unsupervised learning, data augmentation, and domain adaptation [##UREF##22##45##–##REF##35573306##48##] to fully leverage multiple datasets to reduce setup and calibration times for new users. This study used about 30 min of intrasession data for training of 13-class decoders, but with further development of these techniques, similar performance may be achieved with a much shorter decoder recalibration sequence after an initial training session. We demonstrated high performance using only 3 min of training data in a 4-class online decoding scenario, which may be acceptable for some use cases. Our current model architecture does not consider the spatial information available in the sleeve. A future direction for feature extraction and decoder architecture is to include features that capture this relational data between electrodes to create decoders that are less sensitive to positional changes, such as convolutional, transformer, or graph neural networks. Improvements to data quality itself can be accomplished with visual reinforcement to subjects. An online decoding system that displays the decoded intention may be more beneficial to subject engagement over the image cues used in the current study. While we provide the initial proof-of-concept demonstration of the NeuroLife EMG System here, the data collected during the study was not representative of how the system will be ultimately deployed as an assistive device. For example, in the current study, subjects kept their elbow stationary on the table during movements and did not interact with objects, both of which can significantly influence forearm EMG activity and thus decoding performance. Future studies will focus on capturing training data in more complex situations, such as during reach and grasp tasks and object manipulations, to develop decoders robust to movement such as the spatial decoders described above. We also assumed a class distribution based on the target use-case of occupational therapy in which Rest periods occur in between movements (~ 50% of the time). However, this method oversamples the Rest class, which can mask poor performance of other movements which are more functionally relevant thus limiting comparisons to other decoding studies. For this study, we determined the chance level using a naïve decoder (i.e., always predicting the majority class). We acknowledge that this baseline is dependent on the class distribution, thus we also present the success rate metric which is designed to approximate a therapist judging binary success for each movement cue (and ignoring rest periods). Future work will examine different decoders and metrics, including those decoders presented here in Fig. ##FIG##2##3##C. Similarly, the decoding performance presented here was in the absence of assistive device control. Commonly used assistive devices, including FES and exoskeletons, may interfere with EMG activity when active and thus can significantly affect decoding performance [##UREF##14##34##, ##REF##33277517##49##]. Our group is working to integrate FES functionality within the same EMG recording electrodes to eliminate the need for additional hardware, such as an exoskeleton or additional patch electrodes. Future work from our group will focus on developing algorithms that can decode EMG during FES activity. Furthermore, integration with assistive technologies will change the sleeve form factor as well as the backend hardware. The usability assessment in this study focused primarily on the sleeve component of the EMG system. Future work will include optimization of the backend hardware for space efficiency and portability with studies evaluation of the complete system usability. With a technology that incorporates EMG and FES into a single consolidated sleeve, the system has the potential to help support motor recovery and assist in ADLs [##REF##33655789##14##, ##REF##30250141##25##].</p>" ]

[ "<title>Conclusion</title>", "<p id=\"Par46\">The focus of this study was to validate the NeuroLife EMG System by decoding hand, wrist, and forearm movements and collect usability data from subjects with stroke. We demonstrate accurate EMG decoding of 12 different movement classes with a neural network in both able-bodied and stroke subjects. Decoding accuracy in stroke subjects was associated with the movement ability of each subject. The decoding results were consistent with similar myoelectric intention-based studies. We demonstrate online decoding and closed-loop control of a virtual hand with high accuracy, speed, and responsiveness. Finally, we present data on the common usability factors of assistive devices including the simplicity, comfortability, portability, and weight of the sleeve. Overall, all subjects reported good to outstanding ratings for each of the usability categories, indicating that the NeuroLife EMG System can provide accurate decoding of upper extremity motor intention while meeting the usability needs of end users.</p>" ]

[ "<title>Objective</title>", "<p id=\"Par1\">Seventy-five percent of stroke survivors, caregivers, and health care professionals (HCP) believe current therapy practices are insufficient, specifically calling out the upper extremity as an area where innovation is needed to develop highly usable prosthetics/orthotics for the stroke population. A promising method for controlling upper extremity technologies is to infer movement intention non-invasively from surface electromyography (EMG). However, existing technologies are often limited to research settings and struggle to meet user needs.</p>", "<title>Approach</title>", "<p id=\"Par2\">To address these limitations, we have developed the NeuroLife^® EMG System, an investigational device which consists of a wearable forearm sleeve with 150 embedded electrodes and associated hardware and software to record and decode surface EMG. Here, we demonstrate accurate decoding of 12 functional hand, wrist, and forearm movements in chronic stroke survivors, including multiple types of grasps from participants with varying levels of impairment. We also collected usability data to assess how the system meets user needs to inform future design considerations.</p>", "<title>Main results</title>", "<p id=\"Par3\">Our decoding algorithm trained on historical- and within-session data produced an overall accuracy of 77.1 ± 5.6% across 12 movements and rest in stroke participants. For individuals with severe hand impairment, we demonstrate the ability to decode a subset of two fundamental movements and rest at 85.4 ± 6.4% accuracy. In online scenarios, two stroke survivors achieved 91.34 ± 1.53% across three movements and rest, highlighting the potential as a control mechanism for assistive technologies. Feedback from stroke survivors who tested the system indicates that the sleeve’s design meets various user needs, including being comfortable, portable, and lightweight. The sleeve is in a form factor such that it can be used at home without an expert technician and can be worn for multiple hours without discomfort.</p>", "<title>Significance</title>", "<p id=\"Par4\">The NeuroLife EMG System represents a platform technology to record and decode high-resolution EMG for the real-time control of assistive devices in a form factor designed to meet user needs. The NeuroLife EMG System is currently limited by U.S. federal law to investigational use.</p>", "<title>Supplementary Information</title>", "<p>The online version contains supplementary material available at 10.1186/s12984-023-01301-w.</p>" ]

[ "<title>Supplementary Information</title>", "<p>\n</p>" ]

[ "<title>Acknowledgements</title>", "<p>The authors would like to thank our development and management teams at Battelle Memorial Institute including Jesse Keckler, Nick Annetta, Sam Colachis, and Josh Branch for their engineering contributions, Charli Hooper for her assistance with data collection, and Andrew Sweeney for his contribution to the manuscript graphics. Financial support for this study came from Battelle Memorial Institute.</p>", "<title>Author contributions</title>", "<p>EM conducted the experiments, performed analysis, prepared the figures, and wrote the manuscript. DG, performed analysis, prepared figures, and wrote the manuscript. MD, LW conducted the experiments and reviewed the manuscript. NT performed analysis and prepared figures. BS prepared figures and reviewed the manuscript. IB, DF reviewed the manuscript. All authors reviewed the manuscript.</p>", "<title>Funding</title>", "<p>Funding for the study was provided by Battelle Memorial Institute.</p>", "<title>Availability of data and materials</title>", "<p>The data that support the findings of this study are available upon reasonable request from the authors.</p>", "<title>Declarations</title>", "<title>Ethics approval and consent to participate</title>", "<p id=\"Par47\">Data were collected as part of an ongoing clinical study (IRB0773, IRB 0779) being conducted at Battelle Memorial Institute that was approved by the Battelle Memorial Institute Institutional Review Board. All participants provided written informed consent before participation, in accordance with the Declaration of Helsinki.</p>", "<title>Consent for publication</title>", "<p id=\"Par48\">Consent was obtained from participants for publication of these data.</p>", "<title>Competing interests</title>", "<p id=\"Par49\">All authors declare no conflict of interest.</p>" ]

[ "<fig id=\"Fig1\"><label>Fig. 1</label><caption><p>Illustration of experimental data collection procedure. Subjects were seated in front of a computer monitor with the sleeve on their impaired arm, and their arms placed on the table. The sleeve was connected to a custom-built EMG signal acquisition module, which then connected to a laptop computer. Images of hand postures were shown on the monitor and the subject followed along to the best of their ability. Each recording block was approximately 2–3 min in length and involved hand posture cues interleaved with rest periods. The recording block began with an 8-s lead in rest period. Each cue and rest period presentation time were randomly selected between 4 and 6 s for subjects with stroke. An operator ran the data collection software and observed EMG signals during data collection to ensure proper recording of data</p></caption></fig>", "<fig id=\"Fig2\"><label>Fig. 2</label><caption><p>Representative EMG data recorded from subject with stroke. <bold>A</bold> Filtered EMG data recorded from 3 separate channels on the NeuroLife Sleeve during 3 movements: Hand Open (HO), Forearm Supination (FS), and Hand Close (HC). <bold>B</bold> Heatmap of normalized RMS activity, with the channel number on the y-axis and time on the x-axis. Note the activity across clusters of electrodes for each of the 3 separate movements. <bold>C</bold> Normalized RMS activity mapped to the sleeve orientation, with a legend showing the orientation of the sleeve mapping (flex. = flexors, ext. = extensors). Note the location of EMG activity is spatially located near the related musculature for each of the 3 movements</p></caption></fig>", "<fig id=\"Fig3\"><label>Fig. 3</label><caption><p>Decoding hand and wrist movements using the NeuroLife EMG System.<bold> A</bold> Illustration depicting the data used for training and testing the decoder. The presentation of the cue is shown as a black bar on the top of the plot, and the middle 2.5 s of the cue presentation is used for analysis. <bold>B</bold> Heatmaps of various movements from a subject with stroke. <bold>C</bold> Decoding performance comparing 3 models: LR (Logistic Regression), SVM (Support Vector Machine), and NN (Neural Network). The NN outperforms both the LR and SVM models (paired t-test NN vs. SVM, p = 9.3 × 10^–3; NN vs. LR, p = 9.1 × 10^–4). <bold>D</bold> Association between the observed movement score and decoder performance of the neural network (One-way ANOVA, Accuracy (%): F[3, 80] = 13.38, p = 3.7 × 10^–7). The decoder struggles learning to predict movement attempts in which there was no observable movement (movement score = 0), and performs similarly when there is observable movement (movement score ≥ 1). <bold>E</bold> Confusion matrix for a subject with stroke detailing the decoding performance across all movements</p></caption></fig>", "<fig id=\"Fig4\"><label>Fig. 4</label><caption><p>Decoding hand and wrist movements in subjects with severe hand impairment (UEFM-HS &lt; 3). <bold>A</bold>\n<italic>Left:</italic> Comparison of severe (UEFM-HS &lt; 3) and mild (UEFM-HS ≥ 3) subject impairment average movement scores (Average movement score: unpaired t-test UEFM-HS &lt; 3 vs. UEFM-HS ≥ 3, p = 0.02). <italic>Right:</italic> Comparison of NN decoding performance for severe and mild subject impairments (Decoding accuracy: unpaired t-test UEFM-HS &lt; 3 vs. UEFM-HS ≥ 3, p = 0.006). <bold>B</bold> Decoding performance of NN binary classifier for UEFM-HS &lt; 3 subjects comparing Rest and Move in which Move is made up of combining all 12 movements into a single class. Confusion matrix of subject 61,204 for the two-class problem. The observed movement score is the average of all movements observed movement scores. The two-class decoder can reliably distinguish the difference between a resting and moving state. <bold>C</bold> Decoding performance of NN model when restricting classes to Rest, Hand Close, and Hand Open. Confusion matrix of lowest performing subject (61,204) for the three-class problem. The three-class decoder is not sufficient to distinguish the movements reliably. <bold>D</bold> Decoding performance of NN model when restricting classes to Rest and the top 2 movements for each subject for a total of three classes. Confusion matrix of subject 61,204 for the three-class problem. Focusing on movements specific to subjects increases the robustness of decoder performance</p></caption></fig>", "<fig id=\"Fig5\"><label>Fig. 5</label><caption><p>Decoding hand and wrist movements in a continuous EMG dataset. <bold>A</bold> Dynamic cue shifting significantly improved accuracy compared to no cue shift (Cue shift: paired t-test Dynamic vs. None, p = 0.020). There was no significant difference between a dynamic cue shift and static 800-ms cue shift (approximately the average cue shift across subjects) (Cue shift: paired t-test Dynamic vs. Static 800 ms, p = 0.22). <bold>B</bold> Confusion matrix detailing performance from one subject in the continuous dataset. <bold>C</bold> Time series plot depicting decoder class probabilities across time. The presented cue is shown in above the time series plot as a rectangular colored bar with the color corresponding to the movement class</p></caption></fig>", "<fig id=\"Fig6\"><label>Fig. 6</label><caption><p>Online decoding of hand and wrist movements. <bold>A</bold> Confusion matrix of subject 30,458 from the online decoding session. <bold>B</bold> Online decoding performance for both subjects on the real-time demonstration dataset. <bold>C</bold> Time series plot depicting decoder class probabilities across time for subject 30,458. The presented cue is shown above the time series plot as a rectangular colored bar with the color corresponding to the movement class</p></caption></fig>", "<fig id=\"Fig7\"><label>Fig. 7</label><caption><p>Summary of the NeuroLife Sleeve usability data from subjects with stroke. Each subject with stroke ranked the NeuroLife Sleeve based on 6 usability domains. Group data is presented for each of the 6 domains</p></caption></fig>" ]

[ "<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Demographics of subjects with stroke and clinical metrics</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Subject</th><th align=\"left\">UEFM</th><th align=\"left\">Time since stroke, years</th><th align=\"left\">Side of paresis</th><th align=\"left\">UEFM Hand</th><th align=\"left\">MAS Finger</th><th align=\"left\">MAS Wrist</th></tr></thead><tbody><tr><td align=\"left\">13,762</td><td align=\"left\">36</td><td align=\"left\">6</td><td align=\"left\">Right</td><td align=\"left\">6</td><td align=\"left\">1</td><td align=\"left\">1</td></tr><tr><td align=\"left\">29,562</td><td align=\"left\">22</td><td align=\"left\">4</td><td align=\"left\">Right</td><td align=\"left\">2</td><td align=\"left\">1</td><td align=\"left\">1</td></tr><tr><td align=\"left\">30,458</td><td align=\"left\">32</td><td align=\"left\">3</td><td align=\"left\">Left</td><td align=\"left\">6</td><td align=\"left\">1</td><td align=\"left\">1</td></tr><tr><td align=\"left\">47,513</td><td align=\"left\">19</td><td align=\"left\">4</td><td align=\"left\">Right</td><td align=\"left\">4</td><td align=\"left\">1</td><td align=\"left\">0</td></tr><tr><td align=\"left\">61,204</td><td align=\"left\">8</td><td align=\"left\">6</td><td align=\"left\">Right</td><td align=\"left\">0</td><td align=\"left\">4</td><td align=\"left\">3</td></tr><tr><td align=\"left\">87,134</td><td align=\"left\">7</td><td align=\"left\">7</td><td align=\"left\">Right</td><td align=\"left\">0</td><td align=\"left\">4</td><td align=\"left\">4</td></tr><tr><td align=\"left\">98,473</td><td align=\"left\">38</td><td align=\"left\">6</td><td align=\"left\">Right</td><td align=\"left\">7</td><td align=\"left\">0</td><td align=\"left\">0</td></tr></tbody></table></table-wrap>" ]

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[ "<fn-group><fn><p><bold>Publisher's Note</bold></p><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn><fn><p>Eric C. Meyers and David Gabrieli contributed equally to this work.</p></fn></fn-group>" ]

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[ "<media xlink:href=\"12984_2023_1301_MOESM1_ESM.docx\"><caption><p><bold>Additional file 1.</bold> Supplementary Data.</p></caption></media>", "<media xlink:href=\"12984_2023_1301_MOESM2_ESM.mp4\"><caption><p><bold>Additional file 2. Media 1.</bold> Example of online decoding using NN model in stroke subject 13762. In the top video, an experimenter prompted the user with various movement cues (Hand Close, Hand Open, and Forearm Supination) in a random order. A virtual hand on the computer monitor illustrated the real-time decoded movement intention from each subject’s EMG activity. In the bottom left, a heatmap shows the RMS activation across the sleeve at each timepoint. In the bottom right, a time series plot depicting decoder class probability across time. The presented cue is shown above the time series plot as a rectangular colored bar with the color corresponding to the movement class.</p></caption></media>", "<media xlink:href=\"12984_2023_1301_MOESM3_ESM.mp4\"><caption><p><bold>Additional file 3. Media 2.</bold> Example of online decoding using NN model in stroke subject 30458. Refer to Additional File 2 caption for more details.</p></caption></media>" ]

{ "acronym": [], "definition": [] }

[ "<title>Introduction</title>", "<p id=\"Par2\">Lentil (<italic>Lens culinaris</italic> Medik), is one of the diploid annual legumes (2n = 2x = 14); their grains are known for their richness in proteins, minerals (Fe, K, Zn, P) and fibers [##UREF##0##1##]. Lentil grains are an important component of the daily diet for large populations in North Africa, sub-Saharan Africa, the Middle East, and the Indian sub-continent [##UREF##1##2##]. The regular consumption of lentil could help in overcoming mineral deficit for more than half of the world's population [##REF##18314378##3##–##REF##19192191##5##]. Lentil crop residues could also be used as livestock feed [##UREF##3##6##, ##UREF##4##7##].</p>", "<p id=\"Par3\">Moreover lentil is a nitrogen-fixing legume that contributes to enhance soil fertility and promotes the sustainability of agricultural systems [##UREF##5##8##, ##UREF##6##9##]. The world production of lentil was in average 6.315 million tons between 2017 and 2021, with distribution according to the five continents: Asia (42%), America (42%), Oceania (10%), Africa (3%), and Europe (3%) [##UREF##7##10##].</p>", "<p id=\"Par4\">The domestication of lentil began around 7000 B.C<bold>.</bold> [##UREF##8##11##] in the Near East via wild populations of <italic>Lens orientalis</italic> that were found in the mountains between Syria and Turkey [##UREF##9##12##–##UREF##11##14##]. After domestication, lentil with other important basic crops such as pea, faba bean, chickpea, wheat, and barley have been diffused from Near East to Greece, Central Europe, Egypt, Central Asia, and India. It has arrived in Morocco from Central Europe via Mediterranean Islands at ninth century [##UREF##9##12##, ##UREF##12##15##]. While Canada and USA started growing lentil only since 1969 and 1916, respectively [##UREF##13##16##] During domestication, several characteristics were targeted especially seed dormancy and pod indehiscence [##UREF##14##17##]. Lentil has traditionally been cultivated in Morocco, using mostly local varieties selected by farmers on the basis of a number of quality, yield, adaptation and other desired characteristics [##UREF##15##18##]. In Morocco, lentil is currently grown as a rainfed crop, in rotation with cereals. The average cultivated area is around 40,000 ha, yielding an average production of 28,163 to 41,602 tons from 2017 to 2021. [##UREF##7##10##].</p>", "<p id=\"Par5\">The human population is expected to grow to 10 billion by 2050<bold>,</bold> which will put a strain on the world's resources [##REF##31209375##19##]. Climate change and the emergence of new diseases and parasites threaten the productivity of agriculture worldwide [##UREF##16##20##–##REF##32298316##22##]. To meet the needs of this growing population, breeders are investigating efficient methods for developing new cultivars with genetic resistance to diseases and to different abiotic stresses. However, the conventional breeding approaches adopted for enhancing the productivity of lentil take a long time and requires many years to release new and adaptive cultivars.</p>", "<p id=\"Par6\">The extended photoperiod is one of the methods that reduce the duration of the plant cycles [##UREF##18##23##, ##UREF##19##24##]. In many studies, it has been demonstrated that extended photoperiod have a positive benefit effect for breeding, by accelerating flowering and reducing plant life cycle in Safflower [##UREF##20##25##], Strawberry [##UREF##21##26##], Soybean [##REF##31981443##27##], Barley [##UREF##22##28##], Wheat [##UREF##23##29##], Chickpea [##UREF##24##30##], Faba-Bean [##UREF##25##31##] and Lentil [##UREF##18##23##, ##UREF##26##32##, ##UREF##27##33##]. The extended photoperiod can be achieved with artificial light [##UREF##28##34##]. In lentil, it has been reported that the reduction of time to flowering is favored by a photoperiod with a light intensity that can be varied around 500 μmol m⁻² s⁻¹ [##UREF##29##35##], with a duration of 16, 18, 20 and 22 h of light and 8, 6, 4 and 2 h of dark, respectively, [##UREF##18##23##, ##UREF##19##24##, ##UREF##30##36##, ##UREF##31##37##]<bold>.</bold> The lentil is a plant with long or neutral days [##UREF##32##38##–##UREF##34##40##]. For breeding programs based on conventional methods, in normal environmental conditions greenhouses and field conditions, the development of homozygous lines from segregating populations after hybridization takes 7–9 years, if only one generation is produced per year, while a prolonged photoperiod with continuous illumination can reduce time to flowering and accelerate growth resulting in a shorter life cycle [##UREF##18##23##].</p>", "<p id=\"Par7\">The selection of genotypes with early flowering, early development, and high yield are among the challenges of the breeders, to adapt the crops life cycle to available growing season [##UREF##35##41##, ##UREF##36##42##]. The major element for the duration of the crop life cycle is the period between sowing and flowering, this period is regulated by the effect of temperature, photoperiod, genotype, and interactions between these parameters [##UREF##37##43##]. The temperature influence the expression of the transcription factor which directly affects the floral induction [##REF##23790253##44##]. In Soybean, the light environment in which this plant is cultivated significantly influences the genotype, making it the most crucial factor [##UREF##38##45##].</p>", "<p id=\"Par8\">To our knowledge, no studies have been done so far on the evaluation of genetic variability to the response of extended photoperiod using lentil genotypes of different latitudinal origins. The objectives of our study were (1) to analyze the genetic diversity of 80 landraces from three latitudinal origins (low, medium and high latitudes) from different countries (Russia, Serbia, Ukraine, Montenegro, Belgium, Armenia, Chile, Ethiopia, India, Iran, Afghanistan, Morocco, Italy, Turkey, and Greece) in response to the application of an extended photoperiod regime, and (2) to evaluate the sensitivity to photoperiod and select accessions that could be more adapted to use under extended photoperiod in order to use them as parents for rapid generation turnover in speed breeding growth chambers.</p>" ]

[ "<title>Material and methods</title>", "<title>Plant material</title>", "<p id=\"Par9\">A total of 80 lentil (<italic>Lens Culinaris</italic> Medik.) accessions from different countries (Afghanistan [##REF##18314378##3##], Armenia [##UREF##0##1##], Belgium [##REF##19192191##5##], Chile [##UREF##10##13##], Ethiopia [##REF##18314378##3##], Greece [##REF##18314378##3##], India [##UREF##0##1##], Iran [##UREF##2##4##], Italy [##UREF##3##6##], Morocco [##REF##31981443##27##], Montenegro [##UREF##1##2##], Russia [##REF##18314378##3##], Serbia [##UREF##1##2##], Turkey [##UREF##3##6##], Ukraine [##UREF##0##1##]) were characterized under extended photoperiod. The accessions were classified into three latitudinal origins: Low (0°–20°), Medium (21°–40°) and High (41°–60°) (Fig. ##FIG##0##1##; Table ##TAB##0##1##). The low, medium and high categories refer to the latitude 0 in reference to the equator, and therefore to the natural flowering photoperiod in the original latitude. All the accessions used in this study come from our gene bank based at the National Institute of Agricultural Research in Settat, Morocco.</p>", "<title>Photo-thermal regime and plant growth conditions</title>", "<p id=\"Par10\">The experiment was carried out in a growth chamber at the Laboratory of Food legumes breeding, regional center of Settat, the national institute for agricultural research (INRA Morocco), during 150 days, from seed germination to plant physiological maturity. A completely randomized block design was used, with each variety planted three times. In all, three separate planting sessions were carried out, with three plants per pot for each accession, all subjected to a prolonged photoperiod treatment of 22 h of light at 25 °C and 2 h of darkness at 25 °C. Using light emitting diode (LED) Lamps (Standard ECO SLIM LED) (36 lamps of 9 W which each lamp about 14.81–18.51 µmol m⁻² s⁻¹ of light intensity) (Fig. ##FIG##1##2##).</p>", "<p id=\"Par11\">The accessions were planted in plastic pots (500 ml capacity) filled with 2/3 of soil and 1/3 of peat compost. During the experiment, plants were irrigated every 4–7 days depending on the growth stage of the crop and the corresponding water consumption. Plants were harvested at physiological maturity. Then qualitative and quantitative measurements were made.</p>", "<title>Early vegetative growth, development stages and phenological characterization</title>", "<p id=\"Par12\">Percentage of green canopy cover (GCC) corresponding to the proportion of the ground covered by plants was measured using a digital application (Canopeo) which is a mobile application, using images and video, it is an automatic color threshold image analysis tool that uses color values to classify all pixels in the image. The pixel analysis is based on blue to green (B/G) and red to green (R/G) [##UREF##39##46##]. Seedling vigor (SV) was estimated according to a scale modified [##UREF##40##47##] for the photoperiodic stress from 1 to 5, respectively (1 very poor, 2 poor, 3 medium, 4 good, 5 very good). Time to flowering (TF) Measured by counting the days of plant sowing to the day of the first flower's appearance. Time of pods set (TPS) Measured by counting the days from sowing to appearance of the first pod. Time to maturity (TM) Measured by counting the days from sowing to the yellowing and desiccation of the plant and the pods. Harvest Index (HI) was calculated according to the following formula: “Harvest index = Grain yield/Biological yield”, grain yield is a number and weight of seeds in (g), and biological yield is a dry weight of the aerial part measured after drying in an oven at 70 °C for 48 h. The vegetative stage length (VGS) corresponds to the number of days after sowing until the appearance of the first flower. While the reproductive stage length (RPS), corresponds to the number of days from the appearance of the first flower to the formation of the first pod. Finally, the seed filling stage length (SFS), corresponds to the number of days from the appearance of the first pod until 80% maturity.</p>", "<title>Statistical analysis</title>", "<p id=\"Par13\">For each parameter, descriptive statistics, analysis of variance were performed to test the effect of genotype and latitude under speed breeding by extended photoperiod. In addition, in order to assess the hypothesis of differentiation of the accessions according to their geographic origins in response to the application of the extended photoperiod and to determine the contribution of each trait in discriminating between origins, a canonical discriminant analysis was carried out by using the Statistical Package for the Social Sciences (SPSS) database software, version 21 for Windows. Graphical extrapolation of the results was performed using Microsoft Excel and (SPSS). While R software was used for variance analysis and through the \"<italic>agricolae</italic>\" package [##UREF##41##48##]. Duncan post-hoc test was used to test the differences between the different light intensity treatments by the “<italic>multcomp</italic>” R package [##UREF##42##49##]. The principal component analysis was performed using the R package ‘<italic>FactoMineR</italic>, <italic>factoextra’</italic> [##UREF##43##50##].</p>" ]

[ "<title>Results</title>", "<title>Genetic variation of vegetative and phenological traits</title>", "<p id=\"Par14\">The analysis of variance revealed a significant effect (p ≤ 0.05) of genotype on all traits, and a significant variation was also observed according to the latitudinal origins for all traits except reproduction stage and seed filling stage length (Table ##TAB##1##2##).</p>", "<title>Genetic variation of accessions from the latitudinal origins</title>", "<p id=\"Par15\">In this study, an analysis of genetic variation was carried out on accessions from the three distinct latitudinal origins: low, medium and high latitudes. To visualize and compare this genetic variation, we used boxplots (Fig. ##FIG##2##3##). Through observation of these boxplots, we identified interesting trends in genetic variation between latitudinal origins. Important differences in genetic distributions are clearly discernible between the Low, Medium and High latitudinal origins (Fig. ##FIG##2##3##; Tables ##TAB##1##2##, ##TAB##2##3##). These results suggest a distinct genetic structuring between different latitudinal groups, highlighting the potential influence of environmental and latitudinal factors on the genetic diversity of the studied accessions.</p>", "<p id=\"Par16\">These results boost our understanding of genetic diversity in a geographical context, which could have important implications for the preservation and future use of these genetic resources in crop improvement and biodiversity conservation programs.</p>", "<title>Genetic variability of vegetative growth under extended photoperiod</title>", "<p id=\"Par17\">Vegetative cover rate showed significant differences for the 80 accessions, those from low latitudinal origin showed a low percentage of vegetative cover with 1.6%. While, those from Medium latitudinal origin showed a higher percentage of vegetative cover with 3.66%, same trend was observed for seedling vigor (Table ##TAB##2##3##). For vegetative cover and seedling vigor, two distinguished groups were observed according to Duncan test (Table ##TAB##2##3##).</p>", "<title>Genetic variability of phenological stages under extended photoperiod</title>", "<p id=\"Par18\">Significant differences obtained according to Duncan test among the studied accessions were observed for phenological stages between Low, Medium and High latitudes (Table ##TAB##2##3##). For the flowering time, the Low latitudinal accessions were the earliest ones at flowering with an average of 69 days after sowing, while the accessions from the High latitude were the latest at flowering with an average of 96 days after sowing (Table ##TAB##2##3##). For the time of pods set, the Low latitudinal accessions, have averages of 62 days after sowing being the earliest, in contrast to the accessions from High latitude as the latest, with averages of 104 days after sowing (Table ##TAB##2##3##). Time to maturity registered a difference between the accessions, low latitudinal accessions, in average with 75 days after sowing as the earliest, whereas the accessions from the High latitude as the latest in average with 121 days after sowing. Regarding vegetative, reproductive and seed filling stages, the Low latitude accessions have the lowest number of days values.</p>", "<p id=\"Par19\">It should be noted that some accessions of Medium latitudinal origin (18%) and High latitudinal origin (57%) failed to flower, while all accessions from Low latitudinal origin have flowered (Fig. ##FIG##3##4##).</p>", "<title>Variation of harvest index according to genotype under extended photoperiod</title>", "<p id=\"Par20\">The yield and biomass measurements allowed us to estimate the harvest index for the different accessions with a very highly difference, the accessions from the High latitude presented the lowest index with 0.011, while the accessions from Low latitude presented the highest index with 0.12, and three distinguished groups are showed by using Duncan test (Table ##TAB##2##3##).</p>", "<title>Correlation between different traits</title>", "<p id=\"Par21\">The Pearson correlation method was used to examine the links between the variables in our study (Fig. ##FIG##4##5##).</p>", "<p id=\"Par22\">In our study, we observed a Pearson correlation coefficient of 0.79 between the GCC and SV variables, indicating a strong positive correlation between them. This suggests that an increase in vigor is generally associated with an increase in canopy cover and vice versa, in the same trend the phenological traits showed strong positive correlations with each other TF and VGS with (1.00), TF and RPS with (0.72), TF and TPS with (1.00), TF and TM with (0.99). In contrast, the variables HI against TF, VGS and TPS had a correlation coefficient of (-0.70), indicating a negative correlation between them, and this suggests that a prolongation of phenological stages has a negative influence on yield under extended photoperiod.</p>", "<title><bold><italic>Multivariate analysis</italic></bold></title>", "<title>Canonical discriminant analysis</title>", "<p id=\"Par23\">To test the hypothesis of differentiation of the studied accessions according to their latitudinal origins, a canonical discriminant analysis was carried out, providing a graphical view that illustrated the existence of groups using the origin of accessions (Low, Medium and High latitudes) as a dependent variable. Time to flowering, time of pods set, time to maturity, harvest index, green canopy cover, seedling vigor, vegetative stage length, reproduction stage length and seed filing stage length as an explicative variables. The two first functions were significant, for the first function, Wilks' Lambda (0.57), Chi-square (41.48) and P &lt; 0.000, and for the first function, Wilks' Lambda (0.76), Chi-square (20.44) and P &lt; 0.001. The first function explained 50.8% while the second function explained 49.2% of the total variance, and corresponding to the correlations of 0.5 and 0.49, respectively.</p>", "<p id=\"Par24\">The two-dimensional (2D) Scatter diagram of the discriminant space (canonical plot) (Fig. ##FIG##5##6##) presented the distribution of samples separated by the first two functions. Based on the standardized coefficients of the Discriminant Function Analysis (DFA), the accessions from High latitudinal origin were highly weighted in the negative part of DFA-F1, while those from Medium latitudinal origin are the most weighted in the positive part of DFA-F1. The accessions from Low latitudinal origin was clearly distinguished from the other origins by the function 2.</p>", "<p id=\"Par25\">These results suggest that discriminant analysis has successfully identified distinct characteristics between the groups, enabling them to be effectively discriminated in this reduced two-dimensional space.</p>", "<title>Principal component analysis</title>", "<p id=\"Par26\">Principal component analysis was applied based on the mean values of all variables for the extended photoperiod treatment. The first two components explained 59% for the first axis PCA1, 19.4% for the second axis PCA2 (Fig. ##FIG##6##7##A). The first principal component was highly and positively correlated with time to flowering (0.98), vegetative stage length (0.98), time of pod set (0.99), and time of maturity with (0.98), while highly and negatively correlated with harvest index (− 0.75). The second principal component was strongly and positively correlated with green canopy cover (0.93) and with seedling vigor (0.91).</p>" ]

[ "<title>Discussion</title>", "<p id=\"Par27\">Many studies on lentil have focused on the effect of genotype on physiological and morphological traits under normal temperature and photoperiod conditions in either the controlled environments or field. However, studies on the sensitivity to prolonged photoperiod are limited. Therefore, our study aims to investigate the effect of genotype and latitudinal origin of different lentil accessions in an extended photoperiod environment.</p>", "<title>Implications of latitudinal origin on photoperiodic response</title>", "<p id=\"Par28\">The impact of light conditions on flowering and plant development is of great importance, particularly in the context of plant adaptation to varying climates and daylengths. Plants are sensitive to the duration of daylight and darkness, which influences the start of flowering [##UREF##44##51##]. Long or short photoperiods can modify the flowering period according to the specific needs of each species or cultivar. For instance, rice plants from equatorial regions prefer shorter days to start flowering, while those from regions further from the equator require longer days [##UREF##45##52##]. Moreover, plants have the ability to adjust to environmental variations, including changes in photoperiod. When transplanted to new environments, plants can recalibrate their internal circadian clocks to adjust to local photoperiods [##UREF##46##53##]. Plants have photoreceptors, such as phytochromes and cryptochromes, that enable them to detect light, including red (R) and blue (B) light [##UREF##47##54##]. When activated by light, these photoreceptors initiate specific signaling pathways, acting as molecular switches. Light signals are integrated into the plant circadian clock, composed of genes and proteins that regulate gene expression throughout the day, among these genes FLOWERING LOCUS T (FT) plays a key role in regulating flowering in response to specific light signals [##REF##19154317##55##].</p>", "<p id=\"Par29\">The diverse latitudinal origins of the lentil accessions in our study could potentially contribute to the variations observed in their photoperiodic responses. Geographic latitude, longitude and climate may influence the natural photoperiod to which these accessions have adapted over generations [##REF##31981443##27##, ##REF##35894658##56##]. This adaptation could have caused variations in their sensitivity to extended photoperiods, as proven by [##REF##29354150##57##] that some wild lentil genotypes are less sensitive than cultivated ones to light quality. The response to photoperiodism is a major factor in determining the timing of flowering, and is governed by the complex interplay between internal circadian rhythm and external day length, which varies according to geographical latitude [##REF##31981443##27##].</p>", "<title>Impact of extended photoperiod on genetic variation of phenological and reproductive stages</title>", "<p id=\"Par30\">The various lentil genotypes studied comes from different geographical origins and have distinct genetic characteristics, resulting in varying sensitivity to environmental factors such as extended photoperiod. Previous research [##UREF##26##32##] shows that some genotypes from different origins can show increased or reduced sensitivity to specific environmental conditions such as long days, vernalization and temperature. The application of extended light duration induced an early flowering of the long day plants (LDP) such as lentil and chickpea [##UREF##48##58##]. Similar results have been reported by [##UREF##18##23##] on advanced lines, local populations, and wild accessions (<italic>Lens orientalis</italic>) in lentil. According to [##UREF##25##31##], the time from sowing to flowering differed significantly among accessions and also varied with the photoperiodic regimes. Moreover, [##UREF##41##48##] studied the responses of cultivated and wild-type lentil accessions in a growth chamber under controlled conditions (22◦C/16 h during the day and 16◦C/8 h at night) in different light environments (red/far-red ratio (R/FR) and photosynthetically active radiation (PAR)), the authors showed that time to flowering were significantly influenced by genotype, light environment, and the interaction between them, and this is related to the origin of each accession. In the present study, several lentil accessions showed a significant delay in their flowering process, or even failed to flower within the experiment's period. The reason for this response resides mainly in the photoperiod, i.e. the duration of day and night light to which these plants are subjected. The accessions from higher latitudes, such as Russia, showed a marked tendency to delay flowering by more than 80 days, and 57% of these plants failed to flower (Fig. ##FIG##3##4##), in this experiment, even under a prolonged photoperiod. This is explained by their genetic adaptation to environments where flowering days are characterized by a notably prolonged photoperiod, with up to 17 h of natural light as shown in the Table ##TAB##0##1##. Therefore, they may need to be given a longer light duration than their natural light duration, more than 22 h of light or continuous light (24 h of light) in a period between plant emergence to flowering, to become sensitive to this light duration and accelerate their flowering process as LDP [##UREF##49##59##, ##UREF##50##60##]. Another hypothesis is that there is a possible need for vernalization for this group of accessions, a process where prolonged exposure of lentil seeds to cold temperatures may be necessary to induce and accelerate flowering [##UREF##26##32##]. If the genetic variability observed in response to photoperiodism is associated with the potential role of vernalization, it may highlight the complex interaction between genetic and environmental factors in the regulation of flowering. Further research questions, that needs future exploration, related to the response of these accessions and their progenies (after crosses) to extended photoperiod under speed breeding in terms of flowering arise. In contrast, accessions from Ethiopia and India, located at lower latitudes, have evolved naturally to prosper in more balanced light conditions, with days and nights typically lasting 12 to 14 h during their flowering period, as shown in the Table ##TAB##0##1##. Previous studies of lentil response to photoperiod have shown that genotypes from subtropical regions were less sensitive to variations in daylength [##UREF##51##61##]. These observations highlight the essential impact of plants' genetic adaptation to their local environment, particularly with regard to the length of day and night, knowledge that is crucial to breeding and selection programs aimed at developing varieties adapted to specific regions. Therefore, the influence of photoperiod on plant growth and development is mainly linked to the regulation of the long-day-dependent flowering pathway, such as the FLOWERING LOCUS T (FT) pathway [##REF##19154317##55##]. Instead of directly accelerating photosynthesis, a prolonged photoperiod promotes the transition to the early flowering phase by modulating this signaling pathway.</p>", "<p id=\"Par31\">Significant genetic variability was observed for the duration of different development stages. The extended photoperiod significantly influenced the development stages of each genotype as proved by [##UREF##52##62##]. Although vegetative stage length and reproductive stage length were positively correlated with the genotypes earliness, except seed filling stage revealed the opposite, this is explained by the time compensation for the duration of the vegetative phase which will determine the rate and duration of seed filling stage length as proved by [##REF##29089954##63##].</p>", "<title>Effect of extended photoperiod on genetic variability of vegetative growth</title>", "<p id=\"Par32\">In our study, a high genetic variability (p = 0.000) was observed for green canopy cover between the studied accessions. Canopy cover, determined by the Canopeo, the Green canopy cover, is a very important parameter for biomass estimation based on the percentage of green color of plants [##UREF##53##64##]. However, the photoperiod regime can influence the growth and development of the plant as proven by [##UREF##19##24##]. Significant genetic diversity was observed among accessions with regard to seedling vigor. This trend has also been observed in lentil plants exposed to drought stress and well-watered conditions in previous studies [##UREF##40##47##, ##UREF##54##65##], and it means that when plants are subjected to stress conditions, whether controlled or not, they grow differently, tolerating the stress or being sensitive to it.</p>", "<p id=\"Par33\">The importance of our study coincides with current efforts in the field of speed breeding and genetic improvement. The significant genetic variability observed for key traits such as flowering time, developmental stages and harvest index under extended photoperiod conditions has significant implications for accelerated breeding. As breeders and researchers work to develop crop varieties with improved performance and yield potential, it is crucial to understand the genetic basis of rapid development. Our results provide a valuable information on the possibility of exploiting genetic diversity to speed up the breeding process and obtain the desired characteristics through SB techniques. By revealing the complex interaction between genetic diversity and photoperiodic responses, our study offers a valuable gateway to the targeted manipulation of reproductive traits, enabling the rapid creation of high-yielding crop varieties.</p>" ]

[ "<title>Conclusion</title>", "<p id=\"Par34\">The results of this study indicated that extended photoperiod highly influenced the growth and development of different lentil genotypes. However, a large genetic variability for response to prolonged photoperiod was observed among the different accessions. Genotypes of Low latitudinal origin showed early flowering and maturity, and high yield, therefore, higher adaptability and easy use under extended photoperiod conditions without any strategies of initial induction of flowering (vernalization…). While, many other accessions especially from High and Medium latitudinal origins did not flower during this experience, therefore, using these accessions under extended photoperiod would be difficult and needs additional initial steps such as vernalization that could slow down the speed breeding process. Hence, our results suggest that Low latitudinal and some Medium latitudinal accessions are more recommended for breeding programs applying extended photoperiod to accelerate plant growth and flowering.</p>" ]

[ "<p id=\"Par1\">Lentil is an important pulse that contributes to global food security and the sustainability of farming systems. Hence, it is important to increase the production of this crop, especially in the context of climate changes through plant breeding aiming at the development of high-yielding and climate-smart cultivars. However, conventional plant breeding approaches are time and resources consuming. Thus, speed breeding techniques enabling rapid generation turnover could help to accelerate the development of new varieties. The application of extended photoperiod prolonging the duration of the plant’s exposure to light and shortening the duration of the dark phase is among the simplest speed breeding techniques. In this study, genetic variability response under extended photoperiod (22 h of light/2 h of dark at 25 °C) of a lentil collection of 80 landraces from diverse latitudinal origins low (0°–20°), medium (21°–40°) and high (41°–60°), was investigated. Significant genetic variations were observed between accessions, for time to flowering [40 → 120 days], time of pods set [45 → 130 days], time to maturity [64 → 150 days], harvest index [0 → 0.24], green canopy cover [0.39 → 5.62], seedling vigor [2 → 5], vegetative stage length [40 → 120 days], reproduction stage length [3 → 13 days], and seed filing stage length [6 → 25 days]. Overall, the accessions from Low latitudinal origin demonstrated a favorable response to the extended photoperiod application with almost all accessions flowered, while 18% and 57% of accessions originating from medium and high latitudinal areas, respectively, did not successfully reach the flowering stage. These results enhanced our understanding lentil responses to photoperiodism under controlled conditions and are expected to play important roles in speed breeding based on the application of the described protocol for lentil breeding programs in terms of choosing appropriate initial treatments such as vernalization depending on the origin of accession.</p>", "<title>Keywords</title>" ]

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[ "<title>Acknowledgements</title>", "<p>Many thanks to the team at the Laboratory of Food Legumes Breeding at the Regional Center of Agricultural Research in Settat. Your help and expertise were very important to our study. Thank you for your wonderful collaboration, it really made our work great!</p>", "<title>Author contributions</title>", "<p>MM Conceptualization, methodology, software, validation, formal analysis, investigation, data curation, writing—original draft preparation, writing—review and editing. OI Conceptualization, methodology, validation, formal analysis, writing—review and editing, supervision, funding acquisition. AB Validation, formal analysis, writing—review and editing, supervision, funding acquisition. BB Writing—review and editing, supervision.</p>", "<title>Funding</title>", "<p>This research received no external funding.</p>", "<title>Availability of data and materials</title>", "<p>The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.</p>", "<title>Declarations</title>", "<title>Ethics approval and consent to participate</title>", "<p id=\"Par35\">Not applicable.</p>", "<title>Consent for publication</title>", "<p id=\"Par36\">Not applicable.</p>", "<title>Competing interests</title>", "<p id=\"Par37\">The authors declare that they have no competing interests.</p>" ]

[ "<fig id=\"Fig1\"><label>Fig. 1</label><caption><p>Distribution of the studied lentil accessions according to countries, latitude and longitude</p></caption></fig>", "<fig id=\"Fig2\"><label>Fig. 2</label><caption><p>Speed breeding growth chamber</p></caption></fig>", "<fig id=\"Fig3\"><label>Fig. 3</label><caption><p>Boxplot of comparison between lentil origins under the extended photoperiod conditions. <italic>TF</italic> time to flowering, <italic>TPS</italic> time of pods set, <italic>TM</italic> time to maturity, <italic>HI</italic> harvest index, <italic>GCC</italic> green canopy cover, <italic>SV</italic> seedling vigor, <italic>VGS</italic> vegetative stage, <italic>RPS</italic> reproduction stage, <italic>SFS</italic> seed filing stage</p></caption></fig>", "<fig id=\"Fig4\"><label>Fig. 4</label><caption><p>Variation in flowering percentage between different latitudinal origins of lentil accessions</p></caption></fig>", "<fig id=\"Fig5\"><label>Fig. 5</label><caption><p>Pearson correlation matrix of variables under extended photoperiod. <italic>TF</italic> time to flowering, <italic>TPS</italic> time of pods set, <italic>TM</italic> time to maturity, <italic>HI</italic> harvest index, <italic>GCC</italic> green canopy cover, <italic>SV</italic> seedling vigor, <italic>VGS</italic> vegetative stage length, <italic>RPS</italic> reproduction stage length, <italic>SFS</italic> seed filing stage length</p></caption></fig>", "<fig id=\"Fig6\"><label>Fig. 6</label><caption><p>2D scatter plot showing the distribution of accessions according to the two discriminant functions obtained by DFA for the studied traits under extended photoperiod conditions</p></caption></fig>", "<fig id=\"Fig7\"><label>Fig. 7</label><caption><p>Principal component analysis of lentil traits and origins under extended photoperiod. <bold>a</bold> PCA-Biplot, <bold>b</bold> Correlation circle. TF time to flowering, <italic>TPS</italic> time of pods set, <italic>TM</italic> time to maturity, <italic>HI</italic> harvest index, <italic>GCC</italic> green canopy cover, <italic>SV</italic> seedling vigor, <italic>VGS</italic> vegetative stage length, <italic>RPS</italic> reproduction stage length, <italic>SFS</italic> seed filing stage length</p></caption></fig>" ]

[ "<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Classification of lentil accessions</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Level</th><th align=\"left\">Country</th><th align=\"left\">Usual cropping season</th><th align=\"left\">Flowering time (month)</th><th align=\"left\">Natural daylength during flowering in country of origin (h)</th><th align=\"left\">Number of accessions</th><th align=\"left\">Latitude</th></tr></thead><tbody><tr><td align=\"left\" rowspan=\"2\">Low</td><td align=\"left\">Ethiopia</td><td align=\"left\">Rainy</td><td align=\"left\">November–December</td><td char=\"–\" align=\"char\">12–13</td><td char=\".\" align=\"char\">3</td><td char=\".\" align=\"char\">9°</td></tr><tr><td align=\"left\">India</td><td align=\"left\">Summer &amp; Rainy</td><td align=\"left\">August</td><td char=\"–\" align=\"char\">12–14</td><td char=\".\" align=\"char\">1</td><td char=\".\" align=\"char\">20°</td></tr><tr><td align=\"left\" rowspan=\"7\">Medium</td><td align=\"left\">Chile</td><td align=\"left\">Spring</td><td align=\"left\">December-January</td><td char=\"–\" align=\"char\">13–14</td><td char=\".\" align=\"char\">13</td><td char=\".\" align=\"char\">30°</td></tr><tr><td align=\"left\">Morocco</td><td align=\"left\">Autumn</td><td align=\"left\">April–May</td><td char=\"–\" align=\"char\">13–14</td><td char=\".\" align=\"char\">27</td><td char=\".\" align=\"char\">32°</td></tr><tr><td align=\"left\">Iran</td><td align=\"left\">Autumn</td><td align=\"left\">March</td><td char=\"–\" align=\"char\">13–14</td><td char=\".\" align=\"char\">4</td><td char=\".\" align=\"char\">32°</td></tr><tr><td align=\"left\">Afghanistan</td><td align=\"left\">Autumn</td><td align=\"left\">February</td><td char=\"–\" align=\"char\">13–14</td><td char=\".\" align=\"char\">3</td><td char=\".\" align=\"char\">33°</td></tr><tr><td align=\"left\">Greece</td><td align=\"left\">Autumn</td><td align=\"left\">February</td><td char=\"–\" align=\"char\">12–14</td><td char=\".\" align=\"char\">3</td><td char=\".\" align=\"char\">39°</td></tr><tr><td align=\"left\">Turkey</td><td align=\"left\">Autumn</td><td align=\"left\">April</td><td char=\"–\" align=\"char\">12–14</td><td char=\".\" align=\"char\">6</td><td char=\".\" align=\"char\">39°</td></tr><tr><td align=\"left\">Italy</td><td align=\"left\">Autumn</td><td align=\"left\">April–May</td><td char=\"–\" align=\"char\">12–14</td><td char=\".\" align=\"char\">6</td><td char=\".\" align=\"char\">39°</td></tr><tr><td align=\"left\" rowspan=\"6\">High</td><td align=\"left\">Armenia</td><td align=\"left\">Spring</td><td align=\"left\">June</td><td char=\"–\" align=\"char\">14–15</td><td char=\".\" align=\"char\">1</td><td char=\".\" align=\"char\">40°</td></tr><tr><td align=\"left\">Montenegro</td><td align=\"left\">Spring</td><td align=\"left\">May</td><td char=\"–\" align=\"char\">14–15</td><td char=\".\" align=\"char\">2</td><td char=\".\" align=\"char\">42°</td></tr><tr><td align=\"left\">Serbia</td><td align=\"left\">Spring</td><td align=\"left\">May</td><td char=\"–\" align=\"char\">14–15</td><td char=\".\" align=\"char\">2</td><td char=\".\" align=\"char\">44°</td></tr><tr><td align=\"left\">Ukraine</td><td align=\"left\">Spring</td><td align=\"left\">June</td><td char=\"–\" align=\"char\">15–16</td><td char=\".\" align=\"char\">1</td><td char=\".\" align=\"char\">49°</td></tr><tr><td align=\"left\">Belgium</td><td align=\"left\">Spring</td><td align=\"left\">May</td><td char=\"–\" align=\"char\">16–17</td><td char=\".\" align=\"char\">5</td><td char=\".\" align=\"char\">51°</td></tr><tr><td align=\"left\">Russia</td><td align=\"left\">Spring</td><td align=\"left\">June</td><td char=\"–\" align=\"char\">17–18</td><td char=\".\" align=\"char\">3</td><td char=\".\" align=\"char\">60°</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab2\"><label>Table 2</label><caption><p>Mean squares from ANOVA results of quantitative and qualitative traits of lentil as influenced by photoperiodic regime</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">SOV</th><th align=\"left\">Df</th><th align=\"left\">GCC (%)</th><th align=\"left\">HI</th><th align=\"left\">SV</th><th align=\"left\">TF (days)</th><th align=\"left\">TPS (days)</th><th align=\"left\">VGS (days)</th><th align=\"left\">RPS (days)</th><th align=\"left\">SFS (days)</th><th align=\"left\">TM (days)</th></tr></thead><tbody><tr><td align=\"left\">Genotype</td><td char=\".\" align=\"char\">79</td><td char=\".\" align=\"char\">2.7867***</td><td char=\".\" align=\"char\">0.0089***</td><td char=\".\" align=\"char\">0.6784*</td><td align=\"left\">229.83***</td><td align=\"left\">206.59***</td><td align=\"left\">229.83***</td><td char=\".\" align=\"char\">5.996*</td><td char=\".\" align=\"char\">22.35</td><td char=\".\" align=\"char\">91.44**</td></tr><tr><td align=\"left\">Latitude</td><td char=\".\" align=\"char\">2</td><td char=\".\" align=\"char\">25.164***</td><td char=\".\" align=\"char\">0.0531***</td><td char=\".\" align=\"char\">1.878*</td><td align=\"left\">809**</td><td align=\"left\">280</td><td align=\"left\">809**</td><td char=\".\" align=\"char\">1.592</td><td char=\".\" align=\"char\">10.11</td><td char=\".\" align=\"char\">221.92*</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab3\"><label>Table 3</label><caption><p>Effect of photoperiod on traits for different lentil origins</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" colspan=\"2\">Traits latitude</th><th align=\"left\">HI</th><th align=\"left\">GCC (%)</th><th align=\"left\">SV</th><th align=\"left\">TF (days)</th><th align=\"left\">TPS (days)</th><th align=\"left\">TM (days)</th><th align=\"left\">VGS (days)</th><th align=\"left\">RPS (days)</th><th align=\"left\">SFS (days)</th></tr></thead><tbody><tr><td align=\"left\">High</td><td char=\".\" align=\"char\" colspan=\"2\">0.011a</td><td char=\".\" align=\"char\">2.91a</td><td char=\".\" align=\"char\">3.43a</td><td char=\".\" align=\"char\">96.04a</td><td char=\".\" align=\"char\">104.71a</td><td char=\".\" align=\"char\">121.43a</td><td char=\".\" align=\"char\">96.04a</td><td char=\".\" align=\"char\">8.67a</td><td char=\".\" align=\"char\">16.71a</td></tr><tr><td align=\"left\">Medium</td><td char=\".\" align=\"char\" colspan=\"2\">0.063b</td><td char=\".\" align=\"char\">3.55a</td><td char=\".\" align=\"char\">3.66a</td><td char=\".\" align=\"char\">68.88b</td><td char=\".\" align=\"char\">75.7b</td><td char=\".\" align=\"char\">91.51b</td><td char=\".\" align=\"char\">68.88b</td><td char=\".\" align=\"char\">6.83ab</td><td char=\".\" align=\"char\">15.81a</td></tr><tr><td align=\"left\">Low</td><td char=\".\" align=\"char\" colspan=\"2\">0.12c</td><td char=\".\" align=\"char\">1.6b</td><td char=\".\" align=\"char\">2.58b</td><td char=\".\" align=\"char\">56.54b</td><td char=\".\" align=\"char\">62.25b</td><td char=\".\" align=\"char\">75.08b</td><td char=\".\" align=\"char\">56.54b</td><td char=\".\" align=\"char\">5.71b</td><td char=\".\" align=\"char\">12.83a</td></tr></tbody></table></table-wrap>" ]

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[ "<table-wrap-foot><p>Signif. codes: 0.000 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’. </p><p><italic>SOV</italic> source of variation, <italic>TF</italic> time to flowering, <italic>TPS</italic> time of pods set, <italic>TM</italic> time to maturity, <italic>HI</italic> harvest index, <italic>GCC</italic> green canopy cover, <italic>SV</italic> seedling vigor, <italic>VGS</italic> vegetative stage length, <italic>RPS</italic> reproduction stage length, <italic>SFS</italic> seed filing stage length</p></table-wrap-foot>", "<table-wrap-foot><p>The table values represent (Means), “a, b and c” Duncan test</p><p><italic>TF</italic> time to flowering, <italic>TPS</italic> time of pods set, <italic>TM</italic> time to maturity, <italic>HI</italic> harvest index, <italic>GCC</italic> green canopy cover, <italic>SV</italic> seedling vigor, <italic>VGS</italic> vegetative stage length, <italic>RPS</italic> reproduction stage length, <italic>SFS</italic> seed filing stage length</p></table-wrap-foot>", "<fn-group><fn><p><bold>Publisher's Note</bold></p><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn></fn-group>" ]

[ "<graphic xlink:href=\"13007_2024_1135_Fig1_HTML\" id=\"MO1\"/>", "<graphic xlink:href=\"13007_2024_1135_Fig2_HTML\" id=\"MO2\"/>", "<graphic xlink:href=\"13007_2024_1135_Fig3_HTML\" id=\"MO3\"/>", "<graphic xlink:href=\"13007_2024_1135_Fig4_HTML\" id=\"MO4\"/>", "<graphic xlink:href=\"13007_2024_1135_Fig5_HTML\" id=\"MO5\"/>", "<graphic xlink:href=\"13007_2024_1135_Fig6_HTML\" id=\"MO6\"/>", "<graphic xlink:href=\"13007_2024_1135_Fig7_HTML\" id=\"MO7\"/>" ]

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{ "acronym": [], "definition": [] }

[ "<title>Introduction</title>", "<p id=\"Par23\">Asthma is a global bronchial inflammatory disease that affects individuals in all age-groups, and its prevalence has shown an increasing trend in many countries [##REF##34667060##1##]. Asthma is a heterogeneous disease characterized clinically by reversible bronchoconstriction and airway hyperresponsiveness [##REF##33576199##2##]. Asthma can be classified into 4 phenotypes based on the predominant type of inflammatory cells in the sputum: eosinophilic asthma (EA), neutrophilic asthma (NA), paucigranulocytic asthma (PA), and mixed granulocytic asthma (MA) [##REF##30105264##3##]. Distinguishing the asthma phenotypes facilitates the analysis of clinical features, biological markers, and individualized treatment. NA is usually associated with more severe asthma, glucocorticoid resistance, and poor prognosis [##REF##30105264##3##]. Therefore, identifying relevant biomarkers and developing therapeutic strategies for NA are key research imperatives.</p>", "<p id=\"Par24\">Interleukin (IL-36) is a member of the IL-1 superfamily of three endogenous agonists, IL-36α, -β, and -γ, which promote inflammatory cell infiltration through signaling at the IL-36 receptor (IL-36R) [##REF##33463541##4##]. Under physiological conditions, low levels of IL-36 cytokine expression can be observed in organs such as the skin, intestine, lung and brain; during inflammation, IL-36 receptor agonists are predominantly expressed by keratinocytes, epithelial cells, and inflammatory monocytes/macrophages [##REF##33501638##5##]. IL-36 cytokines are activated by neutrophil-derived cathepsin G, elastase, and protease-3, which are mainly released by activated neutrophils [##REF##26776523##6##]. Studies have indicated the potential involvement of IL-36 in a wide range of inflammatory and oncogenic processes in the skin, lung, kidney, liver, and intestine, which is mediated via activation of immune and non-immune cells, such as T cells, keratinocytes, and epithelial cells [##REF##32540133##7##]. In a mouse model of unilateral ureteral obstruction, IL-36α was found to activate the IL-23/IL-17 axis, amplify inflammation, and promote the development of renal lesions. We hypothesized that a similar phenomenon may occur in the context of asthma [##REF##28179433##8##]. A study found that IL-36γ promotes allergic rhinitis by enhancing eosinophil infiltration, and that IL-36α is involved in the allergic inflammatory response by regulating Th17 [##REF##32533268##9##]. There are many similarities in the pathogenesis of allergic asthma and allergic rhinitis, and these are common diseases that frequently occur together [##REF##25295802##10##].</p>", "<p id=\"Par25\">As mentioned above, the heterogeneity of asthma and IL-36 may lead to inconsistency between the results of experimental studies. Therefore, in this study, we compared the sputum concentrations of IL-36 in asthma and healthy non-asthmatic individuals, and investigated the relationship between IL-36 and associated inflammatory cytokines. Furthermore, we investigated the sputum concentration of IL-36 in patients with different asthma phenotypes.</p>" ]

[ "<title>Methods</title>", "<title>Study population</title>", "<p id=\"Par26\">The diagnosis of asthma was based on the Global Initiative for Asthma (GINA) guidelines for current episodes of respiratory symptoms, evidence of variable airflow obstruction, and clinical diagnosis [##REF##32868307##11##]. This study required sputum induction maneuvers; therefore, only asthmatic patients in a mild controlled stage were enrolled. The exclusion criteria were [##REF##34667060##1##] pregnant women [##REF##33576199##2##]; patients with severe cardiovascular diseases [##REF##30105264##3##]; malignant tumors [##REF##33463541##4##]; active tuberculosis or interstitial lung disease [##REF##33501638##5##]; history of oral corticosteroid or antibiotic therapy in the past year [##REF##26776523##6##]; exacerbation of asthma within the 4-week period immediately preceding the study; and [##REF##32540133##7##] previous change of treatment within 4 weeks.</p>", "<p id=\"Par27\">In addition, age- and sex-matched healthy non-asthmatic subjects were also recruited as healthy non-asthmatic controls. All asthma patients and healthy non-asthmatic subjects were recruited from the Second Hospital of Jilin University. All subjects completed a bronchodilator test prior to enrolment. All subjects were of Mongolian ethnicity, i.e., yellow race. All subjects completed the questionnaires, including treatment history, smoking history, and presence of respiratory symptoms. All subjects provided written informed consent. The Ethics Committee of the Second Hospital of Jilin University granted ethical approval for this study (2016-34).</p>", "<title>Sputum collection</title>", "<p id=\"Par28\">All participants inhaled ultrasonically nebulized hypertonic saline (4.5%) for 15 min to induce sputum after adequate cleaning of the oral cavity and pharynx. The induced sputum was collected into petri dishes and the sputum plugs were isolated. Dithiothreitol (DTT) was added to lyse the sputum plugs and the volume of sputum plugs was recorded. After 30 min of rotational mixing at room temperature, phosphate buffer solution (PBS, pH 7.4) with 4 times the volume of sputum was added and mixed. The filtered filtrate (60 μm) was centrifuged at 400×<italic>g</italic> for 10 min and the supernatant was stored at − 80 °C for subsequent experiments. Sputum cell smears were prepared by cell precipitation, fixed in methanol for 10 min, rinsed, stained with hematoxylin for 30 s, rinsed with Chromotrope 2R (C2R acid)-paraffin mixture for 20 min, rinsed again, air-dried, and sealed with neutral resin [##REF##16423202##12##, ##REF##31995587##13##].</p>", "<title>Measurement of IL-36 and other cytokines</title>", "<p id=\"Par29\">The concentrations of IL-36α, IL-36β, IL-36γ, IL-36Ra, and IL-1β were measured using a commercial human ELISA kit (CUSABIO, China). The IL-2, IL-4, IL-6, IL-9, IL-10, IL-13, IL-17 A, IL-17 F, IL-22, IFN-γ, and TNF-α concentrations were determined using the Multi-Analyte Flow Assay Kit (Biolegend, USA) with a Cytometric Bead Array (CBA). The above assay steps were performed according to the manufacturer’s recommended protocol.</p>", "<title>Asthma phenotype classification</title>", "<p id=\"Par30\">The numbers of various inflammatory cells in the induced sputum smear were observed microscopically and recorded. Patients with neutrophils ≥ 61% in sputum were categorized as NA, patients with eosinophils ≥ 3% in sputum as EA, patients with eosinophils &lt; 3% and neutrophils &lt; 61% in sputum as PA, and patients with eosinophils ≥ 3% and neutrophils ≥ 61% in sputum were categorized as MA [##REF##21785157##14##].</p>", "<title>Statistical analysis</title>", "<p id=\"Par31\">All data were analyzed using the Statistical Package for the Social Sciences for Windows (SPSS) statistical software Version 20 (SPSS Inc., IL, USA). Non-normally distributed continuous variables were subjected to logarithmic transformation, after which statistical analysis was performed on normally distributed logged data. Normally distributed variables were expressed as mean ± standard deviation (SD) and statistical analysis was performed using ANOVA with a least significant difference (LSD). Non-normally distributed variables were expressed as median and interquartile range (IQR), and statistical analysis was performed using Kruskal Wallis H test with Bonferroni correction or Mann–Whitney U test. Categorical variables were analyzed using Chi-squared test. Correlations between each inflammatory factor in sputum supernatant, and correlation of inflammatory factors with lung function, and inflammatory cells in sputum were analyzed using partial correlation. <italic>P</italic> values &lt; 0.05 were considered indicative of statistical significance.</p>" ]

[ "<title>Results</title>", "<title>Clinical characteristics of asthmatic patients and healthy non-asthmatic controls</title>", "<p id=\"Par32\">A total of 62 patients with asthma (27 male and 35 female) were included in this study. Sixteen healthy volunteers (10 males and 6 females) were enrolled in the control group. There were no significant between-group differences with respect to the baseline clinical data (<italic>P</italic> &gt; 0.05). The predicted and post values of forced expiratory volume in 1 s (FEV1) in the asthma group were significantly lower than those in the healthy non-asthmatic control group (<italic>P</italic> &lt; 0.001 and 0.002, respectively). The number of eosinophils, neutrophils, macrophages, and lymphocytes in the induced sputum were significantly greater in the asthma group compared to the control group (eosinophils: <italic>P</italic> &lt; 0.001, neutrophils, macrophages, lymphocytes: <italic>P</italic> = 0.001) (Table ##TAB##0##1##).</p>", "<p id=\"Par33\">\n</p>", "<p id=\"Par34\">In the asthma group, the concentrations of IL-36α and IL-36β were significantly higher (<italic>P</italic> = 0.003 and 0.031), while the IL-36Ra concentration was significantly lower compared to the control group (<italic>P</italic> &lt; 0.001). However, there was no significant between-group difference with respect to IL-36γ concentration (<italic>P</italic> = 0.603). The concentrations of IL-10, IL-13, and IL-17 A in the asthma group were significantly lower than those in the control group (IL-10: <italic>P</italic> = 0.043; IL-13: <italic>P</italic> = 0.014; IL-17 A: <italic>P</italic> = 0.026). There were no significant between-group differences with respect to the other inflammatory factors (Fig. ##FIG##0##1##).</p>", "<p id=\"Par35\">\n</p>", "<title>Clinical features of inflammatory phenotypes in asthma</title>", "<p id=\"Par36\">The asthma group was further divided into EA, MA, NA, and PA groups based on the examination of induced sputum; the clinical characteristics of these groups were comparable (<italic>P</italic> &gt; 0.05) (Table ##TAB##1##2## and Fig. ##FIG##1##2##).</p>", "<p id=\"Par37\">\n</p>", "<p id=\"Par38\">\n</p>", "<title>Concentrations of IL-36 and other inflammatory mediators in Asthma phenotypes</title>", "<p id=\"Par39\">Sputum IL-36α and IL-36β concentrations in the NA group were significantly higher than those in the PA and EA groups. Sputum IL-1β concentration in the NA, PA, and MA groups were significantly higher than that in the EA group. Sputum IL-13 and IL-10 concentrations in the NA group were significantly lower than those in the PA and EA groups. Sputum IL-6 concentration in the NA group was significantly higher than that in the EA group. The concentrations of other inflammatory factors were comparable among the groups (Fig. ##FIG##2##3##).</p>", "<p id=\"Par40\">\n</p>", "<title>Association between IL-36 and inflammatory cells</title>", "<p id=\"Par41\">We compared inflammatory mediators and concentrations of inflammatory cells in the induced sputum. IL-36α and IL-36β showed positive correlation with sputum neutrophils and total cell count (TCC) (R = 0.689, <italic>P</italic> &lt; 0.01; R = 0.304, <italic>P</italic> = 0.008; R = 0.689, <italic>P</italic> &lt; 0.042; R = 0.253, <italic>P</italic> = 0.026). In addition, there was a significant positive correlation between IL-36α and IL-36β (R = 0.658, <italic>P</italic> &lt; 0.01) (Fig. ##FIG##3##4##).</p>", "<p id=\"Par42\">\n</p>", "<title>Association of IL-36 with other inflammatory mediators</title>", "<p id=\"Par43\">We compared the concentrations of IL-36 and other inflammatory mediators in the sputum supernatant. IL-36α, IL-36β, and IL-36γ showed strong positive correlation with IL-6, TNF-α, and IL-17 A, respectively (R = 0.592, 0.451, and 0.431, <italic>P</italic> &lt; 0.01) (Table ##TAB##2##3##).</p>", "<p id=\"Par44\">\n</p>", "<title>Association of other inflammatory mediators</title>", "<p id=\"Par45\">In addition, our study also innovatively performed multiple comparisons of IL-2, IL-4, IL-6, IL-9, IL-10, IL-13, IL-17 A, IL-17 F, IL-22, IFN-γ, and TNF-α (Fig. ##FIG##4##5##). Our results found a significant positive correlation between IL-2 (R = 0.614) and IL-4 (R = 0.614), IL-9 (R = 0.710), IL-10 (R = 0.275), IL-13 (R = 0.327), IL-17 A (R = 0.307), IL-17 F (R = 0.628), IL-22 (R = 0.540), IFN-γ (R = 0.546) (<italic>P</italic> &lt; 0.05). IL-1β had a significant positive correlation with IL-6 (R = 0.271; <italic>P</italic> &lt; 0.05). IL-13 had a significant positive correlation with IL-2 (R = 0.327), IL-4 (R = 0.272), IL-10 (R = 0.553), IL-17 F (R = 0.279) (<italic>P</italic> &lt; 0.05). IL-4 had a significant positive correlation with IL-2 (R = 0.614), IL-9 (R=,0.365), IL-10 (R = 0.350), IL-13 (R = 0.272), IL-17 A (R = 0.506), IL-17 F (R = 0.811), IL-22 (R = 0.738), IFN-γ (R = 0.500) (<italic>P</italic> &lt; 0.05). IL-6 was significantly and positively correlated with IL-1β, IFN-γ (R = 0.271, 0.446; <italic>P</italic> &lt; 0.05). IL-9 had significant positive correlation with IL-2 (R = 0.710), IL-4 (R = 0.365), IFN-γ (R = 0.377), IL-17 F (R = 0.314) (<italic>P</italic> &lt; 0.05). IL-10 had significant positive correlation with IL-2 (R = 0.275), IL-13 (R = 0.553), IFN-γ (R = 0.363), IL-17 F (R = 0.258) (<italic>P</italic> &lt; 0.05). iFN-γ was significantly correlated with IL-2 (R = 0.546), IL-4 (R = 0.500), IL-6 (R = 0.446), IL-9 (R = 0.377), IL-10 (R = 0.363), IL-17 A (R = 0.418), IL-17 F (R = 0.525), IL-22 (R = 0.432), and TNF-α (R = 0.366) (<italic>P</italic> &lt; 0.05). TNF-α had significant positive correlation with IL-17 A, IL-22, and IFN-γ (R = 0.333, 0.292, 0.366; <italic>P</italic> &lt; 0.05). IL-17 A had significant positive correlation with IL-2 (R = 0.307), IL-4 (R = 0.506), IL-10 (R = 0.258), IL-17 F (R = 0.512), IL-22 (R = 0.378), IFN-γ (R = 0.418), and TNF-α (R = 0.333) (<italic>P</italic> &lt; 0.05). IL-17 F showed a significant positive correlation with IL-2 (R = 0.628), IL-4 (R = 0.811), IL-9 (R = 0.314), IL-10 (R = 0.472), IL-13 (R = 0.279), IL-17 A (R = 0.512), IL-22(R = 0.755), IFN-γ(R = 0.525) (<italic>P</italic> &lt; 0.05). IL-22 was significantly correlated with IL-2 (R = 0.540), IL-4 (R = 0.738), IL-10 (R = 0.321), IFN-γ (R = 0.432), TNF-α (R = 0.292), IL-17 A (R = 0.378), and IL-17 F (R = 0.755) (<italic>P</italic> &lt; 0.05).</p>", "<p id=\"Par46\">\n</p>" ]

[ "<title>Discussion</title>", "<p id=\"Par47\">The involvement of IL-36 in the pathogenesis of autoimmune diseases is well established. However, its role in the pathogenesis of asthma is not well characterized. IL-Rrp2 is the common binding receptor for all IL-36 isoforms, and IL-36α, IL-36β, and IL-36γ compete with IL-36Ra for binding to this receptor [##REF##11466363##15##]. In our study, asthmatic patients had higher sputum IL-36α and IL-36β concentrations, and lower IL-36Ra concentration compared to healthy non-asthmatic controls. In a mouse model of <italic>S. aureus</italic>-induced epidermal inflammation, IL 36α and IL-4 released from keratinocytes were found to promote B-cell IgE secretion, plasma cell differentiation, and elevated serum IgE concentrations. However, these changes were significantly attenuated in IL-36R-deficient transgenic mice or wild-type mice treated with anti-IL-36R antagonistic antibodies [##REF##33645549##16##]. Our results support this study; however, there is a paucity of studies on IL-36 isoforms in different asthmatic phenotypes. Therefore, we sought to investigate whether IL-36 concentrations differed among asthma phenotypes, and if so, whether these differences could be explained by heterogeneity of asthma inflammation or differences in asthma phenotypes. We further examined the concentrations of various IL-36 subtypes in the sputum supernatant of patients with different asthmatic phenotypes.</p>", "<p id=\"Par48\">Interestingly, sputum IL-36α and IL-36β concentrations were significantly higher in the NA group compared to the PA and EA groups. However, there were no significant differences between the phenotypes with respect to sputum IL-36γ and IL-36Ra. Moreover, IL-36α and IL-36β showed a positive correlation with sputum neutrophils and TCC. These findings indicate a key role of IL-36 isoforms in inducing infiltration and activity of neutrophils in asthma, and underline their involvement in the pathophysiology of airway inflammation in the asthmatic phenotypes. IL-36α has a pro-inflammatory effect on the lung. One study found that the neutrophil environment can activate IL-36α and IL-36γ [##REF##28043870##17##]. Intratracheal administration of IL-36α drops in a mouse model was found to induce the activation of the NF-κB and MAPK pathways, and induce neutrophil chemokine expression, ultimately leading to neutrophil intracellular flow [##REF##20299540##18##, ##REF##23029241##19##]. In addition, IL-36 pro-inflammatory factors can promote the expression of neutrophil chemokines such as CXCL8, CXCL1, and CXCL2, which induce neutrophil endocytosis [##REF##23029241##19##, ##REF##28869889##20##]. IL-36 induces the production of pro-inflammatory factors such as IL-1β, TNF-α, IL-12, and IL-23. IL-36β is involved not only in inducing Th1 cell polarization but also in the Th1 immune response following mycobacterial infection [##REF##22968459##21##]. These studies are consistent with our findings. In addition, IL-36 cytokines have been shown to be mainly involved in the Th1 immune response, while the in vivo expression of IL-36α and IL-36β promotes neutrophil recruitment in asthmatic airways [##REF##31284527##22##, ##REF##23147407##23##]. IL-36R expression is increased in naive CD4+ T cells, and IL-36β, together with IL-12, promotes the Th1 polarization of naive CD4+ T cells [##REF##22968459##21##]. IL-36 has now been shown to be involved in the polarization process of Th17 [##REF##31284527##22##]. IL-36α and IL-17 have a strong feedback loop in switching skin inflammation signaling [##REF##34675805##24##]. Our study also found a significant positive correlation between IL-36γ and IL-17 A concentrations. It has been found that the level of IL-36γ increases after IL-17 stimulation [##REF##32533268##9##]. Perhaps IL-36γ and IL-36β are jointly involved in the enhanced feedback loop of IL-17 for activating the immune response in asthma. IL-36α, IL-36β, IL-36γ, and IL-36Ra may be involved in the pathogenesis of asthma phenotypes via different pathways and may be important biological targets for asthma therapy. Our study also had an interesting finding. It is well known that IL-13 H and IL-17 are classical pro-inflammatory cytokines, usually expressed at higher levels in asthma patients. However, in our study, IL-13 and IL-17 levels were lower in the asthma group. This contradictory result is the reason for the further differentiation of asthma into four different subtypes in our study. The heterogeneity of asthma leads to such contradictory results; therefore, further studies to differentiate asthma into subtypes are important for individualized and precise treatment of asthma. In our study, we found that IL-13 and IL-10 levels were lower in neutrophilic asthma. IL-13 is a cytokine secreted mainly by Th2, typically accompanying Th2 asthma, and IL-13 correlates with the severity of asthma, including eosinophilic airway inflammation, mucus secretion, airway hyperresponsiveness, and remodeling. In addition, anti-IL-13 therapy plays a significant role in targeted asthma therapy. CCL11 (eotaxin1) and CCL17 promote eosinophil and leukocyte infiltration into the lung mediated by IL-13 [##REF##11544308##25##–##REF##29334288##28##]. One study found significantly increased IL-13 in BALF, lung block biopsy specimens, and sputum of asthmatics; however, further differentiation of asthma subtypes revealed that IL-13 was not increased in non-eosinophilic asthma [##REF##29334288##28##, ##REF##15536417##29##]. IL-10 is a cytokine with both anti-inflammatory and pro-inflammatory effects and is mainly produced by activated monocytes, peripheral blood T cells, B lymphocytes, macrophages, mast cells, eosinophils, and dendritic cells. In asthma, IL-10 can negatively regulate the inflammatory response mediated by Th2 and Th17 and can alleviate the severity of neutrophilic asthma [##REF##26976823##30##]. Due to the complex function of IL-36, the results of different studies may not be consistent with each other. A previous study found significantly increased expressions of serum IL-36 cytokine mRNA and protein in patients with allergic rhinitis and asthma [##REF##19710636##31##, ##REF##30831444##32##], which is consistent with our study. However, the serum IL-36γ and IL-36R mRNA and protein expressions were also significantly elevated in patients with allergic rhinitis, which is different from our findings. These inconsistent findings may be attributable to the different proportions of patients with different asthma phenotypes in the study sample. In addition, our study also innovatively performed multiple comparisons of IL-2, IL-4, IL-6, IL-9, IL-10, IL-13, IL-17 A, IL-17 F, IL-22, IFN-γ, and TNF-α (Fig. ##FIG##3##4##). The cytokines that are closely related to IL-36 isoforms are described below. IL-1β plays a pro-inflammatory role in the pathogenesis of asthma. IL-1β expression was found in lavage fluid, epithelial cells, and alveolar macrophages of asthmatic patients. IL-1β is a regulator of airway hyperresponsiveness in asthma and can mediate eosinophil inflammation by inducing chemokines and cytokines. In addition, IL-1β is also involved in neutrophil-mediated inflammation [##REF##26134749##33##]. IL-1β can promote the production of IL-6 and chemokines in the lung, recruit neutrophils, and promote the inflammatory response [##REF##22147847##34##]. In addition, the pathogenesis of neutrophilic asthma is associated with IL-1β/IL-17-induced neutrophil activation [##REF##24136334##35##, ##REF##30267575##36##].</p>", "<p id=\"Par49\">We determined that the pro-inflammatory factor IL-36 can promote neutrophil aggregation in asthma airway inflammation, but the exact underlying mechanisms are not clear [##REF##31284527##22##]. Therefore, we further examined asthma-associated inflammatory factors and assessed their correlation with IL-36. We observed that IL-36α was positively correlated with IL-6; IL-36β was positively correlated with TNF-α, and IL-36γ was positively correlated with IL-17 A. IL-6 is known to induce neutrophil recruitment and its level increases with increasing neutrophil numbers [##REF##20398920##37##]. IL-36α was shown to induce the composition of MyD88 linked molecules to form complexes and induce activation of JNK, MAPK, and ERK1/2 signaling pathways to enhance IL-6 expression [##REF##31867327##38##]. Studies have shown that in the airway epithelium, IL-36α and IL-36γ promote IL-1β, IL-17 A, and TNF-α, an effect that is mediated through Toll-like receptors 2/6, 3, 4, and 5. This is consistent with our findings [##REF##34887860##39##]. We also innovatively found a positive correlation between IL-36β and TNF-α, which was not found in previous experiments. In vitro, treatment of cultured human keratinocytes with TNF-α and IL-17 A resulted in significantly higher levels of IL-36α and IL-36γ, forming a positive feedback loop with Th17 cytokines, which also stimulated the production of pro-inflammatory cytokines such as TNF-α, IL-6, and IL-8 [##REF##21881584##40##]. TNF-α is produced by a variety of pro-inflammatory cells and structural cells during the pathogenesis of asthma, and TNF-α is mainly associated with the Th1 response. It also works with IL-17 A to produce cxcl8, which promotes neutrophil aggregation, and is associated with the inflammatory mechanisms and airway hyperresponsiveness in neutrophilic asthma [##REF##17475560##41##–##REF##31109984##44##]. It plays an important role in airway remodeling and inflammatory response and promoting neutrophil and eosinophil migration by promoting pro-inflammatory factors and adhesion molecules such as vascular cell adhesion molecule 1 and intercellular adhesion molecule 1 [##REF##16505611##45##]. Our study supports these results in that IL-36β showed a positive correlation with TNF-α. IL-17 A is a characteristic cytokine of TH17. A previous study described the association of IL-36 with TH17 cellular responses. In our study, IL-36γ showed a positive correlation with IL-17 A concentration, supporting our previously mentioned point. However, our study also found no significant differences in TNF-α and IL-17 A concentrations between the asthma group and healthy non-asthmatic controls, and between the different asthma phenotypes. This may be related to our sample size and the regional characteristics of asthma patients, and further underlines the heterogeneity of asthma. TNF-α, IL-17 A, and IL-1β act in consort with IL-36 to regulate Th1 cell responses by sharing downstream signals through pathways such as JNK, MAPK, ERK, p38, and NF-κB [##REF##34675805##24##, ##REF##34887860##39##]. IL-36 may promote airway neutrophil aggregation and airway inflammation through the IL-6/IL-17 A/TNF-α axis. Further exploration of the role of IL-36 receptor blockers in animal models of asthma and in vitro experiments are required to better characterize the role of IL-36 in asthma.</p>", "<p id=\"Par50\">Based on our results, we suggest that IL-36 is associated with neutrophil recruitment in the airways and that IL-36 exacerbates the asthmatic airway inflammatory response via Th1-related cytokines. These results may serve as a basis for further investigation of the different pathophysiological mechanisms of IL-36 in NA and EA in the future.</p>" ]

[ "<title>Conclusions</title>", "<p id=\"Par51\">Our study indicates the involvement of IL-36α and IL-36β in the pathophysiology of airway inflammation in asthma, which is likely mediated via promotion of neutrophil recruitment in the airways. Our findings provide insights into the inflammatory pathways of neutrophilic asthma and identify a potential therapeutic target for the asthma phenotypes. However, more in vivo and in vitro experiments are required to investigate the role of IL-36 in various asthma phenotypes to assess the potential of IL-36-based therapeutic targets in asthma.</p>" ]

[ "<p id=\"Par1\">Interleukin (IL)-36 family is closely associated with inflammation and consists of IL-36α, IL-36β, IL-36γ, and IL-36Ra. The role of IL-36 in the context of asthma and asthmatic phenotypes is not well characterized. We examined the sputum IL-36 levels in patients with different asthma phenotypes in order to unravel the mechanism of IL-36 in different asthma phenotypes. Our objective was to investigate the induced sputum IL-36α, IL-36β, IL-36γ, and IL-36Ra concentrations in patients with mild asthma, and to analyze the relationship of these markers with lung function and other cytokines in patients with different asthma phenotypes. Induced sputum samples were collected from patients with mild controlled asthma (n = 62, 27 males, age 54.77 ± 15.49) and healthy non-asthmatic controls (n = 16, 10 males, age 54.25 ± 14.60). Inflammatory cell counts in sputum were determined. The concentrations of IL-36 and other cytokines in the sputum supernatant were measured by ELISA and Cytometric Bead Array. This is the first study to report the differential expression of different isoforms of IL-36 in different asthma phenotypes. IL-36α and IL-36β concentrations were significantly higher in the asthma group (<italic>P</italic> = 0.003 and 0.031), while IL-36Ra concentrations were significantly lower (<italic>P</italic> &lt; 0.001) compared to healthy non-asthmatic controls. Sputum IL-36α and IL-36β concentrations in the neutrophilic asthma group were significantly higher than those in paucigranulocytic asthma (n = 24) and eosinophilic asthma groups (n = 23). IL-36α and IL-36β showed positive correlation with sputum neutrophils and total cell count (R = 0.689, <italic>P</italic> &lt; 0.01; R = 0.304, <italic>P</italic> = 0.008; R = 0.689, <italic>P</italic> &lt; 0.042; R = 0.253, <italic>P</italic> = 0.026). In conclusion, IL-36α and IL-36β may contribute to asthma airway inflammation by promoting neutrophil recruitment in airways. Our study provides insights into the inflammatory pathways of neutrophilic asthma and identifies potential therapeutic target.</p>", "<title>Keywords</title>" ]

[]

[ "<title>Acknowledgements</title>", "<p>Not applicable.</p>", "<title>Author contributions</title>", "<p>PG contributed to the conception of the study. WL and JYL drafted the manuscript. HND, ZDW and YQH reviewed and revised it critically for important intellectual content. All authors revised the manuscript critically and approved the final version.</p>", "<title>Funding</title>", "<p>This research was funded by the Natural Science Foundation of Jilin Province (20210101460JC), National Natural Science Foundation of China (82070037), Jilin Province Natural Science Foundation (202000201384JC), Jilin Province Development and Reform Commission Plan (2019C047-7), and Jilin Provincial Department of Finance, Provincial Talent Project (2019SCZT033). The design of the study and writing of the manuscript were performed in accordance with the rules of the funding bodies.</p>", "<title>Data availability</title>", "<p>All data generated or analyzed during this study are included in this article.</p>", "<title>Declarations</title>", "<title>Ethics approval and consent to participate</title>", "<p id=\"Par52\">This study was approved by the Ethics Committee of the Second Hospital of Jilin University (approval number: 2016-34). Written informed consent was obtained from all subjects prior to their enrollment.</p>", "<title>Consent for publication</title>", "<p id=\"Par53\">Not applicable.</p>", "<title>Competing interests</title>", "<p id=\"Par54\">The authors declare no competing interests.</p>" ]

[ "<fig id=\"Fig1\"><label>Fig. 1</label><caption><p>Relationship between inflammatory factors in asthma and healthy non-asthmatic controls. After logarithmic conversion and adjusting for age, the data are expressed as the individual geometric mean values and statistically analyzed. Horizontal lines represent the mean. <italic>IL</italic> interleukin, <italic>TNF</italic> tumor necrosis factor, <italic>IFN</italic> interferon</p></caption></fig>", "<fig id=\"Fig2\"><label>Fig. 2</label><caption><p>Sputum cell numbers in asthma in inflammatory phenotypes. After logarithmic conversion and adjusting for age, the data are expressed as the individual geometric mean values and statistically analyzed. Horizontal lines represent the mean values. <italic>EA</italic> eosinophilic asthma, <italic>NA</italic> neutrophilic asthma, <italic>PA</italic> paucigranulocytic asthma, <italic>MA</italic> mixed granulocytic asthma. *<italic>P</italic> &lt; 0.05; **<italic>P</italic> &lt; 0.01; ***<italic>P</italic> &lt; 0.001</p></caption></fig>", "<fig id=\"Fig3\"><label>Fig. 3</label><caption><p>Concentrations of inflammatory mediators in sputum supernatant of different subtypes of asthma patients. After logarithmic conversion and adjusting for age, the data are expressed as the individual geometric mean values and statistically analyzed. Horizontal lines represent the geometric mean. Abbreviations as in Fig. ##FIG##0##1##. *<italic>P</italic> &lt; 0.05; **<italic>P</italic> &lt; 0.01; ***<italic>P</italic> &lt; 0.001</p></caption></fig>", "<fig id=\"Fig4\"><label>Fig. 4</label><caption><p>Correlation of IL-36 and cellular levels in sputum supernatant of asthma patients. The data are expressed as individual values and were analyzed by partial correlation after adjusting for age and sex. <italic>TCC</italic> total cell count, <italic>NEU</italic> neutrophils, <italic>EOS</italic> eosinophils, <italic>IL</italic> interleukin. *<italic>P</italic> &lt; 0.05; **<italic>P</italic> &lt; 0.01</p></caption></fig>", "<fig id=\"Fig5\"><label>Fig. 5</label><caption><p>Correlation analysis of other inflammatory factors. Data are expressed as individual values and were analyzed by partial correlation after adjusting for age. Abbreviations as in Fig. ##FIG##0##1##. *<italic>P</italic> &lt; 0.05; **<italic>P</italic> &lt; 0.01</p></caption></fig>" ]

[ "<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Characteristics of asthma patients and healthy non-asthmatic controls</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Variable</th><th align=\"left\">Asthma</th><th align=\"left\">Normal</th><th align=\"left\"><italic>P</italic> value</th></tr></thead><tbody><tr><td align=\"left\">Number</td><td align=\"left\">62</td><td align=\"left\">16</td><td align=\"left\"/></tr><tr><td align=\"left\">Age, years</td><td align=\"left\">54.77 ± 15.49</td><td align=\"left\">54.25 ± 14.60</td><td char=\".\" align=\"char\">0.903</td></tr><tr><td align=\"left\">Sex, male, n (%)</td><td align=\"left\">27 (43.5)</td><td align=\"left\">10 (62.5)</td><td char=\".\" align=\"char\">0.179</td></tr><tr><td align=\"left\">BMI, kg/m²</td><td align=\"left\">24.10 ± 3.76</td><td align=\"left\">23.75 ± 3.45</td><td char=\".\" align=\"char\">0.632</td></tr><tr><td align=\"left\">Ex-smoker, n (%)</td><td align=\"left\">29 (46.8)</td><td align=\"left\">5 (31.3)</td><td char=\".\" align=\"char\">0.267</td></tr><tr><td align=\"left\">Pre-FEV1, L</td><td align=\"left\">1.78 ± 0.80</td><td align=\"left\">2.70 ± 0.78</td><td char=\".\" align=\"char\">&lt; 0.001</td></tr><tr><td align=\"left\">Post-FEV1, L</td><td align=\"left\">2.06 ± 0.80</td><td align=\"left\">2.78 ± 0.80</td><td char=\".\" align=\"char\">0.002</td></tr><tr><td align=\"left\">Post-FVC, L</td><td align=\"left\">3.03 ± 0.81</td><td align=\"left\">3.45 ± 0.91</td><td char=\".\" align=\"char\">0.075</td></tr><tr><td align=\"left\">Post-bronchodilator FEV1/pred (%)</td><td align=\"left\">73.94 ± 23.96</td><td align=\"left\">97.68 ± 11.80</td><td char=\".\" align=\"char\">&lt; 0.001</td></tr><tr><td align=\"left\">Post-bronchodilator FVC/pred (%)</td><td align=\"left\">89.90 ± 18.95</td><td align=\"left\">101.04 ± 12.48</td><td char=\".\" align=\"char\">0.029</td></tr><tr><td align=\"left\">Post-bronchodilator FEV1/FVC (%)</td><td align=\"left\">66.86 ± 14.57</td><td align=\"left\">80.16 ± 3.89</td><td char=\".\" align=\"char\">0.001</td></tr><tr><td align=\"left\">Sputum TCC, 10⁶/mL</td><td align=\"left\">0.97 (0.44,2.60)</td><td align=\"left\">1.65 (0.95,2.35)</td><td char=\".\" align=\"char\">0.319</td></tr><tr><td align=\"left\">Sputum NEU, 10⁴/mL</td><td align=\"left\">4.05 (0.58,49.65)</td><td align=\"left\">0.60 (0.23,0.93)</td><td char=\".\" align=\"char\">0.001</td></tr><tr><td align=\"left\">Sputum EOS, 10⁴/mL</td><td align=\"left\">2.95 (0.20,9.28)</td><td align=\"left\">0.00 (0.00,0.10)</td><td char=\".\" align=\"char\">&lt; 0.001</td></tr><tr><td align=\"left\">Sputum MA, 10⁶/mL</td><td align=\"left\">71.45 (26.75,88.78)</td><td align=\"left\">98.15 (97.85,99.08)</td><td char=\".\" align=\"char\">&lt; 0.001</td></tr><tr><td align=\"left\">Sputum LY, 10⁴/mL</td><td align=\"left\">6.00 (1.68,10.05)</td><td align=\"left\">0.65 (0.28,1.18)</td><td char=\".\" align=\"char\">&lt; 0.001</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab2\"><label>Table 2</label><caption><p>Clinical characteristics and sputum cell numbers in asthma inflammatory phenotypes</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Variable</th><th align=\"left\">EA</th><th align=\"left\">NA</th><th align=\"left\">PA</th><th align=\"left\">MA</th><th align=\"left\"><italic>P</italic> value</th></tr></thead><tbody><tr><td align=\"left\">Number</td><td align=\"left\">23</td><td align=\"left\">9</td><td align=\"left\">24</td><td align=\"left\">6</td><td align=\"left\"/></tr><tr><td align=\"left\">Age, yrs</td><td align=\"left\">50.48 ± 17.33</td><td align=\"left\">59.44 ± 12.36</td><td align=\"left\">54.83 ± 14.80</td><td align=\"left\">64.00 ± 10.58</td><td char=\".\" align=\"char\">0.192</td></tr><tr><td align=\"left\">Male, n (%)</td><td align=\"left\">13 (56.5)</td><td align=\"left\">1 (11.1)</td><td align=\"left\">11 (45.8)</td><td align=\"left\">2 (33.3)</td><td char=\".\" align=\"char\">0.129</td></tr><tr><td align=\"left\">BMI, kg/m²</td><td align=\"left\">23.48 ± 3.99</td><td align=\"left\">24.89 ± 4.05</td><td align=\"left\">24.42 ± 3.62</td><td align=\"left\">24.00 ± 3.52</td><td char=\".\" align=\"char\">0.762</td></tr><tr><td align=\"left\">Ex-smoker, n (%)^a</td><td align=\"left\">10 (43.5)</td><td align=\"left\">5 (55.6)</td><td align=\"left\">12 (50)</td><td align=\"left\">2 (33.3)</td><td char=\".\" align=\"char\">0.801</td></tr><tr><td align=\"left\">Pre-bronchodilator FEV1, L^a</td><td align=\"left\">1.93 ± 0.92</td><td align=\"left\">1.53 ± 0.83</td><td align=\"left\">1.74 ± 0.73</td><td align=\"left\">1.78 ± 0.58</td><td char=\".\" align=\"char\">0.670</td></tr><tr><td align=\"left\">Post-bronchodilator FEV1, L^a</td><td align=\"left\">2.21 ± 0.97</td><td align=\"left\">1.86 ± 0.84</td><td align=\"left\">1.98 ± 0.65</td><td align=\"left\">2.08 ± 0.61</td><td char=\".\" align=\"char\">0.644</td></tr><tr><td align=\"left\">Post-bronchodilator FVC, L^a</td><td align=\"left\">3.20 ± 0.96</td><td align=\"left\">3.08 ± 0.96</td><td align=\"left\">2.87 ± 0.63</td><td align=\"left\">2.96 ± 0.65</td><td char=\".\" align=\"char\">0.616</td></tr><tr><td align=\"left\">Post-bronchodilator FEV1/pred (%)</td><td align=\"left\">72.53 ± 21.33</td><td align=\"left\">64.37 ± 25.94</td><td align=\"left\">77.16 ± 26.99</td><td align=\"left\">80.81 ± 17.17</td><td char=\".\" align=\"char\">0.495</td></tr><tr><td align=\"left\">Post-bronchodilator FVC/pred (%)</td><td align=\"left\">88.78 ± 14.25</td><td align=\"left\">85.24 ± 21.31</td><td align=\"left\">91.47 ± 22.21</td><td align=\"left\">94.90 ± 20.09</td><td char=\".\" align=\"char\">0.761</td></tr><tr><td align=\"left\">Post-bronchodilator FEV1/FVC (%)</td><td align=\"left\">67.14 ± 14.56</td><td align=\"left\">57.92 ± 13.68</td><td align=\"left\">68.90 ± 14.16</td><td align=\"left\">71.06 ± 15.74</td><td char=\".\" align=\"char\">0.227</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab3\"><label>Table 3</label><caption><p>IL-36 and sputum chemokine correlations</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" rowspan=\"2\">Variable</th><th align=\"left\" colspan=\"2\">IL-36α, pg/mL</th><th align=\"left\" colspan=\"2\">IL-36β, pg/mL</th><th align=\"left\" colspan=\"2\">IL-36γ, pg/mL</th><th align=\"left\" colspan=\"2\">IL-36Ra, pg/mL</th></tr><tr><th align=\"left\">R</th><th align=\"left\"><italic>P</italic> value</th><th align=\"left\">R</th><th align=\"left\"><italic>P</italic> value</th><th align=\"left\">R</th><th align=\"left\"><italic>P</italic> value</th><th align=\"left\">R</th><th align=\"left\"><italic>P</italic> value</th></tr></thead><tbody><tr><td align=\"left\">IL-1β, pg/mL</td><td char=\".\" align=\"char\">0.148</td><td char=\".\" align=\"char\">0.201</td><td char=\".\" align=\"char\">0.071</td><td char=\".\" align=\"char\">0.543</td><td char=\".\" align=\"char\">0.02</td><td char=\".\" align=\"char\">0.864</td><td char=\".\" align=\"char\">0.015</td><td char=\".\" align=\"char\">0.899</td></tr><tr><td align=\"left\">IL-2, ng/mL</td><td char=\".\" align=\"char\">0.066</td><td char=\".\" align=\"char\">0.584</td><td char=\".\" align=\"char\">− 0.004</td><td char=\".\" align=\"char\">0.971</td><td char=\".\" align=\"char\">− 0.068</td><td char=\".\" align=\"char\">0.570</td><td char=\".\" align=\"char\">0.073</td><td char=\".\" align=\"char\">0.544</td></tr><tr><td align=\"left\">IL-4, ng/mL</td><td char=\".\" align=\"char\">− 0.47</td><td char=\".\" align=\"char\">0.696</td><td char=\".\" align=\"char\">− 0.062</td><td char=\".\" align=\"char\">0.606</td><td char=\".\" align=\"char\">− 0.066</td><td char=\".\" align=\"char\">0.580</td><td char=\".\" align=\"char\">− 0.010</td><td char=\".\" align=\"char\">0.933</td></tr><tr><td align=\"left\">IL-6, ng/mL</td><td char=\".\" align=\"char\">0.592</td><td char=\".\" align=\"char\">&lt; 0.01</td><td char=\".\" align=\"char\">0.207</td><td char=\".\" align=\"char\">0.082</td><td char=\".\" align=\"char\">− 0.82</td><td char=\".\" align=\"char\">0.494</td><td char=\".\" align=\"char\">− 0.088</td><td char=\".\" align=\"char\">0.464</td></tr><tr><td align=\"left\">IL-9, ng/mL</td><td char=\".\" align=\"char\">0.043</td><td char=\".\" align=\"char\">0.721</td><td char=\".\" align=\"char\">0.051</td><td char=\".\" align=\"char\">0.672</td><td char=\".\" align=\"char\">− 0.22</td><td char=\".\" align=\"char\">0.855</td><td char=\".\" align=\"char\">− 0.012</td><td char=\".\" align=\"char\">0.918</td></tr><tr><td align=\"left\">IL-10, ng/mL</td><td char=\".\" align=\"char\">− 0.166</td><td char=\".\" align=\"char\">0.163</td><td char=\".\" align=\"char\">− 0.203</td><td char=\".\" align=\"char\">0.088</td><td char=\".\" align=\"char\">− 0.107</td><td char=\".\" align=\"char\">0.373</td><td char=\".\" align=\"char\">0.022</td><td char=\".\" align=\"char\">0.852</td></tr><tr><td align=\"left\">IL-13, pg/mL</td><td char=\".\" align=\"char\">− 0.159</td><td char=\".\" align=\"char\">0.181</td><td char=\".\" align=\"char\">− 0.182</td><td char=\".\" align=\"char\">0.126</td><td char=\".\" align=\"char\">− 0.108</td><td char=\".\" align=\"char\">0.366</td><td char=\".\" align=\"char\">0.057</td><td char=\".\" align=\"char\">0.633</td></tr><tr><td align=\"left\">IL-17 A, ng/mL</td><td char=\".\" align=\"char\">− 0.017</td><td char=\".\" align=\"char\">0.889</td><td char=\".\" align=\"char\">− 0.033</td><td char=\".\" align=\"char\">0.785</td><td char=\".\" align=\"char\">0.431</td><td char=\".\" align=\"char\">&lt; 0.01</td><td char=\".\" align=\"char\">0.078</td><td char=\".\" align=\"char\">0.515</td></tr><tr><td align=\"left\">IL-17 F, ng/mL</td><td char=\".\" align=\"char\">− 0.087</td><td char=\".\" align=\"char\">0.465</td><td char=\".\" align=\"char\">− 0.097</td><td char=\".\" align=\"char\">0.417</td><td char=\".\" align=\"char\">− 0.065</td><td char=\".\" align=\"char\">0.587</td><td char=\".\" align=\"char\">− 0.006</td><td char=\".\" align=\"char\">0.958</td></tr><tr><td align=\"left\">IL-22, pg/mL</td><td char=\".\" align=\"char\">− 0.002</td><td char=\".\" align=\"char\">0.988</td><td char=\".\" align=\"char\">0.086</td><td char=\".\" align=\"char\">0.473</td><td char=\".\" align=\"char\">− 0.027</td><td char=\".\" align=\"char\">0.822</td><td char=\".\" align=\"char\">0.213</td><td char=\".\" align=\"char\">0.073</td></tr><tr><td align=\"left\">IFN-γ, ng/mL</td><td char=\".\" align=\"char\">− 0.061</td><td char=\".\" align=\"char\">0.608</td><td char=\".\" align=\"char\">0.048</td><td char=\".\" align=\"char\">0.691</td><td char=\".\" align=\"char\">0.019</td><td char=\".\" align=\"char\">0.873</td><td char=\".\" align=\"char\">− 0.035</td><td char=\".\" align=\"char\">0.770</td></tr><tr><td align=\"left\">TNF-α, ng/mL</td><td char=\".\" align=\"char\">0.249</td><td char=\".\" align=\"char\">0.035</td><td char=\".\" align=\"char\">0.451</td><td char=\".\" align=\"char\">&lt; 0.01</td><td char=\".\" align=\"char\">0.219</td><td char=\".\" align=\"char\">0.065</td><td char=\".\" align=\"char\">0.028</td><td char=\".\" align=\"char\">0.818</td></tr></tbody></table></table-wrap>" ]

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[ "<table-wrap-foot><p>Data are expressed as mean ± SD or median (IQR). Data were analyzed by Student’s <italic>t</italic> test or Mann–Whitney U test after adjusting for age</p><p><italic>BMI</italic> body mass index, <italic>FEV1</italic> forced expiratory volume in 1 s, <italic>FVC</italic> forced vital capacity, <italic>TCC</italic> total cell count, <italic>NEU</italic> neutrophils, <italic>EOS</italic> eosinophils, <italic>MA</italic> Macrophages, <italic>LY</italic> lymphocytes</p></table-wrap-foot>", "<table-wrap-foot><p>The cells were tested in the sputum samples. Data are expressed as mean ± SD or median (IQR). Data were analyzed by ANOVA or Kruskal–Wallis test. Abbreviations as in Table ##TAB##0##1##</p><p>^aAdjusted for age</p></table-wrap-foot>", "<table-wrap-foot><p>Data were analyzed by partial correlation after adjusting for age and BMI</p><p><italic>IL</italic> interleukin, <italic>TNF</italic> tumor necrosis factor, <italic>IFN</italic> interferon</p></table-wrap-foot>", "<fn-group><fn><p><bold>Publisher’s Note</bold></p><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn></fn-group>" ]

[ "<graphic xlink:href=\"13223_2023_868_Fig1_HTML\" id=\"d32e684\"/>", "<graphic xlink:href=\"13223_2023_868_Fig2_HTML\" id=\"d32e921\"/>", "<graphic xlink:href=\"13223_2023_868_Fig3_HTML\" id=\"d32e950\"/>", "<graphic xlink:href=\"13223_2023_868_Fig4_HTML\" id=\"d32e1002\"/>", "<graphic xlink:href=\"13223_2023_868_Fig5_HTML\" id=\"d32e1376\"/>" ]

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{ "acronym": [ "IL", "TNF", "ELISA", "BMI", "AHR", "ACQ", "FeNO", "FEV1", "FVC", "TCC", "SD", "ANOVA", "LSD", "IQR", "OR", "CI", "ICS", "NA", "EA", "PA", "MGA" ], "definition": [ "Interleukin", "Tumor necrosis factor", "Enzyme-linked immunosorbent assay", "Body mass index", "Airway hyperresponsiveness", "Asthma control questionnaire", "Fractional exhaled nitric oxide", "Forced expiratory volume in 1 s", "Forced vital capacity", "Total cell count", "Standard deviation", "Analysis of variance", "Least significant difference", "Interquartile range", "Odd ratio", "Confidence interval", "Inhaled corticosteroid", "Neutrophilic asthma", "Eosinophilic asthma", "Paucigranulocytic asthma", "Mixed granulocytic asthma" ] }

[ "<title>Introduction</title>", "<p id=\"Par5\">Heart failure (HF) is a prevalent ailment worldwide, and despite substantial advancements in medical technology over the past few decades, HF holds the global record for the highest fatality rates [##UREF##0##1##, ##UREF##1##2##]. HF imposes a significant global burden, impacting more than 64 million individuals worldwide and incurring an annual cost exceeding $100 billion US dollars [##UREF##2##3##–##REF##26673558##5##]. Research reveals that one out of every five individuals will encounter HF during their lifetime, and approximately half of these HF patients will not survive beyond five years [##UREF##4##6##, ##REF##31523902##7##]. Consequently, it becomes evident that HF shoulders a substantial share of the burden in terms of CVD-related morbidity, mortality, and healthcare expenditures [##REF##31992061##8##]. Hence, given the global prevalence and significant burden of HF, it is necessary to assess HF-specific mortality and its associated risk factors.</p>", "<p id=\"Par6\">HF presents a debilitating state in which the heart’s inability to pump blood to adequately meet the body’s demands leads to the failure of multiple organs and eventual fatality [##REF##28727739##9##, ##REF##27748494##10##]. Survival in patients with HF is a significant concern. Studies have shown that HF leads to a substantial loss of life expectancy, with comorbidities playing a major role in this loss [##REF##31523902##7##, ##REF##33153996##11##, ##UREF##5##12##]. A collection of factors, including lifestyle elements (such as inadequate diet, sedentary habits, smoking, and drug abuse). ), preexisting medical conditions (e.g., diabetes mellitus, hypertension, hyperlipidemia), physiological anomalies, and therapeutic interventions (such as radiation or chemotherapy), can contribute to the development of HF [##REF##28727739##9##, ##UREF##6##13##]. Analyzing modifiable risk factors can offer valuable insights into effective treatment and preventive measures to improve HF patient survival. Therefore, knowing the distribution of these factors holds significant importance. Despite the existence of numerous studies conducted in some regions, there are limited data in Iran. The allure of this topic will intensify when the risk factors for mortality are scrutinized based on the specific cause. The variety of causes of death in patients with HF is high. Therefore, competing risk models can be used to investigate and analyze the time to death of patients.</p>", "<p id=\"Par7\">Competing risks refer to a situation in which an individual or unit can experience multiple events, but only one event can occur. The Cox proportional hazards (PH) model is commonly used in competing risks for analysis. The survival function estimator conditional on , , in this model, assumes a constant proportional hazard. This means that the relative hazard between individuals remains constant over time. This assumption may not hold in practical scenarios where risks change over time. Additionally, in the estimation of survival probability, the application of traditional survival analysis methods such as CoxPH may lead to biases due to ignoring competing risks that are present [##UREF##7##14##, ##REF##26858290##15##].</p>", "<p id=\"Par8\">CoxPH is by far the most commonly used survival model in competing risk. However, it has limited compatibility with specific probability distributions for survival times. In such cases, the accelerated failure time (AFT) model can be a realistic alternative [##UREF##8##16##]. On the other hand, AFT shifts focus to quantify the direct variable influence on survival time, which is distinct from the hazard assessment in the Cox PH model [##UREF##9##17##]. Within the framework of the PH model, it is not feasible to make predictions without an estimate of the baseline hazard function. Therefore, solely reporting coefficients, which is a common practice, prevents others from predicting survival. As the AFT model follows a log-linear structure, one can easily calculate a point estimate of survival for covariates.</p>", "<p id=\"Par9\">Recent research has focused on improving the CPH model in competing risks. Some papers discuss a combination of Cox and Bayesian survival models to enhance both model interpretability and predictive power [##UREF##10##18##, ##UREF##11##19##]. S.N. Al-Aziz et al. introduced a Bayesian methodology for analyzing competing risk data, utilizing a generalized log-logistic baseline distribution for the proportional hazard (PH) specification [##UREF##12##20##]. Traditional statistical inference techniques typically rely on estimating parameters using available data, with the maximum likelihood estimator (MLE) often being the preferred method. However, when dealing with survival data, it is important to consider the past information available, such as the medical history of patients in medical sciences. The MLE cannot incorporate prior information in data analysis. In contrast, Bayesian reasoning is renowned for its ability to incorporate prior information. Additionally, Bayesian methods provide more accurate estimation results than MLE [##UREF##13##21##].</p>", "<p id=\"Par10\">The analysis of survival Bayesian in competing risks encompasses a range of models and techniques that aid in comprehending the duration of events and the factors that impact them [##UREF##14##22##].</p>", "<p id=\"Par11\">Considering the limitations of the Cox model, another purpose of this study is to consider combining the AFT method and the Bayesian approach in the competing risk. On the other hand, very few studies have simultaneously explored three approaches, competing risks, parametric models, and Bayesian analysis, in investigating risk factors for the survival of patients with HF.</p>", "<p id=\"Par12\">Therefore, the current study using the Bayesian AFT approach was designed to predict patient survival based on the cause of death and identify risk factors, specifically differentiating between causes of death (HF-related mortality and non-HF-related mortality).</p>" ]

[ "<title>Methodology</title>", "<title>Study area</title>", "<p id=\"Par13\">The study was conducted in the Rajaie Cardiovascular Medical and Research Center (RCMRC), Tehran, Iran, which is considered one of the largest tertiary centers for cardiovascular medicine in the Middle East and includes many departments, including the heart failure and transplantation department.</p>", "<title>Study design and population</title>", "<p id=\"Par14\">In this retrospective study, data were derived from the Rajaie Acute Systolic Heart Failure Registry (RASHF), the first HF registry in Iran. This registry was started in RCMRC, based on data from hospitalized patients with acute HF diagnoses. The data were collected and recorded in dedicated forms designed by the medical Information Technology team of the center. The data of interest of the RASHF registry include the following items: medical and drug history of patients, type of HF presentation (decompensated or de novo), cardiomyopathy type (nonischemic or ischemic), admission-time vital signs, initial clinical symptoms (dyspnea, chest pain, edema, etc.), precipitating factors of acute HF, laboratory findings during admission, baseline electrocardiogram and echocardiographic findings, medications during hospital and at discharge, in-hospital course and outcome status. The hospital information system [##REF##35620751##23##] (HIS) was utilized to identify all patients enrolled in the RASHF registry from March 2018 to August 2018. The mortality status of the identified individuals was examined and followed up for up to five years (June 2023). In cases where the hospital records or death registration system lacked sufficient information, efforts were made to contact the individuals themselves or their families to complete the missing details. Utmost care was taken to handle this communication sensitively and without causing any discomfort to the individual or their family. The process was conducted indirectly to ensure that the sensitive nature of the event was respected and that information about the event’s status was obtained discreetly.</p>", "<title>Inclusion criteria</title>", "<p id=\"Par15\">Patients with acute HF with reduced ejection fraction (HFrEF) diagnosis based on international HF guidelines enrolled in the RASHF registry.</p>", "<title>Exclusion criteria</title>", "<p id=\"Par16\">Patients for whom sufficient information was not recorded in their files and individuals who had not received any treatment.</p>", "<title>Ending time</title>", "<p id=\"Par17\">Patients with HF who were enrolled in the study were followed up for mortality status for up to five years (June 2023) and categorized by the cause of death. Individuals whose mortality status was uncertain were censored. This means that the type of survival data is right-censored.</p>", "<p id=\"Par18\">According to the approach of this study, the cause of death was categorized into “HF-related mortality” and “non-HF-related mortality” as competing risks. Additionally, we considered in-hospital mortality.</p>", "<title>HF-related mortality</title>", "<p id=\"Par19\">Death due to HF complications such as causes of decompensation (infection, pulmonary emboli, electrolyte disturbance, etc.), low cardiac output state and shock, and arrhythmias.</p>", "<title>Non-HF-related mortality</title>", "<p id=\"Par20\">Death due to other causes (non-HF). For example, brain stroke, cancer, old age, etc.</p>", "<title>Statistical analysis</title>", "<p id=\"Par21\">In this study, categorical variables are reported as frequencies and percentages, and numeric variables are reported as medians. In addition, we considered the trend effect for ordinal categorical variables. Survival rates across variables were compared through the implementation of a log-rank test.</p>", "<p id=\"Par22\">In this study, we used the Bayesian parametric AFT method with competing risks analysis. Employing the Bayesian AFT method in competing risks survival analysis leads to the creation of more accurate survival models, allowing us to examine the effects of different variables with greater precision, specifically in terms of the cause of death differentiation. In this approach, separate Bayesian models for competing risks are considered, and an appropriate distribution for survival time is selected to conduct the analysis (Fig. ##FIG##0##1##).</p>", "<p id=\"Par23\">\n</p>", "<p id=\"Par24\">\n<list list-type=\"bullet\"><list-item><p id=\"Par25\">Time ratio (TR_A): cause-specific TR HF-related mortality.</p></list-item><list-item><p id=\"Par26\">Time ratio (TR_B): cause-specific TR non-HF-related mortality.</p></list-item></list>\n</p>", "<p id=\"Par27\">Bayesian models were compared with DIC to recognize the true model. The model’s superior fit for the data is indicated by the lower DIC values [##UREF##10##18##]. This part of the analysis was carried out using R 4.3.0 software utilizing the spBayesSurv package [##REF##33713474##24##]. The significance level was set at 0.05.</p>", "<p id=\"Par28\">Then, the association between survival time and other variables was analyzed by univariate and multivariable Bayesian AFT regression by cause of death. These parts of the studies were conducted using Stata17 software (StataCorp, College Station, Texas, USA).</p>", "<p id=\"Par29\">Bayesian survival analysis is a method for calculating the probability of an event occurring based on prior information related to events associated with that phenomenon. The parameters include the regression coefficients of the variables. Various prior distributions can be considered for them. Determining the appropriate form of the prior can often be challenging. There is no definitive rule for selecting the best prior distribution to formulate the Bayes estimator. However, in cases where only limited or vague knowledge about the parameters is available, a noninformative prior can be employed [##UREF##13##21##]. In this study, we utilized sensitivity analysis for the optimal selection and tuning of the prior distribution variance. The reason for using noninformative prior distributions is often to allow the data to speak for themselves, ensuring that inferences are not influenced by external information unrelated to the current data. Consequently, all resulting inferences were entirely objective rather than subjective.</p>", "<title>Prior distribution <italic>π</italic>(<italic>θ</italic>) </title>", "<p id=\"Par30\">In this study, we utilized a normal distribution with a large variance (mean 0 and variance of 10,000; Non-Informative) as the prior distribution for the regression coefficients [##UREF##13##21##].</p>", "<title>Likelihood L(β|X, t)</title>", "<p id=\"Par31\">The likelihood equation is as follows:</p>", "<p id=\"Par32\">\nwhere is the censoring indicator (0 = censored and 1 = death) and in Weibull regression is  and Log-Logistic regression is  where  In Log-Normal regression is</p>", "<p id=\"Par33\">where is the standard normal distribution and </p>", "<title>Posterior distribution</title>", "<p id=\"Par34\"> A mixture of the prior distribution and likelihood.</p>", "<p id=\"Par35\">\n</p>", "<title>Variables in the study</title>", "<p id=\"Par36\">In this study, death was considered an event of interest. The response variable was the survival time of HF patients (in months), which was defined as the difference between the time of diagnosis and time to one of the events “HF-related mortality” and “non-HF-related mortality”. The variables in this study were categorized into three groups: demographic, disease symptoms, and clinical factors.</p>", "<p id=\"Par37\">\n<list list-type=\"bullet\"><list-item><p id=\"Par38\">Demographic variables: Age (years), sex, employment status, education level, place of residence, and marital status.</p></list-item><list-item><p id=\"Par39\">Disease symptom variables: dyspnea, chest pain, limb swelling, temperature, and heart rate.</p></list-item><list-item><p id=\"Par40\">Clinical variables: history of hypertension, history of diabetes mellitus (DM), coronary artery disease (CAD), history of hyperlipidemia, smoking, chronic kidney disease (CKD), atrial fibrillation (AF), stroke, and acute decompensated HF (ADHF).</p></list-item></list>\n</p>" ]

[ "<title>Results</title>", "<title>Participant characteristics</title>", "<p id=\"Par41\">The median survival time for the patients was 43.40 months. Out of 435 HF patients, 61.1% were male. The mean age of the patients was 56.57 years, ranging from 14 to 95 years. In addition, 86% of the patients had education levels below a diploma, 92% lived in the city, and 90% were married. In addition, 34% of patients presented to the hospital with dyspnea, while 88.3% reported chest pain, 89% exhibited limb swelling, 11% of patients had a heart rate &lt; 60, 25% of patients had a heart rate greater than 100 beats/min, and only 10% of patients had a temperature &gt; 37.5 degrees Celsius (see Table ##TAB##1##1## for more information).</p>", "<p id=\"Par42\">\n</p>", "<title>Comparison of mortality rates and participant characteristics between two causes of death</title>", "<p id=\"Par43\">At the end of the follow-up time, 24.6% of the patients were still alive, and the mortality rates due to HF and non-HF were 36.8% and 22.3%, respectively.</p>", "<p id=\"Par44\">In HF-related mortality, 64% were unemployed patients, 64% had education below the diploma level, 63% lived in the city, and 62% were married. Patients 61.5%, 62%, and 63% sought medical attention at the hospital with symptoms such as dyspnea, chest pain, and limb swelling, respectively.</p>", "<p id=\"Par45\">In non-HF-related mortality, 36% were employed patients, 36% had education below the diploma level, 37% lived in the city, 38% were married and 38%, 38%, and 37% had symptoms of dyspnea, chest pain, and limb swelling, respectively.</p>", "<p id=\"Par46\">The average body temperature was 36.56 degrees Celsius for patients who had HF-related mortality and 36.75 degrees Celsius for patients who had non-HF-related mortality (see Table ##TAB##1##1## for more information).</p>", "<p id=\"Par47\">In HF-related mortality, the 1-, 3-, and 5-year survival rates were 80.66% (95% CI: 0.76–0.84), 68.03 (95% CI: 0.63–0.72), 59.52% (95% CI: 0.54-64), respectively, and in non-HF-related mortality, they were 91.78% (95% CI: 0.88–0.94), 79.08% (95% CI: 0.74–0.83), and 70.29% (95% CI: 0.64–0.75), respectively.</p>", "<title>Outcome rates</title>", "<p id=\"Par48\">The mortality rate for HF and non-HF increased significantly with increasing age. Patients with chest pain, hyperlipidemia, and chronic kidney disease were associated with higher outcome rates for both causes of death; however, certain variables exhibited elevated mortality rates in non-HF, and these differences did not have statistical significance in HF-related mortality (<italic>P</italic> &lt; 0.05) (see Table ##TAB##2##2## for more information by cause of death).</p>", "<p id=\"Par49\">\n</p>", "<title>Bayesian model selection criteria</title>", "<p id=\"Par50\">According to the DIC values (Table ##TAB##3##3##), the Bayesian Weibull AFT model had the best fit HF dataset among the three models.</p>", "<p id=\"Par51\">\n</p>", "<title>Univariable bayesian AFT competing risk parametric model</title>", "<p id=\"Par52\">Table ##TAB##4##4## shows the final results for the univariable Bayesian Weibull AFT regression, and as this, the results show that in HF-related mortality, the survival time of patients is statistically significantly affected by age (TR = 0.98), chest pain (TR = 0.30), temperature (&lt; 36 degrees Celsius) (TR = 0.51), hyperlipidemia (TR = 0.30), and ADHF (TR = 0.08). In non-HF-related mortality, age (TR = 0.97), chest pain (TR = 0.32), hypertension (TR = 0.53), CAD (TR = 0.52), hyperlipidemia (TR = 0.54), CKD (TR = 0.38), and AF (TR = 0.53) showed a significant relationship with reducing the survival time of patients. Subsequently, all significant variables determined through univariate analysis were incorporated into the multivariate parametric modeling approach.</p>", "<p id=\"Par53\">\n</p>", "<title>Sensitivity analysis</title>", "<p id=\"Par54\">Considering the sensitivity analysis results, there was a difference of more than 10% in most variables. Therefore, given the sample size and the sensitivity of the analysis to variance changes, results were reported for both causes of death with a larger variance (10,000). This choice allows us to effectively represent the variations in the results (Tables ##TAB##5##5## and ##TAB##6##6##). Additionally, considering the study aims, a larger variance can be a more appropriate choice for better examining and understanding the effects of variables.</p>", "<p id=\"Par55\">\n</p>", "<p id=\"Par56\">\n</p>", "<title>Multivariable bayesian AFT competing risk parametric model</title>", "<p id=\"Par57\">Based on the results of the best model, with the increase in age, the survival time of patients was shorter in HF-related mortality [time ratio = 0.98, confidence interval 95%: 0.96–0.99]. In addition, patients who had ADHF [TR = 0.11, 95% (CI): 0.01–0.44] were associated with a lower survival time for HF-related mortality.</p>", "<p id=\"Par58\">Chest pain in HF-related mortality [TR = 0.41, 95% (CI): 0.10–0.96] and in non-HF-related mortality [TR = 0.38, 95% (CI): 0.12–0.86] was associated with a lower survival time. The next significant variable in HF-related mortality was hyperlipidemia (yes): [TR = 0.34, 95% (CI): 0.13–0.64], and in non-HF-related mortality hyperlipidemia (yes): [TR = 0.60, 95% (CI): 0.37–0.90]. In the Weibull survival model, a one-unit increase in hyperlipidemia was associated with a 66% and 40% decrease in the survival time of patients. In other words, for a unit increase in hyperlipidemia, the risk of both causes of death increases.</p>", "<p id=\"Par59\">CAD [TR = 0.65, 95% (CI): 0.38–0.98], CKD [TR = 0.52, 95% (CI): 0.28–0.87], and AF [TR = 0.53, 95% (CI): 0.32–0.81] were other variables that were directly related to the reduction in survival time of patients with non-HF-related mortality (Table ##TAB##7##7##).</p>", "<p id=\"Par60\">\n</p>" ]

[ "<title>Discussion</title>", "<p id=\"Par61\">In this study, we investigated the survival risk factors in patients with HF using a Bayesian parametric survival modeling approach. Using the Bayesian approach for competing risks has advantages compared with other survival modeling methods. In this manner, by utilizing prior information and background knowledge about the parameters in the analysis of patient survival times, broken down by the cause of death, more precise estimates can be provided. Moreover, it allows for examining the uncertainty in estimates for each parameter and continually updating them with new data. Additionally, this approach provides high flexibility and allows the modeling of different survival models with ease by altering distributions and functions in competing risk AFT models. This enables researchers to consider a broader and more diverse range of variables for examination, categorized by the cause of death. Therefore, Bayesian parametric models provide valuable tools for understanding the relationship between heart disease and survival outcomes [##UREF##15##25##, ##REF##36593459##26##].</p>", "<p id=\"Par62\">In our dataset, among all the parametric models examined for both causes of death (HF-related mortality and non-HF-related mortality), the Weibull model outperformed the other models. Parametric models have been widely used in the analysis of survival data, including in the context of heart disease. These models specify the distribution of the time to event in terms of unknown parameters. In addition, in other studies, the Weibull distribution is suitable for proportional hazard models in the analysis of HF data [##UREF##16##27##, ##UREF##17##28##]. However, in some other studies, the Bayesian log-normal AFT model was found to be the best fit for analyzing the HF dataset [##REF##36182998##29##].</p>", "<p id=\"Par63\">In the current study, in HF-related mortality, the 1-, 3-, and 5-year survival rates were 80.66%, 68.03, and 59.52%, respectively, and in non-HF-related mortality, they were 91.78%, 79.08%, and 70.29%, respectively. In line with this study, Jones NR et al. found that the survival rates for patients with chronic HF at 1, 2, and 5 years were 86.5%, 72.6%, and 56.7%, respectively [##REF##31523902##7##, ##REF##33827738##30##]. Despite improvements in survival over the years, mortality associated with HF remains high [##REF##33827738##30##]. Morbidity and mortality remain high for patients with HF, with a five-year mortality rate of approximately 50% [##REF##35748124##31##]. It remains a prevalent condition among older adults, with a significant five-year mortality risk. Understanding the broader implications of HF can guide research, resource allocation, and policy-making for noncommunicable disease mitigation [##REF##35042750##32##].</p>", "<p id=\"Par64\">In this study, for patients who had mortality due to HF between 2018 and 2023, as age increased, the survival rate of patients decreased. Similar to our results, some research has demonstrated a direct correlation between age and survival rates among patients with HF [##REF##35748124##31##, ##REF##35512983##33##–##REF##37408590##36##]. The median age of our patients with both causes of death was less than 60 years, and the predominant sex was male. In a study in Asia, the prevalence of HF was higher in men and younger than in studies in Europe and the US [##REF##27541646##37##]. HF-related mortality is a common and growing health problem, with a prevalence that increases with age. It affects approximately 2% of the adult population and doubles in prevalence for each decade of age [##UREF##20##38##]. This can be caused by additional chronic ailments, weakness of the immune system due to old age, and delayed diagnosis in elderly patients. Therefore, preventive strategies targeting HF risk factors should be prioritized for individuals aged 50 and above.</p>", "<p id=\"Par65\">Patients with chest pain and hyperlipidemia were associated with a lower survival time. Chest pain is a public sign in patients with HF. Some studies have also reported that chest pain serves as a sign of exacerbation and worsening of patients’ cardiac conditions [##REF##35608172##39##].</p>", "<p id=\"Par66\">Hyperlipidemia emerged as another noteworthy factor associated with mortality, displaying an inverse correlation with patient survival time. Hyperlipidemia in adulthood is associated with an increased risk of mortality from future HF disease. This result aligns with findings from earlier research, which likewise indicated a negative relationship between hyperlipidemia and patient survival [##REF##37408590##36##, ##UREF##21##40##, ##UREF##22##41##]. The association between hyperlipidemia and HF as a risk factor for mortality is significant in patients with HF. Hyperlipidemia can lead to the formation of fatty deposits in the walls of coronary arteries, impairing heart function and causing damage to the blood vessels and heart muscle. Other studies have shown similar results [##REF##36480862##42##, ##REF##12612871##43##]. Therefore, controlling hyperlipidemia can help increase the survival time of patients with HF. These precautions include proper nutrition, regular exercise, and consistent use of lipid-lowering medications.</p>", "<p id=\"Par67\">ADHF was another factor associated with the survival time of patients who had HF mortality. ADHF is a type of HF that requires urgent medical attention and hospitalization [##UREF##23##44##]. ADHF is the leading cause of hospital admissions in patients older than 65 years and is associated with poor outcomes, including rehospitalization and death [##REF##32086714##45##]. The majority of patients with ADHF have a previous history of HF and present with symptoms and/or signs of congestion and normal or increased blood pressure [##REF##32143763##46##]. Different classification criteria have been proposed for ADHF, reflecting the clinical heterogeneity of the syndrome, including classifications based on the history of HF, systolic blood pressure upon presentation, and the presence or absence of congestion and peripheral hypoperfusion [##REF##33769383##47##].</p>", "<p id=\"Par68\">CAD, CKD, and AF had a significant relationship with survival time in non-HF-related mortality in our study. Other studies have shown similar results; patients who have both CAD and HF are at a heightened risk of health complications, including mortality events [##REF##12612871##43##].</p>", "<p id=\"Par69\">Our study examined the relationship between CKD and mortality in patients with HF, with CKD emerging as a severe complication of HF. Individuals afflicted by both conditions exhibit more unfavorable outcomes, including a higher risk of mortality compared with those with a single condition [##UREF##22##41##]. CKD patients face an escalated likelihood of HF development, and the coexistence of HF in CKD patients exacerbates their prognosis [##REF##26419625##48##].</p>", "<p id=\"Par70\">In this study, one of the significant factors contributing to mortality was AF among non-HF-related mortality. According to a study, AF and HF are common cardiac conditions that often co-occur, sharing risk factors. AF can worsen HF, as seen in more than 50% of AF patients [##UREF##24##49##]. Therefore, preventing AF in HF involves lifestyle changes (changes in dietary patterns, increased physical activity, reduced consumption of drugs or alcohol, stress management, and improved sleep quality), screening, and optimal therapy [##REF##26419625##48##].</p>", "<title>Strengths and limitations</title>", "<p id=\"Par71\">The RASHF registry stands as the inaugural heart failure registry in Iran, and the data derived from it holds a unique within our country. The study’s strengths lie in its highly suitable sample, extended follow-up period, and utilization of statistical Bayesian and AFT techniques to identify risk groups. This study is an example of the significant utility of relative survival within HF research, particularly in competing risks. The findings of this study are reinforced by the appropriate sample size of patients visiting this hospital who come from all over the country and Iran’s neighboring countries. Therefore, this study results in a more diverse and representative dataset, thereby enhancing the study’s generalizability. It also enables robust trend analysis and a comprehensive grasp of the broader impact of the topic.</p>", "<p id=\"Par72\">The main limitation of this study was inadequate recording of death by the cause of death. To address this, researchers established contact with individuals or their families based on hospital record information to verify and ensure the accuracy of their status. To prevent bias in data collection and information bias, patient records were reviewed without knowledge of their final status, except for cases where hospital death had occurred.</p>" ]

[ "<title>Conclusion</title>", "<p id=\"Par73\">In this study, using a Bayesian approach, we concluded that chest pain and hyperlipidemia levels are significant risk factors for predicting mortality in HF-related mortality and non-HF-related mortality. Furthermore, we have discussed risk factors separately for each cause of death. Exploring the survival duration of patients with HF by cause of death offers a valuable approach to tackling societal health issues, as it reveals factors linked to mortality. The findings of this study can heighten awareness regarding determinants that contribute to the cause of death in individuals with HF. Moreover, these scientific insights can be shared with health authorities, enabling policymakers to enhance public comprehension of factors that worsen the risk of HF-related mortality. This awareness is crucial because early screening and timely interventions can facilitate effective prevention, treatment, and preservation of lives.</p>" ]

[ "<title>Purpose</title>", "<p id=\"Par1\">Heart failure (HF) is a widespread ailment and is a primary contributor to hospital admissions. The focus of this study was to identify factors affecting the extended-term survival of patients with HF, anticipate patient outcomes through cause-of-death analysis, and identify risk elements for preventive measures.</p>", "<title>Methods</title>", "<p id=\"Par2\">A total of 435 HF patients were enrolled from the medical records of the Rajaie Cardiovascular Medical and Research Center, covering data collected between March and August 2018. After a five-year follow-up (July 2023), patient outcomes were assessed based on the cause of death. The survival analysis was performed with the AFT method with the Bayesian approach in the presence of competing risks.</p>", "<title>Results</title>", "<p id=\"Par3\">Based on the results of the best model for HF-related mortality, age [time ratio = 0.98, confidence interval 95%: 0.96–0.99] and ADHF [TR = 0.11, 95% (CI): 0.01–0.44] were associated with a lower survival time. Chest pain in HF-related mortality [TR = 0.41, 95% (CI): 0.10–0.96] and in non-HF-related mortality [TR = 0.38, 95% (CI): 0.12–0.86] was associated with a lower survival time. The next significant variable in HF-related mortality was hyperlipidemia (yes): [TR = 0.34, 95% (CI): 0.13–0.64], and in non-HF-related mortality hyperlipidemia (yes): [TR = 0.60, 95% (CI): 0.37–0.90]. CAD [TR = 0.65, 95% (CI): 0.38–0.98], CKD [TR = 0.52, 95% (CI): 0.28–0.87], and AF [TR = 0.53, 95% (CI): 0.32–0.81] were other variables that were directly related to the reduction in survival time of patients with non-HF-related mortality.</p>", "<title>Conclusion</title>", "<p id=\"Par4\">The study identified distinct predictive factors for overall survival among patients with HF-related mortality or non-HF-related mortality. This differentiated approach based on the cause of death contributes to the estimation of patient survival time and provides valuable insights for clinical decision-making.</p>", "<title>Keywords</title>" ]

[]

[ "<title>Acknowledgements</title>", "<p>The authors express their sincere gratitude to the Research Deputy of Rajaie Cardiovascular, Medical, and Research Center, the HIS Department of the hospital, and the specialized cardiologist for HF for their invaluable collaboration.</p>", "<title>Informed consent</title>", "<p id=\"Par74\">All participants, or their legal guardians, provided informed written consent on registration in the database. Additionally, all methods were carried out according to relevant guidelines and regulations.</p>", "<title>Authors’ contributions</title>", "<p>Conceptualization: SN, MAJ, SM, EH. Data curation: SN, SM. Formal analysis: SN, MAJ. Methodology: SN, MAJ, EH. Writing – original draft: SN, MAJ. Writing – review &amp; editing: SN, MAJ, SM, EH.</p>", "<title>Funding</title>", "<p>Not applicable.</p>", "<title>Availability of data and materials</title>", "<p>The datasets used in the current study are available from the corresponding author upon reasonable request.</p>", "<title>Declarations</title>", "<title>Ethics approval and consent to participate</title>", "<p id=\"Par75\">This study was approved by the ethics committee of the School of Medical Sciences – Tarbiat Modares University under the approval ID IR.MODARES.REC.1402.012. The participants’ privacy was preserved. All the processes were approved by international agreements (World Medical Association, Declaration of Helsinki, Ethical Principles for Medical Research Involving Human Subjects).</p>", "<title>Consent for publication</title>", "<p id=\"Par76\">Not applicable.</p>", "<title>Competing interests</title>", "<p id=\"Par77\">The authors declare no competing interests.</p>" ]

[ "<fig id=\"Fig1\"><label>Fig. 1</label><caption><p>Graphical display of the competing risks model: the situation where some risks are competing in patients with heart failure. TR_A: Time Ratio in HF-related mortality and TR_B: Time Ratio in Non-HF-related mortality</p></caption></fig>" ]

[ "<table-wrap id=\"Taba\"><table frame=\"hsides\" rules=\"groups\"><tbody><tr><td align=\"left\" colspan=\"2\"><p>\n<bold><italic>Main Points</italic></bold>\n</p><p>• Most studies’ competing risks suffer from an overestimation of the prediction of survival when using the Cox model.</p><p>• A competing risk approach can mitigate the overestimation problem, (providing the probability of death due to HF and the probability of death to non-HF causes).</p><p>• AFT is used when the study aims to compare patient survival times.</p><p>• The Bayesian approach is used to enhance model interpretability and predictive power.</p></td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Participants’ demographic characteristics and clinical characteristics</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\"> Characteristic\n</th><th align=\"left\">Total (<italic>n</italic> = 435)<break/>N (%)</th><th align=\"left\">HF-related mortality<break/>(<italic>n</italic> = 160)<break/>N (%)</th><th align=\"left\">Non-HF related mortality<break/>(<italic>n</italic> = 97)<break/>N (%)</th></tr></thead><tbody><tr><td align=\"left\"><bold><italic>Demographic variables</italic></bold>\n</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\"> Sex</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\">  Male</td><td align=\"left\">266 (61.1)</td><td align=\"left\">101 (66.4)</td><td align=\"left\">51 (33.5)</td></tr><tr><td align=\"left\">  Female</td><td align=\"left\">169 (38.9)</td><td align=\"left\">59 (56.1)</td><td align=\"left\">46 (43.8)</td></tr><tr><td align=\"left\"> Employment Status (unemployed)</td><td align=\"left\">374 (86.0)</td><td align=\"left\">143 (64.4)</td><td align=\"left\">79 (35.5)</td></tr><tr><td align=\"left\"> Education Level (&lt; Diploma)</td><td align=\"left\">362 (83.2)</td><td align=\"left\">140 (63.6)</td><td align=\"left\">80 (36.3)</td></tr><tr><td align=\"left\"> Place of Residence (City)</td><td align=\"left\">404 (92.9)</td><td align=\"left\">149 (63.1)</td><td align=\"left\">87 (36.8)</td></tr><tr><td align=\"left\"> Marital status (married)</td><td align=\"left\">390 (89.7)</td><td align=\"left\">143 (62.1)</td><td align=\"left\">87 (37.8)</td></tr><tr><td align=\"left\">\n<bold><italic>Disease symptoms variables</italic></bold>\n</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\"> Dyspnea(yes)</td><td align=\"left\">148 (34.0)</td><td align=\"left\">48 (61.5)</td><td align=\"left\">30 (38.4)</td></tr><tr><td align=\"left\"> Chest Pain (yes)</td><td align=\"left\">384 (88.3)</td><td align=\"left\">147 (61.7)</td><td align=\"left\">91 (38.2)</td></tr><tr><td align=\"left\"> Limb Swelling (yes)</td><td align=\"left\">387 (89.0)</td><td align=\"left\">142 (62.5)</td><td align=\"left\">85 (37.4)</td></tr><tr><td align=\"left\"> HeartRate (beats per minute)</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\">  &lt; 60</td><td align=\"left\">48 (11.0)</td><td align=\"left\">21 (70.0)</td><td align=\"left\">9 (30.0)</td></tr><tr><td align=\"left\">  60–100</td><td align=\"left\">277 (63.6)</td><td align=\"left\">97 (59.5)</td><td align=\"left\">66 (40.4)</td></tr><tr><td align=\"left\">  &gt; 100</td><td align=\"left\">110 (25.2)</td><td align=\"left\">160 (62.2)</td><td align=\"left\">22 (34.3)</td></tr><tr><td align=\"left\"> Temperature (degrees Celsius)</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\">  &lt; 36</td><td align=\"left\">90 (20.6)</td><td align=\"left\">42 (68.8)</td><td align=\"left\">19 (31.1)</td></tr><tr><td align=\"left\">  36-37.5</td><td align=\"left\">301 (69.2)</td><td align=\"left\">108 (62.0)</td><td align=\"left\">66 (37.9)</td></tr><tr><td align=\"left\">  &gt; 37.5</td><td align=\"left\">44 (10.1)</td><td align=\"left\">10 (45.4)</td><td align=\"left\">12 (54.5)</td></tr><tr><td align=\"left\">\n<bold><italic>Clinical variables</italic></bold>\n</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\"> (yes) History Hypertension</td><td align=\"left\">124 (28.5)</td><td align=\"left\">41 (51.9)</td><td align=\"left\">38 (48.1)</td></tr><tr><td align=\"left\"> (yes) History DiabetesMellitus</td><td align=\"left\"/><td align=\"left\">32 (50.7)</td><td align=\"left\">31 (49.2)</td></tr><tr><td align=\"left\">  Coronary Artery Disease (CAD) (yes)</td><td align=\"left\">150 (34.5)</td><td align=\"left\">53 (54.6)</td><td align=\"left\">44 (45.3)</td></tr><tr><td align=\"left\">  History Hyperlipidemia (yes)</td><td align=\"left\">100 (23.0)</td><td align=\"left\">21 (38.8)</td><td align=\"left\">33 (61.1)</td></tr><tr><td align=\"left\">  Smoking (yes)</td><td align=\"left\">77 (17.7)</td><td align=\"left\">25 (59.5)</td><td align=\"left\">17 (40.4)</td></tr><tr><td align=\"left\">  Chronic kidney disease(CKD) (yes)</td><td align=\"left\">93 (21.4)</td><td align=\"left\">39 (54.9)</td><td align=\"left\">32 (45.0)</td></tr><tr><td align=\"left\">  Atrial Fibrillation ( AF) (yes)</td><td align=\"left\">96 (22.1)</td><td align=\"left\">29 (48.3)</td><td align=\"left\">31 (51.6)</td></tr><tr><td align=\"left\">  Stroke(yes)</td><td align=\"left\">26 (6.0)</td><td align=\"left\">8 (53.3)</td><td align=\"left\">7 (46.6)</td></tr><tr><td align=\"left\">  Acute decompensated heart failure (ADHF)</td><td align=\"left\">413 (94.9)</td><td align=\"left\">158 (63.2)</td><td align=\"left\">92 (36.8)</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab2\"><label>Table 2</label><caption><p>The Rate of death due to HF and Non-HF events in patients with HF</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Characteristic</th><th align=\"left\" colspan=\"2\">HF-related mortality (<italic>n</italic> = 160)</th><th align=\"left\" colspan=\"2\">Non-HF related mortality (<italic>n</italic> = 97)</th></tr><tr><th align=\"left\"/><th align=\"left\">Rate [per 1000] (95%CI)</th><th align=\"left\"><italic>p</italic>-value</th><th align=\"left\">Rate [per 1000] (95%CI)</th><th align=\"left\"><italic>p</italic>-value</th></tr></thead><tbody><tr><td align=\"left\"><bold><italic>Demographic variables</italic></bold></td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\">  Age (Trend effect)</td><td align=\"left\" colspan=\"2\"><bold><italic>p</italic></bold><bold>=0.017</bold></td><td align=\"left\" colspan=\"2\"><bold><italic>P</italic></bold><bold> &lt; 0.001</bold></td></tr><tr><td align=\"left\">  Sex</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\">   Male</td><td align=\"left\">10.10 (8.31–12.27)</td><td align=\"left\">0.690</td><td align=\"left\">5.10 (3.87–6.71)</td><td align=\"left\">0.052</td></tr><tr><td align=\"left\">   Female</td><td align=\"left\">9.65 (7.48–12.46)</td><td align=\"left\"/><td align=\"left\">7.53 (5.64–10.05)</td><td align=\"left\"/></tr><tr><td align=\"left\">  Employment Status</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\">   Employed</td><td align=\"left\">7.80 (4.85–12.55)</td><td align=\"left\">0.248</td><td align=\"left\">8.26 (5.20-13.11)</td><td align=\"left\">0.148</td></tr><tr><td align=\"left\">   Unemployed</td><td align=\"left\">10.26 (8.71–12.09)</td><td align=\"left\"/><td align=\"left\">5.67 (4.54–7.07)</td><td align=\"left\"/></tr><tr><td align=\"left\">  Education Level</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\">   &gt;= Diploma</td><td align=\"left\">7.17 (4.62–11.12)</td><td align=\"left\">0.125</td><td align=\"left\">6.09 (3.79–9.81)</td><td align=\"left\">0.924</td></tr><tr><td align=\"left\">   &lt; Diploma</td><td align=\"left\">10.51 (8.90–12.40)</td><td align=\"left\"/><td align=\"left\">6.08 (4.82–7.47)</td><td align=\"left\"/></tr><tr><td align=\"left\">  Place of Residence</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\">   City</td><td align=\"left\">9.93 (8.46–11.66)</td><td align=\"left\">0.966</td><td align=\"left\">5.80 (4.70–7.15)</td><td align=\"left\">0.199</td></tr><tr><td align=\"left\">   Village</td><td align=\"left\">9.89 (5.47–17.86)</td><td align=\"left\"/><td align=\"left\">8.99 (4.83–16.71)</td><td align=\"left\"/></tr><tr><td align=\"left\">  Marital status</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\">   Married</td><td align=\"left\">9.89 (8.40-11.66)</td><td align=\"left\">0.903</td><td align=\"left\">6.02 (4.88–7.43)</td><td align=\"left\">0.986</td></tr><tr><td align=\"left\">   Single</td><td align=\"left\">10.25 (6.37–16.49)</td><td align=\"left\"/><td align=\"left\">6.03 (3.24–11.21)</td><td align=\"left\"/></tr><tr><td align=\"left\"><bold><italic>Disease symptoms variables</italic></bold></td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\">  Dyspnea</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\">   Yes</td><td align=\"left\">10.89 (9.05–13.10)</td><td align=\"left\">0.140</td><td align=\"left\">6.51 (5.12–8.27)</td><td align=\"left\">0.311</td></tr><tr><td align=\"left\">   No</td><td align=\"left\">8.24 (6.21–10.93)</td><td align=\"left\"/><td align=\"left\">5.15 (3.60–7.36)</td><td align=\"left\"/></tr><tr><td align=\"left\">  Chest Pain</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\">   Yes</td><td align=\"left\">10.70 (9.10-12.58)</td><td align=\"left\"><bold>0.014</bold></td><td align=\"left\">6.62 (5.39–8.13)</td><td align=\"left\"><bold>0.028</bold></td></tr><tr><td align=\"left\">   No</td><td align=\"left\">4.79 (2.65–8.66)</td><td align=\"left\"/><td align=\"left\">2.61 (1.17–5.82)</td><td align=\"left\"/></tr><tr><td align=\"left\">  Limb Swelling</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\">   Yes</td><td align=\"left\">9.98 (8.47–11.77)</td><td align=\"left\">0.842</td><td align=\"left\">6.57 (3.73–11.57)</td><td align=\"left\">0.751</td></tr><tr><td align=\"left\">   No</td><td align=\"left\">9.31 (5.78–14.97)</td><td align=\"left\"/><td align=\"left\">5.97 (4.83–7.39)</td><td align=\"left\"/></tr><tr><td align=\"left\">  HeartRate (beats per minute)</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\">   &lt; 60</td><td align=\"left\">12.02 (7.84–18.44)</td><td align=\"left\"/><td align=\"left\">5.15 (2.68–9.90)</td><td align=\"left\"/></tr><tr><td align=\"left\">   60–100</td><td align=\"left\">9.43 (7.73–11.50)</td><td align=\"left\">0.609</td><td align=\"left\">6.41 (5.04–8.16)</td><td align=\"left\">0.706</td></tr><tr><td align=\"left\">   &gt; 100</td><td align=\"left\">10.30 (7.61–13.94)</td><td align=\"left\"/><td align=\"left\">5.39 (3.55–8.19)</td><td align=\"left\"/></tr><tr><td align=\"left\">  Temperature (degrees Celsius)</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\">   &lt; 36</td><td align=\"left\">5.73 (3.08–10.65)</td><td align=\"left\"><bold>0.008</bold></td><td align=\"left\">6.71 (4.28–10.52)</td><td align=\"left\"/></tr><tr><td align=\"left\">   36-37.5</td><td align=\"left\">9.36 (7.75–11.30)</td><td align=\"left\"/><td align=\"left\">5.72 (4.49–7.28)</td><td align=\"left\">0.750</td></tr><tr><td align=\"left\">   &gt; 37.5</td><td align=\"left\">14.84 (10.96–20.08)</td><td align=\"left\"/><td align=\"left\">6.87 (3.90-12.11)</td><td align=\"left\"/></tr><tr><td align=\"left\"><bold><italic>Clinical variables</italic></bold></td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\">  History Hypertension</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\">   Yes</td><td align=\"left\">10.17 (8.50-12.18)</td><td align=\"left\">0.523</td><td align=\"left\">8.61 (6.26–11.83)</td><td align=\"left\"><bold>0.012</bold></td></tr><tr><td align=\"left\">   No</td><td align=\"left\">9.29 (6.84–12.62)</td><td align=\"left\"/><td align=\"left\">5.04 (3.90–6.51)</td><td align=\"left\"/></tr><tr><td align=\"left\">  History Diabetes Mellitus (DM)</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\">   Yes</td><td align=\"left\">10.40 (8.75–12.37)</td><td align=\"left\">0.242</td><td align=\"left\">8.14 (5.72–11.58)</td><td align=\"left\">0.059</td></tr><tr><td align=\"left\">   No</td><td align=\"left\">8.40 (5.94–11.89)</td><td align=\"left\"/><td align=\"left\">5.36 (4.21–6.83)</td><td align=\"left\"/></tr><tr><td align=\"left\">  Coronary Artery disease (CAD)</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\">   Yes</td><td align=\"left\">10.08 (7.70–13.20)</td><td align=\"left\">0.916</td><td align=\"left\">8.37 (6.23–11.25)</td><td align=\"left\"><bold>&lt; 0.001</bold></td></tr><tr><td align=\"left\">   No</td><td align=\"left\">9.86 (8.15–11.91)</td><td align=\"left\"/><td align=\"left\">4.88 (3.73–6.39)</td><td align=\"left\"/></tr><tr><td align=\"left\">  History HyperLipidemia</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\">   Yes</td><td align=\"left\">11.33 (9.59–13.38)</td><td align=\"left\"><bold>0.001&gt;</bold></td><td align=\"left\">8.59 (6.10-12.08)</td><td align=\"left\"><bold>0.018</bold></td></tr><tr><td align=\"left\">   No</td><td align=\"left\">5.46 (3.56–8.38)</td><td align=\"left\"/><td align=\"left\">5.21 (4.08–6.66)</td><td align=\"left\"/></tr><tr><td align=\"left\">  Smoking</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\">   Yes</td><td align=\"left\">10.18 (8.60-12.05)</td><td align=\"left\">0.491</td><td align=\"left\">6.03 (4.84–7.51)</td><td align=\"left\">0.964</td></tr><tr><td align=\"left\">   No</td><td align=\"left\">8.76 (5.92–12.97)</td><td align=\"left\"/><td align=\"left\">5.96 (3.70–9.58)</td><td align=\"left\"/></tr><tr><td align=\"left\">  Chronic kidney disease (CKD)</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\">   Yes</td><td align=\"left\">13.51 (9.87–18.49)</td><td align=\"left\"><bold>0.049</bold></td><td align=\"left\">11.08 (7.83–15.67)</td><td align=\"left\"><bold>&lt; 0.001</bold></td></tr><tr><td align=\"left\">   No</td><td align=\"left\">9.15 (7.65–10.93)</td><td align=\"left\"/><td align=\"left\">4.91 (3.85–6.27)</td><td align=\"left\"/></tr><tr><td align=\"left\">  Atrial Fibrillation (AF)</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\">   Yes</td><td align=\"left\">10.39 (8.75–12.33)</td><td align=\"left\">0.225</td><td align=\"left\">8.84 (6.22–12.57)</td><td align=\"left\"><bold>0.016</bold></td></tr><tr><td align=\"left\">   No</td><td align=\"left\">8.27 (5.75–11.90)</td><td align=\"left\"/><td align=\"left\">5.23 (4.11–6.66)</td><td align=\"left\"/></tr><tr><td align=\"left\">  Stroke</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\">   Yes</td><td align=\"left\">10.08 (8.60-11.82)</td><td align=\"left\">0.477</td><td align=\"left\">6.77 (3.22–14.20)</td><td align=\"left\">0.762</td></tr><tr><td align=\"left\">   No</td><td align=\"left\">7.73 (3.87–15.47)</td><td align=\"left\"/><td align=\"left\">5.97 (4.85–7.34)</td><td align=\"left\"/></tr><tr><td align=\"left\">  Type of Acute Heart Failure</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\">   ACUTEDENOVOHF</td><td align=\"left\">1.80 (0.45–7.20)</td><td align=\"left\"><bold>0.007</bold></td><td align=\"left\">4.50 (1.87–10.81)</td><td align=\"left\">0.534</td></tr><tr><td align=\"left\">   DECOMPENSATEDHF</td><td align=\"left\">10.53 (9.01–12.31)</td><td align=\"left\"/><td align=\"left\">6.13 (5.01–7.52)</td><td align=\"left\"/></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab3\"><label>Table 3</label><caption><p>Bayesian information criterion values for parametric models</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Distribution</th><th align=\"left\">WAIC</th><th align=\"left\">LPML</th><th align=\"left\">DIC</th></tr></thead><tbody><tr><td align=\"left\">\n<bold>Weibull</bold>\n</td><td align=\"left\">\n<bold>1730.922</bold>\n</td><td align=\"left\">\n<bold>-865.851</bold>\n</td><td align=\"left\">\n<bold>1717.717</bold>\n</td></tr><tr><td align=\"left\">Log-Normal</td><td align=\"left\">1728.042</td><td align=\"left\">-864.430</td><td align=\"left\">1722.320</td></tr><tr><td align=\"left\">Log-Logistic</td><td align=\"left\">1728.594</td><td align=\"left\">-864.470</td><td align=\"left\">1723.892</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab4\"><label>Table 4</label><caption><p>Competing risk parametric utilizing univariable Bayesian Weibull AFT regression</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Characteristic</th><th align=\"left\">HF-related mortality (<italic>n</italic> = 160)</th><th align=\"left\">Non-HF related mortality (<italic>n</italic> = 97)</th></tr><tr><th align=\"left\"/><th align=\"left\">Time Ratio (95%CI)</th><th align=\"left\">Time Ratio (95%CI)</th></tr></thead><tbody><tr><td align=\"left\"><bold><italic>Demographic variables</italic></bold></td><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\"> Age (Trend effect)</td><td align=\"left\"><bold>0.98 (0.96–0.99)</bold></td><td align=\"left\"><bold>0.97 (0.95–0.99)</bold></td></tr><tr><td align=\"left\"> Sex (Male)</td><td align=\"left\">0.92 (0.51–1.57)</td><td align=\"left\">1.70 (0.93–2.88)</td></tr><tr><td align=\"left\"> Employment Status (unemployed)</td><td align=\"left\">0.64 (0.23–1.34)</td><td align=\"left\">1.67 (0.78–3.10)</td></tr><tr><td align=\"left\"> Education Level (&lt; Diploma)</td><td align=\"left\">0.56 (0.23–1.09)</td><td align=\"left\">1.06 (0.48–2.01)</td></tr><tr><td align=\"left\"> Place of Residence (City)</td><td align=\"left\">1.01 (0.29–2.32)</td><td align=\"left\">1.86 (0.68–3.96)</td></tr><tr><td align=\"left\"> Marital status  (Married)</td><td align=\"left\">1.06 (0.39–2.22)</td><td align=\"left\">1.04 (0.38–2.21)</td></tr><tr><td align=\"left\"><bold><italic>Disease symptoms variables</italic></bold></td><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\"> Dyspnea (Yes)</td><td align=\"left\">0.67 (0.35–1.11)</td><td align=\"left\">0.77 (0.41–1.31)</td></tr><tr><td align=\"left\"> Chest Pain (Yes)</td><td align=\"left\"><bold>0.30 (0.08–0.70)</bold></td><td align=\"left\"><bold>0.32 (0.07–0.77)</bold></td></tr><tr><td align=\"left\"> Limb Swelling (Yes)</td><td align=\"left\">0.96 (0.35–2.01)</td><td align=\"left\">0.98 (0.40–2.20)</td></tr><tr><td align=\"left\"> HeartRate (beats per minute)</td><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\">   &lt; 60</td><td align=\"left\">0.73 (0.32–1.52)</td><td align=\"left\">1.64 (0.58–4.13)</td></tr><tr><td align=\"left\">   60–100</td><td align=\"left\">base</td><td align=\"left\">base</td></tr><tr><td align=\"left\">   &gt; 100</td><td align=\"left\">0.88 (0.47–1.54)</td><td align=\"left\">1.34 (0.65–2.60)</td></tr><tr><td align=\"left\"> Temperature (degrees Celsius)</td><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\">   &lt; 36</td><td align=\"left\"><bold>0.51 (0.26–0.93)</bold></td><td align=\"left\">0.87 (0.42–1.63)</td></tr><tr><td align=\"left\">   36-37.5</td><td align=\"left\">base</td><td align=\"left\">base</td></tr><tr><td align=\"left\">   &gt; 37.5</td><td align=\"left\">2.76 (0.89–7.86)</td><td align=\"left\">0.89 (0.36–2.06)</td></tr><tr><td align=\"left\"><bold><italic>Clinical variables</italic></bold></td><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\"> History Hypertension (Yes)</td><td align=\"left\">0.83 (0.42–1.45)</td><td align=\"left\"><bold>0.53 (0.28–0.90)</bold></td></tr><tr><td align=\"left\"> DM (Yes)</td><td align=\"left\">0.70 (0.33–1.26)</td><td align=\"left\">0.62 (0.32–1.04)</td></tr><tr><td align=\"left\"> CAD (Yes)</td><td align=\"left\">1.01 (0.53–1.65)</td><td align=\"left\"><bold>0.52 (0.29–0.85)</bold></td></tr><tr><td align=\"left\"> History HyperLipidemia (Yes)</td><td align=\"left\"><bold>0.30 (0.11–0.60)</bold></td><td align=\"left\"><bold>0.54 (0.28–0.92)</bold></td></tr><tr><td align=\"left\"> Smoking (Yes)</td><td align=\"left\">0.81 (0.35–1.52)</td><td align=\"left\">1.04 (0.46–1.96)</td></tr><tr><td align=\"left\"> CKD (Yes)</td><td align=\"left\">0.63 (0.34–1.09)</td><td align=\"left\"><bold>0.38 (0.19–0.65)</bold></td></tr><tr><td align=\"left\"> AF (Yes)</td><td align=\"left\">0.68 (0.31–1.23)</td><td align=\"left\"><bold>0.53 (0.27–0.92)</bold></td></tr><tr><td align=\"left\"> Stroke (Yes)</td><td align=\"left\">0.68 (0.16–1.67)</td><td align=\"left\">1.11 (0.34–2.93)</td></tr><tr><td align=\"left\"> ADHF</td><td align=\"left\"><bold>0.08 (0.01–0.37)</bold></td><td align=\"left\">0.76 (0.12–1.87)</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab5\"><label>Table 5</label><caption><p>Sensitivity analysis for prior distribution in HF-related mortality</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Characteristic</th><th align=\"left\">Normal (mean 0 and variance of 10,000)</th><th align=\"left\">Normal (mean 0 and variance of 1000)</th><th align=\"left\">Normal (mean 0 and variance of 100)</th><th align=\"left\">Normal (mean 0 and variance of 10)</th><th align=\"left\">Normal (mean 0 and variance of 1)</th></tr></thead><tbody><tr><td align=\"left\">Age (Trend effect)</td><td align=\"left\">0.98 (0.96–0.99)</td><td align=\"left\">0.98 (0.96-1)</td><td align=\"left\">0.98 (0.96–0.99)</td><td align=\"left\">0.98 (0.97-1)</td><td align=\"left\">0.99 (0.98–1.01)</td></tr><tr><td align=\"left\">Chest Pain (Yes)</td><td align=\"left\">0.41 (0.10–0.96)</td><td align=\"left\">0.46 (0.26–0.71)</td><td align=\"left\">0.37 (0.08–1.34)</td><td align=\"left\">0.55 (0.26–1.19)</td><td align=\"left\">0.85 (0.41–1.57)</td></tr><tr><td align=\"left\">Temperature (degrees Celsius)</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\">  &lt; 36</td><td align=\"left\">0.62 (0.33–1.13)</td><td align=\"left\">0.61 (0.38–0.94)</td><td align=\"left\">0.60 (0.32–0.98)</td><td align=\"left\">0.68 (0.39–1.22)</td><td align=\"left\">0.66 (0.39–1.05)</td></tr><tr><td align=\"left\">  36-37.5</td><td align=\"left\">base</td><td align=\"left\">base</td><td align=\"left\">base</td><td align=\"left\">base</td><td align=\"left\">base</td></tr><tr><td align=\"left\">  &gt; 37.5</td><td align=\"left\">2.80 (0.83–7.90)</td><td align=\"left\">2.14 (0.93–3.71)</td><td align=\"left\">2.88 (0.79–7.81)</td><td align=\"left\">2.27 (0.84–5.22)</td><td align=\"left\">2.32 (0.98–4.11)</td></tr><tr><td align=\"left\">  History HyperLipidemia (Yes)</td><td align=\"left\">0.34 (0.13–0.64)</td><td align=\"left\">0.38 (0.17–0.65)</td><td align=\"left\">0.32 (0.16–0.57)</td><td align=\"left\">0.33 (0.17–0.60)</td><td align=\"left\">0.64 (0.39–1.04)</td></tr><tr><td align=\"left\">  ADHF</td><td align=\"left\">0.11 (0.01–0.44)</td><td align=\"left\">0.01 (0.008–0.28)</td><td align=\"left\">0.11 (0.006-0.50)</td><td align=\"left\">0.06 (0.01–0.13)</td><td align=\"left\">1.16 (0.49–2.20)</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab6\"><label>Table 6</label><caption><p>Sensitivity analysis for prior distribution in Non- HF-related mortality</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Characteristic</th><th align=\"left\">Normal (mean 0 and variance of 10,000)</th><th align=\"left\">Normal (mean 0 and variance of 1000)</th><th align=\"left\">Normal (mean 0 and variance of 100)</th><th align=\"left\">Normal (mean 0 and variance of 10)</th><th align=\"left\">Normal (mean 0 and variance of 1)</th></tr></thead><tbody><tr><td align=\"left\">Age (Trend effect)</td><td align=\"left\">0.99 (0.97–1.01)</td><td align=\"left\">0.99 (0.97–1.01)</td><td align=\"left\">0.99 (0.97–1.01)</td><td align=\"left\">0.99 (0.97-1.00)</td><td align=\"left\">0.99 (0.98–1.01)</td></tr><tr><td align=\"left\">Chest Pain (Yes)</td><td align=\"left\">0.38 (0.12–0.86)</td><td align=\"left\">0.32 (0.05–0.89)</td><td align=\"left\">0.38 (0.12–0.83)</td><td align=\"left\">0.33 (0.13–0.72)</td><td align=\"left\">0.58 (0.30–0.91)</td></tr><tr><td align=\"left\">History Hypertension (Yes)</td><td align=\"left\">0.91 (0.57–1.37)</td><td align=\"left\">1.01 (0.47–1.86)</td><td align=\"left\">1.27 (0.57–2.36)</td><td align=\"left\">1.02 (0.67–1.59)</td><td align=\"left\">1.17 (0.61–1.63)</td></tr><tr><td align=\"left\">CAD (Yes)</td><td align=\"left\">0.65 (0.38–0.98)</td><td align=\"left\">0.78 (0.42–1.30)</td><td align=\"left\">0.82 (0.42–1.56)</td><td align=\"left\">0.77 (0.40–1.28)</td><td align=\"left\">0.83 (0.44–1.39)</td></tr><tr><td align=\"left\">History HyperLipidemia (Yes)</td><td align=\"left\">0.60 (0.37–0.90)</td><td align=\"left\">0.62 (0.24–1.11)</td><td align=\"left\">0.47 (0.26–0.67)</td><td align=\"left\">0.70 (0.41–1.18)</td><td align=\"left\">0.80 (0.44–1.39)</td></tr><tr><td align=\"left\">CKD (Yes)</td><td align=\"left\">0.52 (0.28–0.87)</td><td align=\"left\">0.56 (0.27–1.23)</td><td align=\"left\">0.57 (0.33–0.88)</td><td align=\"left\">0.59(0.35–0.95)</td><td align=\"left\">0.57 (0.31–1.12)</td></tr><tr><td align=\"left\">AF (Yes)</td><td align=\"left\">0.53 (0.32–0.81)</td><td align=\"left\">0.71 (0.37–1.23)</td><td align=\"left\">0.74 (0.42–1.19)</td><td align=\"left\">0.79 (0.44–1.35)</td><td align=\"left\">0.77 (0.52–1.30)</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab7\"><label>Table 7</label><caption><p>Competing risk parametric utilizing multivariable Bayesian Weibull AFT regression</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Characteristic</th><th align=\"left\">HF-related mortality (<italic>n</italic> = 160)</th><th align=\"left\">Non-HF related mortality (<italic>n</italic> = 97)</th></tr><tr><th align=\"left\"/><th align=\"left\">Time Ratio (95%CI)</th><th align=\"left\">Time Ratio (95%CI)</th></tr></thead><tbody><tr><td align=\"left\"><bold><italic>Demographic variables</italic></bold></td><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\"> Age (Trend effect)</td><td align=\"left\"><bold>0.98 (0.96–0.99)</bold></td><td align=\"left\">0.99 (0.97–1.01)</td></tr><tr><td align=\"left\"><bold><italic>Disease symptoms variables</italic></bold></td><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\"> Chest Pain (Yes)</td><td align=\"left\"><bold>0.41 (0.10–0.96)</bold></td><td align=\"left\"><bold>0.38 (0.12–0.86)</bold></td></tr><tr><td align=\"left\"> Temperature (degrees Celsius)</td><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\">   &lt; 36</td><td align=\"left\">0.62 (0.33–1.13)</td><td align=\"left\"/></tr><tr><td align=\"left\">   36-37.5</td><td align=\"left\">base</td><td align=\"left\">NC</td></tr><tr><td align=\"left\">   &gt; 37.5</td><td align=\"left\">2.80 (0.83–7.90)</td><td align=\"left\"/></tr><tr><td align=\"left\"><bold><italic>Clinical variables</italic></bold></td><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\"> History Hypertension (Yes)</td><td align=\"left\">NC</td><td align=\"left\">0.91 (0.57–1.37)</td></tr><tr><td align=\"left\"> CAD (Yes)</td><td align=\"left\">NC</td><td align=\"left\"><bold>0.65 (0.38–0.98)</bold></td></tr><tr><td align=\"left\"> History HyperLipidemia (Yes)</td><td align=\"left\"><bold>0.34 (0.13–0.64)</bold></td><td align=\"left\"><bold>0.60 (0.37–0.90)</bold></td></tr><tr><td align=\"left\"> CKD (Yes)</td><td align=\"left\">NC</td><td align=\"left\"><bold>0.52 (0.28–0.87)</bold></td></tr><tr><td align=\"left\"> AF (Yes)</td><td align=\"left\">NC</td><td align=\"left\"><bold>0.53 (0.32–0.81)</bold></td></tr><tr><td align=\"left\"> ADHF</td><td align=\"left\"><bold>0.11 (0.01–0.44)</bold></td><td align=\"left\">NC</td></tr></tbody></table></table-wrap>" ]

[ "<inline-formula id=\"IEq1\"><alternatives><tex-math id=\"M1\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$X$$\\end{document}</tex-math><mml:math id=\"M2\"><mml:mi>X</mml:mi></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq2\"><alternatives><tex-math id=\"M3\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$S(.|X)\\triangleq p(T&gt;t\\left|X\\right)$$\\end{document}</tex-math><mml:math id=\"M4\"><mml:mrow><mml:mi>S</mml:mi><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mo>.</mml:mo><mml:mo stretchy=\"false\">|</mml:mo><mml:mi>X</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mo>≜</mml:mo><mml:mi>p</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>T</mml:mi><mml:mo>&gt;</mml:mo><mml:mi>t</mml:mi><mml:mfenced close=\")\" open=\"|\"><mml:mi>X</mml:mi></mml:mfenced></mml:mrow></mml:math></alternatives></inline-formula>", "<disp-formula id=\"Equa\"><alternatives><tex-math id=\"M5\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\text{L}\\left({\\upbeta }|\\text{X},\\text{t}\\right)=\\prod _{i=1}^{N}{f\\left({t}_{i}\\right)}^{{d}_{i}}{S\\left({t}_{i}\\right)}^{{1-d}_{i}}$$\\end{document}</tex-math><mml:math id=\"M6\" display=\"block\"><mml:mrow><mml:mtext>L</mml:mtext><mml:mfenced close=\")\" open=\"(\"><mml:mi mathvariant=\"normal\">β</mml:mi><mml:mo stretchy=\"false\">|</mml:mo><mml:mtext>X</mml:mtext><mml:mo>,</mml:mo><mml:mtext>t</mml:mtext></mml:mfenced><mml:mo>=</mml:mo><mml:munderover><mml:mo>∏</mml:mo><mml:mrow><mml:mi>i</mml:mi><mml:mo>=</mml:mo><mml:mn>1</mml:mn></mml:mrow><mml:mi>N</mml:mi></mml:munderover><mml:msup><mml:mrow><mml:mi>f</mml:mi><mml:mfenced close=\")\" open=\"(\"><mml:msub><mml:mi>t</mml:mi><mml:mi>i</mml:mi></mml:msub></mml:mfenced></mml:mrow><mml:msub><mml:mi>d</mml:mi><mml:mi>i</mml:mi></mml:msub></mml:msup><mml:msup><mml:mrow><mml:mi>S</mml:mi><mml:mfenced close=\")\" 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\n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$f\\left(t\\right)=\\lambda pt^{p-1}exp\\left[-\\lambda t^p\\right],\\;S\\left(t\\right)=exp\\left[-\\lambda t^p\\right]$$\\end{document}</tex-math><mml:math id=\"M10\"><mml:mrow><mml:mi>f</mml:mi><mml:mfenced close=\")\" open=\"(\"><mml:mi>t</mml:mi></mml:mfenced><mml:mo>=</mml:mo><mml:mi>λ</mml:mi><mml:mi>p</mml:mi><mml:msup><mml:mi>t</mml:mi><mml:mrow><mml:mi>p</mml:mi><mml:mo>-</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:msup><mml:mi>e</mml:mi><mml:mi>x</mml:mi><mml:mi>p</mml:mi><mml:mfenced close=\"]\" open=\"[\"><mml:mo>-</mml:mo><mml:mi>λ</mml:mi><mml:msup><mml:mi>t</mml:mi><mml:mi>p</mml:mi></mml:msup></mml:mfenced><mml:mo>,</mml:mo><mml:mspace width=\"0.277778em\"/><mml:mi>S</mml:mi><mml:mfenced close=\")\" 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id=\"M15\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$f\\left(t\\right)= \\frac{1}{t{\\left(2\\pi \\delta \\right)}^{{}^{1}\\!\\left/ \\!{}_{2}\\right.}}\\text{exp}\\left[ \\frac{-1}{2\\delta } \\left({ln}\\left(t\\right)-{\\mu }^{2}\\right)\\right], S\\left(t\\right)=1-\\varnothing \\left[\\frac{\\text{ln}t-\\mu }{\\delta }\\right]$$\\end{document}</tex-math><mml:math id=\"M16\" display=\"block\"><mml:mrow><mml:mi>f</mml:mi><mml:mfenced close=\")\" open=\"(\"><mml:mi>t</mml:mi></mml:mfenced><mml:mo>=</mml:mo><mml:mfrac><mml:mn>1</mml:mn><mml:mrow><mml:mi>t</mml:mi><mml:msup><mml:mrow><mml:mfenced close=\")\" 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open=\"(\"><mml:mi>t</mml:mi></mml:mfenced><mml:mo>=</mml:mo><mml:mn>1</mml:mn><mml:mo>-</mml:mo><mml:mi>∅</mml:mi><mml:mfenced close=\"]\" open=\"[\"><mml:mfrac><mml:mrow><mml:mtext>ln</mml:mtext><mml:mi>t</mml:mi><mml:mo>-</mml:mo><mml:mi>μ</mml:mi></mml:mrow><mml:mi>δ</mml:mi></mml:mfrac></mml:mfenced></mml:mrow></mml:math></alternatives></disp-formula>", "<inline-formula id=\"IEq7\"><alternatives><tex-math id=\"M17\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\varnothing (.)$$\\end{document}</tex-math><mml:math id=\"M18\"><mml:mrow><mml:mi>∅</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:mo>.</mml:mo><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq8\"><alternatives><tex-math id=\"M19\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\mu ={{\\upbeta }}_{0}+{x}_{1}{\\beta }_{1}+\\dots +{x}_{p}{\\beta }_{p}.$$\\end{document}</tex-math><mml:math id=\"M20\"><mml:mrow><mml:mi>μ</mml:mi><mml:mo>=</mml:mo><mml:msub><mml:mi 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[ "<table-wrap-foot><p>Rate = failures/person-time; <italic>P</italic>-values computed from log-rank test and bold <italic>P</italic>-values indicate significant differences (<italic>P</italic> &lt; 0.05)</p><p><italic>CI </italic>Confidence interval</p></table-wrap-foot>", "<table-wrap-foot><p>Bold values indicate better results than other methods</p><p><italic>WAIC </italic>Watanabe-Akaike information criterion, <italic>LPML </italic>Log pseudo marginal likelihood, <italic>DIC</italic> Deviance information criterion</p></table-wrap-foot>", "<table-wrap-foot><p><italic>CI</italic> Confidence interval and bold <italic>P</italic>-values indicate significant differences</p></table-wrap-foot>", "<table-wrap-foot><p><italic>CI</italic> Confidence interval and bold <italic>P</italic>-values indicate significant differences</p><p><italic>NC</italic> Not computable</p></table-wrap-foot>", "<fn-group><fn><p><bold>Publisher’s Note</bold></p><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn></fn-group>" ]

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[ "<title>Introduction</title>", "<p id=\"Par3\">BLCA is the most frequent diagnosed malignant tumor of the genitourinary system [##REF##35022204##1##]. Although unprecedented progress has been made in early diagnosis of BLCA tumors and multiple treatment options have been established (such as surgery and intravesical BCG) for primary BLCA in the past decade, the high recurrence rate and poor prognosis of this disease still remains invasive, especially for individuals diagnosed with muscle-invasive BLCA [##REF##31676912##2##]. Long-term regular infusion of chemotherapy drugs after surgery is the most effective preventive care to reduce the recurrence of nonmuscle-invasive BLCA, while postoperative chemotherapy is the first-line therapeutic strategy for BLCA with progression. However, due to intratumor heterogeneity and chemotherapy resistance, the efficacy of the current treatment methods for BLCA is largely limited, and the 5 year of survival rate is still unsatisfactory [##UREF##0##3##, ##REF##36436682##4##]. The median survival rate of patients who received the most common chemotherapy regimen, gemcitabine and cisplatin (GC scheme), was limited to a timespan of 14 months [##REF##35064817##5##]. Therefore, exploring the mechanism of tumor resistance is crucial for discovering new targets for chemotherapy sensitivity and promoting the progress of precision therapy.</p>", "<p id=\"Par4\">It is well known that metabolic reprogramming is a hallmark of cancer [##REF##21376230##6##]. More and more evidence has shown that cancer cell response to treatment is controlled by the metabolic state, suggesting that metabolism-related pathways could overcome resistance through the controlled metabolic state [##REF##35931676##7##]. Li Y et al. reported that the GLUT1/ALDOB/G6PD axis regulate glucose metabolism reprogramming and promotes chemotherapy resistance in pancreatic cancer [##REF##37597521##8##]. Zhou et al. proved the important role of lipid metabolism during the process of cancer resistance in the treatment of castration resistant prostate cancer [##REF##33391508##9##]. Wong TL et al. confirmed that SCD1 promotes the formation of lipid droplets to target 5-fluorouracil and cisplatin resistance in gastric cancer [##REF##37208334##10##]. Solanki S et al. identified amino acid metabolism to be essential in the cellular reprogramming process of chemoresistance in chemotherapeutic-resistant patients diagnosed with colon cancer [##REF##36410445##11##]. However, there are few studies on metabolism in chemotherapy-related pathways resistance in BLCA. BLCA cells rely on their own unique metabolic transformation to maintain the energy needed for their growth and proliferation [##REF##26975021##12##]. At the same time, the metabolism of bladder cancer is characterized by increased fatty acid synthesis and the phosphopentose pathway, and decreased AMP-activated protein kinase and Krebs cycle activity. The mRNA modification of PKM2 promotes glucose metabolism in BLCA [##REF##33991457##13##]. Afonso J et al. described that glucose metabolism could be a target to improve BLCA immunotherapy [##REF##31953517##14##]. However, there is a lack of systematic analysis on the relationship between the potential mechanism of chemotherapy resistance and metabolic recombination in BLCA.</p>", "<p id=\"Par5\">In this study, we obtained drug-resistant differentially expressed by RNA sequencing of the established gemcitabine-resistant bladder cancer cell line, combined with MRGs, and then established and justified a prognostic model that is found in several BLCA databases through Cox and LASSO regression analysis. Our studies found that this model is a very accurate predictor of overall survival (OS), and is significantly related to metabolic reprogramming, gene mutation, and the tumor microenvironment. In addition, FASN was considered the representative gene of RM-RM. We proved that FASN promoted BLCA gemcitabine resistance, while TVB-3166, an inhibitor of FASN, reversed BLCA gemcitabine resistance in vitro and in vivo.</p>", "<p id=\"Par6\">In summary, we will provide a new model for predicting the survival and therapeutic strategies for BLCA patients.</p>" ]

[ "<title>Materials and methods</title>", "<title>Cell culture and reagents</title>", "<p id=\"Par31\">T24 and UMUC3 cells were obtained from the American Type Culture Collection (ATCC, Manassas, VA). These BLCA cells were cultured in DMEM (for UMUC3 and UMUC3-R) and RPMI-1640 (for T24 and T24-R) mediums added into 10% foetal bovine serum (Gibco, USA). The lentivirus was used to knock down FASN and the vector were purchased from GeneChem. Our operation steps were strictly used for maintaining the instruction requirements. Gemcitabine (HY-17026) and TVB-3166 were purchased from MCE.</p>", "<title>Establishment of gemcitabine-resistant cell lines</title>", "<p id=\"Par32\">Two types of BLCA cell lines (T24, UMUC3) were first incubated with gemcitabine in several concentration gradients (0–20 μg/ml) for 48 h. The IC50s were calculated depending on their absorbances. Then, the cell lines were cocultured with gemcitabine at the concentration levels at which the IC50s were attached. Repeat the above steps. RI (resistance index) is calculated as the IC50 of the drug-resistant cells divided by the IC50 of the wild-type (WT) cells. 1–5 indicated low drug resistance, 5–15 indicated moderate drug resistance, and more than 15 indicated high drug resistance. Previous studies have found that the IC50 values of gemcitabine-resistant cells in two cell lines are 5 to 10 times higher or more compared to WT cells [##REF##36637036##38##]. When the RI (resistance index) &gt; 5, then we considered that drug-resistant cell lines were successfully constructed.</p>", "<title>RNA sequencing</title>", "<p id=\"Par33\">Three groups of repeated cells were used for RNA sequencing after the establishment of the T24 gemcitabine-resistant cell line. This sequencing was completed by APT (APPLIED PROTEIN TECHNOLOGY). By means of the R “limma” (Version 3.54.0) package [##REF##25605792##39##], we checked out the drug-resistant differential genes of gemcitabine resistance (p &lt; 0.05, |Fold change|&gt; 2).</p>", "<title>Data acquisition</title>", "<p id=\"Par34\">The MRGs were collected from MSigDB [##REF##21546393##40##]. The TCGA BLCA database was acquired from UCSC Xena as the training set, and two BLCA data: GSE69795 [##REF##26343003##41##] and GSE31684 [##UREF##4##42##] were acquired from GEO as the validation set. The marker genes of endothelial cells and fibroblasts were collected from the literature, CellMarker database and R “xCell” (Version1.1.0) package [##REF##29141660##43##].</p>", "<title>Visualization of differentially expressed genes</title>", "<p id=\"Par35\">The volcano plot and heatmap are presented by R “ggplot2” (Version 3.4.0) [##UREF##5##44##] to demonstrate the distribution of DEGs. Moreover, the Venn diagram demonstrated the connection of differentially expressed genes of gemcitabine resistance (R-DEGs) and metabolic-related genes (MRGs) to gain resistance and metabolism-related differentially expressed genes (RM-DEGs).</p>", "<title>Enrichment analysis of genes</title>", "<p id=\"Par36\">GO analysis and KEGG analysis were carried out by the R “clusterProfiler” (Version 4.6.0) package to deeply study the major molecular functions and significantly enriched pathways of the DEGs [##REF##22455463##45##]. We took P &lt; 0.05 as the standard of significant difference.</p>", "<title>Unsupervised clustering analysis</title>", "<p id=\"Par37\">We used the R ConsensusClusterPlus' (version 1.62.0) package to perform hierarchical consistency clustering analysis [##REF##20427518##46##].</p>", "<title>Establishment and validation of the drug resistance and metabolism-related prognosis risk assessment model (RM-RM)</title>", "<p id=\"Par38\">First, we screened out the main genes that correlated with the OS of BLCA patients from RM-DEGs by using univariate Cox regression. Then, the R “glmnet” (Version 4.1–6) package [##UREF##6##47##] was used for LASSO Cox regression to evade the overfitting of characteristics and to narrow the number of factors for predicting OS. Finally, we further evaluated the genes that were recognized by LASSO regression using multiple Cox regression analysis, obtained seven key genes, and used them to create a forecast risk model on the basis of drug resistance and metabolism. The drug resistance and metabolism-related risk score (RM-RS) formula is described below: RM-RS = ∑ (β × Exp), in which β and Exp are respectively representation of coefficient and genes expression that were standardized.</p>", "<title>Survival analysis</title>", "<p id=\"Par39\">In accordance with the median RM-RS, patients with BLCA were subdivided into a high RM-RS group and a low RM-RS group. KM survival analysis was used to prove the variance in OS of different RM-RS groups. In addition, the ROC curves were used to estimate the prognostic value of the RS-RS using the R “Survival ROC” package. Subsequently, through the R “survival” (Version 3.5–5) package, we carried out the analysis of independent factors affecting BLCA prognosis by univariate and multivariate regression. The above determination methods were verified in two independent gene sets.</p>", "<title>Immunohistochemical staining assay</title>", "<p id=\"Par40\">We performed an immunohistochemical staining assay on the tissue chips in accordance with a previously described method [##REF##34185414##48##]. The antibodies we used included anti-GPC2 (1:200, AF2304SP, Goat, IgG, Novus), anti-CNOT6L (1:75, abs108959, Rabbit, IgG Absin), anti-FASN (1:300, 66,591-1-Ig, Mouse, IgG, Proteintech), anti- MAP2 (1:2500, 66,846-1-Ig, Rabbit, IgG, Proteintech), anti-BMP6 (1:500, bs-10090R, Rabbit, IgG, Bioss), anti-CARD10 (1:300, bs-7081R, Rabbit, IgG, Bioss), anti-GALNT12 (1:100, ab201196, Rabbit, IgG, Abcam), anti-IgG (ab238004, Mouse, Abcam), anti-IgG (A7007, Goat, Beyotime), and anti-IgG (30,000-0-AP, Rabbit, Proteintech). The IRS (value, 0–12) was calculated by multiplying the staining strength grade by the positive area grade. The grade of staining strength was prescribed below: 0, negative; l, weak; 2, moderate; and 3, strong. The positive area grade was described as follows: zero-grade, less than 5%; first-grade, 5% to 25%; second-grade, 26% to 50%; third-grade, 51% to 75%; and fourth-grade, greater than 75%.</p>", "<title>GSEA and ssGSEA</title>", "<p id=\"Par41\">GSEA (Gene Set Enrichment Analysis) was carried out by means of the “clusterProfiler” package and GSEA software (4.3.2) to reveal the relevant signaling pathways, and visualization was implemented by means of the R “enrichplot” package (Version 1.20.0) and the “ggplot2” package. The ssGSEA (single sample Gene Set Enrichment Analysis) was performed by the R “GSVA” package (Version 1.48.3), and the individual score of each sample-specific pathway was obtained by the sample-related gene expression. In order to explore the metabolic pathway of BLCA gene sets, we searched for relatable papers [##REF##31968678##49##, ##REF##27386546##50##] and data in the MSigDB.</p>", "<title>Gene mutation analysis</title>", "<p id=\"Par42\">We obtained somatic mutation information using the TCGA BLCA database. Meanwhile, using the R “Maftools” (Version 2.14.0) package, we analyzed various differences in mutations in the two RM-RS subgroups [##REF##30341162##51##].</p>", "<title>Analysis of the TME</title>", "<p id=\"Par43\">In order to evaluate the immune and stromal scores of each BLCA patient, we used different algorithms on the online tools: the xCell, MCP-counter, and EPIC. Subsequently, we evaluated the infiltration of different cells in the two subgroups by box plot visualization. Finally, we performed an association analysis by means of the R “corrplot” package (Version 0.92) to prove the intimate connection between RM-RS and characteristic cell marker genes.</p>", "<title>Multiplex immunofluorescence staining assay</title>", "<p id=\"Par44\">We performed an immunofluorescence staining assay on the tissue in accordance with the method illustrated previously [##REF##33268821##52##]. The antibodies we used included anti-FASN (1:200, 66,591-1-Ig, ProteinTech), anti-BMP6 (1: 3,000, bs-10090R, Bioss), anti-CXCL12 (1:200, 17,402-1-AP, ProteinTech) and anti-CD34 (1:200, ab81219, Abcam). Through ImageJ software analysis, we obtained corrected total cell fluorescence (CTCF) to evaluate the content of protein expression in BLCA and adjacent tissues.</p>", "<title>Prediction of drug sensitivity</title>", "<p id=\"Par45\">With the purpose of predicting the sensitivity of two risk subgroups to multiple chemotherapeutic drugs, we jointly analyzed the TCGA database, Genomics of Drug Sensitivity in Cancer (GDSC) and Cancer Therapeutics Response Portal (CTRP) data. By means of the R “oncoPredict” package [##UREF##7##53##], we obtained the IC50 of each sample in two different RM-RS groups for hundreds of drugs.</p>", "<title>Statistical analyses</title>", "<p id=\"Par46\">Our data processing was performed by R software (version 4. 2. 1).</p>", "<title>Human samples</title>", "<p id=\"Par47\">With the agreement of the Ethics Committee of the First Affiliated Hospital of Zhengzhou University, we gathered BLCA tissues and normal bladder tissues from BLCA patients, partially collected them in a -80 °C freezer and partially embedded them in paraffin.</p>", "<title>BODIPY staining</title>", "<p id=\"Par48\">First, we placed cells or fresh tissues in 4% paraformaldehyde solution. Subsequently, we incubated cells or tissues with BODIPY and DAPI in the dark for 30 min and 10 min. Then ImageJ software was used for analysis.</p>", "<title>FASN, FFA, TG and T-CHO measurement assay</title>", "<p id=\"Par49\">The levels of FASN were assessed by enzyme-linked immunosorbent assay (ELISA) in accordance with the FASN ELISA kit’s instructions (Abcam, ab279412). The contents of FFAs, TGs and T-CHO were correspondingly assessed by an FFA assay kit, TG assay kit, and T-CHO assay kit (Nanjing Jiancheng Bioengineerin), in accordance with the instructions.</p>", "<title>Western blotting</title>", "<p id=\"Par50\">The proteins of cells and tissues were extracted using RIPA buffer containing phosphatase and protease inhibitors. Subsequently, 30 µg of protein was put into a Bis–Tris gel to accomplish protein electrophoresis. Then, we transferred the proteins to polyvinylidene fluoride (PVDF) membranes and blocked the membranes in 5% skim milk. After that, we took the membrane together with primary antibodies overnight, incubated it with the second antibody for 1 h, and exposed the membrane. The antibodies included anti-FASN (1:1,000, 66,591-1-Ig, ProteinTech) and anti-β-actin (1:10,000, 20,536-1-AP, Proteintech).</p>", "<title>Cell proliferation assays</title>", "<p id=\"Par51\">Gemcitabine-resistant cell lines (T-24 and UMUC3 cells) treated with TVB-3166 (1 μmol) or transfected with shRNA were treated with gemcitabine (5 μg/ml). Cell viability was determined by using Cell Counting Kit-8 (CCK-8) in accordance with the manufacturer's instructions [##UREF##8##54##].</p>", "<title>Drug sensitivity test</title>", "<p id=\"Par52\">After transfection with shRNA for 48 h or treatment with TVB-3166 (1 μmol), gemcitabine-resistant cell lines (T-24 and UMUC3 cells) were treated with gemcitabine for 24 h at six concentrations (1 μg, 2 μg, 4 μg, 8 μg, 16 μg and 32 μg per ml). Their viabilities were detected by CCK-8 according to the guidelines provided by the manufacturer.</p>", "<title>Colony formation assay</title>", "<p id=\"Par53\">Different reagents were added as needed: gemcitabine (5 μg/ml) and TVB-3166 (1 μmol). A total of 1000 cells were cultured per well of the 6-well plate for 1 week, followed by colony analysis.</p>" ]

[ "<title>Results</title>", "<title>Identification of gemcitabine resistance and metabolism-related differentially expressed genes in BLCA</title>", "<p id=\"Par7\">With the purpose of studying the molecular biological changes in BLCA cells after gemcitabine resistance, we obtained drug-resistant differential genes by RNA sequencing of the established gemcitabine-resistant BLCA cell line (Fig. ##FIG##0##1##A). Subsequently, we performed GO (Additional file ##SUPPL##0##1##: Figure S1A) and KEGG (Fig. ##FIG##0##1##B) enrichment analyses. The KEGG results revealed that these gene were related to lipids, fatty acid metabolism, cholesterol metabolism and amino acid metabolism. The top ten GO terms were enriched in cholesterol synthesis and metabolism, the response to the lipid, extracellular matrix, and the response to the chemical, etc.</p>", "<p id=\"Par8\">With the purpose of studying the metabolic changes of in BLCA cells after gemcitabine resistance, we further screened 597 resistance- and metabolism-related differentially expressed Genes (RM-DEGs, Fig. ##FIG##0##1##C). Patients with BLCA were divided into two subgroups on the basis of RM-DEG expression in the TCGA BLCA database by consensus clustering (Additional file ##SUPPL##0##1##: Figure S1B–E). The two subgroups included 177 and 189 patients (Fig. ##FIG##0##1##D, E), respectively. Through KM analysis, we discovered noteworthy differences in OS between the two subgroups. (Fig. ##FIG##0##1##F). Then, we carried out further functional analysis of RM-DEGs. The KEGG analysis revealed that the RM-DEGs were significantly linked with fatty acid biosynthesis, steroid biosynthesis, the PPAR signaling pathway, ferroptosis and other metabolic pathways (Fig. ##FIG##0##1##G). The top ten GO enrichment pathways mainly included the following aspects (Fig. ##FIG##0##1##H): biological process included fatty acid, steroid and purine nucleotide metabolism procedure; cellular components included extracellular matrix, endoplasmic reticulum and lipid droplet; molecular function included glycosyltransferase activity, oxidoreductase activity and extracellular matrix binding activity. In short, the above consequences revealed that metabolic reprogramming of tumors play a significant role in drug resistance progression and on the overall survival of BLCA Patients.</p>", "<title>Establishment of the RM-RM to predict the OS of BLCA patients</title>", "<p id=\"Par9\">First, by analyzing the relationship between a single gene and the OS of BLCA, we selected 134 OS-related RM-DEGs (P &lt; 0.05, Additional file ##SUPPL##1##2##: Form S1). As shown in Additional file ##SUPPL##0##1##: Figure S2A, most of the OS-related RM-DEGs were closely correlated, indicating that the progression of BLCA resistance is a whole metabolic rearrangement. Then, we performed LASSO Cox regression analysis on OS-related RM-DEGs to further narrow the range of the primary genes that predict prognostic risk. As shown in Fig. ##FIG##1##2##A, B<bold>, </bold>28 genes were obtained by removing any overfitting data to avoid the minimum likelihood of bias. Finally, through the multivariable Cox regression analysis, we obtained 7 independent prognostic genes. As shown in Fig. ##FIG##1##2##C<bold>,</bold> the hazard ratio and 95% confidence interval of the four genes were greater than 1, and the remaining three genes were less than 1. This suggested that GPC2, CNOT6L, GALNT12 and CARD10 were independent protective factors and that FASN, MAP2 and BMP6 were independent risk factors. Through the gene index obtained from multivariable Cox regression analysis, we constructed RM-RM and drug Resistance and Metabolism-Related risk Score (RM-RS) = (− 0.16) *GPC2 gene expression + (− 0.65) * CNOT6L gene expression + 0.42 * FASN gene expression + 0.18 * MAP2 gene expression + (− 0.15) * GALNT12 gene expression + 0.18 * BMP6 gene expression + (− 0.13) * CARD10 gene expression. After calculating the risk score, we divided 366 BLCA patients into a high-hazard cluster and a low-hazard cluster in accordance with the median of the RM-RS (Fig. ##FIG##1##2##D). As shown in Fig. ##FIG##1##2##E, the OS of the high-hazard cluster was apparently shorter than that of the low-hazard cluster. Compared with patients subjected to low RM-RS, patients with high RM-RS usually have a poor prognosis (Fig. ##FIG##1##2##F). The results showed that the area under the curves (AUCs) were 0.74,0.75, and 0.76 in the first, third, and fifth years, respectively. (Fig. ##FIG##1##2##G). The ROC curve suggested that RM-RM had good sensitivity and specificity and was better than other clinical parameters (Fig. ##FIG##1##2##H). These clinical parameters included sex, age, T stage, N stage, M stage and clinical stage. Finally, we carried out univariate regression and multivariate regression analyses. The results (Fig. 2I) showed that RM-RM was closely related to OS and was potentially the most meaningful independent predictor for BLCA. As shown in Fig. ##FIG##1##2##J and Additional file ##SUPPL##0##1##: Figure S2B, we found that the distribution of RM-RS was routinely consistent with the distribution of other clinical findings. We also found that as RM-RS increased, the expression of FASN, MAP2 and BMP6 increased, and that of GPC2, CNOT6L, GALNT12 and CARD10 decreased.</p>", "<title>Justification of the prognostic value of the RM-RM in two BLCA databases and real-world study</title>", "<p id=\"Par10\">In order to verify the prognostic value of the RM-RM, we checked two databases including OS data of BLCA patients: GSE69795 and GSE31684. According to the calculation formula of RM-RS obtained above, we also calculated the RM-RS of each patient, and divided the patients into high-hazard clusters and low-hazard clusters in accordance with the RM-RS (Additional file ##SUPPL##0##1##: Figure S3A). Similar to the TCGA dataset, we obtained the connection between the RM-RS and the survival rate. The results demonstrated a noteworthy difference between the two clusters (Fig. ##FIG##2##3##A), and the higher the result of the RM-RS, the worse the prognosis of the patients (Additional file ##SUPPL##0##1##: Figure S3B). As shown in Fig. ##FIG##2##3##B, RM-RM has excellent diagnostic value in both short- and long-term survival rates. For the two independent validation sets, RM-RM was also superior to the other only clinical features in terms of diagnostic sensitivity and specificity (Fig. ##FIG##2##3##D). Next, we carried out univariate regression and multivariate regression analyses in accordance with the two databases. Although the clinical data of the two validation sets are not as comprehensive as the TCGA database, RM-RM was still the best independent predictor of OS for only the existing clinical data within the two independent BLCA cohorts. In addition, as shown in Additional file ##SUPPL##0##1##: Figure S3C, we obtained coherent results compared to the TCGA database in two validation sets by analyzing the correlation among RM-RS, clinical characteristics and independent prognostic genes expression.</p>", "<p id=\"Par11\">Finally, we further verified the prognostic value of the RM-RM by using samples of collected tissues in a real-world Study. As shown in Fig. ##FIG##2##3##E and Additional file ##SUPPL##0##1##: Figure S3D, immunohistochemistry (IHC) was finished to detect the expression of genes in RM-RM on the basis of protein expression (pRM-RS), and pRM-RS was obtained according to the immune response score of genes in RM-RM. The results were consistent with the TCGA database. FASN, MAP2 and BMP6 were highly expressed in bladder cancer, while GPC2, CNOT6L, GALNT12 and CARD10 were expressed at low levels in bladder cancer (Fig. ##FIG##2##3##F). According to pRM-RS, BLCA patients with survival data were divided into two subgroups. The KM survival analysis also directly revealed that the OS of the high-hazard cluster was notably shorter than that of the low-hazard cluster (Fig. ##FIG##2##3##G). shows that pRM-RS was closely related to grade, T stage, M stage and clinical stage.</p>", "<p id=\"Par12\">In summary, we concluded that RM-RM had a high diagnostic value of prognosis for BLCA.</p>", "<title>The molecular function and mechanism of RM-RM in BLCA</title>", "<p id=\"Par13\">To analyze the molecular function of RM-RM, we completed GSEA and found that the risk model was strongly linked with the incidence, recurrence, distant metastasis, tumor proliferation and angiogenesis of BLCA <bold>(</bold>Fig. ##FIG##3##4##A<bold>)</bold>. With the purpose of further analyzing the mechanism of the model, we performed gene expression analysis on two risk subgroups and obtained 878 significantly differentially expressed genes (DEGs), of which 687 genes were overexpressed in the high-hazard subgroup and 191 genes were overexpressed in the low-hazard subgroup <bold>(</bold>Fig. ##FIG##3##4##B<bold>)</bold>. Through KEGG analysis, we discovered that the DEGs were strongly connected with drug metabolism, regulation of lipolysis in adipocytes, galactose metabolism and the PPAR signaling pathway (Fig. ##FIG##3##4##C). As shown in Additional file ##SUPPL##0##1##: Figure S4A, the result of GO analysis demonstrated that these DEGs were strongly linked with the reaction to the fibroblast growth factor, intermediate filament organization, cellular response to xenobiotic stimulus and intermediate filament cytoskeleton organization, suggesting that the two subgroups in RM-RM had different microenvironments and tumor stroma.</p>", "<p id=\"Par14\">Energy metabolism is an important support for tumor function. Through ssGSEA, we obtained the metabolic score of each BLCA patient in the TCGA database <bold>(</bold>Fig. ##FIG##3##4##D<bold>)</bold>. As can be seen from the Fig. ##FIG##3##4##E and Additional file ##SUPPL##0##1##: Figure S4B, the high-hazard subclass was extensively higher than the low-hazard subclass in terms of fatty acid synthesis, monocarboxylic acid transport and ATPase (Resp. complex V). In the GSEA of representative metabolic pathways, we obtained the same results <bold>(</bold>Fig. ##FIG##3##4##F<bold>)</bold>. The results suggested that the high-hazard subgroup was mainly involved in fatty acid synthesis, while the low-hazard subgroup correlated with phosphoinositide metabolism. The two subgroups also showed different amino acid metabolism.</p>", "<title>RM-RM is correlated with the mutation and tumor microenvironment characteristics of BLCA</title>", "<p id=\"Par15\">Gene mutations can lead to the development of mutant cells which may have some selective advantages over adjacent cells. To explore the connection between gene mutations and drug resistance in BLCA, we analyzed gene mutations in the RM-RM subgroups, As can be seen from the Fig. ##FIG##4##5##A. By comparing the top twenty genes with the highest mutation rates, we suggested noteworthy variances in the gene mutation levels between the two groups. Missense variation was the most frequent category, and the results demonstrated no obvious differences compared to the transition and transversion of mutant genes between the two subgroups. Among the six transition and transversion events of the subgroups, the proportion of groups c and t demonstrated the highest transitions. By comparing the mutation probability of the two subgroups, we obtained the top ten most distinct differences of mutant genes <bold>(</bold>Fig. ##FIG##4##5##B<bold>)</bold>. These gene mutations may be an important factor leading to the progression of drug resistance in BLCA.</p>", "<p id=\"Par16\">The tumor microenvironment (TME) mainly includes tumor cells, tumor extracellular matrix, immune cells, cancer-associated fibroblasts (CAFs), cancer-associated adipocytes and tumor-derived endothelial cells (TECs). The TME could be subdivided into an immunological microenvironment led by immune cells and a nonimmunological microenvironment led by cancer-associated fibroblasts. As shown in Fig. ##FIG##4##5##C, the stroma score of the high RM-RS cluster was higher than that of the low RM-RS cluster, suggesting that the nonimmunological microenvironment led by fibroblasts in the high-hazard cluster was more vigorous than the nonimmunological microenvironment led by fibroblasts in the low-hazard cluster. The detailed TME regulatory pathway of GSEA enriched by RM-RM mainly included positive regulation of fibroblast proliferation, responses to the drug, carcinoma-associated fibroblasts and angiogenesis, as shown in Additional file ##SUPPL##0##1##: Figure S5A. Then, we used three classical algorithms: xCell, MCP-counter and EPIC, to calculate the ratio of TME cells in BLCA patients from the TCGA database (Fig. ##FIG##4##5##D and Additional file ##SUPPL##0##1##: Figure S5B). We found that CAFs, endothelial cells, adipocytes and CD4⁺ T cells were more abundant in the high RM-RS subgroup. As shown in Fig. ##FIG##4##5##E, F and Additional file ##SUPPL##0##1##: Figure S5C, the RM-RS subgroup was strongly connected with the marker gene expression of TECs and CAFs. The higher the risk genes expression (FASN and BMP6) <bold>(</bold>Fig. ##FIG##4##5##G<bold>)</bold>, the higher the expression of an endothelial cell marker gene (CD34) and a fibroblast cell marker gene (CXCL12).</p>", "<title>Sensitivity of Drugs in the two RM-RS Subgroups</title>", "<p id=\"Par17\">After viewing the previous KEGG analysis <bold>(</bold>Fig. ##FIG##3##4##C<bold>)</bold> which demonstrated that RM-RS is involved in drug metabolism, we further studied the different sensitivities of BLCA individuals to drugs in different RM-RS subgroups. First and foremost, we comprehensively analyzed the pathways of drug metabolism relevant to BLCA resistance through GSEA in the two RM-RS groups (Fig. ##FIG##5##6##A). These results indicated that the high RM-RS cluster was correlated with drug response, aging, hypoxia, and doxorubicin resistance pathways, while the low RM-RS group correlated with endocrine therapy resistance, DNA repair, and decreased resistance to gefitinib. As shown in Fig. ##FIG##5##6##B, the MSI score of the high RM-RS cluster was meaningfully lower than that of the low RM-RS cluster, and the exclusion score was meaningfully higher than that of the low RM-RS cluster. The results suggested that the immune escape potential of the high RM-RS group was enhanced, and the effect of immunotherapy drugs is poor. From the Additional file ##SUPPL##0##1##: Figure S5D it is shown that there was no meaningful difference in dysfunction scores between the two subgroups. To provide treatment guidance for different BLCA clusters, we compared the sensitivity of two RM-RS clusters to various anticancer drugs. For chemotherapeutic drugs commonly used in BLCA, the drug sensitivity of high RM-RS individuals was significantly lower than that of low RM-RS individuals such as gemcitabine, carboplatin, docetaxel and epirubicin <bold>(</bold>Fig. ##FIG##5##6##C<bold>)</bold>. Then, we recommended sensitive drugs in different subgroups. The high-risk <bold>(</bold>Fig. ##FIG##5##6##D<bold>)</bold> subgroup was sensitive to BRD2/3/4 inhibitors (e.g., OTX015_1626), tankyrase inhibitors (e.g., WIKI4_1940), B-RafV600E inhibitors (e.g., PLX-4720_1036), and HMG-CoA reductase inhibitors (e.g., lovastatin), while the low-risk <bold>(</bold>Fig. ##FIG##5##6##E<bold>)</bold> subgroup was sensitive to PARP inhibitors (e.g., Olaparib_1017), tyrosine kinase inhibitors (e.g., Gefitinib_1010), vincristine, and maleimide analogs (e.g., MIRA-1_1931).</p>", "<title>Upregulation of FASN promotes drug resistance and poor prognosis in BLCA</title>", "<p id=\"Par18\">Through the analysis of the molecular function and mechanism of RM-RM in bladder cancer, we found that BLCA resistance is closely related to lipid metabolism. In order to further uncover the connection between genes in RM-RM and gemcitabine resistance in BLCA, we established another bladder cancer gemcitabine-resistant cell line (UMUC3). (Additional file ##SUPPL##0##1##: Figure S6A). The BODIPY assay <bold>(</bold>Fig. ##FIG##6##7##A<bold>)</bold> was conducted, and the results validated our prediction that lipid metabolism in drug-resistant cells is more active. We detected the content of free fatty acids (FFAs), triglycerides (TGs), and total cholesterol (T-CHO) in gemcitabine-resistant cells and normal BLCA cells <bold>(</bold>Fig. ##FIG##6##7##B<bold>)</bold>, and the results showed lipid accumulation in T24 gemcitabine-resistant (T24-R) cells and UMUC3 gemcitabine-resistant (UMUC3-R) cells. Through the establishment of the RM-RM, we found that FASN has the highest risk ratio <bold>(</bold>Fig. ##FIG##1##2##C<bold>)</bold>. As shown in Fig. ##FIG##6##7##C, we demonstrated that FASN is overexpressed in drug-resistant BLCA cells. In order to prove the function of FASN in the development of gemcitabine resistance, we first established T24 and UMUC3 BLCA gemcitabine-resistant cells with stable low expression of FASN <bold>(</bold>Fig. ##FIG##6##7##D<bold>)</bold>.</p>", "<p id=\"Par19\">Subsequently, we used gemcitabine to treat T24-R and UMUC3-R cells with FASN knockdown. The results showed that T24-R and UMUC3-R cells were refractory to gemcitabine, and T24-R and UMUC3-R cells with FASN knockdown had restored sensitivity to gemcitabine, indicating that FASN promotes gemcitabine resistance in BLCA <bold>(</bold>Fig. ##FIG##6##7##E<bold>)</bold>. It also indicated that the overexpression of FASN promotes the proliferation of BLCA cells. The sensitivity of FASN knockdown on T24-R and UMUC3-R cells to gemcitabine was consistent with the above results <bold>(</bold>Fig. ##FIG##6##7##F<bold>)</bold>. In the Colony formation assay shown in Fig. ##FIG##6##7##G, after FASN expression was knocked down, the tumorigenic ability of single cells of drug-resistant cells was inhibited, and the inhibitory effect was more obvious under gemcitabine treatment, while the control group was not sensitive to gemcitabine (Additional file ##SUPPL##0##1##: Figure S6B). Through the BODIPY assay <bold>(</bold>Fig. ##FIG##6##7##H<bold>)</bold> and the determination of lipid content (Fig. 7I), we further verified the relationship between the expression of FASN and cellular lipid metabolism. The results showed that the intracellular lipid content decreased after FASN knockdown. Therefore, we can conclude that FASN further promotes drug resistance progression in BLCA cells by affecting lipid accumulation in BLCA cells. In order to further verify our prediction, we conducted in vivo tumor formation experiments in mice <bold>(</bold>Fig. ##FIG##6##7##J<bold>)</bold>. As shown in Fig. ##FIG##6##7##K, L, after FASN knockdown, the tumor growth rate was significantly repressed and the resistance of the tumor to gemcitabine was reversed. Altogether, the above results revealed that knockdown of FASN inhibit tumorigenesis of gemcitabine-resistant BLCA cells in vitro and in vivo.</p>", "<title>TVB-3166 inhibited BLCA progression and reversed gemcitabine resistance</title>", "<p id=\"Par20\">TVB-3166 is an orally active, reversible and selective inhibitor of FASN. As shown in Additional file ##SUPPL##0##1##: Figure S6C, under the action of TVB-3166, the FASN content of T24-R and UMUC3-R cells was significantly reduced. The results of plate cloning experiments and CCK-8 assays indicated that TVB3166 could almost completely eliminate the influence of FASN on the proliferation and gemcitabine resistance of T24-R and UMUC3-R cells (Fig. ##FIG##7##8##A, B, C). also shows that TVB-3166 reversed gemcitabine resistance in BLCA. Second to the lipid changes presented after TVB-3166 treatment, as shown in the BODIPY assay <bold>(</bold>Fig. ##FIG##7##8##D<bold>)</bold> and the determination of lipid content <bold>(</bold>Fig. ##FIG##7##8##E<bold>)</bold>, the treatment group showed lower lipid aggregation than the contrast group. Next, in vivo experiments, we obtained consistent results <bold>(</bold>Fig. ##FIG##7##8##F–H<bold>)</bold>. These results demonstrated that compared with the control group, the volume, proliferation rate and mass of subcutaneous tumors treated with TVB-3166 decreased, while the volume, proliferation rate and mass of subcutaneous tumors treated with gemcitabine were not meaningfully different from those of the contrast group. The volume, proliferation rate and mass of subcutaneous tumors in mice treated with gemcitabine after TVB-3166 treatment significantly decreased, indicating that TVB-3166 improved the sensitivity to gemcitabine in gemcitabine-resistant BLCA cells. The ELISA results (Fig. 8I) showed that TVB-3166 reduced the FASN gene of the tumor, consistent with the in vitro results. BODIPY staining and IHC assay detection were carried out on subcutaneous tumors <bold>(</bold>Fig. ##FIG##7##8##J<bold>)</bold>. TVB-3166 treatment can reduce lipid accumulation, inhibit cell proliferation and increase the apoptosis rate. Thus, these results proved that by contrast with the control group, the proliferation and apoptosis rate of mice xenograft tumor cells treated with gemcitabine alone did not change significantly, while the proliferation rate of mice xenograft tumor cells treated with gemcitabine after TVB-3166 was inhibited and the apoptosis rate increased, indicating that TVB-3166 reversed gemcitabine resistance.</p>" ]

[ "<title>Discussion</title>", "<p id=\"Par21\">Gemcitabine is the most common drug in cancer chemotherapy, including BLCA. The occurrence of gemcitabine resistance remains the most important challenge in the treatment of tumor patients [##REF##32727463##15##]. Drug-resistant cancers, under pharmacological pressure, exhibit complex molecular mechanisms aimed to inhibit treatment [##REF##36517820##16##]. Gu J et al. found a novel therapeutic target to overcome gemcitabine resistance in pancreatic cancer [##REF##35538494##17##]. Studies have found that cisplatin resistance in BLCA is related to epigenetic mechanisms such as DNA methylation, noncoding RNA regulation, m6A modification and posttranslational modification. Cocetta V et al. described the relationship between cisplatin resistance and cancer metabolism in detail [##REF##32475471##18##]. However, there are a lack of systematic studies on gemcitabine resistance in BLCA cells. In our study, we identified and analyzed RM-DEGs based on RNA sequencing of gemcitabine-resistant BLCA cells and metabolic-related genes (MRGs). We also constructed and validated an RM-RM for predicting the OS of BLCA patients using several BLCA databases.</p>", "<p id=\"Par22\">Tumor cell metabolism is a representative pattern of variable, alloplastic, and adaptive phenotypic characteristics. It is the result of a combination of internal and external factors that enable cancer cells to outlive, pervade the body, and obtain resistance to antineoplastic drugs [##UREF##1##19##]. Bacci M et al. described the function of abnormal lipid metabolism in affecting the antitumor treatment response and maintaining drug resistance [##REF##33281098##20##]. Considering the essential role of tumor metabolism in chemotherapy resistance, we collected all MRGs on the basis of the MSigDB, established a gemcitabine-resistant cell line of BLCA cells, scientifically and thoroughly considered the metabolic model of BLCA resistance, and designed an RM-RM on the basis of OS to support precise prognosis information and guidance of treatment for BLCA patients.</p>", "<p id=\"Par23\">In our research, we initially recognized and considered RM-DEGs in the TCGA BLCA dataset. The RM-DEGs were mostly associated with fatty acid and amino acid metabolism. Notably, these RM-DEGs were also enriched in extracellular matrix organization and drug metabolic processes. According to these RM-DEGs, we subdivided BLCA sufferers into two clusters with noteworthy variances in OS. These findings indicated the heterogeneity of BLCA metabolism, and that BLCA patients with diverse modes of metabolism have different prognoses.</p>", "<p id=\"Par24\">Then, by means of univariate, LASSO and multivariate Cox regression analyses, we selected seven central RM-DEGs related to survival. Based on these seven genes, the TCGA dataset was viewed as the training data to create an RM-RM for predicting the survival of BLCA sufferers. The consequences showed that RM-RS was closely related to T, N, M and clinical stage, revealing that the deterioration of BLCA was associated with the reprogramming of tumor metabolism. Afterward, we used a variety of analytical methods to further prove that RM-RM was a reliable detached predictor and demonstrated the highest accuracy compared with other clinical indicators. Subsequently, two GEO datasets were used to further verify that RM-RM could be a promising clinical predictor for BLCA treatment. To discover the prospect of RM-RM for clinical conversion, we used immunohistochemistry to detect the protein expression levels of clinical specimens. The study further found that RM-RS in these patients was intimately linked with the prognosis and clinical characteristics.</p>", "<p id=\"Par25\">The metabolism of BLCA patients represents a key issue for cancer research. Cao D et al. found that some genes, through inhibiting glucose metabolism, repressed tumor proliferation and improved cisplatin-induced apoptosis of BLCA cells [##UREF##2##21##]. We divided BLCA patients into two subclasses of different RM-RSs subgroups and performed GSEA and ssGSEA analysis. It was found that gemcitabine resistance in BLCA cells was closely related to lipid metabolism. Patients in the high RM-RS group showed more active lipid synthesis than those in the low RM-RS group.</p>", "<p id=\"Par26\">Through gene mutation analysis, we uncovered considerable differences between the two different RM-RS subgroups. Previous studies have shown that ARID1A gene alterations may mediate resistance to platinum-based chemotherapy and estrogen receptor degradation/modulators [##REF##34619527##22##]. Our study also found the top ten genes with the most obvious differential mutations, including ARID1A. The specific mechanisms of these genes are subjected to further research. In addition, current researches have proven that the TME plays a vital role in the procedure of tumor drug resistance [##REF##30925923##23##] and have also proven the cross-link interference between metabolic reprogramming of cancer cells and the changes in the TME [##REF##28605656##24##, ##REF##34272515##25##]. Saw PE et al. proposed targeting cancer-associated fibroblasts (CAFs) to overcome anticancer drug resistance [##REF##35331673##26##]. Particularly, in our study, we discovered that endothelial cells and fibroblasts obviously infiltrated in the TME of the high RM-RS subgroup. This may provide a new therapeutic target for patients with chemotherapy resistance of BLCA.</p>", "<p id=\"Par27\">After that, we also found that the high RM-RS group was insensitive to a variety of classic chemotherapy regimens but was sensitive to other drugs, such as antiangiogenic drugs (B-RafV600E inhibitor: PLX-4720_1036) and lipid-lowering drugs (lovastatin). By predicting the different sensitivities of the two groups to anticarcinogen, we could precisely provide compatible drugs for patients with different metabolic sensitivities, suggesting the potential application of RM-RM in clinical guidance in the future.</p>", "<p id=\"Par28\">The key genes in the RM-RM include FASN (Fatty Acid Synthase), MAP2 (Microtubule Associated Protein 2), BMP6 (Bone Morphogenetic Protein 6), GPC2 (Glypican 2), CNOT6L (CCR4-NOT Transcription Complex Subunit 6 Like), GALNT12 (Polypeptide N-Acetylgalactosaminyltransferase 12) and CARD10 (Caspase Recruitment Domain Family Member 10). We found that FASN, MAP2, and BMP6 were upregulated in BLCA tissues, while GPC2, CNOT6L, GALNT12 and CARD10 were downregulated. FASN is an essential enzyme in fatty acid synthesis [##REF##17882277##27##]. It not only plays a vital role in lipometabolism, but also is related to tumor proliferation. In addition, FASN can adjust the immune microenvironment and take part in epithelial-mesenchymal transition, thereby regulating tumor progression [##REF##33973101##28##]. Li Y et al. found that FASN was associated with sorafenib resistance in patients with liver cancer [##UREF##3##29##]. MAP2 belongs to the microtubule-associated protein of the MAP2/Tau family, which is related to the collection of signal proteins and the modulation of microtubule-mediated transport [##REF##15642108##30##]. Pulkkinen HH et al. found that BMP protein regulates angiogenesis and endothelial cell proliferation [##REF##33021694##31##]. GPC2 protein is a promising therapeutic target for pantumor [##REF##35345673##32##]. Katsumura S et al. found that CNOT6L protein can coordinate energy intake and consumption when stimulated [##REF##35385705##33##]. Guda K et al. identified the mutation of GALNT12 protein in colon cancer patients and explored its function in the occurrence and progression of colon cancer [##REF##19617566##34##]. CARD10 protein mediates the occurrence and progression of various kinds of cancers [##REF##36618379##35##]. Zhu L et al. revealed that CARD10 protein also plays a crucial role in the formation of a growth factor signaling axis that mediates immunosuppression and tumorigenesis by TBKBP1 and TBK1 [##REF##31792381##36##].</p>", "<p id=\"Par29\">FASN, as a representative gene, was further verified as a promoting factor for gemcitabine resistance in vitro and in vivo. Previous researches have proven that the effect of a FASN inhibitor (TVB-3166) on carcinogenic signals and gene expression enhances the antitumor efficacy of various xenograft tumor models [##REF##28159572##37##]. Our study further demonstrated that TVB-3166 can reverse gemcitabine resistance.</p>", "<p id=\"Par30\">In summary, this study constructed an RM-RM with high diagnostic accuracy for predicting OS and treatment response in patients with bladder cancer. We hope that the constructed RM-RM can provide guidance in the treatment of BLCA patients.</p>" ]

[]

[ "<p id=\"Par1\">Bladder cancer (BLCA) is the most frequent malignant tumor of the genitourinary system. Postoperative chemotherapy drug perfusion and chemotherapy are important means for the treatment of BLCA. However, once drug resistance occurs, BLCA develops rapidly after recurrence. BLCA cells rely on unique metabolic rewriting to maintain their growth and proliferation. However, the relationship between the metabolic pattern changes and drug resistance in BLCA is unclear. At present, this problem lacks systematic research. In our research, we identified and analyzed resistance- and metabolism-related differentially expressed genes (RM-DEGs) based on RNA sequencing of a gemcitabine-resistant BLCA cell line and metabolic-related genes (MRGs). Then, we established a drug resistance- and metabolism-related model (RM-RM) through regression analysis to predict the overall survival of BLCA. We also confirmed that RM-RM had a significant correlation with tumor metabolism, gene mutations, tumor microenvironment, and adverse drug reactions. Patients with a high drug resistance- and metabolism-related risk score (RM-RS) showed more active lipid synthesis than those with a low RM-RS. Further in vitro and in vivo studies were implemented using Fatty Acid Synthase (FASN), a representative gene, which promotes gemcitabine resistance, and its inhibitor (TVB-3166) that can reverse this resistance effect.</p>", "<title>Supplementary Information</title>", "<p>The online version contains supplementary material available at 10.1186/s12967-024-04867-8.</p>", "<title>Statement of Significance</title>", "<p id=\"Par2\">The RM-RM aid to accurately predict survival rates and are used to help guide BLCA patients to choose the appropriate treatment option, and by inhibiting the fatty acid synthesis pathway involved in FASN a potential therapeutic strategy for BLCA is presented</p>", "<title>Supplementary Information</title>", "<p>The online version contains supplementary material available at 10.1186/s12967-024-04867-8.</p>" ]

[ "<title>Supplementary Information</title>", "<p>\n</p>" ]

[ "<title>Author contributions</title>", "<p>CHG, FYT, and LJZ conceived the study. LJZ, KXD, and YHD designed experiments. LJZ, KXD, YHD, and YMZ performed experiments. LJZ, K.X.D., YMZ, and YBL assisted with animal experiments. MDR, YHL, and W.B.P. helped to obtain BLCA patients’ clinical information. LJZ, KXD, LLZ, RHZ, and DPF analyzed the data LJZ and KXD wrote the manuscript and all authors reviewed and approved the manuscript for publication.</p>", "<title>Funding</title>", "<p>This work was supported by grants from the National Natural Sciences Foundation of China (NO. 82203099 to L. J. Z, NO. 82173294 to C.H.G.), the Training Program for Middle-aged and Young Discipline Leaders of Health of Henan Province (NO. HNSWJW-2021004 to C.H.G.); the Key Program Jointly Built by Henan Province and the Ministry of Medical Science and Technology(NO.SBGJ202102127 to C.H.G. and SBGJ202102095 to F.Y.T.); the Training Program of Young and Middle-aged Health Science and Technology Innovation Excellent Youth (NO.YXKC2021033 to C.H.G.); the Program of International Training of High-level Talents of Henan Province (NO.202207 to C.H.G.); the Science and Technology Research and Development Plan Joint Foundation of Henan Province (NO. 222301420017 to C.H.G.); the Key Project of Research and Practice of Education and Teaching Reform of Zhengzhou University (NO. 2022ZZUJG082 to C.H.G.); the Professional Degree Graduate Quality Teaching Case Project of Henan Province (NO. YJS2023AL013 to C.H.G.); the Funding for Scientific Research and Innovation Team of The First Affiliated Hospital of Zhengzhou University (NO. QNCXTD2023023 to C.H.G.); the Key Technologies R &amp; D Program of Henan Province (NO. 232102521032 to C.H.G.); the Basic Research Incubation Program for Young Teachers of Zhengzhou University (NO. JC21854035 to F.Y.T.); the Joint Construction Project between Medical Science and Technology Research Project of Henan Province (No. LHGJ20220335 to L.J.Z.).</p>", "<title>Availability of data and materials</title>", "<p>All data are available in a public, open access repository. R and other custom scripts for analyzing data are available upon reasonable request.</p>", "<title>Declarations</title>", "<title>Ethics approval and consent to participate</title>", "<p id=\"Par54\">Not applicable.</p>", "<title>Animal experiments</title>", "<p id=\"Par55\">The subcutaneous xenograft model was agreed by the Ethics Committee of Experimental Animal Center of Zhengzhou University. Male BALB/c nude mice (approximately 4 weeks old), purchased from Beijing Weitong Lihua Experimental Animal Technology, were divided into 8 groups with 5 mice per subgroup. Four groups be used for studying the effect of FASN knockdown on the reversal of gemcitabine resistance induced in vivo: Group I (DMSO: DMSO), group II (DMSO: gemcitabine), group III (FASN knockdown: DMSO) and group IV (FASN knockdown: gemcitabine). Four groups be used for studying the effect of TVB-3166 on the reversal of gemcitabine resistance in vivo: Group I (DMSO: DMSO), group II (DMSO: gemcitabine), group III (DMSO: TVB-3166) and group IV (gemcitabine: TVB-3166). In this experiment, 2*10⁶ BLCA cells were injected subcutaneously into each mouse. After that, the volume of the tumor was recorded every 5 days. Drug therapy was initiated when the average tumor size was measured at 100–200 mm³.After 50 days, we removed the subcutaneous tumor from the mice. After measurement and recording, we partially stored these tumors at in a − 80 °C freezer and partially embedded them in paraffin. Mice treated with gemcitabine were intraperitoneally injected with gemcitabine 50 mg/kg every 2 days. Mice treated with TVB-3166 were intragastrically administered 60 mg/kg TVB-3166 daily for approximately 5 weeks.</p>", "<title>Consent for publication</title>", "<p id=\"Par56\">All authors agree to publish.</p>", "<title>Competing interests</title>", "<p id=\"Par57\">All the authors declared that they had no competing interests.</p>" ]

[ "<fig id=\"Fig1\"><label>Fig. 1</label><caption><p>Identification of Gemcitabine Resistance and Metabolism-Related Differentially Expressed Genes in BLCA. <bold>A</bold> By comparing T24 gemcitabine-resistant cells with nonresistant cells, a volcano map of differentially expressed genes (DEGs) was drawn. Blue represents downregulated genes, and red represents upregulated genes in drug-resistant cells. p &lt; 0.05, |FC|&gt; 2 <bold>B</bold> Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of DEGs. Adjusted p &lt; 0.01, p &lt; 0.05 <bold>C</bold> Venn diagram for the Resistance and Metabolism-related Differentially Expressed Genes (RM-DEGs) <bold>D</bold>, <bold>E</bold>, <bold>F</bold> Consensus clustering of TCGA BLCA cohorts based on the RM-DEGs. Consensus matrix for optimal k = 2. The optimal k = 2 for the principal component analysis (PCA) database. Kaplan‒Meier analysis was used to analyze the overall survival (OS) curve of patients in different groups. <bold>G</bold>, <bold>H</bold> RM-DEGs were concentrated and analyzed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Adjusted p &lt; 0.01 and p &lt; 0.05</p></caption></fig>", "<fig id=\"Fig2\"><label>Fig. 2</label><caption><p>Establishment of an RM-RM to Predict the OS of BLCA Patients. <bold>A</bold>, <bold>B</bold> Least absolute shrinkage and selection operator (LASSO) Cox regression of OS-related key drug resistance and metabolism-related differentially expressed genes (RM-DEGs). <bold>C</bold> Multivariate Cox regression analysis was performed on seven key genes obtained based on cross validation and the minimum partial likelihood deviance. <bold>D</bold> The drug resistance and metabolism-related risk score (RM-RS) distribution of the cancer genome atlas (TCGA) BLCA. The median was the dividing line, blue was the low RM-RS subgroup, and red was the high RM-RS subgroup. <bold>E</bold> The overall survival distribution of the two subgroups. Blue represents alive, while red represent death. <bold>F</bold> Kaplan‒Meier analysis of overall survival (OS) curves of TCGA BLCA patients in the two subgroups. <bold>G</bold> The receiver operating characteristic (ROC) curves of 1-, 3-, and 5 year OS of patients in TCGA BLCA database was predicted based on RM-RS. <bold>H</bold> Comparison of ROC curves between RM-RS and clinical features. <bold>I</bold> Univariate and multivariate Cox regression analyses of RM-RS and clinical features. <bold>J</bold> The heatmap of RM-RM 7 component gene expression in the TCGA BLCA database, including RM-RS and clinical features</p></caption></fig>", "<fig id=\"Fig3\"><label>Fig. 3</label><caption><p>Justification of the Prognostic Value of the RM-RM in Two BLCA Databases and Real-World Study. <bold>A</bold> Kaplan–Meier analysis for overall survival (OS) curves of patients in low or high drug resistance and metabolism-related risk score (RM-RS) subgroups from two independent validation cohorts (GSE69795, GSE31684). <bold>B</bold> The receiver operating characteristic (ROC) curves of 1-, 3-, and 5 year OS of patients in GSE69795 and of 3-, 5-, and 10 year OS of patients in GSE31684 were predicted based on RM-RS. <bold>(C)</bold> The ROC curve of RM-RS was compared with that of only other clinical features in GSE69795 and GSE31684. <bold>D</bold> Univariate and multivariate Cox regression analyses of RM-RS and only other clinical features in GSE69795 and GSE31684. <bold>E</bold>, <bold>F</bold> Immunohistochemical (IHC) staining was used to detect the protein expression of metabolism-related differentially expressed genes (RM-DEGs) (FASN, MAP2, BMP6, GPC2, CNOT6L, GALNT12 and CARD10) in 60 normal tissues and 170 tumor tissues. The immunohistochemical staining immune response score (IRS) score was statistically analyzed and the violin diagram shows a representative image. <bold>G</bold>, <bold>H</bold> pRM-RS was obtained by IRS and RM-RM. The median pRM-RS was divided into a high-risk group and a low-risk group, and KM analysis and difference analysis of other clinical features between the two subgroups were performed. *p &lt; 0.05; **p &lt; 0.01; ***p &lt; 0.001; ****p &lt; 0.0001</p></caption></fig>", "<fig id=\"Fig4\"><label>Fig. 4</label><caption><p>The Molecular Function and Mechanism of RM-RM in BLCA. <bold>A</bold> Gene set enrichment analysis (GSEA) of drug resistance and metabolism-related score (RM-RS) and BLCA occurrence, metastasis and progression signaling pathways. p &lt; 0.05 <bold>B</bold> Heatmap of differentially expressed genes (DEGs) by comparing the expression between the high and low RM-RS groups. p &lt; 0.05 and |FC|&gt; 2. Blue represents the low-risk subgroup and red represents the high-risk subgroup. <bold>C</bold> Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis of these DEGs. <bold>D</bold> Single-sample gene set enrichment analysis (ssGSEA) of metabolic pathway gene sets in the TCGA BLCA database. Heatmap of Metabolic pathway score of TCGA BLCA patients. <bold>E</bold> The violin plot shows the difference analysis of the metabolic scores of the high- and low-risk subgroups. <bold>F</bold> Metabolism-related gene sets enriched in the high- and low-risk subgroups (p &lt; 0.05)</p></caption></fig>", "<fig id=\"Fig5\"><label>Fig. 5</label><caption><p>RM-RM is correlated with the mutation and tumor microenvironment characteristics of BLCA. <bold>A</bold> The top 20 mutated genes in different risk subgroups of TCGA BLCA were sorted according to the mutation rate. The color coding represents the mutation type. The total number of mutations is shown above, the percentage of mutations is shown on the right, and the distribution of base mutation types is shown below. <bold>B</bold> The top 10 genes with significant differences in mutant genes between the high- and low-risk subgroups. <bold>C</bold> Comparison of the immune, stromal and microenvironment scores in different risk subgroups. <bold>D</bold> Tumor microenvironment (TME) cells with significant differences in different RM-RS subgroups based on the x Cell algorithm. <bold>E</bold> Correlation heatmap of RM-RS and endothelial cell marker gene expression. <bold>F</bold> The expression of CD34 and CXCL12 in different risk subgroups. <bold>G</bold> Multiple immunofluorescence analysis (MIF) of different risk subgroups. The staining of these genes was quantified by corrected total cell fluorescence (CTCF). *p &lt; 0.05; **p &lt; 0.01; ***p &lt; 0.001; ****p &lt; 0.0001</p></caption></fig>", "<fig id=\"Fig6\"><label>Fig. 6</label><caption><p>Sensitivity of Drugs in the two RM-RS Subgroups. <bold>A</bold> Drug metabolism-related gene sets enriched in the high- and low-risk subgroups (p &lt; 0.05, false discovery rate (FDR) &lt; 0.25). <bold>B</bold> Comparison of MSI score and exclusion score in different risk subgroups. <bold>C</bold> Sensitivity assessment of different risk subgroups for the current clinical preferred drug therapy. <bold>D</bold>, <bold>E</bold> Prediction of sensitive drugs recommended by different risk subgroups</p></caption></fig>", "<fig id=\"Fig7\"><label>Fig. 7</label><caption><p>Upregulation of FASN promotes drug resistance and poor prognosis in BLCA. <bold>A</bold> The lipid content of bladder cancer cells and gemcitabine-resistant cells was quantified by BODIPY staining corrected total cell fluorescence (CTCF). <bold>B</bold> The contents of free fatty acids (FFAs), triglycerides (TGs) and total cholesterol (T-CHO) were used as intracellular lipid indexes. <bold>C</bold> The expression of FASN in bladder cancer cells resistant to different concentrations of gemcitabine was detected by Western blotting (WB). Density measurement and statistical analysis. Representative images are shown. <bold>D</bold> The expression of FASN in gemcitabine-resistant bladder cancer cells after FASN knockdown was detected by WB. <bold>E</bold>, <bold>F</bold> Cell viability and sensitivity to gemcitabine under all conditions were determined by CCK-8 assay. <bold>G</bold> The tumorigenic ability of single cells under all conditions was determined by colony formation assay. <bold>H</bold>, <bold>I</bold> The lipid content of gemcitabine-resistant cells after FASN knockdown was quantitatively detected by BODIPY staining corrected total cell fluorescence (CTCF) and the contents of free fatty acids (FFAs), triglycerides (TGs) and total bilirubin (T-CHO). <bold>J</bold>, <bold>K</bold>, and<bold> L</bold> Mice with stable knockdown expression of T24-R xenografts were treated with vector control or gemcitabine (50 mg/kg. IP. QOD) for approximately 5 weeks. Tumor volumes were measured every 5 days (n = 5 per group). Tumors were weighed after resection. The graphs show the means ± SEMs. One-way ANOVA followed by Tukey’s multiple comparison test. α = 0.05; *, p &lt; 0.05; **, p &lt; 0.01; ***, p &lt; 0.001; ****p &lt; 0.0001; ns, no significance</p></caption></fig>", "<fig id=\"Fig8\"><label>Fig. 8</label><caption><p><bold>| </bold>TVB-3166 inhibited BLCA progression and reversed gemcitabine resistance. <bold>A</bold>, <bold>B</bold> Cell viability and sensitivity to gemcitabine under all conditions were determined by CCK-8 assay. <bold>C</bold> The tumorigenic ability of single cells under all conditions was determined by colony formation assay. <bold>D</bold>, <bold>E</bold> The lipid content of gemcitabine-resistant cells after treatment with TVB-3166 was quantitatively detected by BODIPY staining corrected total cell fluorescence (CTCF) and the contents of free fatty acids (FFAs), triglycerides (TGs) and total cholesterol (T-CHO). <bold>F</bold>, <bold>G</bold>, and <bold>H</bold> The mice were divided into 4 groups with 5 mice in each group: Group I (DMSO: DMSO), group II (DMSO: gemcitabine), group III (DMSO: TVB-3166) and group IV (gemcitabine: TVB-3166). Tumor volumes were measured every 5 days (n = 5 per group). Tumors were weighed after resection. The graphs show the means ± SEMs. One-way ANOVA followed by Tukey’s multiple comparison test. α = 0.05; *, p &lt; 0.05; **, p &lt; 0.01; ***, p &lt; 0.001; ****p &lt; 0.0001; ns, no significance. <bold>I</bold> Enzyme-linked immunosorbent assay (ELISA) was used to determine the FASN content of xenograft tumors in each group. <bold>J</bold> Statistical analysis was performed on the rate of KI67- and TUNEL-positive cells in each group of xenograft tumors by immunohistochemical staining (IHC). The corrected total cell fluorescence (CTCF) of BODIPY staining was used to quantitatively detect the lipid content of xenograft tumors in each group. Representative images are shown</p></caption></fig>" ]

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[ "<supplementary-material content-type=\"local-data\" id=\"MOESM1\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM2\"></supplementary-material>" ]

[ "<fn-group><fn><p><bold>Publisher's Note</bold></p><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn><fn><p>Lijie Zhou, Kaixuan Du and Yiheng Dai have contributed equally to this work.</p></fn></fn-group>" ]

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[ "<media xlink:href=\"12967_2024_4867_MOESM1_ESM.pdf\"><caption><p><bold>Additional file 1:</bold>Figure S1-6 and the corresponding legends.</p></caption></media>", "<media xlink:href=\"12967_2024_4867_MOESM2_ESM.xlsx\"><caption><p><bold>Additional file 2: </bold>The list of OS-related RM-DEGs.</p></caption></media>" ]

{ "acronym": [], "definition": [] }

[ "<title>Introduction</title>", "<p id=\"Par5\">Currently, 5.32 billion people in the world use a smartphone, and 4 out of 5 mobile devices are active and permanently connected to the Internet [##UREF##0##1##]. In addition, three-quarters of the planet’s inhabitants use social networks as communication channels and for social interaction [##UREF##1##2##–##UREF##2##4##].</p>", "<p id=\"Par6\">Numerous studies have demonstrated the usefulness of new technologies and social networks in information exchange, such as socialisation [##UREF##3##5##], mental health [##UREF##4##6##], improved self-esteem [##REF##35627511##7##], emotional benefit [##UREF##2##4##], self-expression and increased quality of life for individuals [##UREF##5##8##, ##UREF##6##9##]. New technologies and social networks have enabled the improvement of living standards and changes in people’s consumption concepts have greatly boosted the development of tourism [##UREF##7##10##, ##UREF##8##11##], which has further benefited the hospitality sector [##UREF##9##12##, ##REF##35964103##13##].</p>", "<p id=\"Par7\">An increasing number of studies are addressing the need to limit the use of IT and reduce digital hyperconnection [##REF##31430621##3##, ##REF##32780029##14##, ##UREF##10##15##].</p>", "<p id=\"Par8\">In recent years, these lines of research have focused on proposing therapies to combat the adverse health and wellbeing effects of this technological addiction [##REF##35964103##13##, ##REF##35123383##16##].</p>", "<p id=\"Par9\">The research on addiction to the Internet and social media stands out in the scientific literature [##UREF##8##11##, ##UREF##11##17##] analyse the mechanical attitude and behaviour of users that lack self-control and self-awareness [##UREF##12##18##].</p>", "<p id=\"Par10\">Other studies have revealed the negative impacts of IT on society, such as the influence of fake news [##UREF##6##9##], polarization of public opinion [##UREF##13##19##], data protection and privacy,especially in the health sector where patient data is highly sensitive and there are concerns that anonymisation of data is not sufficient to preserve patient privacy, cybercrime, addiction to being connected [##UREF##14##20##], the obsessive attraction of social media [##REF##31518525##21##], hyperconnection of cyber workers [##UREF##15##22##], control of big data and virtual monetary systems without financial regulation [##UREF##9##12##, ##UREF##16##23##] or the new domain of Artificial Intelligence (AI) [##UREF##17##24##] and use of virtual reality [##UREF##1##2##].</p>", "<p id=\"Par11\">The tourism sector has responded to the growing demand for digital disconnection trips and holidays by offering DFT experiences [##REF##35120510##25##].</p>", "<p id=\"Par12\">Digital Free Tourism (DFT) has become an attractive tourism market and an emerging business opportunity. Existing lines of research have studied the application of new technologies in business and the hospitality sector but have not considered the concept of digital disconnection and wellbeing in a holiday context [##UREF##10##15##, ##UREF##11##17##, ##UREF##18##26##].</p>", "<p id=\"Par13\">In the field of tourism, the motivations of tourists on a DFT trip have been examined the effects that a DFT experience can have on well-being [##REF##35627511##7##, ##UREF##19##27##–##UREF##21##29##] and also on health [##UREF##6##9##, ##REF##35123383##16##, ##UREF##22##30##].</p>", "<p id=\"Par14\">The results have identified DFT-derived attributes that provide important findings that can inform strategies in the tourism sector and its promotion as an emerging and future market [##UREF##8##11##, ##UREF##23##31##–##UREF##25##33##].</p>", "<p id=\"Par15\">This phenomenon emerged in 2013 in the United States and extended throughout the world in only a few years, becoming a global emerging market opportunity for the tourism sector, wellness and health and for economic sustainability [##UREF##2##4##, ##UREF##26##34##–##UREF##28##36##].</p>", "<p id=\"Par16\">DFT accommodation and travel agencies offer services for technological disconnection that limit access to information with alternative activities, exclusive stays free of electronic devices or therapies such as yoga, hiking, mindfulness and pilates, which offer to improve the well-being of the customers [##UREF##10##15##, ##REF##34401384##37##].</p>", "<p id=\"Par17\">There are strategies that try to help users temporarily disassociate from their digital devices or use them in a balanced and responsible way [##UREF##29##38##–##UREF##31##40##].</p>", "<p id=\"Par18\">However, there are barriers to the decision to take a DFT travel. There a few studies on the behavioural intention of tourists to use experiences that limit the use of smartphones [##REF##31430621##3##, ##UREF##32##41##].</p>", "<p id=\"Par19\">This study addresses a new problem with a strong impact of technology and tourism. The methodology is based on an exploratory analysis, building on previous studies, using a survey of potential Spanish tourists. The scientific production of studies on Digital Detox and specific studies on Digital Free Tourism is very scarce. The use of structural equations to evaluate the results of the questionnaires is a novelty in the work as it employs a pioneering statistical analysis carried out with PLS-SEM and DFT [##UREF##33##42##].</p>", "<p id=\"Par20\">The aim of this study is to examine the opportunities that DFT can bring in the tourism sector for tourism service providers and, in turn, to investigate the influence of tourists’ behavioural intentions (BI) on the variables offered by DFT attributes linked to social and family engagement, relaxation and wellbeing and connection with nature. It also studies the impact of BI on DFT economic sustainability in the new economic scenario and the complex relationship between digital technologies and tourism.</p>", "<p id=\"Par21\">In summary, we use a quantitative approach that investigates the attitudes and motivations of potential DFT tourists by employing a new dimension, sustainability as a cornerstone of DFT attributes and its influence on the behavioural intention of these potential tourists.</p>", "<p id=\"Par22\">\n\n</p>", "<p id=\"Par23\">This research offers a thoughtful perspective to understand how providers can leverage DFT strategies to achieve greater appeal to potential travellers.</p>", "<p id=\"Par24\">The drive for new technologies and digitalisation has led to the need to rethink business models and segments oriented towards the sustainability and viability of tourism resources. In this sense, service providers are making efforts to turn their destinations into service providers are making efforts to turn their destinations into ideal destinations that meet the needs and experiences of their potential customers [##UREF##9##12##, ##UREF##11##17##].</p>", "<p id=\"Par25\">With these premises, tourism managers are starting to promote sustainable strategies in line with their clients’ offer. Environmental and economic sustainability is a priority objective for the new manifestations of tourism, such as DFT, which proposes and promotes the revaluation of authentic resources [##UREF##34##43##, ##UREF##35##44##]. For these reasons, research population would be clients of this promote sustainable strategies and with issues related with DETOX.</p>", "<p id=\"Par26\">The research questions are as follows:</p>", "<p id=\"Par27\">\n<list list-type=\"order\"><list-item><p id=\"Par28\">Investigate whether in the new digital era DFT can offer a competitive advantage in attracting tourists and be a driver of economic sustainability.</p></list-item><list-item><p id=\"Par29\">To identify DFT as a new business model and generator of business initiatives that promote health and wellness tourism.</p></list-item><list-item><p id=\"Par30\">Expand the field of knowledge of DFT by adding economic sustainability as a factor influencing the behavioural intention of tourists when seeking DFT experiences.</p></list-item></list>\n</p>", "<p id=\"Par31\">After this introduction, the existing scientific literature about DFT is reviewed. Then, the methodology of the research and the data collection system using an online questionnaire of 426 tourists are given, the results obtained are discussed, and the conclusions of the study are presented.</p>" ]

[ "<title>Methods</title>", "<p id=\"Par69\">The objective of this research is to advance the knowledge of new structures of motivational factors that can understand the decision of a tourist to make a DFT trip. To this end, it is investigated whether family and social engagement and health and relaxation have a positive impact on the behavioral intention of the potential tourist and whether this influences sustainability due to the importance of DFT in the new economic framework.</p>", "<p id=\"Par70\">For this purpose, a quantitative approach has been used with an online survey including question areas from previous studies [##REF##35627511##7##, ##UREF##10##15##, ##UREF##17##24##, ##UREF##21##29##].</p>", "<p id=\"Par71\">The questionnaire investigates the profile, attitudes and motivations of DFT tourists [##UREF##2##4##, ##UREF##53##71##, ##UREF##54##72##]. This allows tourism service providers and managers to consult this research and adapt marketing strategies to tourists who demand these types of wellness and health services.</p>", "<title>Data collection</title>", "<p id=\"Par72\">The answers to questions about the proposed relationships and the influence of each dimension of sustainability of DFT were measured with a five-point Likert scale (5 = “strongly agree”, 1 = “strongly disagree”) [##UREF##55##73##]. The study uses a conceptual model that analyses the interrelationships of the variables that contribute to behavioural intention for the DFT experience.</p>", "<p id=\"Par73\">The methodology employed is a questionnaire in an attempt to reach a broad audience. In our research, we conceptualise sustainability DFT as a pioneering study through an analysis of PLS-SEM results that can contribute to critical debates in technology and tourism studies. The common method of bias with the Harm test has been taken into account [##UREF##34##43##]. The model is used to analyse the influence of the above variables on economic sustainability and sustainable tourism.</p>", "<p id=\"Par74\">The theoretical model in the proposal above (see Fig. ##FIG##0##1##) connects social and family engagement, nature connectedness and health-relaxation variables to behavioural intention for DFT and the contribution to economic sustainability.</p>", "<p id=\"Par75\">The indicators selected in previous studies were also analysed. The most important studies and elements in the literature were reviewed [##REF##35627511##7##, ##UREF##10##15##, ##UREF##17##24##, ##UREF##21##29##]. To measure sustainability, the scales proposed in previous work were adapted [##UREF##2##4##, ##UREF##17##24##, ##UREF##26##34##], such as DFT experiences generate profitability for the tourism sector, DFT is a driver of future economic sustainability, DFT promotes new jobs and DFT creates new companies and entrepreneurs.</p>", "<p id=\"Par76\">This study uses the proposal of [##UREF##29##38##] to evaluate health relaxation.</p>", "<p id=\"Par77\">For nature connectedness, the items were proposed using the work of [##REF##35627511##7##, ##UREF##10##15##, ##UREF##18##26##]. Social and family engagement items were adapted from those used by [##UREF##8##11##, ##REF##35123383##16##, ##UREF##29##38##]. The analysis of behavioural intention was based on previous work by [##REF##35627511##7##, ##UREF##10##15##, ##UREF##21##29##, ##UREF##40##49##].</p>", "<p id=\"Par78\">The items included for each construct are shown below in Tables ##TAB##0##1## and ##TAB##1##2##. The measurement scales that were developed and adapted using the literature on previous research are also shown in Table ##TAB##1##2##.</p>", "<title>Sampling procedure</title>", "<p id=\"Par79\">A specially created online questionnaire was used in the research, and respondents were asked to answer questions about DFT. It is important to note that the questionnaires were anonymous.</p>", "<p id=\"Par80\">The questionnaire has been previously validated with experts from the tourism sector and academics using a Google Forms format. Of the experts, 5 are academics from the University of Extremadura, 2 are researchers from the Lisbon Research Centre and 5 are professionals from the Spanish tourism sector.</p>", "<p id=\"Par81\">The procedure was to use no probabilistic convenience sampling. The questionnaire about tourist destinations, entrepreneurship, mindfulness, relaxation and meditation was advertised on social networks in Spain with the corresponding permission and rights of the respondent.</p>", "<p id=\"Par82\">Stratified sampling by age group was used in training sessions for businesspeople, academics and entrepreneurs, as well as public administration staff and industry professionals who were given the questionnaire for research purposes and collaboration with the study.</p>", "<p id=\"Par83\">The data were collected between July and October 2022 and were first analysed for missing values. Of the 435 questionnaires received, 9 were eliminated due to incomplete or unanswered items and did not count towards the total sample. In the end, 426 questionnaires were obtained with valid responses.</p>", "<p id=\"Par84\">In the questionnaire’s preparation, wording, order and characteristics, it is possible to indicate that [##REF##21838546##74##] recommendations have been taken into account.</p>", "<p id=\"Par85\">In particular, it should be noted that a control question was included to eliminate questionnaires that did not pass this question. Likewise, an item was added to control the error, which turned out to be lower than indicated by these authors.</p>", "<title>Statistical analysis</title>", "<p id=\"Par86\">The statistical programs SPSS and Smart PLS 4 were used to analyse the results [##UREF##33##42##]. All questionnaire variables were pre-coded.</p>", "<p id=\"Par87\">IBM SPSS Statistics 26.0 statistics software was used to evaluate the data obtained by descriptive analysis using Smart PLS 4 software to confirm the relationships in the model and the research hypotheses [##UREF##56##75##]. PLS is the most efficient way to analyse data using the SEM methodology since it provides the theoretical and empirical conditions of behavioural and social science and is especially applicable when the conditions for a closed system are not met [##UREF##57##76##].</p>", "<p id=\"Par88\">PLS was chosen for several reasons: first, PLS imposes no requirement of normality on the data and is a suitable technique for predicting dependent variables in small samples, given a certain degree of quality in the model [##UREF##58##77##]. Furthermore, PLS is more appropriate when the objective is to predict and investigate relatively new phenomena [##UREF##59##78##] as is the case of DFT and technology in the tourism sector also applied to business management research [##REF##34499044##66##, ##UREF##57##76##].</p>" ]

[ "<title>Results</title>", "<title>Analysis of the measurement model</title>", "<p id=\"Par89\">The reliability and validity of the proposed model are checked to verify that the observed variables accurately measure the theoretical concepts. All the constructs are reflective, which means that the model uses data that have item reliability, with all factorial loads greater than 0.505 [##UREF##60##79##], presenting values between 0.759 and 0.949. Bootstrapping with significant loads (99.99%) was used to find the t statistics.</p>", "<p id=\"Par90\">\n\n</p>", "<p id=\"Par91\">The calculations for Cronbach’s alpha for each of the constructs gave values higher than 0.7, which is the established minimum [##UREF##61##80##]. These values were between 0.869 (Social and family engagement) and 0.920 (Health-Relaxation).</p>", "<p id=\"Par92\">The composite reliability was seen to be internally consistent because all the constructs had values greater than 0.9, which are higher than the proposed minimum of 0.7 (Hair et al. 2011). The results for the average variance extracted (AVE) resulted in values between 0.718 (social and family engagement) and 0.890 (behavioural intention), which verify convergent validity, as they are all greater than the minimum of 0.50 (Fornell &amp; Larcker, 1981) (see Table ##TAB##1##2##). The calculation of AVE ≥ 0.5 means that more than half the variance of each indicator is explained by the construct [##UREF##33##42##, ##UREF##62##81##, ##UREF##63##82##].</p>", "<p id=\"Par93\">\n\n</p>", "<p id=\"Par94\">Table ##TAB##2##3## shows how all the indicators used in the research meet the requirements established for discriminant validity, since the diagonal values are all higher than the other values in the same columns and rows [##UREF##60##79##].</p>", "<p id=\"Par95\">In addition, the heterotrait-monotrait criterion (HTMT) was calculated to find the discriminant validity. The values of HTMT must be less than 1 to show discrimination of two factors [##UREF##58##77##]. Table ##TAB##2##3## (final columns) shows that all variables had discriminant validity when following the criteria for HTMT.</p>", "<p id=\"Par96\">From the results obtained, the measurement model was considered to have sufficient levels of validity and reliability, and the evaluation of the structural model can proceed.</p>", "<title>Structural model analysis</title>", "<p id=\"Par97\">Once the measurement model validity has been verified, the structural model of the different constructs is analysed to evaluate the coefficient and path significance [##UREF##61##80##]. The values of R², which is the explained variance of the latent dependent variables, verify that the endogenous constructs of the model are predictive and explanatory [##UREF##64##83##] (see Table ##TAB##3##4##).</p>", "<p id=\"Par98\">\n\n</p>", "<p id=\"Par99\">The model explains 61.5% of nature connectedness, 36.9% of behavioural intention and 63.8% of economic sustainability.</p>", "<p id=\"Par100\">Student’s two-tailed t-distribution was used to compare the significance of β coefficients using a bootstrapping process with 5000 samples [##UREF##61##80##]. The values for the constructs of the model (standardized β path coefficients) are greater than 0.2 [##UREF##65##84##] or have t values greater than 1.96, apart from the relationship between social and family engagement and behavioural intention.</p>", "<p id=\"Par101\">This means that all the proposed hypotheses used in the structural model were significant except for the hypothesis about the relationship of social and family engagement and Behavioural Intention because this does not reach the minimum accepted value for the t statistic (see Table ##TAB##4##5##).</p>", "<p id=\"Par102\">\n\n</p>", "<p id=\"Par103\">Similarly, the p-values are also less than 0.05 level of significance, except for H3 which is the positive influence of Social and family engagement on Behavioural intention. The value obtained is higher (0.41) and is not supported because the significance level is higher than the 0.05 threshold, which means that the confidence level is lower than 95%.</p>" ]

[ "<title>Discussion</title>", "<p id=\"Par104\">The first and second hypotheses are validated, which show that both ENG and REL have a positive influence on BI and NAT [##UREF##19##27##, ##UREF##24##32##, ##UREF##32##41##]. This coincides with the findings of [##UREF##21##29##, ##UREF##40##49##] and therefore validates the research hypothesis.</p>", "<p id=\"Par105\">Other authors, however, consider that the constant need for commitment to the family is an obstacle to enjoyment and creates an obligation to communicate. This can cause frustration and discomfort and means that tourists are under pressure because they do not have the necessary language skills to communicate [##UREF##53##71##, ##UREF##59##78##]. This negative feeling is highest in a natural, isolated and unconnected environment [##UREF##41##50##, ##UREF##66##85##]. proposes that this drawback does not influence behavioural intention for connection with nature with DFT. Being in a cabin in the forest can help visitors gain self-knowledge and immerse themselves in the environment, but this does not happen in places such as hotels or urban resorts where the feeling of being in a natural environment can be blurred and therefore reduce the enjoyment of the natural environment.</p>", "<p id=\"Par106\">On the other hand, social commitment, defined as the process of establishing and improving ties with family and friends, has a positive influence on tourist motivation to participate in a DFT trip and therefore has an influence on tourist intention to experience DFT [##REF##35627511##7##, ##UREF##21##29##, ##UREF##54##72##].</p>", "<p id=\"Par107\">However, contrary to what is proposed in the third research hypothesis, ENG does not positively influence BI. Some authors affirm that it is not a predictor of DFT intention [##UREF##41##50##] because social bonding does not necessarily occur due to DFT experience but is gained from different activities that tourists do together in the company of others while on holiday. This may be because our family and friends are connected to the Internet and social media, and the best way to connect with them is digitally; thus, in these circumstances, being disconnected does not benefit social relationships [##UREF##10##15##, ##UREF##39##48##].</p>", "<p id=\"Par108\">On the other hand, the results suggest that nature connectedness and health relaxation contribute positively to behavioural intention, especially the first construct, so the fourth and fifth hypotheses are validated.</p>", "<p id=\"Par109\">These results are consistent with the idea that factors of health relaxation and nature connectivity during a trip are decisive when recommending or repeating a DFT trip. The feeling of unity with the natural environment is an attractive reason for a DFT experience is an idea proposed in the scientific literature [##UREF##4##6##, ##REF##35627511##7##].</p>", "<p id=\"Par110\">On the other hand, relaxation influences motivation to make a DFT trip. A better sensory experience, feeling of freedom, sensory experience and relaxation are possible rewards after engaging in activities without digital media [##UREF##30##39##]. Relaxation means feeling peaceful and quiet while refreshing the body and mind, which is in line with studies that have found that relaxation can motivate tourists to go sightseeing without digital devices [##UREF##24##32##, ##UREF##40##49##]. The results of these studies try to explain that tourist intention to not use digital devices during their holidays has its origin in the belief that a DFT trip will allow a person to feel relaxed and mindful, allow them to express themselves and help them avoid technostress [##UREF##21##29##, ##REF##33978589##68##]. Other studies also support this theory about the benefits of DFT for improving health and well-being and increasing relaxation and satisfaction [##UREF##4##6##, ##UREF##18##26##, ##UREF##29##38##]. In addition, relaxation and mindfulness have positive impacts on tourist intention to travel by limiting the use of technology [##REF##31430621##3##, ##UREF##6##9##].</p>", "<p id=\"Par111\">In other lines of research, it is concluded that the excessive use of new technologies minimizes commitment to family and social relationships [##UREF##21##29##].</p>", "<p id=\"Par112\">The sixth hypothesis predicts that Behavioural Intention positively influences economic sustainability [##UREF##26##34##, ##UREF##44##54##]. The results reveal (β = 0.xxx. t = 2. xxxx) that the hypothesis is supported. These data are in line with the studies of [##UREF##17##24##, ##UREF##31##40##, ##UREF##52##70##].</p>", "<p id=\"Par113\">Figure ##FIG##1##2## presents the model with the confirmed relationships of the research, the trajectory results and their statistical significance.</p>", "<p id=\"Par114\">\n\n</p>", "<p id=\"Par115\">The above data justify proposing a new tourism product based on the voluntary absence of technology during a trip [##REF##35627511##7##, ##REF##35123383##16##] to promote the sustainable economy of a territory [##UREF##2##4##] because behavioural intention clearly influences economic sustainability.</p>", "<p id=\"Par116\">This confirms what most authors in the peer-review literature propose [##REF##32780029##14##, ##UREF##10##15##] for five of the research hypotheses used in this study. Four different elements of motivation that positively affect behavioural intention to go on a DFT trip have been identified. These are economic sustainability, social and family engagement, nature connectedness and health relaxation.</p>" ]

[ "<title>Conclusions</title>", "<title>Theoretical implications</title>", "<p id=\"Par117\">Theoretical contribution with DFT as a driver for attracting potential tourists to help service providers to offer efficient, sustainable services to support the health and wellbeing demanded by tourists who wish to digitally disconnect. DFT can be a driver of economic sustainability and health and wellness therapy in tourism in the digital age.</p>", "<p id=\"Par118\">Innovative technologies are increasingly important as a fundamental part of the tourist experience, and this study contributes to the scientific literature on the topic and adds to the limited number of studies on the motivation of tourists to go on a DFT trip. It advances knowledge by proposing a new structure of motivational factors that could explain the decision of a tourist to make a DFT trip.</p>", "<p id=\"Par119\">To this end, it empirically proposes how variables such as social and family commitment, connection with nature, relaxation or preference for economic sustainability influence the decision to make a trip that is free from technology and digital devices. Study participants have consistently indicated the positive impacts that temporary abandonment of digital devices can have during holiday periods.</p>", "<p id=\"Par120\">This empirical study also expands the lines of research on DFT and proposes new dimensions to try to lay theoretical foundations for future studies into DFT, such as disconnection from work, privacy or sustainable tourism and the positive impacts on the decision to choose to disconnect digitally while taking a trip.</p>", "<p id=\"Par121\">The study shows a great variation in the traveller’s desire to disconnect, as some already want to disconnect digitally, while others live attached to their devices and make them an integral part of their lives. Much of the debate about hyper connectedness and the ubiquity of new technologies has focused on data given empirically in this research, concluding that the decision to disconnect from DFT is complex and that it is not just an individual choice but has other factors inhibiting voluntary disconnection that are all influenced mainly by the social environment of work and family.</p>", "<title>Practical implications</title>", "<p id=\"Par122\">Being disconnected while travelling is an added value for DFT tourists. This has obvious advantages and means it can become part of the creation and design of products and DFT service packages with companies in the sector. All this can result in increased productivity and contribution to well-being, sustainability and an improved lifestyle.</p>", "<p id=\"Par123\">At the same time, it offers an opportunity for small and medium-sized companies to turn the disadvantage of lack of technology into a defining advantage for their product. DFT proposes an adequate use of existing resources that can be improved with efficient strategies and does not require large infrastructures and investment.</p>", "<p id=\"Par124\">Therefore, the practical findings of this research are that digital connections alter the travel experience and the evolution of the rapid adoption of recent technologies in tourism. The omnipresence of digital connections is also changing, and a social transition is beginning for the connection-disconnection dilemma in tourism.</p>", "<title>Limitations and future research</title>", "<p id=\"Par125\">DFT is an alternative and emerging trend that companies and the tourism sector can use to adapt offers to changing market needs. Disconnecting from the digital world, for leisure and for treatment, can be used to create a catalogue of services that can generate new jobs and specialize areas, spaces and regions for this type of tourism.</p>", "<p id=\"Par126\">First, this study is limited to only one target audience made up of people of legal age who travel regularly. The complexity of making the decision to disconnect is latent since most of the scientific literature focuses on opinions and not on empirical data. There are individual choices and an age bias that allows us to distinguish digital profiles such as natives, immigrants, generation z, and millennials. There is also only limited empirical research in this area [##REF##35627511##7##, ##UREF##19##27##, ##UREF##24##32##, ##UREF##67##86##].</p>", "<p id=\"Par127\">Second, potential DFT travellers supplied the data collected in this quantitative study. Future research could develop this conceptual model with travellers who have already taken a DFT trip and check the degree of loyalty and recommendations to future tourists, which would allow the factors of intention for these experiences to be researched. A temporary digital disconnection is accepted and considered positive. Encouraging self-awareness, control and moderation at different types of DFT accommodation (resort, hotel, mountain hut, rural accommodation), the various sizes of travel groups (singles, couples, with family, with friends) and a research agenda of travel-related factors can all be used to predict enjoyable elements for DFT travellers and therefore suggest a future roadmap including other conceptual models such as well-being, DFT experience, and loyalty that could all influence decision-making and give a predictive model for DFT traveller services and products.</p>", "<p id=\"Par128\">Third, the conceptual framework can be useful for the future of tourist destinations that promote or specialize in DFT by generating a collaborative ecosystem that would allow for the expansion of the results of other studies, such as creating a network of disconnected tourist destinations or for potential use by addiction treatment centres.</p>", "<p id=\"Par129\">However, some situations are given which help to focus on the study aims. Tourists on a disconnection experience trip may be limited by the potential recall and forgetfulness bias that could be felt in a hypothetical situation and may differ from the way the traveller behaves once disconnected, so this area of research may warrant future lines of research examining how tourists on a DFT experience trip behave.</p>", "<p id=\"Par130\">Studies still must analyse patterns that analyse the intentions of tourists regarding digital disconnection experiences. The relationships between diverse types of DFT in various places around the world can suggest lines and areas of future research whose results can be used by professionals in the tourism sector to make pragmatic efforts to meet the potential DFT demand in the market. The aim is to generate strength and power for remote areas with reduced means of communication and without current tourism development, which can be rural and undeveloped areas away from busy tourist routes and mass tourism destinations. It is also an opportunity for combined destinations to establish a catalogue of innovative DFT services complying with the following characteristics: lack or limited access to IT with leisure activities in an exclusive and healthy environment. This would allow entities to plan strategies and alternatives for tourism development and marketing policies focused on sustainability, relaxation and social and family commitment as valuable elements of well-being when taking part in the experience.</p>" ]

[ "<title>Background</title>", "<p id=\"Par1\">The excessive use of information technologies (IT) and online digital devices are causing symptoms of burnout, anxiety, stress and dependency that affect the physical and mental health of our society, extending to leisure time and work relationships. Digital free tourism (DFT) is a phenomenon that emerges as a solution to technostress and pathologies derived from digital hyperconnection. The objective of this research is to advance the knowledge of new structures of motivational factors that can understand the decision of a tourist to make a DFT trip. To this end, it is investigated whether family and social engagement and health and relaxation have a positive impact on the behavioral intention of the potential tourist and whether this influences sustainability due to the importance of DFT in the new economic framework.</p>", "<title>Methods</title>", "<p id=\"Par2\">With a quantitative approach, the methodology used consisted of an online questionnaire among potential travelers. IBM SPSS Statistics 22.0 statistical software was used to evaluate the data obtained and confirm the relationships of the model and the research hypotheses.</p>", "<title>Results</title>", "<p id=\"Par3\">The results of the questionnaire assessed the contribution of each construct to the tourist’s behavioral intention and the tourist’s decision to make the decision to undertake a DFT experience.</p>", "<title>Conclusions</title>", "<p id=\"Par4\">DFT can be a driver of economic sustainability and health therapy in tourism in the digital age. This study aims to expand the lines of research on DFT and determine the complex factors that can lead a tourist to participate in the DFT experience. The results obtained can help managers of companies in the sector to offer more efficient and sustainable services that contribute to the health and wellbeing of tourists as a differentiating factor.</p>", "<title>Supplementary Information</title>", "<p>The online version contains supplementary material available at 10.1186/s12889-023-17584-6.</p>", "<title>Keywords</title>" ]

[ "<title>Background</title>", "<title>Social and family engagement</title>", "<p id=\"Par32\">Every individual wants to have social relationships with others [##UREF##10##15##, ##REF##35120510##25##]. Therefore, the influence exerted by family and friends is very important in our lives since it can directly affect decision-making and condition attitudes and behaviour [##UREF##32##41##].</p>", "<p id=\"Par33\">Several studies have shown that enjoying a vacation with family or friends is beneficial for social relationships [##REF##35123383##16##]. In addition, vacations help create close bonds that increase sociability [##UREF##21##29##], promote face-to-face communication [##UREF##36##45##], build trust [##UREF##18##26##] and generate social and family commitment [##UREF##30##39##].</p>", "<p id=\"Par34\">Jiang and Balaji’s (2021) research identifies several reasons for tourists to participate in a DFT trip, including social and family engagement, connection with nature, relaxation and novelty, which all increase well-being during holidays. It can even reinforce bonding points with loved ones without the constant need to send social media notifications and enhance that active engagement with a DFT experience [##REF##35627511##7##].</p>", "<p id=\"Par35\">Family and social commitment can represent a barrier to holiday enjoyment that must be negotiated and addressed personally by the potential tourist as technology wields extensive power in the experience [##UREF##20##28##].</p>", "<title>Nature connectedness</title>", "<p id=\"Par36\">Other research suggests that social and family engagement can be enhanced with an immersive experience in the natural environment [##REF##35627511##7##]. Nature connectedness has been defined as the subjective feeling of association with the environment that implies meaningful participation in something greater than oneself and that can be related to scales of natural emotional, social and psychological well-being [##UREF##37##46##, ##UREF##38##47##]. Researchers have found that immersion in a natural environment creates positive communication bonds, enhances the development of personal skills, reinforces attachment and interpersonal harmony, and increases sociability [##UREF##10##15##, ##UREF##37##46##, ##UREF##39##48##]. When choosing an experience of well-being and relaxation with family and friends, DFT in nature gives the tourist a chance to try new activities that favour full enjoyment of the environment [##UREF##18##26##] and strengthen social and family bonds [##UREF##29##38##]. Nature can enhance self-expression and self-control and contribute to a healthy experience [##UREF##8##11##].</p>", "<p id=\"Par37\">DFT limits the constant presence of IT with activities in environments that allow tourists to enter the natural environment [##UREF##40##49##] and engage with family, friends and fellow travellers in activities that improve interpersonal relationships without the need to rely on mobile devices. Thus, bonds of unions are reinforced without the constant obligation to send emails, upload photos to social networks or publish videos on the Internet [##UREF##29##38##, ##UREF##31##40##].</p>", "<p id=\"Par38\">Hence, the following hypothesis is proposed for research:</p>", "<p id=\"Par39\"><italic>H1. Social and family engagement positively influences nature connectedness</italic>.</p>", "<title>Health and relaxation</title>", "<p id=\"Par40\">One of the consequences of a world with digital communication without limits is the increase in stress levels [##REF##31518525##21##, ##UREF##41##50##].</p>", "<p id=\"Par41\">Some studies conclude that DFT is a way for tourists to reduce technostress, which is a subtype of stress that is characterized by a loss of control due to being connected to the Internet with devices such as smartphones, causing frustration, anxiety and an absence of privacy [##UREF##42##51##].</p>", "<p id=\"Par42\">DFT can allow tourists to escape from their usual work routines and disconnect in the middle of nature with limited use of IT [##UREF##21##29##, ##UREF##43##52##]. This increases the feeling of well-being and relaxation [##UREF##4##6##, ##UREF##10##15##, ##REF##32294052##53##, ##UREF##44##54##]. It also improves the participants’ health by avoiding compulsive use of the Internet in daily online activities, such as posting on social networks, instant messaging, sending and receiving emails or watching online videos [##UREF##10##15##, ##REF##31518525##21##, ##UREF##29##38##].</p>", "<p id=\"Par43\">Hence. The following research hypothesis is proposed:</p>", "<p id=\"Par44\"><italic>H2. Health relaxation positively influences nature connectedness</italic>.</p>", "<title>Behavioural intention</title>", "<p id=\"Par45\">Behavioural intention is the subjective probability that a person is going to act in some way and have certain behaviour [##UREF##32##41##]. In the tourism sector, conceptual models have tried to investigate what factors influence the behaviour of a tourist when choosing a type of experience and how these affect the tourist’s intention to book a trip [##UREF##41##50##].</p>", "<p id=\"Par46\">DFT reduces the negative impact of technology and the Internet during leisure activities and holidays by limiting the use of digital devices that cause distractions and pathologies [##REF##32780029##14##]. An excessive use of technology causes technostress, depression, low self-esteem, anxiety and other new diseases associated with technological addiction, such as nomophobia, FOMO disorder, and phubbing [##REF##19770258##55##, ##REF##33996506##56##].</p>", "<p id=\"Par47\">This study is based in other research works, but this model has a lot of new apports.</p>", "<p id=\"Par48\">Similar model as [##REF##35627511##7##]. These authors present a model Digital-Free tourism holiday as a new approach for tourism well-being.</p>", "<p id=\"Par49\">Additionally, Previous studies such as Zhuang et al. [##UREF##45##57##] and Jiang and Balaji [##UREF##10##15##] have showed different models and relations with some variables, as the positive relation between ‘Use digital technologies during holidays’ in ‘Tourist self-control during holidays’. Egger et al. [##UREF##24##32##] and Dickinson et al. [##UREF##18##26##] presented the negative influence of ‘Use digital technologies during holidays’ in ‘Technology dark traits in holidays’.</p>", "<p id=\"Par50\">On the hand, Technology dark traits in holidays’ have a positive influence in DFT [##UREF##46##58##]. Finally, Jackson [##UREF##47##59##] and Fong et al. [##UREF##48##60##] established the influence of ‘Tourist attribution’ in DFT.</p>", "<p id=\"Par51\">Several investigations have concluded that certain factors, such as social and family engagement, nature connectedness and health relaxation, favour the intention to participate in a DFT experience and positively affect tourists’ behavioural intention [##REF##35627511##7##, ##UREF##10##15##, ##UREF##21##29##].</p>", "<p id=\"Par52\">Social and family engagement can influence tourists’ intention to choose a DFT experience, and an increasing number of friends, family and private circles recommend enjoying DFT trips [##UREF##40##49##].</p>", "<p id=\"Par53\">Due to the above literature, the following research hypothesis was proposed:</p>", "<p id=\"Par54\"><italic>H3. Social and family engagement positively influences behavioural intention</italic>.</p>", "<p id=\"Par55\">As seen above, an immersive trip in nature can motivate a person to escape from a hyperconnected world [##UREF##12##18##, ##UREF##18##26##]. This increases tourists’ enjoyment of the trip [##UREF##21##29##]. This approach has been supported by other studies researching digital disconnection experiences at destinations surrounded by nature, such as campsites [##UREF##18##26##], detox retreats [##REF##32834340##61##] or mountain huts [##UREF##40##49##]. All of these factors provoke positive and authentic emotions in tourists who consider them decisive elements when making a DFT trip with full immersion in nature [##UREF##4##6##, ##REF##25586982##62##].</p>", "<p id=\"Par56\">The contributions to well-being and health of this type of trip means that Behavioural Intention is positive when connecting with nature on a DFT experience [##REF##35627511##7##, ##UREF##10##15##, ##UREF##49##63##].</p>", "<p id=\"Par57\">Hence, the following research hypothesis is proposed:</p>", "<p id=\"Par58\"><italic>H4. Nature connectedness influences Behavioural intention</italic>.</p>", "<p id=\"Par59\">In addition to social and family engagement and nature connectedness, the desire for relaxation and health is also an element that can condition the decision to choose a DFT destination [##UREF##14##20##, ##UREF##41##50##]. Numerous studies address the negative impacts of technology addiction and its harmful effects on health [##REF##35162570##64##]. DFT has a high demand from users who want to mitigate the negative effects of hyperconnection and find enjoyment, pleasure and spirituality [##REF##35627511##7##, ##UREF##18##26##, ##UREF##21##29##]. Suppliers in the tourism sector have tried to channel this intention to meet the demand for the well-being of their customers [##UREF##12##18##, ##UREF##27##35##, ##REF##26290327##65##].</p>", "<p id=\"Par60\">The following research hypothesis is proposed using the above:</p>", "<p id=\"Par61\"><italic>H5. Health relaxation positively influences behavioural intention</italic>.</p>", "<title>Economic sustainability and sustainable tourism</title>", "<p id=\"Par62\">The revolution and transformation of tourism caused by IT plays a fundamental role in world economies [##REF##35627511##7##]. In 2030, the United Nations World Tourism Organization program predicts that there will be over 1,800 million tourists [##UREF##0##1##]. This will generate income, create new jobs and promote economic opportunities that can increase the sustainability and profitability of the tourism industry [##REF##34499044##66##].</p>", "<p id=\"Par63\">Technical, social, environmental, economic and political challenges all affect demand and sustainability in many countries that already promote tourism in nature [##UREF##50##67##]. The economic sustainability of tourism should allow for viable economic projects in the long term, which produce socioeconomic benefits for all stakeholders. These include alleviating poverty, income-generating opportunities, stable employment, and social services for host communities [##UREF##0##1##]. Therefore, sustainability must satisfy the different stakeholders so that there are positive feelings in social commitments, defence of natural resources and improvements of the tourist experience [##UREF##26##34##]. DFT can be relevant for the sustainability and profitability of tourist destinations and is important for their economy [##UREF##2##4##]. In addition, DFT aims to maintain tourist satisfaction and ensure that tourists live a meaningful experience that will make them aware of sustainability issues and sustainable tourism. Existing studies indicate that tourist awareness is being attracted to new sustainable experiences that are completely different from saturated mass tourism and focus on well-being and authenticity at a DFT destination [##UREF##40##49##, ##REF##33978589##68##]. A DFT tourist seeks a balance between good infrastructure, safety, healthy activities, new experiences, personalized offerings and respect for the environment [##UREF##6##9##, ##UREF##11##17##, ##UREF##28##36##] and an experience that includes quality services that protect nature, ecology and control to reach more efficient, sustainable services without noise or light pollution [##REF##35627511##7##, ##UREF##44##54##]. All these elements are an integral part of sustainable tourism for economic development, society and the environment [##UREF##51##69##].</p>", "<p id=\"Par64\">The opportunity that DFT gives for business growth and job creation [##UREF##27##35##, ##UREF##52##70##] as a new market niche for companies and new entrepreneurs can have an impact on the decision of the DFT tourist and condition their behavioural intention for a trip [##UREF##17##24##, ##UREF##26##34##]. This means that tourist destinations must promote and specialize in these types of experiences [##REF##35627511##7##, ##REF##35123383##16##, ##UREF##41##50##].</p>", "<p id=\"Par65\">The last research hypothesis is proposed based on these studies:</p>", "<p id=\"Par66\"><italic>H6. Behavioural intention positively influences economic sustainability</italic>.</p>", "<p id=\"Par67\">The relationships between the distinct factors are shown in Fig. ##FIG##0##1##.</p>", "<p id=\"Par68\">\n\n</p>", "<title>Electronic supplementary material</title>", "<p>Below is the link to the electronic supplementary material.</p>", "<p>\n\n</p>", "<p>\n\n</p>" ]

[ "<title>Acknowledgements</title>", "<p>The authors would like to thank all who responded to the questionnaire for their participation. We would also like to thank the reviewers, experts and colleagues who commented on the drafts.</p>", "<title>Author contributions</title>", "<p>All authors made substantial contributions to the literature review and the analysis and interpretation of the data in producing the combined framework. All authors reviewed, revised, and approved the final manuscript. This research confirms that all methods used, anonymous questionnaire, have been carried out in accordance with all relevant guidelines and regulations. The questionnaire on tourism destinations, entrepreneurship, mindfulness, relaxation and meditation was advertised on social media in Spain with the permission and rights of the respondent. However, I am sending you the link to the official publication. <ext-link ext-link-type=\"uri\" xlink:href=\"https://doe.juntaex.es/pdfs/doe/2017/740o/17060728.pdf\">https://doe.juntaex.es/pdfs/doe/2017/740o/17060728.pdf</ext-link>. The UEX regulations are transparent and guarantee the right of access to the public information of the University of Extremadura, presenting to the citizenship the most relevant information of its governance, processes, procedures and accountability. The datasets are offered in international standard formats so that they are easily reusable by software applications that wish to use these data and represent the information in open linked data, with the maximum level of reusability of 5 stars, recommended by the W3C. If you need any datasets, or queries combining information on existing datasets, please send an email to [email protected].</p>", "<title>Funding</title>", "<p>Not applicable.</p>", "<title>Data availability</title>", "<p>All data generated or analysed during this study are included in this published article.</p>", "<title>Declarations</title>", "<title>Ethics approval and consent to participate</title>", "<p id=\"Par131\">This research confirms that all methods used, anonymous questionnaire, have been carried out in accordance with all relevant guidelines and regulations. Participants in this questionnaire were informed that all data provided were anonymous. All participants were informed that the research was for academic purposes for a thesis at the University of Extremadura. This article does not report the results of a health intervention in human participants.</p>", "<title>Consent for publication</title>", "<p id=\"Par132\">Not applicable.</p>", "<title>Competing interests</title>", "<p id=\"Par133\">The authors declare no competing interests.</p>" ]

[ "<fig id=\"Fig1\"><label>Fig. 1</label><caption><p>Theoretical model. <italic>Note</italic>: ENG (Social and Family Engagement), NAT (Nature Connectedness), H-REL (Health-Relaxation, BI (Behavioural Intention), ECO (Economic sustainability). <italic>Source</italic>: Authors </p></caption></fig>", "<fig id=\"Fig2\"><label>Fig. 2</label><caption><p>Graph of the structural model analysis results. <italic>Note</italic>: ENG (Social and Family Engagement). NAT (Nature Connectedness). H-REL (Health-Relaxation). BI (Behavioral Intention). ECO (Sustainable Economy). <italic>Source</italic>: Authors</p></caption></fig>" ]

[ "<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Terminology in digital free tourism travel and digital disconnection travel</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Terminology DFT</th><th align=\"left\">Authors</th><th align=\"left\">Year</th><th align=\"left\">Topics</th></tr></thead><tbody><tr><td align=\"left\" rowspan=\"2\">Digital detox holidays (DDH)</td><td align=\"left\">Jiang &amp; Balaji</td><td char=\".\" align=\"char\">2022</td><td align=\"left\">Disconected in travels and holidays</td></tr><tr><td align=\"left\">Winke</td><td char=\".\" align=\"char\">2010</td><td align=\"left\">Addiction and holidays</td></tr><tr><td align=\"left\" rowspan=\"4\">Digital detox tourism (DDT)</td><td align=\"left\">Gaafar</td><td char=\".\" align=\"char\">2021</td><td align=\"left\">Attitudes and motivators in Egyptian tourists</td></tr><tr><td align=\"left\">Hoving</td><td char=\".\" align=\"char\">2017</td><td align=\"left\">Motivations Dutch tourists’ digital detox</td></tr><tr><td align=\"left\">Pawloska-Legwad &amp; Matoga</td><td char=\".\" align=\"char\">2020</td><td align=\"left\">Disconnect from digital world</td></tr><tr><td align=\"left\">Wilckonson</td><td char=\".\" align=\"char\">2019</td><td align=\"left\">Effects smartphone: anxiety and craving</td></tr><tr><td align=\"left\" rowspan=\"2\">Digital free holidays (DFH)</td><td align=\"left\">Emek</td><td char=\".\" align=\"char\">2014</td><td align=\"left\">Addiction and digital free holidays</td></tr><tr><td align=\"left\">Ozdemir</td><td char=\".\" align=\"char\">2021</td><td align=\"left\">Bibliometrics analysis about digital holidays</td></tr><tr><td align=\"left\" rowspan=\"11\">Digital free tourism (DFT)</td><td align=\"left\">Arenas et al.</td><td char=\".\" align=\"char\">2022</td><td align=\"left\">Opportunities DFT</td></tr><tr><td align=\"left\">Dickinson et al.</td><td char=\".\" align=\"char\">2016</td><td align=\"left\">Disconnection at campsite</td></tr><tr><td align=\"left\">Egger et al.</td><td char=\".\" align=\"char\">2020</td><td align=\"left\">Exploratory study DFT motivations</td></tr><tr><td align=\"left\">Floros et al.</td><td char=\".\" align=\"char\">2019</td><td align=\"left\">Millennials</td></tr><tr><td align=\"left\">Fryman &amp; William</td><td char=\".\" align=\"char\">2021</td><td align=\"left\">Smartphones dependency</td></tr><tr><td align=\"left\">Hassan et al.</td><td char=\".\" align=\"char\">2022</td><td align=\"left\">DFT Tourism and Well Being</td></tr><tr><td align=\"left\">Li et al.</td><td char=\".\" align=\"char\">2018</td><td align=\"left\">Critical discourse digital free tourism</td></tr><tr><td align=\"left\">Li et al.</td><td char=\".\" align=\"char\">2020</td><td align=\"left\">Character Strengths digital free tourist</td></tr><tr><td align=\"left\">Liu &amp; Hu</td><td char=\".\" align=\"char\">2021</td><td align=\"left\">Technostress perspective in digital free tourism</td></tr><tr><td align=\"left\">Cai et al.</td><td char=\".\" align=\"char\">2020</td><td align=\"left\">Turn it off in travels</td></tr><tr><td align=\"left\">Cai et al.</td><td char=\".\" align=\"char\">2023</td><td align=\"left\">Power and resistance in a connected world</td></tr><tr><td align=\"left\">Tourism offline</td><td align=\"left\">Syvertsen</td><td char=\".\" align=\"char\">2022</td><td align=\"left\">No access internet mountain. Experiences.</td></tr><tr><td align=\"left\" rowspan=\"9\">Unplugged in experiences. Motivations, attitudes</td><td align=\"left\">Ayeh</td><td char=\".\" align=\"char\">2018</td><td align=\"left\">Problematic use technology in holidays</td></tr><tr><td align=\"left\">Durán-Román et al.</td><td char=\".\" align=\"char\">2021</td><td align=\"left\">Sustainability and experience at destination</td></tr><tr><td align=\"left\">Kirillova &amp; Wang</td><td char=\".\" align=\"char\">2020</td><td align=\"left\">Smartphones disconnected in holidays</td></tr><tr><td align=\"left\">Kuntsman &amp; Miyake</td><td char=\".\" align=\"char\">2015</td><td align=\"left\">Digital disengagement</td></tr><tr><td align=\"left\">Paris et al.</td><td char=\".\" align=\"char\">2015</td><td align=\"left\">Campsites and disconnection</td></tr><tr><td align=\"left\">Fan et al.</td><td char=\".\" align=\"char\">2019</td><td align=\"left\">Face to face contact in destination immersion</td></tr><tr><td align=\"left\">Thomas et al.</td><td char=\".\" align=\"char\">2016</td><td align=\"left\">Benefits connection and disconnection</td></tr><tr><td align=\"left\">Benedict et al.</td><td char=\".\" align=\"char\">2019</td><td align=\"left\">Benefits connection and disconnection</td></tr><tr><td align=\"left\">Zhuang et al.</td><td char=\".\" align=\"char\">2021</td><td align=\"left\">Tourism experiences of AR technology use</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab2\"><label>Table 2</label><caption><p>Construct variable measurements: average variance extracted (AVE), composite reliability, Cronbach’s alpha and loadings</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Construct</th><th align=\"left\">Items</th><th align=\"left\">Loadings</th><th align=\"left\">Cronbach’s alpha</th><th align=\"left\">Composite reliability</th><th align=\"left\">AVE</th></tr></thead><tbody><tr><td align=\"left\" rowspan=\"2\">BI-behavioural intention</td><td align=\"left\">[ECO1 I would recommend DFT destinations]</td><td char=\".\" align=\"char\">0.949***</td><td char=\".\" align=\"char\" rowspan=\"2\">0.876</td><td char=\".\" align=\"char\" rowspan=\"2\">0.942</td><td char=\".\" align=\"char\" rowspan=\"2\">0.890</td></tr><tr><td align=\"left\">[ECO2 I would repeat a DFT experience]</td><td char=\".\" align=\"char\">0.937***</td></tr><tr><td align=\"left\" rowspan=\"4\">ECO-economic sustainability</td><td align=\"left\">[ECO3 DFT experiences are profitable for the tourism sector]</td><td char=\".\" align=\"char\">0.812***</td><td char=\".\" align=\"char\" rowspan=\"4\">0.879</td><td char=\".\" align=\"char\" rowspan=\"4\">0.917</td><td char=\".\" align=\"char\" rowspan=\"4\">0.733</td></tr><tr><td align=\"left\">[ECO DFT is a driver of future economic sustainability</td><td char=\".\" align=\"char\">0.854***</td></tr><tr><td align=\"left\">[ECO5 DFT promotes new jobs in the region]</td><td char=\".\" align=\"char\">0.888***</td></tr><tr><td align=\"left\">[ECO6 DFT creates new companies and entrepreneurs]</td><td char=\".\" align=\"char\">0.870***</td></tr><tr><td align=\"left\" rowspan=\"4\">ENG-social and family engagement</td><td align=\"left\">[ENG1 Being offline benefits my social relationships]</td><td char=\".\" align=\"char\">0.833***</td><td char=\".\" align=\"char\" rowspan=\"4\">0.869</td><td char=\".\" align=\"char\" rowspan=\"4\">0.911</td><td char=\".\" align=\"char\" rowspan=\"4\">0.718</td></tr><tr><td align=\"left\">[ENG2 When I disconnect, I spend more time with my family]</td><td char=\".\" align=\"char\">0.894***</td></tr><tr><td align=\"left\">[ENG3 Disconnecting favours face-to-face relationships]</td><td char=\".\" align=\"char\">0.808***</td></tr><tr><td align=\"left\">[ENG5, I enjoy the local culture when I disconnect on my trip]</td><td char=\".\" align=\"char\">0.853***</td></tr><tr><td align=\"left\" rowspan=\"3\">NAT-nature connectedness</td><td align=\"left\">[NAT1 I am more at one with nature when I disconnect]</td><td char=\".\" align=\"char\">0.895***</td><td char=\".\" align=\"char\" rowspan=\"3\">0.894</td><td char=\".\" align=\"char\" rowspan=\"3\">0.934</td><td char=\".\" align=\"char\" rowspan=\"3\">0.825</td></tr><tr><td align=\"left\">[NAT2 Disconnecting allows me to fully enjoy nature]</td><td char=\".\" align=\"char\">0.916***</td></tr><tr><td align=\"left\">[NAT3 When I disconnect, I feel good in nature]</td><td char=\".\" align=\"char\">0.913***</td></tr><tr><td align=\"left\" rowspan=\"6\">H-REL-health-relaxation</td><td align=\"left\">[REL, I relax when I am offline]</td><td char=\".\" align=\"char\">0.865***</td><td char=\".\" align=\"char\" rowspan=\"6\">0.920</td><td char=\".\" align=\"char\" rowspan=\"6\">0.938</td><td char=\".\" align=\"char\" rowspan=\"6\">0.718</td></tr><tr><td align=\"left\">[REL2 When I disconnect, I enjoy things]</td><td char=\".\" align=\"char\">0.877***</td></tr><tr><td align=\"left\">[REL3 Being disconnected gives me peace and well-being]</td><td char=\".\" align=\"char\">0.890***</td></tr><tr><td align=\"left\">[REL4, I feel mindfulness when I disconnect]</td><td char=\".\" align=\"char\">0.783***</td></tr><tr><td align=\"left\">[REL5 Being disconnected allows me to enjoy pleasant sensations]</td><td char=\".\" align=\"char\">0.897***</td></tr><tr><td align=\"left\">[REL6 I am open-minded when I am offline]</td><td char=\".\" align=\"char\">0.759***</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab3\"><label>Table 3</label><caption><p>Discriminant validity</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Discriminant validity</th><th align=\"left\" colspan=\"5\">Fornell-Larcker</th><th align=\"left\" colspan=\"4\">HTMT criterion</th></tr><tr><th align=\"left\">Construct</th><th align=\"left\">BI</th><th align=\"left\">ECO</th><th align=\"left\">ENG</th><th align=\"left\">NAT</th><th align=\"left\">H-REL</th><th align=\"left\">BI</th><th align=\"left\">ECO</th><th align=\"left\">ENG</th><th align=\"left\">NAT-</th></tr></thead><tbody><tr><td align=\"left\">BI-behavioural intention</td><td char=\".\" align=\"char\">0.943</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\">ECO-economic sustainability</td><td char=\".\" align=\"char\">0.798</td><td char=\".\" align=\"char\">0.856</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td char=\".\" align=\"char\">0.904</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\">ENG-social and family engagement</td><td char=\".\" align=\"char\">0.526</td><td char=\".\" align=\"char\">0.460</td><td char=\".\" align=\"char\">0.848</td><td align=\"left\"/><td align=\"left\"/><td char=\".\" align=\"char\">0.595</td><td char=\".\" align=\"char\">0.520</td><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\">NAT-nature connectedness</td><td char=\".\" align=\"char\">0.516</td><td char=\".\" align=\"char\">0.449</td><td char=\".\" align=\"char\">0.748</td><td char=\".\" align=\"char\">0.908</td><td align=\"left\"/><td char=\".\" align=\"char\">0.580</td><td char=\".\" align=\"char\">0.502</td><td char=\".\" align=\"char\">0.845</td><td align=\"left\"/></tr><tr><td align=\"left\">H-REL-health-relaxation</td><td char=\".\" align=\"char\">0.597</td><td char=\".\" align=\"char\">0.548</td><td char=\".\" align=\"char\">0.813</td><td char=\".\" align=\"char\">0.745</td><td char=\".\" align=\"char\">0.847</td><td char=\".\" align=\"char\">0.664</td><td char=\".\" align=\"char\">0.608</td><td char=\".\" align=\"char\">0.908</td><td char=\".\" align=\"char\">0.819</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab4\"><label>Table 4</label><caption><p>Endogenous variables</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Hypothesis/construct</th><th align=\"left\">R²</th><th align=\"left\">Direct effect (β)</th><th align=\"left\">Correlation</th><th align=\"left\">Explained variance</th></tr></thead><tbody><tr><td align=\"left\">H1(+). ENG <italic>&gt;</italic> NAT</td><td align=\"left\"/><td char=\".\" align=\"char\">0.798</td><td char=\".\" align=\"char\">0.748</td><td char=\".\" align=\"char\">31.21%</td></tr><tr><td align=\"left\">H2(+). REL <italic>&gt;</italic> NAT</td><td align=\"left\"/><td char=\".\" align=\"char\">0.061</td><td char=\".\" align=\"char\">0.745</td><td char=\".\" align=\"char\">30.29%</td></tr><tr><td align=\"left\">\n<bold><italic>NAT</italic></bold>\n</td><td char=\".\" align=\"char\">0.615</td><td align=\"left\"/><td align=\"left\"/><td char=\".\" align=\"char\">61.5%</td></tr><tr><td align=\"left\">H3(+). ENG <italic>&gt;</italic> BI</td><td align=\"left\"/><td char=\".\" align=\"char\">0.421</td><td char=\".\" align=\"char\">0.526</td><td char=\".\" align=\"char\">3.33%</td></tr><tr><td align=\"left\">H4(+). NAT <italic>&gt;</italic> BI</td><td align=\"left\"/><td char=\".\" align=\"char\">0.140</td><td char=\".\" align=\"char\">0.516</td><td char=\".\" align=\"char\">9.67%</td></tr><tr><td align=\"left\">H5(+). REL <italic>&gt;</italic> BI</td><td align=\"left\"/><td char=\".\" align=\"char\">0.443</td><td char=\".\" align=\"char\">0.597</td><td char=\".\" align=\"char\">23.90%</td></tr><tr><td align=\"left\">\n<bold><italic>BI</italic></bold>\n</td><td char=\".\" align=\"char\">0.369</td><td align=\"left\"/><td align=\"left\"/><td char=\".\" align=\"char\">36.9%</td></tr><tr><td align=\"left\">H6(+). BI &gt; ECO</td><td align=\"left\"/><td char=\".\" align=\"char\">0.403</td><td char=\".\" align=\"char\">0.798</td><td char=\".\" align=\"char\">63.8%</td></tr><tr><td align=\"left\">\n<bold><italic>ECO</italic></bold>\n</td><td char=\".\" align=\"char\">0.638</td><td align=\"left\"/><td align=\"left\"/><td char=\".\" align=\"char\">63.8%</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab5\"><label>Table 5</label><caption><p>Hypothesis support</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Hypothesis</th><th align=\"left\">β</th><th align=\"left\">T-value bootstrap</th><th align=\"left\">P values</th><th align=\"left\">Support</th></tr></thead><tbody><tr><td align=\"left\">H1. Social and family engagement -&gt; Nature connectedness</td><td char=\".\" align=\"char\">0.421***</td><td char=\".\" align=\"char\">7.682</td><td char=\".\" align=\"char\">0.000</td><td align=\"left\">Yes</td></tr><tr><td align=\"left\">H2. Health-Relaxation -&gt; Nature connectedness</td><td char=\".\" align=\"char\">0.403***</td><td char=\".\" align=\"char\">7.310</td><td char=\".\" align=\"char\">0.000</td><td align=\"left\">Yes</td></tr><tr><td align=\"left\">H3. Social and family engagement -&gt; Behavioural intention</td><td char=\".\" align=\"char\">0.061***</td><td char=\".\" align=\"char\">0.805</td><td char=\".\" align=\"char\">0.421</td><td align=\"left\">No</td></tr><tr><td align=\"left\">H4. Nature connectedness -&gt; Behavioural intention</td><td char=\".\" align=\"char\">0.140***</td><td char=\".\" align=\"char\">2.397</td><td char=\".\" align=\"char\">0.017</td><td align=\"left\">Yes</td></tr><tr><td align=\"left\">H5. Health-Relaxation -&gt; Behavioural intention</td><td char=\".\" align=\"char\">0.443***</td><td char=\".\" align=\"char\">5.925</td><td char=\".\" align=\"char\">0.000</td><td align=\"left\">Yes</td></tr><tr><td align=\"left\">H6. Behavioural intention -&gt; Economic sustainability</td><td char=\".\" align=\"char\">0.798***</td><td char=\".\" align=\"char\">37.876</td><td char=\".\" align=\"char\">0.000</td><td align=\"left\">Yes</td></tr></tbody></table></table-wrap>" ]

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[ "<supplementary-material content-type=\"local-data\" id=\"MOESM1\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM2\"></supplementary-material>" ]

[ "<table-wrap-foot><p>Own authors</p></table-wrap-foot>", "<table-wrap-foot><p><italic>Note</italic>: Bootstrapping 95% confidence interval using 5000 samples</p><p>*p value &lt; 0.05, using t (4999), one-tailed test, **p value &lt; 0.01, using t (4999), one-tailed test and ***p value &lt; 0.001, using t (4999), one-tailed test</p><p><italic>Source</italic>: Author</p></table-wrap-foot>", "<fn-group><fn><p><bold>Publisher’s Note</bold></p><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn><fn><p>José A. Folgado-Fernández and Pedro R. Palos-Sánchez contributed equally to this work.</p></fn></fn-group>" ]

[ "<graphic xlink:href=\"12889_2023_17584_Fig1_HTML\" id=\"d32e1082\"/>", "<graphic xlink:href=\"12889_2023_17584_Fig2_HTML\" id=\"d32e2022\"/>" ]

[ "<media xlink:href=\"12889_2023_17584_MOESM1_ESM.xlsx\"><caption><p>Supplementary Material 1</p></caption></media>", "<media xlink:href=\"12889_2023_17584_MOESM2_ESM.xlsx\"><caption><p>Supplementary Material 2</p></caption></media>" ]

{ "acronym": [ "AI", "BI", "DFT", "HTMT", "IT" ], "definition": [ "Artificial Intelligence", "Behavioural intentions", "Digital Free Tourism", "Heterotrait-monotrait criterion", "Information Technology" ] }

[ "<title>Introduction</title>", "<p id=\"Par5\">Acute pancreatitis (AP) is a common gastroenterological condition, with approximately 80% of patients developing mild to moderately severe disease (no organ failure &gt; 48 h) and the rest progressing into severe acute pancreatitis (SAP) [##UREF##0##1##]. The death rate of SAP is as high as 20%, therefore, early assessment of severity in AP is crucial. Despite the large number of studies exploring early prediction of AP severity [##REF##31507427##2##, ##UREF##1##3##], no ideal multifactorial scoring system and/or biochemical markers have been identified for early assessment of AP severity [##REF##31947993##4##]. Therefore, early identification of the development of severe AP remains a great challenge.</p>", "<p id=\"Par6\">In clinical studies, the components of metabolic syndrome have been found to be associated with the occurrence and deterioration of AP [##REF##31620021##5##, ##REF##33407178##6##]. In particular, obesity is an independent risk factor for the AP morbidity and mortality [##REF##35322789##7##–##REF##32891531##10##]. Depending on its location, adipose tissue can be divided into subcutaneous adipose tissue (SAT) and visceral adipose tissue (VAT). SAT, accounting for approximately 80% of all adipose tissue, acts as a reservoir for excess lipids. However, once the storage capacity is exceeded, which can only accommodate a limited number of adipocytes with limited expandability, fat begins to accumulate in areas outside the SAT, such as the liver, heart, skeletal muscles, and other sites [##REF##32158768##11##, ##REF##30067159##12##]. Numerous studies have shown that VAT, associated with the occurrence and development of AP [##REF##34341894##13##–##REF##34838056##15##], is a key site of inflammation and responsible for driving the systemic inflammatory response and exacerbating AP [##REF##31085992##16##, ##REF##32763428##17##], thus serving as an important prognostic indicator of AP severity. Being highly metabolically active, VAT can continuously release adipokines such as resistin, leptin, adiponectin, and visfatin into the portal circulation [##REF##32559663##18##], which may involve in the development and progression of AP by modulating oxidative stress and inflammatory responses and influencing the severity of AP. Furthermore, resistin, leptin, adiponectin, and visfatin are well-known biomarkers for Nonalcoholic Fatty Liver Disease (NAFLD), which is a strong risk factor for AP and SAP.</p>", "<p id=\"Par7\">Resistin has been found to increase the production of pro-inflammatory cytokines such as TNF-α, IL-1β, and IL-6 in mononuclear cells and macrophages [##REF##15843582##19##, ##REF##16039994##20##]. Additionally, it stimulates the production of cell adhesion molecules, including vascular cellular adhesion molecule-1 (VCAM-1), intercellular adhesion molecule-1 (ICAM-1), and monocyte chemoattractant protein (MCP)-1, as well as chemokine (C-C motif) ligand 2 (CCL 2), which contribute to chemotaxis and leukocyte recruitment to sites of inflammation [##REF##14733921##21##, ##REF##16159596##22##]. Leptin, which is mainly secreted by adipocytes, is a potent chemoattractant for immune cells, causing monocytes and macrophages to accumulate towards adipose tissue, and promoting increased expression of the inflammatory cytokines IL-6 and tumor necrosis factor (TNF) as well as toll-like receptor 4 (TLR4) [##REF##17728393##23##]. At the same time, leptin is required for T-cell development and promotes the production of pro-inflammatory cytokines in CD4[+] T cells [##REF##20227394##24##–##REF##27222115##26##]. Adiponectin, a hormone mainly produced by white adipose tissue, can inhibit M1 macrophage activation [##REF##26993045##27##, ##REF##32059381##28##], exert anti-inflammatory effects by regulating JmJC family histone demethylase 3, which contributes to M2 polarization [##REF##26399931##29##], and inhibit macrophage infiltration [##UREF##2##30##]. In animal studies, adiponectin-deficient mice exhibited more severe AP than wild-type mice, and adiponectin overexpression reduced the severity of AP [##REF##18579666##31##]. Administration of exogenous recombinant adiponectin to AP mice significantly reduced NF-kB activity, cytokine levels, and tissue damage [##REF##30345768##32##]. Visfatin has nicotinamide phosphoribose transferase (Nampt) activity, the rate-limiting enzyme of the nicotinamide adenine dinucleotide (NAD) salvage synthesis pathway, and macrophages rely on the NAD salvage pathway to meet their energy requirements and maintain their pro-inflammatory phenotype. Visfatin also promotes the release of pro-inflammatory cytokines IL-1β, IL-6, and TNF-α from peripheral monocytes [##REF##33535169##33##–##REF##33853482##35##].</p>", "<p id=\"Par8\">Despite many studies that have explored the relationship between adipokines and SAP, the findings have been inconsistent. Furthermore, even though a meta-analysis of the relationship between adipokines and SAP has recently been published [##REF##35770007##36##], it only examined the statistical correlation between resistin and SAP, without addressing the correlation between other adipokines and SAP. Therefore, we performed this meta-analysis, involving such adipokines as resistin, leptin, adiponectin, and visfatin, to explore the correlation between adipokines and SAP.</p>" ]

[ "<title>Method</title>", "<p id=\"Par9\">This study was performed in accordance with the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines.</p>", "<title>Search strategy</title>", "<p id=\"Par10\">We conducted a systematic literature search on Embase, Cochrane library, PubMed and Web of Science, using the following keywords: (“adipokines”, “resistin”, “leptin”, “visfatin” or “adiponectin”) AND (“acute pancreatitis”) and MeSH/Emtree terms as well (Table ##SUPPL##7##S1##). The deadline for the search was July 20, 2023. In addition, we checked the references of the screened literature to identify any additional relevant studies.</p>", "<title>Study selection</title>", "<p id=\"Par11\">Inclusion criteria: (1) study subjects with a confirmed diagnosis of AP were included; (2) the severity of the AP was assessed; (3) the concentration of resistin, leptin, endolipin or lipocalin in peripheral blood was measured; (4) complete data calculation metrics were available: including the mean of the concentrations of resistin, leptin, visfatin or adiponectin with corresponding standard deviations (SD) or 95% confidence intervals (CI); (5) studies republished after additional data in the literature on the same topic, using the most recent study data.</p>", "<p id=\"Par12\">Exclusion criteria: (1) duplicate articles; (2) reviews, meta-analyses, editorials, and letters; (3) animal studies or in vitro experiments; (4) articles whose data were unavailable; (5) studies that were subgroup analyses of included multicenter studies.</p>", "<p id=\"Par13\">Both the study selection and exclusion procedures described above were conducted by two independent investigators (Xuehua Yu and Ning Zhang). Once disagreements occur, a third independent reviewer (Jing Wu) was invited to make the final decision.</p>", "<title>Data extraction and quality assessment</title>", "<p id=\"Par14\">Data were extracted and cross-checked independently by two authors (Xuehua Yu and Ning Zhang) using a pre-developed data extraction form, and in case of disagreement, they were referred to a third investigator (Yunhong Zhao) for verification. Extractions included: first author, year of publication, country, types of adipokines, the time of the blood test, assay method, AP diagnostic criteria, sample size, sample characteristics, etiology, adipokine concentration (mean, SD), and fund.</p>", "<p id=\"Par15\">To evaluate the risk of bias and quality of all included studies, we used the Quality Assessment of Diagnostic Accuracy Studies tool (QUADAS) [##REF##27222115##26##], which was adapted to the studies included in this meta-analysis. All assessments were performed by two independent investigators (Xuehua Yu and Ning Zhang), and any disputes were resolved through consultation or discussion with a third party (Chengjiang Liu).</p>", "<title>Statistical analysis</title>", "<p id=\"Par16\">Continuous outcomes measured on the same scale were expressed as a mean value and standard deviation and were analyzed by using standardized mean difference (SMD). Statistical analyses of heterogeneity were conducted using the chi-squared Q test and the I-square (<italic>I</italic>^{<italic>2</italic>}) statistic. <italic>P</italic> &lt; 0.10 and <italic>I</italic>^{<italic>2</italic>} &gt; 50% were considered statistically significant heterogeneity thresholds. Calculation of the pooled SMD was performed using a random effects model. Moreover, subgroup and sensitivity analyses were used to further explore the sources of heterogeneity. All <italic>P</italic>-values were 2-tailed, and <italic>P</italic> &lt; 0.05 (except for tests of heterogeneity) was considered statistically significant. Publication bias was assessed by Egger’s test and Begg’s test.</p>" ]

[ "<title>Results</title>", "<title>Literature search and research characteristics</title>", "<p id=\"Par17\">According to a predefined search strategy, we searched PubMed, EMBASE, Web of Science, and Cochrane Library, generating 1266 articles. By strictly following the inclusion and exclusion criteria, 20 articles were finally included, and the specific screening process is shown in Fig. ##FIG##0##1##.</p>", "<p id=\"Par18\">\n\n</p>", "<p id=\"Par19\">The main characteristics of the included studies are summarized in Table ##TAB##0##1##, Table ##SUPPL##8##S2##, ##SUPPL##9##S3## and ##SUPPL##10##S4##. A total of 1332 AP patients were evaluated in studies conducted in countries (4 in Turkey, 3 in the United States, 3 in China, 2 in Czech Republic, 2 in Germany, 1 in India, 1 in México, 1 in Poland, 1 in Finland, 1 in Saudi Arabia, and 1 in Lithuania). Among the 20 studies, 10 evaluated the predictive effect of resistin on SAP, 8 focused on the predictive effect of leptin on SAP, 7 evaluated the predictive effect of adiponectin on SAP, and 3 investigated the predictive effect of visfatin on SAP. The detailed statistics of each adipocytokine are shown in Table ##TAB##1##2##. The quality assessment of all included studies that applied the QUADAS risk of bias assessment tool is shown in Table ##SUPPL##11##S5##.</p>", "<p id=\"Par20\">\n\n</p>", "<p id=\"Par21\">\n\n</p>", "<title>Relationship between adipokines and SAP</title>", "<p id=\"Par22\">A total of 7 of 10 studies showed significantly increased levels of resistin in patients with SAP relative to patients with mild acute pancreatitis (MAP). A total of 275 SAP patients and 541 MAP patients were included in the summary analysis, as shown in Fig. ##FIG##1##2##A. The pooled analysis showed significantly higher resistin levels in SAP patients as compared to MAP patients (SMD = 0.78, 95% CI:0.37 to 1.19, z = 3.75, <italic>P</italic> = 0.000). However, statistically significant heterogeneity was observed in these studies (<italic>P</italic> = 0.000, <italic>I</italic>^{<italic>2</italic>} = 83.9%).</p>", "<p id=\"Par23\">For leptin, 3 out of 8 studies saw significantly higher levels in patients with SAP. A total of 160 SAP patients and 310 MAP patients were analyzed. Leptin levels were not significantly higher in SAP patients than in MAP patients (SMD = 0.30, 95% CI: -0.08 to 0.68, z = 1.53, <italic>P</italic> = 0.127) (Fig. ##FIG##1##2##B). Again, significant heterogeneity was observed in the study (<italic>P</italic> = 0.004, <italic>I</italic>^{<italic>2</italic>} = 66.2%).</p>", "<p id=\"Par24\">A total of 1 out of 7 studies showed significantly lower adiponectin levels in patients with SAP as compared to those with MAP. Pooled analysis showed no significant difference in adiponectin levels between 131 SAP patients and 308 MAP patients (SMD = 0.11, 95% CI: -0.17 to 0.40, z = 0.80, <italic>P</italic> = 0.425) (Fig. ##FIG##1##2##C). Significant heterogeneity was found in these 10 studies (<italic>P</italic> = 0.190, <italic>I</italic>^{<italic>2</italic>} = 31.2%).</p>", "<p id=\"Par25\">Only 3 studies have examined blood visfatin levels in SAP patients and MAP patients. A total of 91 patients with SAP and 126 patients with MAP were analyzed. Visfatin levels were not significantly higher in patients with SAP than in those with MAP (SMD = 1.20, 95% CI: -0.48 to 2.88, z = 1.40, <italic>P</italic> = 0.162) (Fig. ##FIG##1##2##D). Again, significant heterogeneity was observed in the study (<italic>P</italic> = 0.000, <italic>I</italic>^{<italic>2</italic>} = 95.2%).</p>", "<p id=\"Par26\">\n\n</p>", "<title>Subgroup analysis</title>", "<p id=\"Par27\">According to year of publication, sample size, mean age of patients, and definition of SAP group and MAP group (Table ##SUPPL##10##S4##), subgroup analysis was performed to explore the impact of these three factors on outcomes as well as to identify potential sources of resistin and leptin heterogeneity.</p>", "<p id=\"Par28\">As shown in Fig ##SUPPL##0##S1##A, pooled results from the literature published before 2014 and in 2014 and after showed that resistin was predictive of SAP. Pooled results from studies in which the mean age of patients with AP was &lt; 50 years versus age ≥ 50 years also indicated that resistin was a predictor of SAP (Fig ##SUPPL##0##S1##B). Studies with a sample size of &lt; 100 patients showed significantly higher resistin levels in SAP patients than in MAP patients (SMD = 0.83, 95% CI: 0.42 to 1.24, z = 3.98, <italic>P</italic> = 0.000, <italic>I</italic>^{<italic>2</italic>} = 64.1%, Fig ##SUPPL##0##S1##C), but studies having a sample size of ≥ 100 patients showed no statistically significant difference in resistin levels between the two groups (SMD = 0.72, 95% CI: -0.08 to 1.52, z = 1.77, <italic>P</italic> = 0.076, <italic>I</italic>^{<italic>2</italic>} = 92.6%, Fig ##SUPPL##0##S1##C). In addition, SAP was defined as persistent organ failure (&gt; 48 h) in 6 studies that tested resistin levels, showing a significant difference between in the MAP group and the SAP group (SMD = 0.80, 95% CI: 0.23 to 1.37, z = 2.73, <italic>P</italic> = 0.006, <italic>I</italic>^{<italic>2</italic>} = 88.7%, Fig ##SUPPL##0##S1##D).</p>", "<p id=\"Par29\">Regarding leptin, as shown in Fig ##SUPPL##1##S2##, different publication years, ages, and definition of SAP and MAP showed no statistically significant difference in leptin levels between the two groups. However, the pooled results of the 7 studies with sample sizes &lt; 100 showed that leptin levels were higher in the SAP group than in the MAP group, and the difference was statistically significant (SMD = 0.40, 95% CI: 0.02 to 0.77, z = 2.07, <italic>P</italic> = 0.038, <italic>I</italic>^{<italic>2</italic>} = 57.2%, Fig ##SUPPL##1##S2##C).</p>", "<p id=\"Par30\">There were no statistically significant differences in lipocalin levels between the two groups for different publication years, sample sizes, ages, and definitions of SAP and MAP, as shown in Figure ##SUPPL##2##S3##.</p>", "<title>Sensitivity analysis</title>", "<p id=\"Par31\">Sensitivity analysis was performed whereby each study was excluded in turn to assess the stability of the results and the impact of each study on the pooled SMD was also determined (Fig. ##FIG##2##3##). It can be seen from Fig. ##FIG##2##3##A that the studies by Kibar YI et al., Singh AK et al. and Langmead C et al. had the greatest influence on the results regarding resistin. Although these 3 studies were removed, SAP patients showed significantly higher resistance levels than MAP patients (SMD = 0.66, 95% CI: 0.45 to 0.87, z = 6.21, <italic>P</italic> = 0.000, <italic>I</italic>^{<italic>2</italic>} = 0.0%, Fig ##SUPPL##3##S4##). As shown in Fig. ##FIG##2##3##B, the results of the study by Türkoğlu A et al. had the greatest impact on the results regarding leptin, and removal of this study still showed no significant increase in leptin levels in SAP patients compared to MAP patients (SMD = 0.13, 95% CI: -0.15 to 0.41, z = 0.88, <italic>P</italic> = 0.379, <italic>I</italic>^{<italic>2</italic>} = 23.9%, Fig ##SUPPL##4##S5##).</p>", "<p id=\"Par32\">\n\n</p>", "<title>Publication bias</title>", "<p id=\"Par33\">For resistin, leptin and lipocalin, symmetry was observed in Begg’s funnel plot (Fig ##SUPPL##5##S6##), with Egger’s test results <italic>(P</italic> = 0.444, <italic>P</italic> = 0.869, <italic>P</italic> = 0.920, respectively, Fig ##SUPPL##6##S7##), suggesting no publication bias.</p>" ]

[ "<title>Discussion</title>", "<p id=\"Par34\">The results of this meta-analysis showed that increased resistin levels were associated with SAP, whereas leptin and adiponectin levels were not linked to SAP. Only three of the studies included visfatin, not enough to draw any conclusions.</p>", "<p id=\"Par35\">Resistin is a small protein rich in cysteine, with a molecular weight of either 11 or 12.5 kDa. It was first identified in mice in 2001 as a signal molecule produced by adipocytes, and named resistin because it was thought to be involved in the development of insulin resistance [##REF##34220862##37##]. Resistin belongs to the resistin-like molecule (RELM) family, which includes RELM-α, RELM-β, and RELM-γ [##REF##36691020##38##]. Unlike mice, where resistin is produced by adipocytes, humans mainly express resistin in monocytes and macrophages [##REF##19740705##39##]. Despite only sharing 59% of the same amino acids [##REF##12594039##40##], resistin functions similarly in both humans and rodents, even though they are produced from different sources. Resistin has been identified as a molecule that promotes inflammation and regulates various chronic inflammatory, metabolic, and infectious diseases in humans [##REF##16549046##41##–##REF##30517161##44##]. It modulates many cellular responses in the host, such as recruiting and activating immune cells, promoting the release of pro-inflammatory cytokines, enhancing interferon (IFN) expression, and promoting the formation of neutrophil extracellular trap networks (NETs) [##REF##24719460##45##–##REF##27477870##47##].</p>", "<p id=\"Par36\">The role of resistin in regulating inflammatory pathways has been demonstrated in the context of AP. Resistin increases the levels of calcium in pancreatic follicular cells, as well as the activity of NADPH oxidase, leading to an increase in the production of reactive oxygen species (ROS) within the cells. Additionally, resistin activates the NF-κB pathway, resulting in the expression of pro-inflammatory cytokines such as TNF-α and IL-6 [##REF##34349610##48##, ##REF##23765438##49##]. Jiang et al. demonstrated in a laboratory model of AP induced by cerulein that resistin increases the production of pro-inflammatory cytokines TNF-α and IL-6 via an NF-κB-dependent pathway. However, the increased mRNA expression levels of TNF α and IL 6 induced by resistin can be significantly reduced by using an NF-κB inhibitor [##REF##27959400##50##]. Furthermore, Wang et al. discovered that the severity of SAP lung injury was positively associated with RELMα levels. Moreover, overexpression of RELMα worsened the release of inflammatory cytokines such as interleukin (IL)-1β, IL-6, IL-8, tumor necrosis factor-α, and serum C-reactive protein. This led to an increase in the expression of inflammatory mediators such as phosphorylated (p)-AKT, p-P65, p-P38 mitogen activated protein kinase, p-extracellular regulated kinase, and intracellular adhesion molecule-1, ultimately resulting in lung injury. On the other hand, knocking down RELMα had the opposite effect. It improved the expression of proliferative cellular nuclear antigen, Bcl-2, zonal occludin-1, and Claudin-1 in lung tissue of SAP rats [##REF##27492801##51##]. Furthermore, numerous studies have confirmed the correlation between resistin and the severity of AP. This suggests that resistin may serve as a valuable marker and potential therapeutic target for SAP [##REF##35730599##52##].</p>", "<p id=\"Par37\">Leptin is mainly secreted by fat cells and plays a crucial role in the immune response as an immune modulator [##REF##33584724##53##, ##REF##33857282##54##]. Monocytes treated with leptin increase the production of type 1 cytokines, including IL-1β, IL-6, TNF, and resistin [##REF##23484124##55##, ##REF##29565180##56##]. Adiponectin can inhibit the ROS/NF-κB/NLRP3 inflammatory pathway [##REF##34107901##57##], activate the anti-inflammatory cytokine interleukin-10 (IL-10), and reduce pro-inflammatory cytokines such as interferon-gamma (IFN-γ), IL-6 and TNF-α in human macrophages [##REF##15369797##58##]. The results of this meta-analysis showed that leptin and adiponectin levels were not linked to SAP. However, it is still unclear whether leptin and adiponectin have different effects at different stages of inflammation, or whether an imbalance among leptin, adiponectin and other adipokines may inhibit their regulation of the immune response, or whether there are other possible mechanisms, which need to be confirmed by more studies. Although most studies show that visfatin appears to have pro-inflammatory effects [##REF##33535169##33##–##REF##33853482##35##, ##REF##35731253##59##–##REF##33901513##62##], there are some studies that show the opposite [##REF##26399931##29##, ##UREF##2##30##, ##REF##34403808##63##]. In response to this seemingly contradictory result, the study by Sayers et al. may give us some insight. They found a possible bimodal effect of extracellular Nampt (eNampt) monomer on the stimulation of insulin secretion by β-cells [##REF##31732790##64##]. Whether this bimodal effect is equally reflected in the stimulatory effect of endolipin on inflammatory factors and the modulation of the inflammatory response, and whether it is this bimodal effect that leads to the unstable prediction of SAP by visfatin, remain to be further explored.</p>", "<p id=\"Par38\">Heterogeneity was observed in our pooled analysis. The resistin results were greatly influenced by two studies, while the leptin results were mainly affected by one study. Several factors such as regions, research samples, and detection reagents can affect the outcomes. Small sample sizes can also lead to accidental findings, making heterogeneity between studies inevitable. However, the stability of the results was confirmed even after removing the heterogeneous studies. Furthermore, sample size and mean age of the patients may be associated with resistin heterogeneity. It has been shown that the adverse effects of obesity appear to be reduced in older populations [##REF##32297267##65##]. Khatua et al. suggested that the different visceral triglyceride saturation status could have varying effects on AP severity, explaining the obesity paradox [##REF##33514548##66##]. Based on the results of the subgroup analysis in this meta-analysis, it appears that the mean age of patients has an effect on adiposity factors and resultantly affects AP severity, which may provide a new thought for the obesity paradox.</p>", "<p id=\"Par39\">There are some limitations to this meta-analysis. Firstly, all studies included were case-control studies with inherent selection, information and confounding biases. Secondly, the sample size was moderate for the included studies and a few of the eligible studies had small sample sizes. Thirdly, changes in testing methods and diagnostic criteria over time may have contributed to the different pooled results between publication years in the subgroup analysis.</p>", "<p id=\"Par40\">In conclusion, the results of this meta-analysis suggest high levels of resistin levels are associated with an increased risk of SAP, indicating resistin may be a potential biomarker. Moreover, serum or plasma samples can be easily obtained for resistin detection, and the assay is uncomplicated and can be performed in many laboratories. Since it is often challenging for a single indicator to accurately predict the severity of AP, it may be possible in the future to predict SAP by testing for the levels of resistin in conjunction with other indicators or by incorporating resistin into a scoring system.</p>" ]

[]

[ "<title>Background</title>", "<p id=\"Par1\">Severe acute pancreatitis (SAP) is a dangerous condition with a high mortality rate. Many studies have found an association between adipokines and the development of SAP, but the results are controversial. Therefore, we performed a meta-analysis of the association of inflammatory adipokines with SAP.</p>", "<title>Methods</title>", "<p id=\"Par2\">We screened PubMed, EMBASE, Web of Science and Cochrane Library for articles on adipokines and SAP published before July 20, 2023. The quality of the literature was assessed using QUADAS criteria. Standardized mean differences (SMD) with 95% confidence intervals (CI) were calculated to assess the combined effect. Subgroup analysis, sensitivity analysis and publication bias tests were also performed on the information obtained.</p>", "<title>Result</title>", "<p id=\"Par3\">Fifteen eligible studies included 1332 patients with acute pancreatitis (AP). Pooled analysis showed that patients with SAP had significantly higher serum levels of resistin (SMD = 0.78, 95% CI:0.37 to 1.19, z = 3.75, <italic>P</italic> = 0.000). The difference in leptin and adiponectin levels between SAP and mild acute pancreatitis (MAP) patients were not significant (SMD = 0.30, 95% CI: -0.08 to 0.68, z = 1.53, <italic>P</italic> = 0.127 and SMD = 0.11, 95% CI: -0.17 to 0.40, z = 0.80, <italic>P</italic> = 0.425, respectively). In patients with SAP, visfatin levels were not significantly different from that in patients with MAP (SMD = 1.20, 95% CI: -0.48 to 2.88, z = 1.40, <italic>P</italic> = 0.162).</p>", "<title>Conclusion</title>", "<p id=\"Par4\">Elevated levels of resistin are associated with the development of SAP. Resistin may serve as biomarker for SAP and has promise as therapeutic target.</p>", "<title>Supplementary Information</title>", "<p>The online version contains supplementary material available at 10.1186/s12876-024-03126-w.</p>", "<title>Keywords</title>" ]

[ "<title>Electronic supplementary material</title>", "<p>Below is the link to the electronic supplementary material.</p>", "<p>\n\n</p>", "<p>\n\n</p>", "<p>\n\n</p>", "<p>\n\n</p>", "<p>\n\n</p>", "<p>\n\n</p>", "<p>\n\n</p>", "<p>\n\n</p>", "<p>\n\n</p>", "<p>\n\n</p>", "<p>\n\n</p>", "<p>\n\n</p>" ]

[ "<title>Acknowledgements</title>", "<p>We appreciate from both Department of Gastroenterology, Hebei Provincial People’s Hospital and Graduate School of Hebei North University.</p>", "<title>Author contributions</title>", "<p>Jing Wu, Xuehua Yu, and Ning Zhang participated in literature collection.Yunhong Zhao, Xuehua Yu, and Ning Zhang participated in data extraction.Chengjiang Liu, Xuehua Yu, and Ning Zhang involved in article quality assessment.Xuehua Yu wrote the manuscript, and Gaifang Liu revised the article critically for important intellectual content.</p>", "<title>Funding</title>", "<p>Special Project for the Construction of Academician Workstation of Hebei Provincial People’s Hospital (Project No. 199A7745H). Clinical significance and mechanism of leukocyte elevation in the third condition of severe acute pancreatitis (Project No. 20200747). Molecular mechanism of NNMT/CCL8/VEGF-C signaling axis regulating lymph node metastasis in gastric cancer (Project No. H2022307040).</p>", "<title>Data availability</title>", "<p>All data generated or analyzed during this study are included in this published article and its supplementary information files.</p>", "<title>Declarations</title>", "<title>Ethics approval and consent to participate</title>", "<p id=\"Par41\">Not applicable (this paper was provided based on researching in global databases).</p>", "<title>Consent for publication</title>", "<p id=\"Par42\">Not applicable.</p>", "<title>Competing interests</title>", "<p id=\"Par43\">The authors declare no competing interests.</p>" ]

[ "<fig id=\"Fig1\"><label>Fig. 1</label><caption><p>The PRISMA flow chart of literature screening</p></caption></fig>", "<fig id=\"Fig2\"><label>Fig. 2</label><caption><p>Forest plots of SMD with 95% CI of peripheral blood levels of resistin (<bold>A</bold>), leptin (<bold>B</bold>), adiponectin (<bold>C</bold>) and visfatin (<bold>D</bold>) levels between SAP patients and MAP patients</p></caption></fig>", "<fig id=\"Fig3\"><label>Fig. 3</label><caption><p>The pooled SMD and 95%CI of eligible studies of resistin (<bold>A</bold>) and leptin (<bold>B</bold>) through sensitivity analysis</p></caption></fig>" ]

[ "<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Characteristics of 20 studies included in the meta-analysis (1)</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" rowspan=\"2\">Author, year</th><th align=\"left\" rowspan=\"2\">Country</th><th align=\"left\" colspan=\"4\">Assay Method</th><th align=\"left\" colspan=\"2\">Sample size,n</th><th align=\"left\" rowspan=\"2\">Collecting time</th><th align=\"left\" rowspan=\"2\">Etiology</th></tr><tr><th align=\"left\">Resistin</th><th align=\"left\">Leptin</th><th align=\"left\">Adiponectin</th><th align=\"left\">Visfatin</th><th align=\"left\">SAP</th><th align=\"left\">MAP</th></tr></thead><tbody><tr><td align=\"left\"><p>Kisaoglu,2014</p><p> [##UREF##3##67##]</p></td><td align=\"left\">Turkey</td><td align=\"left\">ELISA</td><td align=\"left\">-</td><td align=\"left\">-</td><td align=\"left\">-</td><td char=\".\" align=\"char\">17</td><td char=\".\" align=\"char\">17</td><td align=\"left\">on the 1st day</td><td align=\"left\">n/r</td></tr><tr><td align=\"left\">Schäffler A, 2010 [##REF##20648005##68##]</td><td align=\"left\">Germany</td><td align=\"left\">ELISA</td><td align=\"left\">ELISA</td><td align=\"left\">ELISA</td><td align=\"left\">-</td><td char=\".\" align=\"char\">41</td><td char=\".\" align=\"char\">9</td><td align=\"left\">at admission</td><td align=\"left\">gallstones, alcohol, metabolic, ERCP, toxic,</td></tr><tr><td align=\"left\">Kibar YI, 2016 [##REF##26857860##69##]</td><td align=\"left\">Turkey</td><td align=\"left\">ELISA</td><td align=\"left\">-</td><td align=\"left\">-</td><td align=\"left\">-</td><td char=\".\" align=\"char\">22</td><td char=\".\" align=\"char\">37</td><td align=\"left\">at admission</td><td align=\"left\">biliary/nonbiliary</td></tr><tr><td align=\"left\">Singh AK, 2021 [##REF##33089708##70##]</td><td align=\"left\">India</td><td align=\"left\">ELISA</td><td align=\"left\">-</td><td align=\"left\">-</td><td align=\"left\">-</td><td char=\".\" align=\"char\">53</td><td char=\".\" align=\"char\">77</td><td align=\"left\">at admission</td><td align=\"left\">alcohol, gallstone, others</td></tr><tr><td align=\"left\">Karpavicius A, 2016 [##REF##27549125##71##]</td><td align=\"left\">Lithuania</td><td align=\"left\">ELISA</td><td align=\"left\">ELISA</td><td align=\"left\">ELISA</td><td align=\"left\">ELISA</td><td char=\".\" align=\"char\">20</td><td char=\".\" align=\"char\">82</td><td align=\"left\">at admission</td><td align=\"left\">biliary stones, alcohol, others</td></tr><tr><td align=\"left\">Al-Maramhy, 2014 [##REF##25386084##72##]</td><td align=\"left\">Saudi Arabia</td><td align=\"left\">ELISA</td><td align=\"left\">-</td><td align=\"left\">-</td><td align=\"left\">-</td><td char=\".\" align=\"char\">22</td><td char=\".\" align=\"char\">80</td><td align=\"left\">at admission</td><td align=\"left\">gallstone</td></tr><tr><td align=\"left\"><p>Yu P,</p><p>2016 [##REF##27654573##73##]</p></td><td align=\"left\">China</td><td align=\"left\">Luminex xMAP</td><td align=\"left\">ELISA</td><td align=\"left\"><p>Luminex</p><p>xMAP</p></td><td align=\"left\">-</td><td char=\".\" align=\"char\">24</td><td char=\".\" align=\"char\">66</td><td align=\"left\">at admission</td><td align=\"left\">biliary, alcoholic, hypertriglyceridaemia, others</td></tr><tr><td align=\"left\">Muddana V, 2010 [##UREF##4##74##]</td><td align=\"left\">America</td><td align=\"left\">Luminex assay</td><td align=\"left\">-</td><td align=\"left\">-</td><td align=\"left\">-</td><td char=\".\" align=\"char\">19</td><td char=\".\" align=\"char\">27</td><td align=\"left\">in early time</td><td align=\"left\">n/r</td></tr><tr><td align=\"left\">Novotny D, 2015 [##UREF##5##75##]</td><td align=\"left\">Czech Republic</td><td align=\"left\">-</td><td align=\"left\">-</td><td align=\"left\">ELISA</td><td align=\"left\">-</td><td char=\".\" align=\"char\">14</td><td char=\".\" align=\"char\">70</td><td align=\"left\">at admission</td><td align=\"left\">alcohol, biliary, CHP (chronic pancreatitis) exacerbation, idiopathic, others</td></tr><tr><td align=\"left\">Sharma A, 2009 [##REF##19696691##76##]</td><td align=\"left\">America</td><td align=\"left\">-</td><td align=\"left\">-</td><td align=\"left\">IF</td><td align=\"left\">-</td><td char=\".\" align=\"char\">10</td><td char=\".\" align=\"char\">26</td><td align=\"left\">days 1 to 3</td><td align=\"left\">n/r</td></tr><tr><td align=\"left\">Tukiainen E, 2006 [##REF##16552343##77##]</td><td align=\"left\">Finland</td><td align=\"left\">-</td><td align=\"left\">IA</td><td align=\"left\">IA</td><td align=\"left\">-</td><td char=\".\" align=\"char\">12</td><td char=\".\" align=\"char\">12</td><td align=\"left\">at admission</td><td align=\"left\">alcohol, biliary, idiopathic</td></tr><tr><td align=\"left\">Türkoğlu A, 2014 [##REF##25448651##78##]</td><td align=\"left\">Turkey</td><td align=\"left\">-</td><td align=\"left\">ELISA</td><td align=\"left\">-</td><td align=\"left\">-</td><td char=\".\" align=\"char\">30</td><td char=\".\" align=\"char\">62</td><td align=\"left\">within 24 h of admission</td><td align=\"left\">biliary, alcoholic, hypertriglyceridemia, idiopathic, ERCP</td></tr><tr><td align=\"left\">Panek J, 2014 [##REF##24988238##79##]</td><td align=\"left\">Poland</td><td align=\"left\">-</td><td align=\"left\">RIA</td><td align=\"left\">-</td><td align=\"left\">-</td><td char=\".\" align=\"char\">11</td><td char=\".\" align=\"char\">9</td><td align=\"left\">1st</td><td align=\"left\">biliary</td></tr><tr><td align=\"left\">Duarte-Rojo A, 2006 [##REF##16865784##80##]</td><td align=\"left\">México</td><td align=\"left\">-</td><td align=\"left\">ELISA</td><td align=\"left\">-</td><td align=\"left\">-</td><td char=\".\" align=\"char\">14</td><td char=\".\" align=\"char\">38</td><td align=\"left\">at admission</td><td align=\"left\">biliary, hypertriglyceridemia, alcoholic, ERCP, others</td></tr><tr><td align=\"left\">Schäffler A, 2011 [##REF##21245835##81##]</td><td align=\"left\">Germany</td><td align=\"left\">-</td><td align=\"left\">-</td><td align=\"left\">-</td><td align=\"left\">ELISA</td><td char=\".\" align=\"char\">41</td><td char=\".\" align=\"char\">9</td><td align=\"left\">at admission</td><td align=\"left\">gallstones, alcohol, metabolic, ERCP, toxic,</td></tr><tr><td align=\"left\"><p>Ülger BV,</p><p>2014 [##REF##25599787##82##]</p></td><td align=\"left\">Turkey</td><td align=\"left\">-</td><td align=\"left\">ELISA</td><td align=\"left\">-</td><td align=\"left\">-</td><td char=\".\" align=\"char\">8</td><td char=\".\" align=\"char\">32</td><td align=\"left\">at admission</td><td align=\"left\">gallstones</td></tr><tr><td align=\"left\"><p>Deng LH,</p><p>2017 [##UREF##6##83##]</p></td><td align=\"left\">China</td><td align=\"left\">Human Obesity Premixed Kit</td><td align=\"left\">-</td><td align=\"left\">-</td><td align=\"left\">-</td><td char=\".\" align=\"char\">20</td><td char=\".\" align=\"char\">50</td><td align=\"left\"><p>within 24 h of</p><p>admission</p></td><td align=\"left\">biliary, hypertriglyceridemia, alcoholic, others</td></tr><tr><td align=\"left\"><p>Langmead C,</p><p>2021 [##REF##33955376##84##]</p></td><td align=\"left\">America</td><td align=\"left\">custom human duplex kits</td><td align=\"left\">-</td><td align=\"left\">-</td><td align=\"left\">-</td><td char=\".\" align=\"char\">37</td><td char=\".\" align=\"char\">96</td><td align=\"left\">at days 2, 3, and 4</td><td align=\"left\">biliary, alcoholic, idiopathic, other</td></tr><tr><td align=\"left\"><p>Malina P,</p><p>2014 [##UREF##7##85##]</p></td><td align=\"left\">Czech Republic</td><td align=\"left\">-</td><td align=\"left\">-</td><td align=\"left\">ELISA</td><td align=\"left\">-</td><td char=\".\" align=\"char\">10</td><td char=\".\" align=\"char\">43</td><td align=\"left\">at admission</td><td align=\"left\">biliary, alcoholic, exacerbation of chronic pancreatitis, idiopathic</td></tr><tr><td align=\"left\"><p>Guo F,</p><p>2021 [##UREF##8##86##]</p></td><td align=\"left\">China</td><td align=\"left\">-</td><td align=\"left\">-</td><td align=\"left\">-</td><td align=\"left\">ELISA</td><td char=\".\" align=\"char\">30</td><td char=\".\" align=\"char\">35</td><td align=\"left\">at admission</td><td align=\"left\">biliary, alcoholic, other</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab2\"><label>Table 2</label><caption><p>Circulating levels of resistin, leptin, adiponectin and visfatin in SAP and MAP patients</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" rowspan=\"2\">Author, year</th><th align=\"left\" colspan=\"4\">SAP</th><th align=\"left\" colspan=\"4\">MAP</th></tr><tr><th align=\"left\">Mean</th><th align=\"left\">SD</th><th align=\"left\">unit</th><th align=\"left\">N</th><th align=\"left\">Mean</th><th align=\"left\">SD</th><th align=\"left\">unit</th><th align=\"left\">N</th></tr></thead><tbody><tr><td align=\"left\" colspan=\"3\">\n<bold>Circulating resistin levels</bold>\n</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\">Kisaoglu, 2014 [##UREF##3##67##]</td><td align=\"left\">26.48</td><td align=\"left\">12.03</td><td align=\"left\">ng/dl</td><td char=\".\" align=\"char\">17</td><td align=\"left\">23.50</td><td align=\"left\">12.30</td><td align=\"left\">ng/dl</td><td align=\"left\">17</td></tr><tr><td align=\"left\">Schäffler A, 2010 [##REF##20648005##68##]</td><td align=\"left\">74.1</td><td align=\"left\">94.9</td><td align=\"left\">ng/ml</td><td char=\".\" align=\"char\">41</td><td align=\"left\">35.9</td><td align=\"left\">54.6</td><td align=\"left\">ng/ml</td><td align=\"left\">9</td></tr><tr><td align=\"left\">Kibar YI, 2016 [##REF##26857860##69##]</td><td align=\"left\">28.9</td><td align=\"left\">5.22</td><td align=\"left\">ng/ml</td><td char=\".\" align=\"char\">22</td><td align=\"left\">18.3</td><td align=\"left\">6.95</td><td align=\"left\">ng/ml</td><td align=\"left\">37</td></tr><tr><td align=\"left\">Singh AK, 2021 [##REF##33089708##70##]</td><td align=\"left\">1.24</td><td align=\"left\">1.72</td><td align=\"left\">ng/ml</td><td char=\".\" align=\"char\">53</td><td align=\"left\">1.39</td><td align=\"left\">2.45</td><td align=\"left\">ng/ml</td><td align=\"left\">77</td></tr><tr><td align=\"left\">Karpavicius A, 2016 [##REF##27549125##71##]</td><td align=\"left\">20.2</td><td align=\"left\">31.75</td><td align=\"left\">ng/ml</td><td char=\".\" align=\"char\">20</td><td align=\"left\">10.7</td><td align=\"left\">8.65</td><td align=\"left\">ng/ml</td><td align=\"left\">82</td></tr><tr><td align=\"left\">Al-Maramhy, 2014 [##REF##25386084##72##]</td><td align=\"left\">17.5</td><td align=\"left\">0.96</td><td align=\"left\">ng/ml</td><td char=\".\" align=\"char\">22</td><td align=\"left\">16.82</td><td align=\"left\">1.10</td><td align=\"left\">ng/ml</td><td align=\"left\">80</td></tr><tr><td align=\"left\"><p>Yu P,</p><p>2016 [##REF##27654573##73##]</p></td><td align=\"left\">230.94</td><td align=\"left\">215.79</td><td align=\"left\">ng/ml</td><td char=\".\" align=\"char\">24</td><td align=\"left\">107.95</td><td align=\"left\">85.76</td><td align=\"left\">ng/ml</td><td align=\"left\">66</td></tr><tr><td align=\"left\">Muddana V, 2010 [##UREF##4##74##]</td><td align=\"left\">51,316</td><td align=\"left\">59023.7</td><td align=\"left\">pg/ml</td><td char=\".\" align=\"char\">19</td><td align=\"left\">7504</td><td align=\"left\">4199.26</td><td align=\"left\">pg/ml</td><td align=\"left\">27</td></tr><tr><td align=\"left\"><p>Deng LH,</p><p>2017 [##UREF##6##83##]</p></td><td align=\"left\">53264.28</td><td align=\"left\">125153.22</td><td align=\"left\">n/r</td><td char=\".\" align=\"char\">20</td><td align=\"left\">11686.23</td><td align=\"left\">59253.78</td><td align=\"left\">n/r</td><td align=\"left\">50</td></tr><tr><td align=\"left\"><p>Langmead C,</p><p>2021 [##REF##33955376##84##]</p></td><td align=\"left\">12,104</td><td align=\"left\">10,359</td><td align=\"left\">n/r</td><td char=\".\" align=\"char\">37</td><td align=\"left\">2175</td><td align=\"left\">2089</td><td align=\"left\">n/r</td><td align=\"left\">96</td></tr><tr><td align=\"left\" colspan=\"3\">\n<bold>Circulating leptin levels</bold>\n</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\">Schäffler A, 2010 [##REF##20648005##68##]</td><td align=\"left\">20.9</td><td align=\"left\">30.7</td><td align=\"left\">ng/ml</td><td char=\".\" align=\"char\">41</td><td align=\"left\">17.5</td><td align=\"left\">13.9</td><td align=\"left\">ng/ml</td><td align=\"left\">9</td></tr><tr><td align=\"left\">Karpavicius A, 2016 [##REF##27549125##71##]</td><td align=\"left\">4.17</td><td align=\"left\">8.14</td><td align=\"left\">ng/ml</td><td char=\".\" align=\"char\">20</td><td align=\"left\">7.21</td><td align=\"left\">11.83</td><td align=\"left\">ng/ml</td><td align=\"left\">82</td></tr><tr><td align=\"left\"><p>Yu P,</p><p>2016 [##REF##27654573##73##]</p></td><td align=\"left\">7.07</td><td align=\"left\">6.61</td><td align=\"left\">ng/ml</td><td char=\".\" align=\"char\">24</td><td align=\"left\">5.01</td><td align=\"left\">6.48</td><td align=\"left\">ng/ml</td><td align=\"left\">66</td></tr><tr><td align=\"left\">Tukiainen E, 2006 [##REF##16552343##77##]</td><td align=\"left\">6.1</td><td align=\"left\">52.8</td><td align=\"left\">ng/ml</td><td char=\".\" align=\"char\">12</td><td align=\"left\">9.0</td><td align=\"left\">25.2</td><td align=\"left\">ng/ml</td><td align=\"left\">12</td></tr><tr><td align=\"left\">Türkoğlu A, 2014 [##REF##25448651##78##]</td><td align=\"left\">9.32</td><td align=\"left\">5.80</td><td align=\"left\">ng/ml</td><td char=\".\" align=\"char\">30</td><td align=\"left\">4.69</td><td align=\"left\">3.46</td><td align=\"left\">ng/ml</td><td align=\"left\">62</td></tr><tr><td align=\"left\">Panek J, 2014 [##REF##24988238##79##]</td><td align=\"left\">24.7</td><td align=\"left\">20.0</td><td align=\"left\">ng/ml</td><td char=\".\" align=\"char\">11</td><td align=\"left\">6.8</td><td align=\"left\">6.7</td><td align=\"left\">ng/ml</td><td align=\"left\">9</td></tr><tr><td align=\"left\">Duarte-Rojo A, 2006 [##REF##16865784##80##]</td><td align=\"left\">10.3</td><td align=\"left\">44.8</td><td align=\"left\">ng/ml</td><td char=\".\" align=\"char\">14</td><td align=\"left\">7.7</td><td align=\"left\">158.9</td><td align=\"left\">ng/ml</td><td align=\"left\">38</td></tr><tr><td align=\"left\"><p>Ülger BV,</p><p>2014 [##REF##25599787##82##]</p></td><td align=\"left\">7.63</td><td align=\"left\">6.27</td><td align=\"left\">ng/ml</td><td char=\".\" align=\"char\">8</td><td align=\"left\">6.93</td><td align=\"left\">4.35</td><td align=\"left\">ng/ml</td><td align=\"left\">32</td></tr><tr><td align=\"left\" colspan=\"3\">\n<bold>Circulating adiponectin levels</bold>\n</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\">Schäffler A, 2010 [##REF##20648005##68##]</td><td align=\"left\">12.2</td><td align=\"left\">20.8</td><td align=\"left\">µg/ml</td><td char=\".\" align=\"char\">41</td><td align=\"left\">9.9</td><td align=\"left\">8.1</td><td align=\"left\">µg/ml</td><td align=\"left\">9</td></tr><tr><td align=\"left\">Sharma A, 2009 [##REF##19696691##76##]</td><td align=\"left\">3.74</td><td align=\"left\">5.99</td><td align=\"left\">µg/L</td><td char=\".\" align=\"char\">10</td><td align=\"left\">6.58</td><td align=\"left\">10.41</td><td align=\"left\">µg/L</td><td align=\"left\">26</td></tr><tr><td align=\"left\">Karpavicius A, 2016 [##REF##27549125##71##]</td><td align=\"left\">7.91</td><td align=\"left\">10.07</td><td align=\"left\">ng/ml</td><td char=\".\" align=\"char\">20</td><td align=\"left\">11.10</td><td align=\"left\">9.58</td><td align=\"left\">ng/ml</td><td align=\"left\">82</td></tr><tr><td align=\"left\"><p>Yu P,</p><p>2016 [##REF##27654573##73##]</p></td><td align=\"left\">13.65</td><td align=\"left\">8.08</td><td align=\"left\">ng/ml</td><td char=\".\" align=\"char\">24</td><td align=\"left\">10.17</td><td align=\"left\">11.34</td><td align=\"left\">ng/ml</td><td align=\"left\">66</td></tr><tr><td align=\"left\">Novotny D, 2015 [##UREF##5##75##]</td><td align=\"left\">8.3</td><td align=\"left\">2.6</td><td align=\"left\">mg/L</td><td char=\".\" align=\"char\">14</td><td align=\"left\">7.4</td><td align=\"left\">3.0</td><td align=\"left\">mg/L</td><td align=\"left\">70</td></tr><tr><td align=\"left\">Tukiainen E, 2006 [##REF##16552343##77##]</td><td align=\"left\">5642</td><td align=\"left\">13,481</td><td align=\"left\">ng/ml</td><td char=\".\" align=\"char\">12</td><td align=\"left\">6314</td><td align=\"left\">16,563</td><td align=\"left\">ng/ml</td><td align=\"left\">12</td></tr><tr><td align=\"left\"><p>Malina P,</p><p>2014 [##UREF##7##85##]</p></td><td align=\"left\">8.45</td><td align=\"left\">1.56</td><td align=\"left\">mg/L</td><td char=\".\" align=\"char\">10</td><td align=\"left\">6.4</td><td align=\"left\">3.0</td><td align=\"left\">mg/L</td><td align=\"left\">43</td></tr><tr><td align=\"left\" colspan=\"3\">\n<bold>Circulating visfatin levels</bold>\n</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\">Schäffler A, 2011 [##REF##21245835##81##]</td><td align=\"left\">6.8</td><td align=\"left\">9.6</td><td align=\"left\">ng/ml</td><td char=\".\" align=\"char\">41</td><td align=\"left\">3.3</td><td align=\"left\">1.5</td><td align=\"left\">ng/ml</td><td align=\"left\">9</td></tr><tr><td align=\"left\">Karpavicius A, 2016 [##REF##27549125##71##]</td><td align=\"left\">5.42</td><td align=\"left\">4.74</td><td align=\"left\">ng/ml</td><td char=\".\" align=\"char\">20</td><td align=\"left\">4.15</td><td align=\"left\">5.45</td><td align=\"left\">ng/ml</td><td align=\"left\">82</td></tr><tr><td align=\"left\"><p>Guo F,</p><p>2021 [##UREF##8##86##]</p></td><td align=\"left\">10.75</td><td align=\"left\">2.92</td><td align=\"left\">ng/ml</td><td char=\".\" align=\"char\">30</td><td align=\"left\">3.70</td><td align=\"left\">1.73</td><td align=\"left\">ng/ml</td><td align=\"left\">35</td></tr></tbody></table></table-wrap>" ]

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[ "<supplementary-material content-type=\"local-data\" id=\"MOESM1\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM2\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM3\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM4\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM5\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM6\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM7\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM8\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM9\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM10\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM11\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM12\"></supplementary-material>" ]

[ "<table-wrap-foot><p>n/r, not reported; ELISA, Enzyme Linked Immunosorbent Assay; IA, immunoassays; IF, immunofluorescence</p></table-wrap-foot>", "<table-wrap-foot><p>SAP, Severe acute pancreatitis; MAP, Mild acute pancreatitis; SD, standard deviation; N, number; n/r, not reported</p></table-wrap-foot>", "<fn-group><fn><p><bold>Publisher’s Note</bold></p><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn></fn-group>" ]

[ "<graphic xlink:href=\"12876_2024_3126_Fig1_HTML\" id=\"d32e422\"/>", "<graphic xlink:href=\"12876_2024_3126_Fig2_HTML\" id=\"d32e1848\"/>", "<graphic xlink:href=\"12876_2024_3126_Fig3_HTML\" id=\"d32e1978\"/>" ]

[ "<media xlink:href=\"12876_2024_3126_MOESM1_ESM.tif\"><caption><p><bold>Supplementary Material 1:</bold> Forest plots of subgroup analysis by year of publication (A), age (B), sample size (C), and definition of SAP group and MAP group (D) in resistin</p></caption></media>", "<media xlink:href=\"12876_2024_3126_MOESM2_ESM.tif\"><caption><p><bold>Supplementary Material 2:</bold> Forest plots of subgroup analysis by year of publication (A), age (B), sample size (C), and definition of SAP group and MAP group (D) in leptin</p></caption></media>", "<media xlink:href=\"12876_2024_3126_MOESM3_ESM.tif\"><caption><p><bold>Supplementary Material 3:</bold> Forest plots of subgroup analysis by year of publication (A), age (B), sample size (C), and definition of SAP group and MAP group (D) in adiponectin</p></caption></media>", "<media xlink:href=\"12876_2024_3126_MOESM4_ESM.tif\"><caption><p><bold>Supplementary Material 4:</bold> Forest plots of SMD with 95% CI of peripheral blood levels of resistin excluding the studies of Kibar YI et al., Singh AK et al. and Langmead C et al</p></caption></media>", "<media xlink:href=\"12876_2024_3126_MOESM5_ESM.tif\"><caption><p><bold>Supplementary Material 5:</bold> Forest plots of SMD with 95% CI of peripheral blood levels of leptin excluding the studies of Türkoğlu A et al</p></caption></media>", "<media xlink:href=\"12876_2024_3126_MOESM6_ESM.tif\"><caption><p><bold>Supplementary Material 6:</bold> Begg’s funnel plot of peripheral blood levels of resistin (A), leptin (B), and adiponectin (C) levels between SAP patients and MAP patients</p></caption></media>", "<media xlink:href=\"12876_2024_3126_MOESM7_ESM.tif\"><caption><p><bold>Supplementary Material 7:</bold> Egger’s publication bias plot of peripheral blood levels of resistin (A), leptin (B), and adiponectin (C) levels between SAP patients and MAP patients</p></caption></media>", "<media xlink:href=\"12876_2024_3126_MOESM8_ESM.docx\"><caption><p><bold>Supplementary Material 8:</bold> Search strategy in PubMed</p></caption></media>", "<media xlink:href=\"12876_2024_3126_MOESM9_ESM.docx\"><caption><p><bold>Supplementary Material 9:</bold> Characteristics of 20 studies included in the meta-analysis (2)</p></caption></media>", "<media xlink:href=\"12876_2024_3126_MOESM10_ESM.docx\"><caption><p><bold>Supplementary Material 10:</bold> Characteristics of 20 studies included in the meta-analysis (3)</p></caption></media>", "<media xlink:href=\"12876_2024_3126_MOESM11_ESM.docx\"><caption><p><bold>Supplementary Material 11:</bold> Characteristics of 20 studies included in the meta-analysis (4)</p></caption></media>", "<media xlink:href=\"12876_2024_3126_MOESM12_ESM.docx\"><caption><p><bold>Supplementary Material 12:</bold> The quality assessment of all included studies applying Quality Assessment of Diagnostic Accuracy Studies tool (QUADAS)</p></caption></media>" ]

{ "acronym": [], "definition": [] }

[ "<title>Introduction</title>", "<p id=\"Par6\">Typically the main aim of a phase I dose-finding trials is to identify the maximum tolerated dose (MTD) of the treatment being investigated. The MTD is usually determined under the monotonicity assumption which assumes that as dose increases so does the probability of toxicity. With model-based designs such as the continual reassessment method (CRM) escalation occurs to identify the dose with an associated probability of toxicity based on a pre-defined target.</p>", "<p id=\"Par7\">The investigation of multiple-agent treatments in phase I dose-finding trials, where the monotonicity assumption in relation to the dose-toxicity model may not hold, is increasing in early phase trials. Finding the MTD in combinations of treatments, compared to single-agents, presents methodological challenges. Each drug individually may obey the monotonicity assumption we can refer to this as the doses being fully ordered. However, when multiple treatments are combined, the ordering of doses in terms of toxicity may not be fully apparent or may only be partially ordered. An order may be identified for a subset of the doses which would result in a partial order. Without a fully understood ordering it is uncertain which dose should be chosen in decisions of escalation and de-escalation and ultimately as the MTD. This issue is not exclusively reserved for trials with multiple-agents. The monotonicity assumption may not hold for certain drugs in single-agent studies leading to partial orders of dose toxicity. For example, when dose and frequency of administration vary between dose levels. Monotonicity is a very strong assumption. It requires that the probability of toxicity is always increasing - staying the same is not enough. At high enough doses, this assumption is almost surely violated for all interventions when the event probability reaches its maximum. Thus, even when total ordering is possible, the monotonicity assumption could be violated [##REF##33397301##1##]. This can occur in scenarios where multiple parameters of the treatment schedule are altered for each dose level. For example, two doses could prescribe the same overall total dose but be over different treatment durations and hence have higher and lower daily doses. In this situation, it could be unclear as to whether prolonged exposure to a lower daily dose is more toxic than short exposure to a higher daily dose, which implies a partial ordering of toxicity probabilities. This is the case for the proposed dose levels in the ADePT-DDR trial.</p>", "<p id=\"Par8\">Worldwide there are approximately 600,000 new cases of Head and Neck Squamous Cell Carcinoma (HNSCC) each year [##REF##21798893##2##]. Of which, 12,000 occur in the UK with the most common forms of treatment being surgery, radiotherapy and/or chemotherapy. Radiotherapy is essential for the treatment of cancer. It has been estimated that more than 40% of patients will receive radiotherapy at some point in their treatment [##UREF##0##3##]. However, despite recent advancements in radiation techniques and the use of concomitant chemoradiotherapy, patients with solid tumours such as head and neck cancer have suboptimal cure rates [##REF##18798532##4##]. For those with advanced HNSCC, primary radiotherapy with concurrent chemotherapy is often offered but, it has not been shown to improve survival in patients aged over 70 compared to radiotherapy alone [##UREF##1##5##]. Therefore, any strategy to improve the efficacy of radiotherapy without increasing toxicity would have a significant impact on patient outcomes. DNA damage repair (DDR) inhibition is a potential technique which could be utilised as it potentiates the therapeutic effects of ionising radiation in cancer cells [##UREF##2##6##]. Combining radiotherapy with DDR inhibition could improve clinical outcomes for these patients [##UREF##3##7##].</p>", "<p id=\"Par9\">The ADePT-DDR trial is a platform trial which aims to evaluate the safety and efficacy of different DDR agents, or different immunotherapy agents and/or DDR and immunotherapy combinations, together with radiotherapy in patients with HNSCC. The initial component of this trial is a single-arm dose-finding trial investigating the ataxia telangiectasis and Rad3-related (ATR) inhibitor AZD6738 in combination with radiotherapy. ATR inhibitors not only stop DNA repair but impair the mechanism that allows for repairs to take place. Preclinical models have shown this double blocking to be effective in killing cancer cells [##REF##30606227##8##]. The aim of this trial is to determine a maximum tolerated dose of AZD6738 in combination with radiotherapy.</p>", "<p id=\"Par10\">Further methodological challenges revolve around the issue of late-onset toxicities. Typically, early phase trials implement a short window to observe DLTs (Dose Limiting Toxicities). This works well in situations where toxicities are likely to occur rapidly after treatment. However, this is not optimal for treatments that could cause late-onset toxicities such as radiotherapy. The aim with ADePT-DDR would be to incorporate a larger observation window to account for potential late-onset toxicities from radiotherapy whilst also minimising the trial duration.</p>", "<p id=\"Par11\">Due to the historical use of rule-based designs, the majority of the terminology used to describe them, and the ambiguity they raise, have been inherited by modern designs such as the CRM. The MTD in the context of a CRM is not the ‘maximum’ dose patients could tolerate but rather a dose in which there would be an acceptable target probability of a DLT occurring. For example, if the target is set at 25% the MTD would be the dose at which there is a 25% probability of experiencing a DLT. Rather than using the term MTD, the dose to be found will be referred to as the target dose (TD%%, where the %’s are replaced by the target probability), i.e. TD25 would be the dose expected to be toxic in 25% of patients. We will use this terminology throughout the paper.</p>", "<p id=\"Par12\">The continual reassessment method for partial orders (PO-CRM) developed by Wages et al. [##REF##21361888##9##] extends the CRM design by relaxing the assumption of monotonicity and by modelling different potential orders. Wages et al. [##REF##21361888##9##, ##REF##22806898##10##] further developed their work on the PO-CRM to deal with late-onset toxicities by implementing a TITE component. This trial design, referred to as the time-to-event continual reassessment method in the presence of partial orders (PO-TITE-CRM) by the authors, was chosen to be used in ADePT-DDR. We aim to provide insight into the methodology of PO-TITE-CRM through application in a real-world scenario.</p>" ]

[ "<title>Methods</title>", "<title>The PO-TITE-CRM design</title>", "<p id=\"Par13\">Wages et al. [##REF##22806898##10##] introduced the PO-TITE-CRM design which builds directly upon the PO-CRM design by incorporating a TITE component into the dose-toxicity model. The aim of which is to determine the target dose for combinations of drugs where the monotonicity assumption does not hold, in a setting where late-onset toxicities are possible.</p>", "<p id=\"Par14\">Using the notation of Wages et al. [##REF##21361888##9##, ##REF##22806898##10##], let <italic>M</italic> denote the number of possible orders and <italic>Y</italic> be an indicator of a DLT event. Then for a trial investigating <italic>k</italic> combinations, ,...,, the dose for the <italic>j</italic>th patient, , <italic>j</italic> = 1,...,<italic>n</italic> can be thought of as random . For a specific ordering <italic>m</italic>, the toxicity probability is modelled byfor a weighted dose response model where is the model parameter of the working dose toxicity model. The weight, <italic>w</italic> as defined by Cheung and Chappel [##REF##11129476##11##], is a function of the time-to-event of each patient and is incorporated linearly within the dose-toxicity model so that . Each patient is followed for a fixed amount of time <italic>T</italic>. Let represent the time-to-toxicity of patient <italic>j</italic>. Then for ,</p>", "<p id=\"Par15\">For simplicity we will refer to the weight function <italic>w</italic>(<italic>u</italic>; <italic>T</italic>) as <italic>w</italic>. The weight function will have to be decided upon by the trials team, dependent on the scenario, a simple linear function or a more complex adaptive weights function could be utilised. There are also several working dose toxicity models which could be used for . Wages et al. [##REF##21361888##9##, ##REF##22806898##10##] present their design with the power parameter model given by</p>", "<p id=\"Par16\">Here are the prior estimates of DLT probabilities, or skeleton, for each potential ordering. Furthermore, prior probabilities are assigned to each order <italic>M</italic> to account for any prior information regarding the plausibility of each model such that, , where and . When all orders are equally likely or there is no prior information available on possible orderings the prior is discretely uniform and would be .</p>", "<p id=\"Par17\">A Bayesian framework is used and a prior probability distribution is assigned to the parameter . The ordering with the largest prior probability is selected as the starting ordering, in the scenario where all priors are equal an ordering is selected at random, subsequently a starting dose is also chosen. After <italic>j</italic> patients have been entered into the trial, data is collected in the form of . A weighted likelihood for the parameter is used to establish running probabilities of toxicity for each treatment combination. The weighted likelihood under ordering <italic>m</italic>, is given bywhich can be used to generate a summary value for each ordering. With the likelihood and the data , the posterior density for can be calculated using</p>", "<p id=\"Par18\">This can then be used to establish posterior probabilities of the orderings given the data as</p>", "<p id=\"Par19\">We select the single ordering, <italic>h</italic>, with the largest posterior probability along with its associated working model and generate toxicity probabilities for each dose level. Once the <italic>j</italic>th patient has been included the posterior probability of DLT can be calculated for so that</p>", "<p id=\"Par20\">In turn, the dose level assigned to the (1)th patient is the dose, , which minimiseswhere is the target DLT rate. Similarly, once all patients have been recruited and observed and the trial ends, the target dose (TD) is the dose, , which minimises (##FORMU##37##8##).</p>", "<title>PO-TITE-CRM in ADePT-DDR</title>", "<p id=\"Par21\">The intended use of this design is for dose-finding in combinations of therapies, as this is the main source of the partial ordering issue. ADePT-DDR however, is a unique implementation of the design as, even though it involves a combination of therapies (radiotherapy and AZD6738), the dose of radiotherapy is fixed and dose-finding is only planned for AZD6738. PO-TITE-CRM is still applicable in this case as the design includes combinations of dose and duration for AZD6738 which are partially ordered. A summary of the proposed dose levels can be found in Table ##TAB##0##1##.</p>", "<p id=\"Par22\">A two-stage PO-TITE-CRM will be used to find the TD25 of AZD6738. This will be determined by DLTs evaluated by Common Terminology Criteria for Adverse Events (CTCAE) v5.0 and Radiation Therapy Oncology Group (RTOG) late toxicity score. The binary DLT events are pre-defined by a variety of grade 3-4 adverse events notably, haematological, cardiovascular and gastrointestinal/hepatic toxicities as well as significant non-haematological events and specific treatment-related toxicities. DLTs will be monitored for the duration of treatment (seven weeks) and throughout the follow-up period. The total follow-up period post treatment is 52 weeks, so patients will spend a total of 59 weeks in the trial.</p>", "<p id=\"Par23\">A maximum of 60 patients will be recruited for the dose-finding aspect of this trial and up to 20 patients as controls. Controls will be utilised to make comparisons for secondary outcomes such as survival and efficacy. Control patients will only be receiving radiotherapy, the dose of which is fixed at 70Gy/35F (control patients will not be included in any of the dose-finding aspects of the trial). Controls will be recruited in the interim period between the recruitment of the third patient in a cohort and the completion of the minimum follow-up period. Additionally, patients can also be recruited to the control dose if they do not wish to receive AZD6738 whilst the dose-finding cohort is actively recruiting.</p>", "<p id=\"Par24\">The first cohorts of patients will be allocated to dose level 0. The first stage of the design will follow an initial escalation scheme escalating cohorts of three patients to dose level 1, 2a, 2b then 3 if no DLTs occur. If a DLT occurs stage I of the design ends and stage II begins. In stage II cohorts of three patients are assigned to dose levels chosen by the PO-TITE-CRM.</p>", "<p id=\"Par25\">Each patient entered into ADePT-DDR will receive fixed dose radiation, totalling 70 Gy in 35 fractions over seven weeks. For the dose-finding aspect we investigate six doses of AZD6738 detailed in Table ##TAB##0##1##. Treatment dose and duration to be selected for dose level 3 will be determined based on a combination of data observed, adverse events and compliance. The issue of partial ordering is illustrated in Fig. ##FIG##0##1## inspired from plots by Wages et al. [##REF##22806898##10##]. The doses to be used in this trial are detailed in their appropriate box. Additionally, each dot represents a potential dose combination which theoretically could be investigated. The combinations are colour coordinated to indicate where partial ordering exists in this dose combination space. Doses across the same colour (each diagonal) cannot be distinguished from each other in terms of probability of toxicity. However, it forms a hierarchy in which doses of the same colour can be thought of as less/more toxic that doses in another colour i.e the red dose levels would have a higher probability of toxicity than the yellow dose levels. It is clear that dose levels 2a and 2b would be considered more toxic than dose level 1 due to the increase in treatment duration and treatment dose respectively. However, when comparing 2a and 2b it is unknown whether the increase in dose or duration will be more toxic. Hence there are two possible orderings for ADePT-DDR.</p>", "<p id=\"Par26\">Traditionally, dose-finding trials for combinations would select dose levels to form a ‘path’ through the dose combination space such that each subsequent dose level was logically more toxic. This avoids the issue of partial ordering but means doses of interest or effective dose combinations may be missed or not investigated. Specifically, for ADePT-DDR this allows two ‘paths’ from dose level 1 extending to 2a and 2b. In terms of dose level 3 only one of the doses in that tier will be investigated, it was unclear as to which dose level would be best due to a lack of historical data. The choice of dosing for this dose-level will be determined based on data observed throughout the trial. Even though dose level 3 is not yet specified in terms of modelling and simulations it was treated as singular dose. This was done as clinicians thought that it would be unlikely that we would reach these doses and that the probability of toxicity between them would be similar.</p>", "<p id=\"Par27\">Preliminary designs of the trial included only five dose levels and planned to use dose level 0 as the starting dose. During the trial design phase it was decided a new lower dose (dose level -1) would be introduced to allow for de-escalation if the initial dose was found to be too toxic. Dose escalation/de-escalation for subsequent cohorts would be determined from the two-stage PO-TITE-CRM. A two-stage design allows for escalation according to a pre-defined escalation scheme similar to a ‘3+3’ design. The first stage dictates that if no DLT’s are observed in the current cohort the dose allocated to the next cohort is the following dose in the escalation scheme. Dose levels continue to be incremented in this fashion until the first DLT is observed. In stage two, dose levels are determined by the PO-TITE-CRM.</p>", "<p id=\"Par28\">Typically CRM designs begin by testing the first patient, or cohort, at the prior guess of TD or at a lower dose to be safe. However, clinicians may have safety concerns beginning the trial at higher dose levels as well as escalating to higher dose levels without testing lower ones. Investigators in ADePT-DDR expressed similar concerns as such a two-stage design was adopted. The escalation scheme used in stage one of ADePT-DDR will follow that of the first ordering (). If patients in the first cohort (assigned to dose level 0) don’t experience a DLT the next cohort will be allocated to dose level 1 and then if no DLTs are observed again the third cohort will be allocated to dose level 2a and so on and so forth. The dose escalation scheme was determined based on the prior probabilities of toxicity generated for each dose level.</p>", "<p id=\"Par29\">Information elicited from the investigators helped generate prior probabilities of toxicity for each dose level. They believed that dose level 2b would be the TD25 with 2a being less toxic. This was used in conjunction with the getprior function from the dfcrm R package [##UREF##4##12##] which yielded priors of 0.01, 0.04, 0.08, 0.16, 0.25 and 0.35 for dose levels -1, 0, 1, 2a, 2b and 3 respectively. The half-width of the indifference interval was set at 0.05. The indifference interval is an interval in which the toxicity probability of the selected dose will eventually fall. Prior probabilities are also required for the plausibility of each model and even though the clinicians think that 2b will be more toxic than 2a there is no clear evidence and still a lot of uncertainty. As such it is sensible to assume a plausibility probability of 0.5 for each ordering, implying both orders are equally likely to be the true ordering of these dose levels.</p>", "<title>The TITE component</title>", "<p id=\"Par30\">The observation window for this trial will be up to a year post-treatment as the combination of radiotherapy with AZD6738 is anticipated to cause late-onset toxicity. The acute DLT observation period is 12 weeks (84 days) post radiotherapy end with a minimum of 8 weeks (56 days) for the last patient of each cohort. However, patients will continuously be monitored for occurrence of DLT for at least 12 weeks (84 days), i.e. at least 12 weeks (84 days) from the end of radiotherapy. The full window will last for 52 weeks (365 days) post-treatment.</p>", "<p id=\"Par31\">The TITE component incorporates a weighting contribution for each patient dependent on how long that patient has been evaluable in the study. This allows a patient to be evaluated once they have been observed for the minimum DLT period of 8 weeks (56 days). The weighting at this point is 60% rising to 80% at 12 weeks (84 days). A patient will not contribute fully to the model until they have completed 52 weeks (365 days) follow up (or have experienced a DLT at any stage in which case they will be weighted as a whole contribution). Linear weighting functions will be employed for any patient with a length of follow up between these three time points. One weight function to calculate weights between 8-12 weeks and another for weights between 12-52 weeks. For the weighting function where <italic>u</italic> is the time-to-toxicity of patient <italic>j</italic> and is the time period with values 8, 12 and 52 respectively. Then for </p>", "<p id=\"Par32\">All patients will have a minimum weight of 60% as that is the prescribed weighting to the minimum follow up period before dose escalation/de-escalation decisions can be made. For each additional week the patient is observed, without a DLT occurring, between weeks 8 and 12 their weighting increases by 5%. Similarly for each week between 12 and 52 weeks, without a DLT, weighting increases by 0.5%. Figure ##FIG##1##2## illustrates the weight function and how the weight changes for patients dependent on how long they have been followed-up. The dotted lines represent key time points in the trial. The first being after treatment (7 weeks), the second being the minimum follow-up period at 8 weeks post-treatment (15 weeks into the trial) and the third being at 12 weeks post-treatment (19 weeks into the trial).</p>", "<p id=\"Par33\">The TITE-CRM originally presented by Cheung and Chappel [##REF##11129476##11##] did not incorporate a minimum follow-up period and their design allowed for the continual recruitment of patients whenever they became available. There are some practical considerations which make this infeasible in ADePT-DDR. The model would need to be run each time a new patient entered the study which requires statistical input hence the introduction of cohorts. Clinicians may also have safety concerns if we see rapid recruitment at the start of the trial and the model keeps escalating so we impose a minimum follow-up period. Initially this was set at 12 weeks (at 80% weighting) however, this would have meant that dose escalation/de-escalation decisions would have to take place 19 weeks (7 weeks treatment and 12 weeks follow-up) after recruitment of the third patient in the cohort. Dependent on the recruitment rates this could extend the duration of the trial and negates the benefits of using a TITE design. Consultation with the trial clinicians and the Trial Management Group (TMG) indicated that the trial duration would be too lengthy and settled on lowering this period to 8 weeks (at 60% weighting) whilst also including the original 12 week weighting of 80%.</p>", "<title>Stopping rules</title>", "<p id=\"Par34\">A practical modification was included to allow for early stopping of the trial if there is sufficient evidence that the TD25 has been reached. Sufficient evidence is achieved once 15 patients (five cohorts) have been treated at the same dose level and the model allocates that dose level again to a sixth cohort. This rule evolved from the original designs of the trial which involved 30 patients with a dose expansion cohort to ensure at least 15 patients were treated at the TD25.</p>", "<p id=\"Par35\">Initial simulations highlighted the inadequacy of these design parameters, as operating characteristics for various scenarios were poor, specifically in terms of correct TD25 selection. Clinicians explained the inclusion of the dose expansion cohort was to ensure the dose-finding aspect of the trial did not take a large amount of time whilst also allowing safety to be assessed at the TD25. In order to ensure that a reasonable amount of patients would be treated at the TD25, the trial wouldn’t take longer than necessary and operating characteristics improved, the sample size was increase and this rule was introduced.</p>", "<p id=\"Par36\">A rule was also implemented to allow for early termination of the trial in the case of excess toxicity at the lowest dose. If the probability of DLT at the lowest dose is higher than 0.35 with a probability of 80% and has been tested the trials safety committee will be alerted and will recommend if the trial should be stopped. As the trial starts at dose level 0, which is not the lowest dose, it’s hypothetically possible for the trial to recommend terminating without ever allocating patients to the lowest dose level. As such it was decided early termination would only occur once at least 3 patients (1 cohort) have been allocated dose level -1.</p>", "<p id=\"Par37\">An approximate estimate of the variance was calculated using methodology presented by O’Quigley and Shen [##REF##8672707##13##]. The observed information matrix is obtained by taking the second derivative of the likelihood (eq. ##FORMU##25##4##) which is then used to calculate the variance , for estimate which becomes more accurate with larger sample sizes. After each cohort, we sample many times from a normal distribution with parameters based on the estimate of and its variance. These samples are then plugged into our dose-toxicity model to ascertain the probability of toxicity at the lowest dose. The trial will be recommended to stop if it breaks the rule based on the criteria above.</p>" ]

[ "<title>Results</title>", "<p id=\"Par38\">Simulations were repeatedly utilised during the design process of the trial to assess how various changes to design features impact the overall performance. Changes to design features such as the sample size, weight function and stopping rules helped inform decisions which led to this design.</p>", "<p id=\"Par39\">Functions from pocrm package in R were modified in order to perform simulations. These modified functions will also be used for analysis during the conduct of the trial. The majority of work involved integrating the TITE component and the stopping rules into the code. In standard CRM designs a binary outcome for toxicity is generated for each patient based on a pre-specified true DLT rates for the dose they are assigned. Adding the TITE component means the time the toxicity occurs also has to be generated, the simulation must also track this time and incorporate this information into the PO-TITE-CRM model when it needs to make dose allocation decisions for the next cohort. We defined multiple scenarios to reflect various real life possibilities in order to assess the designs performance. Simulations presented here were based on the design specified in the previous section, which included six dose levels (-1, 0, 1, 2a, 2b and 3) with dose level 3 treated as a singular dose.</p>", "<p id=\"Par40\">Standard scenarios include adjusting the true DLT rates to reflect each dose being the TD25. For each of these we calculate the probability of selecting each dose as the TD25. It would be expected that the dose with the highest probability of being selected has its true DLT rate set at 25% to match the target rate. A high probability of selecting the correct dose implies the design works well in the specified scenario. Additional characteristics such as the average number of patients at each dose level and how many receive the ideal dose were also investigated. This can be used to look at how many patients may potentially be allocated to a toxic dose. It is also necessary to consider performance when all doses are too toxic, in which case we would want the design to recommend stopping early. Usually the true DLT rates used to define these scenarios abide by the monotonicity assumption. Due to the partial ordering we consider scenarios in which the true DLT rates follow both orders. For trials with a large amount of orders it may be unfeasible to run so many simulations. However, as ADePT-DDR only has two orders we explored all scenarios for each ordering.</p>", "<p id=\"Par41\">We simulated 10000 trials for each scenario using this design detailed in <xref rid=\"Sec2\" ref-type=\"sec\">Methods</xref> section. It is recommended by Morris et al. [##REF##30652356##14##] to detail the Monte Carlo standard error in order to quantify the simulations uncertainty. The Monte Carlo standard error for probabilities estimated by 10000 simulations is . This implies that for any differences in selection probabilities greater than 1% are due to more than simulation error. Simulations were based on the assumption that the trial would recruit one patient per month. The occurrence of DLT’s were randomly generated for patients in each cohort using a Bernoulli distribution with the probability set at the true DLT rate for that cohort’s assigned dose level in the specific scenario. For patients who had a DLT occur, the time at which the DLT occurred was randomly generated using a uniform distribution which spanned the start of treatment to the end of follow-up.</p>", "<p id=\"Par42\">Table ##TAB##1##2## details simulations for eight scenarios to test the performance of the PO-TITE-CRM design using true DLT rates which reflect the first ordering. We analyse scenarios where each dose is the TD25 (scenarios 1-6) and when all doses are too toxic (scenario 8). Additionally, we also investigate performance under conditions where the probability of DLT is fairly similar between doses (scenario 7). This is a notoriously difficult circumstance for CRM designs to deal with as the limited number of patients and events at each dose make it hard to accurately estimate toxicity probabilities if they are similar. Simulation results for the second ordering are shown in Table ##TAB##2##3## where dose level 2a is considered more toxic than 2b. This is achieved by altering the true DLT rates so 2b has a lower probability of DLT compared to 2a.</p>", "<p id=\"Par43\">In scenarios 1 - 6 (Table ##TAB##1##2##), this design correctly selects the TD25 with probabilities between 43% and 78%, under the assumption 2b is more toxic than 2a. Likewise, for the ordering where 2a is more toxic than 2b, scenarios 9-14 (Table ##TAB##2##3##) have probabilities between 43% and 78% of correctly selecting the TD25. Correct selection probabilities are generally higher when the TD25 is at the first and last dose levels compared to dose levels 2a and 2b. However, these dose levels are still chosen with the highest probability as the TD25 in their given scenarios. For scenarios 7 and 15, the probabilities of toxicity are equally spaced, approximately 5% apart. This is a relatively diffcult scenario for dose-finding studies to handle. The probability of selecting the TD25 is 28% and 32% for orderings 1 and 2 respectively and even if the performance is poor the correct dose is still likely to be selected. In scenarios 8 and 16, where all the doses are too toxic, the design very seldom allocates patients higher than the first three doses and there is a high chance (74% and 73% respectively) that the trial will recommend early stopping.</p>", "<p id=\"Par44\">Additionally, we assess designs based on the distribution of patients across doses. Designs may correctly select the TD25 however, this could be undesirable and unethical if the majority of patients are over dosed at the more toxic dose levels. The average number and the percentage of patients at each dose level, for each scenario, is recorded in Tables ##TAB##1##2## and ##TAB##2##3##.</p>", "<p id=\"Par45\">The percentage of patients treated at the TD25 ranges between 23% and 43% for each scenario under both orderings. The design also allocates the most patients on average to the TD25 apart from in scenario 7. In this case more patients were allocated to the next lowest dose, we have already discussed the difficulties of this scenario so this characteristic is not too concerning. The mean number of patients recruited for scenarios 1-6 is 26, 30, 32, 33, 34 and 31 respectively. Similarly for scenarios 9-14 its 26, 30, 32, 34, 33 and 31. Even though we allow for up to 60 patients the majority of trials terminate early based on the pre-defined rules for selecting the TD25. This information is presented in Table ##TAB##3##4## which also shows how often the max sample size is reached from the 10000 trials for each scenario. We can see in all scenarios, except those where doses are all toxic, we reach the maximum sample size in a small number of simulations. This is largest for scenario 1 where 21 of the 10000 (0.21%) needed the full sample size of 60 patients.</p>", "<p id=\"Par46\">Overall, the simulation results show the specification of this design performs relatively well in a number of scenarios. We have shown there is a high probability of the trial stopping early if all dose-levels are too toxic. We have also shown the design behaves in an appropriate manner when there is a lack of disparity between dose-levels in terms of toxicity. Finally, we have demonstrated that regardless of the ordering we observe the PO-TITE-CRM has a high probability of selecting the correct dose. There are a number of limitations to the operating characteristics presented here which are due to the specification of the simulations and trial design.</p>" ]

[ "<title>Discussion</title>", "<p id=\"Par47\">The PO-CRM and PO-TITE-CRM designs offer solutions to the issue of partial ordering where the order of the doses of treatments are only partially known. The original methodology details that this issue commonly arises in trials of multiple agents, where each drug individually may follow the monotonicity assumption but when combined at certain dose levels this may not hold. This issue is typically dealt with by fixing the dose of one of the agents and escalating the other or escalating both agents simultaneously. This means certain drug combinations that are clinically relevant may not be investigated or even considered.</p>", "<p id=\"Par48\">Here we have shown that these issues can also arise in other situations. Even though the ADePT-DDR trial uses multiple agents the issue of partial ordering occurs due to the varying treatment dose and schedule for one of its agents AZD6738. Implementing the PO-TITE-CRM design allowed us to deal with this issue effectively. There may be other factors or variables in single-agent dose-finding trials that would lead to the issue of partial ordering and would warrant the use of either PO-CRM or PO-TITE-CRM. A limited literature review highlighted that this may be the first instance of the PO-TITE-CRM design being applied. It is important to note that although this methodology takes into account all the various orderings the main aim is to identify the TD%% and does not attempt to identify the order that is more correct.</p>", "<p id=\"Par49\">Compared to other CRM based designs only a few additional pieces of information are required to implement the PO-CRM design, specifically the number of toxicity orderings and prior probabilities for the orders. Dependent on how many dose combinations are available it may not be feasible to investigate all combinations and all orderings. Careful thought and consideration should be given to the combinations and orderings selected which would require input from all relevant investigators (TMG, clinical investigators and other relevant stake holders). In terms of priors for orderings, if no prior information is available all orders should be treated as equally likely to occur. Extending this design to the PO-TITE-CRM requires a fit for purpose weight function and is applied in a similar way to the TITE-CRM methodology. There is an R package available with functions that can be used to run and simulate a PO-CRM trial. These functions were extended to included weighted dose toxicity models as described in this chapter to implement PO-TITE-CRM into ADePT-DDR. The lack of available software for PO-TITE-CRM specifically may be one of the reasons for its lack of use.</p>", "<p id=\"Par50\">In terms of the ADePT-DDR trial, dose combinations were decided upon by the clinical investigators. The issue of partial ordering was due to the dose-levels 2a and 2b and as such this methodology was employed to deal with that scenario. This is a very simple example of partial ordering as we only have two possible orderings and six dose levels. The necessity of implementing this methodology was discussed and whether or not adopting an easier solution by simply altering the dose levels would have been better. Ultimately, the dose levels selected by the clinicians were deemed the most relevant with the TD25 likely to be one of these doses.</p>", "<p id=\"Par51\">Our design used the power model as the working dose-toxicity model. Alternative models such as the one and two parameter logistic model could also be implemented. Whilst a two parameter model may better estimate the dose-toxicity relationship it is unclear if this is still applicable in the presence of partial orders. Therefore, for the purposes of this trial aiming to identify a TD25 a one parameter model was used. As the original authors of the methodology utilised the power model we felt this would be appropriate to use in this trial as well. Further work could be done via simulations to investigate how other models would perform with this design.</p>", "<p id=\"Par52\">Similarly, alternative weight functions such as a polynomial function could also be explored. Our selection of weight function was motivated to a large extent by clinical input. We chose to use a two piecewise linear function due to its simplicity in interpretation. Also, due to the lack of data and certainty around how the weights should actually change over time.</p>", "<p id=\"Par53\">Simulations to generate operating characteristics were the main tools used to assess the designs performance as well as help understand the impact of sample size and stopping rules. This was an iterative process that involved running multiple iterations of simulations under various scenarios until the design was finalised. A key point is that scenarios from simulations should account for each of the possible orderings. ADePT-DDR only has two orderings and we ran scenarios for both. For a trial with a greater number of orderings, this may be unfeasible but at least some scenarios should be assessed to ensure the design is behaving as expected. Overall, the design operating characteristics performed reasonably well even in difficult scenarios.</p>", "<p id=\"Par54\">One limitation of the simulations is how the time-to-event data is generated. The time of DLTs is sampled from a uniform distribution <italic>U</italic>(0, 413), where the time of the DLT can occur at any time between the patient beginning treatment and the end of follow-up (413 days). Using this uniform distribution implies that a DLT has an equal probability of occurring at any time-point in the observation window. This may not be an accurate representation of what happens in the actual trial. Similar comments can be made about the accrual rate used in the simulations. Here we specified the recruitment of one patient per month which is in no way guaranteed for the actual trial. Wages et al. [##REF##22806898##10##], when presenting this methodology investigated four different applications of the PO-TITE-CRM which used different models to enroll patients and allocate DLTs. Results across these four applications were comparable and therefore we assume similar conclusions for this study.</p>", "<p id=\"Par55\">The simulations are also able to instantaneously determine dose-levels for incoming cohorts with all available information. This does not fully reflect the process in which dose-escalation decisions would be made during the actual running of the trial. The analysis would require a data snapshot and time would have to be spent cleaning the data and determining the next dose-level. Meaning any data from the point of the snapshot would not be included in any dose escalation/de-escalation decisions.</p>" ]

[ "<title>Conclusion</title>", "<p id=\"Par56\">We detail the issue of partial ordering and how we implemented the trial design, in what we believe is the first real-world application of this design. A large amount of simulation work is required to assess the performance of the design. We recommend running several varied scenarios for each potential ordering that will be investigated. This is often an iterative process to refine decisions that were made and often requires input from both clinical and statistical investigators to ensure that the trial design is fit for purpose.</p>" ]

[ "<title>Background</title>", "<p id=\"Par1\">In this article we describe the methodology of the time-to-event continual reassessment method in the presence of partial orders (PO-TITE-CRM) and the process of implementing this trial design into a phase I trial in head and neck cancer called ADePT-DDR. The ADePT-DDR trial aims to find the maximum tolerated dose of an ATR inhibitor given in conjunction with radiotherapy in patients with head and neck squamous cell carcinoma.</p>", "<title>Methods</title>", "<p id=\"Par2\">The PO-TITE-CRM is a phase I trial design that builds upon the time-to-event continual reassessment method (TITE-CRM) to allow for the presence of partial ordering of doses. Partial orders occur in the case where the monotonicity assumption does not hold and the ordering of doses in terms of toxicity is not fully known.</p>", "<title>Results</title>", "<p id=\"Par3\">We arrived at a parameterisation of the design which performed well over a range of scenarios. Results from simulations were used iteratively to determine the best parameterisation of the design and we present the final set of simulations. We provide details on the methodology as well as insight into how it is applied to the trial.</p>", "<title>Conclusions</title>", "<p id=\"Par4\">Whilst being a very efficient design we highlight some of the difficulties and challenges that come with implementing such a design. As the issue of partial ordering may become more frequent due to the increasing investigations of combination therapies we believe this account will be beneficial to those wishing to implement a design with partial orders.</p>", "<title>Trial registration</title>", "<p id=\"Par5\">ADePT-DDR was added to the European Clinical Trials Database (EudraCT number: 2020-001034-35) on 2020-08-07.</p>", "<title>Keywords</title>" ]

[]

[ "<title>Acknowledgements</title>", "<p>We would like to thank the members of the ADePT-DDR Trial Management Group and Trial Safety Committee for their contributions to the trial. We also thank V.Homer for her help validating the code used to conduct simulations. Finally, thank you to the editor and reviewers whose comments helped improve the manuscript.</p>", "<title>Authors’ contributions</title>", "<p>AP wrote the main manuscript text, prepared figures and conducted the simulations. AP and PG designed the trial and worked on the implementation of the design with DS. LB and KB contributed as statistical methodology reviewers. AK and HM are Co Chief Investigators for the trial and CG is the trial management lead. All authors reviewed the manuscript.</p>", "<title>Funding</title>", "<p>The ADePT-DDR trial is funded by AstraZeneca. This research was conducted with support from AstraZeneca UK Limited.</p>", "<title>Availability of data and materials</title>", "<p>All data presented in this manuscript is simulated data. The results presented here are summaries of the simulations.</p>", "<title>Declarations</title>", "<p>Professor Mehanna is a National Institute for Health Research (NIHR) Senior Investigator. The views expressed in this article are those of the author(s) and not necessarily those of the NIHR, or the Department of Health and Social Care.</p>", "<title>Ethics approval and consent to participate</title>", "<p id=\"Par57\">The ADePT-DDR trial has been approved by the South Central - Berkshire B Research Ethics Committee. The trial continues to be conducted in accordance with the protocol, Good Clinical Practice guidelines, and the Declaration of Helsinki. All patients provide written informed consent.</p>", "<title>Consent for publication</title>", "<p id=\"Par58\">Not applicable.</p>", "<title>Competing interests</title>", "<p id=\"Par59\">The authors declare no competing interests.</p>" ]

[ "<fig id=\"Fig1\"><label>Fig. 1</label><caption><p>ADePT-DDR dose levels across dose and duration</p></caption></fig>", "<fig id=\"Fig2\"><label>Fig. 2</label><caption><p>Weights of patients who have not experienced a DLT across the observation window</p></caption></fig>" ]

[ "<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>ADePT-DDR dose-levels and duration of treatment for AZD6738</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Dose Level</th><th align=\"left\">AZD6738 Daily dose (mg BD)</th><th align=\"left\">Weeks</th><th align=\"left\">Duration (days)</th><th align=\"left\">Radiotherapy</th></tr></thead><tbody><tr><td align=\"left\">-1</td><td align=\"left\">20</td><td align=\"left\">1</td><td align=\"left\">5</td><td align=\"left\">70 Gy/ 35 F</td></tr><tr><td align=\"left\">0</td><td align=\"left\">20</td><td align=\"left\">1 &amp;4</td><td align=\"left\">10</td><td align=\"left\">70 Gy/ 35 F</td></tr><tr><td align=\"left\">1</td><td align=\"left\">40</td><td align=\"left\">1 &amp;4</td><td align=\"left\">10</td><td align=\"left\">70 Gy/ 35 F</td></tr><tr><td align=\"left\">2a</td><td align=\"left\">40</td><td align=\"left\">1,2,4 &amp;5</td><td align=\"left\">20</td><td align=\"left\">70 Gy/ 35 F</td></tr><tr><td align=\"left\">2b</td><td align=\"left\">80</td><td align=\"left\">1 &amp;4</td><td align=\"left\">10</td><td align=\"left\">70 Gy/ 35 F</td></tr><tr><td align=\"left\" rowspan=\"2\">3</td><td align=\"left\">120</td><td align=\"left\">1 &amp;4</td><td align=\"left\">10</td><td align=\"left\">70 Gy/ 35 F</td></tr><tr><td align=\"left\">80</td><td align=\"left\">1,2,4 &amp;5</td><td align=\"left\">20</td><td align=\"left\">70 Gy/ 35 F</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab2\"><label>Table 2</label><caption><p>Operating Characteristics of the two-stage PO-TITE-CRM (with true DLT rates that imply 2b is more toxic than 2a) based on 10000 simulated trials. Definitions: DLT: Dose-limiting toxicity. P(select): Probability of selecting a dose as the TD25. Bold values indicate the correct decision</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\"/><th align=\"left\"/><th align=\"left\" colspan=\"6\">Dose Levels</th><th align=\"left\"/></tr><tr><th align=\"left\"/><th align=\"left\"/><th align=\"left\">-1</th><th align=\"left\">0</th><th align=\"left\">1</th><th align=\"left\">2a</th><th align=\"left\">2b</th><th align=\"left\">3</th><th align=\"left\">Stop</th></tr></thead><tbody><tr><td align=\"left\">Scenario</td><td align=\"left\">Prior DLT</td><td align=\"left\">0.01</td><td align=\"left\">0.04</td><td align=\"left\">0.08</td><td align=\"left\">0.16</td><td align=\"left\">0.25</td><td align=\"left\">0.35</td><td align=\"left\"/></tr><tr><td align=\"left\" rowspan=\"4\">1: TD25 @-1</td><td align=\"left\">True DLT rate</td><td align=\"left\">0.25</td><td align=\"left\">0.4</td><td align=\"left\">0.45</td><td align=\"left\">0.5</td><td align=\"left\">0.55</td><td align=\"left\">0.6</td><td align=\"left\"/></tr><tr><td align=\"left\">P(select)</td><td align=\"left\"><bold>0.68</bold></td><td align=\"left\">0.18</td><td align=\"left\">0.05</td><td align=\"left\">0.01</td><td align=\"left\">0</td><td align=\"left\">0</td><td align=\"left\">0.08</td></tr><tr><td align=\"left\">% of patients</td><td align=\"left\">39</td><td align=\"left\">32</td><td align=\"left\">20</td><td align=\"left\">6</td><td align=\"left\">3</td><td align=\"left\">0</td><td align=\"left\"/></tr><tr><td align=\"left\">Mean number of patients</td><td align=\"left\">10.17</td><td align=\"left\">8.46</td><td align=\"left\">5.33</td><td align=\"left\">1.67</td><td align=\"left\">0.69</td><td align=\"left\">0.07</td><td align=\"left\"/></tr><tr><td align=\"left\" rowspan=\"4\">2: TD25 @0</td><td align=\"left\">True DLT rate</td><td align=\"left\">0.12</td><td align=\"left\">0.25</td><td align=\"left\">0.4</td><td align=\"left\">0.45</td><td align=\"left\">0.5</td><td align=\"left\">0.55</td><td align=\"left\"/></tr><tr><td align=\"left\">P(select)</td><td align=\"left\">0.23</td><td align=\"left\"><bold>0.51</bold></td><td align=\"left\">0.2</td><td align=\"left\">0.03</td><td align=\"left\">0.02</td><td align=\"left\">0</td><td align=\"left\">0.01</td></tr><tr><td align=\"left\">% of patients</td><td align=\"left\">17</td><td align=\"left\">35</td><td align=\"left\">29</td><td align=\"left\">11</td><td align=\"left\">6</td><td align=\"left\">1</td><td align=\"left\"/></tr><tr><td align=\"left\">Mean number of patients</td><td align=\"left\">5.24</td><td align=\"left\">10.48</td><td align=\"left\">8.75</td><td align=\"left\">3.4</td><td align=\"left\">1.83</td><td align=\"left\">0.26</td><td align=\"left\"/></tr><tr><td align=\"left\" rowspan=\"4\">3: TD25 @1</td><td align=\"left\">True DLT rate</td><td align=\"left\">0.09</td><td align=\"left\">0.12</td><td align=\"left\">0.25</td><td align=\"left\">0.4</td><td align=\"left\">0.45</td><td align=\"left\">0.5</td><td align=\"left\"/></tr><tr><td align=\"left\">P(select)</td><td align=\"left\">0.02</td><td align=\"left\">0.2</td><td align=\"left\"><bold>0.55</bold></td><td align=\"left\">0.14</td><td align=\"left\">0.09</td><td align=\"left\">0.01</td><td align=\"left\">&lt;0.01</td></tr><tr><td align=\"left\">% of patients</td><td align=\"left\">4</td><td align=\"left\">20</td><td align=\"left\">34</td><td align=\"left\">23</td><td align=\"left\">16</td><td align=\"left\">3</td><td align=\"left\"/></tr><tr><td align=\"left\">Mean number of patients</td><td align=\"left\">1.22</td><td align=\"left\">6.41</td><td align=\"left\">10.97</td><td align=\"left\">7.23</td><td align=\"left\">5.14</td><td align=\"left\">1.02</td><td align=\"left\"/></tr><tr><td align=\"left\" rowspan=\"4\">4: TD25 @2a</td><td align=\"left\">True DLT rate</td><td align=\"left\">0.06</td><td align=\"left\">0.09</td><td align=\"left\">0.12</td><td align=\"left\">0.25</td><td align=\"left\">0.4</td><td align=\"left\">0.45</td><td align=\"left\"/></tr><tr><td align=\"left\">P(select)</td><td align=\"left\">0</td><td align=\"left\">0.02</td><td align=\"left\">0.22</td><td align=\"left\"><bold>0.48</bold></td><td align=\"left\">0.23</td><td align=\"left\">0.05</td><td align=\"left\">&lt;0.01</td></tr><tr><td align=\"left\">% of patients</td><td align=\"left\">1</td><td align=\"left\">12</td><td align=\"left\">20</td><td align=\"left\">31</td><td align=\"left\">25</td><td align=\"left\">11</td><td align=\"left\"/></tr><tr><td align=\"left\">Mean number of patients</td><td align=\"left\">0.47</td><td align=\"left\">3.88</td><td align=\"left\">6.74</td><td align=\"left\">10.43</td><td align=\"left\">8.2</td><td align=\"left\">3.5</td><td align=\"left\"/></tr><tr><td align=\"left\" rowspan=\"4\">5: TD25 @2b</td><td align=\"left\">True DLT rate</td><td align=\"left\">0.03</td><td align=\"left\">0.06</td><td align=\"left\">0.09</td><td align=\"left\">0.12</td><td align=\"left\">0.25</td><td align=\"left\">0.4</td><td align=\"left\"/></tr><tr><td align=\"left\">P(select)</td><td align=\"left\">0</td><td align=\"left\">0</td><td align=\"left\">0.02</td><td align=\"left\">0.3</td><td align=\"left\"><bold>0.43</bold></td><td align=\"left\">0.25</td><td align=\"left\">0</td></tr><tr><td align=\"left\">% of patients</td><td align=\"left\">1</td><td align=\"left\">10</td><td align=\"left\">12</td><td align=\"left\">24</td><td align=\"left\">28</td><td align=\"left\">25</td><td align=\"left\"/></tr><tr><td align=\"left\">Mean number of patients</td><td align=\"left\">0.25</td><td align=\"left\">3.36</td><td align=\"left\">4.15</td><td align=\"left\">8.17</td><td align=\"left\">9.33</td><td align=\"left\">8.33</td><td align=\"left\"/></tr><tr><td align=\"left\" rowspan=\"4\">6: TD25 @3</td><td align=\"left\">True DLT rate</td><td align=\"left\">0.01</td><td align=\"left\">0.03</td><td align=\"left\">0.06</td><td align=\"left\">0.09</td><td align=\"left\">0.12</td><td align=\"left\">0.25</td><td align=\"left\"/></tr><tr><td align=\"left\">P(select)</td><td align=\"left\">0</td><td align=\"left\">0</td><td align=\"left\">0</td><td align=\"left\">0.09</td><td align=\"left\">0.13</td><td align=\"left\"><bold>0.78</bold></td><td align=\"left\">0</td></tr><tr><td align=\"left\">% of patients</td><td align=\"left\">0</td><td align=\"left\">10</td><td align=\"left\">11</td><td align=\"left\">18</td><td align=\"left\">18</td><td align=\"left\">42</td><td align=\"left\"/></tr><tr><td align=\"left\">Mean number of patients</td><td align=\"left\">0.1</td><td align=\"left\">3.13</td><td align=\"left\">3.49</td><td align=\"left\">5.46</td><td align=\"left\">5.6</td><td align=\"left\">13.14</td><td align=\"left\"/></tr><tr><td align=\"left\" rowspan=\"4\">7: Equal steps in DLT rate</td><td align=\"left\">True DLT rate</td><td align=\"left\">0.05</td><td align=\"left\">0.1</td><td align=\"left\">0.15</td><td align=\"left\">0.2</td><td align=\"left\">0.25</td><td align=\"left\">0.3</td><td align=\"left\"/></tr><tr><td align=\"left\">P(select)</td><td align=\"left\">0</td><td align=\"left\">0.03</td><td align=\"left\">0.12</td><td align=\"left\">0.31</td><td align=\"left\"><bold>0.28</bold></td><td align=\"left\">0.26</td><td align=\"left\">&lt;0.01</td></tr><tr><td align=\"left\">% of patients</td><td align=\"left\">2</td><td align=\"left\">13</td><td align=\"left\">18</td><td align=\"left\">26</td><td align=\"left\">23</td><td align=\"left\">19</td><td align=\"left\"/></tr><tr><td align=\"left\">Mean number of patients</td><td align=\"left\">0.55</td><td align=\"left\">4.03</td><td align=\"left\">5.72</td><td align=\"left\">8.32</td><td align=\"left\">7.15</td><td align=\"left\">5.96</td><td align=\"left\"/></tr><tr><td align=\"left\" rowspan=\"4\">8: All toxic</td><td align=\"left\">True DLT rate</td><td align=\"left\">0.5</td><td align=\"left\">0.6</td><td align=\"left\">0.65</td><td align=\"left\">0.7</td><td align=\"left\">0.75</td><td align=\"left\">0.8</td><td align=\"left\"/></tr><tr><td align=\"left\">P(select)</td><td align=\"left\">0.26</td><td align=\"left\">0</td><td align=\"left\">0</td><td align=\"left\">0</td><td align=\"left\">0</td><td align=\"left\">0</td><td align=\"left\"><bold>0.74</bold></td></tr><tr><td align=\"left\">% of patients</td><td align=\"left\">56</td><td align=\"left\">26</td><td align=\"left\">15</td><td align=\"left\">2</td><td align=\"left\">0</td><td align=\"left\">0</td><td align=\"left\"/></tr><tr><td align=\"left\">Mean number of patients</td><td align=\"left\">9.05</td><td align=\"left\">4.27</td><td align=\"left\">2.4</td><td align=\"left\">0.37</td><td align=\"left\">0.04</td><td align=\"left\">0</td><td align=\"left\"/></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab3\"><label>Table 3</label><caption><p>Operating Characteristics of the two-stage PO-TITE-CRM (with true DLT rates that imply 2a is more toxic than 2b) based on 10000 simulated trials. Definitions: DLT: Dose-limiting toxicity. P(select): Probability of selecting a dose as the TD25. Bold values indicate the correct decision</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\"/><th align=\"left\"/><th align=\"left\" colspan=\"6\">Dose Levels</th><th align=\"left\"/></tr><tr><th align=\"left\"/><th align=\"left\"/><th align=\"left\">-1</th><th align=\"left\">0</th><th align=\"left\">1</th><th align=\"left\">2a</th><th align=\"left\">2b</th><th align=\"left\">3</th><th align=\"left\">Stop</th></tr></thead><tbody><tr><td align=\"left\">Scenario</td><td align=\"left\">Prior DLT</td><td align=\"left\">0.01</td><td align=\"left\">0.04</td><td align=\"left\">0.08</td><td align=\"left\">0.16</td><td align=\"left\">0.25</td><td align=\"left\">0.35</td><td align=\"left\"/></tr><tr><td align=\"left\" rowspan=\"4\">9: TD25 @-1</td><td align=\"left\">True DLT rate</td><td align=\"left\">0.25</td><td align=\"left\">0.4</td><td align=\"left\">0.45</td><td align=\"left\">0.55</td><td align=\"left\">0.5</td><td align=\"left\">0.6</td><td align=\"left\"/></tr><tr><td align=\"left\">P(select)</td><td align=\"left\"><bold>0.67</bold></td><td align=\"left\">0.19</td><td align=\"left\">0.05</td><td align=\"left\">0</td><td align=\"left\">0.01</td><td align=\"left\">0</td><td align=\"left\">0.08</td></tr><tr><td align=\"left\">% of patients</td><td align=\"left\">39</td><td align=\"left\">32</td><td align=\"left\">20</td><td align=\"left\">6</td><td align=\"left\">3</td><td align=\"left\">0</td><td align=\"left\"/></tr><tr><td align=\"left\">Mean number of patients</td><td align=\"left\">10.19</td><td align=\"left\">8.43</td><td align=\"left\">5.27</td><td align=\"left\">1.6</td><td align=\"left\">0.68</td><td align=\"left\">0.07</td><td align=\"left\"/></tr><tr><td align=\"left\" rowspan=\"4\">10: TD25 @0</td><td align=\"left\">True DLT rate</td><td align=\"left\">0.12</td><td align=\"left\">0.25</td><td align=\"left\">0.4</td><td align=\"left\">0.5</td><td align=\"left\">0.45</td><td align=\"left\">0.55</td><td align=\"left\"/></tr><tr><td align=\"left\">P(select)</td><td align=\"left\">0.23</td><td align=\"left\"><bold>0.52</bold></td><td align=\"left\">0.2</td><td align=\"left\">0.02</td><td align=\"left\">0.02</td><td align=\"left\">0</td><td align=\"left\">0.01</td></tr><tr><td align=\"left\">% of patients</td><td align=\"left\">18</td><td align=\"left\">36</td><td align=\"left\">29</td><td align=\"left\">11</td><td align=\"left\">6</td><td align=\"left\">1</td><td align=\"left\"/></tr><tr><td align=\"left\">Mean number of patients</td><td align=\"left\">5.24</td><td align=\"left\">10.64</td><td align=\"left\">8.82</td><td align=\"left\">3.16</td><td align=\"left\">1.85</td><td align=\"left\">0.24</td><td align=\"left\"/></tr><tr><td align=\"left\" rowspan=\"4\">11: TD25 @1</td><td align=\"left\">True DLT rate</td><td align=\"left\">0.09</td><td align=\"left\">0.12</td><td align=\"left\">0.25</td><td align=\"left\">0.45</td><td align=\"left\">0.4</td><td align=\"left\">0.5</td><td align=\"left\"/></tr><tr><td align=\"left\">P(select)</td><td align=\"left\">0.02</td><td align=\"left\">0.2</td><td align=\"left\"><bold>0.55</bold></td><td align=\"left\">0.09</td><td align=\"left\">0.14</td><td align=\"left\">0.01</td><td align=\"left\">&lt;0.01</td></tr><tr><td align=\"left\">% of patients</td><td align=\"left\">4</td><td align=\"left\">20</td><td align=\"left\">34</td><td align=\"left\">21</td><td align=\"left\">17</td><td align=\"left\">3</td><td align=\"left\"/></tr><tr><td align=\"left\">Mean number of patients</td><td align=\"left\">1.16</td><td align=\"left\">6.43</td><td align=\"left\">11.07</td><td align=\"left\">6.83</td><td align=\"left\">5.6</td><td align=\"left\">1.07</td><td align=\"left\"/></tr><tr><td align=\"left\" rowspan=\"4\">12: TD25 @2a</td><td align=\"left\">True DLT rate</td><td align=\"left\">0.06</td><td align=\"left\">0.09</td><td align=\"left\">0.12</td><td align=\"left\">0.25</td><td align=\"left\">0.15</td><td align=\"left\">0.45</td><td align=\"left\"/></tr><tr><td align=\"left\">P(select)</td><td align=\"left\">0</td><td align=\"left\">0.01</td><td align=\"left\">0.08</td><td align=\"left\"><bold>0.44</bold></td><td align=\"left\">0.33</td><td align=\"left\">0.14</td><td align=\"left\">&lt;0.01</td></tr><tr><td align=\"left\">% of patients</td><td align=\"left\">1</td><td align=\"left\">11</td><td align=\"left\">16</td><td align=\"left\">30</td><td align=\"left\">24</td><td align=\"left\">18</td><td align=\"left\"/></tr><tr><td align=\"left\">Mean number of patients</td><td align=\"left\">0.48</td><td align=\"left\">3.78</td><td align=\"left\">5.24</td><td align=\"left\">10.1</td><td align=\"left\">7.9</td><td align=\"left\">6.07</td><td align=\"left\"/></tr><tr><td align=\"left\" rowspan=\"4\">13: TD25 @2b</td><td align=\"left\">True DLT rate</td><td align=\"left\">0.03</td><td align=\"left\">0.06</td><td align=\"left\">0.09</td><td align=\"left\">0.35</td><td align=\"left\">0.25</td><td align=\"left\">0.4</td><td align=\"left\"/></tr><tr><td align=\"left\">P(select)</td><td align=\"left\">0</td><td align=\"left\">0</td><td align=\"left\">0.15</td><td align=\"left\">0.31</td><td align=\"left\"><bold>0.43</bold></td><td align=\"left\">0.11</td><td align=\"left\">0</td></tr><tr><td align=\"left\">% of patients</td><td align=\"left\">1</td><td align=\"left\">11</td><td align=\"left\">18</td><td align=\"left\">30</td><td align=\"left\">28</td><td align=\"left\">14</td><td align=\"left\"/></tr><tr><td align=\"left\">Mean number of patients</td><td align=\"left\">0.25</td><td align=\"left\">3.5</td><td align=\"left\">5.9</td><td align=\"left\">9.82</td><td align=\"left\">9.14</td><td align=\"left\">4.54</td><td align=\"left\"/></tr><tr><td align=\"left\" rowspan=\"4\">14: TD25 @3</td><td align=\"left\">True DLT rate</td><td align=\"left\">0.01</td><td align=\"left\">0.03</td><td align=\"left\">0.06</td><td align=\"left\">0.12</td><td align=\"left\">0.09</td><td align=\"left\">0.25</td><td align=\"left\"/></tr><tr><td align=\"left\">P(select)</td><td align=\"left\">0</td><td align=\"left\">0</td><td align=\"left\">0</td><td align=\"left\">0.13</td><td align=\"left\">0.09</td><td align=\"left\"><bold>0.78</bold></td><td align=\"left\">0</td></tr><tr><td align=\"left\">% of patients</td><td align=\"left\">0</td><td align=\"left\">10</td><td align=\"left\">11</td><td align=\"left\">19</td><td align=\"left\">16</td><td align=\"left\">43</td><td align=\"left\"/></tr><tr><td align=\"left\">Mean number of patients</td><td align=\"left\">0.1</td><td align=\"left\">3.13</td><td align=\"left\">3.51</td><td align=\"left\">5.88</td><td align=\"left\">5.06</td><td align=\"left\">13.13</td><td align=\"left\"/></tr><tr><td align=\"left\" rowspan=\"4\">15: Equal steps in DLT rate</td><td align=\"left\">True DLT rate</td><td align=\"left\">0.05</td><td align=\"left\">0.1</td><td align=\"left\">0.15</td><td align=\"left\">0.25</td><td align=\"left\">0.2</td><td align=\"left\">0.3</td><td align=\"left\"/></tr><tr><td align=\"left\">P(select)</td><td align=\"left\">0</td><td align=\"left\">0.02</td><td align=\"left\">0.12</td><td align=\"left\"><bold>0.32</bold></td><td align=\"left\">0.27</td><td align=\"left\">0.26</td><td align=\"left\">&lt;0.01</td></tr><tr><td align=\"left\">% of patients</td><td align=\"left\">2</td><td align=\"left\">13</td><td align=\"left\">19</td><td align=\"left\">27</td><td align=\"left\">22</td><td align=\"left\">18</td><td align=\"left\"/></tr><tr><td align=\"left\">Mean number of patients</td><td align=\"left\">0.54</td><td align=\"left\">4.02</td><td align=\"left\">5.93</td><td align=\"left\">8.56</td><td align=\"left\">6.89</td><td align=\"left\">5.75</td><td align=\"left\"/></tr><tr><td align=\"left\" rowspan=\"4\">16: All toxic</td><td align=\"left\">True DLT rate</td><td align=\"left\">0.5</td><td align=\"left\">0.6</td><td align=\"left\">0.65</td><td align=\"left\">0.75</td><td align=\"left\">0.7</td><td align=\"left\">0.8</td><td align=\"left\"/></tr><tr><td align=\"left\">P(select)</td><td align=\"left\">0.27</td><td align=\"left\">0</td><td align=\"left\">0</td><td align=\"left\">0</td><td align=\"left\">0</td><td align=\"left\">0</td><td align=\"left\"><bold>0.73</bold></td></tr><tr><td align=\"left\">% of patients</td><td align=\"left\">56</td><td align=\"left\">27</td><td align=\"left\">15</td><td align=\"left\">2</td><td align=\"left\">0</td><td align=\"left\">0</td><td align=\"left\"/></tr><tr><td align=\"left\">Mean number of patients</td><td align=\"left\">9.01</td><td align=\"left\">4.28</td><td align=\"left\">2.39</td><td align=\"left\">0.38</td><td align=\"left\">0.05</td><td align=\"left\">0</td><td align=\"left\"/></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab4\"><label>Table 4</label><caption><p>Summary of simulated patient numbers for each scenario</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Scenario</th><th align=\"left\">Max no. of patients</th><th align=\"left\">% max reached</th><th align=\"left\">Mean no. of patients</th></tr></thead><tbody><tr><td align=\"left\">1: TD25 @-1</td><td align=\"left\">60</td><td align=\"left\">0.21</td><td align=\"left\">26.38</td></tr><tr><td align=\"left\">2: TD25 @0</td><td align=\"left\">60</td><td align=\"left\">0.08</td><td align=\"left\">29.97</td></tr><tr><td align=\"left\">3: TD25 @1</td><td align=\"left\">60</td><td align=\"left\">0.05</td><td align=\"left\">32.01</td></tr><tr><td align=\"left\">4: TD25 @2a</td><td align=\"left\">60</td><td align=\"left\">0.12</td><td align=\"left\">33.22</td></tr><tr><td align=\"left\">5: TD25 @2b</td><td align=\"left\">60</td><td align=\"left\">0.06</td><td align=\"left\">33.60</td></tr><tr><td align=\"left\">6: TD25 @3</td><td align=\"left\">60</td><td align=\"left\">0.02</td><td align=\"left\">30.92</td></tr><tr><td align=\"left\">7: Equal steps</td><td align=\"left\">60</td><td align=\"left\">0.01</td><td align=\"left\">31.74</td></tr><tr><td align=\"left\">8: All toxic</td><td align=\"left\">54</td><td align=\"left\">0.01</td><td align=\"left\">16.14</td></tr><tr><td align=\"left\">9: TD25 @-1</td><td align=\"left\">60</td><td align=\"left\">0.17</td><td align=\"left\">26.24</td></tr><tr><td align=\"left\">10: TD25 @0</td><td align=\"left\">60</td><td align=\"left\">0.11</td><td align=\"left\">29.95</td></tr><tr><td align=\"left\">11: TD25 @1</td><td align=\"left\">60</td><td align=\"left\">0.06</td><td align=\"left\">32.15</td></tr><tr><td align=\"left\">12: TD25 @2a</td><td align=\"left\">60</td><td align=\"left\">0.07</td><td align=\"left\">33.56</td></tr><tr><td align=\"left\">13: TD25 @2b</td><td align=\"left\">60</td><td align=\"left\">0.03</td><td align=\"left\">33.16</td></tr><tr><td align=\"left\">14: TD25 @3</td><td align=\"left\">60</td><td align=\"left\">0.08</td><td align=\"left\">30.81</td></tr><tr><td align=\"left\">15: Equal steps</td><td align=\"left\">60</td><td align=\"left\">0.02</td><td align=\"left\">31.69</td></tr><tr><td align=\"left\">16: All toxic</td><td align=\"left\">51</td><td align=\"left\">0.01</td><td align=\"left\">16.11</td></tr></tbody></table></table-wrap>" ]

[ "<inline-formula id=\"IEq1\"><alternatives><tex-math id=\"M1\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$d_{1}$$\\end{document}</tex-math><mml:math id=\"M2\"><mml:msub><mml:mi>d</mml:mi><mml:mn>1</mml:mn></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq2\"><alternatives><tex-math id=\"M3\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$d_{k}$$\\end{document}</tex-math><mml:math id=\"M4\"><mml:msub><mml:mi>d</mml:mi><mml:mi>k</mml:mi></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq3\"><alternatives><tex-math id=\"M5\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$X_{j}$$\\end{document}</tex-math><mml:math id=\"M6\"><mml:msub><mml:mi>X</mml:mi><mml:mi>j</mml:mi></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq4\"><alternatives><tex-math id=\"M7\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$x_{j} \\in (d_{1}, ..., d_{k})$$\\end{document}</tex-math><mml:math id=\"M8\"><mml:mrow><mml:msub><mml:mi>x</mml:mi><mml:mi>j</mml:mi></mml:msub><mml:mo>∈</mml:mo><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:msub><mml:mi>d</mml:mi><mml:mn>1</mml:mn></mml:msub><mml:mo>,</mml:mo><mml:mo>.</mml:mo><mml:mo>.</mml:mo><mml:mo>.</mml:mo><mml:mo>,</mml:mo><mml:msub><mml:mi>d</mml:mi><mml:mi>k</mml:mi></mml:msub><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq5\"><alternatives><tex-math id=\"M9\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$m = 1,...,M$$\\end{document}</tex-math><mml:math id=\"M10\"><mml:mrow><mml:mi>m</mml:mi><mml:mo>=</mml:mo><mml:mn>1</mml:mn><mml:mo>,</mml:mo><mml:mo>.</mml:mo><mml:mo>.</mml:mo><mml:mo>.</mml:mo><mml:mo>,</mml:mo><mml:mi>M</mml:mi></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq6\"><alternatives><tex-math id=\"M11\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$R(d_{i})$$\\end{document}</tex-math><mml:math id=\"M12\"><mml:mrow><mml:mi>R</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:msub><mml:mi>d</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:math></alternatives></inline-formula>", "<disp-formula id=\"Equ1\"><label>1</label><alternatives><tex-math id=\"M13\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\begin{aligned} R(d_{i}) = \\phi _m(d_i,w,\\beta ) = w\\psi _m(d_i,\\beta ) \\; i = 1, ..., k; \\; m = 1, ...,M \\end{aligned}$$\\end{document}</tex-math><mml:math id=\"M14\" display=\"block\"><mml:mrow><mml:mtable><mml:mtr><mml:mtd columnalign=\"right\"><mml:mrow><mml:mi>R</mml:mi><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:msub><mml:mi>d</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mo>=</mml:mo><mml:msub><mml:mi>ϕ</mml:mi><mml:mi>m</mml:mi></mml:msub><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:msub><mml:mi>d</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:mo>,</mml:mo><mml:mi>w</mml:mi><mml:mo>,</mml:mo><mml:mi>β</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mo>=</mml:mo><mml:mi>w</mml:mi><mml:msub><mml:mi>ψ</mml:mi><mml:mi>m</mml:mi></mml:msub><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:msub><mml:mi>d</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:mo>,</mml:mo><mml:mi>β</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mspace width=\"0.277778em\"/><mml:mi>i</mml:mi><mml:mo>=</mml:mo><mml:mn>1</mml:mn><mml:mo>,</mml:mo><mml:mo>.</mml:mo><mml:mo>.</mml:mo><mml:mo>.</mml:mo><mml:mo>,</mml:mo><mml:mi>k</mml:mi><mml:mo>;</mml:mo><mml:mspace width=\"0.277778em\"/><mml:mi>m</mml:mi><mml:mo>=</mml:mo><mml:mn>1</mml:mn><mml:mo>,</mml:mo><mml:mo>.</mml:mo><mml:mo>.</mml:mo><mml:mo>.</mml:mo><mml:mo>,</mml:mo><mml:mi>M</mml:mi></mml:mrow></mml:mtd></mml:mtr></mml:mtable></mml:mrow></mml:math></alternatives></disp-formula>", "<inline-formula id=\"IEq7\"><alternatives><tex-math id=\"M15\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\phi _m(d_i,w,\\beta )$$\\end{document}</tex-math><mml:math id=\"M16\"><mml:mrow><mml:msub><mml:mi>ϕ</mml:mi><mml:mi>m</mml:mi></mml:msub><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:msub><mml:mi>d</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:mo>,</mml:mo><mml:mi>w</mml:mi><mml:mo>,</mml:mo><mml:mi>β</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq8\"><alternatives><tex-math id=\"M17\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\beta \\in (-\\infty , \\infty )$$\\end{document}</tex-math><mml:math id=\"M18\"><mml:mrow><mml:mi>β</mml:mi><mml:mo>∈</mml:mo><mml:mo stretchy=\"false\">(</mml:mo><mml:mo>-</mml:mo><mml:mi>∞</mml:mi><mml:mo>,</mml:mo><mml:mi>∞</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq9\"><alternatives><tex-math id=\"M19\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\psi$$\\end{document}</tex-math><mml:math id=\"M20\"><mml:mi>ψ</mml:mi></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq10\"><alternatives><tex-math id=\"M21\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$0 \\le w \\le 1$$\\end{document}</tex-math><mml:math id=\"M22\"><mml:mrow><mml:mn>0</mml:mn><mml:mo>≤</mml:mo><mml:mi>w</mml:mi><mml:mo>≤</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq11\"><alternatives><tex-math id=\"M23\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$U_j$$\\end{document}</tex-math><mml:math id=\"M24\"><mml:msub><mml:mi>U</mml:mi><mml:mi>j</mml:mi></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq12\"><alternatives><tex-math id=\"M25\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$u \\le T$$\\end{document}</tex-math><mml:math id=\"M26\"><mml:mrow><mml:mi>u</mml:mi><mml:mo>≤</mml:mo><mml:mi>T</mml:mi></mml:mrow></mml:math></alternatives></inline-formula>", "<disp-formula id=\"Equ2\"><label>2</label><alternatives><tex-math id=\"M27\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\begin{aligned} P(U_j \\le u ) = P(U_j \\le u |U_j \\le T)P(U_j \\le T) \\equiv w(u;T) \\psi _m(d_i,\\beta ). \\end{aligned}$$\\end{document}</tex-math><mml:math id=\"M28\" display=\"block\"><mml:mrow><mml:mtable><mml:mtr><mml:mtd columnalign=\"right\"><mml:mrow><mml:mi>P</mml:mi><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:msub><mml:mi>U</mml:mi><mml:mi>j</mml:mi></mml:msub><mml:mo>≤</mml:mo><mml:mi>u</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mo>=</mml:mo><mml:mi>P</mml:mi><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:msub><mml:mi>U</mml:mi><mml:mi>j</mml:mi></mml:msub><mml:mo>≤</mml:mo><mml:mi>u</mml:mi><mml:mo stretchy=\"false\">|</mml:mo><mml:msub><mml:mi>U</mml:mi><mml:mi>j</mml:mi></mml:msub><mml:mo>≤</mml:mo><mml:mi>T</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mi>P</mml:mi><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:msub><mml:mi>U</mml:mi><mml:mi>j</mml:mi></mml:msub><mml:mo>≤</mml:mo><mml:mi>T</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mo>≡</mml:mo><mml:mi>w</mml:mi><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>u</mml:mi><mml:mo>;</mml:mo><mml:mi>T</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:msub><mml:mi>ψ</mml:mi><mml:mi>m</mml:mi></mml:msub><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:msub><mml:mi>d</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:mo>,</mml:mo><mml:mi>β</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mo>.</mml:mo></mml:mrow></mml:mtd></mml:mtr></mml:mtable></mml:mrow></mml:math></alternatives></disp-formula>", "<inline-formula id=\"IEq13\"><alternatives><tex-math id=\"M29\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\psi$$\\end{document}</tex-math><mml:math id=\"M30\"><mml:mi>ψ</mml:mi></mml:math></alternatives></inline-formula>", "<disp-formula id=\"Equ3\"><label>3</label><alternatives><tex-math id=\"M31\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\begin{aligned} \\psi _m(d_i,\\beta ) = \\alpha _{mi}^{exp(\\beta )} \\; i = 1,...,k; \\; m = 1,\\ldots ,M. \\end{aligned}$$\\end{document}</tex-math><mml:math id=\"M32\" display=\"block\"><mml:mrow><mml:mtable><mml:mtr><mml:mtd columnalign=\"right\"><mml:mrow><mml:msub><mml:mi>ψ</mml:mi><mml:mi>m</mml:mi></mml:msub><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:msub><mml:mi>d</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:mo>,</mml:mo><mml:mi>β</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mo>=</mml:mo><mml:msubsup><mml:mi>α</mml:mi><mml:mrow><mml:mi mathvariant=\"italic\">mi</mml:mi></mml:mrow><mml:mrow><mml:mi>e</mml:mi><mml:mi>x</mml:mi><mml:mi>p</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>β</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:msubsup><mml:mspace width=\"0.277778em\"/><mml:mi>i</mml:mi><mml:mo>=</mml:mo><mml:mn>1</mml:mn><mml:mo>,</mml:mo><mml:mo>.</mml:mo><mml:mo>.</mml:mo><mml:mo>.</mml:mo><mml:mo>,</mml:mo><mml:mi>k</mml:mi><mml:mo>;</mml:mo><mml:mspace width=\"0.277778em\"/><mml:mi>m</mml:mi><mml:mo>=</mml:mo><mml:mn>1</mml:mn><mml:mo>,</mml:mo><mml:mo>…</mml:mo><mml:mo>,</mml:mo><mml:mi>M</mml:mi><mml:mo>.</mml:mo></mml:mrow></mml:mtd></mml:mtr></mml:mtable></mml:mrow></mml:math></alternatives></disp-formula>", "<inline-formula id=\"IEq14\"><alternatives><tex-math id=\"M33\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$0&lt; \\alpha _{m1}&lt; ...&lt; \\alpha _{mk} &lt; 1$$\\end{document}</tex-math><mml:math id=\"M34\"><mml:mrow><mml:mn>0</mml:mn><mml:mo>&lt;</mml:mo><mml:msub><mml:mi>α</mml:mi><mml:mrow><mml:mi>m</mml:mi><mml:mn>1</mml:mn></mml:mrow></mml:msub><mml:mo>&lt;</mml:mo><mml:mo>.</mml:mo><mml:mo>.</mml:mo><mml:mo>.</mml:mo><mml:mo>&lt;</mml:mo><mml:msub><mml:mi>α</mml:mi><mml:mrow><mml:mi mathvariant=\"italic\">mk</mml:mi></mml:mrow></mml:msub><mml:mo>&lt;</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq15\"><alternatives><tex-math id=\"M35\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$p(m) = \\{p(1),...,p(M)\\}$$\\end{document}</tex-math><mml:math id=\"M36\"><mml:mrow><mml:mi>p</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>m</mml:mi><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>=</mml:mo><mml:mo stretchy=\"false\">{</mml:mo><mml:mi>p</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:mn>1</mml:mn><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>,</mml:mo><mml:mo>.</mml:mo><mml:mo>.</mml:mo><mml:mo>.</mml:mo><mml:mo>,</mml:mo><mml:mi>p</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>M</mml:mi><mml:mo stretchy=\"false\">)</mml:mo><mml:mo stretchy=\"false\">}</mml:mo></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq16\"><alternatives><tex-math id=\"M37\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$p(m) \\ge 0$$\\end{document}</tex-math><mml:math id=\"M38\"><mml:mrow><mml:mi>p</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>m</mml:mi><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>≥</mml:mo><mml:mn>0</mml:mn></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq17\"><alternatives><tex-math id=\"M39\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\sum _mp(m)=1$$\\end{document}</tex-math><mml:math id=\"M40\"><mml:mrow><mml:msub><mml:mo>∑</mml:mo><mml:mi>m</mml:mi></mml:msub><mml:mi>p</mml:mi><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>m</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mo>=</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq18\"><alternatives><tex-math id=\"M41\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$p(m) = 1/M$$\\end{document}</tex-math><mml:math id=\"M42\"><mml:mrow><mml:mi>p</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>m</mml:mi><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>=</mml:mo><mml:mn>1</mml:mn><mml:mo stretchy=\"false\">/</mml:mo><mml:mi>M</mml:mi></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq19\"><alternatives><tex-math id=\"M43\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$g(\\beta )$$\\end{document}</tex-math><mml:math id=\"M44\"><mml:mrow><mml:mi>g</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>β</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq20\"><alternatives><tex-math id=\"M45\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\beta$$\\end{document}</tex-math><mml:math id=\"M46\"><mml:mi>β</mml:mi></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq21\"><alternatives><tex-math id=\"M47\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\Omega _j = \\{x_1,y_1, ..., x_j,y_j\\}$$\\end{document}</tex-math><mml:math id=\"M48\"><mml:mrow><mml:msub><mml:mi mathvariant=\"normal\">Ω</mml:mi><mml:mi>j</mml:mi></mml:msub><mml:mo>=</mml:mo><mml:mrow><mml:mo stretchy=\"false\">{</mml:mo><mml:msub><mml:mi>x</mml:mi><mml:mn>1</mml:mn></mml:msub><mml:mo>,</mml:mo><mml:msub><mml:mi>y</mml:mi><mml:mn>1</mml:mn></mml:msub><mml:mo>,</mml:mo><mml:mo>.</mml:mo><mml:mo>.</mml:mo><mml:mo>.</mml:mo><mml:mo>,</mml:mo><mml:msub><mml:mi>x</mml:mi><mml:mi>j</mml:mi></mml:msub><mml:mo>,</mml:mo><mml:msub><mml:mi>y</mml:mi><mml:mi>j</mml:mi></mml:msub><mml:mo stretchy=\"false\">}</mml:mo></mml:mrow></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq22\"><alternatives><tex-math id=\"M49\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\beta$$\\end{document}</tex-math><mml:math id=\"M50\"><mml:mi>β</mml:mi></mml:math></alternatives></inline-formula>", "<disp-formula id=\"Equ4\"><label>4</label><alternatives><tex-math id=\"M51\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\begin{aligned} \\tilde{L}_m(\\beta |\\Omega _j)=\\prod _{l=1}^{j}\\phi _m^{y_l}(x_l,w_l,\\beta )\\{1-\\phi _m(x_l,w_l,\\beta )\\}^{(1-y_l)} \\end{aligned}$$\\end{document}</tex-math><mml:math id=\"M52\" display=\"block\"><mml:mrow><mml:mtable><mml:mtr><mml:mtd columnalign=\"right\"><mml:mrow><mml:msub><mml:mover accent=\"true\"><mml:mi>L</mml:mi><mml:mo stretchy=\"false\">~</mml:mo></mml:mover><mml:mi>m</mml:mi></mml:msub><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>β</mml:mi><mml:mo stretchy=\"false\">|</mml:mo><mml:msub><mml:mi mathvariant=\"normal\">Ω</mml:mi><mml:mi>j</mml:mi></mml:msub><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mo>=</mml:mo><mml:munderover><mml:mo>∏</mml:mo><mml:mrow><mml:mi>l</mml:mi><mml:mo>=</mml:mo><mml:mn>1</mml:mn></mml:mrow><mml:mi>j</mml:mi></mml:munderover><mml:msubsup><mml:mi>ϕ</mml:mi><mml:mi>m</mml:mi><mml:msub><mml:mi>y</mml:mi><mml:mi>l</mml:mi></mml:msub></mml:msubsup><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:msub><mml:mi>x</mml:mi><mml:mi>l</mml:mi></mml:msub><mml:mo>,</mml:mo><mml:msub><mml:mi>w</mml:mi><mml:mi>l</mml:mi></mml:msub><mml:mo>,</mml:mo><mml:mi>β</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:msup><mml:mrow><mml:mo stretchy=\"false\">{</mml:mo><mml:mn>1</mml:mn><mml:mo>-</mml:mo><mml:msub><mml:mi>ϕ</mml:mi><mml:mi>m</mml:mi></mml:msub><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:msub><mml:mi>x</mml:mi><mml:mi>l</mml:mi></mml:msub><mml:mo>,</mml:mo><mml:msub><mml:mi>w</mml:mi><mml:mi>l</mml:mi></mml:msub><mml:mo>,</mml:mo><mml:mi>β</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mo stretchy=\"false\">}</mml:mo></mml:mrow><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mn>1</mml:mn><mml:mo>-</mml:mo><mml:msub><mml:mi>y</mml:mi><mml:mi>l</mml:mi></mml:msub><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:msup></mml:mrow></mml:mtd></mml:mtr></mml:mtable></mml:mrow></mml:math></alternatives></disp-formula>", "<inline-formula id=\"IEq23\"><alternatives><tex-math id=\"M53\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\hat{\\beta }_{mj}$$\\end{document}</tex-math><mml:math id=\"M54\"><mml:msub><mml:mover accent=\"true\"><mml:mi>β</mml:mi><mml:mo stretchy=\"false\">^</mml:mo></mml:mover><mml:mrow><mml:mi mathvariant=\"italic\">mj</mml:mi></mml:mrow></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq24\"><alternatives><tex-math id=\"M55\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\Omega _j$$\\end{document}</tex-math><mml:math id=\"M56\"><mml:msub><mml:mi mathvariant=\"normal\">Ω</mml:mi><mml:mi>j</mml:mi></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq25\"><alternatives><tex-math id=\"M57\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\beta$$\\end{document}</tex-math><mml:math id=\"M58\"><mml:mi>β</mml:mi></mml:math></alternatives></inline-formula>", "<disp-formula id=\"Equ5\"><label>5</label><alternatives><tex-math id=\"M59\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\begin{aligned} \\tilde{f}_m(\\beta |\\Omega _j)=\\frac{\\tilde{L}_m(\\beta |\\Omega _j)g(\\beta )}{\\int _{\\beta }\\tilde{L}_m(\\beta |\\Omega _j)g(\\beta )d\\beta } \\end{aligned}$$\\end{document}</tex-math><mml:math id=\"M60\" display=\"block\"><mml:mrow><mml:mtable><mml:mtr><mml:mtd columnalign=\"right\"><mml:mrow><mml:msub><mml:mover accent=\"true\"><mml:mi>f</mml:mi><mml:mo stretchy=\"false\">~</mml:mo></mml:mover><mml:mi>m</mml:mi></mml:msub><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>β</mml:mi><mml:mo stretchy=\"false\">|</mml:mo><mml:msub><mml:mi mathvariant=\"normal\">Ω</mml:mi><mml:mi>j</mml:mi></mml:msub><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mo>=</mml:mo><mml:mfrac><mml:mrow><mml:msub><mml:mover accent=\"true\"><mml:mi>L</mml:mi><mml:mo stretchy=\"false\">~</mml:mo></mml:mover><mml:mi>m</mml:mi></mml:msub><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>β</mml:mi><mml:mo stretchy=\"false\">|</mml:mo><mml:msub><mml:mi mathvariant=\"normal\">Ω</mml:mi><mml:mi>j</mml:mi></mml:msub><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mi>g</mml:mi><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>β</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:mrow><mml:mrow><mml:msub><mml:mo>∫</mml:mo><mml:mi>β</mml:mi></mml:msub><mml:msub><mml:mover accent=\"true\"><mml:mi>L</mml:mi><mml:mo stretchy=\"false\">~</mml:mo></mml:mover><mml:mi>m</mml:mi></mml:msub><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>β</mml:mi><mml:mo stretchy=\"false\">|</mml:mo><mml:msub><mml:mi mathvariant=\"normal\">Ω</mml:mi><mml:mi>j</mml:mi></mml:msub><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mi>g</mml:mi><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>β</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mi>d</mml:mi><mml:mi>β</mml:mi></mml:mrow></mml:mfrac></mml:mrow></mml:mtd></mml:mtr></mml:mtable></mml:mrow></mml:math></alternatives></disp-formula>", "<disp-formula id=\"Equ6\"><label>6</label><alternatives><tex-math id=\"M61\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\begin{aligned} \\tilde{\\pi }(m|\\Omega _j)=\\frac{p(m)\\int _{\\beta }\\tilde{L}_m(\\beta |\\Omega _j)g(\\beta )d\\beta }{\\sum _{m=1}^{M}p(m)\\int _{\\beta }\\tilde{L}_m(\\beta |\\Omega _j)g(\\beta )d\\beta }. \\end{aligned}$$\\end{document}</tex-math><mml:math id=\"M62\" display=\"block\"><mml:mrow><mml:mtable><mml:mtr><mml:mtd columnalign=\"right\"><mml:mrow><mml:mover accent=\"true\"><mml:mi>π</mml:mi><mml:mo stretchy=\"false\">~</mml:mo></mml:mover><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>m</mml:mi><mml:mo stretchy=\"false\">|</mml:mo><mml:msub><mml:mi mathvariant=\"normal\">Ω</mml:mi><mml:mi>j</mml:mi></mml:msub><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mo>=</mml:mo><mml:mfrac><mml:mrow><mml:mi>p</mml:mi><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>m</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:msub><mml:mo>∫</mml:mo><mml:mi>β</mml:mi></mml:msub><mml:msub><mml:mover accent=\"true\"><mml:mi>L</mml:mi><mml:mo stretchy=\"false\">~</mml:mo></mml:mover><mml:mi>m</mml:mi></mml:msub><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>β</mml:mi><mml:mo stretchy=\"false\">|</mml:mo><mml:msub><mml:mi mathvariant=\"normal\">Ω</mml:mi><mml:mi>j</mml:mi></mml:msub><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mi>g</mml:mi><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>β</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mi>d</mml:mi><mml:mi>β</mml:mi></mml:mrow><mml:mrow><mml:msubsup><mml:mo>∑</mml:mo><mml:mrow><mml:mi>m</mml:mi><mml:mo>=</mml:mo><mml:mn>1</mml:mn></mml:mrow><mml:mi>M</mml:mi></mml:msubsup><mml:mi>p</mml:mi><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>m</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:msub><mml:mo>∫</mml:mo><mml:mi>β</mml:mi></mml:msub><mml:msub><mml:mover accent=\"true\"><mml:mi>L</mml:mi><mml:mo stretchy=\"false\">~</mml:mo></mml:mover><mml:mi>m</mml:mi></mml:msub><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>β</mml:mi><mml:mo stretchy=\"false\">|</mml:mo><mml:msub><mml:mi mathvariant=\"normal\">Ω</mml:mi><mml:mi>j</mml:mi></mml:msub><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mi>g</mml:mi><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>β</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mi>d</mml:mi><mml:mi>β</mml:mi></mml:mrow></mml:mfrac><mml:mo>.</mml:mo></mml:mrow></mml:mtd></mml:mtr></mml:mtable></mml:mrow></mml:math></alternatives></disp-formula>", "<inline-formula id=\"IEq26\"><alternatives><tex-math id=\"M63\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\psi _h(d_i,\\beta )$$\\end{document}</tex-math><mml:math id=\"M64\"><mml:mrow><mml:msub><mml:mi>ψ</mml:mi><mml:mi>h</mml:mi></mml:msub><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:msub><mml:mi>d</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:mo>,</mml:mo><mml:mi>β</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq27\"><alternatives><tex-math id=\"M65\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$d_{i}$$\\end{document}</tex-math><mml:math id=\"M66\"><mml:msub><mml:mi>d</mml:mi><mml:mi>i</mml:mi></mml:msub></mml:math></alternatives></inline-formula>", "<disp-formula id=\"Equ7\"><label>7</label><alternatives><tex-math id=\"M67\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\begin{aligned} \\hat{R}(d_i) = \\psi _h(d_i,\\hat{\\beta }_{hj}); \\; \\hat{\\beta }_h = \\int _{\\beta }\\beta \\tilde{f}_h(\\beta |\\Omega _j)d\\beta . \\end{aligned}$$\\end{document}</tex-math><mml:math id=\"M68\" display=\"block\"><mml:mrow><mml:mtable><mml:mtr><mml:mtd columnalign=\"right\"><mml:mrow><mml:mover accent=\"true\"><mml:mi>R</mml:mi><mml:mo stretchy=\"false\">^</mml:mo></mml:mover><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:msub><mml:mi>d</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mo>=</mml:mo><mml:msub><mml:mi>ψ</mml:mi><mml:mi>h</mml:mi></mml:msub><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:msub><mml:mi>d</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:mo>,</mml:mo><mml:msub><mml:mover accent=\"true\"><mml:mi>β</mml:mi><mml:mo stretchy=\"false\">^</mml:mo></mml:mover><mml:mrow><mml:mi mathvariant=\"italic\">hj</mml:mi></mml:mrow></mml:msub><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mo>;</mml:mo><mml:mspace width=\"0.277778em\"/><mml:msub><mml:mover accent=\"true\"><mml:mi>β</mml:mi><mml:mo stretchy=\"false\">^</mml:mo></mml:mover><mml:mi>h</mml:mi></mml:msub><mml:mo>=</mml:mo><mml:msub><mml:mo>∫</mml:mo><mml:mi>β</mml:mi></mml:msub><mml:mi>β</mml:mi><mml:msub><mml:mover accent=\"true\"><mml:mi>f</mml:mi><mml:mo stretchy=\"false\">~</mml:mo></mml:mover><mml:mi>h</mml:mi></mml:msub><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>β</mml:mi><mml:mo stretchy=\"false\">|</mml:mo><mml:msub><mml:mi mathvariant=\"normal\">Ω</mml:mi><mml:mi>j</mml:mi></mml:msub><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mi>d</mml:mi><mml:mi>β</mml:mi><mml:mo>.</mml:mo></mml:mrow></mml:mtd></mml:mtr></mml:mtable></mml:mrow></mml:math></alternatives></disp-formula>", "<inline-formula id=\"IEq28\"><alternatives><tex-math id=\"M69\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$x_j \\in \\{d_1,...,d_k\\}$$\\end{document}</tex-math><mml:math id=\"M70\"><mml:mrow><mml:msub><mml:mi>x</mml:mi><mml:mi>j</mml:mi></mml:msub><mml:mo>∈</mml:mo><mml:mrow><mml:mo stretchy=\"false\">{</mml:mo><mml:msub><mml:mi>d</mml:mi><mml:mn>1</mml:mn></mml:msub><mml:mo>,</mml:mo><mml:mo>.</mml:mo><mml:mo>.</mml:mo><mml:mo>.</mml:mo><mml:mo>,</mml:mo><mml:msub><mml:mi>d</mml:mi><mml:mi>k</mml:mi></mml:msub><mml:mo stretchy=\"false\">}</mml:mo></mml:mrow></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq29\"><alternatives><tex-math id=\"M71\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$j+$$\\end{document}</tex-math><mml:math id=\"M72\"><mml:mrow><mml:mi>j</mml:mi><mml:mo>+</mml:mo></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq30\"><alternatives><tex-math id=\"M73\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$d_i$$\\end{document}</tex-math><mml:math id=\"M74\"><mml:msub><mml:mi>d</mml:mi><mml:mi>i</mml:mi></mml:msub></mml:math></alternatives></inline-formula>", "<disp-formula id=\"Equ8\"><label>8</label><alternatives><tex-math id=\"M75\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\begin{aligned} \\triangle (\\hat{R}(d_i),\\theta ) = |\\hat{R}(d_i)-\\theta |, \\; i=1,...,k \\end{aligned}$$\\end{document}</tex-math><mml:math id=\"M76\" display=\"block\"><mml:mrow><mml:mtable><mml:mtr><mml:mtd columnalign=\"right\"><mml:mrow><mml:mi>▵</mml:mi><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mover accent=\"true\"><mml:mi>R</mml:mi><mml:mo stretchy=\"false\">^</mml:mo></mml:mover><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:msub><mml:mi>d</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mo>,</mml:mo><mml:mi>θ</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mo>=</mml:mo><mml:mrow><mml:mo stretchy=\"false\">|</mml:mo><mml:mover accent=\"true\"><mml:mi>R</mml:mi><mml:mo stretchy=\"false\">^</mml:mo></mml:mover><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:msub><mml:mi>d</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mo>-</mml:mo><mml:mi>θ</mml:mi><mml:mo stretchy=\"false\">|</mml:mo></mml:mrow><mml:mo>,</mml:mo><mml:mspace width=\"0.277778em\"/><mml:mi>i</mml:mi><mml:mo>=</mml:mo><mml:mn>1</mml:mn><mml:mo>,</mml:mo><mml:mo>.</mml:mo><mml:mo>.</mml:mo><mml:mo>.</mml:mo><mml:mo>,</mml:mo><mml:mi>k</mml:mi></mml:mrow></mml:mtd></mml:mtr></mml:mtable></mml:mrow></mml:math></alternatives></disp-formula>", "<inline-formula id=\"IEq31\"><alternatives><tex-math id=\"M77\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\theta$$\\end{document}</tex-math><mml:math id=\"M78\"><mml:mi>θ</mml:mi></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq32\"><alternatives><tex-math id=\"M79\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\theta$$\\end{document}</tex-math><mml:math id=\"M80\"><mml:mi>θ</mml:mi></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq33\"><alternatives><tex-math id=\"M81\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$d_{i}$$\\end{document}</tex-math><mml:math id=\"M82\"><mml:msub><mml:mi>d</mml:mi><mml:mi>i</mml:mi></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq34\"><alternatives><tex-math id=\"M83\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$d_{-1} \\rightarrow d_{0} \\rightarrow d_{1} \\rightarrow d_{2a} \\rightarrow d_{2b} \\rightarrow d_{3}$$\\end{document}</tex-math><mml:math id=\"M84\"><mml:mrow><mml:msub><mml:mi>d</mml:mi><mml:mrow><mml:mo>-</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:msub><mml:mo stretchy=\"false\">→</mml:mo><mml:msub><mml:mi>d</mml:mi><mml:mn>0</mml:mn></mml:msub><mml:mo stretchy=\"false\">→</mml:mo><mml:msub><mml:mi>d</mml:mi><mml:mn>1</mml:mn></mml:msub><mml:mo stretchy=\"false\">→</mml:mo><mml:msub><mml:mi>d</mml:mi><mml:mrow><mml:mn>2</mml:mn><mml:mi>a</mml:mi></mml:mrow></mml:msub><mml:mo stretchy=\"false\">→</mml:mo><mml:msub><mml:mi>d</mml:mi><mml:mrow><mml:mn>2</mml:mn><mml:mi>b</mml:mi></mml:mrow></mml:msub><mml:mo stretchy=\"false\">→</mml:mo><mml:msub><mml:mi>d</mml:mi><mml:mn>3</mml:mn></mml:msub></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq35\"><alternatives><tex-math id=\"M85\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$w(u;t_1, t_2, t_3)$$\\end{document}</tex-math><mml:math id=\"M86\"><mml:mrow><mml:mi>w</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>u</mml:mi><mml:mo>;</mml:mo><mml:msub><mml:mi>t</mml:mi><mml:mn>1</mml:mn></mml:msub><mml:mo>,</mml:mo><mml:msub><mml:mi>t</mml:mi><mml:mn>2</mml:mn></mml:msub><mml:mo>,</mml:mo><mml:msub><mml:mi>t</mml:mi><mml:mn>3</mml:mn></mml:msub><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq36\"><alternatives><tex-math id=\"M87\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$t_1, t_2, t_3$$\\end{document}</tex-math><mml:math id=\"M88\"><mml:mrow><mml:msub><mml:mi>t</mml:mi><mml:mn>1</mml:mn></mml:msub><mml:mo>,</mml:mo><mml:msub><mml:mi>t</mml:mi><mml:mn>2</mml:mn></mml:msub><mml:mo>,</mml:mo><mml:msub><mml:mi>t</mml:mi><mml:mn>3</mml:mn></mml:msub></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq37\"><alternatives><tex-math id=\"M89\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$t_1 \\le u \\le t_3$$\\end{document}</tex-math><mml:math id=\"M90\"><mml:mrow><mml:msub><mml:mi>t</mml:mi><mml:mn>1</mml:mn></mml:msub><mml:mo>≤</mml:mo><mml:mi>u</mml:mi><mml:mo>≤</mml:mo><mml:msub><mml:mi>t</mml:mi><mml:mn>3</mml:mn></mml:msub></mml:mrow></mml:math></alternatives></inline-formula>", "<disp-formula 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[ "<title>Introduction</title>", "<p id=\"Par5\">Informed consent among patients undergoing surgical procedure is the process of shared decision-making made by the client or his/her surrogates after fully explained what he/she is consenting [##UREF##0##1##]. It is a voluntary agreement by a competent individual after adequate information regarding the procedure performed, potential benefits and risks, and alternative options of management to make decisions without corrosion [##UREF##1##2##]. One of the medical practices associated with high risks that require informed consent is surgical invasive procedures [##UREF##2##3##]. The patient has the right to obtain appropriate expression of all risks and benefits, type of producer, options of treatment, and consequences with scientific justification and evidence [##UREF##3##4##]. One of the fundamental pillars of surgical treatment is the patient’s informed consent [##UREF##4##5##].</p>", "<p id=\"Par6\">A globally recognized safeguard for clients undergoing invasive procedures is informed consent [##UREF##5##6##]. The requirement to make informed consent is patient autonomy (a) the ability of the client to self-determination regarding the procedure that will be done on his/her body. It is self-rule and choice regarding what treatment options physicians propose [##UREF##1##2##, ##REF##22392947##7##]. Patient comprehension (b) the ability of the client to understand what is explained by health care providers [##REF##22392947##7##]. Adequate information (c) means health care provider disclose in sufficient detail the diagnosis, prognosis, treatment option, potential risks, and benefits by using understandable language to his/her expert decision [##UREF##1##2##, ##UREF##6##8##]. Competency (d) the capacity of the client to understand the information, voluntariness (e) decision of consent based on the information rather than coercion, consent (f) agreement between the patient and treating clinician in the proposed treatment procedure with full understanding. Consent form (g) is a written document signed by the client before the surgical procedure [##REF##24387594##9##–##UREF##8##11##]. Informed consent is the safeguarding of the patient in medical practice at different standards such as ethical, legal, and administrative purposes [##UREF##1##2##, ##UREF##5##6##, ##REF##28077127##12##]. The informed consent document builds trust between patients and physicians and enhances the shared decision-making of the client in the surgical procedure. All surgeons check the informed consent document before entering into operation room. Any invasive procedure without signed consent is illegal as well as unethical [##REF##28077127##12##].</p>", "<p id=\"Par7\">Knowledge and perception of the client towards informed consent in the primary study were assessed in the composite variable. Knowledge of informed consent is measured by the know reason why they had surgery, the option of alternative treatment, type of surgery, anesthesia-related risks, postoperative care, the complication of surgery, the legal requirement of informed consent, the right to change their mind after sign, and who protects [##UREF##9##13##, ##UREF##10##14##]. Different literature indicated that patient knowledge of informed consent is low. Research conducted in Benin indicated that one-third of the clients (32.3%) experienced good knowledge regarding informed consent [##UREF##1##2##]. Another similar study in Sudan revealed that 46% of clients had good knowledge of informed consent [##UREF##11##15##, ##UREF##12##16##]. In Rwanda, only 5% of patients had a high level of knowledge, 12% had moderate, and the rest 83% of the patients had a low level of knowledge towards informed consent [##UREF##13##17##]. A study done in Kenya revealed that knowledge regarding informed consent is limited, 46% of the patients stated that the purpose of informed consent is for hospital protection and 41% of them stated their wishes [##UREF##13##17##].</p>", "<p id=\"Par8\">In Ethiopia, the magnitude of good knowledge of informed consent among surgical patients is low ranging from 10.5% [##UREF##9##13##] to 46.9% [##UREF##14##18##].</p>", "<p id=\"Par9\">Client perception towards informed consent includes perception of the importance and function of consent forms, the legal and ethical status of consent, and the scope of consent [##UREF##14##18##–##REF##16880192##21##]. Research in the different countries indicated that the perception of clients towards informed consent is low. A study done in Saudi Arabia indicated that 23.7% of the clients had poor perceptions of informed consent [##UREF##14##18##, ##UREF##17##22##]. In Rwanda, 23% of patients experience poor perception, and 50% and 31% of clients had moderate and high levels of perception towards surgical informed consent [##UREF##13##17##, ##UREF##14##18##].</p>", "<p id=\"Par10\">The magnitude of client perception towards informed consent in Ethiopia among post-operated patients is low, ranging from 13.7 to 66.8% [##UREF##12##16##, ##UREF##14##18##].</p>", "<p id=\"Par11\">Factors affecting knowledge and perception of patient informed consent in surgical procedures are level of education, residence, age, history of signing before, type of surgery, marital status, and occupation significant variables [##UREF##1##2##, ##UREF##9##13##, ##UREF##13##17##].</p>", "<p id=\"Par12\">Many patients around the world, particularly in developing countries undergo surgery without the knowledge of the reason for the surgery, the type of surgery, and identifying the identity of the surgeon [##UREF##9##13##, ##UREF##18##23##]. The consequence of poor knowledge and perception of clients towards informed consent is patient dissatisfaction, feeling low power in their determination, low control, patient anxiety, and unaccountable for the management [##UREF##14##18##, ##UREF##16##20##, ##REF##16880192##21##, ##UREF##19##24##].</p>", "<p id=\"Par13\">Despite patient knowledge and perception of informed consent being one of the priority concerns in surgical procedures, the problem still exists in Ethiopia. In addition, studies in small-scale findings are inconsistent and inconclusive about the knowledge, perception, and determinants of informed consent. Therefore, the purpose of this systematic review and meta-analysis study was to determine the pooled prevalence and factors of knowledge and perception of patients towards informed consent among surgical patients in Ethiopia. The findings of this nationwide study will generate evidence with implications to improve physician intervention, health facility managers, and policymakers to establish guidelines for informed consent practice.</p>" ]

[ "<title>Methods</title>", "<title>Study design and protocol registration</title>", "<p id=\"Par14\">A Systematic Review and Meta-Analysis (SRMA) was conducted to quantify the pooled level of patient knowledge and perception towards informed consent and determinants among surgical patients in Ethiopia. A preliminary assessment was done to check whether a similar study was performed or not through Prospero, Epistemonikos, Semantic Scholar, and PubMed and there was no similar study. We prepared this systematic review and meta-analysis according to the preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA-2020) follow diagrams (##SUPPL##0##S1## Table ##SUPPL##0##1##). The protocol was registered at Prospero with number CRD42023445409 and is available from: <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.crd.york.ac.uk/PROSPERO/#myprospero\">https://www.crd.york.ac.uk/PROSPERO/#myprospero</ext-link>.</p>", "<p id=\"Par15\">\n\n</p>", "<title>Search strategies</title>", "<p id=\"Par16\">We searched major databases such as PubMed, Hinary, MEDLINE, Cochrane Library, EMBASE, Scopus, African Journal Online (AJO), Semantic Scholar, Google Scholar, google, and reference lists. Besides this, University databases in the country were also searched from August 20, 2023, until September 30, 2023,. Studies conducted between January 01, 2015 to September 30, 2023, were included.</p>", "<p id=\"Par17\">This systematic review and meta-analysis used PECO (Population, Exposure, Comparison, and outcome) to identify eligible studies. The study population (P) are surgical patients, exposure (E) associated factors, comparison (C) reference of the factors, and outcome (O) level of knowledge and perception towards informed consent. Boolean operators “OR” and “AND” were used to combine search terms. Keywords used to search includes knowledge, perception, patient, client, “informed consent”, consent, factors, determinants, predictors, “surgical patient”, “post operated patient”, “after surgery”, and Ethiopia.</p>", "<p id=\"Par18\">Studies obtained by the reviewers’ search strategy were exported into EndNote for management. All duplicated studies obtained for different database searches were excluded. Studies eligibility was assessed first from the title, then the abstract, and finally, a full-text review was performed.</p>", "<title>Eligibility criteria</title>", "<p id=\"Par19\">All observational studies (cross-sectional, case-control, and cohort) on patient knowledge and perception towards informed consent among surgical patients conducted in Ethiopia were included. Both published and unpublished studies reported the prevalence of patient knowledge and perception toward informed consent and its associated factors were included. All studies reported in English were included. Studies conducted between January 01, 2015 to September 30, 2023 were included. Articles that cannot access full text after failing to contact the primary authors were excluded.</p>", "<title>Outcome measurement</title>", "<p id=\"Par20\">This systematic review and meta-analysis measured three main outcomes. The first outcome of the study was to estimate the pooled level of appropriate knowledge towards informed consent. The second outcome was to estimate the pooled level of perception towards informed consent. The third outcome was the associated factors with knowledge of informed consent among surgical patients. The level of knowledge towards informed consent was measured by 12 items and the level of perception by 8 items of questions. Patients who scored less than the mean for knowledge and perception questions had poor knowledge and poor perception respectively.</p>", "<title>Data extraction</title>", "<p id=\"Par21\">The selection of studies in all the searched databases was conducted by three authors (YT, NK, and FDB) independently. The primary author, study year, year of publication, regions where the study was done, study design, sample size, prevalence, response rate, method of outcome measurement, all associated factors odds ratio, relative risk, lower confidence interval, and upper confidence interval were extracted by using Microsoft Excel format. The corresponding author was supportive of clarification on the inclusion criteria. Disagreements among data extractors were solved by consensus.</p>", "<title>Quality assessment and risk of bias</title>", "<p id=\"Par22\">Three reviewers (MMM, NK, and YT) independently screened the articles that fulfilled the inclusion criteria to avoid the risk of bias. The Newcastle-Ottawa Scale (NOS) checklist was used to appraise the quality of the studies. The tool includes three parts. The first part included methodology [##UREF##4##5##] rate with five stars, the second part was comparability [##UREF##1##2##] rate with two stars and the third part was outcome with statistical test [##UREF##2##3##] rate with three stars (##SUPPL##1##S2## Table). Three authors (MMM, NK, and YT) independently assessed the quality of the studies. Disagreements among reviewers were resolved by consensus and a third party (FDB).</p>", "<title>Data processing and analysis</title>", "<p id=\"Par23\">Data were extracted by using Microsoft Excel format and imported into STATA version 17 for processing and analysis. The pooled prevalence of patient knowledge and perception towards informed consent was estimated by random effect model meta-analysis. The heterogeneity of the studies was assessed by observing the p-value and I² statistics test. Factors associated with patient knowledge for informed consent were estimated by a log odds ratio at 95% CI. The potential source of heterogeneity was identified by subgroup analysis. In addition, Egger’s test statics and funnel plot were performed to identify potential publication bias among the included studies. The result of this meta-analysis was presented by tables, funnel plots, forest plots, and narrations.</p>" ]

[ "<title>Results</title>", "<p id=\"Par24\">A total of 1635 studies were searched by using a searching strategy in this systematic review and meta-analysis study. Among those studies, 452 articles were excluded due to redundancy. From the remaining 1115 articles, 1148 studies were excluded in the review of the abstract and title of the study that did not report the level of patient knowledge or perception and its determinants. Of the studies, 28 articles were excluded because of the study location outside Ethiopia. Finally, seven studies were included in this systematic review and meta-analysis study that met the minimum eligibility criteria (Fig. ##FIG##0##1##). Of those articles, seven studies estimated pooled level of knowledge [##UREF##9##13##, ##UREF##11##15##, ##UREF##14##18##, ##UREF##16##20##, ##UREF##20##25##–##UREF##22##27##], and four studies estimated the prevalence of perception [##UREF##12##16##, ##UREF##14##18##, ##UREF##16##20##, ##UREF##23##28##].</p>", "<p id=\"Par25\">\n\n</p>", "<title>Characteristics of the included studies characteristics of the included studies</title>", "<p id=\"Par26\">From all included seven articles 2,690 study participants were used to estimate the pooled level of patient knowledge of informed consent among surgical patients in Ethiopia. The maximum sample size was 423 [##UREF##12##16##] and the minimum sample size was 302 [##UREF##8##11##]. All included studies are cross-sectional study design. The prevalence of patient knowledge of informed consent ranges from 10.5% [##UREF##9##13##] to 46.9% [##UREF##14##18##] (Table ##TAB##0##1##).</p>", "<title>Prevalence of patient knowledge and perception for informed consent among surgical patent in Ethiopia</title>", "<p id=\"Par27\">We observed that there is a variation in the prevalence of patient knowledge and perception of informed consent among surgical patients in Ethiopia. A random effect meta-analysis model for seven studies pooled prevalence of patient knowledge for informed consent was 32% (95% CI: 21, 43) with (I² = 97.87% and p_value &lt; 0.001) (Fig. ##FIG##1##2##). Similarly, four studies pooled the prevalence of perception of patients towards informed consent at 40% (95% CI: 16, 65) with (I² = 99.21% and p_value &lt; 0.001) (Fig. ##FIG##2##3##).</p>", "<p id=\"Par28\">\n\n</p>", "<p id=\"Par29\">\n\n</p>", "<p id=\"Par30\">To identify potential causes of publication bias among the included studies Egger’s test statistics and funnel plot were performed. As a result, the funnel plot indicated that there was asymmetric distribution in the included studies. In addition, Egger’s test statics indicated that there was evidence to show publication bias (p = 0.009) with a standard error of 7.39. Besides this, we performed a sensitivity analysis to identify any outlier that causes a source of heterogeneity to estimate the pooled prevalence of patient knowledge of informed consent among surgical patients in Ethiopia. The finding indicated that there was one outlier study far apart from the confidence interval of the rest included studies. As a result, we were confident enough, that in this systematic review and meta-analysis study, there was a single study that affected the overall pooled prevalence of patient knowledge of informed consent among surgical patients in Ethiopia (Fig. ##FIG##3##4##).</p>", "<p id=\"Par31\">\n\n</p>", "<p id=\"Par32\">Accordingly, we omitted a single study that lies outside the confidence interval and performed a random effect meta-analysis model in six studies. The pooled level of patient knowledge of informed consent after removing one study changed from 32 to 36% (95% CI: 27,44) with (I² = 95.33% and p &lt; 0.00) (Fig. ##FIG##4##5##). In addition, the funnel plot changes are somehow symmetrical, and Egger’s test statistics result also revealed that there was no evidence of publication bias (p = 0.17) with a standard error of 20.56.</p>", "<p id=\"Par33\">\n\n</p>", "<title>Subgroup analysis</title>", "<p id=\"Par34\">Subgroup analysis was performed by using sample size, study period, and region of the study to identify the potential source of heterogeneity. As a result, studies conducted after 2020 were the possible cause of heterogeneity with the higher pooled prevalence estimated which was 44% (95% CI: 40, 48). Besides this, studies conducted in the Oromia region were other sources of heterogeneity with a lower pooled prevalence of 23% (95% CI: 20,26) (Table ##TAB##1##2##).</p>", "<p id=\"Par35\">\n\n</p>", "<title>Factors affected patient knowledge of informed consent among surgical patients in Ethiopia</title>", "<p id=\"Par36\">The factors of residence, formal education, history of signed informed consent before, and type of surgery were investigated in the pooled effect on patient knowledge of informed consent.</p>", "<p id=\"Par37\">The association between formal education and patient knowledge towards informed consent was examined by using three studies, of which there was no association [##UREF##21##26##] and the rest two were positive associations with patient knowledge towards informed consent [##UREF##9##13##, ##UREF##12##16##]. Hence, there was a positive relationship between formal education and patient knowledge of informed consent. The pooled effect of appropriate patient knowledge of informed consent is nearly three times more likely among formally educated patients than counterparts 2.69 (95% CI: 1.18, 6.15) (Table ##TAB##2##3##).</p>", "<p id=\"Par38\">\n\n</p>", "<p id=\"Par39\">Similarly, we examined the association between having history of signed informed consent before and patient knowledge of informed consent by using three studies [##UREF##12##16##, ##UREF##16##20##, ##UREF##22##27##]. Accordingly, there was a statistically positive relation between history of sign before and patient knowledge of informed consent. Patients who had experienced signing informed consent before the pooled effect of appropriate patient knowledge were more than three times more likely than had no history of signing before 3.65 (95% CI:1.02,13.11) (Table ##TAB##2##3##).</p>", "<p id=\"Par40\">In this meta-analysis, the pooled effect of residence on patient knowledge of informed consent was examined by using four studies. Of which being urban residence, 2 studies had no effect [##UREF##12##16##, ##UREF##21##26##] and 2 had a positive relation with patient knowledge of informed consent [##UREF##9##13##, ##UREF##16##20##]. As a result, there was no statistically significant pooled effect of residence on patient knowledge of informed consent 1.06 (95% CI: 0.26, 3.87) (Table ##TAB##2##3##).</p>", "<p id=\"Par41\">Finally, the pooled effect of the type of surgery on patient knowledge of informed consent was assessed using two studies [##UREF##9##13##, ##UREF##21##26##]. The result of these two studies indicated that there was no statistically significant pooled effect of type of surgery on patient knowledge of informed consent among surgical patients 0.81(95% CI:0.16,4.21) (Table ##TAB##2##3##).</p>" ]

[ "<title>Discussion</title>", "<p id=\"Par42\">Patient knowledge and perception of informed consent are important to increase client satisfaction and better health outcomes for surgical patients. Evidence on patient knowledge, perception, and its determinants is crucial for physicians, health managers, and policymakers. Therefore, this systematic review and meta-analysis were performed by using available primary studies in Ethiopia. The finding of the study revealed that the pooled prevalence of appropriate patient perception of informed consent was 40% (95% CI: 16%, 65%) among surgical patients in Ethiopia. This finding was congruent with studies conducted in Egypt 27.3% [##UREF##2##3##] and in South Africa 27% of patients perceived signed consent with understanding [##UREF##24##29##]. However, the result of this finding was lower than the study conducted in Nigeria 97% of patients were satisfied with the explanation of informed consent [##UREF##25##30##] and University of Colorado on repeat back and no repeat back participants, favorable perception of patients towards informed consent was 88% [##UREF##26##31##]. The possible justification for this variation might be due to the different methods of the study, the sample size in Nigeria was 398 whereas this study incorporates 2690 participants in the primary study.</p>", "<p id=\"Par43\">The pooled prevalence of appropriate patient knowledge of informed consent was 32% (95% CI:21, 43) among surgical patients in Ethiopia. This finding was incongruent with the study finding in German 32.6% of patients correctly answered knowledge questions [##UREF##27##32##]. However, this finding is higher than the study done in Rwanda 5% of the participants had a high level of knowledge, 12% moderate, and the rest 83% had a low level of knowledge towards informed consent [##UREF##13##17##]. The possible reason for this discrepancy might be due to the difference in sample size in Rwanda was 147 and it was conducted in one military hospital. However, this finding was lower than a systematic review study done in Pakistan 50% [##UREF##28##33##], India 68% understood the type and consequence of the study [##UREF##29##34##], Portuguese 44.7%, Croatia level of knowledge average, and 60% had partial knowledge [##UREF##30##35##]. These variations might be due to the difference in the educational status of study participants, differences in economic status, and giving value for informed consent during surgical producer of the patient. It may vary the culture and behavior of physicians who focus on informed consent. Developed countries have a high-level concern for patient rights and informed consent; whereas in developing countries including Ethiopia focus on patient rights is limited.</p>", "<p id=\"Par44\">Subgroup analysis was performed by taking the study setting, sample size, and study period. In this regrade, the study conducted after 2020 indicated a source of heterogeneity of 44% (95% CI: 40, 48) as compared to studies conducted before or in 2020. This implies that as the period of study increases the patient knowledge towards informed consent also increases. This variation might be explained as the period of study is more recent the patient may get more information about informed consent. It might be due to the increase in the number of health professionals from time to time who had room to explain informed consent. In addition, studies conducted with a sample size greater than or equal to 385 were another source of heterogeneity of 36% (95% CI: 16, 55) than a sample size less than 385. This difference might be as the sample size increases and also increases the representativeness of the finding.</p>", "<p id=\"Par45\">The pooled effects of patient knowledge towards informed consent among formally educated patients were 2.69 times more likely than counterparts (Table ##TAB##2##3##). This finding is in line with the study in South Africa [##UREF##24##29##], Pakistan [##UREF##31##36##], and India [##UREF##19##24##]. The possible explanation for this finding might be those educated patients can easily understand the physician’s explanation of informed consent [##UREF##32##37##]. There may be a language barrier to the understanding of the consent formats.</p>", "<p id=\"Par46\">For patients who had experienced signed informed consent before, the pooled effect of patient knowledge towards informed consent was 3.65 times more likely than those not signed before. This finding is consistent with a systematic review done on client comprehension; those patients demonstrated the highest understanding of informed consent (Systematic review) [##UREF##33##38##]. The implication of this finding is once the patient was exposed for signed informed consent, had more understanding. Besides this, those patients had more knowledge of diagnosis, treatment, and possible outcomes of treatment.</p>", "<p id=\"Par47\">This meta-analysis revealed that there is no statistically significant pooled effect residence on patient knowledge towards informed consent in Ethiopia. In addition, the type of surgery had no statistically significant pooled effect on patient knowledge of informed consent.</p>", "<p id=\"Par48\">The limitation of this study primary studies included in this meta-analysis were found to be in Southern Ethiopia, Amhara, Oromia, and Addis Ababa city, which is under-represented in other regions in the country. In addition, a limited number of primary studies are available in Ethiopia. Besides this, only a few systematic review and meta-analysis studies on patient knowledge and perception of informed consent to compare the findings.</p>" ]

[ "<title>Conclusion</title>", "<p id=\"Par49\">The appropriate patient knowledge and perception of informed consent in Ethiopia is low. Formal education and history of signed informed consent were positive factors for the level of patient knowledge of informed consent in Ethiopia. Physicians, policymakers, and health facility managers should focus on patients without prior experience with signed informed consent and not had formal education to improve patient knowledge towards informed consent. Physicians should provide clear information regarding the content of informed consent those patients had no formal education and experience before to increase their knowledge of informed consent.</p>" ]

[ "<title>Background</title>", "<p id=\"Par1\">Informed consent is one of the safeguarding of the patient in medical practice at different standards such as ethical, legal, and administrative purposes. Patient knowledge and perception of informed consent are one of the priority concerns in surgical procedures. Patient knowledge and perception towards informed consent increased patient satisfaction, feeling high power on their determination, and accountability for the management, and facilitated positive treatment outcomes. Despite this, in Ethiopia, there are small-scale primary studies with inconsistent and inconclusive findings. Therefore, this systematic review and meta-analysis study estimated the pooled prevalence of patient knowledge and perception of informed consent and its determinants in Ethiopia.</p>", "<title>Methods</title>", "<p id=\"Par2\">We searched major databases such as PubMed, Hinary, MEDLINE, Cochrane Library, EMBASE, Scopus, African Journal Online (AJO), Semantic Scholar, Google Scholar, google, and reference lists. Besides this, University databases in the country were also searched from August 20, 2023, until September 30, 2023,. All published and unpublished studies that report the prevalence of patient knowledge and perception toward informed consent and its associated factors were included. All studies reported in English were included. Studies conducted between January 01, 2015 to September 30, 2023 were included. There are three outcome measurements pooled level of patient knowledge towards informed consent, pooled level of patient perception towards informed consent, and pooled effect that affects patient knowledge of informed consent. Three reviewers (MMM, NK, and YT) independently screened the articles that fulfilled the inclusion criteria to avoid the risk of bias. The studies’ quality was appraised using a modified Newcastle-Ottawa Scale (NOS) version.</p>", "<title>Results</title>", "<p id=\"Par3\">The pooled prevalence of appropriate patient knowledge and perception towards informed consent was 32% (95% CI: 21, 43) and 40% (95% CI: 16, 65) respectively. Having formal education 2.69 (95% CI: 1.18, 6.15) and having a history of signed informed consent before 3.65 (95% CI:1.02,13.11) had a statistically significant association with good patient knowledge towards informed consent.</p>", "<title>Conclusion</title>", "<p id=\"Par4\">The appropriate patient knowledge and perception of informed consent in Ethiopia is low. Formal education and history of signed informed consent were positive factors for appropriate patient knowledge of informed consent in Ethiopia. Physicians, policymakers, and health facility managers should focus on patients without prior experience with signed informed consent and not have formal education to improve patient knowledge towards informed consent. The protocol was registered at Prospero with number CRD42023445409 and is available from: <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.crd.york.ac.uk/PROSPERO/#myprospero\">https://www.crd.york.ac.uk/PROSPERO/#myprospero</ext-link>.</p>", "<title>Supplementary Information</title>", "<p>The online version contains supplementary material available at 10.1186/s13037-023-00386-5.</p>", "<title>Keywords</title>" ]

[ "<title>Electronic supplementary material</title>", "<p>Below is the link to the electronic supplementary material.</p>", "<p>\n\n</p>", "<p>\n\n</p>" ]

[ "<title>Acknowledgements</title>", "<p>We have special thanks to all authors, data collectors, and supervisors of the primary studies included in this systematic review and meta-analysis.</p>", "<title>Author contributions</title>", "<p>MMM designed the study, performed analysis, interpreted the data, and prepared the manuscript. EB assisted in the design, participated in data analysis, approved the article with revisions, and prepared the manuscript. YT assisted in the design, approved the article with revisions, and revised the subsequent write-up of the paper. KA participated in data analysis, approved the article with revisions, and prepared the manuscript. FDB assisted in the design, participated in data analysis, approved the article with revisions, and prepared the manuscript. LA Methodology performed analysis, interpreted the data, and prepared the manuscript. AE performed analysis, interpreted the data, and prepared the manuscript. SDK participated in data analysis and approved the article with revisions. MAMethodology performed analysis, interpreted the data, and prepared the manuscript. NK performed analysis, interpreted the data, and prepared the manuscript. All authors reviewed and approved the manuscript.</p>", "<title>Funding</title>", "<p>Not applicable.</p>", "<title>Data availability</title>", "<p>The datasets used and analyzed during the current study are available from the first author.</p>", "<title>Declarations</title>", "<title>Ethical approval and consent to participate</title>", "<p id=\"Par50\">Not applicable.</p>", "<title>Competing interests</title>", "<p id=\"Par51\">The authors declare no competing interests.</p>" ]

[ "<fig id=\"Fig1\"><label>Fig. 1</label><caption><p>PRISMA 2020 flow diagram</p></caption></fig>", "<fig id=\"Fig2\"><label>Fig. 2</label><caption><p>Forest plot pooled prevalence of patient knowledge towards informed consent among surgical patients in Ethiopia 2023</p></caption></fig>", "<fig id=\"Fig3\"><label>Fig. 3</label><caption><p>Forest plot pooled prevalence of patient perception towards informed consent among surgical patients in Ethiopia 2023</p></caption></fig>", "<fig id=\"Fig4\"><label>Fig. 4</label><caption><p>Forest plot for patient knowledge towards informed consent among surgical patients in Ethiopia, sensitivity analysis, 2023</p></caption></fig>", "<fig id=\"Fig5\"><label>Fig. 5</label><caption><p>Forest plot pooled prevalence of patient knowledge towards informed consent among surgical patients after omitting a single outlier study in Ethiopia 2023</p></caption></fig>" ]

[ "<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Characteristics of studies included in this systemic review and meta-analysis, in Ethiopia, 2023 (n = 7)</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Author</th><th align=\"left\">Study period</th><th align=\"left\">Region</th><th align=\"left\">Study design</th><th align=\"left\">Sample size</th><th align=\"left\">Prevalence</th><th align=\"left\">Quality score</th></tr></thead><tbody><tr><td align=\"left\">Lemmu et al. [##UREF##9##13##]</td><td char=\".\" align=\"char\">2018</td><td align=\"left\">Addis Ababa</td><td align=\"left\">Cross-sectional</td><td char=\".\" align=\"char\">385</td><td align=\"left\">10.5</td><td char=\".\" align=\"char\">7</td></tr><tr><td align=\"left\">Daniel et al. [##UREF##12##16##]</td><td char=\".\" align=\"char\">2022</td><td align=\"left\">Southern Ethiopia</td><td align=\"left\">Cross-sectional</td><td char=\".\" align=\"char\">423</td><td align=\"left\">44.4</td><td char=\".\" align=\"char\">8</td></tr><tr><td align=\"left\">Gebrehiwot et al. [##UREF##14##18##]</td><td char=\".\" align=\"char\">2021</td><td align=\"left\">Amhara</td><td align=\"left\">Cross-sectional</td><td char=\".\" align=\"char\">422</td><td align=\"left\">46.9</td><td char=\".\" align=\"char\">7</td></tr><tr><td align=\"left\">Nurhusien Nuru Yesuf et al. [##UREF##16##20##]</td><td char=\".\" align=\"char\">2018</td><td align=\"left\">Amhara</td><td align=\"left\">Cross-sectional</td><td char=\".\" align=\"char\">302</td><td align=\"left\">36</td><td char=\".\" align=\"char\">7</td></tr><tr><td align=\"left\">Kebede et al. [##UREF##21##26##]</td><td char=\".\" align=\"char\">2020</td><td align=\"left\">Oromia</td><td align=\"left\">Cross-sectional</td><td char=\".\" align=\"char\">372</td><td align=\"left\">22.8</td><td char=\".\" align=\"char\">6</td></tr><tr><td align=\"left\">Biyazin et al. [##UREF##20##25##]</td><td char=\".\" align=\"char\">2020</td><td align=\"left\">Oromia</td><td align=\"left\">Cross-sectional</td><td char=\".\" align=\"char\">372</td><td align=\"left\">22.8</td><td char=\".\" align=\"char\">7</td></tr><tr><td align=\"left\">Tsegahun Amilaku et al. [##UREF##23##28##]</td><td char=\".\" align=\"char\">2022</td><td align=\"left\">Southern Ethiopia</td><td align=\"left\">Cross-sectional</td><td char=\".\" align=\"char\">414</td><td align=\"left\">40.6</td><td char=\".\" align=\"char\">9</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab2\"><label>Table 2</label><caption><p>Subgroup analysis of the pooled prevalence of patient knowledge towards informed consent in Ethiopia, 2023 (n = 7)</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Variables</th><th align=\"left\">Subgroup</th><th align=\"left\">Number of studies</th><th align=\"left\">Prevalence of subgroup with 95% CI</th><th align=\"left\">I² (%)</th><th align=\"left\">P</th></tr></thead><tbody><tr><td align=\"left\" rowspan=\"4\">Region</td><td align=\"left\">Southern Ethiopia</td><td char=\".\" align=\"char\">2</td><td align=\"left\">43% (39%, 46%)</td><td align=\"left\">–</td><td align=\"left\">–</td></tr><tr><td align=\"left\">Amhara</td><td char=\".\" align=\"char\">2</td><td align=\"left\">42% (39%, 46%)</td><td align=\"left\">–</td><td align=\"left\">–</td></tr><tr><td align=\"left\">Oromia</td><td char=\".\" align=\"char\">2</td><td align=\"left\">23% (20%, 26%)</td><td align=\"left\">–</td><td align=\"left\">–</td></tr><tr><td align=\"left\">Others</td><td char=\".\" align=\"char\">1</td><td align=\"left\">10% (8%, 14%)</td><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\" rowspan=\"2\">Year of the study</td><td align=\"left\">≤ 2020</td><td char=\".\" align=\"char\">4</td><td align=\"left\">23% (13%, 33%)</td><td align=\"left\">96%</td><td align=\"left\">&lt; 0.001</td></tr><tr><td align=\"left\">&gt; 2020</td><td char=\".\" align=\"char\">3</td><td align=\"left\">44% (40%, 48%)</td><td align=\"left\">–</td><td align=\"left\">–</td></tr><tr><td align=\"left\" rowspan=\"2\">Sample size</td><td align=\"left\">≥ 385</td><td char=\".\" align=\"char\">4</td><td align=\"left\">36% (16%, 55%)</td><td align=\"left\">98.85%</td><td align=\"left\">&lt; 0.001</td></tr><tr><td align=\"left\">&lt; 385</td><td char=\".\" align=\"char\">3</td><td align=\"left\">27% (19%, 35%)</td><td align=\"left\">–</td><td align=\"left\">–</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab3\"><label>Table 3</label><caption><p>Effect of formal education, history of informed consent signed before, residence, and type of surgery on patient knowledge towards informed consent in Ethiopia; systematic review and meta-analysis</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" colspan=\"2\">Study</th><th align=\"left\">Estimated effect</th><th align=\"left\">95% CI</th><th align=\"left\">Weight</th></tr></thead><tbody><tr><td align=\"left\" rowspan=\"4\">Formal education</td><td align=\"left\">Lemmu et al. (2018)</td><td char=\".\" align=\"char\">4.80</td><td char=\".\" align=\"char\">0.05, 483.78</td><td align=\"left\">3.20</td></tr><tr><td align=\"left\">Daniel et al. (2022)</td><td char=\".\" align=\"char\">3.26</td><td char=\".\" align=\"char\">1.30, 8.17</td><td align=\"left\">80.80</td></tr><tr><td align=\"left\">Kebede et al. (2020)</td><td char=\".\" align=\"char\">0.92</td><td char=\".\" align=\"char\">0.12, 7.22</td><td align=\"left\">16.00</td></tr><tr><td align=\"left\">Pooled effect</td><td char=\".\" align=\"char\">\n<bold>2.69</bold>\n</td><td char=\".\" align=\"char\">\n<bold>1.18, 6.15</bold>\n</td><td align=\"left\">100</td></tr><tr><td align=\"left\" rowspan=\"5\">Residence</td><td align=\"left\">Lemmu et al. (2018)</td><td char=\".\" align=\"char\">4.70</td><td char=\".\" align=\"char\">0.12, 187.93</td><td align=\"left\">12.43</td></tr><tr><td align=\"left\">Daniel et al. (2022)</td><td char=\".\" align=\"char\">0.25</td><td char=\".\" align=\"char\">0.00, 12.81</td><td align=\"left\">10.82</td></tr><tr><td align=\"left\">Nurhusien Nuru Yesuf et al. (2018)</td><td char=\".\" align=\"char\">1.52</td><td char=\".\" align=\"char\">0.18, 12.62</td><td align=\"left\">37.76</td></tr><tr><td align=\"left\">Kebede et al. (2020)</td><td char=\".\" align=\"char\">0.69</td><td char=\".\" align=\"char\">0.09, 5.54</td><td align=\"left\">38.99</td></tr><tr><td align=\"left\">Pooled effect</td><td char=\".\" align=\"char\">1.06</td><td char=\".\" align=\"char\">0.29, 3.87</td><td align=\"left\">100</td></tr><tr><td align=\"left\" rowspan=\"4\">Informed consent signed before</td><td align=\"left\">Daniel et al. (2020)</td><td char=\".\" align=\"char\">4.06</td><td char=\".\" align=\"char\">0.70, 23.52</td><td align=\"left\">52.26</td></tr><tr><td align=\"left\">Nurhusien Nuru Yesuf et al. (2018)</td><td char=\".\" align=\"char\">2.20</td><td char=\".\" align=\"char\">0.12, 41,57</td><td align=\"left\">18.92</td></tr><tr><td align=\"left\">Tsegahun Amilaku et al. (2022)</td><td char=\".\" align=\"char\">4.21</td><td char=\".\" align=\"char\">0.36, 45.87</td><td align=\"left\">28.13</td></tr><tr><td align=\"left\">Pooled effect</td><td char=\".\" align=\"char\">\n<bold>3.65</bold>\n</td><td char=\".\" align=\"char\">\n<bold>1.02, 13.11</bold>\n</td><td align=\"left\">100</td></tr><tr><td align=\"left\" rowspan=\"3\">Type of surgery</td><td align=\"left\">Lemmu et al. (2018)</td><td char=\".\" align=\"char\">0.96</td><td char=\".\" align=\"char\">0.07, 13.08</td><td align=\"left\">39.54</td></tr><tr><td align=\"left\">Kebede et al. (2020)</td><td char=\".\" align=\"char\">0.73</td><td char=\".\" align=\"char\">0.09, 6.08</td><td align=\"left\">60.46</td></tr><tr><td align=\"left\">Pooled effect</td><td char=\".\" align=\"char\">0.81</td><td char=\".\" align=\"char\">0.16, 4.21</td><td align=\"left\">100</td></tr></tbody></table></table-wrap>" ]

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[ "<supplementary-material content-type=\"local-data\" id=\"MOESM1\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM2\"></supplementary-material>" ]

[ "<fn-group><fn><p><bold>Publisher’s Note</bold></p><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn></fn-group>" ]

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[ "<media xlink:href=\"13037_2023_386_MOESM1_ESM.docx\"><caption><p>Supplementary Material 1</p></caption></media>", "<media xlink:href=\"13037_2023_386_MOESM2_ESM.docx\"><caption><p>Supplementary Material 2</p></caption></media>" ]

{ "acronym": [], "definition": [] }

[ "<title>Introduction</title>", "<p id=\"Par5\">Uncontrolled asthma can affect sleep quality as increased nocturnal symptoms are synonymous with uncontrolled disease. However, short or excessive sleep duration and poor sleep quality are risk factors for asthma exacerbations and healthcare usage, poorer quality of life and mortality [##REF##32389780##1##]. Accelerometers provide a novel opportunity to evaluate sleep parameters relative to asthma severity.</p>", "<p id=\"Par6\">Accelerometery has been validated against polysomnography for measurement of sleep-related variables in asthma and sleep measures can be obtained using a validated algorithm for wrist-worn accelerometers without the use of accompanying sleep diaries [##REF##18569232##2##]. These tri-axial devices measure acceleration, allowing estimates of physical activity, sedentary time and sleep. Accelerometers are less cumbersome than sleep diaries, encouraging adherence, provide additional data such as sleep onset and efficiency, and are a cost-effective option compared to polysomnography.</p>", "<p id=\"Par7\">We hypothesised that sleep patterns differ between mild and difficult-to-treat asthma populations. We performed a cross-sectional, proof-of-concept analysis comparing sleep parameters from participants with mild and difficult-to-treat asthma utilising accelerometer technology.</p>" ]

[ "<title>Materials and methods</title>", "<p id=\"Par8\">Data for this analysis was retrieved from two recent local trials approved by the West of Scotland Regional Ethics Committee (references 16/WS/0200 and 18/WS/0216) and undertaken between 2017 and 2021: one of pulmonary rehabilitation in difficult-to-treat asthma associated with raised body mass index (BMI) alongside a sub-study of activity levels in mild asthma, and a second trial studying weight loss in difficult-to-treat asthma and obesity (trial identifiers: NCT03630432, NCT03858608). Full trial protocols are described elsewhere [##UREF##0##3##, ##UREF##1##4##]. Both trials were funded by an NHS Greater Glasgow and Clyde Endowment Fund, and none of the contributors to the fund had any input in trial design, results or interpretation, nor any input into this retrospective analysis. All participants provided written consent for data use in future studies.</p>", "<p id=\"Par9\">Briefly, difficult-to-treat asthma was defined as per SIGN/BTS and GINA guidelines [##UREF##2##5##, ##UREF##3##6##], including presence of characteristic symptoms, reversibility (≥ 12% and 200mls increase in FEV₁ post-bronchodilator) or bronchial hyper-reactivity on bronchial challenge testing; asthma treatment with high-dose inhaled corticosteroid (ICS); poor asthma control (Asthma Control Questionnaire score &gt; 1.5) or ≥ 2 exacerbations requiring oral corticosteroids (OCS) or ≥ 1 asthma exacerbation requiring hospitalisation in the preceding 12 months. Patients with mild active asthma (asthma treatment within the preceding 12 months) were recruited from primary care. Mild disease was categorised by maximum preventer treatment with moderate-dose ICS/long-acting β-agonist combination, ACQ ≤ 1.5, &lt; 2 exacerbations requiring OCS treatment and no hospital admissions with asthma in the preceding 12 months.</p>", "<p id=\"Par10\">As part of the trial assessments, participants wore an ActiGraph wGT3X-BT accelerometer (ActiGraph, Pensacola, USA) on their non-dominant wrist continually for 7 days (excluding bathing). Devices were initialised to capture data at 30 Hz. Raw data was downloaded using ActiLife software (v.6.14.3; ActiGraph) and saved as .gt3x files and converted to .csv files. Data was exported into R v4.1.2 (R Foundation for Statistical Computing, Vienna, Austria) for subsequent processing using the GGIR package (v2.6.0).</p>", "<p id=\"Par11\">Among the variables extracted were number of nights devices were worn; mean sleep window time (time from initial sleep-onset to waking); mean sleep time (accumulated sustained inactivity sojourns overnight); sleep efficiency (sleep time: sleep window); sleep-onset time and wake time. Time variables were described as hours and minutes or 24-hour clock where appropriate. Variables were non-parametric and so summarised as median (IQR) and compared between mild and difficult-to-treat asthma groups using the Mann-Whitney U test. Data was analysed using IBM SPSS Statistics (version 28.0) and significance was set at 0.05.</p>" ]

[ "<title>Results</title>", "<p id=\"Par12\">Of 133-patient data-sets available, nine were excluded due to lack of data (defined ≤ 3 nights use), leaving 124 participants (44 with mild asthma, 80 with difficult-to-treat asthma). Of the 124, 56% were female, median (IQR) age was 57 (47, 64) years and the majority were never and ex-smokers (56% and 38% respectively). Baseline characteristics (Table ##TAB##0##1##) showed differences between mild and difficult-to-treat participants in atopy, weight, BMI, asthma control and quality of life, long-acting β-agonist (LABA) use and number of annual exacerbations.Higher baseline fractional exhaled nitric oxide (FeNO) and peripheral eosinophils were observed in the difficult-to-treat asthma group compared to mild asthma.</p>", "<p id=\"Par13\">Table ##TAB##1##2## summarises sleep-metric findings. Overall, the median number of nights accelerometery was available was 6 (6, 6). Median sleep time was 6hrs35mins (5hrs2mins, 7hrs45mins), with a median sleep window time of 7hrs49 mins (6hrs29mins, 8hrs56mins) and median sleep efficiency of 85% (81, 90). Median time of sleep-onset was 00:08 (23:02, 01:23) and wake time 07:54 (06:48, 09:22).</p>", "<p id=\"Par14\">No differences were observed in sleep time, sleep window, sleep efficiency or wake time between the mild and difficult-to-treat groups, though sleep-onset time was later in the difficult-to-treat asthma group (00:24; 23:16, 02:02) compared to mild asthma (23:41; 22:52, 00:45; <italic>p</italic> = 0.019). In the overall dataset (I.e., mild and difficult-to-treat groups together), Spearman’s rank showed no correlation between sleep-onset time and ACQ (marker of asthma control); rho = 0.049, <italic>p</italic> = 0.589. Additionally, both unadjusted and adjusted (correcting for weight) linear regression using sleep-onset time as the dependent variable and ACQ as the independent variable showed no relationship between asthma control and sleep-onset time: unadjusted F(1,122) = 0.28, <italic>p</italic> = 0.866; adjusted for weight F(2,121) = 0.160, <italic>p</italic> = 0.852.</p>" ]

[ "<title>Discussion</title>", "<p id=\"Par15\">We observed no differences in sleep duration or efficiency between mild and difficult-to-treat groups, but whilst there was no difference in wake time, there was a later time of sleep-onset in the difficult-to-treat group which may reflect greater difficulty in sleep initiation in this cohort. The clinical significance of this difference (~ 40 min) is uncertain, however, interestingly correlation and regression analysis suggest this difference is not related to asthma control even when adjusted for weight, a key factor in sleep health. There was a significant between-group difference in proportion of participants with regular LABA use and it is feasible that β-agonist-mediated stimulation could be related to the delay in sleep initiation in the difficult-to-treat group. Compared to the recommended sleep duration, patients from our cohort appear to be on the lower side (6.59 h; 5.04, 7.75) suggesting poorer sleep health despite good sleep efficiency. Factors associated with delayed sleep initiation and reduced sleep duration in difficult-to-treat asthma therefore remain to be elucidated and require further study.</p>", "<p id=\"Par16\">Our results are similar to a study performed in 56 healthy adults (mean age 24.5 ± 4.5 years) also using ActiGraph devices (non-dominant wrist) without sleep logs that showed (mean ± SD) sleep time (6 h 56 min ± 49 min), sleep window (7 h 59 min ± 51 min) and sleep efficiency (87%±4), as well as similar sleep-onset (00:05 ± 90 min) and wake times (08:20 ± 84 min) [##UREF##4##7##]. A small study of 10 patients with mild-to-moderate asthma [##REF##18569232##2##] showed reduced sleep time of 5 h 54 min ± 74 min with a similar mean sleep window time of 7 h 34 min ± 40 min. However, this study is clearly limited by the small sample size.</p>", "<p id=\"Par17\">Our retrospective analysis has potential limitations. Firstly, groups were not equally weighted with more patients with difficult-to-treat asthma than mild asthma. Secondly, the initial trials data did not include objective assessments of daytime or nocturnal sleep (e.g., Epworth sleep score, Pittsburgh sleep quality index), nor any sleep logs. Thirdly, this analysis was not powered to assess sleep outcomes. Finally, this analysis did not account for factors such as sleep-disordered breathing that may influence outcomes, which should be addressed in future studies. Despite this, key strengths of our study are the sample size, higher than in previous studies, and observed excellent tolerance of accelerometer use (93%). To our knowledge this is the first comparison of mild and difficult-to-treat asthma sleep outcomes using accelerometery and we highlight a difference in sleep initiation between groups unrelated to asthma control and weight. Further study is warranted to explore the relationship between asthma severity and sleep-metrics and whether interventions targeting sleep health can improve asthma outcomes.</p>", "<p id=\"Par18\">In summary, patients with difficult-to-treat asthma may have delayed initiation of sleep compared to mild asthma, though this observation appears to be independent of asthma control and obesity. Other sleep parameters are broadly comparable to the general population. Accelerometers are well tolerated, offer more pragmatism than polysomnography and can be used to assess sleep outcomes in asthma but dedicated trials are needed before any definitive conclusions can be drawn.</p>", "<p id=\"Par19\">\n\n</p>", "<p id=\"Par24\">\n\n</p>" ]

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[ "<title>Introduction</title>", "<p id=\"Par1\">Poor sleep health is associated with increased asthma morbidity and mortality. Accelerometers have been validated to assess sleep parameters though studies using this method in patients with asthma are sparse and none have compared mild to difficult-to-treat asthma populations.</p>", "<title>Methods</title>", "<p id=\"Par2\">We performed a retrospective analysis from two recent in-house trials comparing sleep metrics between patients with mild and difficult-to-treat asthma. Participants wore accelerometers for 24-hours/day for seven days.</p>", "<title>Results</title>", "<p id=\"Par3\">Of 124 participants (44 mild, 80 difficult-to-treat), no between-group differences were observed in sleep-window, sleep-time, sleep efficiency or wake time. Sleep-onset time was ~ 40 min later in the difficult-to-treat group (<italic>p</italic> = 0.019).</p>", "<title>Discussion</title>", "<p id=\"Par4\">Broadly, we observed no difference in accelerometer-derived sleep-metrics between mild and difficult-to-treat asthma. This is the largest analysis of accelerometer-derived sleep parameters in asthma and the first comparing groups by asthma severity. Sleep-onset initiation may be delayed in difficult-to-treat asthma but a dedicated study is needed to confirm.</p>", "<title>Keywords</title>" ]

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[ "<title>Acknowledgements</title>", "<p>The authors are grateful to the all participants from the two trials.</p>", "<title>Author contributions</title>", "<p>VS aided with study design, data collection and performed data analysis and manuscript preparation. HCR, FS and AG aided with data collection and review of manuscript. DSB aided with analysis of data, manuscript preparation and review of manuscript. DCC aided with study design and manuscript review.</p>", "<title>Funding</title>", "<p>None.</p>", "<title>Data availability</title>", "<p>Data is available upon reasonable request.</p>", "<title>Declarations</title>", "<title>Ethics approval and consent to participate</title>", "<p id=\"Par27\">all participants provided written consent and ethical approval was granted for both trials from which this data were taken by the West of Scotland Regional Ethics Committee (references 16/WS/0200 and 18/WS/0216).</p>", "<title>Consent for publication</title>", "<p id=\"Par28\">All trial participants consented to publication of data for the initial trials and any subsequent analyses.</p>", "<title>Competing interests</title>", "<p id=\"Par29\">The authors report there are no competing interests to declare.</p>" ]

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[ "<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Baseline characteristics</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Variable</th><th align=\"left\">Overall <italic>n</italic> = 124</th><th align=\"left\">Mild asthma <italic>n</italic> = 44</th><th align=\"left\">Difficult-to-treat <italic>n</italic> = 80</th><th align=\"left\"><italic>p</italic> value</th></tr></thead><tbody><tr><td align=\"left\">Age, years</td><td align=\"left\">57 (47 to 64)</td><td align=\"left\">60 (48 to 72)</td><td align=\"left\">56 (48 to 65)</td><td align=\"left\">0.843</td></tr><tr><td align=\"left\">Female sex, no. (%)</td><td align=\"left\">69 (55.6)</td><td align=\"left\">25 (56.8)</td><td align=\"left\">44 (55.0)</td><td align=\"left\">0.845</td></tr><tr><td align=\"left\"><p>Smoking status:</p><p>Never smoker</p><p>Ex-smoker</p><p>Current smoker</p></td><td align=\"left\"><p>69 (55.6)</p><p>47 (37.9)</p><p>8 (6.5)</p></td><td align=\"left\"><p>30 (68.2)</p><p>12 (27.3)</p><p>2 (4.5)</p></td><td align=\"left\"><p>39 (48.8)</p><p>35 (43.8)</p><p>6 (7.5)</p></td><td align=\"left\">0.114</td></tr><tr><td align=\"left\">Atopy, no. (%)</td><td align=\"left\">55 (44.4)</td><td align=\"left\">6 (13.6)</td><td align=\"left\">49 (61.3)</td><td align=\"left\">\n<bold>&lt; 0.001</bold>\n</td></tr><tr><td align=\"left\">Weight, kg</td><td align=\"left\">84.6 (73.0 to 99.5)</td><td align=\"left\">75.3 (65.7 to 84.9)</td><td align=\"left\">92.3 (76.8 to 107.8)</td><td align=\"left\">\n<bold>&lt; 0.001</bold>\n</td></tr><tr><td align=\"left\">BMI, kg/m²</td><td align=\"left\">31.0 (26.5 to 36.4)</td><td align=\"left\">25.7 (21.9 to 29.6)</td><td align=\"left\">33.6 (28.8 to 38.5)</td><td align=\"left\">\n<bold>&lt; 0.001</bold>\n</td></tr><tr><td align=\"left\">SABA</td><td align=\"left\">122 (98.4)</td><td align=\"left\">42 (95.5)</td><td align=\"left\">80 (100.0)</td><td align=\"left\">0.124</td></tr><tr><td align=\"left\">LABA/ICS</td><td align=\"left\">101 (81.5)</td><td align=\"left\">21 (47.7)</td><td align=\"left\">80 (100.0)</td><td align=\"left\">\n<bold>&lt; 0.001</bold>\n</td></tr><tr><td align=\"left\">Maintenance prednisolone, no. (%)</td><td align=\"left\">28 (22.6)</td><td align=\"left\">n/a</td><td align=\"left\">28 (35.0)</td><td align=\"left\">n/a</td></tr><tr><td align=\"left\">Biologic, no. (%)</td><td align=\"left\">13 (10.5)</td><td align=\"left\">n/a</td><td align=\"left\">13 (16.3)</td><td align=\"left\">n/a</td></tr><tr><td align=\"left\">Prednisolone boosts</td><td align=\"left\">2 (0 to 4)</td><td align=\"left\">0 (0 to 0)</td><td align=\"left\">4 (3 to 6)</td><td align=\"left\">\n<bold>&lt; 0.001</bold>\n</td></tr><tr><td align=\"left\">ACQ6</td><td align=\"left\">1.7 (0.5 to 3.0)</td><td align=\"left\">0.4 (0.0 to 0.8)</td><td align=\"left\">2.7 (1.9 to 3.6)</td><td align=\"left\">\n<bold>&lt; 0.001</bold>\n</td></tr><tr><td align=\"left\">AQLQ overall</td><td align=\"left\">4.6 (3.8 to 6.2)</td><td align=\"left\">6.4 (5.9 to 6.9)</td><td align=\"left\">4.0 (3.3 to 4.8)</td><td align=\"left\">\n<bold>&lt; 0.001</bold>\n</td></tr><tr><td align=\"left\">FeNO, ppb</td><td align=\"left\">23 (16 to 45)</td><td align=\"left\">21 (16 to 26)</td><td align=\"left\">33 (12 to 54)</td><td align=\"left\">\n<bold>0.023</bold>\n</td></tr><tr><td align=\"left\">Eosinophils, x10⁹/L</td><td align=\"left\">0.2 (0.1 to 0.4)</td><td align=\"left\">0.1 (0.0 to 0.2)</td><td align=\"left\">0.3 (0.2 to 0.5)</td><td align=\"left\">\n<bold>0.017</bold>\n</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab2\"><label>Table 2</label><caption><p>Sleep parameters of asthma patients overall and by disease severity</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Variable</th><th align=\"left\">Overall <italic>n</italic> = 124</th><th align=\"left\">Mild <italic>n</italic> = 44</th><th align=\"left\">Difficult-to-treat <italic>n</italic> = 80</th><th align=\"left\"><italic>p</italic> value*</th></tr></thead><tbody><tr><td align=\"left\">No. of nights used</td><td align=\"left\">6 (6, 6)</td><td align=\"left\">6 (5, 6)</td><td align=\"left\">6 (6, 6)</td><td align=\"left\">0.333</td></tr><tr><td align=\"left\">Sleep time</td><td align=\"left\">6:35 (5:02, 7:45)</td><td align=\"left\">6:50 (6:05, 7:45)</td><td align=\"left\">6:26 (4:56, 7:44)</td><td align=\"left\">0.353</td></tr><tr><td align=\"left\">Sleep window</td><td align=\"left\">7:49 (6:29, 8:56)</td><td align=\"left\">8:03 (7:02, 8:50)</td><td align=\"left\">7:38 (6:08, 8:59)</td><td align=\"left\">0.339</td></tr><tr><td align=\"left\">Sleep efficiency (%)</td><td align=\"left\">85.4 (81.0, 90.2)</td><td align=\"left\">86.3 (82.1, 90.5)</td><td align=\"left\">85.4 (80.2, 90.0)</td><td align=\"left\">0.471</td></tr><tr><td align=\"left\">Sleep onset</td><td align=\"left\">00:08 (23:02, 01:23)</td><td align=\"left\">23:41 (22:52, 00:45)</td><td align=\"left\">00:24 (23:16, 02:02)</td><td align=\"left\">\n<bold>0.019</bold>\n</td></tr><tr><td align=\"left\">Wake onset</td><td align=\"left\">07:54 (06:48, 09:22)</td><td align=\"left\">07:41 (06:43, 08:13)</td><td align=\"left\">08:03 (06:48, 10:01)</td><td align=\"left\">0.097</td></tr></tbody></table></table-wrap>" ]

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[ "<table-wrap-foot><p>Continuous variables described as median (interquartile range)</p><p>Categorical variables described as n (%)</p><p><italic>p</italic>-value compares mild vs. difficult-to-treat groups with Mann Whitney U for continuous and chi square or Fisher’s exact for categorical variables</p><p>Abbreviations: ACQ6 (Asthma Control Questionnaire), AQLQ (Asthma Quality of Life Questionnaire), FeNO (fractional exhaled nitric oxide), LABA/ICS (long-acting β-agonist/inhaled corticosteroid combination inhaler), ppb (parts per billion), SABA (short-acting β-agonist inhaler)</p></table-wrap-foot>", "<table-wrap-foot><p>Variables described as median (IQR) in hours:mins unless specified.</p><p>*Mann Whitney U test comparing mild vs. difficult-to-treat asthma groups</p></table-wrap-foot>", "<fn-group><fn><p><bold>Publisher’s Note</bold></p><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn></fn-group>" ]

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{ "acronym": [], "definition": [] }

[ "<title>Introduction</title>", "<p id=\"Par8\">Lung cancer is the second most common malignancies in China, where up to 39.8% of all 2.2 million worldwide newly diagnosed cases were from China in 2020 [##REF##33538338##1##, ##UREF##0##2##]. Only 17.3% of the lung cancer patients are diagnosed at stage I, others are found with advanced stage [##UREF##1##3##]. Given the large number of patients with lung cancer and the poor prognosis [##UREF##2##4##], lung cancer contributes prominently to the cancer burden in China with substantial economic and societal impacts in future [##REF##31777579##5##]. To achieve effective cancer prevention, there is a growing focus on improving cancer control through screening and early diagnosis. Several organizations or medical societies worldwide, including National Cancer Center of China, recommended annual low-dose CT (LDCT) screening for people at high risk of developing lung cancer [##REF##33752304##6##–##REF##29208441##9##]. As a result, millions of participants were diagnosed with lung nodules by undergoing LDCT screening every year [##REF##25282284##10##]. However, the false positive rate (FPR) of LDCT test was reported as 96.4% and 56.5% in the National Lung Screening Trial(NLST) and Dutch-Belgian Randomized Lung Cancer Screening Trial(NELSON), respectively [##REF##21714641##11##, ##REF##31995683##12##]. Consequently, a substantial part of subjects undergo unnecessary clinical examinations following a false-positive screening result which results in extra radiation exposure and over-diagnosis.</p>", "<p id=\"Par9\">To make the existing cancer screening programs more efficient targeting, polygenic risk scores (PRSs) are introduced. PRS have the potential to identify individuals at risk of different type of cancers, optimizing treatment, and predicting survival outcomes [##REF##35693291##13##]. Though translation of PRSs into clinically relevant prediction models is a challenge [##REF##31828333##14##, ##REF##34021222##15##]. Recent case–control cohort study suggested that the PRSs could significantly improve discrimination in high risk populations, compared to clinical risk factors (e.g. age, sex, smoking history, cancer histology, etc.) alone [##UREF##3##16##]. A large-scale prospective cohort study identified 19 susceptibility loci to be significantly associated with non-small cell lung cancer risk at p ≤ 5.0 × 10⁻⁸,and confirmed that PRS was an independent effective risk stratification indicator beyond age and smoking pack-years in Chinese populations, makes PRS a potential candidate for realizing precision screening [##REF##31326317##17##]. Although promising, none of the candidate PRSs are regularly used in clinical practice, despite studies reporting benefits from using PRS to assess eligibility of several types of cancer screening programs (i.e. breast, prostate and colorectal cancer) [##UREF##4##18##]. As the PRS could be used as an indicator to guide risk stratification, we propose to use PRS on the basis of former risk assessment criteria to further assess the eligibility of lung cancer screening, might be one of the potential approaches to realize its utility in population-based cancer screening programs. Few results have been reported to date using these PRSs in screening practice; thus, the health outcomes associated with adjunctive strategies with LDCT as well as the cost-effectiveness remain unclear.</p>", "<p id=\"Par10\">Here, we assessed the impact of the current PRS introduced in conjunction with LDCT screening on the effectiveness and cost-effectiveness of lung cancer screening from a societal perspective. Using a Markov model, we evaluated the long-term benefits and harms of lung cancer screening with and without a PRS in Chinese populations.</p>" ]

[ "<title>Methods</title>", "<title>Study design and model description</title>", "<p id=\"Par11\">In this modelling study, the Markov model on lung cancer screening that developed by our previous work was used and adapted for the purpose of assessing the potential impact of LDCT screening with and without a PRS from a societal perspective. Important assumptions and the overall structure of the model have been thoroughly described before and in supplementary material [##REF##35608858##19##, ##REF##35793127##20##]. Per China guideline for the screening and early detection of lung cancer (2021,Beijing) [##UREF##5##21##] recommended, 3 hypothetical cohorts of 10,000 current and former smokers aged 50–74 years old were simulated until death or age 79 years (mean life expectancy in China),named non-screening cohort, LDCT screening cohort and LDCT&amp;PRS screening cohort. Unlike the normal LDCT screening modality, individuals who enter the cohort of LDCT&amp;PRS were assumed to have received PRS assessment and were included to the top 5% high risk based on PRS. All the simulated individuals from two screening cohorts undergo annual screening until the simulation ended. We further superimposed screening and diagnostic follow-up interventions onto the natural history model for lung cancer and obtained population-level outcomes. Data sources, main outcomes, and the full research design are shown in Fig. ##FIG##0##1##. The model was run with a cycle length of 1 year and a discount rate of 5% was applied to both costs and effectiveness. The model construction and all the simulations were conducted using Treeage Pro, version 2021 (Treeage Software). The study was performed according to the Consolidated Health Economic Evaluation Reporting Standards (CHEERS) and was approved by the ethics committee of the Jiangsu Province Hospital of Chinese Medicine; informed consent was not applicable because this was a modeling study.</p>", "<title>Model input parameters</title>", "<p id=\"Par12\">For this modelling analysis, we used China age-stratified data for lung cancer incidence and integrated the effect of smoking rate to model incidence rates for the initial probability of lung cancer for those in the cohorts of non-screening or LDCT screening alone [##UREF##6##22##–##UREF##8##24##]. According to the 3 PRS-defined quantiles (ie, the top 5%,5%-95%, and the bottom 5%), we then calculated the relative risk(RR) of the PRS for lung cancer based on the published estimates of the standardized rates of lung cancer events of the three groups of heavy smokers with diverse genetic risk in China Kadoorie Biobank (CKB) cohort [##REF##31326317##17##]. The proportion of clinical stage for lung cancer detected by LDCT was derived from screening results of the Wenling Lung Cancer Screening Program, which was initiated in 2018 to conduct annual LDCT screening for local populations at high risk of lung cancer with follow-up for 3 years. A total of 20130 asymptomatic individuals were screened by the program by the end of December, 2022, and 287 patients were diagnosed with lung cancer; details of the proportions by cancer stage are presented in Table ##TAB##0##1##. Annual screening followed the same screening protocol as in the Cancer Screening Program in Urban China, which determined positive findings by morphologic features and the size of the nodule [##UREF##9##25##]. As for those diagnosed by normal clinical pathways, the probability that diagnosed clinically is detailed by stage in Table ##TAB##0##1## based on a hospital-based multi-center lung cancer retrospective clinical epidemiological survey in China(LuCCRES) [##REF##30642458##26##]. The probability of health to all-cause death was estimated as all-cause mortality for smokers by age [##UREF##8##24##, ##REF##9822393##27##]. The probability of lung cancer-specific death was derived from a study by Zhang et al. [##UREF##10##28##] and was adjusted for smoking status [##UREF##11##29##, ##REF##27521774##30##]. The probability that a cancerous state progressed to a more advanced state or to a maintenance state is detailed by cancer stage in Table ##TAB##0##1## according to Haaf’s work [##UREF##12##31##]. The sensitivity and specificity for LDCT were based on a study that enrolled 9,522 person-times over five screening rounds from 2014 to 2018 in Sichuan, China [##REF##24419137##32##]. Perfect attendance to screening was assumed for base-case analysis and the uptake rates by different screening modality were incorporated in scenario analysis [##REF##34885217##33##].\n</p>", "<p id=\"Par13\">A total estimated cost for the lung cancer screening program consisted of two parts, the direct screening cost and the indirect screening cost. Screening related cost data were surveyed by the work team of a local lung cancer screening program for the expenses for public advertising, screening invitation management, staff salary and depreciation of screening machinery. For the indirect screening cost, we conducted a survey to estimate the expenses for transportation and wage for missed work for the participants.</p>", "<p id=\"Par14\">We estimated the treatment cost of lung cancer by stage based on the database of local medical insurance bureau, which including 4,947 patients and 107,248 relevant records. Given the potential diversity in treatment cost across the nation, we adapt the treatment cost by stage using published metrics form China Health Statistics Yearbook 2020 [##UREF##14##38##]. The cost of maintenance by stage was calculated using the standard follow-up process and the unit price of each test per the price list of medical services in public medical institutions. All the costs in this study are expressed in CNY and are discounted to the price level of 2022 at a discount rate of 5%.</p>", "<p id=\"Par15\">For quality-of-life adjustment, we used the utility values for lung cancer state by stage based on a EQ-5D-3L survey from 2586 lung cancer patients in 8 provinces and 12 cities in China through the Cancer Screening Program in Urban China(CanSPUC). In addition to, we derived the utility value of CIS stage from a global systematic review by Sturza et al. [##REF##20448248##36##]. The utility value for the maintenance state of each stage was derived from a domestic thesis in 2016 [##UREF##13##37##].</p>", "<title>Evaluated strategies</title>", "<p id=\"Par16\">We compared 15 alternative strategies as shown in Table ##TAB##1##2##. The first 5 strategies involved non-screening for all the heavy smokers as blank control. The remaining 10 strategies were defined by combinations of risk stratification approaches (smoking pack-years or PRS) and initial screening age from 50 to 70 years by 5-year age bands. We describe these strategies in Table ##TAB##1##2##.\n</p>", "<title>Outcome measures</title>", "<p id=\"Par17\">In this study, primary outcomes included life years (LYs), quality adjusted life years (QALYs), and costs of different strategies. Given the #0 Non-screening strategy as reference, a strategy was deemed cost-effective if the incremental cost-effectiveness ratio (ICER), namely the difference between the overall costs of the two strategies divided by the difference between the total QALYs gained, was lower than the cost-effectiveness threshold of 1–3 times Gross Domestic Production (GDP) per capita per QALY gained (CNY 85,698–257,094) [##REF##35421181##39##].</p>", "<title>Sensitivity analysis and scenario analysis</title>", "<p id=\"Par18\">The robustness of the outcomes to uncertainties in the parameter estimates was examined through a series of univariate sensitivity analyses. The cost of screening, treatment cost as well as maintenance cost and consumer price index (CPI) rate were set to vary by 30% compared to base case values. The discount rate was set to range from 0 to 8%. The RR of the PRS for lung cancer was set to range from 2.64 to 5.99. The sensitivity and specificity of LDCT test were set to range from (0.632, 0.648) to (0.948, 0.972). Furthermore, Probability sensitivity analysis (PSA) was also performed with 10,000 iterations to assess the joint uncertainties in the values of input parameters. Input parameters were randomly drawn from beta, lognormal or gamma distribution (see Table ##TAB##0##1##). As for the scenario analysis, we evaluated the health benefits and harms associated with a lung cancer screening program that incorporated the uptake rate of different screening modalities among Chinese high-risk population for lung cancer.</p>", "<title>Software</title>", "<p id=\"Par19\">Modelling was performed in TreeAge Pro 2021 Version R2.1 (TreeAge Software, Williamstown, Massachusetts).</p>", "<title>IRB approval</title>", "<p id=\"Par20\">This project has been approved by Ethics Committee of the Taizhou cancer hospital (code: IRB-[2020]NO.6).</p>", "<title>Role of the funding source</title>", "<p id=\"Par21\">No specific funding was received for this analysis.</p>" ]

[ "<title>Results</title>", "<title>Base-case analysis</title>", "<p id=\"Par22\">In the absence of screening, the total number of lung cancer death per 100,000 heavy smokers aged between 50–79 years were estimated to range from 4,434 to 10,586. The introduction of a screening program led to a decrease of lung cancer deaths, with the reduction rate of lung cancer death ranging from 0.31% to 15.80% across a diverse set of screening strategies. About 95% false-positive cases could be averted by incorporating PRS in the screening program in relative to LDCT screening alone. The LYs and QALYs across all the screening strategies compared with non-screening ranged from 60.26 to 134.93 and from 59.83 to 134.27, respectively. To be specific, screening strategies using PRS as extra eligible criteria obtained lower LY and QALY gained than LDCT screening alone (see Table ##TAB##2##3##). Compared to non-screening, the #1LDCT strategies cost between CNY 104,998.56 and CNY 176,565.66 per LY gained. The #2 PRS&amp;LDCT strategies cost between CNY 191,110.06 and CNY 260,918.20 per LY gained. When adjusting to QALYs, the #1LDCT strategies would cost between CNY 808,80.85 and CNY 150,050.15 per QALY gained. The #2 PRS&amp;LDCT strategies would cost between CNY 156,691.93 and CNY 221,741.84 per QALY gained. All showed an ICER below 3 times GDP per capita (CNY257,094) per QALY gained. Assuming a cost-effectiveness threshold of 1time GDP per capita (CNY 85,698) per QALY gained for the Chinese healthcare system, only annual LDCT screening with the start age of 65–74 and 70–74 years old were cost-effective, yielding an ICER of CNY 85,332.16 and CNY 80,880.85 per QALY gained compared with non-screening. Table ##TAB##2##3## provides the outcomes of the model simulation.\n</p>", "<title>Sensitivity analysis and scenario analysis</title>", "<p id=\"Par23\">Results of sensitivity analyses are shown in Fig. ##FIG##1##2## and Fig. ##FIG##2##3##. The most influential factors on the ICER were specificity and sensitivity of LDCT, as well as discount rate. The results were robust to the changes of the important values from base-case analysis with no variation exceeding 3 times GDP per capita (CNY257,094) per QALY gained, but also generally exceeding 1 times GDP per capita (CNY85,698) (Fig. ##FIG##2##3##). Notably, the #1LDCT screening strategy compared with the #0 Non-screening strategy with a start age older than 55 years had better than 90% likelihood of being cost-effective when the willingness-to-pay threshold was 3 times GDP per capita (CNY257,094). Meanwhile, the probability of #2 PRS&amp;LDCT screening strategy to be cost-effective ranged from 33.77%-79.68%, varying from different start age. While the per capita GDP (CNY 85,698) serves as the threshold for absolutely cost-effective, the acceptability at willingness-to-pay threshold ranged from 1.44% to 34.18% for #1LDCT screening strategy and from 0.26% to 2.54% for #2 PRS&amp;LDCT screening strategy (Table ##TAB##3##4##).</p>", "<p id=\"Par24\">The tornado diagram illustrates the change in the incremental cost-effectiveness ratio (ICER), which was defined as the cost of the PRS&amp;LDCT screening strategy minus the cost of the LDCT screening divided by the difference of the quality-adjusted life-year of the two strategies when important input parameters were varied for both strategies (1 strategy at a time) by 10% ~ 30% higher or lower than their base-case values (shown in Sect. 2.5 Sensitivity analysis and scenario analysis). The vertical axis (dotted dark line) on the left shows the estimated ICER for the base-case analysis, and the vertical axis on the right showed the willingness-to-pay. The column with black color in the tornado diagram showed when the input parameters decrease, their impact for the results. Similarly, the column with grey color showed when the input parameters increase, their impact for the results. </p>", "<p id=\"Par25\">Abbreviations: LDCT, low-dose computed tomography; PRS, polygenic risk score; LC, lung cancer; CIS, carcinoma in situ; CPI, consumer price index.</p>", "<p id=\"Par26\">The dashed circle is the 95% confidence interval, which indicates the robustness of the model operation. The dashed lines are displayed as the cost-effectiveness threshold of 1 times GDP per capita (CNY85,698) and 3 times GDP per capita (CNY257,094) per QALY gained, respectively. The dots above the dashed line are cost-effective.</p>", "<p id=\"Par27\">Abbreviations: LDCT, low-dose computed tomography; PRS, polygenic risk score; WTP, willingness-to-pay threshold. In a previous study, a discrete choice experiment was used to create scenarios on several different possible modalities for the implementation of lung cancer screening in Chinese context [##UREF##14##38##]. The uptake rate varied from different screening modalities by mixed-logit model. The uptake rate of screening by blood test would be decreased by 0.08 compared with the baseline, i.e. LDCT screening. The compliance rate of LDCT screening in CanSPUC from 2013 to 2018 remained 34.41%, 37.25%, and 48.21% in urban areas of Shanxi, Henan, and Zhejiang Provinces, respectively [##REF##31777579##5##–##REF##33687470##7##]. However, we found a substantial improvement (91%) on the compliance rate of LDCT in Wenling lung cancer screening program than those reported by CanSPUC. As CanSUPC was a national cancer screening program targeting five cancer types (lung cancer, female breast cancer, liver cancer, upper gastrointestinal cancer, and colorectal cancer) using a combined screening modality. Given the effect on the compliance rate for the combined screening modality of five cancer type might varied from separate screening for each cancer type, we hence used the compliance rate of LDCT screening from the Wenling lung cancer screening program in this study. The compliance rate of PRS test was then estimated as 83.72% for scenario analysis. When we analysed the impact of the compliance rate of LDCT and PRS test, we observed similar patterns as those obtained from our base case analysis with a perfect attendance, despite some differences on the absolute effects due to discrepancies in the compliance rate of the two cohorts (Supplementary Table S##SUPPL##0##6##).</p>" ]

[ "<title>Discussion</title>", "<p id=\"Par28\">We assessed the effectiveness and cost-effectiveness of lung cancer screening per the NCC recommendation when PRS is introduced to further assess the eligibility of lung cancer screening on the basis of the current definitions of high-risk population for lung cancer in China. The results showed that lung cancer screening programs incorporating PRS of current performance would be cost-effective with the start age of 50–74 years, using a willingness-to-pay threshold of 3 times GDP per capita (CNY257,094) per QALY gained. We demonstrated that as the compliance rate of the screening test decreased by 10%-20% (i.e. a real-world like scenario), its start age must be postponed to 55 years for the screening program to be cost-effective. However, when applied the willingness-to-pay threshold of 1 time GDP per capita (CNY85,698) per QALY gained, all the screening strategies incorporating PRS were not able to be cost-effective anymore. Note that the #1LDCT screening strategy were more cost-effective than #2 PRS&amp;LDCT screening strategy using existing PRS tool in general, yielding more LYs or QALYs at lower cost. These results were sensitive to the sensitivity and the specificity of LDCT, as well as the discount rate. The results were robust when incorporating real-world compliance rate of the LDCT and PRS test in place of the perfect attendance. Overall, our results suggested that we should be more conservative in considering LDCT screening with PRS for lung cancer, unless optimized PRS with better performance emerged.</p>", "<p id=\"Par29\">In a modelling study, the Huntley et al. modelled the application of PRS stratification using UK metrics and demonstrated that the PRS-defined high-risk quintile (20%) of the UK population was estimated to capture 26% of lung cancer cases [##UREF##4##18##]. However, lung cancer was not presented as being the most plausible use cases for PRS stratification on account of the current PRS predictiveness and the availability of established cancer screening tools than other cancer types like breast, prostate, or colorectal cancer [##UREF##4##18##]. Furthermore, rather than considering age and PRS as mutually exclusive options, it is more rational to consider stratification based on a combination of age and PRS, and the other risk factors (notably, for lung cancer, smoking pack-years and family history) [##REF##37178709##40##].</p>", "<p id=\"Par30\">Nevertheless, a downside of our study is that the modeled strategies cover only one possible group at high risk, i.e. the top 5% based on PRS in the CKB cohort. Due to the crucial effect of smoking status for lung cancer incidence, we were not able to reliably estimate the actual ability to capture lung cancer cases using the area under the receiver operating characteristic curve for PRS alone, nor assess the effect and cost-effectiveness of the scenarios incorporating diverse PRS-defined high-risk quantiles. Hence, there is still a need to further assess the alternative strategies by generating empirical evidence on the utility of risk stratification in population-based screening programs in future. Furthermore, as histologic type was also determinant of long-term outcomes of lung cancer patients, the application for the average probability in the transition probabilities between cancerous states might affect the analytical precision in this work. Further research may benefit from incorporating the histology data for the construction of natural history model for lung cancer.</p>", "<p id=\"Par31\">By introduction of new PRS-stratified screening tool, the application in cancer screening could be considered from diverse perspectives. For mass screening based on population, Huntley et al. focused on providing additional screening to the PRS-defined high-risk group [##UREF##4##18##], this study explored the modality that adding PRS to the former high-risk criteria to assess eligibility of lung cancer screening. Conversely, using PRS-stratified screening tool to provide less intensive screening to low-risk individuals could also help to reduce the unnecessary harms (i.e. radiation exposure or invasive biopsy) and costs of overscreening. Moreover, several studies have shown that the risk-stratified screening programs [##REF##34199804##41##, ##REF##36230559##42##] and personalized screening randomised trials for breast cancer [##REF##35524202##43##, ##UREF##15##44##] were ongoing in the Europe and the United States. The risk-tailored screening modality which determine the screening age range, frequency, and method to each risk group according to the PRS might be a potential solution for lung cancer screening programs as well.</p>", "<p id=\"Par32\">Research into new application of PRS in screening programs typically involves breast cancer [##REF##32853342##45##, ##REF##30643217##46##], prostate cancer [##REF##33704474##47##, ##REF##36201131##48##] and colorectal cancer [##REF##31748260##49##, ##REF##35575786##50##]. Current findings can be informative for researchers in the field of cancer epidemiology to guide early adoption of PRS in screening programs or trials for lung cancer, given that they provide extensive information on expected costs, effects, and even cost-effectiveness based on current status. According to our findings, the field of cancer screening and early-detection could move into a direction where PRS will become cost-effective as a molecular diagnostic test in participants with high risk of lung cancer. Although currently the #1LDCT screening strategy were more cost-effective than #2 PRS&amp;LDCT screening strategy using existing PRS tool in general, the obtained data could then potentially be used for a better stratification leading to more participants receiving better screening service. By the time real-world data relevant to the modeled scenarios become available, a more comprehensive and precise cost-effectiveness analysis should be performed for validation purposes. In light of the uncertainties and insufficient performance of the current modality, it seems advisable to accompany adoption with further research to optimize the performance by risk assessment and tailoring of screening frequency and age range of screening for lung cancer.</p>", "<p id=\"Par33\">Our findings suggest that lung cancer screening programs incorporating PRS of current performance would hardly be cost-effective using the willingness-to-pay threshold of 1 time GDP per capita, and the optimal screening strategy for lung cancer still remains to be LDCT screening alone for now. Further optimization of the screening modality can be useful to consider early adoption of PRS, in order to identify the best ways to implement lung cancer screening programs that could improve the benefit–harm trade-offs and cost-effectiveness relevant to its implementation.</p>" ]

[]

[ "<title>Introduction</title>", "<p id=\"Par1\">Several studies have proved that Polygenic Risk Score (PRS) is a potential candidate for realizing precision screening. The effectiveness of low-dose computed tomography (LDCT) screening for lung cancer has been proved to reduce lung cancer specific and overall mortality, but the cost-effectiveness of diverse screening strategies remained unclear.</p>", "<title>Methods</title>", "<p id=\"Par2\">The comparative cost-effectiveness analysis used a Markov state-transition model to assess the potential effect and costs of the screening strategies incorporating PRS or not. A hypothetical cohort of 300,000 heavy smokers entered the study at age 50–74 years and were followed up until death or age 79 years. The model was run with a cycle length of 1 year. All the transition probabilities were validated and the performance value of PRS was extracted from published literature. A societal perspective was adopted and cost parameters were derived from databases of local medical insurance bureau. Sensitivity analyses and scenario analyses were conducted.</p>", "<title>Results</title>", "<p id=\"Par3\">The strategy incorporating PRS was estimated to obtain an ICER of CNY 156,691.93 to CNY 221,741.84 per QALY gained compared with non-screening with the initial start age range across 50–74 years. The strategy that screened using LDCT alone from 70–74 years annually could obtain an ICER of CNY 80,880.85 per QALY gained, which was the most cost-effective strategy. The introduction of PRS as an extra eligible criteria was associated with making strategies cost-saving but also lose the capability of gaining more LYs compared with LDCT screening alone.</p>", "<title>Conclusion</title>", "<p id=\"Par4\">The PRS-based conjunctive screening strategy for lung cancer screening in China was not cost-effective using the willingness-to-pay threshold of 1 time Gross Domestic Product (GDP) per capita, and the optimal screening strategy for lung cancer still remains to be LDCT screening for now. Further optimization of the screening modality can be useful to consider adoption of PRS and prospective evaluation remains a research priority.</p>", "<title>Supplementary Information</title>", "<p>The online version contains supplementary material available at 10.1186/s12885-023-11800-7.</p>", "<title>Keywords</title>" ]

[ "<title>Summary</title>", "<title>Evidence before this study</title>", "<p id=\"Par5\">China, with 1/3 proportion of smoking population across the world has substantial cancer burden while lung cancer remains the leading cause of cancer-related death. The effectiveness for mortality reduction of lung cancer screening programs has been well confirmed by several trials (e.g. National Lung Screening Trail) and the challenge for lung cancer screening now seemed to be the high false-positive rate of Low-Dose Computed Tomography (LDCT). To make the existing cancer screening programs more efficient targeting, polygenic risk scores (PRSs) are introduced. PRS have the potential to identify individuals at risk of different type of cancers, optimizing treatment, and predicting survival outcomes. We searched PubMed, EMBASE, and Web of Science between January 1, 2000, and July 30, 2023, with no language restrictions, using the terms “China” or “Chinese”, “lung cancer”, “polygenic risk score” or “PRS” or “genetic test” and “cost-effectiveness”, to identify published economic evaluations on PRS-based strategy for lung cancer screening in China. We found no previous studies describing the cost-effectiveness of PRS-based lung screening in China. Only one previous study evaluated the effect of PRS-based screening based on modelling using UK metrics.</p>", "<title>Added value of this study</title>", "<p id=\"Par6\">The comparative cost-effectiveness analysis used a Markov state-transition model to assess the potential effect and costs of the screening strategies incorporating PRS or not. We found that the screening strategy incorporating PRS was estimated to be cost-effective compared with non-screening, with an ICUR of CNY 156,691.93 to CNY 221,741.84 (initial start age range across 50-74 years) per QALY gained. The strategy that screened using LDCT alone from 70-74 years annually could obtain an ICER of CNY 80,880.85 per QALY gained, which was the most cost-effective strategy. The introduction of PRS as an extra eligible criteria was associated with making strategies cost-saving but also lose the capability of gaining more LYs compared with LDCT screening alone.</p>", "<title>Implications of all the available evidence</title>", "<p id=\"Par7\">Our findings suggest that lung cancer screening programs incorporating PRS of existing performance would hardly be cost-effective using the willingness-to-pay threshold of 1 time GDP per capita, and the optimal screening strategy for lung cancer still remains to be LDCT screening alone for now, suggesting that we should be more conservative in considering LDCT screening with PRS for lung cancer.</p>", "<title>Supplementary Information</title>", "<p>\n</p>" ]

[ "<title>Acknowledgements</title>", "<p>The authors would like to thank all participants who took part in the survey.</p>", "<title>Data sharing statement</title>", "<p id=\"Par34\">The datasets used during the current study are available from the corresponding author on reasonable request.</p>", "<title>Authors’ contributions</title>", "<p>Concept and design: Zixuan Zhao; Acquisition of data: Lingbin Du, Shuyan Gu; Analysis and interpretation of data: Yi Yang, Weijia Wu; Drafting of the manuscript: Zixuan Zhao,Yi Yang;Critical revision of paper for important intellectual content: Hengjin Dong, Shuyan Gu; Statistical analysis: Yi Yang, Weijia Wu; Obtaining funding: Zixuan Zhao, Lingbin Du; Supervision:Gaoling WangHengjin Dong.</p>", "<title>Funding</title>", "<p>This work was supported by the Scientific Research Foundation of Nanjing University of Chinese Medicine (Grant No.013038029001).</p>", "<title>Declarations</title>", "<title>Ethics approval and consent to participate</title>", "<p id=\"Par35\">The study was conducted according to the report guidelines of CHEERS and approved by the Ethics Committee of the Taizhou cancer hospital (code: IRB-[2020]NO.6). Written informed consent was obtained from study participants before their enrollment into the study.</p>", "<title>Consent for publication</title>", "<p id=\"Par36\">Not applicable.</p>", "<title>Competing interests</title>", "<p id=\"Par37\">All authors declare that they have no competing interests.</p>" ]

[ "<fig id=\"Fig1\"><label>Fig. 1</label><caption><p>The schematic diagram of research design. Abbreviations: RR, relative risk; LDCT, low-dose computed tomography; PRS, polygenic risk score; CKB, China Kadoorie Biobank; ICER, incremental cost -effectiveness ratio; ICUR, incremental cost-utility ratio</p></caption></fig>", "<fig id=\"Fig2\"><label>Fig. 2</label><caption><p>Univariate sensitivity analyses of annual LDCT screening vs PRS&amp;LDCT screening for lung cancer</p></caption></fig>", "<fig id=\"Fig3\"><label>Fig. 3</label><caption><p>Probabilistic sensitivity analyses of diverse screening strategies for lung cancer</p></caption></fig>" ]

[ "<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Input parameters of Markov model</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Variables</th><th align=\"left\" colspan=\"2\">Base-case value</th><th align=\"left\">Distribution</th><th align=\"left\">Source</th></tr><tr><th align=\"left\">Lung cancer incidence rate in general population(100,000^–1)</th><th align=\"left\">Male</th><th align=\"left\">Female</th><th align=\"left\"/><th align=\"left\"/></tr></thead><tbody><tr><td align=\"left\">50–54</td><td align=\"left\">84.34</td><td align=\"left\">50.87</td><td align=\"left\">Beta</td><td align=\"left\">[##UREF##5##21##]</td></tr><tr><td align=\"left\">55–59</td><td align=\"left\">121.85</td><td align=\"left\">56.99</td><td align=\"left\">Beta</td><td align=\"left\">[##UREF##5##21##]</td></tr><tr><td align=\"left\">60–64</td><td align=\"left\">237.82</td><td align=\"left\">104.22</td><td align=\"left\">Beta</td><td align=\"left\">[##UREF##5##21##]</td></tr><tr><td align=\"left\">65–69</td><td align=\"left\">329.68</td><td align=\"left\">137.74</td><td align=\"left\">Beta</td><td align=\"left\">[##UREF##5##21##]</td></tr><tr><td align=\"left\">70–74</td><td align=\"left\">418.52</td><td align=\"left\">178.38</td><td align=\"left\">Beta</td><td align=\"left\">[##UREF##5##21##]</td></tr><tr><td align=\"left\">RR(&gt; 30 pack-years)</td><td align=\"left\" colspan=\"2\">3.87</td><td align=\"left\">Lognormal</td><td align=\"left\">[##REF##34210726##34##]</td></tr><tr><td align=\"left\">RR(&gt; 30 pack-years at top 5% based on PRS)</td><td align=\"left\" colspan=\"2\">3.98</td><td align=\"left\">Lognormal</td><td align=\"left\">[##REF##31326317##17##]</td></tr><tr><td align=\"left\">Proportion of lung cancer by stage</td><td align=\"left\" colspan=\"3\"/><td align=\"left\">Wenling lung cancer screening program</td></tr><tr><td align=\"left\"> CIS</td><td align=\"left\" colspan=\"2\">0.0370</td><td align=\"left\">Beta</td><td align=\"left\"/></tr><tr><td align=\"left\"> I</td><td align=\"left\" colspan=\"2\">0.6852</td><td align=\"left\">Beta</td><td align=\"left\"/></tr><tr><td align=\"left\"> II</td><td align=\"left\" colspan=\"2\">0.0370</td><td align=\"left\">Beta</td><td align=\"left\"/></tr><tr><td align=\"left\"> III</td><td align=\"left\" colspan=\"2\">0.1852</td><td align=\"left\">Beta</td><td align=\"left\"/></tr><tr><td align=\"left\"> IV</td><td align=\"left\" colspan=\"2\">0.0556</td><td align=\"left\">Beta</td><td align=\"left\"/></tr><tr><td align=\"left\"> Sensitivity of LDCT</td><td align=\"left\" colspan=\"2\">0.79</td><td align=\"left\">Beta</td><td align=\"left\">[##REF##24419137##32##]</td></tr><tr><td align=\"left\"> Specificity of LDCT</td><td align=\"left\" colspan=\"2\">0.81</td><td align=\"left\">Beta</td><td align=\"left\">[##REF##24419137##32##]</td></tr><tr><td align=\"left\" colspan=\"5\">Mortality of all-cause death (%)</td></tr><tr><td align=\"left\"> 50–54</td><td align=\"left\" colspan=\"2\">0.45</td><td align=\"left\">Beta</td><td align=\"left\">Estimated [##UREF##8##24##, ##REF##9822393##27##]</td></tr><tr><td align=\"left\"> 55–59</td><td align=\"left\" colspan=\"2\">0.65</td><td align=\"left\">Beta</td><td align=\"left\">Estimated [##UREF##8##24##, ##REF##9822393##27##]</td></tr><tr><td align=\"left\"> 60–64</td><td align=\"left\" colspan=\"2\">1.08</td><td align=\"left\">Beta</td><td align=\"left\">Estimated [##UREF##8##24##, ##REF##9822393##27##]</td></tr><tr><td align=\"left\"> 65–69</td><td align=\"left\" colspan=\"2\">1.88</td><td align=\"left\">Beta</td><td align=\"left\">Estimated [##UREF##8##24##, ##REF##9822393##27##]</td></tr><tr><td align=\"left\"> 70–74</td><td align=\"left\" colspan=\"2\">3.36</td><td align=\"left\">Beta</td><td align=\"left\">Estimated [##UREF##8##24##, ##REF##9822393##27##]</td></tr><tr><td align=\"left\"> 75–79</td><td align=\"left\" colspan=\"2\">5.40</td><td align=\"left\">Beta</td><td align=\"left\">Estimated [##UREF##8##24##, ##REF##9822393##27##]</td></tr><tr><td align=\"left\" colspan=\"5\">Lung cancer mortality rate in general population(100,000^–1)</td></tr><tr><td align=\"left\"> 50–54</td><td align=\"left\" colspan=\"2\">28.81</td><td align=\"left\">Beta</td><td align=\"left\">[##UREF##10##28##]</td></tr><tr><td align=\"left\"> 55–59</td><td align=\"left\" colspan=\"2\">52.86</td><td align=\"left\">Beta</td><td align=\"left\">[##UREF##10##28##]</td></tr><tr><td align=\"left\"> 60–64</td><td align=\"left\" colspan=\"2\">101.93</td><td align=\"left\">Beta</td><td align=\"left\">[##UREF##10##28##]</td></tr><tr><td align=\"left\"> 65–69</td><td align=\"left\" colspan=\"2\">153.34</td><td align=\"left\">Beta</td><td align=\"left\">[##UREF##10##28##]</td></tr><tr><td align=\"left\"> 70–74</td><td align=\"left\" colspan=\"2\">248.57</td><td align=\"left\">Beta</td><td align=\"left\">[##UREF##10##28##]</td></tr><tr><td align=\"left\" colspan=\"5\">Transition probabilities(1 year)</td></tr><tr><td align=\"left\"> Lung cancer stage CIS to lung cancer stage I</td><td align=\"left\" colspan=\"2\">0.0980</td><td align=\"left\">Beta</td><td align=\"left\">[##UREF##9##25##]</td></tr><tr><td align=\"left\"> Lung cancer stage I to lung cancer stage II</td><td align=\"left\" colspan=\"2\">0.3682</td><td align=\"left\">Beta</td><td align=\"left\">[##REF##30268459##35##]</td></tr><tr><td align=\"left\"> Lung cancer stage I to lung cancer stage III</td><td align=\"left\" colspan=\"2\">0.0328</td><td align=\"left\">Beta</td><td align=\"left\">[##REF##30268459##35##]</td></tr><tr><td align=\"left\"> Lung cancer stage I to lung cancer stage IV</td><td align=\"left\" colspan=\"2\">0.0745</td><td align=\"left\">Beta</td><td align=\"left\">[##REF##30268459##35##]</td></tr><tr><td align=\"left\"> Lung cancer stage II to lung cancer stage III</td><td align=\"left\" colspan=\"2\">0.2260</td><td align=\"left\">Beta</td><td align=\"left\">[##REF##30268459##35##]</td></tr><tr><td align=\"left\"> Lung cancer stage II to lung cancer stage IV</td><td align=\"left\" colspan=\"2\">0.1510</td><td align=\"left\">Beta</td><td align=\"left\">[##REF##30268459##35##]</td></tr><tr><td align=\"left\"> Lung cancer stage III to lung cancer stage IV</td><td align=\"left\" colspan=\"2\">0.1455</td><td align=\"left\">Beta</td><td align=\"left\">[##REF##30268459##35##]</td></tr><tr><td align=\"left\"> Lung cancer stage CIS to death</td><td align=\"left\" colspan=\"2\">0</td><td align=\"left\">Beta</td><td align=\"left\">Estimated [##UREF##10##28##–##REF##27521774##30##]</td></tr><tr><td align=\"left\"> Lung cancer stage I to death</td><td align=\"left\" colspan=\"2\">0.1739</td><td align=\"left\">Beta</td><td align=\"left\">Estimated [##UREF##10##28##–##REF##27521774##30##]</td></tr><tr><td align=\"left\"> Lung cancer stage II to death</td><td align=\"left\" colspan=\"2\">0.2842</td><td align=\"left\">Beta</td><td align=\"left\">Estimated [##UREF##10##28##–##REF##27521774##30##]</td></tr><tr><td align=\"left\"> Lung cancer stage III to death</td><td align=\"left\" colspan=\"2\">0.4626</td><td align=\"left\">Beta</td><td align=\"left\">Estimated [##UREF##10##28##–##REF##27521774##30##]</td></tr><tr><td align=\"left\"> Lung cancer stage IV to death</td><td align=\"left\" colspan=\"2\">0.5880</td><td align=\"left\">Beta</td><td align=\"left\">Estimated [##UREF##10##28##–##REF##27521774##30##]</td></tr><tr><td align=\"left\" colspan=\"5\">Utility</td></tr><tr><td align=\"left\"> CIS</td><td align=\"left\" colspan=\"2\">0.92</td><td align=\"left\">Beta</td><td align=\"left\">[##REF##20448248##36##]</td></tr><tr><td align=\"left\"> I</td><td align=\"left\" colspan=\"2\">0.92</td><td align=\"left\">Beta</td><td align=\"left\">CanSPUC data</td></tr><tr><td align=\"left\"> II</td><td align=\"left\" colspan=\"2\">0.87</td><td align=\"left\">Beta</td><td align=\"left\">CanSPUC data</td></tr><tr><td align=\"left\"> III</td><td align=\"left\" colspan=\"2\">0.71</td><td align=\"left\">Beta</td><td align=\"left\">CanSPUC data</td></tr><tr><td align=\"left\"> IV</td><td align=\"left\" colspan=\"2\">0.60</td><td align=\"left\">Beta</td><td align=\"left\">CanSPUC data</td></tr><tr><td align=\"left\"> Maintenance state</td><td align=\"left\" colspan=\"2\">0.87</td><td align=\"left\">Beta</td><td align=\"left\">[##UREF##13##37##]</td></tr><tr><td align=\"left\"> Costs(CNY)</td><td align=\"left\" colspan=\"2\"/><td align=\"left\"/><td align=\"left\">Survey data</td></tr><tr><td align=\"left\"> Screening cost(LDCT)</td><td align=\"left\" colspan=\"2\">245.86</td><td align=\"left\">Gamma</td><td align=\"left\">-</td></tr><tr><td align=\"left\"> Screening cost(PRS)</td><td align=\"left\" colspan=\"2\">280.00</td><td align=\"left\">Gamma</td><td align=\"left\">-</td></tr><tr><td align=\"left\"> Pre-diagnosis cost</td><td align=\"left\" colspan=\"2\">628.36</td><td align=\"left\">Gamma</td><td align=\"left\">-</td></tr><tr><td align=\"left\"> Biopsy diagnosis cost</td><td align=\"left\" colspan=\"2\">1,232.44</td><td align=\"left\">Gamma</td><td align=\"left\">-</td></tr><tr><td align=\"left\" colspan=\"5\">Treatment cost</td></tr><tr><td align=\"left\"> CIS</td><td align=\"left\" colspan=\"2\">47,341.85</td><td align=\"left\">Gamma</td><td align=\"left\">-</td></tr><tr><td align=\"left\"> I</td><td align=\"left\" colspan=\"2\">53,344.51</td><td align=\"left\">Gamma</td><td align=\"left\">-</td></tr><tr><td align=\"left\"> II</td><td align=\"left\" colspan=\"2\">83,365.95</td><td align=\"left\">Gamma</td><td align=\"left\">-</td></tr><tr><td align=\"left\"> III</td><td align=\"left\" colspan=\"2\">90,643.18</td><td align=\"left\">Gamma</td><td align=\"left\">-</td></tr><tr><td align=\"left\"> IV</td><td align=\"left\" colspan=\"2\">116,471.34</td><td align=\"left\">Gamma</td><td align=\"left\">-</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab2\"><label>Table 2</label><caption><p>Characters of the evaluated strategies</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Strategy</th><th align=\"left\">Eligible criteria</th><th align=\"left\">Screening tool</th><th align=\"left\">Start age</th></tr></thead><tbody><tr><td align=\"left\">#0 Non-screening</td><td align=\"left\">-</td><td align=\"left\">-</td><td align=\"left\">50;55;60;65;70</td></tr><tr><td align=\"left\">#1 LDCT</td><td align=\"left\">Smoking &gt; 30 pack-years</td><td align=\"left\">LDCT</td><td align=\"left\">50;55;60;65;70</td></tr><tr><td align=\"left\">#2 PRS&amp; LDCT</td><td align=\"left\">Top 5% based on PRS and smoking &gt; 30 pack-years</td><td align=\"left\">LDCT</td><td align=\"left\">50;55;60;65;70</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab3\"><label>Table 3</label><caption><p>Outcomes of base-case analysis among alternative strategies</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Start age</th><th align=\"left\">Strategies</th><th align=\"left\">Costs (CNY:million)</th><th align=\"left\">LYs (10,000 years)</th><th align=\"left\">QALYs (10,000 years)</th><th align=\"left\">ICER</th><th align=\"left\">ICUR</th></tr></thead><tbody><tr><td align=\"left\" rowspan=\"3\">50</td><td align=\"left\">#0 Non-screening</td><td char=\".\" align=\"char\">1339.49</td><td char=\".\" align=\"char\">134.58</td><td char=\".\" align=\"char\">133.86</td><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/></tr><tr><td align=\"left\">#1 LDCT</td><td char=\".\" align=\"char\">1956.54</td><td char=\".\" align=\"char\">134.93</td><td char=\".\" align=\"char\">134.27</td><td char=\".\" align=\"char\">176,565.66</td><td char=\".\" align=\"char\">150,050.15</td></tr><tr><td align=\"left\">#2 PRS&amp;LDCT</td><td char=\".\" align=\"char\">1386.32</td><td char=\".\" align=\"char\">134.60</td><td char=\".\" align=\"char\">133.88</td><td char=\".\" align=\"char\">260,918.20</td><td char=\".\" align=\"char\">221,741.84</td></tr><tr><td align=\"left\" rowspan=\"3\">55</td><td align=\"left\">#0 Non-screening</td><td char=\".\" align=\"char\">1283.70</td><td char=\".\" align=\"char\">119.71</td><td char=\".\" align=\"char\">118.92</td><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/></tr><tr><td align=\"left\">#1 LDCT</td><td char=\".\" align=\"char\">1832.75</td><td char=\".\" align=\"char\">120.09</td><td char=\".\" align=\"char\">119.38</td><td char=\".\" align=\"char\">142,128.81</td><td char=\".\" align=\"char\">119,991.71</td></tr><tr><td align=\"left\">#2 PRS&amp;LDCT</td><td char=\".\" align=\"char\">1327.11</td><td char=\".\" align=\"char\">119.73</td><td char=\".\" align=\"char\">118.95</td><td char=\".\" align=\"char\">218,831.81</td><td char=\".\" align=\"char\">184,753.98</td></tr><tr><td align=\"left\" rowspan=\"3\">60</td><td align=\"left\">#0 Non-screening</td><td char=\".\" align=\"char\">1169.75</td><td char=\".\" align=\"char\">102.33</td><td char=\".\" align=\"char\">101.55</td><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/></tr><tr><td align=\"left\">#1 LDCT</td><td char=\".\" align=\"char\">1634.73</td><td char=\".\" align=\"char\">102.73</td><td char=\".\" align=\"char\">102.02</td><td char=\".\" align=\"char\">116,463.62</td><td char=\".\" align=\"char\">97,566.13</td></tr><tr><td align=\"left\">#2 PRS&amp;LDCT</td><td char=\".\" align=\"char\">1208.94</td><td char=\".\" align=\"char\">102.35</td><td char=\".\" align=\"char\">101.57</td><td char=\".\" align=\"char\">191,110.06</td><td char=\".\" align=\"char\">160,107.15</td></tr><tr><td align=\"left\" rowspan=\"3\">65</td><td align=\"left\">#0 Non-screening</td><td char=\".\" align=\"char\">950.81</td><td char=\".\" align=\"char\">82.70</td><td char=\".\" align=\"char\">82.03</td><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/></tr><tr><td align=\"left\">#1 LDCT</td><td char=\".\" align=\"char\">1309.47</td><td char=\".\" align=\"char\">83.04</td><td char=\".\" align=\"char\">82.45</td><td char=\".\" align=\"char\">104,998.56</td><td char=\".\" align=\"char\">85,332.16</td></tr><tr><td align=\"left\">#2 PRS&amp;LDCT</td><td char=\".\" align=\"char\">984.64</td><td char=\".\" align=\"char\">82.72</td><td char=\".\" align=\"char\">82.05</td><td char=\".\" align=\"char\">192,795.29</td><td char=\".\" align=\"char\">156,691.93</td></tr><tr><td align=\"left\" rowspan=\"3\">70</td><td align=\"left\">#0 Non-screening</td><td char=\".\" align=\"char\">631.94</td><td char=\".\" align=\"char\">60.26</td><td char=\".\" align=\"char\">59.83</td><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/></tr><tr><td align=\"left\">#1 LDCT</td><td char=\".\" align=\"char\">864.37</td><td char=\".\" align=\"char\">60.48</td><td char=\".\" align=\"char\">60.11</td><td char=\".\" align=\"char\">105,370.21</td><td char=\".\" align=\"char\">80,880.85</td></tr><tr><td align=\"left\">#2 PRS&amp;LDCT</td><td char=\".\" align=\"char\">659.40</td><td char=\".\" align=\"char\">60.27</td><td char=\".\" align=\"char\">59.84</td><td char=\".\" align=\"char\">242,247.42</td><td char=\".\" align=\"char\">185,958.48</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab4\"><label>Table 4</label><caption><p>Acceptability at different level of willingness-to-pay</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" rowspan=\"2\"><bold>Start age</bold></th><th align=\"left\" rowspan=\"2\"><bold>Strategies</bold></th><th align=\"left\" colspan=\"2\"><bold>Acceptability at willingness-to-pay (%)</bold></th><th align=\"left\"/></tr><tr><th align=\"left\"><bold>1 time GDP per capita (CNY85,698)</bold></th><th align=\"left\"><bold>2 times GDP per capita (CNY171,396)</bold></th><th align=\"left\"><bold>3 times GDP per capita (CNY257,094)</bold></th></tr></thead><tbody><tr><td align=\"left\" rowspan=\"2\">50</td><td align=\"left\">#1 LDCT</td><td char=\".\" align=\"char\">1.44</td><td char=\".\" align=\"char\">27.99</td><td char=\".\" align=\"char\">75.07</td></tr><tr><td align=\"left\">#2 PRS&amp;LDCT</td><td char=\".\" align=\"char\">0.26</td><td char=\".\" align=\"char\">9.89</td><td char=\".\" align=\"char\">33.77</td></tr><tr><td align=\"left\" rowspan=\"2\">55</td><td align=\"left\">#1 LDCT</td><td char=\".\" align=\"char\">4.31</td><td char=\".\" align=\"char\">54.76</td><td char=\".\" align=\"char\">93.52</td></tr><tr><td align=\"left\">#2 PRS&amp;LDCT</td><td char=\".\" align=\"char\">0.77</td><td char=\".\" align=\"char\">16.68</td><td char=\".\" align=\"char\">54.34</td></tr><tr><td align=\"left\" rowspan=\"2\">60</td><td align=\"left\">#1 LDCT</td><td char=\".\" align=\"char\">10.47</td><td char=\".\" align=\"char\">83.35</td><td char=\".\" align=\"char\">99.06</td></tr><tr><td align=\"left\">#2 PRS&amp;LDCT</td><td char=\".\" align=\"char\">1.88</td><td char=\".\" align=\"char\">26.94</td><td char=\".\" align=\"char\">73.49</td></tr><tr><td align=\"left\" rowspan=\"2\">65</td><td align=\"left\">#1 LDCT</td><td char=\".\" align=\"char\">19.77</td><td char=\".\" align=\"char\">94.28</td><td char=\".\" align=\"char\">99.81</td></tr><tr><td align=\"left\">#2 PRS&amp;LDCT</td><td char=\".\" align=\"char\">2.54</td><td char=\".\" align=\"char\">31.9</td><td char=\".\" align=\"char\">79.68</td></tr><tr><td align=\"left\" rowspan=\"2\">70</td><td align=\"left\">#1 LDCT</td><td char=\".\" align=\"char\">34.18</td><td char=\".\" align=\"char\">98.13</td><td char=\".\" align=\"char\">99.95</td></tr><tr><td align=\"left\">#2 PRS&amp;LDCT</td><td char=\".\" align=\"char\">1.45</td><td char=\".\" align=\"char\">22.66</td><td char=\".\" align=\"char\">67.41</td></tr></tbody></table></table-wrap>" ]

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[ "<supplementary-material content-type=\"local-data\" id=\"MOESM1\"></supplementary-material>" ]

[ "<table-wrap-foot><p><italic>Abbreviations: RR</italic> Relative risk ratio, <italic>CIS</italic> Carcinoma in situ, <italic>PRS</italic> Polygenic risk score, <italic>LDCT</italic> Low-dose computed tomography, <italic>CNY</italic> Chinese yuan, <italic>CanSPUC</italic> Cancer Screening Program in Urban China</p></table-wrap-foot>", "<table-wrap-foot><p><italic>Abbreviations: LDCT</italic> Low-dose computed tomography, <italic>PRS</italic> Polygenic risk score</p><p>Pack-years, 1 pack-year equivalent to 20 cigarettes per day for 1 year</p></table-wrap-foot>", "<table-wrap-foot><p><italic>Abbreviations: LDCT</italic> Low-dose computed tomography, <italic>PRS</italic> Polygenic risk score, <italic>CNY</italic> Chinese yuan, <italic>LYs</italic> Life years, <italic>QALYs</italic> Quality-adjusted life years, <italic>ICER</italic> Incremental cost-effectiveness ratio, <italic>ICUR</italic> incremental cost-utility ratio</p></table-wrap-foot>", "<table-wrap-foot><p><italic>Abbreviations</italic>:<italic> LDCT</italic> Low-dose computed tomography, <italic>PRS</italic> Polygenic risk score, <italic>CNY</italic> Chinese yuan, <italic>GDP</italic> Gross Domestic Production</p></table-wrap-foot>", "<fn-group><fn><p><bold>Publisher’s Note</bold></p><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn></fn-group>" ]

[ "<graphic xlink:href=\"12885_2023_11800_Fig1_HTML\" id=\"MO1\"/>", "<graphic xlink:href=\"12885_2023_11800_Fig2_HTML\" id=\"MO2\"/>", "<graphic xlink:href=\"12885_2023_11800_Fig3_HTML\" id=\"MO3\"/>" ]

[ "<media xlink:href=\"12885_2023_11800_MOESM1_ESM.docx\"><caption><p><bold>Additional file 1. Appendix 1. </bold>Operational validation for the natural history model of lung cancer. <bold>Figure S1.</bold> Schematic diagram of Markov model for lung cancer screening. <bold>Table S1.</bold> Initial and death probability of natural history model. <bold>Table S2.</bold> Transition probabilities in natural history model for lung cancer. <bold>Table S3.</bold> Validity indicators and sources. <bold>Table S4.</bold> Standard population of China and Segi’s population. <bold>Table S5.</bold> Validity indicator: incidence and mortality of lung cancer. <bold>Figure S2.</bold> Proportion for clinical stages. <bold>Figure S3.</bold> Comparison between GBD observed value and simulation value in life expectancy. <bold>Appendix 2.</bold> Scenario analysis. <bold>Table S6.</bold> Outcomes of scenario analysis with diverse compliance rates. <bold>Appendix 3.</bold> CHEERS Checklist.</p></caption></media>" ]

{ "acronym": [], "definition": [] }

[ "<title>Background</title>", "<p id=\"Par5\">Poor oral health is a major global public health problem [##UREF##0##1##]. Around 3.5 billion people worldwide are affected by oral diseases, predominantly untreated dental caries (tooth decay), severe periodontal disease, and tooth loss [##REF##32122215##2##]. These oral conditions not only impact the health of the teeth and mouth but also systemic health [##UREF##1##3##]. Periodontal disease has been associated with various systemic diseases, such as diabetes, cardiovascular disease, and cancer [##UREF##1##3##, ##REF##21819493##4##]. Observational studies have repeatedly shown associations between tooth loss– often resulting from periodontal disease– and several cancer types, particularly cancers of the upper gastrointestinal tract [##REF##26462879##5##, ##REF##28449041##6##]. Regular oral hygiene practices, namely toothbrushing, have been associated with a decreased risk of developing certain cancers [##UREF##2##7##, ##UREF##3##8##]. These associations between poor oral health and systemic diseases, including cancer, are suspected to share a common pathway mediated by the oral microbiome [##REF##32811287##9##]. The mechanism of these associations may involve carcinogenic bacterial metabolites (e.g., acetaldehyde produced by ethanol-metabolizing oral microbes [##REF##29303995##10##], and nitrosamines formed from nitrate reduced to nitrite by nitrate-reducing oral microbes [##REF##1761254##11##, ##REF##8688158##12##]), chronic systemic inflammation triggered by the oral microbiome, or specific periodontal pathogens and their interplay with the host immune response [##REF##32811287##9##].</p>", "<p id=\"Par6\">Several prospective studies have previously reported adverse associations between poor oral health, as measured by tooth loss and/or periodontal disease, and lung cancer incidence or mortality [##REF##31871854##13##–##UREF##4##15##]. However, the relationship between oral health and lung cancer risk remains inconclusive, particularly since smoking may modify associations. Some studies found that smokers may have greater risk of lung cancer if they have poor oral health [##REF##35626036##14##, ##REF##30642558##16##, ##REF##24913780##17##], and other studies found no significant associations between oral health and lung cancer in never smokers [##REF##35626036##14##, ##REF##24913780##17##–##REF##12821269##21##]. In addition, many of the existing studies have been from the United States [##REF##24913780##17##, ##REF##26811350##20##–##UREF##5##22##] where smoking is a common exposure that may have altered the relationship between oral health and lung cancer. The current evidence is lacking studies from diverse populations, particularly studies from prospective cohorts outside of the US with adjustment for smoking and other major confounders of lung cancer associations.</p>", "<p id=\"Par7\">Here, we examined the association between poor dental health and lung cancer incidence and mortality in the Golestan Cohort Study, a large-scale, population-based prospective study with more than 50,000 participants in Golestan Province, located in northeastern Iran. We used multiple dental health measures, including tooth loss; the sum of decayed, missing, or filled teeth (DMFT score); and frequency of toothbrushing, to investigate the impact of poor dental health on lung cancer risk.</p>" ]

[ "<title>Methods</title>", "<title>Study population and questionnaire data</title>", "<p id=\"Par8\">As described in detail previously [##REF##19332502##23##], the Golestan Cohort Study is a prospective, population-based cohort of 50,045 individuals between ages 40 and 75 years at baseline in Golestan Province, Iran. Participants were recruited from January 2004 to June 2008 and continue to be followed up. Written informed consent was obtained from all study participants at the time of enrollment. The Golestan Cohort Study was approved by the Institutional Review Boards of the Digestive Disease Research Institute of Tehran University of Medical Sciences, the International Agency for Research on Cancer, and the United States National Cancer Institute.</p>", "<p id=\"Par9\">At baseline, participants were interviewed in-person by trained staff using a structured questionnaire to collect sociodemographic and lifestyle information, including age, sex, ethnicity, place of residence, education, and detailed information on the use of cigarettes, nass (a local chewing tobacco product), and opium (e.g., age at initiation and cessation and amount of use per day). Opium consumption is a known carcinogen [##REF##33038940##24##] and risk factor for different cancers including lung cancer [##REF##32353313##25##]. Individuals who use opium are exposed to most of the carcinogens present in tobacco smoke [##REF##31915141##26##]. Fruit and vegetable intake were assessed at baseline using a food frequency questionnaire. Socioeconomic status (SES) was estimated based on a composite wealth score determined by ownership of vehicles, property, and household appliances [##REF##19416955##27##]. The high reliability and validity of self-reported cigarette smoking and opium use in this population have been demonstrated previously [##REF##15184266##28##, ##REF##30622099##29##].</p>", "<title>Dental health assessment</title>", "<p id=\"Par10\">As part of the baseline interview, trained medical staff counted each participant’s total number of teeth and the number of decayed, missing, or filled teeth, the sum of which constitutes the DMFT score. Participants were also asked about toothbrushing habits, and toothbrushing frequency was categorized as never, non-daily, and daily. The reliability of tooth counts and self-reported brushing frequency have both been shown to be high in this population [##UREF##3##8##, ##REF##15597107##30##]. Specifically, a pilot study was previously conducted for the Golestan Cohort Study where the reliability of teeth counts was evaluated based on repeated examinations of 130 participants occurring two months apart [##REF##15597107##30##]. These results showed that the reliability of the teeth counts was high, with 88.3% agreement and a kappa statistic of 0.86. Similarly, the reliability of self-reported toothbrushing frequency has been evaluated based on a subset of the cohort (11,418 randomly selected participants) who completed a repeat questionnaire approximately 5 years after the baseline interview where participants were asked how often they brush their teeth [##UREF##3##8##]. The self-reported toothbrushing frequency at baseline and from the repeated assessment showed excellent agreement with 77.9% concordance (<italic>p</italic> &lt; 0.001). The maximum number of teeth and DMFT score were coded as 32 to represent the total number of adult teeth including third molars because these are not routinely extracted in this population.</p>", "<title>Case ascertainment</title>", "<p id=\"Par11\">All study participants were followed annually through telephone surveys or home visits, and provincial death and cancer registry data were reviewed monthly to identify all incident cancers and deaths due to any cause. In the case of death, a validated verbal autopsy was performed where the closest relative of the deceased was interviewed by a trained physician to obtain information about the cause of death [##REF##20567597##31##]. Cancer diagnoses and deaths were confirmed by linking to the Golestan population-based cancer registry [##REF##29306787##32##]. Primary lung cancer was defined using International Classification of Diseases, Tenth Revision (ICD-10) codes C34.0-C34.9. Six subjects diagnosed with nonepithelial malignancies (i.e., 4 subjects with lymphoma and 2 subjects with neuroendocrine carcinoma) of the lung were excluded from the present analysis.</p>", "<title>Statistical analysis</title>", "<p id=\"Par12\">Of the 50,045 cohort participants, 9 subjects missing dental status variables and 83 subjects with other missing covariates were excluded, in addition to the 6 subjects with nonepithelial lung cancer diagnoses, leaving a total of 49,947 subjects remaining in the analysis. We used age-dependent exposure metrics to account for the strong correlation between oral variables and age and sex [##REF##15659476##33##]. Specifically, a loess model was fit to estimate the predicted number of lost teeth or DMFT score at each integer year of age, stratified by sex. The loess smoothing parameter was selected based on the bias-corrected Akaike information criterion. Excess numbers of lost teeth and DMFT score were calculated for each participant by taking the difference between the loess predicted age- and sex-specific number of lost teeth/DMFT score and the observed number of lost teeth/DMFT score. Those with a difference of 0 or fewer than the expected number were categorized into the reference group, and the remaining subjects with excess tooth loss/DMFT were categorized into tertiles.</p>", "<p id=\"Par13\">Cox proportional hazards regression models were used to estimate hazard ratios (HRs) and 95% CIs for the association between oral health variables (i.e., tooth loss, DMFT, and toothbrushing frequency), other potential risk factors (described below) and lung cancer incidence and mortality. The entry time was defined as the date of enrollment into the Golestan Cohort Study. Follow-up ended on the date of lung cancer or other cancer diagnosis (for lung cancer incidence analysis only), death, or last follow-up through March 31, 2021, whichever came first. A total of 518 participants (1.04%) were lost to follow-up during the study period.</p>", "<p id=\"Par14\">Cox models were run separately for each dental health variable, including the following sociodemographic and lifestyle variables: age, sex, SES (in quartiles) [##REF##19416955##27##], ethnicity (Turkmen or non-Turkmen), residence (urban or rural), education (illiterate or literate), nass use (never or ever), cigarette use, and opium use. For cigarette smoking, participants were categorized as never smokers or in tertile categories of their cumulative pack-years of smoked cigarettes, with separate analyses run for former and current smokers. Cumulative pack-years of cigarette smoking was calculated as the number of packs (20 cigarettes in each pack) smoked per day multiplied by the number of years of smoking. For opium use, participants were categorized as never users or in tertile categories of their number of years of consumption. We further performed analyses stratified by cigarette smoking and opium use (never smoker/opium user or ever smoker/opium user) and tested for interactions between oral health variables and smoking/opium use (coded as a binary variable of never or ever smoker/opium user) using the likelihood ratio test. Dental health variables were tested for a linear trend by assigning ordinal numbers to each category, and the Wald test was used for testing for a global trend. Deviations from the proportional hazard assumption were not detected in any of the models based on the Schoenfeld residuals test. All statistical tests were two-sided with a significance level of 0.05. The R programming environment [##UREF##6##34##] (version 4.2.2) was used for all statistical analyses.</p>" ]

[ "<title>Results</title>", "<p id=\"Par15\">Table ##TAB##0##1## shows the baseline characteristics of the cohort participants, overall and by DMFT category. The majority of cohort participants had never smoked cigarettes (82.8%) or used nass (92.3%) or opium (83.1%). Overall, the mean cigarette smoking pack-years was 16.9 (SD 18.6) for ever smokers (mean smoking pack-years was 16.3 [SD 21.0] and 17.3 [SD 16.9] for former and current smokers, respectively), and the mean duration of opium use was 12.2 (SD 10.7) years for ever opium users. The mean number of missing teeth and the mean DMFT score were 18.3 (SD 9.55) and 23.4 (SD 8.73), respectively, and more than half of the cohort participants (55.7%) reported never brushing their teeth. Relative to subjects with the expected DMFT score or lower, a larger proportion of individuals in the highest tertile of DMFT were male, lived in rural areas, smoked cigarettes, and used opium or nass (Table ##TAB##0##1##). During a median 14 years of follow-up there were 119 incident lung cancer cases (crude incidence rate of 17.9 cases per 100,000 person-years), and 98 of these people died of lung cancer. Of the 119 lung cancer cases, 53 (44.5%) were never cigarette smokers, 66 (55.5%) were never opium users, and 45 (37.8%) used neither.</p>", "<p id=\"Par16\">\n\n</p>", "<p id=\"Par17\">We first examined associations between cigarette smoking, opium use, and nass use and lung cancer incidence (Fig. ##FIG##0##1##, Table ##SUPPL##0##S1##). Age, cigarette smoking, and opium use were significantly associated with an increased risk of lung cancer, whereas sex, SES, ethnicity, area of residence, education, and nass use did not have a significant association with lung cancer risk, with mutual adjustment for all potential risk factors including the dental health variables. Compared with never smokers, former smokers with over 20 pack-years of smoked cigarettes had a higher risk of lung cancer (HR 2.78 [95% CI: 1.14, 6.80] in a model that included DMFT), but former smokers with 20 pack-years or less did not. All current smokers had higher lung cancer risk compared with never smokers regardless of the number of pack-years. Current smokers with 5.5 pack-years or less and current smokers with 5.5–20 pack-years had HRs of 4.05 (95% CI: 1.87, 8.75) and 4.27 (95% CI: 2.09, 8.71), respectively. Lung cancer risk was further increased for current smokers with over 20 pack-years with HR of 7.98 (95% CI: 4.39, 14.5). Ever using opium for over 5 years was also significantly associated with an increased lung cancer risk with a HR of around 2.2 compared with never users. Ever use of nass was not significantly associated with an increased risk of lung cancer compared with never use.</p>", "<p id=\"Par18\">\n\n</p>", "<p id=\"Par19\">Poor dental status was associated with an increased risk of incident lung cancer (Fig. ##FIG##0##1##, Table ##SUPPL##0##S1##) in models adjusted for known and suspected lung cancer risk factors. Specifically, there was an increasing trend in lung cancer risk across the DMFT tertiles (linear trend, <italic>p</italic> = 0.011; global trend, <italic>p</italic> = 0.011). Relative to individuals with the expected DMFT score or less, the HR increased from 1.27 (95% CI: 0.73, 2.22) to 2.15 (95% CI: 1.34, 3.43) across the first two tertiles of DMFT but dropped to 1.52 (95% CI: 0.81, 2.84) for the highest tertile (Fig. ##FIG##0##1##, Table ##SUPPL##0##S1##). The highest tertile of tooth loss was also associated with an increased lung cancer risk with a HR of 1.68 (95% CI: 1.04, 2.70) compared with subjects with the expected number of lost teeth or fewer, but no associations were found for the first two tertiles of tooth loss (linear trend, <italic>p</italic> = 0.043; global trend, <italic>p</italic> = 0.19) (Fig. ##FIG##0##1##, Table ##SUPPL##0##S1##). There were no significant associations between toothbrushing frequency and lung cancer risk (Fig. ##FIG##0##1##, Table ##SUPPL##0##S1##).</p>", "<p id=\"Par20\">We further examined associations between dental status, other potential risk factors, and lung cancer incidence, stratified by cigarette smoking and opium use, important risk factors in this population. Subjects were stratified into binary groups of never (<italic>n</italic> = 37,358; 45 cases) and ever (<italic>n</italic> = 12,589; 74 cases) users of cigarettes or opium. For the non-oral health related risk factors (i.e., age, sex, SES, ethnicity, area of residence, education, former and current smoking pack-years, and opium and nass use), the results did not change upon stratification (Table ##SUPPL##0##S1##). For DMFT, the results were similar among never and ever cigarette/opium users, with significant associations for the second tertile of DMFT (Fig. ##FIG##0##1##, Table ##SUPPL##0##S2##). For never smoker/opium users, HRs were 1.59 (95% CI: 0.71, 3.60), 2.02 (95% CI: 0.94, 4.33), and 1.77 (95% CI: 0.55, 5.66) from the first to the third tertile of DMFT. For ever smoker/opium users, HRs were 1.06 (95% CI: 0.49, 2.30), 2.23 (95% CI: 1.21, 4.09), and 1.42 (95% CI: 0.66, 3.03) from the first to the third tertile of DMFT. Strata-specific HRs were similar to the overall unstratified HRs (2.15 for the second DMFT tertile; Fig. ##FIG##0##1##, Table ##SUPPL##0##S1##). Stratification also did not change the results for tooth loss or toothbrushing frequency (Fig. ##FIG##0##1##, Table ##SUPPL##1##S2##). We found no evidence of a statistical interaction between smoking/opium use and any of the dental status variables (<italic>p</italic> &gt; 0.49).</p>", "<p id=\"Par21\">For lung cancer mortality, associations with dental health variables were similar to those for incidence but had slightly elevated risk estimates for DMFT (Table ##SUPPL##0##S3##, Fig. ##SUPPL##0##S1##). The second tertile of DMFT was significantly associated with an increased risk of lung cancer mortality, with a HR of 2.55 (95% CI: 1.50, 4.33), and mortality risk significantly increased with higher DMFT tertiles (linear trend, <italic>p</italic> = 0.0038; global trend, <italic>p</italic> = 0.0046). For tooth loss, the highest tertile of tooth loss had a HR of 1.71 (95% CI: 1.01, 2.92), and there was a marginally significant linear trend across the tertiles of tooth loss (<italic>p</italic> = 0.049). Associations with toothbrushing frequency remained null for lung cancer mortality.</p>", "<p id=\"Par22\">Sensitivity analyses excluding the first two years of follow-up did not meaningfully change the Cox regression analysis results for either lung cancer incidence or mortality (Table ##SUPPL##0##S4##, Fig. ##SUPPL##1##S2##). Excluding subjects with no teeth (8,709 subjects with no teeth, including 34 incident lung cancer cases) did not change the results for associations between DMFT and lung cancer incidence, but associations with tooth loss and toothbrushing frequency were null (Table ##SUPPL##0##S5##, Fig. ##SUPPL##1##S3##). Adjusting for daily fruit and vegetable intake also did not substantially change associations with lung cancer incidence (Table ##SUPPL##0##S6##, Fig. ##SUPPL##1##S3##).</p>" ]

[ "<title>Discussion</title>", "<p id=\"Par23\">In this large, prospective cohort study, more than half of the cohort members reported never brushing their teeth, and the participants had on average 23.4 decayed, missing, or filled teeth. Higher DMFT scores were associated with a progressively higher risk of both lung cancer incidence and mortality, and the second tertile of individuals with higher-than-expected DMFT score had more than a two-fold risk of lung cancer compared with subjects who had the expected DMFT score or less. Similarly, there was a ~ 1.7-fold increased risk of lung cancer for subjects in the highest tertile of increased tooth loss compared with those with the expected number of lost teeth or fewer. These dental health variables were significantly associated with lung cancer risk after simultaneous adjustment for other risk factors, including age, cigarette smoking, and opium use. We found no associations between toothbrushing frequency and lung cancer risk.</p>", "<p id=\"Par24\">Our results from the Golestan Cohort Study show that poor dentition (i.e. higher numbers of tooth loss or higher DMFT score) is independently associated with lung cancer risk, and it is unlikely that these results can be explained by residual confounding by tobacco or opium use. This is in line with previous studies of tooth loss and lung cancer, with a recent meta-analysis including seven studies showing a relative risk of 1.64 (95% CI: 1.44, 1.86) comparing the highest and lowest category of tooth loss for incident lung cancer [##REF##35626036##14##]. Tooth loss often results from periodontal disease, which has also been shown to be associated with an increased risk of lung cancer in multiple prospective cohort studies (meta-analyzed HR of 1.40 (95% CI: 1.25, 1.58) [##REF##32583879##35##]). Also similar to our results, a cohort study in Japan found that higher numbers of teeth lost were associated with an increased risk of lung cancer mortality (0–9 teeth remaining vs. 20 or more teeth remaining, HR 1.75; 95% CI: 1.08, 2.83), with adjustment for smoking and other potential confounders [##REF##31006716##36##]. However, there have also been other studies, such as the prospective cohort analysis of the Sister Study cohort in the US, that did not find a significant association of periodontal disease or tooth loss with lung cancer mortality [##REF##34605056##37##].</p>", "<p id=\"Par25\">Almost half of the lung cancer cases in our study were never smokers. In addition, more than half of the cases had never used opium, which is another known lung cancer risk factor that is relevant in this population [##REF##32353313##25##], and 37.8% used neither cigarettes nor opium. We furthermore showed that associations with dental status remained largely unchanged upon stratification by smoking status and opium use. In previous cohort studies, some found no significant associations between poor oral health (tooth loss and/or periodontal disease) and lung cancer incidence [##REF##35626036##14##, ##REF##24913780##17##, ##REF##18462995##19##, ##REF##26811350##20##] or mortality [##REF##12821269##21##] in never smokers but found poor oral health to increase risk for current [##REF##35626036##14##] or former [##REF##24913780##17##] smokers. It is possible that smoking may modify associations between poor oral health and lung cancer risk, but more studies are needed to clarify this.</p>", "<p id=\"Par26\">The mechanism for the association between oral health and lung cancer likely involves the oral microbiome. Oral microbes produce various metabolites that have been linked to carcinogenesis, such as acetaldehyde [##REF##29303995##10##], nitrosamines [##REF##33785324##38##], and reactive oxygen species [##REF##32811287##9##]. Some authors have suggested that edentulism and the healing of gum tissue may ameliorate the negative effects of tooth loss by altering the oral microbiome against the overgrowth of bacterial species that produce carcinogenic metabolites [##REF##15888779##39##], but we did not find strong evidence to support this hypothesis when we excluded subjects with no teeth from the analysis (Table ##SUPPL##0##S5##). The oral microbiome can also impact cancer risk at distant sites through systemic inflammation, which is a key component of both periodontal disease and carcinogenesis [##REF##30301974##40##, ##REF##24132111##41##]. Recently, there have been a few studies that have found potential links between the oral microbiome and lung cancer. A case-cohort study of three US cohorts found that greater diversity in the oral microbiome was associated with lower risk of developing lung cancer, and relative abundances/presence of certain genera were associated with risk; for example, higher relative abundances of <italic>Streptococcus</italic> was associated with increased lung cancer risk [##UREF##7##42##]. In addition, two nested case-control studies (one from a low-income population in the southeastern US [##REF##34432217##43##] and another among never smokers in China [##REF##33318237##44##]) found different specific taxa to be associated with increased or decreased lung cancer risk. Another recent nested case-control study conducted in the US found that serum antibodies to 13 periodontal bacteria were mostly inversely associated with lung cancer risk, possibly indicating immunity against certain bacteria that may help reduce cancer risk [##UREF##8##45##]. Additional types of evidence beyond observational studies are warranted to understand the exact mechanism of association between poor oral health, the oral microbiome, and lung cancer.</p>", "<p id=\"Par27\">Our study has several strengths and limitations. The major strengths of this study include its prospective design and low loss to follow-up. We used multiple measures to evaluate dental status, which were assessed by trained interviewers. However, our study did not examine the participants’ periodontal status, so we could not evaluate the effect of this component of poor oral health. We carefully adjusted (and stratified by when necessary) for multiple potential confounders, including cigarette smoking, opium use, and SES, but, as with all observational epidemiologic studies, our findings may have been impacted by unmeasured confounders or residual confounding. We also accrued a limited number of lung cancer cases and this precluded analysis by histology and restricted power. Finally, all dental health measures were ascertained at a single time point and accounting for changes in dental status over the follow-up period might have led to different exposure ranking of cohort members.</p>" ]

[ "<title>Conclusion</title>", "<p id=\"Par28\">We found evidence in this cohort that poor dental status, as indicated by higher DMFT scores and greater tooth loss, was associated with an increased risk of lung cancer incidence and mortality after controlling for other important risk factors such as cigarette smoking and opium use. These results persisted even when the analysis was restricted to never users of cigarettes or opium. We did not find significant associations for toothbrushing frequency. While known risk factors such as smoking and opium use remain important, our results indicate that poor oral health may also contribute to lung cancer risk.</p>" ]

[ "<title>Background</title>", "<p id=\"Par1\">Poor oral health has been linked to various systemic diseases, including multiple cancer types, but studies of its association with lung cancer have been inconclusive.</p>", "<title>Methods</title>", "<p id=\"Par2\">We examined the relationship between dental status and lung cancer incidence and mortality in the Golestan Cohort Study, a large, prospective cohort of 50,045 adults in northeastern Iran. Cox proportional hazards models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for associations between three dental health measures (i.e., number of missing teeth; the sum of decayed, missing, or filled teeth (DMFT); and toothbrushing frequency) and lung cancer incidence or mortality with adjustment for multiple potential confounders, including cigarette smoking and opium use. We created tertiles of the number of lost teeth/DMFT score in excess of the loess adjusted, age- and sex-specific predicted numbers, with subjects with the expected number of lost teeth/DMFT or fewer as the reference group.</p>", "<title>Results</title>", "<p id=\"Par3\">During a median follow-up of 14 years, there were 119 incident lung cancer cases and 98 lung cancer deaths. Higher DMFT scores were associated with a progressively increased risk of lung cancer (linear trend, <italic>p</italic> = 0.011). Compared with individuals with the expected DMFT score or less, the HRs were 1.27 (95% CI: 0.73, 2.22), 2.15 (95% CI: 1.34, 3.43), and 1.52 (95% CI: 0.81, 2.84) for the first to the third tertiles of DMFT, respectively. The highest tertile of tooth loss also had an increased risk of lung cancer, with a HR of 1.68 (95% CI: 1.04, 2.70) compared with subjects with the expected number of lost teeth or fewer (linear trend, <italic>p</italic> = 0.043). The results were similar for lung cancer mortality and did not change substantially when the analysis was restricted to never users of cigarettes or opium. We found no associations between toothbrushing frequency and lung cancer incidence or mortality.</p>", "<title>Conclusion</title>", "<p id=\"Par4\">Poor dental health indicated by tooth loss or DMFT, but not lack of toothbrushing, was associated with increased lung cancer incidence and mortality in this rural Middle Eastern population.</p>", "<title>Supplementary Information</title>", "<p>The online version contains supplementary material available at 10.1186/s12885-024-11850-5.</p>", "<title>Keywords</title>" ]

[ "<title>Electronic supplementary material</title>", "<p>Below is the link to the electronic supplementary material.</p>", "<p>\n\n</p>", "<p>\n\n</p>" ]

[ "<title>Author contributions</title>", "<p>Y.Y.: Conceptualization, data curation, software, formal analysis, methodology, writing–original draft. C.C.A.: Conceptualization, formal analysis, methodology, writing–original draft, project administration. G.R.: Data curation, investigation, writing–review and editing. A.G.: Formal analysis, writing–review and editing. H.P.: Resources, investigation, writing–review and editing. M.K.: Investigation, writing–review and editing. A.P.: Investigation, writing–review and editing. F.K.: Methodology, project administration, writing–review and editing. P. Bo.: Project administration, writing–review and editing. P. Br.: Project administration, writing–review and editing. S.M.D.: Project administration, writing–review and editing. E.V.: Data curation, writing–review and editing. R.M.: Resources, supervision, investigation, project administration, writing–review and editing. A.E.: Conceptualization, data curation, software, formal analysis, supervision, investigation, methodology, writing–original draft, project administration.</p>", "<title>Funding</title>", "<p>The Golestan Cohort Study was supported by Tehran University of Medical Sciences (grant no: 81/15), Cancer Research UK (grant no: C20/ A5860), the Intramural Research Program of the National Cancer Institute, National Institutes of Health, and various collaborative research agreements with the International Agency for Research on Cancer.</p>", "<title>Data availability</title>", "<p>The data that support the findings in this study are available from the corresponding authors upon request.</p>", "<title>Declarations</title>", "<title>Ethics approval and consent to participate</title>", "<p id=\"Par29\">Written informed consent was obtained from all study participants at the time of enrollment. The Golestan Cohort Study was approved by the Institutional Review Boards of the Digestive Disease Research Institute of Tehran University of Medical Sciences, the International Agency for Research on Cancer, and the United States National Cancer Institute.</p>", "<title>Consent for publication</title>", "<p id=\"Par30\">Not applicable.</p>", "<title>Competing interests</title>", "<p id=\"Par31\">The authors declare no competing interests.</p>" ]

[ "<fig id=\"Fig1\"><label>Fig. 1</label><caption><p>Associations between dental status, substance use, and incident lung cancer, overall and stratified by cigarette smoking and opium use status. Subjects were stratified into binary groups of those that ever smoked cigarettes or used opium and those that never used either cigarettes or opium. DMFT, the sum of decayed, missing, or filled teeth; HR, hazard ratio; T, tertile. HRs for cigarette, opium, and nass use are from the model including DMFT. In the stratified analysis of ever users of cigarettes/opium, the reference group for cigarette pack-years included subjects that never smoked cigarettes but used opium, and vice versa for the opium use reference group. Full results, including associations with sociodemographic factors, can be found in Tables ##SUPPL##0##S1## and ##SUPPL##0##S2##. *<italic>P</italic> &lt; 0.05</p></caption></fig>" ]

[ "<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Baseline characteristics of the Golestan Cohort Study participants, overall and by DMFT group</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\"/><th align=\"left\"/><th align=\"left\"/><th align=\"left\" colspan=\"4\">DMFT group</th></tr><tr><th align=\"left\"/><th align=\"left\"/><th align=\"left\">Overall</th><th align=\"left\">Expected or fewer</th><th align=\"left\">T1</th><th align=\"left\">T2</th><th align=\"left\">T3</th></tr></thead><tbody><tr><td align=\"left\">N</td><td align=\"left\"/><td align=\"left\">49,947</td><td align=\"left\">22,257 (44.6)</td><td align=\"left\">10,082 (20.2)</td><td align=\"left\">10,906 (21.8)</td><td align=\"left\">6702 (13.4)</td></tr><tr><td align=\"left\">Age, years, mean (SD)</td><td align=\"left\"/><td align=\"left\">51.6 (8.92)</td><td align=\"left\">50.1 (8.31)</td><td align=\"left\">57.5 (10.7)</td><td align=\"left\">52.9 (6.75)</td><td align=\"left\">45.3 (3.87)</td></tr><tr><td align=\"left\">Sex, n (%)</td><td align=\"left\">Female</td><td align=\"left\">28,792 (57.6)</td><td align=\"left\">12,384 (55.6)</td><td align=\"left\">6599 (65.5)</td><td align=\"left\">6772 (62.1)</td><td align=\"left\">3037 (45.3)</td></tr><tr><td align=\"left\"/><td align=\"left\">Male</td><td align=\"left\">21,155 (42.4)</td><td align=\"left\">9873 (44.4)</td><td align=\"left\">3483 (34.5)</td><td align=\"left\">4134 (37.9)</td><td align=\"left\">3665 (54.7)</td></tr><tr><td align=\"left\">SES, quartile, n (%)</td><td align=\"left\">Q1 (low SES)</td><td align=\"left\">13,909 (27.8)</td><td align=\"left\">5655 (25.4)</td><td align=\"left\">3116 (30.9)</td><td align=\"left\">3186 (29.2)</td><td align=\"left\">1952 (29.1)</td></tr><tr><td align=\"left\"/><td align=\"left\">Q2</td><td align=\"left\">11,125 (22.3)</td><td align=\"left\">4611 (20.7)</td><td align=\"left\">2304 (22.9)</td><td align=\"left\">2535 (23.2)</td><td align=\"left\">1675 (25.0)</td></tr><tr><td align=\"left\"/><td align=\"left\">Q3</td><td align=\"left\">12,567 (25.2)</td><td align=\"left\">5465 (24.6)</td><td align=\"left\">2514 (24.9)</td><td align=\"left\">2871 (26.3)</td><td align=\"left\">1717 (25.6)</td></tr><tr><td align=\"left\"/><td align=\"left\">Q4 (high SES)</td><td align=\"left\">12,346 (24.7)</td><td align=\"left\">6526 (29.3)</td><td align=\"left\">2148 (21.3)</td><td align=\"left\">2314 (21.2)</td><td align=\"left\">1358 (20.3)</td></tr><tr><td align=\"left\">Ethnicity, n (%)</td><td align=\"left\">Turkmen</td><td align=\"left\">37,176 (74.4)</td><td align=\"left\">16,040 (72.1)</td><td align=\"left\">7525 (74.6)</td><td align=\"left\">8409 (77.1)</td><td align=\"left\">5202 (77.6)</td></tr><tr><td align=\"left\"/><td align=\"left\">Non-Turkmen</td><td align=\"left\">12,771 (25.6)</td><td align=\"left\">6217 (27.9)</td><td align=\"left\">2557 (25.4)</td><td align=\"left\">2497 (22.9)</td><td align=\"left\">1500 (22.4)</td></tr><tr><td align=\"left\">Residence, n (%)</td><td align=\"left\">Urban</td><td align=\"left\">10,006 (20.0)</td><td align=\"left\">5496 (24.7)</td><td align=\"left\">2057 (20.4)</td><td align=\"left\">1744 (16.0)</td><td align=\"left\">709 (10.6)</td></tr><tr><td align=\"left\"/><td align=\"left\">Rural</td><td align=\"left\">39,941 (80.0)</td><td align=\"left\">16,761 (75.3)</td><td align=\"left\">8025 (79.6)</td><td align=\"left\">9162 (84.0)</td><td align=\"left\">5993 (89.4)</td></tr><tr><td align=\"left\">Education, n (%)</td><td align=\"left\">None</td><td align=\"left\">35,059 (70.2)</td><td align=\"left\">14,122 (63.4)</td><td align=\"left\">8135 (80.7)</td><td align=\"left\">8665 (79.5)</td><td align=\"left\">4137 (61.7)</td></tr><tr><td align=\"left\"/><td align=\"left\">Any</td><td align=\"left\">14,888 (29.8)</td><td align=\"left\">8135 (36.6)</td><td align=\"left\">1947 (19.3)</td><td align=\"left\">2241 (20.5)</td><td align=\"left\">2565 (38.3)</td></tr><tr><td align=\"left\">Cigarette use status, n (%)</td><td align=\"left\">Never</td><td align=\"left\">41,366 (82.8)</td><td align=\"left\">18,968 (85.2)</td><td align=\"left\">8564 (84.9)</td><td align=\"left\">8971 (82.3)</td><td align=\"left\">4863 (72.6)</td></tr><tr><td align=\"left\"/><td align=\"left\">Former</td><td align=\"left\">3193 (6.39)</td><td align=\"left\">1271 (5.71)</td><td align=\"left\">651 (6.46)</td><td align=\"left\">749 (6.87)</td><td align=\"left\">522 (7.79)</td></tr><tr><td align=\"left\"/><td align=\"left\">Current</td><td align=\"left\">5388 (10.8)</td><td align=\"left\">2018 (9.07)</td><td align=\"left\">867 (8.60)</td><td align=\"left\">1186 (10.9)</td><td align=\"left\">1317 (19.7)</td></tr><tr><td align=\"left\">Cigarette smoking pack-years (former/current smokers only), mean (SD)</td><td align=\"left\"/><td align=\"left\">16.9 (18.6)</td><td align=\"left\">13.8 (16.0)</td><td align=\"left\">19.0 (22.5)</td><td align=\"left\">21.0 (21.4)</td><td align=\"left\">16.5 (14.7)</td></tr><tr><td align=\"left\">Opium use, n (%)</td><td align=\"left\">Never</td><td align=\"left\">41,501 (83.1)</td><td align=\"left\">19,267 (86.6)</td><td align=\"left\">8333 (82.7)</td><td align=\"left\">8838 (81.0)</td><td align=\"left\">5063 (75.5)</td></tr><tr><td align=\"left\"/><td align=\"left\">Ever</td><td align=\"left\">8446 (16.9)</td><td align=\"left\">2990 (13.4)</td><td align=\"left\">1749 (17.3)</td><td align=\"left\">2068 (19.0)</td><td align=\"left\">1639 (24.5)</td></tr><tr><td align=\"left\">Opium use years (ever users only), mean (SD)</td><td align=\"left\"/><td align=\"left\">12.2 (10.7)</td><td align=\"left\">10.9 (9.85)</td><td align=\"left\">13.7 (12.5)</td><td align=\"left\">13.5 (11.4)</td><td align=\"left\">11.3 (8.71)</td></tr><tr><td align=\"left\">Cigarette or opium use, n (%)</td><td align=\"left\">Never used either</td><td align=\"left\">37,358 (74.8)</td><td align=\"left\">17,439 (78.4)</td><td align=\"left\">7627 (75.6)</td><td align=\"left\">7992 (73.3)</td><td align=\"left\">4300 (64.2)</td></tr><tr><td align=\"left\"/><td align=\"left\">Ever used either</td><td align=\"left\">12,589 (25.2)</td><td align=\"left\">4818 (21.6)</td><td align=\"left\">2455 (24.4)</td><td align=\"left\">2914 (26.7)</td><td align=\"left\">2402 (35.8)</td></tr><tr><td align=\"left\">Nass use, n (%)</td><td align=\"left\">Never</td><td align=\"left\">46,077 (92.3)</td><td align=\"left\">20,757 (93.3)</td><td align=\"left\">9277 (92.0)</td><td align=\"left\">10,000 (91.7)</td><td align=\"left\">6043 (90.2)</td></tr><tr><td align=\"left\"/><td align=\"left\">Ever</td><td align=\"left\">3870 (7.75)</td><td align=\"left\">1500 (6.74)</td><td align=\"left\">805 (7.98)</td><td align=\"left\">906 (8.31)</td><td align=\"left\">659 (9.83)</td></tr><tr><td align=\"left\">DMFT score, mean (SD)</td><td align=\"left\"/><td align=\"left\">23.4 (8.73)</td><td align=\"left\">15.3 (6.20)</td><td align=\"left\">28.2 (3.95)</td><td align=\"left\">30.7 (2.29)</td><td align=\"left\">31.6 (1.08)</td></tr><tr><td align=\"left\">Number of teeth missing, mean (SD)</td><td align=\"left\"/><td align=\"left\">18.3 (9.55)</td><td align=\"left\">11.4 (6.11)</td><td align=\"left\">23.3 (7.67)</td><td align=\"left\">24.8 (7.86)</td><td align=\"left\">22.9 (9.02)</td></tr><tr><td align=\"left\">Tooth loss group, n (%)</td><td align=\"left\">Expected or fewer</td><td align=\"left\">26,303 (52.7)</td><td align=\"left\">19,466 (87.5)</td><td align=\"left\">2783 (27.6)</td><td align=\"left\">2463 (22.6)</td><td align=\"left\">1591 (23.7)</td></tr><tr><td align=\"left\"/><td align=\"left\">T1</td><td align=\"left\">8478 (17.0)</td><td align=\"left\">2705 (12.2)</td><td align=\"left\">3232 (32.1)</td><td align=\"left\">1667 (15.3)</td><td align=\"left\">874 (13.0)</td></tr><tr><td align=\"left\"/><td align=\"left\">T2</td><td align=\"left\">7337 (14.7)</td><td align=\"left\">86 (0.39)</td><td align=\"left\">3978 (39.5)</td><td align=\"left\">2509 (23.0)</td><td align=\"left\">764 (11.4)</td></tr><tr><td align=\"left\"/><td align=\"left\">T3</td><td align=\"left\">7829 (15.7)</td><td align=\"left\">0 (0)</td><td align=\"left\">89 (0.88)</td><td align=\"left\">4267 (39.1)</td><td align=\"left\">3473 (51.8)</td></tr><tr><td align=\"left\">Toothbrushing frequency, n (%)</td><td align=\"left\">Never</td><td align=\"left\">27,815 (55.7)</td><td align=\"left\">10,062 (45.2)</td><td align=\"left\">6742 (66.9)</td><td align=\"left\">7024 (64.4)</td><td align=\"left\">3987 (59.5)</td></tr><tr><td align=\"left\"/><td align=\"left\">Non-daily</td><td align=\"left\">8341 (16.7)</td><td align=\"left\">4756 (21.4)</td><td align=\"left\">1296 (12.9)</td><td align=\"left\">1320 (12.1)</td><td align=\"left\">969 (14.5)</td></tr><tr><td align=\"left\"/><td align=\"left\">Daily</td><td align=\"left\">13,791 (27.6)</td><td align=\"left\">7439 (33.4)</td><td align=\"left\">2044 (20.3)</td><td align=\"left\">2562 (23.5)</td><td align=\"left\">1746 (26.1)</td></tr></tbody></table></table-wrap>" ]

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[ "<supplementary-material content-type=\"local-data\" id=\"MOESM1\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM2\"></supplementary-material>" ]

[ "<table-wrap-foot><p>DMFT, the sum of decayed, missing, or filled teeth; Q, quartile; SD, standard deviation; SES, socioeconomic status; T, tertile</p></table-wrap-foot>", "<fn-group><fn><p><bold>Publisher’s Note</bold></p><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn></fn-group>" ]

[ "<graphic xlink:href=\"12885_2024_11850_Fig1_HTML\" id=\"d32e1086\"/>" ]

[ "<media xlink:href=\"12885_2024_11850_MOESM1_ESM.xlsx\"><caption><p><bold>Supplementary Material 1:</bold> Supplementary tables</p></caption></media>", "<media xlink:href=\"12885_2024_11850_MOESM2_ESM.docx\"><caption><p><bold>Supplementary Material 2:</bold> Supplementary figures</p></caption></media>" ]

{ "acronym": [], "definition": [] }

[ "<title>Introduction</title>", "<p id=\"Par5\">Vitamin D deficiency is one of the most ignored and undiagnosed conditions in the general population [##REF##21872807##1##, ##REF##19543765##2##]. Studies conducted in the past among various communities and locations of Indian population exhibited prevalence of Vitamin D deficiencies ranging from 50 to 94%, which was indicative of the magnitude of the problem in the country. These deficiencies are associated with individuals with detected systemic illness therefore [##REF##30090772##3##].The major causes for vitamin D deficiency in Indian population can be attributed to a low vitamin D dietary intake, increased indoor lifestyle, decreased exposure to sunlight, and increased air pollution which in turn hampers synthesis of Vitamin D by the skin after absorption of UV rays [##REF##17413106##4##, ##REF##20440690##5##]. Past research has shown that it is imperative to address Vitamin D deficiency and correlate it to systemic health issues since Vitamin D deficiency causes autoimmune diseases, infectious diseases, cancer, skeletal manifestation, and depression [##REF##18400738##6##]. Research has also shown that Vitamin D deficiency leads to problems in oral health viz. poor tooth formation, development and calcification in younger adults, poor periodontal health, and malignant oral lesions [##REF##32438644##7##].</p>", "<p id=\"Par6\">Vitamin D is also plays a vital role in innate immune responses by promoting immune cell differentiation and cell maturation. Active Vitamin D is absorbed onto the Vitamin D receptors (VDR) on the immune cells, which in turn promote gene expression and regulation of protective peptides [##REF##24529992##8##]. This non-classical action of the VDR and CYP27B1 expressed on various cells and tissues are not associated with calcium haemostasis but are instead dependent on pathogen detection and cytokine production via interleukin production. The circulating vitamin D 1,25-dihydroxy vitamin forms complexes with the Retinoid X receptor vitamin D binding protein complex (RXR + VDBP) to attach to the VDRs on various target cells and tissues. Finally, this complex attaches to the vitamin D promoter regions on the vitamin D receptor genes in order to stimulate the production of the antimicrobial peptide LL-37 [##REF##21845364##9##, ##REF##15985530##10##]. Vitamin D regulates the innate immune response via production of these peptides in various concentrations in body fluids [##REF##16497887##11##] (illustrated in Fig. ##FIG##0##1##).</p>", "<p id=\"Par7\">The cytokines released during this immune response contains pro-inflammatory cytokines to stimulate the cells, which in turn undergo an immuno-modulatory response. Vitamin D also affects to regulation of T-helper cells by contributing to anti-inflammatory effects. Interleukin IL-17A is produced by Th17A, which in turn regulates NF-KB and other nitrogen activated protein kinases to regulate the IL-6 expression. IL-6 is an important interleukin to promote host defence and immune response, especially in the oral cavity where an absence of the immune response leads to destruction of tooth structure and causing caries [##REF##26188623##12##]. Keeping this in mind, the present study evaluated the association of salivary Vitamin D levels and the levels of LL-37, IL-6, and IL-17A in the severity of dental caries.</p>" ]

[ "<title>Materials and methods</title>", "<p id=\"Par8\">For conducting the present study, necessary approvals were obtained from the Central ethics committee, Nitte (deemed to be) University. Approval obtained dated NU/CEC/2020/0339 and NU/CEC/2022/291, prior initiation of the study, also renewal done.</p>", "<p id=\"Par9\">Informed consent was obtained from each individual patient, after they were provided with the patient information sheet.The experimental protocols were approved by the scientific committee prior to the commencement of the study. A total of 377 patients who visited the outpatient department of conservative dentistry and endodontics at the A.B. Shetty Memorial Institute of Dental Services, Deralakatte, Mangalore, were included in the study. Out of these, 272 patients were designated as Caries active and 105 were designated as Caries free.</p>", "<p id=\"Par10\">The Sample size (N) was calculated estimating the difference between two means and by using the formula presented below.</p>", "<p id=\"Par11\">Where, P1 was the proportion of the 1st group (39%), P2 was the proportion of the 2nd group (24%), α was the significance level (5%), and β was the power of the test (20%).</p>", "<p id=\"Par12\">All the laboratory work was conducted at the Central Research Laboratory, K.S. Hegde Medical Academy, Deralakatte, Mangalore. The time period of the study was from August 2018 to January 2022 (4 years). During the study, all patients were evaluated and informed consent was obtained from them for the purpose of the study using an information sheet. Designation as Caries Free and Caries Active was done based on certain inclusion and exclusion criteria. The inclusion criteria pertained to individuals in the age group of 18–40 years who were not exhibiting symptoms of any systemic and/or local illnesses that could potentially hamper salivary flow. Those individuals who were following a restricted diet, exhibiting symptoms of generalized gingivitis or periodontitis, undergoing long-term medication, having poor oral hygiene habits, who were chronic smokers and/or alcoholics, and individuals consuming specific nutritional supplements were also excluded from the study.</p>", "<p id=\"Par13\">For evaluating the presence of caries, patients were seated in a dental chair and under ideal illumination and evaluated using a mouth mirror and straight probe. In order to create a DMFT index (Decayed, Missing, and Filled Teeth), the WHO Oral health survey format Annexure 1 was used [##UREF##0##13##]. Individuals were then divided into two groups based on prevalence of caries as well as their DMFT score. The Caries Free group had a DMFT score of 0 while the Caries Active group had DMFT scores ranging from 1–10. The individuals in the Caries Active Group were further subdivided into Decay group 1 (1–3 caries), Decay Group 2 (4–10 caries), and Decay group 3 (&gt; 10 caries).</p>", "<p id=\"Par14\">The general information of the individuals like age, sex, dietary habits like frequency of food intake, type of food habits like non-vegetarian/vegetarian diet, and their brushing habits were also obtained and noted in addition to their medical history.</p>", "<p id=\"Par15\">Following the generation of the DMFT index, a PUFA Index (Pulpal involvement, Ulceration, Fistula, and Abcess) [##REF##20002630##14##] was recorded to delineate the oral conditions in the individuals who were Caries Active. Based on visible root pulp, ulceration of oral mucosa from root fragments, appearance of a fistula or abscess, a score was assigned and recorded.</p>", "<p id=\"Par16\">Pulpal involvement (P/p) was recorded when individuals were noted to have a visible opening of the pulp chamber in their teeth due to caries, thereby leaving only the roots and root fragments. Ulceration (U/u) was recorded in individuals who exhibited significant sharp object trauma from either a broken/dislocated tooth or root fragments as a result of caries. Fistula (F/f) was recorded in those individuals where the pulpal involvement was accompanied by pus releasing sinus tract. Finally, Abscess (A/a) was recorded in those individuals who exhibited a pus contained swelling as a result of pulpal involvement.</p>", "<p id=\"Par17\">In order to collect saliva from the individuals, the Navazesh protocol was used [##REF##8215087##15##]. Individuals were informed to abstain from eating or drinking, brushing their teeth, using mouthwash, or smoking two hours prior to salivary sample collection. Samples were collected between 10 and 11 a.m. In order to maintain a stress free atmosphere and not to hinder salivary flow, the individuals were seated in regular chairs. A Tarson's saliva collection tube was used to collect 5 ml of saliva that had gathered on the floor of the mouth of the individuals. The collected saliva was then centrifuged and the supernatant was stored at -20 °C till further analysis.</p>", "<title>Analysis of salivary vitamin D levels</title>", "<p id=\"Par18\">The analysis of salivary Vitamin D levels was carried out using the 25OH Vitamin D Total ELISA Kit Microtiter Plates (Epitope Diagnostics). 20µL of sample, calibrators, and controls were added to the wells of the plate along with 100µL of the Vitamin D Assay buffer. The plates were covered with aluminium foil and static incubated at room temperature for 30 min. Following this, 25µL of Biotinylated Vitamin D analog was added to each well and static incubated at room temperature for 1 h. Then, each well was washed 5 times with 350µL of the wash solution. This was followed by addition of 100µL Streptavidin Horseradish Peroxidase (HRP) and static incubated at room temperature for 30 min to form the Vitamin D antibody – Vitamin D, Biotin D and HRP conjugated streptavidin complex. The unbound complexes were removed from the wells by washing them five times with 350µL buffer solution and 100µL of tetramethylbenzidine (TMB) was added. The plates were static incubated one final time for 20 min after which 100µL of the stop solution was added. Finally, the reaction mixture was measured spectrophotometrically at 450 nm absorbance with a maximum absorbance time of 10 min.</p>", "<title>Analysis of salivary Cathelicidin levels</title>", "<p id=\"Par19\">For the analysis of the salivary cathelicidins levels, a pre-coated micro-ELISA plate containing the human LL-37-specific antibody (Sincere Biotech) was used. Controls and samples were loaded into the wells of the ELISA plate along with the specific antibody and incubated at room temperature. Following this, the biotinylated detection antibody (specific to human LL-37) and avidin conjugated HRP were added to the wells and incubated once again in static condition. This was followed by a washing step to remove the unconjugated complexes, following which a substrate was added to each well. Those wells in which the complexation occurred turned blue. A final stop solution was added to halt the reaction and the optical density was measured spectrophotometrically at 450 ± 2 nm.</p>", "<title>Analysis of salivary IL-6, IL-17A levels</title>", "<p id=\"Par20\">The analysis of IL-17A and IL-6 were done using commercially available ELISA kits (Booster Biologicals). For the IL-17A, the principle used was the Solid Phase Sandwich ELISA. The samples and standards were added to the wells of the ELISA microtiter plates, facilitating the binding of IL-17A to the immobilized antibodies. Following a washing step, HRP conjugated anti-IL-17A antibody solution was added to the wells, creating an antibody-antigen–antibody sandwich in the process. TMB substrate solution was added to the ‘sandwich’ and incubated followed by stopping the reaction using a stop solution. Finally, the absorbance was measured spectrophotometrically at 620 nm. by booster biological technology.</p>", "<p id=\"Par21\">Similar to IL-17A, a sandwich ELISA approach was also used to measure the IL-6 levels. The microtiter ELISA plates contained immobilized rat monoclonal antibodies, to which standards and samples were added to facilitate the binding of the IL-6. Following, this, a anti-IL-6 antibody was added to create the antibody-antigen–antibody sandwich. After a period of incubation, HRP conjugated streptavidin was added to the wells and incubated. Following a wash step to remove unconjugated elements, TMB was added to the wells followed by a stop solution. The final absorbance was measured spectrophotmetrically at 450 nm and absorbance of samples were compared to the standards.</p>", "<title>Statistical analysis</title>", "<p id=\"Par22\">Qualitative statistical analysis was performed on the collected data pertaining to frequency, percentage, mean, and standard deviation. A chi square test was performed for comparing the salivary parameters between Caries Active and Caries Free groups. Analysis of Variance (ANOVA) and t-test were also performed for the two groups. The Receiver Operating Characteristic analysis was performed in order to obtain the optimum cut off levels of sensitivity and specificity for Salivary Vitamin D, LL-37, IL-17A and IL-6. SPSS (version 23; IBM SPSS Corp, Armonk, NY, USA) software was used to perform the statistical comparisons and all the statistical analyses for the <italic>P</italic> value were observed to be two-sided. The significance level was set to <italic>P</italic> ≤ 0.05 in order to eliminate overfitting of data.</p>" ]

[ "<title>Results</title>", "<title>Demographic characteristics</title>", "<p id=\"Par23\">Of the 272 Caries Active and 105 Caries Free individuals, 239 were females and 138 were males (Tables ##TAB##0##1## and ##TAB##1##2##). Data highlighted in Tables ##TAB##9##10## and ##TAB##10##11## exhibits the comparison between the demographic data and decay groups. Based on the demographics, a significant population of individuals were from urban areas. When decay groups were compared with the demographic data like age groups, sex, location and gender, individuals from urban population showed significant correlation with the decay group 1(i.e., 1–3 caries) with <italic>p</italic> value of 0.011. From the data in Table ##TAB##1##2##, it can be observed that individuals in the age group 18–25 years were associated to Decay group 1 (1–3 caries). It can also be observed that the 26–35 years age group were closely associated with Decay group 2 (4–10 caries) and Decay group 3 (&gt; 10 caries). For the individuals hailing from urban areas, the PUFA score was observed to be 0 (<italic>p</italic> = 0.0) (data highlighted in Table ##TAB##2##3##).\n</p>", "<title>Evaluation of salivary antimicrobial peptide LL-37,Vitamin D, IL6, IL-17A levels in dental caries</title>", "<p id=\"Par24\">Among the individuals classified in the caries active group, the mean salivary vitamin D level was observed to be 20.85 pg/ml in comparison to the significantly higher (<italic>p</italic> &lt; 0.001) 28.56 pg/ml for the individuals in the caries free group (Table ##TAB##3##4##). It was also observed that the mean salivary Vitamin D decreased with increasing severity of caries in the individuals. In the different subgroups of the Caries Active group, mean salivary vitamin D levels of 16.31 pg/ml was observed in decay group 2 and 3, whereas decay group 1 had a mean salivary vitamin D level of 28.77 pg/ml, which was significantly higher (<italic>p</italic> = 0.00). When the PUFA index scores were correlated to the salivary vitamin D levels, it was observed that the salivary vitamin D was significantly lower (13.46 pg/ml, <italic>p</italic> = 0.026) in individuals with PUFA score of 2–5 when compared to individuals with a PUFA score of 1 (21.13 pg/ml) (data highlighted in Table ##TAB##4##5##).\n</p>", "<p id=\"Par25\">The logistic regression performed to establish the odds ratio depicted 0.939 effect of salivary vitamin D on caries active group. A 1 unit decrease in salivary vitamin D levels meant that an individual had a 1.064 chance of being classified as caries active (Table ##TAB##5##6##) The ROC analysis which was performed since the data was significant in the Univariate analysis indicated that the optimal cut-off value for the salivary Vitamin-D was 28.33 pg/ml with a sensitivity of 71% and a specificity of 57%, with AUC of 0.694.\n</p>", "<p id=\"Par26\">The LL-37 assay results exhibited a 7.07 ng/ µl of salivary LL-37 in the caries free group individuals, in comparison to 7.05 ng/ µl for the caries active individuals (data not statistically significant, highlighted in Table ##TAB##6##7##). It was also observed that the salivary LL-37 levels did not significantly vary with severity of caries in decay groups and was not significantly associated with the PUFA scores (Table ##TAB##7##8##).\n</p>", "<p id=\"Par27\">The logistic regression was performed and the odds ratio depicted 1.309 effect of salivary LL-37 on caries active group. ROC analysis was performed and the optimal cut off for LL-37 is 6.81 ng/ µl with low sensitivity and specificity and area under the curve is 0.506. However, the results were not statistically significant (Table ##TAB##8##9##).\n</p>", "<p id=\"Par28\">The salivary levels of IL-17A and IL-6 among the individuals in caries active and caries free groups were observed to be 155.01 ng/ml and 174.20 ng/ml respectively. However, the data was deemed to be statistically insignificant upon further analyses. It was also observed that the salivary IL-17A levels did not vary with the severity of caries in individuals nor did the PUFA scores significantly vary (Tables ##TAB##9##10## and ##TAB##10##11##). Logistic regression was performed, and odds ratio showed salivary IL-17A 0.999 effect on caries active group. The ROC analysis that was performed exhibited an optimal cut-off of 189.9 ng/ml of IL-17a, with low sensitivity and specificity and a curve area of 0.556. The data was not statistically significant (data highlighted in Table ##TAB##12##13##). The IL-6 levels in the individuals of the caries active and caries free groups were observed to be 31.15 ng/ml and 28.33 ng/ml respectively with the data not considered to be statistically significant. It was also observed that the salivary IL-6 levels did not vary with the severity of caries or the associated PUFA scores (data highlighted in Tables ##TAB##9##10## and ##TAB##11##12##). Logistic regression was performed, and odds ratio showed salivary IL-6 1.006 effect on caries active group. The ROC analysis that was performed exhibited an optimal cut-off of 17.60 ng/ml of IL-6 with low sensitivity and specificity, and a curve area of 0.521. However, the data was not statistically significant (highlighted in Table ##TAB##12##13##).\n</p>" ]

[ "<title>Discussion</title>", "<p id=\"Par29\">Past research has shown that genetic factors play a vital role in risk of dental caries, which in turn is due to the multifaceted nature of caries itself [##REF##9808140##16##]. Vitamin D has been shown to control calcium haemostasis which in turn significantly influences immune responses and anti-inflammatory activity [##REF##24102630##17##]. Studies have also noted that bone phenotype, hormonal balance, food, and sun exposure may all play a vital role in the variation of vitamin D receptor gene polymorphism that is observed among different races and age groups [##REF##24510435##18##–##REF##10913060##20##]. In the current study, the practices of individuals in both caries free and caries active groups were similar e.g. brushing teeth once a day, no significant food intake in between meals, and low intake of sugary or sticky food items. Therefore, these factors were not considered for the study. Research has shown that environmental and underlying genetic factors are associated with various other factors that cause development of dental caries [##REF##13196991##21##]. It has also been shown that susceptibility to caries may differ from individual to individual, even though an individual may be considered as high risk of developing caries [##REF##15315818##22##].</p>", "<p id=\"Par30\">In the current study, salivary vitamin D levels were observed to be significantly higher in the caries-free group as compared to the caries active group. This can be attributed to the production of protective peptides (LL-37/cathelicidins) following their activation via the TLR2-vitamin D LL-37 mechanism, where the production of these peptides occur as a result of the binding of 1,25(OH)2 D to the Vitamin D receptor. The LL-37 has been noted to possess the potential for increasing the antimicrobial capacity of anti-inflammatory cells like neutrophils [##REF##16497887##23##]. In the current study, the elevated levels of salivary vitamin D in the caries free group exhibit the effectiveness of vitamin D to play an antibacterial role by regulating the production of these naturally occurring peptides. Vitamin D has also been noted to upregulate numerous proteins such enamelin, dentin sialoproteins, amelogenins, and dentin phosphoproteins, while also stabilizing the demineralization and disintegration of tooth surface while preserving the appropriate surface proteins [##REF##1362507##24##]. In the current study, the salivary vitamin D levels among participants in both the groups could be linked to normal to average sun exposure and to a variety of dietary sources. A past study by Gyll et al. evaluated the association of dental caries and salivary vitamin D levels post vitamin D supplementation, and noted high vitamin D levels in individuals without caries [##REF##29338758##25##]. A similar study conducted by Chhonkar et al. exhibited that vitamin D was an important factor in preventing dental caries. Studies have also shown that absence of caries can be attributed to the role of vitamin D in the production of LL-37 peptides via the TLR2-Vitamin D pathway [##REF##22336091##26##, ##REF##20511058##27##], which is in accordance with the results of the present study where we evaluated the protective role of vitamin D levels in dental caries progression and prevalence.</p>", "<p id=\"Par31\">Antimicrobial peptide LL-37 was evaluated in the current study for both caries active and caries free individuals, with the results exhibiting that the levels of LL-37 were higher in individuals without caries as compared to those having caries (not statistically significant). LL-37 has been shown to reduce biofilm formation on the tooth surface, reduce thickness of existing biofilms, and decreasing the adherence of microbes onto the tooth surface, thereby decreasing the production of inflammatory markers [##REF##34740262##28##]. Similarly, another study evaluated the LL-37 levels in children wherein it was noted that lower levels were associated with higher caries activity, albeit statistically insignificant. The same study also noted that LL-37 had the potential to be a prognostic marker against caries in children, adolescents, and adults [##REF##35625823##29##, ##REF##19805540##30##]. LL-37 has been observed in the past in the carpet, toroidal, and barrel stave models to have potency against Streptococcus mutans by preventing growth and colonization [##REF##34740262##28##]. Another past study showed that the direct effect of the LL-37 peptide was to cause enzyme mediated destruction of bacteria while the indirect effect was to regulate inflammatory markers [##REF##35625823##29##]. In another study, the production and biochemical levels of cathelicidins were noted to be directly affected by the levels of inflammation and vitamin D [##REF##35098213##31##].</p>", "<p id=\"Par32\">The current study also evaluated the IL-6 levels among the individuals as noted an increase in the caries active group in comparison to the caries free group (data statistically insignificant). This could be as a result of the pro-inflammatory function of the IL-6 interleukin. Studies have shown that IL-6 is a key factor in the inflammatory response since it activates neutrophil proliferation at the inflammatory sites. The study also noted that IL-6 plays a vital role in the pathology of diseases due to its pleitropy, role in immunosenescence, and caries formation [##UREF##1##32##] therefore the IL-6 may be a potential indicator for inflammation in oral cavity, however needs to be validated with more supporting studies. Apart from IL-6, IL-17A levels were also analyzed in the individuals participating in the current study and it was observed that IL-17A levels were higher in those individuals without caries (data statistically insignificant). This could have been due to the levels of LL-37 maintaining inflammatory balance to promote repair in individuals with caries. Past studies have shown that immune processes, both innate and adaptive, affect dental biofilm formation, which in turn affect caries formation. However, the studies were limited to evaluating a single nucleotide polymorphism in the vitamin D receptor and the polymorphisms in the CAMP gene, another factor affecting LL-37 levels in saliva, were excluded from the scope [##UREF##2##33##–##UREF##3##35##].</p>" ]

[ "<title>Conclusions</title>", "<p id=\"Par33\">The present study focused on evaluating the levels of Vitamin D, IL-17A, IL-6, and LL-37 in saliva of individuals with and without caries. Salivary vitamin D was higher in caries free individuals as compared to those with caries. This could be because vitamin D plays an important role in preventing caries by activating enzymes which in turn convert 25, hydroxyl vitamin D to 1,25-dihydroxy vitamin D. This in turn binds to vitamin D binding protein to form a complex to activate the LL-37 peptides via binding to vitamin D receptors. The current study noted that LL-37 was higher in caries free individuals but not statistically significant in comparison to individuals with caries, possibly due to LL-37’s role in preventing and neutralizing biofilms and bacterial colonization to hinder caries formation. Interleukins IL-6 and IL-17A were also higher in caries free individuals but not statistically significant from those with caries. This could be attributed to the pro-inflammatory activity of IL-6 and the regulating role of LL-37 in IL-17A production to promote repair respectively. Therefore it can be said that all three biochemical markers could be used as a prognostic marker to predict incidence of caries in individuals.</p>" ]

[ "<title>Introduction</title>", "<p id=\"Par1\">Vitamin D performs various functions as a hormone by promoting calcium absorption but plays a major role in innate immunity,cell differentiation, cell maturation through its genomic effects via vitamin D receptor. The immune response also plays a major role in tooth surface and supporting structure destruction and playing a major factor in high caries formation. The inflammatory cytokines are released has proinflammatory cytokines and stimulate cells in disease process. Therefore, in the present study we have evaluated the association of salivary vitamin D, LL-37, interleukins 6 and 17A in various levels of severity of dental caries.</p>", "<title>Method</title>", "<p id=\"Par2\">Ethical approval was obtained (NU/CEC/2020/0339), 377 individuals reporting to department of conservative dentistry and endodontics, AB Shetty memorial institute of dental sciences were included based on inclusion criteria. The individuals were further divided into caries free(<italic>N</italic> = 105) and caries active(<italic>N</italic> = 272) based on their caries prevalence. The salivary were collected and evaluated for vitamin D, LL-37,IL-17A and IL-6.Results were statistically analysed with SPSS vs 22 (IBM Corp, USA). Normally distributed data were expressed as mean ± SD. Skewed data were expressed as median and interquartile range. To compare (mean) outcome measures between the two groups unpaired independent t-test was applied and for values in median IQR, Mann Whitney U test was used. All statistical analysis for <italic>P</italic> value were two-sided and significance was set to <italic>P</italic> ≤ 0.05.</p>", "<title>Results</title>", "<p id=\"Par3\">The study showed that, the salivary vitamin D statistically decreased with increasing severity of caries which showed that vitamin D plays an important role in prevention of caries. Antimicrobial peptide LL-37 was higher in caries free group but was not statistically significant, salivary IL-6 level was higher in caries active group but intergroup comparison did not show significant difference. Salivary IL-17A did not show statistically significant between caries active and caries free group.</p>", "<title>Conclusion</title>", "<p id=\"Par4\">The salivary levels of vitamin D may play a vital role in prevalence of dental caries and its severity which can be a underlying cause in presence of other etiological factors.</p>", "<title>Keywords</title>" ]

[]

[ "<title>Acknowledgements</title>", "<p>Central research laboratory for immense support through conducting the analysis.</p>", "<title>Authors’ contributions</title>", "<p>N wrote and planned the study, Hegde MN and Kumari SN reviewed the manuscript and help conduct the study.</p>", "<title>Funding</title>", "<p>The following study was funded by Vision group on Science and technology (VGST/RGS-F/GRD-895/2019–20/2020–21/198).</p>", "<title>Availability of data and materials</title>", "<p>The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.</p>", "<title>Declarations</title>", "<title>Ethics approval and consent to participate</title>", "<p id=\"Par34\">Approvals were obtained from the Central ethics committee, Nitte (deemed to be) University. Approval obtained dated NU/CEC/2020/0339 and NU/CEC/2022/291, prior initiation of the study, also renewal done.</p>", "<p id=\"Par35\">Informed consent was obtained from each individual patient, after they were provided with the patient information sheet.</p>", "<title>Consent for publication</title>", "<p id=\"Par36\">Not applicable.</p>", "<title>Competing interests</title>", "<p id=\"Par37\">The authors declare no competing interests.</p>" ]

[ "<fig id=\"Fig1\"><label>Fig. 1</label><caption><p>Immunomodulatory role of vitamin D through production of antimicrobial peptides. Presence pathogenic microorganisms stimulate the activation of TLR1/2 and stimulate CYP27B1, which in turn converts the inactive vitamin D to active vitamin D; leading to complexation with RXR and VDR. This whole complex adheres to the Vitamin D response element on the CAMP gene to produce Cathelicidins/LL-37</p></caption></fig>" ]

[ "<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Depicts the association of gender, location, diet and age groups with  caries active group</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" rowspan=\"2\" colspan=\"2\">Characteristics</th><th align=\"left\" colspan=\"3\">Caries active group</th></tr><tr><th align=\"left\"><bold>Group 1</bold></th><th align=\"left\"><bold>Group 2 and 3</bold></th><th align=\"left\"><bold><italic>P</italic></bold><bold> Value</bold></th></tr></thead><tbody><tr><td align=\"left\" rowspan=\"2\">Gender</td><td align=\"left\">Female</td><td align=\"left\">34.3%</td><td align=\"left\">65.7%</td><td align=\"left\" rowspan=\"2\">0.346</td></tr><tr><td align=\"left\">Male</td><td align=\"left\">40.0%</td><td align=\"left\">60.0%</td></tr><tr><td align=\"left\" rowspan=\"2\">Diet</td><td align=\"left\">Non veg</td><td align=\"left\">35.0%</td><td align=\"left\">65.0%</td><td align=\"left\" rowspan=\"2\">0.130</td></tr><tr><td align=\"left\">vegetarian</td><td align=\"left\">50.0%</td><td align=\"left\">50.0%</td></tr><tr><td align=\"left\" rowspan=\"2\">Location</td><td align=\"left\">Other</td><td align=\"left\">26.3%</td><td align=\"left\">73.7%</td><td align=\"left\" rowspan=\"2\">.011</td></tr><tr><td align=\"left\">Urban</td><td align=\"left\">41.8%</td><td align=\"left\">58.2%</td></tr><tr><td align=\"left\" rowspan=\"3\">Age group</td><td align=\"left\">18- 25Y</td><td align=\"left\">42.6%</td><td align=\"left\">57.4%</td><td align=\"left\" rowspan=\"3\">.003</td></tr><tr><td align=\"left\">26-35Y</td><td align=\"left\">17.0%</td><td align=\"left\">83.0%</td></tr><tr><td align=\"left\">36- 40 Y</td><td align=\"left\">28.6%</td><td align=\"left\">71.4%</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab2\"><label>Table 2</label><caption><p>Depicts the association of demographic data with caries active group and caries free group</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Characteristics</th><th align=\"left\">Caries active</th><th align=\"left\">Caries free</th><th align=\"left\"><italic>P</italic> Value</th></tr></thead><tbody><tr><td align=\"left\" colspan=\"3\">Gender</td><td align=\"left\" rowspan=\"3\">0.917</td></tr><tr><td align=\"left\"> Male</td><td align=\"left\">36.8%</td><td align=\"left\">36.2%</td></tr><tr><td align=\"left\"> Female</td><td align=\"left\">63.2%</td><td align=\"left\">63.8%</td></tr><tr><td align=\"left\" colspan=\"3\">Location</td><td align=\"left\" rowspan=\"3\">.000</td></tr><tr><td align=\"left\"> Urban</td><td align=\"left\">65.1%</td><td align=\"left\">87.6%</td></tr><tr><td align=\"left\"> Other (Semiurban, rural)</td><td align=\"left\">34.9%</td><td align=\"left\">12.4%</td></tr><tr><td align=\"left\" colspan=\"3\">Diet</td><td align=\"left\" rowspan=\"3\">0.186</td></tr><tr><td align=\"left\"> Vegetarian</td><td align=\"left\">9.6%</td><td align=\"left\">14.3%</td></tr><tr><td align=\"left\"> Non-vegetarian</td><td align=\"left\">90.4%</td><td align=\"left\">85.7%</td></tr><tr><td align=\"left\" colspan=\"3\">Age group</td><td align=\"left\" rowspan=\"4\">0.835</td></tr><tr><td align=\"left\"> 18-25Y</td><td align=\"left\">69.9%</td><td align=\"left\">66.7%</td></tr><tr><td align=\"left\"> 26-35Y</td><td align=\"left\">17.3%</td><td align=\"left\">19.0%</td></tr><tr><td align=\"left\"> 36-40Y</td><td align=\"left\">12.9%</td><td align=\"left\">14.3%</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab3\"><label>Table 3</label><caption><p>Depicts association of demographic data with PUFA scores among caries active individuals</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" rowspan=\"3\" colspan=\"2\">Characteristics</th><th align=\"left\" colspan=\"3\">PUFA INDEX</th><th align=\"left\" rowspan=\"3\"><italic>P</italic> value</th></tr><tr><th align=\"left\" colspan=\"2\"><bold>Caries active (pufa score = 1)</bold></th><th align=\"left\"><bold>Caries active (pufa score = 2–5)</bold></th></tr><tr><th align=\"left\" colspan=\"2\"><bold>ROW N %</bold></th><th align=\"left\"><bold>ROW N %</bold></th></tr></thead><tbody><tr><td align=\"left\" rowspan=\"2\">Gender</td><td align=\"left\">Female</td><td align=\"left\" colspan=\"2\">7.0%</td><td align=\"left\">7.0%</td><td align=\"left\" rowspan=\"2\">0.006</td></tr><tr><td align=\"left\">Male</td><td align=\"left\" colspan=\"2\">19.0%</td><td align=\"left\">3.0%</td></tr><tr><td align=\"left\" rowspan=\"2\">Diet</td><td align=\"left\">Non veg</td><td align=\"left\" colspan=\"2\">11.8%</td><td align=\"left\">6.1%</td><td align=\"left\" rowspan=\"2\">0.326</td></tr><tr><td align=\"left\">Vegetarian</td><td align=\"left\" colspan=\"2\">7.7%</td><td align=\"left\">0.0%</td></tr><tr><td align=\"left\" rowspan=\"2\">Location</td><td align=\"left\">Other</td><td align=\"left\" colspan=\"2\">22.1%</td><td align=\"left\">7.4%</td><td align=\"left\" rowspan=\"2\">.001</td></tr><tr><td align=\"left\">Urban</td><td align=\"left\" colspan=\"2\">5.7%</td><td align=\"left\">4.5%</td></tr><tr><td align=\"left\" rowspan=\"3\">Age group</td><td align=\"left\">18- 25y</td><td align=\"left\" colspan=\"2\">9.5%</td><td align=\"left\">3.2%</td><td align=\"left\" rowspan=\"3\">.028</td></tr><tr><td align=\"left\">26-35y</td><td align=\"left\" colspan=\"2\">19.1%</td><td align=\"left\">10.6%</td></tr><tr><td align=\"left\">36- 40 y</td><td align=\"left\" colspan=\"2\">11.4%</td><td align=\"left\">11.4%</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab4\"><label>Table 4</label><caption><p>Association of salivary vitamin D with study groups</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" rowspan=\"2\"/><th align=\"left\" rowspan=\"2\">N</th><th align=\"left\" rowspan=\"2\">Mean</th><th align=\"left\" rowspan=\"2\">Std. Deviation</th><th align=\"left\" colspan=\"2\">95% Confidence Interval for Mean</th><th align=\"left\" rowspan=\"2\">t test <italic>p</italic> value</th></tr><tr><th align=\"left\">Lower Bound</th><th align=\"left\">Upper Bound</th></tr></thead><tbody><tr><td align=\"left\">Caries free</td><td align=\"left\">105</td><td align=\"left\">28.56</td><td align=\"left\">10.42</td><td align=\"left\">26.54</td><td align=\"left\">30.58</td><td align=\"left\" rowspan=\"3\">0.000</td></tr><tr><td align=\"left\">Caries active</td><td align=\"left\">272</td><td align=\"left\">20.85</td><td align=\"left\">11.20</td><td align=\"left\">19.51</td><td align=\"left\">22.18</td></tr><tr><td align=\"left\">Total</td><td align=\"left\">377</td><td align=\"left\">23.00</td><td align=\"left\">11.51</td><td align=\"left\">21.83</td><td align=\"left\">24.16</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab5\"><label>Table 5</label><caption><p>Represents comparison of salivary levels of vitamin D with decay groups and PUFA scores</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\"/><th align=\"left\">Parameter</th><th align=\"left\">Groups</th><th align=\"left\">Mean ± S. D</th><th align=\"left\">Significance (<italic>P</italic> Value)</th></tr></thead><tbody><tr><td align=\"left\" rowspan=\"5\">Salivary Vitamin D (pg/ml)</td><td align=\"left\" rowspan=\"2\">Decay Group</td><td align=\"left\">1</td><td align=\"left\">28.77 ± 10.50</td><td align=\"left\" rowspan=\"2\">0.000</td></tr><tr><td align=\"left\">2 – 3</td><td align=\"left\">16.31 ± 8.83</td></tr><tr><td align=\"left\" rowspan=\"3\">PUFA Score</td><td align=\"left\">0</td><td align=\"left\">21.13 ± 11.11</td><td align=\"left\" rowspan=\"3\">0.026</td></tr><tr><td align=\"left\">1</td><td align=\"left\">22.44 ± 12.14</td></tr><tr><td align=\"left\">2 – 5</td><td align=\"left\">13.46 ± 8.53</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab6\"><label>Table 6</label><caption><p>Logistic regression to establish odds ratio</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" rowspan=\"2\"/><th align=\"left\" rowspan=\"2\">B</th><th align=\"left\" rowspan=\"2\">S.E</th><th align=\"left\" rowspan=\"2\">Wald</th><th align=\"left\" rowspan=\"2\">Df</th><th align=\"left\" rowspan=\"2\">Sig</th><th align=\"left\" rowspan=\"2\">Exp(B)</th><th align=\"left\" colspan=\"2\">95% C.I.for EXP(B)</th></tr><tr><th align=\"left\">Lower</th><th align=\"left\">Upper</th></tr></thead><tbody><tr><td align=\"left\">Vitamin D</td><td align=\"left\">-.063</td><td align=\"left\">.011</td><td align=\"left\">31.171</td><td align=\"left\">1</td><td align=\"left\">.000</td><td align=\"left\">.939</td><td align=\"left\">.919</td><td align=\"left\">.960</td></tr><tr><td align=\"left\">Constant</td><td align=\"left\">2.502</td><td align=\"left\">.319</td><td align=\"left\">61.629</td><td align=\"left\">1</td><td align=\"left\">.000</td><td align=\"left\">12.202</td><td align=\"left\"/><td align=\"left\"/></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab7\"><label>Table 7</label><caption><p>Association salivary LL-37 levels and study groups</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" colspan=\"2\">Group</th><th align=\"left\">N</th><th align=\"left\">Mean</th><th align=\"left\">Std. Deviation</th><th align=\"left\">Median</th><th align=\"left\">IQR</th><th align=\"left\">Mann Whitney test <italic>p</italic> value</th></tr></thead><tbody><tr><td align=\"left\" rowspan=\"2\">LL-37</td><td align=\"left\">Caries active</td><td align=\"left\">272</td><td align=\"left\">7.05</td><td align=\"left\">1.28</td><td align=\"left\">7.06</td><td align=\"left\">6.1–7.9</td><td align=\"left\" rowspan=\"2\">0.861</td></tr><tr><td align=\"left\">Caries free</td><td align=\"left\">105</td><td align=\"left\">7.07</td><td align=\"left\">1.07</td><td align=\"left\">7.10</td><td align=\"left\">6.2–7.9</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab8\"><label>Table 8</label><caption><p>A comparison of salivary levels of LL-37 with decay groups and PUFA scores</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\"/><th align=\"left\">Parameter</th><th align=\"left\">Groups</th><th align=\"left\">Mean ± S. D</th><th align=\"left\">Significance (<italic>P</italic> Value)</th></tr></thead><tbody><tr><td align=\"left\" rowspan=\"5\">Salivary LL-37(ng/ µl)</td><td align=\"left\" rowspan=\"2\">Decay Group</td><td align=\"left\">1</td><td align=\"left\">6.90 ± 1.43</td><td align=\"left\" rowspan=\"2\">0.161</td></tr><tr><td align=\"left\">2 – 3</td><td align=\"left\">7.13 ± 1.18</td></tr><tr><td align=\"left\" rowspan=\"3\">PUFA Score</td><td align=\"left\">0</td><td align=\"left\">7.02 ± 1.28</td><td align=\"left\" rowspan=\"3\">0.174</td></tr><tr><td align=\"left\">1</td><td align=\"left\">6.98 ± 1.31</td></tr><tr><td align=\"left\">2 – 5</td><td align=\"left\">7.65 ± 1.19</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab9\"><label>Table 9</label><caption><p>Logistic regression to establish odds ratio</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" rowspan=\"2\"/><th align=\"left\" rowspan=\"2\">B</th><th align=\"left\" rowspan=\"2\">S.E</th><th align=\"left\" rowspan=\"2\">Wald</th><th align=\"left\" rowspan=\"2\">df</th><th align=\"left\" rowspan=\"2\">Sig</th><th align=\"left\" rowspan=\"2\">Exp(B)</th><th align=\"left\" colspan=\"2\">95% C.I.for EXP(B)</th></tr><tr><th align=\"left\">Lower</th><th align=\"left\">Upper</th></tr></thead><tbody><tr><td align=\"left\">LL-37</td><td align=\"left\">.270</td><td align=\"left\">.286</td><td align=\"left\">.890</td><td align=\"left\">1</td><td align=\"left\">.345</td><td align=\"left\">1.309</td><td align=\"left\">.748</td><td align=\"left\">2.293</td></tr><tr><td align=\"left\">Constant</td><td align=\"left\">-.126</td><td align=\"left\">2.507</td><td align=\"left\">.003</td><td align=\"left\">1</td><td align=\"left\">.960</td><td align=\"left\">.882</td><td align=\"left\"/><td align=\"left\"/></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab10\"><label>Table 10</label><caption><p>Associating salivary IL-6 and IL-17A levels in between the study groups</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" colspan=\"2\">Group</th><th align=\"left\">Mean</th><th align=\"left\">Std Deviation</th><th align=\"left\">IQR</th><th align=\"left\">Mann whitney test <italic>p</italic> value</th></tr></thead><tbody><tr><td align=\"left\" rowspan=\"2\">IL-17A (ng/ml)</td><td align=\"left\">Caries active</td><td align=\"left\">155.01</td><td align=\"left\">91.48</td><td align=\"left\">99.5–198.9</td><td align=\"left\" rowspan=\"2\">0.537</td></tr><tr><td align=\"left\">Caries free</td><td align=\"left\">174.20</td><td align=\"left\">84.77</td><td align=\"left\">159–221.7</td></tr><tr><td align=\"left\" rowspan=\"2\">IL-6 (ng/ml)</td><td align=\"left\">Caries active</td><td align=\"left\">31.15</td><td align=\"left\">40.98</td><td align=\"left\">2.8–36.7</td><td align=\"left\" rowspan=\"2\">0.813</td></tr><tr><td align=\"left\">Caries free</td><td align=\"left\">28.33</td><td align=\"left\">31.81</td><td align=\"left\">11.9–24.3</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab11\"><label>Table 11</label><caption><p>A comparison of salivary levels of IL-17A with decay groups and PUFA scores</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\"/><th align=\"left\">Parameter</th><th align=\"left\">Groups</th><th align=\"left\">Mean ± S. D</th><th align=\"left\">Significance (<italic>P</italic> Value)</th></tr></thead><tbody><tr><td align=\"left\" rowspan=\"5\">Salivary IL-17A</td><td align=\"left\" rowspan=\"2\">Decay Group</td><td align=\"left\">1</td><td align=\"left\">143.53 ± 8.8</td><td align=\"left\" rowspan=\"2\">0.431</td></tr><tr><td align=\"left\">2—3</td><td align=\"left\">161.23 ± 9.3</td></tr><tr><td align=\"left\" rowspan=\"3\">PUFA Score</td><td align=\"left\">0</td><td align=\"left\">156.24 ± 8.2</td><td align=\"left\" rowspan=\"3\">0.935</td></tr><tr><td align=\"left\">1</td><td align=\"left\">156 ± 11.4</td></tr><tr><td align=\"left\">2—5</td><td align=\"left\">141.75 ± 10.2</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab12\"><label>Table 12</label><caption><p>A comparison of salivary levels of IL-6 with decay groups and PUFA scores  </p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\"/><th align=\"left\">Parameter</th><th align=\"left\">Groups</th><th align=\"left\">Mean ± S. D</th><th align=\"left\">Significance (<italic>P</italic> Value)</th></tr></thead><tbody><tr><td align=\"left\" rowspan=\"5\">Salivary IL-6</td><td align=\"left\" rowspan=\"2\">Decay Group</td><td align=\"left\">1</td><td align=\"left\">29.44 ± 36.59</td><td align=\"left\" rowspan=\"2\">0.794</td></tr><tr><td align=\"left\">2 – 3</td><td align=\"left\">32.07 ± 43.52</td></tr><tr><td align=\"left\" rowspan=\"3\">PUFA Score</td><td align=\"left\">0</td><td align=\"left\">30.68 ± 38.89</td><td align=\"left\" rowspan=\"3\">0.873</td></tr><tr><td align=\"left\">1</td><td align=\"left\">29.65 ± 40.65</td></tr><tr><td align=\"left\">2 – 5</td><td align=\"left\">39.49 ± 63.11</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab13\"><label>Table 13</label><caption><p>Logistic regression to establish odds ratio</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" rowspan=\"2\"/><th align=\"left\" rowspan=\"2\">B</th><th align=\"left\" rowspan=\"2\">S.E</th><th align=\"left\" rowspan=\"2\">Wald</th><th align=\"left\" rowspan=\"2\">df</th><th align=\"left\" rowspan=\"2\">Sig</th><th align=\"left\" rowspan=\"2\">Exp(B)</th><th align=\"left\" colspan=\"2\">95% C.I. for EXP(B)</th></tr><tr><th align=\"left\">Lower</th><th align=\"left\">Upper</th></tr></thead><tbody><tr><td align=\"left\">IL-17A</td><td align=\"left\">-.001</td><td align=\"left\">.004</td><td align=\"left\">.115</td><td align=\"left\">1</td><td align=\"left\">.735</td><td align=\"left\">.999</td><td align=\"left\">.991</td><td align=\"left\">1.007</td></tr><tr><td align=\"left\">IL-6</td><td align=\"left\">.006</td><td align=\"left\">.010</td><td align=\"left\">.366</td><td align=\"left\">1</td><td align=\"left\">.545</td><td align=\"left\">1.006</td><td align=\"left\">.987</td><td align=\"left\">1.026</td></tr><tr><td align=\"left\">LL-37</td><td align=\"left\">.270</td><td align=\"left\">.286</td><td align=\"left\">.890</td><td align=\"left\">1</td><td align=\"left\">.345</td><td align=\"left\">1.309</td><td align=\"left\">.748</td><td align=\"left\">2.293</td></tr><tr><td align=\"left\">Constant</td><td align=\"left\">-.126</td><td align=\"left\">2.507</td><td align=\"left\">.003</td><td align=\"left\">1</td><td align=\"left\">.960</td><td align=\"left\">.882</td><td align=\"left\"/><td align=\"left\"/></tr></tbody></table></table-wrap>" ]

[ "<disp-formula id=\"Equa\"><alternatives><tex-math id=\"M1\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\text{N}}=\\frac{{\\left({{\\text{Z}}}_{1-\\mathrm{\\alpha }/2}+{{\\text{Z}}}_{\\upbeta }\\right)}^{2}\\left[{{\\text{P}}}_{1}\\left(1-{{\\text{P}}}_{1}\\right)+{{\\text{P}}}_{2}\\left(1-{{\\text{P}}}_{2}\\right)\\right]}{{{\\text{P}}}_{1}-{{\\text{P}}}_{2}}$$\\end{document}</tex-math><mml:math id=\"M2\" display=\"block\"><mml:mrow><mml:mtext>N</mml:mtext><mml:mo>=</mml:mo><mml:mfrac><mml:mrow><mml:msup><mml:mrow><mml:mfenced close=\")\" open=\"(\"><mml:msub><mml:mtext>Z</mml:mtext><mml:mrow><mml:mn>1</mml:mn><mml:mo>-</mml:mo><mml:mi>α</mml:mi><mml:mo stretchy=\"false\">/</mml:mo><mml:mn>2</mml:mn></mml:mrow></mml:msub><mml:mo>+</mml:mo><mml:msub><mml:mtext>Z</mml:mtext><mml:mi mathvariant=\"normal\">β</mml:mi></mml:msub></mml:mfenced></mml:mrow><mml:mn>2</mml:mn></mml:msup><mml:mfenced close=\"]\" open=\"[\"><mml:msub><mml:mtext>P</mml:mtext><mml:mn>1</mml:mn></mml:msub><mml:mfenced close=\")\" open=\"(\"><mml:mn>1</mml:mn><mml:mo>-</mml:mo><mml:msub><mml:mtext>P</mml:mtext><mml:mn>1</mml:mn></mml:msub></mml:mfenced><mml:mo>+</mml:mo><mml:msub><mml:mtext>P</mml:mtext><mml:mn>2</mml:mn></mml:msub><mml:mfenced close=\")\" open=\"(\"><mml:mn>1</mml:mn><mml:mo>-</mml:mo><mml:msub><mml:mtext>P</mml:mtext><mml:mn>2</mml:mn></mml:msub></mml:mfenced></mml:mfenced></mml:mrow><mml:mrow><mml:msub><mml:mtext>P</mml:mtext><mml:mn>1</mml:mn></mml:msub><mml:mo>-</mml:mo><mml:msub><mml:mtext>P</mml:mtext><mml:mn>2</mml:mn></mml:msub></mml:mrow></mml:mfrac></mml:mrow></mml:math></alternatives></disp-formula>" ]

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[ "<table-wrap-foot><p><italic>P</italic> &lt; 0.05 was considered statistically significant</p></table-wrap-foot>", "<table-wrap-foot><p><italic>P</italic> &lt; 0.05 was considered statistically significant</p></table-wrap-foot>", "<table-wrap-foot><p><italic>P</italic> &lt; 0.05 was considered statistically significant</p></table-wrap-foot>", "<table-wrap-foot><p><italic>P</italic> &lt; 0.05 was considered statistically significant</p><p>Variable(s) entered on step 1: VITAMIN D</p></table-wrap-foot>", "<table-wrap-foot><p><italic>P</italic> &lt; 0.05 was considered statistically significant</p></table-wrap-foot>", "<table-wrap-foot><p><italic>P</italic> &lt; 0.05 was considered statistically significant</p></table-wrap-foot>", "<table-wrap-foot><p><italic>P</italic> &lt; 0.05 was considered statistically significant</p><p>Variable(s) entered on step 1: LL-37</p></table-wrap-foot>", "<table-wrap-foot><p><italic>P</italic> &lt; 0.05 was considered statistically significant</p></table-wrap-foot>", "<table-wrap-foot><p><italic>P</italic> &lt; 0.05 was considered statistically significant</p></table-wrap-foot>", "<table-wrap-foot><p><italic>P</italic> &lt; 0.05 was considered statistically significant</p></table-wrap-foot>", "<table-wrap-foot><p><italic>P</italic> &lt; 0.05 was considered statistically significant</p></table-wrap-foot>", "<fn-group><fn><p><bold>Publisher’s Note</bold></p><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn><fn><p>Mithra N. Hegde and Suchetha Kumari N contributed equally to this work.</p></fn></fn-group>" ]

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[ "<title>Introduction</title>", "<p id=\"Par9\">Preparation of dental hard tissues using high-speed rotary instruments generates heat; therefore, adequate cooling of the preparation area must be provided to prevent collateral thermal damage of the surrounding tissues [##REF##26051868##1##]. Heat generation and cooling have been widely investigated for dental implant site preparation including the use of navigation guides. However, circumstances during navigated endodontic drilling in dentine significantly differ from navigated implant site preparation in human bone.</p>", "<p id=\"Par10\">The most common cause of artificial root canal preparation is a narrow and/or calcified root canal; therefore, the drill encounters high resistance. This leads to increased heat generation in the case of implant site preparation in human bone, which is generally softer than dentine [##REF##34091687##2##]. Although bone is not a particularly well-vascularized tissue, its blood flow may decrease collateral thermal damage contrary to dentine, which has absolutely no blood supply. In the case of bone, the thermally affected tissue is at the site of the preparation, while in the case of root preparation, the entire root membrane must be protected from the heat generated during drilling procedures [##REF##24474355##3##].</p>", "<p id=\"Par11\">The working length of endodontic drills is generally longer than those of implant drills. The efficiency of cooling decreases with a longer distance of the working end of the instrument from the cooling source and with longer preparation depths (effective working length) [##REF##22417718##4##]. Cooling efficiency may be further decreased with the use of navigation guides. To overcome this negative effect, a gap between the drill guide sleeve and the gingiva is often maintained during the fabrication of dental implant surgical guides to ensure the access of the coolant to the drill [##REF##33942432##5##]. However, due to the flexibility of narrower and longer drills used in endodontics, this is rarely possible during navigated endodontic drilling. Another disadvantage of drills thinner than 1.5 mm is they do not have a heat-retaining mass, and their temperature increases faster during the process of drilling.</p>", "<p id=\"Par12\">Due to these circumstances, clinicians may expect more heat generation during guided endodontic drilling than during guided implant site preparation.</p>", "<p id=\"Par13\">Although guided endodontic drilling is a cutting-edge technology [##REF##34814892##6##], there are a limited number of reports in scientific literature referencing temperature changes during guided endodontic drilling, of which, the effect of different drilling parameters has not been investigated in detail. [##REF##36384556##7##]</p>", "<p id=\"Par14\">The aim of our study was to determine the temperature changes of root surfaces during guided endodontic drilling with various parameters. Due to the anatomical differences between natural teeth and the varying amounts of calcified dentine embedded in teeth, a large variance of results is expected.</p>" ]

[ "<title>Materials and methods</title>", "<title>Sample preparation</title>", "<p id=\"Par15\">In this study, seventy-two teeth with presumably narrow root canals were used. Navigated endodontic drilling enables straight preparation due to the relative rigidity of drills compared to conventional endodontic instruments. Therefore, only teeth bearing a straight root were selected.</p>", "<p id=\"Par16\">Inclusion criteria:</p>", "<p id=\"Par17\">\n<list list-type=\"bullet\"><list-item><p id=\"Par18\">Tooth extracted from a patient older than 50 years of age.</p></list-item><list-item><p id=\"Par19\">Tooth extracted due to poor periodontal prognosis.</p></list-item><list-item><p id=\"Par20\">Straight root.</p></list-item></list>\n</p>", "<p id=\"Par21\">Exclusion criteria:</p>", "<p id=\"Par22\">\n<list list-type=\"bullet\"><list-item><p id=\"Par23\">Prior endodontic treatment of the tooth.</p></list-item><list-item><p id=\"Par24\">Presence of any of the following conditions: crown restoration, caries, periapical lesions, root resorption and/or root fracture.</p></list-item></list>\n</p>", "<p id=\"Par25\">Root length was not standardized. However, the same effective working length was used during preparations. Variances in root canal morphology were evenly distributed among the test groups. Roots of teeth were embedded in a stable support made of plaster and acrylic resin. Each support contained twelve teeth. A channel for the thermocouple electrode was created in the support for each tooth leading to the middle of the root (Fig. ##FIG##0##1##).</p>", "<p id=\"Par58610\">\n\n</p>", "<p id=\"Par27\">A CBCT scan of each support was performed utilizing the Planmeca ProMax 3D imaging system (Planmeca, Helsinki, Finland) with a resolution of 200 microns and an FOV size of 8 × 8 mm (Fig. ##FIG##1##2##).</p>", "<p id=\"Par0510\">\n\n</p>", "<p id=\"Par29\">The image set was uploaded to navigated surgical planning software (coDiagnostiX - Dental Wings Inc., Montréal, Canada) (Figs. ##FIG##2##3## and ##FIG##3##4##)).</p>", "<p id=\"Par25910\">\n\n</p>", "<p id=\"Par19870\">\n\n</p>", "<p id=\"Par32\">The type of endodontic drill (1 mm diameter spiral drill - Steco-System-Technik GmbH &amp; Co. KG, Hamburg, Germany) and the corresponding guide sleeve were selected based on the recommendation of the software manufacturer. In the design software, sleeves were positioned as close as possible to the tooth surface to minimize the effective working length. The body of the guide holding the sleeves was generated automatically by the software and 3D printed (Form2, Formlabs Inc., Somerville, USA) using clear resin (Clear Resin, Formlabs Inc., Somerville, USA).</p>", "<p id=\"Par33\">The thermocouple channel in the support was filled with PK-Zero thermal compound (Prolimatech, Taiwan), and the thermocouple was fed into the channel up to the root surface. The other end of the thermocouple was connected to a digital thermometer (EL-EnviroPad-TC, Lascar Electronics Ltd., Salisbury, UK) (Fig. ##FIG##4##5##).</p>", "<p id=\"Par105975\">\n\n</p>", "<p id=\"Par34\">A marking on the tooth was made through the guide sleeve, enamel was removed for all teeth using a diamond bur and dentin was removed for certain sets of teeth, creating an access cavity (AC) (see group descriptions). Access cavities were prepared with the same sized round diamond burs parallel to the long axis of the tooth. Access cavity width was set by the diameter of this bur. Cavities were prepared until the pulp chamber was reached, or in the case of calcified pulp chambers, preparation was continued until the depth of the cementoenamel junction was reached.</p>", "<title>Drilling protocol</title>", "<p id=\"Par36\">Endodontic preparation through the guide was performed by the same operator, with over five years of experience in guided implantology and endodontics (A.M.). The drill feed rate was standardized using a digital scale. The same micromotor (Bien-Air Chiropro 980, Bien-Air Surgery SA, Le Noirmont, Switzerland) with a 6:1 endodontic handpiece (VDW, München, Germany) was used for the preparation of all teeth.</p>", "<title>Study groups</title>", "<p id=\"Par37\">Four parameters affecting temperature change were investigated in the study: (a) access cavity preparation prior to endodontic drilling, (b) drill speed, (c) cooling and (d) cooling fluid temperature. Twelve teeth were allocated into each of the following test groups:</p>", "<p id=\"Par38\">Group 1:</p>", "<p id=\"Par39\">Guided drilling without access cavity preparation (w/o AC) at 800 RPM without cooling (w/o C).</p>", "<p id=\"Par40\">Group 2:</p>", "<p id=\"Par41\">Guided drilling without access cavity preparation (w/o AC) at 1000 RPM without cooling (w/o C).</p>", "<p id=\"Par42\">Group 3:</p>", "<p id=\"Par43\">Access cavity (w/AC) preparation prior to endodontic drilling and guided drilling at 1000 RPM without cooling (w/o C).</p>", "<p id=\"Par44\">Group 4:</p>", "<p id=\"Par45\">Access cavity (w/AC) preparation prior to endodontic drilling, guided drilling at 800 RPM without cooling (w/o C).</p>", "<p id=\"Par46\">Group 5:</p>", "<p id=\"Par47\">Access cavity (w/AC) preparation prior to endodontic drilling, guided drilling at 1000 RPM speed, cooling (w/C) with a room temperature (21 °C) coolant.</p>", "<p id=\"Par48\">Group 6:</p>", "<p id=\"Par49\">Access cavity (w/AC) preparation prior to endodontic drilling, guided drilling at 1000 RPM speed, cooling (w/C) with a chilled (4–6 °C) coolant.</p>", "<title>Statistical analysis</title>", "<p id=\"Par50\">Sample size was calculated in G*Power version 3.1.9.7. Considering 80% power, 5% alpha error and effect size of 0.5, a minimum of ten samples per group were required. Since the size of the support enabled the fit of more teeth, we analyzed twelve samples per group. This sample size was in accordance with previous studies regarding the subject [##REF##36384556##7##]. The statistical analyses were performed with SPSS v. 25.0 (SPSS, Chicago, IL). The Kolmogorov‒Smirnov test was applied to test the normality of the distribution of the data. The changes in temperatures were compared between guided endodontic root canal preparation groups with one-way ANOVA, followed by Tukey’s HSD post hoc test. P values below 0.05 were considered significant.</p>" ]

[ "<title>Results</title>", "<p id=\"Par51\">No prior recommendation for drill speed in guided endodontic drilling was found among published scientific literature; therefore, we conducted a preliminary study to determine optimal drill speeds. In this preliminary experiment (data not shown) it was found rotary speeds of 1200 RPM and above did not improve drilling efficiency; however, rapid heating of the drill was observed and drill breakage often occurred. Therefore, 1000 RPM was chosen for the cooling efficiency test. On the other end of the spectrum, speeds below 800 RPM were associated with drastically reduced drilling efficiency and with a prolonged temperature rise, resulting in higher peak temperatures than speeds of 800 RPM and above.</p>", "<p id=\"Par52\">Mean temperature elevations are shown in Table ##TAB##0##1##.</p>", "<p id=\"Par53\">\n\n</p>", "<p id=\"Par54\">The highest mean temperatures were observed for drilling without prior access cavity preparation. In this setup, drill speeds of 800 RPM (Group 1.) resulted in higher mean temperatures (14.62 °C ± 0.63) than drill speeds of 1000 RPM (Group 2.) (13.76 °C ± 1.24). The difference between these two groups was not statistically significant (p = 0.243), however, both groups showed significantly higher (p &lt; 0.01) temperatures than any of the access cavity groups (3.,4.,5.,6.)</p>", "<p id=\"Par55\">In groups in which access cavity preparation was applied (Groups 3 and 4) significantly lower (p &lt; 0.01) mean temperature values (10.09 °C ± 1.32 and 8.90 °C ± 0.50, respectively) were measured in comparison to the no access cavity groups (Groups 1 and 2). However, both groups 3 and 4 showed significantly higher mean temperatures than the groups in which cooling was used (Groups 5 and 6; p &lt; 0.01). In this setup (access cavity prepared, no cooling applied), the drill speed had a significant effect, in which 1000 RPM resulted in significantly higher mean temperatures than when compared with 800 RPM (p &lt; 0.05).</p>", "<p id=\"Par56\">Cooling significantly decreased (p &lt; 0.01) the mean temperature increase in both groups (5., 6.) (4.01 °C ± 0.22) and 6. (1.60 °C ± 1.17) compared to any of the uncooled groups (1., 2., 3., 4.). The temperature of the cooling liquid had a significant effect (p &lt; 0.01), and the application of a chilled cooling liquid (Group 6.) proved more beneficial than using a room temperature liquid (Group 5.) at the same drill speeds (1000 RPM).</p>", "<p id=\"Par57\">The results of the intergroup comparisons are shown in Fig. ##FIG##5##6##.</p>", "<p id=\"Par109875\">\n\n</p>" ]

[ "<title>Discussion</title>", "<p id=\"Par59\">Guided root canal drilling leads to heat generation at the drill-dentine interface. Excessive heat generation may lead to collateral thermal damage of the tissues of the periodontal ligament surrounding the root [##REF##31520416##8##]. According to Sauk et al. [##REF##3150436##9##], hyperthermia at 43 °C can lead to decreased protein synthesis, thus altering the functions of periodontal ligament cells. Eriksson and Albrektsson [##REF##6576145##10##] found 47 °C temperature for at least 1 min is necessary for bone damage visible by light microscopy. Kniha et al., in their systematic review, discovered a wide range of published threshold values and concluded, due to the heterogeneity of experimental setups, no exact temperature for bone necrosis can be determined [##UREF##0##11##]. Cunha et al. in their systematic review demonstrated how many factors may contribute to postoperative pain and discomfort in patients who underwent endodontic treatment [##REF##32571285##12##]. It can be assumed temperature elevations even below the necrotic threshold values may also contribute to postoperative pain, therefore, any temperature elevation is to be avoided during endodontic treatments, if possible.</p>", "<p id=\"Par60\">Most of the studies conducted on thermal bone damage are focused on direct heat transfer to the bone when examining critical temperatures. During guided endodontic drilling, heat is first transferred to the nonvital structure of dentine and only secondarily to bone. In this regard, preparation in the root canal is more similar to broken abutment screw removal from dental implants [##REF##36517777##13##]. However, conclusions derived from these studies cannot be directly applied to guided endodontics for two main reasons. One premise implies titanium features better heat conductivity when compared with dentine, and the other premise is blood flow in the periodontal ligament has an attenuating effect upon heat transfer from the unvital structure to the bone.</p>", "<p id=\"Par61\">Although various anatomical factors, including the length of the root, width of the remaining root canal and calcified tissue inside the root canal may contribute to heat generation, they are difficult to control. Procedural factors, such as the type of drill used, presence of a properly prepared access cavity, drill speed, cooling and temperature of the coolant may also contribute to heat generation. However, the importance and effect of these procedural factors have not yet been fully investigated in published scientific literature.</p>", "<p id=\"Par62\">The results show all four tested drilling parameters affected heat generation during in vitro investigation.</p>", "<p id=\"Par63\">The lack of access cavity preparation prior to guided endodontic drilling reportedly bears a detrimental effect, increasing root surface temperature by more than 10 °C regardless of the drilling speed applied.</p>", "<p id=\"Par64\">Our data implies drilling speed also has a major effect on heat generation when the access cavity is prepared prior to guided drilling. Seemingly, a lower speed (800 RPM) results in less heat generation than higher speed (1000 RPM) drilling. The temperature values were also more consistent with lower speed preparations. This may indicate lower speed preparations are less sensitive to different root canal anatomies.</p>", "<p id=\"Par65\">Additionally, cooling of the drill as well as the temperature of the cooling liquid have major effects on heat generation even when higher drill speeds were used. The highest measured temperature elevation with cooling was still lower than the lowest temperature elevation without cooling. In the two cases with the use of refrigerated cooling liquid, no temperature elevation was observed during the entire drilling process. Therefore, it can be assumed cooling the drill is the most predictable method to reduce collateral thermal damage.</p>", "<p id=\"Par66\">The mean temperature data (4.01 °C ± 0.22) of Group 5 of our study (access cavity preparation followed by guided drilling at 1000 RPM and cooling with room temperature coolant) were consistent with the mean temperature data (5.07 °C) of the guided endodontic drilling group (access cavity preparation followed by drilling at 800 RPM for 120 s without cooling) from the study published by Zhang et al. [##REF##36384556##7##]</p>", "<p id=\"Par67\">It must be noted, these data only refer to the one specific drill type used for this study. Bur material, diameter, shape and blade configuration may also contribute to accuracy and heat generation; however, investigation of these parameters was beyond the scope of our study [##REF##28843405##14##, ##UREF##1##15##].</p>" ]

[ "<title>Conclusion</title>", "<p id=\"Par68\">There is a growing need for the development of technical recommendations and protocols as the technique of guided root canal drilling becomes increasingly more accessible to dental practitioners. With the cautious evaluation of unswayed anatomical factors of the tooth and with the thorough understanding of influential procedural factors, the risk of collateral thermal damage during guided endodontic drilling can be minimized. Based on the results of our study, guided endodontic drilling at drill speeds not exceeding 1000 RPM following access cavity preparation, with constant cooling using a fluid cooler than room temperature, provides the best results in avoiding collateral thermal damage.</p>" ]

[ "<title>Background</title>", "<p id=\"Par1\">Navigated endodontics is a cutting-edge technology becoming increasingly more accessible for dental practitioners. Therefore, it is necessary to clarify the ideal technical parameters of this procedure to prevent collateral damage of the surrounding tissues. There is a limited number of studies available in published scientific literature referencing the possible collateral thermal damage due to high-speed rotary instruments used in guided endodontic drilling. The aim of our study was to investigate the different drilling parameters and their effect upon the temperature elevations measured on the outer surface of teeth during guided endodontic drilling.</p>", "<title>Methods</title>", "<p id=\"Par01\">In our in vitro study, 72 teeth with presumably narrow root canals were prepared using a guided endodontic approach through a 3D-printed guide. Teeth were randomly allocated into six different test groups consisting of 12 teeth each, of which, four parameters affecting temperature change were investigated: (a) access cavity preparation prior to endodontic drilling, (b) drill speed, (c) cooling, and (d) cooling fluid temperature. Temperature changes were recorded using a contact thermocouple electrode connected to a digital thermometer.</p>", "<title>Results</title>", "<p id=\"Par001\">The highest temperature elevations (14.62 °C ± 0.60 at 800 rpm and 13.76 °C ± 1.24 at 1000 rpm) were recorded in the groups in which drilling was performed without prior access cavity preparation nor without a significant difference between the different drill speeds (p = 0.243). Access cavity preparation significantly decreased temperature elevations (p &lt; 0.01) while drilling at 800 rpm (8.90 °C ± 0.50) produced significantly less heating of the root surface (p &lt; 0.05) than drilling at 1000 rpm (10.09 °C ± 1.32). Cooling significantly decreased (p &lt; 0.01) temperature elevations at a drill speed of 1000 rpm, and cooling liquid temperatures of 4–6 °C proved significantly (p &lt; 0.01) more beneficial in decreasing temperature elevations (1.60 °C ± 1.17) than when compared with room temperature (21 °C) liquids (4.01 °C ± 0.22).</p>", "<title>Conclusions</title>", "<p id=\"Par10000\">Based on the results of our study, guided endodontic drilling at drill speeds not exceeding 1000 rpm following access cavity preparation, with constant cooling using a fluid cooler than room temperature, provides the best results in avoiding collateral thermal damage during navigated endodontic drilling of root canals.</p>", "<title>Keywords</title>" ]

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[ "<title>Acknowledgements</title>", "<p>None.</p>", "<title>Author contributions</title>", "<p>Zs. R.: Formal analysis, Investigation, Writing - Original Draft.I. M.: Data Curation, Validation, Review &amp; Editing.Á. N.: Funding acquisition, Writing - Review &amp; Editing.K. T.: Project administration, Validation. A. M.: Conceptualization, Methodology, Investigation, Writing - Review &amp; Editing. Gy. M.: Conceptualization, Investigation, Writing - Review &amp; Editing.</p>", "<title>Funding</title>", "<p>This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.</p>", "<title>Data availability</title>", "<p>The datasets used and/or analyzed during the current in vitro study are available from the corresponding author on reasonable request.</p>", "<title>Declarations</title>", "<title>Ethics approval and consent to participate</title>", "<p id=\"Par76\">Ethical approval was obtained by Regional Research Ethics Committee of the Medical Center, Pécs. Written informed consent was acquired from all participants for study participation.</p>", "<title>Consent for publication</title>", "<p id=\"Par77\">Not Applicable (NA).</p>", "<title>Competing interests</title>", "<p id=\"Par75\">The authors declare no competing interests.</p>" ]

[ "<fig id=\"Fig1\"><label>Fig. 1</label><caption><p>The unique stable support structure used during the study. The blue arrow indicates the external orifice of the channel for the thermocouple electrode</p></caption></fig>", "<fig id=\"Fig2\"><label>Fig. 2</label><caption><p>CBCT scan of the teeth inside the support structure</p></caption></fig>", "<fig id=\"Fig3\"><label>Fig. 3</label><caption><p>Designing the surgical template</p></caption></fig>", "<fig id=\"Fig4\"><label>Fig. 4</label><caption><p>The designed surgical template</p></caption></fig>", "<fig id=\"Fig5\"><label>Fig. 5</label><caption><p>Setup for thermal measurement</p></caption></fig>", "<fig id=\"Fig6\"><label>Fig. 6</label><caption><p>Temperature elevation in different groups (n.s.: not significant; *: p &lt; 0.05; **: p &lt; 0.01)</p></caption></fig>" ]

[ "<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Mean temperature elevations for each group</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\"/><th align=\"left\">RPM</th><th align=\"left\">Cooling</th><th align=\"left\">Trepanation</th><th align=\"left\">Number of teeth</th><th align=\"left\">Mean temperature elevation (°C)</th><th align=\"left\">Standard deviation</th></tr></thead><tbody><tr><td align=\"left\">Group 1.</td><td char=\".\" align=\"char\">800</td><td align=\"left\">No</td><td align=\"left\">No</td><td char=\".\" align=\"char\">12</td><td align=\"left\">14.62 °C</td><td char=\".\" align=\"char\">0.63</td></tr><tr><td align=\"left\">Group 2.</td><td char=\".\" align=\"char\">1000</td><td align=\"left\">No</td><td align=\"left\">No</td><td char=\".\" align=\"char\">12</td><td align=\"left\">13.76 °C</td><td char=\".\" align=\"char\">1.24</td></tr><tr><td align=\"left\">Group 3.</td><td char=\".\" align=\"char\">1000</td><td align=\"left\">No</td><td align=\"left\">Yes</td><td char=\".\" align=\"char\">12</td><td align=\"left\">10.09 °C</td><td char=\".\" align=\"char\">1.32</td></tr><tr><td align=\"left\">Group 4.</td><td char=\".\" align=\"char\">800</td><td align=\"left\">No</td><td align=\"left\">Yes</td><td char=\".\" align=\"char\">12</td><td align=\"left\">8.90 °C</td><td char=\".\" align=\"char\">0.50</td></tr><tr><td align=\"left\">Group 5.</td><td char=\".\" align=\"char\">1000</td><td align=\"left\">Yes (21 °C)</td><td align=\"left\">Yes</td><td char=\".\" align=\"char\">12</td><td align=\"left\">4.01 °C</td><td char=\".\" align=\"char\">0.22</td></tr><tr><td align=\"left\">Group 6.</td><td char=\".\" align=\"char\">1000</td><td align=\"left\">Yes (4–6 °C)</td><td align=\"left\">Yes</td><td char=\".\" align=\"char\">12</td><td align=\"left\">1.60 °C</td><td char=\".\" align=\"char\">1.17</td></tr></tbody></table></table-wrap>" ]

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[ "<title>Background</title>", "<p id=\"Par11\">The global technical strategy for malaria 2016–2030 of the World Health Organization (WHO) recommends strengthening malaria surveillance as a fundamental activity to inform programme planning and implementation for improved outbreak detection in malaria-endemic countries [##UREF##0##1##]. According to the World Malaria Report of 2022, Uganda is ranked as the third-highest contributor to malaria burden globally, with 95% of the country being highly endemic and 5% prone to malaria epidemics [##UREF##1##2##, ##UREF##2##3##].</p>", "<p id=\"Par12\">A malaria outbreak is characterized as an increase in case counts above the threshold for the normal seasonal pattern of malaria in an area. This threshold is usually calculated based on historical routine data at the district level for a minimum of 5 years [##UREF##3##4##, ##REF##22443235##5##]. The WHO recommends various methods to calculate thresholds, including the 75th percentile, mean ± 2 standard deviations (SD), cumulative sum (C-SUM), and constant case counts [##UREF##3##4##]. The 75th percentile method considers the threshold as the 75th percentile of the average number of cases for a specific epidemiological week in that district over the past 5 years. The mean + 2SD method takes the mean number of cases for that week over the last 5 years and adds 2SD to establish the threshold. The C-SUM method involves a running average of cases for the current epi week, the previous week, and the following week over the past 5 years [##UREF##3##4##]. To accommodate seasonal malaria peaks that are not necessarily epidemics, modifications to these methods have been proposed, including raising the 75th percentile to the 85th percentile, and increasing the C-SUM method threshold by adding two standard deviations (C-SUM + 2SD) [##UREF##3##4##]. These adaptations are meant to improve the ability to distinguish between true outbreaks and regular seasonal variations.</p>", "<p id=\"Par13\">The threshold calculation method that is recommended depends on the extent of malaria transmission in a given area. The WHO defines high transmission as an annual parasite index (API) &gt; 450/1000, medium transmission as 251–450/1000 API, low transmission as 101–250/1000 API, and very low transmission as ≤ 100/1000 API [##UREF##3##4##]. The C-SUM method is recommended for areas with very low to low transmission; however, it is considered too sensitive for outbreak detection in medium- to high-transmission areas [##UREF##3##4##]. In the medium- to high-transmission areas, the 75th percentile method and mean + 2SD methods are both recommended by the WHO; however, they are considered too insensitive to accurately detect outbreaks in low-transmission areas [##UREF##3##4##]. For any method used, a malaria epidemic is declared when the malaria cases are above the threshold for &gt; 2 weeks consecutively. Uganda’s malaria epidemic preparedness and response plan for 2019 suggests using the 75th percentile method at the national level and for all districts [##UREF##4##6##]. However, some districts use the mean + 2SD and others use the 75th percentile methods, based on the WHO recommendation for similar settings.</p>", "<p id=\"Par14\">From 2019 to 2022, Uganda’s health information system reported a rise in confirmed malaria cases [##UREF##5##7##]. During the first half of 2022, more than half of the districts in Uganda were in outbreak mode for at least 10 weeks, according to the 75th percentile method used [##UREF##6##8##]. While every outbreak should be investigated and responded to by the national rapid response team, limited resources for logistics and human resources forced the national malaria control programme to restrict its response to only a few districts, using the number of complicated malaria presentations and malaria deaths as the prioritization measure. With the rate of progress slowing in terms of malaria control, not only in Uganda but also in other sub-Saharan African countries [##UREF##7##9##, ##UREF##8##10##], there will be a need to ensure that appropriate methods are being used to identify malaria outbreaks and that prioritization methods are available when sufficient resources are not. The three threshold approaches were evaluated to compare their outbreak-signaling outputs in Uganda for improved malaria epidemic detection and response.</p>" ]

[ "<title>Methods</title>", "<title>Study setting</title>", "<p id=\"Par15\">Uganda comprises 15 health regions, of which 2 (West Nile and Acholi Regions) are considered areas with high annual malaria transmission rates. Five (Lango, Karamoja, Teso, Bukedi, and Busoga Regions) are considered medium malaria transmission areas and seven (South Central, North Central, Kampala, Ankole, Tooro, Bugisu and Bunyoro Regions) are considered low malaria transmission areas. Kigezi Region is considered to have very low malaria transmission and is targeted for malaria elimination in the Uganda National Malaria Strategic Plan 2025 [##UREF##3##4##, ##UREF##5##7##, ##UREF##9##11##].</p>" ]

[ "<title>Results</title>", "<title>Characteristics of the study data</title>", "<p id=\"Par20\">Varying malaria incidence levels were identified for districts in the same malaria transmission region (Table ##TAB##0##1##). Overall, 8 of the 16 districts were recategorized based on the use of district data rather than regional data. These included one district (Nwoya) reassigned from ‘high’ to ‘medium’, two districts (Butambala and Bundibugyo) re-categorized from ‘low’ to ‘medium’, 1 district (Kanungu) recategorized from ‘very low’ to ‘low’, two districts (Alebtong and Kibuku) recategorized from ‘medium’ to ‘low’, two districts (Ntoroko and Bukwo) re-categorized from ‘’low’ to ‘very low’. Due to this identified granularity in actual transmission levels, districts were re-categorized by transmission level using district-level data and these assignments were used in the rest of the analysis (Table ##TAB##0##1##).</p>" ]

[ "<title>Discussion</title>", "<p id=\"Par26\">Identifying the appropriate situations to respond to an apparent increase in cases of a disease in an endemic setting is challenging. The use of transmission intensity-specific thresholds, based on historical data, is meant to facilitate the identification of malaria outbreaks and distinguish true increases from seasonal upsurges in endemic areas. Using real examples from Uganda, major differences between threshold calculation approaches in terms of the number of weeks above the threshold detected as well as the number of outbreaks that would require epidemic response were identified. Specifically, two approaches that are both meant to be acceptable for outbreak detection in medium-to-high transmission areas (mean + 2SD and 75th percentile) yielded large differences in the number of outbreak weeks detected across all levels of transmission. The 75th percentile method yielded outbreak weeks more similar to those identified by the very sensitive C-SUM method across all transmission levels. In addition, the true transmission levels in districts were often not reflective of the region to which they were assigned.</p>", "<p id=\"Par27\">Both the 75th percentile and mean + 2SD methods have been recommended for malaria outbreak detection in medium- to high-transmission areas, suggesting their comparability and possible interchangeability. However, significant differences in the number of weeks exceeding the outbreak threshold between these two methods were identified, with the mean + 2SD method identifying significantly fewer outbreak weeks. A Kenyan study in three different regions similarly found that the 75th percentile method identified approximately 3 times as many months as being ‘epidemic’ as the mean + 2SD method [##REF##12023909##12##]. Clear guidance on the application of these methods for specific transmission areas is required for improved malaria outbreak surveillance and detection.</p>", "<p id=\"Par28\">While only the C-SUM method is recommended for low- or very low-transmission areas, no significant difference in the number of weeks above the threshold detected by the 75th percentile and C-SUM methods in these districts was observed. Existing guidance discourages the use of the 75th percentile method in low- and very low-transmission areas due to the potential for missing outbreaks [##UREF##3##4##, ##REF##21910855##13##, ##UREF##10##14##]. In this evaluation, outbreaks were not missed. However, in medium- and high-transmission areas, the C-SUM method detected significantly more outbreak weeks than the 75th percentile method. This supports not using the C-SUM method in medium- and high-transmission areas to avoid false alarms, as it does not account for seasonal peaks [##UREF##3##4##]. Studies conducted in Sudan and Ethiopia for early malaria epidemic detection have suggested the use of both the 75th percentile and C-SUM methods as pre-malaria-outbreak warnings in areas with medium to high malaria transmission [##REF##30963119##15##, ##UREF##11##16##].</p>", "<p id=\"Par29\">The comparable sensitivity of the 75th percentile method and the C-SUM method in very low- and low-transmission areas and the significant differences observed in medium to high transmission areas suggests that the 75th percentile method could be applicable across all transmission levels. Since one objective of surveillance is the timely detection of outbreaks, the sensitivity of the 75th percentile method would provide timely detection of malaria epidemics, especially in medium- and high-malaria transmission areas. However, the use of this approach yielded more outbreaks than were feasible to respond to in Uganda during 2022. Thus, it may be useful to consider whether an alternate, less sensitive approach, such as the mean + SD method, could be applied for epidemic response prioritization when the 75th percentile yields more outbreak districts than can be adequately addressed with existing resources.</p>", "<p id=\"Par30\">On adjustment of the 75th percentile to the 85th percentile, no statistically significant difference was observed in the number of outbreak weeks for low and medium transmission areas. Other studies have proposed adjusting the 75th percentile to the 90th percentile instead of the 85th to better accommodate malaria seasonal peaks and improve outbreak detection [##UREF##3##4##, ##REF##28193215##17##–##REF##25778501##19##]. However, the small differences in outbreak weeks detected between the 75th percentile and the 85Th percentile might not suffice to recommend this adjustment for better accommodation of seasonal peaks. It may be useful to consider other modified approaches, such as modifying the 75th percentile to the 90th percentile to better accommodate seasonal peaks in some situations.</p>", "<p id=\"Par31\">On adjustment of the C-SUM method to the C-SUM + 2SD method, there was a significant decrease in the number of outbreak weeks detected, but no difference from the number of outbreak weeks detected by the mean + 2SD method. This similarity can be attributed to both methods using averages, with the main difference lying in their respective methodologies (the mean + 2SD method takes the mean number of cases for that week over the last five years and adds 2SD to establish the threshold. The C-SUM + 2SD method takes the running average of cases for the current epi week, the previous week, and the week after over the past 5 years and adds 2SD to establish a threshold). Similar findings were observed in Madagascar in a study analysing trends and forecasting malaria epidemics using a sentinel surveillance network which indicated improved specificity when the 2SD is added to the C-SUM [##REF##28193215##17##]. A consideration of C-SUM + 2SD for epidemic detection in medium to high malaria transmission districts could provide an alternative method for malaria epidemic detection to the mean + 2SD method.</p>", "<p id=\"Par32\">In Uganda, transmission levels, on which threshold approaches are meant to be based, are assessed using regional (larger; n = 15 in Uganda) data rather than the district (smaller; n = 146 in Uganda) data. Granularity in the actual malaria transmission levels, different from the regional transmission levels for the districts evaluated was identified. The study revealed notable differences in the malaria transmission of the evaluated districts and their nationally allocated regional malaria transmission levels. Districts in high-transmission regions were found to have medium- or low-transmission levels, while some districts in low-or very low-transmission regions had medium-transmission levels. These findings highlight the need for stratification of the malaria burden at district level rather than regional level. Stratification at district level could be helpful for instances when prioritization for epidemic response is required as it only applies to medium and high transmission areas. This could also support appropriate allocation of resources for improved malaria epidemic surveillance and response at district level.</p>", "<title>Limitations</title>", "<p id=\"Par33\">The study's limitations include the absence of a definitive gold standard approach for identifying outbreaks; however, this is inherent to a highly endemic setting for any disease. Additionally, methods were evaluated in only 16 out of 146 districts in Uganda due to under-reporting by most districts. However, the selected districts were distributed around the country and across all transmission levels, which may enhance the generalizability of the study findings.</p>" ]

[ "<title>Conclusion</title>", "<p id=\"Par34\">Our study demonstrated notable differences in district malaria transmission levels from the assigned regional malaria transmission levels. Among the districts evaluated, the 75th percentile approach proved most applicable for all transmission areas. However, the number of epidemic weeks detected for medium- and high-transmission areas was significantly higher than the mean + 2SD method. This would challenge response in resource-limited settings which is the majority of Africa where the malaria burden is high. We recommend use of the 75th percentile method for epidemic detection in all malaria transmission areas and the use of mean + 2SD for prioritization of districts for response in situations of low resources. Furthermore, the stratification of areas to the smallest geographical unit possible would ensure detection of localized malaria outbreaks. Additionally, re-calculation of malaria transmission levels at district level and re-categorization of districts rather than regions would ensure appropriate malaria outbreak surveillance and detection for appropriate response.</p>" ]

[ "<title>Background</title>", "<p id=\"Par1\">Malaria outbreaks are detected by applying the World Health Organization (WHO)-recommended thresholds (the less sensitive 75th percentile or mean + 2 standard deviations [2SD] for medium-to high-transmission areas, and the more sensitive cumulative sum [C-SUM] method for low and very low-transmission areas). During 2022, &gt; 50% of districts in Uganda were in an epidemic mode according to the 75th percentile method used, resulting in a need to restrict national response to districts with the highest rates of complicated malaria. The three threshold approaches were evaluated to compare their outbreak-signaling outputs and help identify prioritization approaches and method appropriateness across Uganda.</p>", "<title>Methods</title>", "<p id=\"Par2\">The three methods were applied as well as adjusted approaches (85th percentile and C-SUM + 2SD) for all weeks in 2022 for 16 districts with good reporting rates ( ≥ 80%). Districts were selected from regions originally categorized as very low, low, medium, and high transmission; district thresholds were calculated based on 2017–2021 data and re-categorized them for this analysis.</p>", "<title>Results</title>", "<p id=\"Par3\">Using district-level data to categorize transmission levels resulted in re-categorization of 8/16 districts from their original transmission level categories. In all districts, more outbreak weeks were detected by the 75th percentile than the mean + 2SD method (p &lt; 0.001). For all 9 very low or low-transmission districts, the number of outbreak weeks detected by C-SUM were similar to those detected by the 75th percentile. On adjustment of the 75th percentile method to the 85th percentile, there was no significant difference in the number of outbreak weeks detected for medium and low transmission districts. The number of outbreak weeks detected by C-SUM + 2SD was similar to those detected by the mean + 2SD method for all districts across all transmission intensities.</p>", "<title>Conclusion</title>", "<p id=\"Par4\">District data may be more appropriate than regional data to categorize malaria transmission and choose epidemic threshold approaches. The 75th percentile method, meant for medium- to high-transmission areas, was as sensitive as C-SUM for low- and very low-transmission areas. For medium and high-transmission areas, more outbreak weeks were detected with the 75th percentile than the mean + 2SD method. Using the 75th percentile method for outbreak detection in all areas and the mean + 2SD for prioritization of medium- and high-transmission areas in response may be helpful.</p>", "<title>Keywords</title>" ]

[ "<title>Data source</title>", "<p id=\"Par16\">Historic weekly malaria surveillance data from the District Health Information System version 2 (DHIS2) during 2017–2021 was used for the calculation of thresholds. The health facility malaria data are routinely generated at health facilities in outpatient registers. The data are aggregated weekly into health facility weekly surveillance reports, which are submitted to the DHIS2 using a short message system (SMS). This captures information for all health facilities in the districts. The weekly reporting rates for the districts can also be calculated based on data from this system using submitted reports (numerator) divided by expected reports (denominator). Districts with reporting rates of &lt; 80% are considered to have incomplete data submitted.</p>", "<title>Study variables, data abstraction, and analysis</title>", "<p id=\"Par17\">Pivot tables were used to filter secondary data on weekly confirmed malaria cases by both rapid diagnostic test (RDT) and microscopy from the health information management system weekly disease surveillance reports (HMIS 033b report) from 2017 to 2022 available in the DHIS2. Additionally, data on weekly reporting rates for all districts was extracted. Data were extracted for each year for each district. The Ministry of Health (MoH) considers a reporting rate of ≥ 80% as the minimal level for usable data. Sixteen out of 146 districts were selected for the evaluation based on having reporting rates ≥ 80% over the 5-year period and based on their stated regional malaria transmission intensity (four each in the high, medium, low, and very low transmission regions). District API was calculated using malaria cases (numerator) and the total population (denominator) obtained from Uganda Bureau of Statistics census data for the selected districts. Malaria transmission levels by district were re-calculated using district data to enable us evaluate the accuracy of regional-level assignment of transmission levels and evaluate the different threshold approaches accurately.</p>", "<p id=\"Par18\">Using 2022 as the year of review, thresholds were calculated using historic data from 2017 to 2021 for the selected districts. Thresholds were calculated using the three recommended approaches: Mean + 2SD, 75th percentile, and C-SUM to establish their outbreak detection sensitivity, using the highly sensitive C-SUM method as the reference. Case counts were not considered since Uganda is highly endemic for malaria and they are not recommended for such settings [##UREF##3##4##]. Malaria cases for 2022 were plotted together with the thresholds and displayed using line graphs.</p>", "<p id=\"Par19\">The 85th percentile and C-SUM + 2SD adjusted approaches were also evaluated to see how outbreak week detection changed from the original approaches. The difference in malaria outbreak weeks detected by the various methods were compared for significance using chi-square in STATA software version 14. Finally, the number of outbreak weeks detected by the method used during 2022 and the recommended threshold method were compared, based on the district transmission level. The level of significance was considered at p &lt; 0.05. For graphical presentation in this report, one district was picked randomly from each transmission level category (Fig. ##FIG##0##1##).</p>", "<title>Outbreak weeks detected per threshold approach and the difference in weeks detected for specific threshold approaches</title>", "<p id=\"Par21\">The number ‘outbreak weeks’ varied by method used across the different transmission levels. For all transmission levels, the difference in malaria outbreak weeks detected by the 75th percentile method and the mean + 2SD was statistically significant, with the 75th percentile method detecting ~ 1.5 to 30 times the number of outbreak weeks as the mean + 2SD method (p &lt; 0.001). In low- and very low-transmission areas, the more sensitive C-SUM method usually detected similar numbers of malaria outbreak weeks as the 75th percentile method. As transmission levels increased, there was a tendency for greater differences between the C-SUM method and the 75th percentile method, with the C-SUM method detecting more outbreak weeks (Table ##TAB##1##2##). On adjustment of the 75th percentile method to the 85th percentile, there was no difference in the number of outbreak weeks detected for low and medium transmission levels. The adjustment of C-SUM to C-SUM + 2SD reduced its sensitivity to make it equivalent to the mean + 2SD method (Table ##TAB##1##2##).</p>", "<title>Graphical presentation of malaria outbreak detection in a high-transmission district</title>", "<p id=\"Par22\">The 75th percentile and mean + 2SD methods are both meant to be used for medium- to high-transmission districts. Using Yumbe District (high-transmission district) data, malaria cases using the 75th percentile method exceeded the threshold in 31 weeks compared to 2 (non-sequential) weeks detected by the mean + 2SD method (p-value &lt; 0.001). Since a malaria outbreak is declared with 2 or more sequential outbreak weeks, with mean + 2SD, no malaria outbreak would be detected for Yumbe District. The 75th percentile method classified epidemics from weeks 1–15 and weeks 21–24 (Fig. ##FIG##1##2##).</p>", "<title>Graphical presentation of malaria outbreak detection in a medium transmission district</title>", "<p id=\"Par23\">Bundibugyo District, a medium-transmission district, showed 36 weeks exceeding the threshold using the 75th percentile method and 26 weeks using the mean + 2SD method. This would have resulted in the district having a malaria outbreak requiring epidemiologic investigation from weeks 5 to 25 using the mean + 2SD method, and weeks 4–25, 29–36, and 41–43 using the 75th percentile method (Fig. ##FIG##2##3##).</p>", "<title>Graphical presentation of malaria outbreak detection in a low malaria transmission district</title>", "<p id=\"Par24\">Alebtong District, a low-transmission district, showed 50 weeks exceeding the threshold using the 75th percentile method and 52 weeks using the C-SUM method. The district would have had a malaria outbreak requiring epidemic investigation for 49 weeks in 2022 using the 75th percentile method, and 52 epidemic weeks using the C-SUM method (Fig. ##FIG##3##4##).</p>", "<title>Graphical presentation of malaria outbreak detection in a very-low malaria transmission district</title>", "<p id=\"Par25\">For Kisoro District, Kigezi Region, an area of very low transmission also targeted for malaria elimination in the 2020–2025 Malaria Strategic Plan, the 75th percentile method detected 34 weeks above the threshold while the recommended C-SUM detected 26 weeks. This would have resulted in the district having a malaria outbreak requiring epidemic investigation from weeks 3–6 and 21–33 in 2022 using the C-SUM method, and weeks 3–6, 21–22, and 26–43 using the 75th percentile method (Fig. ##FIG##4##5##).</p>" ]

[ "<title>Acknowledgements</title>", "<p>We appreciate the National Malaria Control Division and other national malaria stakeholders for raising the questions that initiated this analysis.</p>", "<title>Author contributions</title>", "<p>GMZ conducted data extraction, analysis, and interpretation of the data under the technical guidance and supervision of JRH, ARA, DK, RM, BK and LB. GMZ drafted the manuscript. GMZ, JFZ, LB, RM, BBA, MKM, DK, BK, JO, ARA, and JRH, critically reviewed the manuscript for intellectual content. All co-authors read and approved the final manuscript. GMZ is the guarantor of the paper.</p>", "<title>Funding</title>", "<p>This project was supported by the President’s Emergency Plan for AIDS Relief (PEPFAR) through the US Centers for Disease Control and Prevention Cooperative Agreement number GH001353 through Makerere University School of Public Health to the Uganda Public Health Fellowship Program, Uganda National Institute of Public Health. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the US Centers for Disease Control and Prevention, the Department of Health and Human Services, Makerere University School of Public Health, or the MoH. The staff of the funding body provided technical guidance in the design of the study, ethical clearance and collection, analysis, and interpretation of data, and in writing the manuscript.</p>", "<title>Availability of data and materials</title>", "<p>The datasets upon which our findings are based belong to the Uganda Ministry of Health. However, the datasets can be availed upon reasonable request from the corresponding author and with permission from the Uganda Public Health Fellowship Program.</p>", "<title>Declarations</title>", "<title>Ethics approval and consent to participate</title>", "<p id=\"Par35\">We used routinely collected malaria surveillance data in the national health information management system DHIS2 that is publicly available for analysis and use to inform public health intervention. The data is aggregated with no individual identifiers. This activity was reviewed by the CDC and was conducted consistent with applicable federal law and CDC policy.§ §See e.g., 45 C.F.R. part 46, 21 C.F.R. part 56; 42 U.S.C. §241(d); 5 U.S.C. §552a; 44 U.S.C. §3501 et seq. This determination was made because the project aimed to address a public health problem and had the primary intent of public health practice. Additional clearance was obtained from the National Malaria Control division (NMCD) and the Division of Health Information (DHI).</p>", "<title>Consent for publication</title>", "<p id=\"Par36\">Not applicable.</p>", "<title>Competing interests</title>", "<p id=\"Par37\">The authors declare no conflicts of interest.</p>" ]

[ "<fig id=\"Fig1\"><label>Fig. 1</label><caption><p>Average regional malaria transmission rates, Uganda, 2017–2021</p></caption></fig>", "<fig id=\"Fig2\"><label>Fig. 2</label><caption><p>Weekly malaria cases and thresholds on the currently used 75th percentile and mean + 2SD for the year 2022 for the high transmission Yumbe District in West Nile Region, Northern Uganda</p></caption></fig>", "<fig id=\"Fig3\"><label>Fig. 3</label><caption><p>Weekly malaria cases on the currently used 75th percentile and mean + 2SD for the year 2022 for the medium-transmission Bundibugyo District in Tooro Region, Western Uganda</p></caption></fig>", "<fig id=\"Fig4\"><label>Fig. 4</label><caption><p>Weekly malaria cases on the currently used 75th percentile and C-SUM for the year 2022 for the low transmission Alebtong District in Lango Region, Northern Uganda</p></caption></fig>", "<fig id=\"Fig5\"><label>Fig. 5</label><caption><p>Weekly malaria cases on the currently used 75th percentile and C-SUM for the year 2022 for the low transmission Kisoro District in Southwestern Region, Uganda. This image shows clearly how the C-SUM method smooths out outliers in the data</p></caption></fig>" ]

[ "<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Malaria transmission levels and reporting rates over time for the study districts</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" rowspan=\"2\">Region</th><th align=\"left\" rowspan=\"2\">District</th><th align=\"left\" rowspan=\"2\">Original transmission category</th><th align=\"left\" colspan=\"5\">Malaria incidence/1000 population</th><th align=\"left\" colspan=\"5\">% Reporting rates</th></tr><tr><th align=\"left\">2017</th><th align=\"left\">2018</th><th align=\"left\">2019</th><th align=\"left\">2020</th><th align=\"left\">2021</th><th align=\"left\">2017</th><th align=\"left\">2018</th><th align=\"left\">2019</th><th align=\"left\">2020</th><th align=\"left\">2021</th></tr></thead><tbody><tr><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\" colspan=\"10\">High Transmission Regions</td></tr><tr><td align=\"left\">West Nile</td><td align=\"left\">Yumbe</td><td align=\"left\">High</td><td char=\".\" align=\"char\">483</td><td char=\".\" align=\"char\">551</td><td char=\".\" align=\"char\">659</td><td char=\".\" align=\"char\">696</td><td char=\".\" align=\"char\">565</td><td char=\".\" align=\"char\">89</td><td char=\".\" align=\"char\">98</td><td char=\".\" align=\"char\">100</td><td char=\".\" align=\"char\">97</td><td char=\".\" align=\"char\">100</td></tr><tr><td align=\"left\">West Nile</td><td align=\"left\">Nebbi</td><td align=\"left\">High</td><td char=\".\" align=\"char\">390</td><td char=\".\" align=\"char\">420</td><td char=\".\" align=\"char\">741</td><td char=\".\" align=\"char\">517</td><td char=\".\" align=\"char\">463</td><td char=\".\" align=\"char\">81</td><td char=\".\" align=\"char\">95</td><td char=\".\" align=\"char\">94</td><td char=\".\" align=\"char\">87</td><td char=\".\" align=\"char\">80</td></tr><tr><td align=\"left\">Acholi</td><td align=\"left\">Lamwo</td><td align=\"left\">High</td><td char=\".\" align=\"char\">960</td><td char=\".\" align=\"char\">356</td><td char=\".\" align=\"char\">567</td><td char=\".\" align=\"char\">756</td><td char=\".\" align=\"char\">753</td><td char=\".\" align=\"char\">81</td><td char=\".\" align=\"char\">85</td><td char=\".\" align=\"char\">93</td><td char=\".\" align=\"char\">92</td><td char=\".\" align=\"char\">86</td></tr><tr><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td char=\".\" align=\"char\" colspan=\"10\">Medium Transmission Regions</td></tr><tr><td align=\"left\">Acholi</td><td align=\"left\">Nwoya</td><td align=\"left\">High</td><td char=\".\" align=\"char\">264</td><td char=\".\" align=\"char\">347</td><td char=\".\" align=\"char\">659</td><td char=\".\" align=\"char\">473</td><td char=\".\" align=\"char\">277</td><td char=\".\" align=\"char\">86</td><td char=\".\" align=\"char\">84</td><td char=\".\" align=\"char\">82</td><td char=\".\" align=\"char\">94</td><td char=\".\" align=\"char\">100</td></tr><tr><td align=\"left\">Karamoja</td><td align=\"left\">Moroto</td><td align=\"left\">Medium</td><td char=\".\" align=\"char\">294</td><td char=\".\" align=\"char\">224</td><td char=\".\" align=\"char\">389</td><td char=\".\" align=\"char\">473</td><td char=\".\" align=\"char\">458</td><td char=\".\" align=\"char\">80</td><td char=\".\" align=\"char\">96</td><td char=\".\" align=\"char\">89</td><td char=\".\" align=\"char\">90</td><td char=\".\" align=\"char\">90</td></tr><tr><td align=\"left\">South Central</td><td align=\"left\">Butambala</td><td align=\"left\">Low</td><td char=\".\" align=\"char\">593</td><td char=\".\" align=\"char\">309</td><td char=\".\" align=\"char\">508</td><td char=\".\" align=\"char\">383</td><td char=\".\" align=\"char\">241</td><td char=\".\" align=\"char\">86</td><td char=\".\" align=\"char\">80</td><td char=\".\" align=\"char\">95</td><td char=\".\" align=\"char\">84</td><td char=\".\" align=\"char\">88</td></tr><tr><td align=\"left\">Tooro</td><td align=\"left\">Bundibugyo</td><td align=\"left\">Low</td><td char=\".\" align=\"char\">437</td><td char=\".\" align=\"char\">300</td><td char=\".\" align=\"char\">359</td><td char=\".\" align=\"char\">340</td><td char=\".\" align=\"char\">380</td><td char=\".\" align=\"char\">89</td><td char=\".\" align=\"char\">96</td><td char=\".\" align=\"char\">95</td><td char=\".\" align=\"char\">91</td><td char=\".\" align=\"char\">96</td></tr><tr><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td char=\".\" align=\"char\" colspan=\"10\">Low Transmission Regions</td></tr><tr><td align=\"left\">Kigezi</td><td align=\"left\">Kanungu</td><td align=\"left\">Very low</td><td char=\".\" align=\"char\">247</td><td char=\".\" align=\"char\">148</td><td char=\".\" align=\"char\">211</td><td char=\".\" align=\"char\">247</td><td char=\".\" align=\"char\">219</td><td char=\".\" align=\"char\">80</td><td char=\".\" align=\"char\">80</td><td char=\".\" align=\"char\">80</td><td char=\".\" align=\"char\">80</td><td char=\".\" align=\"char\">84</td></tr><tr><td align=\"left\">Lango</td><td align=\"left\">Alebtong</td><td align=\"left\">Medium</td><td char=\".\" align=\"char\">117</td><td char=\".\" align=\"char\">40</td><td char=\".\" align=\"char\">72</td><td char=\".\" align=\"char\">152</td><td char=\".\" align=\"char\">394</td><td char=\".\" align=\"char\">80</td><td char=\".\" align=\"char\">83</td><td char=\".\" align=\"char\">90</td><td char=\".\" align=\"char\">87</td><td char=\".\" align=\"char\">90</td></tr><tr><td align=\"left\">Bukedi</td><td align=\"left\">Kibuku</td><td align=\"left\">Medium</td><td char=\".\" align=\"char\">60</td><td char=\".\" align=\"char\">19</td><td char=\".\" align=\"char\">25</td><td char=\".\" align=\"char\">60</td><td char=\".\" align=\"char\">470</td><td char=\".\" align=\"char\">89</td><td char=\".\" align=\"char\">97</td><td char=\".\" align=\"char\">100</td><td char=\".\" align=\"char\">96</td><td char=\".\" align=\"char\">100</td></tr><tr><td align=\"left\">South Central</td><td align=\"left\">Mpigi</td><td align=\"left\">Low</td><td char=\".\" align=\"char\">332</td><td char=\".\" align=\"char\">114</td><td char=\".\" align=\"char\">168</td><td char=\".\" align=\"char\">131</td><td char=\".\" align=\"char\">84</td><td char=\".\" align=\"char\">97</td><td char=\".\" align=\"char\">93</td><td char=\".\" align=\"char\">85</td><td char=\".\" align=\"char\">80</td><td char=\".\" align=\"char\">82</td></tr><tr><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td char=\".\" align=\"char\" colspan=\"10\">Very Low Transmission Regions</td></tr><tr><td align=\"left\">Tooro</td><td align=\"left\">Ntoroko</td><td align=\"left\">Low</td><td char=\".\" align=\"char\">100</td><td char=\".\" align=\"char\">35</td><td char=\".\" align=\"char\">53</td><td char=\".\" align=\"char\">88</td><td char=\".\" align=\"char\">121</td><td char=\".\" align=\"char\">91</td><td char=\".\" align=\"char\">91</td><td char=\".\" align=\"char\">82</td><td char=\".\" align=\"char\">98</td><td char=\".\" align=\"char\">89</td></tr><tr><td align=\"left\">Kigezi</td><td align=\"left\">Rukiga</td><td align=\"left\">Very low</td><td char=\".\" align=\"char\">42</td><td char=\".\" align=\"char\">16</td><td char=\".\" align=\"char\">16</td><td char=\".\" align=\"char\">14</td><td char=\".\" align=\"char\">51</td><td char=\".\" align=\"char\">80</td><td char=\".\" align=\"char\">83</td><td char=\".\" align=\"char\">81</td><td char=\".\" align=\"char\">93</td><td char=\".\" align=\"char\">100</td></tr><tr><td align=\"left\">Kigezi</td><td align=\"left\">Kisoro</td><td align=\"left\">Very low</td><td char=\".\" align=\"char\">31</td><td char=\".\" align=\"char\">29</td><td char=\".\" align=\"char\">99</td><td char=\".\" align=\"char\">32</td><td char=\".\" align=\"char\">16</td><td char=\".\" align=\"char\">80</td><td char=\".\" align=\"char\">84</td><td char=\".\" align=\"char\">84</td><td char=\".\" align=\"char\">80</td><td char=\".\" align=\"char\">84</td></tr><tr><td align=\"left\">Kigezi</td><td align=\"left\">Rubanda</td><td align=\"left\">Very low</td><td char=\".\" align=\"char\">24</td><td char=\".\" align=\"char\">13</td><td char=\".\" align=\"char\">15</td><td char=\".\" align=\"char\">16</td><td char=\".\" align=\"char\">14</td><td char=\".\" align=\"char\">95</td><td char=\".\" align=\"char\">97</td><td char=\".\" align=\"char\">93</td><td char=\".\" align=\"char\">92</td><td char=\".\" align=\"char\">97</td></tr><tr><td align=\"left\">Bugisu</td><td align=\"left\">Bukwo</td><td align=\"left\">Low</td><td char=\".\" align=\"char\">68</td><td char=\".\" align=\"char\">37</td><td char=\".\" align=\"char\">70</td><td char=\".\" align=\"char\">36</td><td char=\".\" align=\"char\">90</td><td char=\".\" align=\"char\">92</td><td char=\".\" align=\"char\">94</td><td char=\".\" align=\"char\">100</td><td char=\".\" align=\"char\">89</td><td char=\".\" align=\"char\">90</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab2\"><label>Table 2</label><caption><p>Outbreak weeks detected per threshold approach and the difference in weeks detected for specific threshold approaches for selected districts in Uganda, 2022</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" rowspan=\"2\">Region</th><th align=\"left\" rowspan=\"2\">District</th><th align=\"left\" colspan=\"5\">Total number of outbreak weeks detected per method</th><th align=\"left\" colspan=\"4\">Statistical difference in weeks detected by the methods (p-value)</th></tr><tr><th align=\"left\">C-SUM</th><th align=\"left\">75%</th><th align=\"left\">Mean + 2SD</th><th align=\"left\">C-SUM + 2SD</th><th align=\"left\">85%</th><th align=\"left\">C-SUM vs. 75th Perc</th><th align=\"left\">75% vs. Mean + 2 SD</th><th align=\"left\">75% vs. 85%</th><th align=\"left\">Mean + 2SD vs. C-SUM + 2SD</th></tr></thead><tbody><tr><td align=\"left\" colspan=\"2\"/><td align=\"left\" colspan=\"9\">High transmission regions</td></tr><tr><td align=\"left\">West Nile</td><td align=\"left\">Yumbe</td><td align=\"left\">42</td><td align=\"left\">31</td><td char=\".\" align=\"char\">2</td><td char=\".\" align=\"char\">2</td><td align=\"left\">21</td><td align=\"left\">0.02</td><td align=\"left\"> &lt; 0.001</td><td align=\"left\">0.04</td><td align=\"left\">1</td></tr><tr><td align=\"left\">West Nile</td><td align=\"left\">Nebbi</td><td align=\"left\">32</td><td align=\"left\">23</td><td char=\".\" align=\"char\">2</td><td char=\".\" align=\"char\">2</td><td align=\"left\">13</td><td align=\"left\">0.08</td><td char=\".\" align=\"char\"> &lt; 0.001</td><td align=\"left\">0.04</td><td align=\"left\">1</td></tr><tr><td align=\"left\">Acholi</td><td align=\"left\">Lamwo</td><td align=\"left\">46</td><td align=\"left\">33</td><td char=\".\" align=\"char\">6</td><td char=\".\" align=\"char\">6</td><td align=\"left\">26</td><td align=\"left\">0.003</td><td char=\".\" align=\"char\"> &lt; 0.001</td><td align=\"left\">0.02</td><td align=\"left\">1</td></tr><tr><td align=\"left\"/><td align=\"left\"/><td align=\"left\" colspan=\"9\">Medium transmission regions</td></tr><tr><td align=\"left\">Acholi</td><td align=\"left\">Nwoya</td><td align=\"left\">42</td><td align=\"left\">30</td><td char=\".\" align=\"char\">1</td><td char=\".\" align=\"char\">1</td><td align=\"left\">17</td><td align=\"left\">0.02</td><td char=\".\" align=\"char\"> &lt; 0.001</td><td align=\"left\">0.01</td><td align=\"left\">1</td></tr><tr><td align=\"left\">Karamoja</td><td align=\"left\">Moroto</td><td align=\"left\">48</td><td align=\"left\">37</td><td char=\".\" align=\"char\">10</td><td char=\".\" align=\"char\">11</td><td align=\"left\">29</td><td align=\"left\">0.01</td><td char=\".\" align=\"char\"> &lt; 0.001</td><td align=\"left\">0.1</td><td align=\"left\">1</td></tr><tr><td align=\"left\">South Central</td><td align=\"left\">Butambala</td><td align=\"left\">38</td><td align=\"left\">20</td><td char=\".\" align=\"char\">5</td><td char=\".\" align=\"char\">5</td><td align=\"left\">14</td><td align=\"left\"> &lt; 0.001</td><td char=\".\" align=\"char\"> &lt; 0.001</td><td align=\"left\">0.21</td><td align=\"left\">1</td></tr><tr><td align=\"left\">Tooro</td><td align=\"left\">Bundibugyo</td><td align=\"left\">45</td><td align=\"left\">39</td><td char=\".\" align=\"char\">26</td><td char=\".\" align=\"char\">25</td><td align=\"left\">35</td><td align=\"left\">0.14</td><td char=\".\" align=\"char\">0.01</td><td align=\"left\">0.39</td><td align=\"left\">0.84</td></tr><tr><td align=\"left\"/><td align=\"left\"/><td align=\"left\" colspan=\"9\">Low transmission regions</td></tr><tr><td align=\"left\">Kigezi</td><td align=\"left\">Kanungu</td><td align=\"left\">35</td><td align=\"left\">27</td><td char=\".\" align=\"char\">11</td><td char=\".\" align=\"char\">11</td><td align=\"left\">17</td><td align=\"left\">0.06</td><td char=\".\" align=\"char\">0.03</td><td align=\"left\">0.42</td><td align=\"left\">1</td></tr><tr><td align=\"left\">Lango</td><td align=\"left\">Alebtong</td><td align=\"left\">52</td><td align=\"left\">50</td><td char=\".\" align=\"char\">39</td><td char=\".\" align=\"char\">40</td><td align=\"left\">49</td><td align=\"left\">0.15</td><td char=\".\" align=\"char\">0.004</td><td align=\"left\">1</td><td align=\"left\">0.8</td></tr><tr><td align=\"left\">Bukedi</td><td align=\"left\">Kibuku</td><td align=\"left\">52</td><td align=\"left\">52</td><td char=\".\" align=\"char\">47</td><td char=\".\" align=\"char\">47</td><td align=\"left\">52</td><td align=\"left\">1</td><td char=\".\" align=\"char\">0.05</td><td align=\"left\">1</td><td align=\"left\">1</td></tr><tr><td align=\"left\">South Central</td><td align=\"left\">Mpigi</td><td align=\"left\">19</td><td align=\"left\">21</td><td char=\".\" align=\"char\">3</td><td char=\".\" align=\"char\">2</td><td align=\"left\">9</td><td align=\"left\">0.69</td><td char=\".\" align=\"char\"> &lt; 0.01</td><td align=\"left\">1</td><td align=\"left\">1</td></tr><tr><td align=\"left\"/><td align=\"left\"/><td align=\"left\" colspan=\"9\">Very low transmission regions</td></tr><tr><td align=\"left\">Tooro</td><td align=\"left\">Ntoroko</td><td align=\"left\">50</td><td align=\"left\">46</td><td char=\".\" align=\"char\">38</td><td char=\".\" align=\"char\">37</td><td align=\"left\">44</td><td align=\"left\">0.37</td><td char=\".\" align=\"char\">0.04</td><td align=\"left\">0.57</td><td align=\"left\">0.83</td></tr><tr><td align=\"left\">Kigezi</td><td align=\"left\">Rukiga</td><td align=\"left\">47</td><td align=\"left\">47</td><td char=\".\" align=\"char\">29</td><td char=\".\" align=\"char\">29</td><td align=\"left\">38</td><td align=\"left\">1</td><td char=\".\" align=\"char\"> &lt; 0.001</td><td align=\"left\">0.02</td><td align=\"left\">1</td></tr><tr><td align=\"left\">Kigezi</td><td align=\"left\">Kisoro</td><td align=\"left\">26</td><td align=\"left\">34</td><td char=\".\" align=\"char\">0</td><td char=\".\" align=\"char\">0</td><td align=\"left\">7</td><td align=\"left\">0.17</td><td char=\".\" align=\"char\"> &lt; 0.001</td><td align=\"left\"> &lt; 0.001</td><td align=\"left\">1</td></tr><tr><td align=\"left\">Kigezi</td><td align=\"left\">Rubanda</td><td align=\"left\">20</td><td align=\"left\">14</td><td char=\".\" align=\"char\">0</td><td char=\".\" align=\"char\">0</td><td align=\"left\">7</td><td align=\"left\">0.14</td><td char=\".\" align=\"char\"> &lt; 0.001</td><td align=\"left\">0.05</td><td align=\"left\">1</td></tr><tr><td align=\"left\">Bugisu</td><td align=\"left\">Bukwo</td><td align=\"left\">38</td><td align=\"left\">34</td><td char=\".\" align=\"char\">12</td><td char=\".\" align=\"char\">9</td><td align=\"left\">25</td><td align=\"left\">0.07</td><td char=\".\" align=\"char\"> &lt; 0.001</td><td align=\"left\">0.08</td><td align=\"left\">0.46</td></tr></tbody></table></table-wrap>" ]

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[ "<table-wrap-foot><p>75%: 75th percentile; 85%: 85th percentile</p></table-wrap-foot>", "<fn-group><fn><p><bold>Publisher's Note</bold></p><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn></fn-group>" ]

[ "<graphic xlink:href=\"12936_2024_4838_Fig1_HTML\" id=\"MO1\"/>", "<graphic xlink:href=\"12936_2024_4838_Fig2_HTML\" id=\"MO2\"/>", "<graphic xlink:href=\"12936_2024_4838_Fig3_HTML\" id=\"MO3\"/>", "<graphic xlink:href=\"12936_2024_4838_Fig4_HTML\" id=\"MO4\"/>", "<graphic xlink:href=\"12936_2024_4838_Fig5_HTML\" id=\"MO5\"/>" ]

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{ "acronym": [ "API", "DHIS2", "HC", "SD", "C-SUM", "HMIS" ], "definition": [ "Annual parasite incidence", "District Health Information System (DHIS2)", "Health facility", "Standard deviation", "Cumulative sum", "Health Management Information System" ] }

[ "<title>Introduction</title>", "<p id=\"Par5\">As a common malignancy threatening women's health, endometrial cancer (EC) is characterized with a continuously increasing incidence over the past decade. At present, EC has become the second leading cause of gynecological cancer-related death in women [##REF##35397864##1##]. For a long time, there are no clear standard classification methods for the molecular classification of EC. In 2013, the Cancer Genome Atlas (TCGA) research network broke through the limitations of the CE classification by integrating molecular characterization. However, such classification method is limited by the main disadvantages of its considerable complexity and impracticality in clinical practice [##REF##36833105##2##, ##REF##36983243##3##]. According to the classification by the World Health Organization (WHO) in 2014, EC is divided into atypical hyperplasia and atypical hyperplasia. Among patients with atypical hyperplasia, 32 to 37% of cases may develop EC, with a high risk of progression up to 25% [##REF##36979434##4##]. It was reported that EC patients diagnosed in the early stage have a good prognosis with a 5-year survival rate of ≥ 95%. However, the survival rate is significantly reduced in patients diagnosed with advanced or recurrent EC, and their 5-year survival rate is less than 20% even after combination therapy [##REF##31074865##5##]. Chemotherapy and hormone therapy are the main management strategies for patients with advanced EC. The main reason for unsatisfactory treatment outcomes is the emergence of drug-resistant tumor cells during treatment. Long-term use of chemotherapeutic drugs makes tumor cells that initially respond to anticancer agents become insensitive or even resistant [##REF##31388127##6##]. Doxorubicin, sorafenib, cisplatin and paclitaxel (also known as taxol) all are prevalent drugs for the treatment of EC, and although the mechanisms are different, they can all induce the death of cancer cells. However, tumor cells resistant to most chemotherapeutic agents, including EC, have been found clinically [##REF##30867105##7##]. Previous studies have revealed the effect of salinomycin on mRNA and miRNA expression of drug-resistant genes in Ishikawa EC cell lines by microarray analysis and RT-qPCR. According to the analysis results, the expression of TUFT1, MTMR11 and SLC30A5 differed most significantly; besides, the influence probability between TUFT1 and hsa-miR-3188 (FC + 2.48), mtmr11 and has-miR-16 (FC-1.74), SLC30A5 and hsa-mir-30d (FC-2.01) was the highest. These results indicated changes in mRNA and miRNA activity involved in drug resistance, and these characteristic changes were expected as a result of anticancer therapy [##REF##32598254##8##]. The underlying causes of drug resistance in malignancies are complex. Vermij L et al. demonstrated that resistance to paclitaxel in EC was attributed to the multidrug resistance 1 (MDR-1) gene expressing the P-glycoprotein (P-gp), and point mutations in the tubulin binding sites that interacted with paclitaxel [##REF##31846532##9##]. For improving the treatment of EC, the molecular mechanism of resistance to anticancer agents needs further investigation.</p>", "<p id=\"Par6\">Fanconi anemia complementation group D2 (Fancd2) is a nuclear protein involved in DNA damage repair. Fancd2 was initially found to be an essential protein for the development of Fanconi anemia [##REF##29376519##10##], but subsequent studies have revealed its association with cancer development. For example, Houghtaling et al. showed that mice lacking Fancd2 were prone to cancers, including acute myeloid leukemia and squamous cell carcinoma [##REF##12893777##11##]. Lisa et al. observed that high expression of Fancd2 promoted excessive proliferation and metastasis of esophageal squamous cell carcinoma cells [##REF##32906798##12##]. Sonali et al. suggested a correlation between subcellular localization of Fancd2 and ovarian cancer survival; and Fancd2 localized in the nucleus led to reduced patient survival [##REF##32165999##13##]. Collectively, the role of Fancd2 in cancers is evident. Fancd2, moreover, has recently been reported to be associated with chemoresistance in cancer cells. As early as 2005, a study by Chirnomas D et al. pointed out that inhibition of the Fanconi anemia pathway was effective in restoring cisplatin sensitivity in ovarian and breast tumor cell lines [##REF##16648566##14##]. Alex et al. also proved that reducing Fancd2 expression could not only restore the sensitivity of the human breast epithelial cell line MCF10A to mitomycin C, but also inhibit the repopulation ability of the cancer cells [##REF##17643815##15##]. In addition, the association between Fancd2 and drug resistance has been reported in multiple myeloma, ovarian cancer, non-small cell lung cancer, and head and neck cancer in vitro [##REF##17643815##15##]. Based on previous studies, we attempted to propose new solutions to solve EC drug resistance through exploring the correlation of Fancd2 with EC development and chemoresistance in EC.</p>" ]

[ "<title>Materials and methods</title>", "<title>Tissue specimens</title>", "<p id=\"Par7\">Tissue specimens were collected from 20 patients pathologically diagnosed as EC in Meizhou People’s Hospital, Meizhou Academy of Medical Sciences between January 2016 and May 2019. These patients did not receive any chemotherapy or radiotherapy before surgery, and tumor tissue (EC group) and adjacent normal tissue (Normal group) were obtained after surgery. The tissue specimen collection was approved by the ethical committee of Meizhou People’s Hospital, Meizhou Academy of Medical Sciences (Ethical No.: 2022-C-83) and written informed consent was acquired from all patients. The clinical information of patients was shown in Table ##TAB##0##1##.\n</p>", "<title>Cell culture</title>", "<p id=\"Par8\">Human endometrial epithelial cells (hEECs, XY-XB-1546) and EC cells (Ishikawa, SNL-171) were purchased from the American Type Culture Institute. Cell culture was achieved in a RPMI-1640 medium supplemented with 10% fetal bovine serum (FBS) and 1% penicillin/streptomycin, with incubation conditions of 37 °C and 5% CO₂. The Ishikawa cells were exposed to different concentrations of paclitaxel to obtain paclitaxel-resistant cells (Ishikawa/TAX).</p>", "<title>Cell transfection</title>", "<p id=\"Par9\">Ishikawa cells were transfected with negative pcDNA3.1 (Vector group), over-expression plasmid pcDNA3.1-Fancd2 (Fancd2 group), negative siRNA (siNC group), and Fancd2 siRNA (si-Fancd2 group) by lipo3000 transfection kit (LMRNA001, Invitrogen, California, USA). Ishikawa/TAX cells were transfected with negative siRNA (siNC group), Fancd2 siRNA (si-Fancd2 group), and treated with Ferrostatin-1 (Fer-1, 20 μM).</p>", "<title>Real-time quantitative PCR (RT-qPCR)</title>", "<p id=\"Par10\">The tissue specimens were added with 1 mL TRizol (12183555, Invitrogen, California, USA), and then subjected to a thorough homogenization in a homogenizer and centrifugation at 12,000 rpm for 10 min. The obtained supernatant was centrifuged with 200 uL chloroform to acquire new supernatant, followed by addition of 500 uL isopropanol and centrifugation to allow precipitation of RNA. The acquired RNA was dissolved with nuclease-free water. The same procedures were followed to extract RNA from the cells. After that, 1 μg RNA was reversely transcribed into cDNA using M-MLV reverse transcriptase (28025013, Invitrogen, California, USA). Subsequently, real-time quantitative PCR (RT-qPCR) assay was performed using the SYBR Green PCR kit (4344463, Invitrogen, California, USA) on the Quant Studio 6 Flex system (Applied Biosystems, USA), and the cycle threshold (Ct) of each gene was recorded. The relative expression of the target gene was calculated using the 2^−ΔΔCt method, with glyceraldehyde-3-phosphate dehydrogenase (GAPDH) as the internal reference gene. Real-time PCR cycles included: 95 °C for 10 min, 40 cycles (95 °C for 15 s, 67 °C for 30 s, 72 °C for 30 s), and 72 °C for 5 min. Primers used were shown in Table ##TAB##1##2## 3-(4,5-dimethyl-2-thiazolyl)-2,5-diphenyl-2-H-tetrazolium bromide (MTT) assay Ishikawa or Ishikawa/TAX cells were seeded in 96-well plates at 5 × 10³ cells/well. Upon completion of transfection with/without Fer-1 treatment, the cells were incubated with different concentrations of paclitaxel, cisplatin, doxorubicin, and sorafenib for 24 h. Then, 3-(4,5-dimethyl-2-thiazolyl)-2,5-diphenyl-2-H-tetrazolium bromide (MTT, C0009S, Beyotime Biotechnology, Shanghai, China) solution (20 μL) was added to each well. Following 4 h of incubation, the reaction was stopped with 150 μL DMSO (ST1276, Beyotime Biotechnology, Shanghai, China) and the cells were shaken for 10 min at room temperature. Subsequently, the absorbance was measured at 490 nm by a microplate reader (Model: 550, BIO-RAD, China) and the viability of cells was calculated.\n</p>", "<title>Detection of reactive oxygen species level</title>", "<p id=\"Par11\">Ishikawa or Ishikawa/TAX cells were seeded in 12-well plates at 5 × 10⁵ cells/well. Firstly, the cells were subject to transfection with/without Fer-1 treatment. Then, 10 μM carboxy-H2DCFDA (C400, ThermoFisher Scientific, California, USA) was added for 30 min of cell incubation at 37 °C in the dark. Subsequently, the cells were washed twice with PBS, resuspended with trypsin, and then collected. Fluorescence values were measured by flow cytometry (emission 495 nm and absorption 525 nm).</p>", "<title>Apoptosis</title>", "<p id=\"Par12\">In strict accordance with the corresponding instructions, Annexin V-FITC (fluorescein Isothiocyanate)/PI (propidium iodide) apoptosis detection kits (APOAF, Sigma-Aldrich, St. Louis, Missouri, USA) were used to detect the apoptosis level of Ishikawa or Ishikawa/TAX cells after different treatments. In short, the cells were washed with PBS, and the cell density was adjusted. Then, the cells were suspended in a 500 μL binding buffer and incubated with 5 μL Annexin V-FITC and 5 μL PI for 30 min at room temperature. After that, the cells were transferred into a flow tube and examined on the Accuri C6 flow cytometer (Tomy Digital Biology, CA, USA).</p>", "<title>Colony formation assay</title>", "<p id=\"Par13\">Ishikawa or Ishikawa/TAX cells were seeded in 6-well plates with 1 × 10³ cells per well. After 2 weeks of culture, the cells were fixed with 4% paraformaldehyde and then stained with 0.5% crystal violet. The results were observed with a microscope (Nikon, Japan) and analyzed using the ImageJ software.</p>", "<title>Detection of malondialdehyde, glutathione, and Fe²⁺ levels</title>", "<p id=\"Par14\">Ishikawa or Ishikawa/TAX cells were seeded in 6-well plates at 1 × 10⁶ cells/well. Then, the cells were transfected to knock down or over-express Fancd2 and treated with Fer-1. After that, the cells were washed twice with PBS, followed by cell lysis and centrifugation at 12,000 rpm for 10 min. Next, the supernatant was collected for the detection of malondialdehyde (MDA, S0131S) activity, glutathione (GSH, S0052) level, and Fe²⁺ level (S0116) according to the protocol of corresponding kits (Beyotime Biotechnology, Shanghai, China).</p>", "<title>Western blot</title>", "<p id=\"Par15\">Cells were solubilized in RIPA lysis buffer (P0013B, Beyotime Biotechnology, Shanghai, China), and the supernatant was collected after centrifugation at 12,000 rpm for 20 min. Total protein concentration was detected by BCA assay (P0009, Beyotime Biotechnology, Shanghai, China). Proteins were then separated using 12% SDS-PAGE (P0012A, Beyotime Biotechnology, Shanghai, China), and transferred onto polyvinylidene fluoride membranes (PVDF, 88585, ThermoFisher Scientific, California, USA). Upon completion of blocking step in 5% skimmed milk for 1 h, the membranes were incubated overnight at 4 °C with primary antibodies Fancd2 (1:1000, ab108928; Abcam, Cambridge, UK), solute farrier family 7 member 11 (SLC7A11, 1:1000, ab175186; Abcam, Cambridge, UK), and glutathione peroxidase 4 (GPX4, 1:1000, ab252833; Abcam, Cambridge, UK). On the next day, the membranes were incubated with HRP-conjugated secondary antibodies (1:5000, ab205719; Abcam, Cambridge, UK) for 2 h. After that, the protein bands were visualized using the ECL Western blotting Kit (32109, ThermoFisher Scientific, California, USA). Finally, the relative expression of proteins was calculated using GAPDH as an internal reference.</p>", "<title>Statistical analysis</title>", "<p id=\"Par16\">Data were analyzed by Statistical Package for the Social Sciences version 26.0. Differences between two groups were analyzed using paired t tests, and comparisons among multiple groups were analyzed by one-way analysis of variance and Tukey 's post hoc test. <italic>P</italic> &lt; 0.05 was used as the criterion for a significant difference. All experiments were repeated three times.</p>" ]

[ "<title>Results</title>", "<title>Fancd2 was up-regulated in endometrial cancer and associated with chemoresistance</title>", "<p id=\"Par17\">Fancd2 expression in EC tissues and cells was measured to explore the role of Fancd2 in EC. RT-qPCR and western blot showed that Fancd2 expression was significantly increased in the EC group compared with the Normal group (Fig. ##FIG##0##1##A, B), and was markedly higher in Ishikawa cells than in hEECs (Fig. ##FIG##0##1##C, D). Further, to observe the effect of Fancd2 on EC chemoresistance, Ishikawa cells were subjected to transfection to inhibit or enhance Fancd2 expression. RT-qPCR and western blot confirmed the up-regulation of Fancd2 in the Fancd2 group compared with the Vector group and the down-regulation of Fancd2 in the si-Fancd2 group compared with the siNC group (Fig. ##FIG##0##1##E, F). Subsequently, the effect of over-expression or knock-down of Fancd2 on drug resistance in Ishikawa cells was assessed by MTT assay. The assay results demonstrated that Ishikawa cells over-expressing Fancd2 presented with significantly increased inhibitory concentration (IC)50 under paclitaxel, cisplatin, doxorubicin, and sorafenib treatment, while knock-down of Fancd2 showed the opposite outcome (Fig. ##FIG##0##1##G–J). These results indicated that Fancd2 was up-regulated in EC and was associated with resistance to chemotherapy.</p>", "<title>Fancd2 was up-regulated in Ishikawa/TAX cells and associated with paclitaxel resistance</title>", "<p id=\"Par18\">Measurement of Fancd2 expression in Ishikawa/TAX cells was conducted for further observing the effect of Fancd2 on chemoresistance in Ishikawa cells. Based on the RT-qPCR and western blot results, the expression of Fancd2 was much higher in Ishikawa/TAX cells than in Ishikawa cells (Fig. ##FIG##1##2##A, B). Subsequently, MTT assay revealed that compared with Ishikawa cells, IC50 was markedly increased in Ishikawa/TAX cells after paclitaxel treatment (Fig. ##FIG##1##2##C). Clonal formation experiment exhibited that the number of cell clones in Ishikawa-PR cells increased significantly compared with Ishikawa cells (Fig. ##FIG##1##2##D). Besides, flow cytometry further revealed that compared with Ishikawa cells, the level of apoptosis in Ishikawa PR cells was significantly reduced (Fig. ##FIG##1##2##E). These results suggested that up-regulation of Fancd2 expression was possibly associated with cellular resistance to paclitaxel.</p>", "<title>Ferroptosis was decreased in Ishikawa/TAX cells</title>", "<p id=\"Par19\">Ferroptosis is a common mode of death in cancer cells [##REF##36317423##16##]. Detection of ROS level, MDA activity, GSH, and Fe²⁺ levels in Ishikawa/TAX cells allowed the determination of ferroptosis level in cells. Compared with Ishikawa cells, Ishikawa/TAX cells showed a significant decline in ROS level, MDA activity, GSH, and Fe²⁺ levels (Fig. ##FIG##2##3##A–D). Subsequently, the protein expression levels of SLC7A11 and GPX4 in the cells were detected by western blot. The western blot outcomes showed that the protein expression levels of SLC7A11 and GPX4 in the Ishikawa/TAX group were significantly higher than those in the Ishikawa group (Fig. ##FIG##2##3##E). The above results indicated a decrease in ferroptosis levels in paclitaxel-resistant Ishikawa cells.</p>", "<title>Knock-down of Fancd2 improved paclitaxel sensitivity by promoting ferroptosis</title>", "<p id=\"Par20\">Subsequently, Fancd2 expression was knocked down by transfection of Fancd2 siRNA into Ishikawa/TAX cells. RT-qPCR showed that Fancd2 expression levels were significantly reduced in Ishikawa/TAX cells in the si-Fancd2 group compared with the siNC group (Fig. ##FIG##3##4##A). Additionally, MTT assay results indicated that the IC50 of cells in the si-Fancd2 group was significantly lower than that in the siNC group (Fig. ##FIG##3##4##B). Furthermore, the clone formation experiment showed that compared with the siNC group, the number of cell clones in the si-Fancd2 group significantly decreased, while the number of cell clones in the Fer-1 group significantly increased; relative to the si-Fancd2 group, the number of cell clones in the si-Fancd2 + Fer-1 group significantly increased; in contrast to the Fer-1 group, the number of cell clones in the si-Fancd2 + Fer-1 group was significantly reduced (Fig. ##FIG##3##4##C). Flow cytometry showed that compared with the siNC group, the apoptosis level in the si-Fancd2 group was significantly increased, while that in the Fer-1 group was significantly decreased. In comparison with the si-Fancd2 group, the level of apoptosis in the si-Fancd2 + Fer-1 group was significantly reduced; while compared with the Fer-1 group, the apoptosis level in the si-Fancd2 + Fer-1 group was significantly increased (Fig. ##FIG##3##4##D).</p>", "<p id=\"Par21\">To verify the relationship of Fancd2 over-expression with cellular resistance and ferroptosis, Fancd2 was knocked down, and the ferroptosis inhibitor Fer-1 was employed to treat cells. The results showed that knock-down of Fancd2 significantly increased the levels of ROS, GSH, and Fe²⁺ and the activity of MDA in Ishikawa/TAX cells compared with the siNC group (Fig. ##FIG##4##5##A–E). Western blot results also revealed that knock-down of Fancd2 significantly reduced the protein expression levels of SLC7A11 and GPX4 in Ishikawa/TAX cells (Fig. ##FIG##4##5##F). However, the effect of Fancd2 knock-down on ferroptosis in Ishikawa/TAX cells could be significantly inhibited after Fer-1 treatment. Collectively, knock-down of Fancd2 improved the sensitivity of Ishikawa/TAX cells to paclitaxel by inducing ferroptosis.</p>" ]

[ "<title>Discussion</title>", "<p id=\"Par22\">Although treatment strategies for EC have gradually improved, patients with advanced EC are prone to develop drug resistance during chemotherapy, seriously affecting the therapeutic effect [##REF##31910163##17##]. Several recent studies have revealed genes associated with chemosensitivity, and proposed some novel and promising approaches to address resistance [##REF##33082238##18##]. In this study, Fancd2 expression was up-regulated in EC tissues, and more importantly, Fancd2 was associated with sensitivity to chemotherapeutic agents in EC.</p>", "<p id=\"Par23\">Previous studies have linked Fancd2 to susceptibility to cancer. Fancd2 is a key player in the DNA repair pathway and is important for maintaining genomic stability in response to various gene damage [##REF##32165999##13##]. Recently, there are studies that up-regulation of Fancd2 expression is positively correlated with tumor size and poor prognosis in ovarian cancer, nasopharyngeal carcinoma, glioblastoma, and EC [##REF##32186434##19##]. Consistent with previous reports, we observed a significant up-regulation of Fancd2 expression in EC tissues and EC cell lines (Ishikawa); and interestingly, after knock-down of Fancd2 expression in Ishikawa, the cells showed a marked decrease in resistance to the above-mentioned chemotherapeutic agents. Yao C et al. demonstrated that Fancd2 was associated with doxorubicin resistance in leukemia [##REF##24996439##20##]. Dai et al. found that cisplatin resistance in drug-resistant lung cancer cells could be effectively reversed by inhibiting the gene expression level of the Fancd2/BRCA pathway [##REF##26385482##21##]. In addition, the results of this study showed that Fancd2 expression was significantly increased in Ishikawa/TAX cells compared with Ishikawa cells, and knocking down Fancd2 could restore the sensitivity of Ishikawa/TAX cells to paclitaxel. Also Xiao et al. reported that curcumin reversed the multidrug resistance of multiple myeloma cells MOLP-2/R by inhibiting the Fancd2 pathway [##REF##19756599##22##]. Taken together, these findings suggest that high expression of Fancd2 may be associated with drug resistance.</p>", "<p id=\"Par24\">In the course of exploring the mechanism of Fancd2 in EC, we observed that Fancd2 expression was associated with ferroptosis. Different from apoptosis, necrosis, and autophagy, ferroptosis is an intracellular iron-dependent form of cell death [##REF##35151318##23##]. Briefly, ferroptosis is characterized by an imbalance in the redox state and manifested as an increase in the ROS level [##REF##35338310##24##]. Friedmann A J et al. pointed out that tumor cells could significantly enhance their defense against oxidative stress by negatively regulating ferroptosis [##REF##31101865##25##]. In this study, we found a significant decline in ROS level, MDA activity, GSH level and Fe²⁺ level, and a marked increase in SLC7A11 and GPX4 expression in Ishikawa/TAX cells. Such results indicated a low level of ferroptosis in Ishikawa/TAX cells. After knock-down of Fancd2 expression, the above-mentioned ferroptosis-related indicators showed a significant opposite change, indicating a marked increase in ferroptosis levels. Zhang C et al. revealed that Fancd2 was a protein associated with ferroptosis, and high levels of Fancd2 significantly inhibited cellular ferroptosis levels [##REF##35151318##23##]. Song et al. demonstrated that abnormal expression of Fancd2 led to ferroptosis and was associated with temozolomide resistance in glioblastoma [##REF##35754466##26##]. These findings yield a conclusion that knock-down of Fancd2 in Ishikawa/TAX cells induces cellular ferroptosis and increases drug sensitivity.</p>", "<p id=\"Par25\">This study preliminarily demonstrated in vitro that chemotherapy resistance of Fancd2 in Ishikawa cells was closely related to the reduction of ferroptosis levels. However, there are many shortcomings in this study. First, the results of this study were not further verified through animal experiments. Second, in vitro studies, we used only one cell line and did not perform similar experiments in multiple cell lines and corresponding cell lines of the G2 and G3 phases of the EC. These defects need to be further discussed in the future research.</p>" ]

[ "<title>Conclusion</title>", "<p id=\"Par26\">Fancd2 expression was significantly up-regulated in EC. Besides, Fancd2 led to chemoresistance by decreasing ferroptosis levels in Ishikawa EC cell lines. Therefore, Fancd2 may serve as a biomarker and therapeutic target for chemoresistance in EC. This study provides a new approach to address multi-drug resistance in EC cells.</p>" ]

[ "<title>Background</title>", "<p id=\"Par1\">Resistance can develop during treatment of advanced endometrial cancer (EC), leading to unsatisfactory results. Fanconi anemia complementation group D2 (Fancd2) has been shown to be closely related to drug resistance in cancer cells. Therefore, this study was designed to explore the correlation of Fancd2 with EC resistance and the mechanism of Fancd2.</p>", "<title>Methods</title>", "<p id=\"Par2\">Real-time quantitative PCR (RT-qPCR) was used to detect the expression of Fancd2 in EC tissues and cells. EC cells (Ishikawa) and paclitaxel-resistant EC cells (Ishikawa/TAX) were transfected to knock down Fancd2. In addition, the ferroptosis inhibitor Ferrostatin-1 was adopted to treat Ishikawa/TAX cells. The sensitivity of cancer cells to chemotherapeutic agents was observed via 3-(4,5-dimethyl-2-thiazolyl)-2,5-diphenyl-2-H-tetrazolium bromide (MTT) assay, and inhibitory concentration (IC)50 was calculated. Reactive oxygen species (ROS) levels were measured by flow cytometry, the activity of malondialdehyde (MDA) and the levels of glutathione (GSH) and Fe²⁺ in cells were detected by corresponding kits, and protein expression of solute farrier family 7 member 11 (SLC7A11) and glutathione peroxidase 4 (GPX4) was obtained through western blot.</p>", "<title>Results</title>", "<p id=\"Par3\">Compared with the normal tissues and endometrial epithelial cells, Fancd2 expression was significantly increased in EC tissues and Ishikawa cells, respectively. After knock-down of Fancd2, Ishikawa cells showed significantly increased sensitivity to chemotherapeutic agents. Besides, compared with Ishikawa cells, the levels of ROS, the activity of MDA, and the levels of GSH and Fe²⁺ were significantly decreased in Ishikawa/TAX cells, while the expression levels of SLC7A11 and GPX4 were significantly increased. Knock-down of Fancd2 significantly increased the ferroptosis levels in Ishikawa/TAX cells, but this effect could be reversed by Ferrostatin-1.</p>", "<title>Conclusion</title>", "<p id=\"Par4\">Fancd2 increases drug resistance in EC cells by inhibiting the cellular ferroptosis pathway.</p>", "<title>Supplementary Information</title>", "<p>The online version contains supplementary material available at 10.1186/s12905-023-02857-4.</p>", "<title>Keywords</title>" ]

[ "<title>Supplementary Information</title>", "<p>\n</p>" ]

[ "<title>Acknowledgements</title>", "<p>Not applicable.</p>", "<title>Authors’ contributions</title>", "<p>Hai-Hong Lin and Wei-Hong Zeng designed and coordinated the study. Ru Pan and Nan-Xiang Lei collected and analyzed the data. All authors contributed to the interpretations and conclusions presented. Hai-Kun Yang and Li-Shan Huang wrote the manuscript.</p>", "<title>Funding</title>", "<p>This study is supported by Meizhou City Science and Technology Plan Project (No. 2022C0301003).</p>", "<title>Availability of data and materials</title>", "<p>The authors confirm that the data supporting the findings of this research are available within the article.</p>", "<title>Declarations</title>", "<title>Ethics approval and consent to participate</title>", "<p id=\"Par27\">The study was approved by the ethical committee of Meizhou People’s Hospital, Meizhou Academy of Medical Sciences (Ethical No.: 2022-C-83) and written informed consent was acquired from all patients. All methods were carried out in accordance with relevant guidelines and regulations.</p>", "<title>Consent for publication</title>", "<p id=\"Par28\">Not applicable.</p>", "<title>Competing interests</title>", "<p id=\"Par29\">The authors declare no competing interests.</p>" ]

[ "<fig id=\"Fig1\"><label>Fig. 1</label><caption><p>Fancd2 was up-regulated in endometrial cancer and associated with chemoresistance. <bold>A</bold> RT-qPCR was used to detect Fancd2 expression in normal and endometrial cancer (EC) tissues. <bold>B</bold> Western blot was adopted to check the expression level of Fancd2 in the Normal group and EC group. <bold>C</bold> RT-qPCR was employed to detect Fancd2 expression in human endometrial epithelial cells (hEEC) and EC cells (Ishikawa). <bold>D</bold> Western blot was adopted for detecting Fancd2 expression levels in hEEC and Ishikawa cells. <bold>E</bold> RT-qPCR was used for measuring the transfection efficiency of Fancd2 in Ishikawa cells. <bold>F</bold> Western blot was used to examine the expression level of Fancd2 in the Ishikawa cells in Vector group, Fancd2 group, siNC group and si-Fancd2 group. <bold>G</bold>–<bold>J</bold> MTT was utilized to test the cell activity of Ishikawa cells treated with different concentrations of paclitaxel (also known as taxol) (<bold>G</bold>), cisplatin (<bold>H</bold>), doxorubicin (<bold>I</bold>), and sorafenib (<bold>J</bold>). * <italic>P</italic> &lt; 0.05, ** <italic>P</italic> &lt; 0.01</p></caption></fig>", "<fig id=\"Fig2\"><label>Fig. 2</label><caption><p>Fancd2 was up-regulated in Ishikawa/TAX cells and associated with paclitaxel resistance. <bold>A</bold> Fancd2 mRNA expression levels in Ishikawa cells and Ishikawa/TAX cells were assessed by RT-qPCR. <bold>B</bold> Western blot was used to detect Fancd2 protein expression levels in Ishikawa cells and Ishikawa/TAX cells. <bold>C</bold> MTT assay was applied to test the cellular activity of Ishikawa cells and Ishikawa/TAX cells under paclitaxel treatment. <bold>D</bold> Clonal formation assay was utilized to determine the number of cell clones in Ishikawa cells and Ishikawa-TAX cells. <bold>E</bold> Flow cytometry was employed to detect apoptosis in Ishikawa cells and Ishikawa-TAX cells. ** <italic>P</italic> &lt; 0.01</p></caption></fig>", "<fig id=\"Fig3\"><label>Fig. 3</label><caption><p>Ferroptosis was decreased in Ishikawa/TAX cells. <bold>A</bold> The level of reactive oxygen species (ROS) level in Ishikawa cells and Ishikawa/TAX cells was detected by flow cytometry. <bold>B</bold>–<bold>D</bold> Malondialdehyde (MDA) activity (<bold>B</bold>), glutathione (GSH) level (<bold>C</bold>), and Fe²⁺ (<bold>D</bold>) in Ishikawa cells and Ishikawa/TAX cells. <bold>E</bold> The protein expression levels of SLC7A11 and GPX4 in Ishikawa cells and Ishikawa/TAX cells were detected by western blot. *<italic>P</italic> &lt; 0.05, ** <italic>P</italic> &lt; 0.01</p></caption></fig>", "<fig id=\"Fig4\"><label>Fig. 4</label><caption><p>Knock-down of Fancd2 promoted ferroptosis in Ishikawa/TAX cells. <bold>A</bold> RT-qPCR was used to detect the expression level of Fancd2 in Ishikawa/TAX cells. <bold>B</bold> MTT assay was adopted to assess the cell viability of Ishikawa/TAX cells under paclitaxel treatment, and IC50 was calculated. <bold>C</bold> Clonal formation assay was employed to examine the number of cell clones in the siNC group, si-Fancd2 group, Fer-1 group and si-Fancd2 + Fer-1 group. <bold>D</bold> Flow cytometry was utilized for evaluating the apoptosis level in the siNC group, si-Fancd2 group, Fer-1 group, and si-Fancd2 + Fer-1 group. ** <italic>P</italic> &lt; 0.01 <italic>vs</italic>. siNC; ## <italic>P</italic> &lt; 0.01 <italic>vs</italic>. si-Fancd2; $$ <italic>P</italic> &lt; 0.01, <italic>vs</italic>. Fer-1</p></caption></fig>", "<fig id=\"Fig5\"><label>Fig. 5</label><caption><p>Knock-down of Fancd2 increased the sensitivity of Ishikawa/TAX cells to paclitaxel by promoting ferroptosis. <bold>A</bold>, <bold>B</bold> Flow cytometry was adopted to detect the level of reactive oxygen species (ROS) in Ishikawa/TAX cells. <bold>C</bold>–B Malondialdehyde (MDA) activity (<bold>C</bold>), glutathione (GSH) level (<bold>D</bold>), and Fe²⁺ (<bold>E</bold>) in Ishikawa/TAX cells. <bold>F</bold> The protein expression levels of SLC7A11 and GPX4 in Ishikawa/TAX cells were assessed via western blot. ** <italic>P</italic> &lt; 0.01 <italic>vs</italic>. siNC; ## <italic>P</italic> &lt; 0.01 <italic>vs</italic>. si-Fancd2; $$ <italic>P</italic> &lt; 0.01, <italic>vs</italic>. Fer-1</p></caption></fig>" ]

[ "<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>The patient's clinical information</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Patient information</th><th align=\"left\">Tumor (<italic>n</italic> = 20)</th></tr></thead><tbody><tr><td align=\"left\" colspan=\"2\">Age (year)</td></tr><tr><td align=\"left\"> ≤ 60</td><td align=\"left\">16</td></tr><tr><td align=\"left\"> &gt; 60</td><td align=\"left\">4</td></tr><tr><td align=\"left\">Body mass index (BMI, kg/m²)</td><td align=\"left\">23.55 ± 2.60</td></tr><tr><td align=\"left\" colspan=\"2\">International Federation of Gynecology and Obstetrics (FIGO) staging</td></tr><tr><td align=\"left\"> Stage I</td><td align=\"left\">18</td></tr><tr><td align=\"left\"> Stage II</td><td align=\"left\">0</td></tr><tr><td align=\"left\"> Stage III</td><td align=\"left\">2</td></tr><tr><td align=\"left\"> Stage IV</td><td align=\"left\">0</td></tr><tr><td align=\"left\" colspan=\"2\">Histopathological type</td></tr><tr><td align=\"left\"> Endometrioid</td><td align=\"left\">19</td></tr><tr><td align=\"left\"> Serous</td><td align=\"left\">1</td></tr><tr><td align=\"left\"> Clear</td><td align=\"left\">0</td></tr><tr><td align=\"left\"> Others</td><td align=\"left\">0</td></tr><tr><td align=\"left\" colspan=\"2\">Recurrence risk</td></tr><tr><td align=\"left\"> Low-risk</td><td align=\"left\">2</td></tr><tr><td align=\"left\"> Moderate-risk</td><td align=\"left\">10</td></tr><tr><td align=\"left\"> High-risk</td><td align=\"left\">8</td></tr><tr><td align=\"left\"> Unknown</td><td align=\"left\">0</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab2\"><label>Table 2</label><caption><p>RT-qPCR primer sequences</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Gene</th><th align=\"left\">Sequences (5’ to 3’)</th></tr></thead><tbody><tr><td align=\"left\" rowspan=\"2\">Fancd2</td><td align=\"left\">F: 5’- TTCCAGGATGCCTTCGTAGTGG</td></tr><tr><td align=\"left\">R: 5’- GCAGGAGGTTTATGGCAATCCC</td></tr><tr><td align=\"left\" rowspan=\"2\">GAPDH</td><td align=\"left\">F: 5’- GTCTCCTCTGACTTCAACAGCG</td></tr><tr><td align=\"left\">R: 5’- ACCACCCTGTTGCTGTAGCCAA</td></tr></tbody></table></table-wrap>" ]

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[ "<supplementary-material content-type=\"local-data\" id=\"MOESM1\"></supplementary-material>" ]

[ "<fn-group><fn><p><bold>Publisher’s Note</bold></p><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn></fn-group>" ]

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[ "<media xlink:href=\"12905_2023_2857_MOESM1_ESM.pdf\"><caption><p><bold>Additional file 1.</bold></p></caption></media>" ]

{ "acronym": [], "definition": [] }

[ "<title>Introduction</title>", "<p id=\"Par9\">Postoperative knee rehabilitation is paramount to maintaining joint motion function [##REF##33337819##1##–##REF##23690452##4##]. The incidence of knee stiffness is reported to be as high as 35% with no or inappropriate rehabilitation [##REF##31728379##2##, ##REF##23690452##4##–##REF##25031380##6##] and significantly affects patient quality of life and satisfaction. Clinically, two intervention strategies can be used to guide patients in postoperative rehabilitation: continuous passive motion (CPM) and traditional physical therapy. CPM was introduced in the 1970s and relies primarily on moving mechanical clips to improve joint mobility and thereby achieve improvement [##UREF##0##7##–##REF##6481515##9##].</p>", "<p id=\"Par10\">CPM has a positive biological effect on tissue healing, edema, and hematoma [##REF##23890952##10##–##REF##1512910##12##]. Vasileiadis et al. [##REF##34310347##13##] confirmed the role of CPM in the maturation of heterotopic ossification by performing CPM rehabilitation in a 46-year-old male patient with right deviation. Stopping the progression and maintenance of heterotopic ossification became a useful aid in increasing joint mobility. Traditional physical therapy mainly includes dynamic floor exercises, suspension, gait training, closed chain exercises, open chain exercises, and pedal exercises, and the basic idea is an active activity. At present, both rehabilitation strategies are used to guide postoperative rehabilitation, but there is high controversy in the industry regarding the clinical application of both. Therefore, it is extremely important to conduct high-quality clinical evidence-based studies to explore reasonable rehabilitation strategies after knee surgery to guide clinical practice.</p>", "<p id=\"Par11\">Previous studies have reported that the use of CPM has advantages over physical therapy, including reduced swelling, faster return of joint mobility, and reduced analgesia [##REF##33008432##14##]. However, there is still a great deal of controversy about whether it is beneficial for patients' postoperative recovery in the past two decades of research [##REF##23690452##4##, ##UREF##1##15##–##REF##36927464##18##]. Many researchers support these benefits; on the contrary, many studies show that the advantages of CPM compared to physical therapy are not as clear [##REF##33337819##1##, ##REF##31728379##2##].</p>", "<p id=\"Par12\">Hence, based on previously presented evidence from high-quality randomized controlled trials, our evidence-based study aimed to determine the effectiveness of CPM compared to physical therapy in postoperative orthopedic rehabilitation, comparing key outcomes including knee range of motion (ROM), The Western Ontario and McMaster University Osteoarthritis Index (WOMAC) pain scores, length of stay, satisfaction of patients, postoperative complications, and medical costs.</p>" ]

[ "<title>Method</title>", "<p id=\"Par13\">This systematic review and meta-analysis following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) Statement protocol. This study was registered in the International Prospective Register of Systematic Reviews (PROSPERO) (CRD42023410252).</p>", "<title>Search strategy and eligibility criteria</title>", "<p id=\"Par14\">PubMed, Embase, and Web of Science databases were searched. We take PubMed as an example to demonstrate the search strategy for this study (Additional file ##SUPPL##0##1##: Appendice 1). We developed specific search strategies for each database, and references of the identified studies were checked for potential eligibility. Relevant clinical outcomes published in January 2000 and April 2023 were retrieved.</p>", "<p id=\"Par15\">Our inclusion criteria for this meta-analysis included: (1) publications comparing the results for both (physiotherapy interventions including active ground exercise, suspension, gait training, closed chain exercise, open chain exercise, and pedal exercise in the control and experimental groups, and CPM in the experimental group); (2) randomized controlled trial and clinical study; (3) the sample size is feasible and the statistical analysis is scientific; (4) primary selection of patients after knee arthroplasty; (5) published literature in English.</p>", "<p id=\"Par16\">Using a standardized data form, we extracted several data elements from the included studies, and two investigators (JZFand WDF), independent of each other, extracted and screened the literature as well as the data according to the inclusion as well as data extraction. If any disagreements arose, they were resolved by discussion or validation by a third-party investigator (XC).</p>", "<title>Data abstraction</title>", "<p id=\"Par17\">We extracted general details and categories mainly including (1) demographics, (2) study characteristics, (3) outcome and prospective measures. Patient statistics included gender, age, and the total number of patients. Characteristics of the trial included author, publication date, study type, CPM, or physical therapy. Outcome measures for this study included ROM (active knee flexion extension and passive knee flexion extension), pain, function, complications, length of hospital stay, and patient satisfaction and were cross-checked.</p>", "<p id=\"Par18\">Prognostic indicators such as postoperative pain were evaluated using the Western Ontario and McMaster University Osteoarthritis Index (WOMAC) score and Visual Analog Scale (VAS) score. The WOMAC Osteoarthritis Index [##REF##29991079##19##] was developed by Bellamy et al. and is one of the most commonly used, patient-reported prognostic indicators for patients with lower extremity osteoarthritis. The WOMAC contains 24 items covering three dimensions: pain (5 items), stiffness (2 items), and function (17 items). The WOMAC has been extensively tested for validity, reliability, feasibility, and responsiveness over time. The VAS has been used since the 1920s to measure intangible indicators of pain, quality of life, and anxiety, and in recent years, the VAS has become a very popular tool for measuring pain [##REF##28850536##20##].</p>", "<title>Risk of bias</title>", "<p id=\"Par19\">The quality of the included studies was assessed independently by two reviewers. In this regard, the Jadad Scale (four categories: (1) Randomization, (2) Concealment, (3) Blinded, and (4) Withdraw or drop-out) for RCT , The Cochrane Risk of Bias (ROB) tool for randomized controlled trials was used to assess the methodological quality of the included studies in our study [##REF##34310347##13##]. Each standard was grouped into three various categories of \"low risk\", \"high risk\", or \"unclear risk\" of bias, and then, the quality of the randomized studies was determined according to institutional health research and quality standards.</p>", "<title>Statistical analysis</title>", "<p id=\"Par20\">We used RevMan version 5.4 Review Manager software for meta-analysis. Weighted mean differences (WMDs) were used to represent the results for continuous data, and 95% CI was used for interval estimation. If <italic>p</italic> &lt; 0.05 was satisfied suggesting that the difference was statistically significant. Meanwhile, the heterogeneity test was performed on the included literature, and when <italic>p</italic> ≥ 0.10 and <italic>I</italic>² ≤ 50%, there was no significant heterogeneity, and the fixed-effect model was used to combine the effect sizes for analysis; if <italic>p</italic> &lt; 0.10 and <italic>I</italic>² &gt; 50%, it indicated that the heterogeneity among the included studies was large, and the sources of heterogeneity were further examined, and after excluding the obvious heterogeneity, the randomized effect model was applied for analysis [##UREF##2##21##].</p>", "<p id=\"Par21\">If the heterogeneity between studies is significant, subgroup or sensitivity analyses are required to clarify the source of heterogeneity. Trials are subject to clinical and methodological differences, and in this study subgroup analysis based on available data according to follow-up time was performed to generate a final forest plot for description.</p>" ]

[ "<title>Results</title>", "<p id=\"Par22\">A total of 1025 publications was retrieved according to the search method, and a total of 6 clinical articles were screened for inclusion in the analysis based on the minimum standard. Figure ##FIG##0##1## represents the screening process. Six direct comparisons of 557 cases of CPM after TKA as well as other physiotherapy RCT were included in this meta-analysis [##REF##18442423##8##, ##REF##18608367##22##–##REF##24886619##26##]. The baseline information for these studies is listed in Table ##TAB##0##1##.</p>", "<title>Range of motion</title>", "<p id=\"Par23\">A comparative analysis of passive knee flexion, passive knee extension, active knee flexion, and active knee extension included in the study was mainly conducted to compare the range of knee motion at different periods.</p>", "<title>Passive knee flexion</title>", "<p id=\"Par24\">For short-term postoperative recovery, CPM produced better results in the first three days of postoperative recovery compared to physical therapy, and six studies reported long-term (3-month postoperative follow-up) results for passive knee flexion, and we analyzed the results using a random-effects model in which WMD was similar between the experimental and control groups (WMD,  − 0.17; 95% CI,  − 0.98 to 0.64; <italic>p</italic> =0.68). There was no clear evidence of statistically significant heterogeneity throughout the analysis (<italic>I</italic>²=28%; <italic>p</italic> = 0.23) (Fig. ##FIG##1##2##A).</p>", "<title>Passive knee extension</title>", "<p id=\"Par25\">A total of five studies with 471 patients was analyzed, and we used a random-effects model to analyze the results. There were no significant differences between the two groups at long-term follow-up (WMD,  − 0.28; 95% CI,  − 1.47 to  − 0.92; <italic>I</italic>²=65%, <italic>p</italic> = 0.65) (Fig. ##FIG##1##2##B).</p>", "<title>Length of hospitalization</title>", "<p id=\"Par26\">A total of 74 patients were included in 2 studies for analysis of length of stay; CPM generates significantly higher in length of stay (WMD, 0.50; 95% CI,  − 0.31 to 0.69; <italic>I</italic>²=3%, <italic>p</italic> &lt; 0.00001) (Fig. ##FIG##2##3##).</p>", "<title>Pain evaluation</title>", "<p id=\"Par27\">Two studies were scored by WOMAC and analyzed by taking a random-effects model (WMD, 6.75; 95% CI,  − 6.75 to 8.10; <italic>p</italic> = 0.86), with a large heterogeneity between the studies' results. The experimental group scored slightly higher on the WOMAC functional difficulty score, but no significant differences were found after two weeks or on any follow-up measures (Fig. ##FIG##3##4##A). Moreover, two studies were scored by VAS and analyzed by taking a random-effects model (WMD, 9.41; 95% CI, 3.37–5.45; <italic>p</italic> = 0.002), with a large heterogeneity between the study results. The experimental group scored slightly higher on the VAS functional difficulty score. The VAS was performed, and there was a significant difference between the experimental and control groups (Fig. ##FIG##3##4##B).</p>", "<title>Satisfaction with treatment</title>", "<p id=\"Par28\">For most patients, their status (perceived outcomes) was \"better\" compared to preoperative. Patients were generally satisfied with their treatment and outcomes in both the experimental and control groups. The CPM group also did not show a significant advantage in terms of patient-perceived outcomes [##REF##26165955##24##].</p>", "<title>Cost in hospital</title>", "<p id=\"Par29\">Compared with other physical treatments [##REF##26165955##24##], CPM generates significantly higher treatment costs and incurs more care costs.</p>", "<title>Risks of bias</title>", "<p id=\"Par30\">All six included RCTs were unblinded. The six RCTs were relatively well designed with a Jadad score range from 4 to 6 points, which indicated that they were of high quality. The Jadad score is summarized in Table ##TAB##1##2##. None of the included literature mentioned allocation concealment; the methodological assessment of the quality of the included literature is shown in Fig. ##FIG##4##5##A, as CPM requires patient consent and signed informed consent, so such studies were unblinded and highly biased in the blinded method. Of the 7 risks of bias domains (blinding of participants and personnel, performance bias) proved to have a high risk of bias. The graph shows \"+\" for attainment and \"-\" for non-attainment. Figure ##FIG##4##5##B shows the quality assessment of each entry of the methodological assessment.</p>" ]

[ "<title>Discussion</title>", "<p id=\"Par31\">The present study finds that combined with CPM did not significantly improve postoperative functional recovery compared to physical therapy. There was no difference between the two in terms of time to discharge and patient satisfaction. Overall, CPM did not show an advantage in postoperative patient recovery. Rather, it was associated with increased equipment costs and costs of care. Therefore, the current findings are insufficient to support the routine use of CPM to facilitate the recovery process after arthroplasty. In addition, the heterogeneity of included studies was significant. However, we performed a subgroup analysis of WMD to investigate the source of heterogeneity. The association between CPM and ROM is described at different times, i.e., baseline, day 3 or when the maximum value is reached, probably because these times are highly dependent on the time and angle set by the CPM device. Nonetheless, our subgroup analysis showed that regardless of when ROM was measured, the increase in CPM still produced the same results as physical rehabilitation.</p>", "<p id=\"Par32\">Although several previous studies have confirmed that CPM improves ROM only in the initial postoperative period and does not have much effect on long-term postoperative recovery, this is consistent with the results of our present meta-analysis. The association between CPM and ROM was described at different times, i.e., baseline, day 3, or at the time of maximal value, possibly because these times were highly dependent on the timing and angle of the CPM device settings. However, our subgroup analyses showed that regardless of when ROM was measured, an increase in CPM still produced the same results as physical recovery. Yang et al [##REF##30831093##27##] found that CPM use was not frequently associated with improved knee ROM and functional outcomes from hospital discharge to a final follow-up. In our study, the analysis of patient satisfaction was added, as well as the conclusion that CPM generates more inpatient spending and longer hospital stays. In actual clinical practice, however, the use of CPM devices remains the standard of care in many institutions for rehabilitation [##REF##9408304##28##], although the provision of CPM to patients has now been shown to be associated with insignificant long-term benefits and the short-term therapeutic role of the procedure remains controversial [##REF##33605578##16##, ##REF##35689278##29##]. The primary goal of using a CPM device is to increase short-term postoperative knee ROM, as several studies have reported short-term efficacy of CPM in improving CPM [##REF##16504087##30##], Although most studies have shown nonsignificant results for CPM, CPM is also heavily used, which is related to subjective patient factors as well as recovery expectations, and should be validated by including a larger sample of patients for follow-up. Lee et al studied new CPM machines compared to previous conventional CPM machines to form a clinical assessment of the usefulness and effectiveness of seated CPM machines in patients undergoing total knee arthroplasty, using more objective tools such as digital inclinometers and handheld dynamometers to measure ROM [##REF##35689278##29##].</p>", "<p id=\"Par33\">We clarified that the difference in the effect of CPM and PT on patients' motor function recovery was not significant. On this basis, the patient's satisfaction is important [##REF##33008432##14##]. Several previous studies have shown no statistical difference between the two in terms of patient satisfaction. In Gatewood et al. [##REF##27695905##31##] by analyzing the efficacy of the means of rehabilitation after knee surgery, it was noted that CPM did not improve in terms of patient satisfaction. Wirries et al. [##REF##32985036##32##] prospectively randomized the analysis of patient satisfaction with CPM after TKA through 40 patients, using the WOMAC and the Knee Social Score (KSS), to assess patient satisfaction and knee function, ultimately concluding that there was no significant difference between the both. Our findings also show that CPM does not improve patient satisfaction, possibly because CPM does not show benefit in any of the outcome indicators assessed, provides additional costs, and requires additional training for implementation [##UREF##3##33##].</p>", "<p id=\"Par34\">In the study conducted by Joshi et al [##REF##32985036##32##], two patients in the CPM group had postoperative complications. One patient was discharged with an acute quadriceps tendon tear and the other had a deep hematoma. One patient in the no-CPM group had a very deep wound dehiscence after a fall. Mau-Moeller et al. [##REF##24886619##26##] systematically evaluated the effectiveness of TKA's new active sling inpatient ROM exercise program; this physical therapy was easy to perform during hospitalization and was less expensive than CPM treatment. Musa Eymir et al.  [##REF##32778907##34##] held that AHSE (active heel gliding exercise) therapy provides more practical rehabilitation and leads to beneficial outcomes for patients with TKA. Therefore, their active exercise approach that encourages patients to participate in rehabilitation should be the first choice for acute postoperative rehabilitation after TKA rather than CPM.</p>", "<p id=\"Par35\">Postoperative knee rehabilitation is essential to maintain joint motor function and significantly affects the quality of life and satisfaction of patients. This study describes in detail the clinical applicability of CPM and PT through meta-analysis, which is of great significance for the selection of rehabilitation exercises and the development of the next rehabilitation program for patients in clinical practice. Meanwhile, our meta-analysis also has some limitations. Firstly, CPM protocols and follow-up periods were inconsistent across all studies, which may lead to the possibility of bias. The long-term impact of CPM should be further assessed. Furthermore, due to the nature of the CPM equipment, it was not possible to blind the subjects to CPM grouping. In addition, some patients had received TKA before this study and therefore knew that the use of CPM devices as standard, could lead to effects that could have uncontrolled patient implications. Therefore, an assessment of the risk of bias revealed a generally high risk of bias in allocation concealment (selection bias) and participant blindness (performance bias). In the case of CPM application, however, these situations are unavoidable. These inconsistent results may be due to inappropriate matching of the CPM machine to the patient as well as measurement errors in ROM between studies.</p>" ]

[ "<title>Conclusion</title>", "<p id=\"Par36\">Combined with CPM did not significantly improve postoperative functional recovery relative to physical therapy. There was no difference between the two in terms of time to hospital discharge and patient satisfaction. Overall, CPM did not show superior benefits for postoperative patient recovery. On the contrary, it was associated with increased equipment costs and care expenses. Therefore, the results of the current study are insufficient to support the routine use of CPM to facilitate the recovery process after arthroplasty. We believe that as CPM is used more in orthopedics, further optimization of measurement structures and device innovations are needed for additional evaluation.</p>" ]

[ "<title>Background</title>", "<p id=\"Par1\">Continuous passive motion (CPM) is commonly used as a postoperative rehabilitation treatment, along with physical therapy, for postoperative knee rehabilitation. However, the comparison between the two in terms of efficacy in postoperative knee replacement recovery is unclear.</p>", "<title>Purpose</title>", "<p id=\"Par2\">To compare efficacy and safety of combined CPM versus physical therapy alone in postoperative rehabilitation after knee arthroplasty.</p>", "<title>Methods</title>", "<p id=\"Par3\">PubMed, Embase, and Web of Science databases were used to retrieve and access clinical studies on the efficacy of CPM compared with physical therapy. Review Manager software was used for study publication bias assessment and data analysis based on inclusion criteria.</p>", "<title>Results</title>", "<p id=\"Par4\">A total of 6 articles covering 557 patients were included in the study. In terms of range of motion (ROM), passive knee flexion was similar between CPM and physical therapy (PT) (WMD, − 0.17; 95% CI,  − 0.98–0.64; <italic>p</italic> = 0.68). At long-term follow-up, passive knee extension was similar between CPM and physical therapy (PT) (WMD,  − 0.28; 95% CI,  − 1.47 to  − 0.92; <italic>I</italic>² = 65%, <italic>p</italic> =0.65). In addition, CPM generates significantly higher in length of stay (WMD, 0.50; 95% CI,  − 0.31 to 0.69; <italic>I</italic>² = 3%, <italic>p</italic> &lt; 0.001). CPM generates significantly higher treatment costs and incurs more care costs relative to physical therapy.</p>", "<title>Conclusion</title>", "<p id=\"Par5\">Compared to PT, combined with CPM failed to significantly improve ROM of the knees and patient’s satisfaction. In addition, CPM treatment significantly increased the cost of hospitalization.</p>", "<title>Supplementary Information</title>", "<p>The online version contains supplementary material available at 10.1186/s13018-024-04536-y.</p>", "<title>Keywords</title>" ]

[ "<title>Supplementary Information</title>", "<p>\n</p>" ]

[ "<title>Acknowledgements</title>", "<p>We thank DaoFeng Wang for his outstanding statistical analysis guidance.</p>", "<title>Author contributions</title>", "<p>LWH did conceptualization; JZF collected the data; JZF, ZWP analyzed the data; JZF done original manuscript writing; ZWP and XC checked the language; GWL contributed to writing—review and editing. All authors read the final manuscript and approved the publication.</p>", "<title>Funding</title>", "<p>This work was supported by the special fund of the National Clinical Research Center for Orthopedics, Sports Medicine and Rehabilitation.</p>", "<title>Availability of data and materials</title>", "<p>Relevant data can be available by contacting the corresponding author.</p>", "<title>Declarations</title>", "<title>Ethics approval and consent to participate</title>", "<p id=\"Par37\">There are no any ethical/legal conflicts involved in the article.</p>", "<title>Consent for publication</title>", "<p id=\"Par38\">All authors have read and approved the content, and agree to submit for consideration for publication in the journal.</p>", "<title>Competing interests</title>", "<p id=\"Par39\">Each authors certifies that there is no conflict of interest relevant to this article.</p>" ]

[ "<fig id=\"Fig1\"><label>Fig. 1</label><caption><p>PRISMA flowchart</p></caption></fig>", "<fig id=\"Fig2\"><label>Fig. 2</label><caption><p>Forest plots of range of motion. <bold>A</bold> Passive knee flexion; <bold>B</bold> passive knee extension</p></caption></fig>", "<fig id=\"Fig3\"><label>Fig. 3</label><caption><p>Forest plots of length of hospital</p></caption></fig>", "<fig id=\"Fig4\"><label>Fig. 4</label><caption><p>Forest plots of pain scale. <bold>A</bold> WOMAC; <bold>B</bold> VAS</p></caption></fig>", "<fig id=\"Fig5\"><label>Fig. 5</label><caption><p><bold>A</bold> Assessment of the risk of bias. The traffic lights with “x,” “+,” and “−” represent that the corresponding domains are of high, low, and unclear risk of biases, respectively; <bold>B</bold> risk of bias summary. The plus sign means low risk, the question mark means unclear risk, and the minus sign means high risk.</p></caption></fig>" ]

[ "<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Basic characteristics of the included papers</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Author</th><th align=\"left\">Year</th><th align=\"left\">Nation</th><th align=\"left\">CPM</th><th align=\"left\">PT</th><th align=\"left\">Age (Mean ± SD)</th><th align=\"left\">Sex, Men (%)</th><th align=\"left\">Outcomes</th></tr></thead><tbody><tr><td align=\"left\">Joshi et al. [##REF##26165955##24##]</td><td align=\"left\">2015</td><td align=\"left\">American</td><td align=\"left\">50</td><td align=\"left\">50</td><td align=\"left\"><p>CPM: 68.5 ± 7.8</p><p>PT : 70.5 ± 8.7</p></td><td align=\"left\"><p>CPM: 40%</p><p>PT : 24%</p></td><td align=\"left\">ROM; Complication; WOMAC; PAQ scores; Discharge location; Cost;</td></tr><tr><td align=\"left\">Lenssen et al. [##REF##18442423##8##]</td><td align=\"left\">2008</td><td align=\"left\">Netherlands</td><td align=\"left\">30</td><td align=\"left\">30</td><td align=\"left\"><p>CPM: 64.1±8.1</p><p>PT : 65±9.1</p></td><td align=\"left\"><p>CPM: 40%</p><p>PT : 30%</p></td><td align=\"left\"><p>ROM-active knee flexion; ROM-passive knee flexion</p><p>ROM-active knee extension; ROM-passive knee extension</p><p>Pain, function (WOMAC, Knee Society Score);</p><p>Pain medication; Satisfaction with treatment; Satisfaction with treatment results; Compliance; Quantity, duration and kind of treatment;</p></td></tr><tr><td align=\"left\">Mau-Moeller et al [##REF##24886619##26##]</td><td align=\"left\">2014</td><td align=\"left\">Germany</td><td align=\"left\">19</td><td align=\"left\">19</td><td align=\"left\"><p>CPM: 67.1 ±8.8</p><p>PT : 68.8 ±8.0</p></td><td align=\"left\"><p>CPM: 63%</p><p>PT : 53%</p></td><td align=\"left\"><p>passive knee flexion range of motion;</p><p>active knee flexion range of motion; active and passive knee extension ROM; static postural control;</p><p>physical activity; pain; length of hospital stay as well as clinical; functional and quality-of-life outcomes (SF-36, HSS and WOMAC scores);</p></td></tr><tr><td align=\"left\">Schulz et al. [##REF##29630570##25##]</td><td align=\"left\">2018</td><td align=\"left\">Germany</td><td align=\"left\">38</td><td align=\"left\">38</td><td align=\"left\"><p>CPM: 71.0± 8.0</p><p>PT : 69.0 ± 8.0</p></td><td align=\"left\"><p>CPM: 44%</p><p>PT : 52%</p></td><td align=\"left\">Pre-op Flexion; Discharge; Length of stay in days;</td></tr><tr><td align=\"left\">Gil‑González et al.[##REF##35033133##23##]</td><td align=\"left\">2022</td><td align=\"left\">Spain</td><td align=\"left\">105</td><td align=\"left\">115</td><td align=\"left\"><p>CPM: 74.2±6.8</p><p>PT : 73.3±6.9</p></td><td align=\"left\"><p>CPM: 36%</p><p>PT : 39%</p></td><td align=\"left\"><p>ROM-active knee flexion; ROM-passive knee flexion</p><p>ROM-active knee extension; ROM-passive knee extension</p><p>Pain medication;</p></td></tr><tr><td align=\"left\">Bruun-olsen et al. [##REF##18608367##22##]</td><td align=\"left\">2009</td><td align=\"left\">Norway</td><td align=\"left\">30</td><td align=\"left\">33</td><td align=\"left\"><p>CPM: 68.0±10.0</p><p>PT : 71.0±10.0</p></td><td align=\"left\"><p>CPM: 27%</p><p>PT : 33%</p></td><td align=\"left\"><p>Knee circumference; Pain intensity (VAS 0 – 100);</p><p>Active knee flexion; Passive knee flexion; Active knee extension; Time Up and Go;40 m walking test;</p></td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab2\"><label>Table 2</label><caption><p>Quality assessment by the Jadad scale for RCT</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Authors</th><th align=\"left\">Randomization</th><th align=\"left\">Concealment</th><th align=\"left\">Blinded</th><th align=\"left\">Withdraw or drop-out</th><th align=\"left\">Total</th></tr></thead><tbody><tr><td align=\"left\">Joshi et al. [##REF##26165955##24##]</td><td align=\"left\">2</td><td align=\"left\">2</td><td align=\"left\">1</td><td align=\"left\">1</td><td align=\"left\">6</td></tr><tr><td align=\"left\">Lenssen et al. [##REF##18442423##8##]</td><td align=\"left\">1</td><td align=\"left\">1</td><td align=\"left\">1</td><td align=\"left\">1</td><td align=\"left\">4</td></tr><tr><td align=\"left\">Mau-Moeller et al. [##REF##24886619##26##]</td><td align=\"left\">1</td><td align=\"left\">2</td><td align=\"left\">1</td><td align=\"left\">1</td><td align=\"left\">5</td></tr><tr><td align=\"left\">Schulz et al. [##REF##29630570##25##]</td><td align=\"left\">1</td><td align=\"left\">1</td><td align=\"left\">1</td><td align=\"left\">1</td><td align=\"left\">4</td></tr><tr><td align=\"left\">Gil‑González et al. [##REF##35033133##23##]</td><td align=\"left\">1</td><td align=\"left\">1</td><td align=\"left\">1</td><td align=\"left\">1</td><td align=\"left\">4</td></tr><tr><td align=\"left\">Bruun-olsen et al. [##REF##18608367##22##]</td><td align=\"left\">1</td><td align=\"left\">2</td><td align=\"left\">1</td><td align=\"left\">1</td><td align=\"left\">5</td></tr></tbody></table></table-wrap>" ]

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[ "<supplementary-material content-type=\"local-data\" id=\"MOESM1\"></supplementary-material>" ]

[ "<table-wrap-foot><p><italic>SF-36</italic> 36-item Short Form Health Survey; <italic>VAS</italic> Visual Analog Scale; <italic>ROM</italic> range of motion; <italic>PAQ</italic> patient-administered questionnaire</p></table-wrap-foot>", "<table-wrap-foot><p><italic>RCT</italic> randomized control trial</p></table-wrap-foot>", "<fn-group><fn><p><bold>Publisher's Note</bold></p><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn><fn><p>Zhengfeng Jia, Yan Zhang and Wupeng Zhang have contributed equally to this work.</p></fn></fn-group>" ]

[ "<graphic xlink:href=\"13018_2024_4536_Fig1_HTML\" id=\"MO1\"/>", "<graphic xlink:href=\"13018_2024_4536_Fig2_HTML\" id=\"MO2\"/>", "<graphic xlink:href=\"13018_2024_4536_Fig3_HTML\" id=\"MO3\"/>", "<graphic xlink:href=\"13018_2024_4536_Fig4_HTML\" id=\"MO4\"/>", "<graphic xlink:href=\"13018_2024_4536_Fig5_HTML\" id=\"MO5\"/>" ]

[ "<media xlink:href=\"13018_2024_4536_MOESM1_ESM.docx\"><caption><p><bold>Additional file 1:</bold> Appendice 1.</p></caption></media>" ]

{ "acronym": [ "CPM", "PT", "ROM" ], "definition": [ "continuous passive motion", "physical therapy", "range of motion" ] }

[ "<title>Introduction</title>", "<p id=\"Par5\">Lung malignancy stands as the foremost contributor to sickness and demise linked to neoplasms. Non–small-cell lung cancer (NSCLC) is the most common variant and exhibits a disheartening outlook; this is primarily due to the frequent occurrence of locally advanced or disseminated metastasis in the majority of patients upon initial diagnosis or after surgical intervention [##REF##36075878##1##]. Classical chemotherapy exhibits restricted efficacy in the management of NSCLC, with a wide range of overall response rates varying from 6.7 to 10.8%, and a meager 5-year survival rate ranging from 7 to 14% [##REF##36223558##2##]. However, there have been significant changes in the treatment landscape for NSCLC in recent years primarily as a result of the introduction of immunotherapy [##REF##35992832##3##]. The field of immunotherapy has recently brought about a revolutionary shift in the treatment of NSCLC across diverse scenarios, thus playing a vital role in augmenting the well-being of these individuals [##REF##34337973##4##].</p>", "<p id=\"Par6\">Numerous clinical studies have consistently demonstrated the effectiveness of immune-checkpoint inhibitors (ICIs) in the treatment of diverse conditions. Evidence has corroborated the efficacy of anti-programmed death 1 (PD-1) antibodies, anti–PD-1 ligand (PD-L1) antibodies, and anti–cytotoxic T-lymphocyte–associated protein 4 (CTLA-4) antibodies [##REF##28434399##5##]. Nonetheless, most patients with NSCLC do not note substantial advantages solely from immunotherapy [##REF##26028407##6##]; therefore, it is essential to investigate the potential of combination therapies to enhance the efficacy of immunotherapy.</p>", "<p id=\"Par7\">Anlotinib, a multi-targeted anti-angiogenic agent, is a small-molecule compound that has been shown to have inhibitory effects on both tumor cells and angiogenesis [##REF##30231931##7##]. New findings from recent scientific research have provided strong evidence suggesting that the combination of anlotinib and PD-1 inhibitors could boost results for individuals with advanced lung cancer, specifically improving both progression-free survival (PFS) and overall survival (OS) [##REF##33566148##8##]. Despite positive results observed in patients with NSCLC with negative driver mutations, there are still some individuals who do not benefit from this treatment. The precise factors contributing to this lack of response have not yet been elucidated [##REF##26412456##9##, ##REF##37513975##10##].</p>", "<p id=\"Par8\">Lipids have a vital function as structural constituents of cellular membranes and as secondary messengers within cells. Emerging data have progressively underscored the noteworthy involvement of lipids in the development of diverse forms of cancer, such as lung cancer [##REF##36261043##11##–##REF##35108060##13##]. Long-chain fatty acyl-CoA synthetases (ACSLs), which have been discovered to have the potential to promote the upregulation of lipids, are recognized for their significant role in breast and colorectal cancer and their possible oncogenic properties. However, interestingly, they also exhibit potential tumor-suppressor properties in lung cancer [##REF##31344914##14##]. Increased levels of lipids, including phospholipids, neutral lipids, and triglycerides, have been observed in lung cancer [##REF##35159223##15##]. Furthermore, alterations in sphingolipid metabolism have also been identified in lung cancer. The presence of sphingosine kinase 2 (SPHK2) has been linked to unfavorable survival outcomes in NSCLC as well as resistance to gefitinib EGFR TKI therapy [##REF##29057430##16##]. In general, the reprogramming of lipid metabolism has become a crucial contributor to the advancement and progression of lung cancer.</p>", "<p id=\"Par9\">As described in this article, our research revealed that patients with advanced NSCLC lacking driver mutations displayed distinct responses upon receiving a combination therapy involving a PD-1 inhibitor and anlotinib. Further, a comparative analysis of lipid composition in patients who underwent treatment with anlotinib in conjunction with PD-1/PD-L1 inhibitors was conducted. The findings demonstrated that, in the group showing partial responses(PR), there were no notable alterations in lipids between before and after treatment. However, in the group with stable disease (SD), only one phosphatidylglycerol (PG) and three phosphatidylinositol (PIs) exhibited a significant increase after therapy. Conversely, among patients with progressive disease (PD), there was a substantial upregulation in two PGs and 17 PIs. The aforementioned results suggest that ensuring a well-balanced lipid profile is crucial for effectively treating patients with advanced NSCLC lacking driver mutations. This can be achieved by employing the combination of anlotinib and PD-1/PD-L1 inhibitors. Notably, an elevation in PG levels—specifically, the level of PI—after treatment may result in an unfavorable treatment response. By broadening the comprehension of lipid metabolism in lung cancer, this investigation enhances the understanding of potential therapeutic strategies and facilitates the discovery of novel therapeutic biomarkers.</p>" ]

[ "<title>Materials and methods</title>", "<title>Participants</title>", "<p id=\"Par10\">Clinical records were collected from patients with advanced NSCLC with negative driver mutations at Hunan Cancer Hospital between July 2018 and March 2022. In the case of adenocarcinoma patients, it was recommended to utilize tissue samples for NGS sequencing. Patients without EGFR/ALK/ROS-1 driver mutations were included in the analysis. However, according to the 2022 Chinese Society of Clinical Oncology (CSCO) Guidelines for the Diagnosis and Treatment of non-small cell lung Cancer, genetic testing is not recommended for patients with advanced squamous cell carcinoma due to the extremely low EGFR/ALK/ROS-1 mutation ratio. The guidelines suggest that patients with lung squamous cell carcinoma, who can be considered genetically negative drivers, should receive conventional anti-tumor therapy. In this study, 7 out of the 15 recruited lung squamous cell carcinoma patients voluntarily underwent genetic testing, and the results showed that they tested negative for EGFR/ALK/ROS-1 mutations. This finding further supports the observation of a low mutation rate of these genes in lung squamous cell carcinoma. Therefore, the remaining 8 advanced lung squamous cell carcinoma patients who did not undergo genetic testing can also be considered as negative gene drive patients based on CSCO guidelines. The enrolled 30 participants who underwent a treatment regimen consisting of the administration of chemotherapy in conjunction with ICIs, either as their initial or subsequent therapeutic approach. After, when resistance to chemotherapy and ICIs emerged, the patients were given ICIs in combination with anlotinib. Blood samples were taken from enrolled patients before and after treatment involving ICIs and anlotinib. The clinical stage of each patient was determined using the eighth edition of the TNM classification. Prior to the administration of ICIs and anlotinib, the Eastern Cooperative Oncology Group (ECOG) guideline was used to evaluate the performance status. To qualify for inclusion in the present investigation, individuals needed to: (i) exhibit an ECOG performance status ranging from 0 to 1 as well as a histologically confirmed NSCLC clinical stage IIIb–IIIc or IV, (ii) have completed at least two courses of ICI plus anlotinib therapy, (iii) have evaluable disease, and (iv) not have any organ dysfunction. Participants with severe autoimmune diseases or those requiring systemic treatment with corticosteroids or other immunosuppressive medications were excluded from the study. The ethical approval document for the study (no. SBQLL-2021-092) was granted by the Hunan Cancer Hospital. All of the enrolled individuals provided informed consent by signing consent forms prior to their participation in the experiment.</p>", "<title>Therapy</title>", "<p id=\"Par11\">Patients with advanced NSCLC who experienced progression after receiving at least one round of chemotherapy as well as ICIs were subjected to a re-challenge involving the combination of ICIs and anlotinib. For a total of 14 days, patients were administered anlotinib orally at a dosage of 12 mg per day, which was followed by a one-week pause in the regimen. In a 21-day cycle, patients were administered a PD-1 inhibitor via intravenous injection on the first day, such as toripalimab (240 mg), carrelizumab (200 mg), sintilimab (200 mg), or pembrolizumab (200 mg). The treatment was continued until any of the following conditions occurred: progressive disease or death, patient refusal, unacceptable toxicity, pregnancy, or treatment withdrawal for any other reason. The evaluation of the response was conducted based on the Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1, using enhanced computed tomography (CT) scans recorded at two-month intervals [##REF##36096156##17##].</p>", "<title>Lipidomic analysis</title>", "<p id=\"Par12\">A revised approach inspired by the methodology outlined in Xuan et al.‘s study was used to analyze the lipid samples [##REF##32333076##18##]. In brief, venous blood was collected into tubes containing heparin to prevent coagulation. The blood was then centrifuged using 2000 g lasting 15 min under 4 °C to obtain the serum component. The serum was mixed with 80 µL of methanol and 400 µL of MTBE (tert-butyl methyl ether). To obtain the serum component, the blood was centrifuged under 2000 g for 15 min at a temperature of 4 °C. Next, the serum was combined with 80 µL of methanol as well as 400 µL of MTBE (tert-butyl methyl ether), along with lipid standards.</p>", "<p id=\"Par13\">This mixture was vigorously vortexed for 30 s and then subjected to centrifugation to separate the upper phase. The separated phases were carefully collected and dried using vacuum evaporation. Finally, the desiccated samples were reconstituted by utilizing 100 µL of a blend consisting of methanol and methylene chloride in an equal proportion of 1:1.</p>", "<p id=\"Par14\">For lipid analysis, a mass spectrometer (QTRAP 6500; Danaher Corporation, Toronto, Canada) coupled by a Shimadzu LC-30 A (Shimadzu, Japan) system was used. To separate the lipid components, a CQUITY UPLC® BEH C18 column (2.1 × 100 mm, 1.7 μm; Waters Corp., Milford, MA, USA) was employed. To ensure optimal chromatographic performance, the following set of conditions was established: the oven temperature was maintained for 55 °C, 0.26 mL/min was established as flow rate, along with an injection volume of 5 µl. The mobile phase was composed of two solutions: solution A (a mixture of H₂O and acetonitrile in a 40:60 ratio, v/v, containing 10 mM of ammonium acetate) and solution B (a mixture of acetonitrile and isopropanol in a 10:90 ratio, v/v, containing 10 mM of ammonium acetate).</p>", "<p id=\"Par15\">A gradient, consisting of varying proportions of two solutions (referred to as solutions A and B), was employed in this study. The mobile-phase composition during different time intervals was as follows: from 0 to1.5 min, 68% solution A and 32% solution B were used; from 1.5 to 15.5 min, the proportions of solution A and solution B were 15% and 85%, respectively; from 15.5 to 15.6 min, 3% solution A and 97% solution B were utilized; from 15.6 to 18 min, the same proportions of solution A and solution B were used as in the previous time interval; from 18 to 18.1 min, the mobile phase reverted back to 68%solution A and 32% solution B of; and, from 18.1 to 20 min, the same proportions of solution A and solution B were maintained. For the electrospray ionization, specific parameters were set. The curtain gas pressure was maintained at 20 psi, while the atomizing gas pressure was set at 60 psi. The ion source voltage was alternated between − 4500 and 5500 V, depending on the specific condition. The ion source temperature was carefully controlled and maintained at a constant 600 °C. In addition, an auxiliary gas pressure of 60 psi was applied. Multiple reaction monitoring was employed to monitor the reactions and obtain precise data. Moreover, quality control samples were incorporated to ensure the accuracy and reliability of the liquid chromatography-mass spectrometry examination. These quality control samples were created by blending samples under the same test conditions, and they were analyzed every third sample in order to evaluate the overall quality of the data.</p>", "<title>Statistical analysis</title>", "<p id=\"Par16\">The measurement of peak area determined the abundance of lipids in this study. After, the obtained data were processed and normalized using the website <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.metaboanalyst.ca/\">https://www.metaboanalyst.ca/</ext-link>. This online platform is primarily focused on processing raw spectra, conducting general statistical analysis, and performing functional analysis [##REF##21637195##19##, ##REF##35715522##20##]. In order to evaluate the highest covariance between lipidomic samples, i.e., both those treated with a PD-1 inhibitor and anlotinib, and those left untreated, the researchers conducted the discriminant analysis of partial least squares (PLS-DA). Examination of lipid molecule correlations was conducted using correlation heatmaps. The mean ± standard error of the mean format was used to present the data. A paired two-tailed Student’s <italic>t</italic> test was carried out to analyze the comparison between the non-treatment group and the conjunction of anlotinib and a PD-1 inhibitor–treated group. <italic>P</italic> &lt; 0.05 was considered statistical significance.</p>" ]

[ "<title>Results</title>", "<title>Demographic information and therapeutic effect among patients</title>", "<p id=\"Par17\">A total of 30 advanced NSCLC patients lacking driver mutations were enrolled in this study. Tables ##TAB##0##1## and ##TAB##1##2## present the demographic details of these patients. As outlined in the methods section, the patients received the specified treatment regimen. Based on the combined effects of anlotinib and PD-1/PD-L1 inhibitors, patients were stratified into three groups. The first group (<italic>n</italic> = 6) showed partial remission of the tumor after therapy (PR), the second group (<italic>n</italic> = 17) had a tumor that remained stable after therapy (SD), and the third group (<italic>n</italic> = 7) experienced tumor progression after therapy (PD).</p>", "<p id=\"Par18\">\n</p>", "<p id=\"Par19\">\n</p>", "<title>A lipid composition analysis was conducted on patients exhibiting advanced NSCLC who underwent a combination treatment involving PD-1/PD-L1 inhibitors and anlotinib</title>", "<p id=\"Par20\">We conducted an analysis of the lipid profiles to investigate the factors contributing to the varying treatment outcomes among patients. The lipid components of all three of the patient groups before and after therapy were examined. Through lipidomic analysis, a total of 460 lipids were identified, which can be classified into 18 subclasses. The different categories encompass various subclasses of lipids—namely, phosphatidylethanolamine, phosphatidic acid, phosphatidylcholine, PI, PG, phosphatidylserine, lysophosphatidylethanolamine, lysophosphatidic acid, lysophosphatidylcholines, lysophosphatidylinositol, lysophosphatidylglycerol, triacylglycerol, diacylglycerol, cholesteryl ester, fatty acid, ceramide, hexosylceramide, and sphingomyelin.</p>", "<p id=\"Par21\">The MetaboAnalyst R software package for conducting PLS-DA was employed to deploy a statistical analysis method relying on multivariate techniques in order to enhance the differentiation and detect unique metabolites among various groups. This analysis yielded evident disparities in the patients’ lipidomic profiles before (pink) and after (green) therapy, indicating a noticeable distinction among individuals from the PR, SD, and PD groups (Fig. ##FIG##0##1##A, C and E). In addition, a Pearson correlation analysis to evaluate the resemblance among various types of lipids within three distinct groups was performed (Fig. ##FIG##0##1##B, D and F). An examination of the patients’ lipidomic makeup across the three groups was conducted using lipid volume measurements. The findings revealed that there were no significant changes in lipids among the PR and SD groups between before and after treatment (Figs. ##FIG##1##2## and ##FIG##2##3##). However, in the PD group, there was a notable increase in both PG and PI contents after treatment (Fig. ##FIG##3##4##). These results indicated that the maintenance of lipid equilibrium holds significant importance in the impressive efficacy of the conjunction of anlotinib and PD-1/PD-L1 inhibitors in treating advanced NSCLC. In addition, an imbalance in particular lipids, such as PG and PI, following treatment could suggest undesirable consequences.</p>", "<title>To investigate the lipid components closely associated with the therapeutic effect of PD-1/PD-L1 inhibitors in combination with anlotinib</title>", "<p id=\"Par22\">Specific constituents of PG and PI were systematically examined and analyzed in order to gain a deeper understanding of the lipid changes. According to the outcomes of this study, it was observed that the composition of PG exhibited a consistent upward trend. However, there were no significant changes observed in individuals of the PR group before and after treatment. Similarly, in the SD group consisting of patients with advanced NSCLC, the only notable change detected post-treatment was a significant up-regulation in the levels of PG36:1. In contrast, the PD group exhibited a substantial increase not only in PG36:1 but also in PG36:0 after treatment. Moreover, the observations revealed an overall upward trend in most PG components within the PD group following treatment, despite the lack of statistical significance in the observed differences (Fig. ##FIG##4##5##).</p>", "<p id=\"Par23\">After assessing the distinct elements of PI, it was observed that the composition of PI exhibited an increasing trend. However, there were no significant changes observed in individuals with advanced NSCLC from the PR group who were administered PD-1/PD-L1 inhibitors concurrently with anlotinib. A noteworthy upsurge in PI38:0, PI40:2, and PI44:4 concentrations was exhibited in advanced NSCLC patients in the SD group after therapy. Interestingly, we observed a significant change in PI levels following treatment in patients with advanced NSCLC in the PD group. After treatment, a significant up-regulation was observed in more than half of the PIs, including PI 34:0, PI 34:1, PI 34:2, PI 34:3, PI 36:0, PI 36:1, PI 36:2, PI 38:0, PI 38:1, PI 38:2, PI 38:3, PI 38:4, PI 38:6, PI 40:2, PI 40:3, PI 40:4, PI 40:5, and PI 40:6 (Fig. ##FIG##5##6##).</p>" ]

[ "<title>Discussion</title>", "<p id=\"Par24\">For individuals diagnosed with advanced NSCLC lacking driver mutations, the administration of anlotinib in conjunction with PD-1/PD-L1 inhibitors is considered a viable alternative treatment choice for later stages of the disease, and it has demonstrated noteworthy therapeutic efficacy [##REF##34956561##21##, ##REF##36250532##22##]. However, there are still patients who do not benefit from this treatment approach. Therefore, a major challenge in clinical practice is to understand why individuals respond differently to drugs. One effective strategy to solve this problem is to identify and exploit potential targets that are triggered by and downstream of cancer-causing signaling pathways. Previous investigations have shown a significant increase in the de novo synthesis of endogenous lipids in numerous cancerous cells [##REF##28325263##23##–##REF##33601415##25##]. Studies have demonstrated that serum metabolomic profiling can reveal metabolic alterations associated with lung cancer, including amino acids, organic acids, and nitrogen compounds [##REF##32374740##26##, ##REF##36295809##27##]. Additionally, lipid and lipid-like molecules have been identified as potential biomarkers for NSCLC. Lipids, as crucial components of cell membranes, undoubtedly influence the activity of proteins on the membrane [##REF##35303882##28##, ##REF##35351993##29##]. Membrane proteins such as epidermal growth factor receptor and tumor necrosis factor receptors play critical roles in tumor signaling pathways [##REF##37264016##30##]. In this particular study, noteworthy disparities in the lipid composition among individuals with advanced NSCLC who received a combination treatment of anlotinib and PD-1/PD-L1 inhibitors were observed. The objective of this investigation was to identify lipid components that may be associated with the therapeutic efficacy of advanced NSCLC treated with a combination of anlotinib and PD-1/PD-L1 inhibitors, from a lipidomics perspective. The results derived from this research provide valuable insights into potential innovative therapeutic strategies.</p>", "<p id=\"Par25\">During this study, the lipid compositions of individuals in the PR, PD, and SD groups were analyzed, revealing remarkable variations in the lipid composition across these groups. Further analysis identified 19 differential lipids, including two PGs and 17 PIs. PG and PI are two important classes of glycerophospholipids with diverse roles in cell signaling and lipid–protein interactions [##REF##32825894##31##]. These molecules can be potential targets for novel drug development in the fight against cancer [##REF##28527945##32##, ##REF##32711004##33##].</p>", "<p id=\"Par26\">As a crucial structural lipid, PG acts as a precursor of cardiolipin, which is primarily found in mitochondrial membranes, and it plays a key role in mitochondrial functionality and membrane integrity [##REF##18703489##34##, ##REF##29559686##35##]. Studies have shown that elevated levels of PG are present in renal cell and hepatocellular carcinomas [##REF##23560736##36##, ##REF##25964345##37##]. During the investigation, a notable rise in the levels of two PGs was observed among patients with advanced NSCLC belonging to the PD group who were administered PD-1/PD-L1 inhibitors alongside anlotinib. Furthermore, one PG also exhibited a significant increase in patients with advanced NSCLC in the SD group. However, no PGs that showed significant differences in patients with advanced NSCLC in the PR group. These findings suggest that the abnormal accumulation of PG after treatment could result in irreversible respiratory injury and hinder the use of alternative energy sources to glucose, ultimately leading to tumor progression [##REF##18703489##34##].</p>", "<p id=\"Par27\">PIs make up only a small portion of the phospholipid content found in cells, yet they exert a pivotal influence on the progress and development of cancer [##REF##32276377##38##]. The findings in this paper indicated that more than half of the PIs showed a significant increase among individuals with advanced NSCLC in the PD group after treatment. However, only three PIs showed a significant increase in advanced NSCLC patients of the SD group after treatment, and no PIs showed significant changes in patients with advanced NSCLC in the PR group. Prior investigations established that PIs have the ability to function as building blocks for the creation of phosphatidylinositol 4,5-bisphosphate as well as phosphatidylinositol 3,4,5-trisphosphate. These substances are known to be essential in the PI3K-AKT pathway, which regulates cell survival, proliferation, invasion, and growth [##REF##12094235##39##]. The observations propose that an abnormal elevation in PI concentrations following therapy could impede the advantageous effects of anlotinib combined with PD-1/PD-L1 inhibitors for patients.</p>", "<p id=\"Par28\">To summarize, adopting a lipidomics approach, an investigation was performed that aimed at analyzing the elements that contribute to the divergent response of patients with advanced NSCLC harboring negative driver mutations when subjected to a combined therapeutic regimen of anlotinib and PD-1/PD-L1 inhibitors. Based on the results, we propose a possible mechanism by which abnormal elevations of PG and PI hinder the beneficial effects of anlotinib combined with PD-1/PD-L1 inhibitors for patients (Supplemental Fig. ##SUPPL##0##1##). We observed a positive correlation between PG and PI in each response group (PR, SD, and PD) during the analysis of Pearson correlation. This suggests that an increase in PG content corresponds to an increase in PI content, and vice versa. We hypothesize that the elevation of PG/PI levels could activate the PI3K-AKT pathway. This is because PI can serve as a substrate for PIP2, which is phosphorylated by PI3K. Activation of the PI3K-AKT pathway promotes tumor growth, which may explain the observed tumor progression in patients in the PD group. In these patients, the levels of PG/PI were abnormally elevated after treatment with anlotinib and a PD-1 inhibitor. However, we did not detect the activity of proteins involved in the PI3K-AKT signaling pathway. This limitation prevents us from fully supporting the proposed working model of our investigation. To validate our hypothesis, further investigations should be conducted to determine phosphoinositide 3-kinase kinase activity, as well as the concentrations of phosphoinositide (4,5) bisphosphate and phosphoinositide (3,4,5) trisphosphate. The findings provide valuable insights into lipid metabolism in advanced NSCLC, which not only offers potential for novel therapeutic approaches but also aids in the identification of new therapeutic biomarkers. In addition, lipid metabolism status has the potential to function as a prognostic indicator for determining the eligibility of patients with advanced NSCLC, who may gain advantages from the combination of anlotinib and PD-1/PD-L1 inhibitors.</p>", "<title>Study strengths and limitations</title>", "<p id=\"Par29\">The main advantage of this research is to identify potential factors that may influence the therapeutic outcomes observed in advanced NSCLC patients with negative driver mutations when treated with a combination of anlotinib and PD-1/PD-L1 inhibitors. However, the investigation solely focused on changes in lipids, and the specific mechanisms through which these lipids affect advanced NSCLC treatment are still unclear. The enrolled patients included not only those with lung adenocarcinoma but also those with lung squamous cell carcinoma. It is worth mentioning that the sample size in the PD group was relatively small. Therefore, further evidence is required to comprehensively examine and understand these mechanisms.</p>" ]

[ "<title>Conclusions and clinical perspective</title>", "<p id=\"Par30\">The administration of anlotinib along with PD-1/PD-L1 inhibitors has demonstrated promise as a viable tactic for managing individuals with advanced NSCLC lacking driver mutations. The plausible rationale behind this lies in the capacity of the therapy to induce modifications in the lipidomics of patients with advanced NSCLC. Within this study, novel insights have been unveiled regarding the correlation between the combination of anlotinib with PD-1/PD-L1 inhibitors and distinct lipids in advanced NSCLC patients. Such discoveries propose that directing interventions toward these lipid modifications could present a hopeful and encouraging methodology for managing patients with advanced NSCLC.</p>" ]

[ "<title>Background</title>", "<p id=\"Par1\">Studies have shown that integrating anlotinib with programmed death 1 (PD-1)/programmed death-ligand 1 (PD-L1) inhibitors enhances survival rates among progressive non–small-cell lung cancer (NSCLC) patients lacking driver mutations. However, not all individuals experience clinical benefits from this therapy. As a result, it is critical to investigate the factors that contribute to the inconsistent response of patients. Recent investigations have emphasized the importance of lipid metabolic reprogramming in the development and progression of NSCLC.</p>", "<title>Methods</title>", "<p id=\"Par2\">The objective of this investigation was to examine the correlation between lipid variations and observed treatment outcomes in advanced NSCLC patients who were administered PD-1/PD-L1 inhibitors alongside anlotinib. A cohort composed of 30 individuals diagnosed with advanced NSCLC without any driver mutations was divided into three distinct groups based on the clinical response to the combination treatment, namely, a group exhibiting partial responses, a group manifesting progressive disease, and a group demonstrating stable disease. The lipid composition of patients in these groups was assessed both before and after treatment.</p>", "<title>Results</title>", "<p id=\"Par3\">Significant differences in lipid composition among the three groups were observed. Further analysis revealed 19 differential lipids, including 2 phosphatidylglycerols and 17 phosphoinositides.</p>", "<title>Conclusion</title>", "<p id=\"Par4\">This preliminary study aimed to explore the specific impact of anlotinib in combination with PD-1/PD-L1 inhibitors on lipid metabolism in patients with advanced NSCLC. By investigating the effects of using both anlotinib and PD-1/PD-L1 inhibitors, this study enhances our understanding of lipid metabolism in lung cancer treatment. The findings from this research provide valuable insights into potential therapeutic approaches and the identification of new therapeutic biomarkers.</p>", "<title>Supplementary Information</title>", "<p>The online version contains supplementary material available at 10.1186/s12944-023-01960-7.</p>", "<title>Keywords</title>" ]

[ "<title>Supplementary Information</title>", "<p>\n</p>" ]

[ "<title>Acknowledgements</title>", "<p>We would like to thank the Changsha SmallAnt Biotechnology Co., Ltd for providing the technical support.</p>", "<title>Authors' contributions</title>", "<p>LL and YT conceived and designed the experiments. LL, SZ, HYY performed the experiments. LL, CHZ and YX analyzed the data. LL, NY and YT wrote the article. The author(s) read and approved the final manuscript.</p>", "<title>Funding</title>", "<p>This work was supported by the science and technology innovation Program of Hunan Province (2021SK51101, 2021SK51103).</p>", "<title>Availability of data and materials</title>", "<p>All data in this study can be obtained from the corresponding author up on request.</p>", "<title>Declarations</title>", "<title>Ethics approval and consent to participate</title>", "<p id=\"Par31\">This work was approved by the ethics committee of Hunan Cancer Hospital (ethics: no. SBQLL-2021-092) All recruited subjects provided informed consent by signing consent forms prior to participating in the experiment.</p>", "<title>Consent for publication</title>", "<p id=\"Par32\">Not applicable.</p>", "<title>Competing interests</title>", "<p id=\"Par33\">The authors declare no competing interests.</p>" ]

[ "<fig id=\"Fig1\"><label>Fig. 1</label><caption><p>Lipidomic patterns in advanced NSCLC individuals pre and post administration of PD-1/PD-L1 inhibitors along with anlotinib. <bold>A </bold>The PLS-DA analysis was performed on advanced NSCLC patients in the PR group (<italic>n</italic> = 6). The non-treated PR group was represented by ‘1’ and the anlotinib along with PD-1/PD-L1 inhibitors-treated PR group was represented by ‘2’. <bold>B</bold> Correlation analysis was carried out on PR group subjects with advanced NSCLC to examine the associations among distinct lipids. Various colors were utilized to depict the correlation strength, employing the Pearson’s correlation coefficient. <bold>C </bold>The PLS-DA analysis was carried out on SD group subjects with advanced NSCLC patients (<italic>n </italic>= 17). The non-treated PR group was represented by ‘1’ and anlotinib along with PD-1/PD-L1 inhibitor -treated SD group was represented by ‘2’. <bold>D </bold>Correlation analysis was carried out on SD group subjects with advanced NSCLC to examine the associations among distinct lipids. Various colors were utilized to depict the correlation strength, employing the Pearson’s correlation coefficient. <bold>E </bold>The PLS-DA analysis was performed on advanced NSCLC patients in the PD group (<italic>n </italic>= 7). The non-treated PD group was represented by ‘1’ and anlotinib along with PD-1/PD-L1 inhibitor-treated PD group was represented by ‘2’. <bold>F </bold>Correlation analysis was performed on advanced NSCLC patients in the PD group to investigate the significantly diverse lipids. The level of Pearson’s correlation coefficient was represented using various colors</p></caption></fig>", "<fig id=\"Fig2\"><label>Fig. 2</label><caption><p>Lipid identification in advanced NSCLC patients in the PR group. In advanced NSCLC patients, lipid identification was performed on the PR group. The analysis of sample composition within the PR group involved assessing the lipid volume in each lipid category before and after administering PD-1/PD-L1 inhibitors and anlotinib treatment. Statistical significance compared to pre-treatment patients was determined using Student’s t-tests. Asterisks indicate the existence of these noteworthy disparities when <italic>P</italic> -value was lower than 0.05</p></caption></fig>", "<fig id=\"Fig3\"><label>Fig. 3</label><caption><p>Lipid identification in advanced NSCLC patients in the SD group. The lipid composition in the SD group samples before and after administering anlotinib along with PD-1/PD-L1 inhibitor was analyzed, by assessing the lipid volume in each lipid category. Significant variations were determined using Student’s t-tests during the comparison of the patients prior to the commencement of treatment. Asterisks indicate the presence of such significant differences when <italic>P</italic> -value was lower than 0.05</p></caption></fig>", "<fig id=\"Fig4\"><label>Fig. 4</label><caption><p>Lipid identification in advanced NSCLC patients in the PD group. After the administering anlotinib along with PD-1/PD-L1 inhibitor, the analysis of lipid volume in various lipid categories was conducted on the samples collected from the PR group. Significant variations in relation to the pre-treatment patient lipid levels were determined using Student’s t-test. Asterisks indicate the presence of such significant differences when <italic>P</italic> -value was lower than 0.05</p></caption></fig>", "<fig id=\"Fig5\"><label>Fig. 5</label><caption><p>Detecting significant variations in the constituents of PG among three patient groups. <bold>A</bold> The PG levels in the PR group did not show any significant alteration. <bold>B</bold> There was a significant increase in PG 36:1 levels in the SD group after treatment. <bold>C </bold>PG 36:0 and PG 36:1 levels showed a significant increase in the PD group after treatment. To compare the alterations in PG levels before and after treatment, a paired two-tailed Student’s t-test was conducted within each group (PR, SD, PD). The comparison was made between the non-treatment group and the group treated with a combination of anlotinib and a PD-1 inhibitor. A <italic>P</italic> -value of less than 0.05 was considered statistically significant and marked with an asterisk</p></caption></fig>", "<fig id=\"Fig6\"><label>Fig. 6</label><caption><p>Detection of substantial alterations in the constituents of PI among three distinct groups of patients prior to and subsequent to intervention. <bold>A </bold>Significant alterations were not observed in the PI levels of the PR group. <bold>B </bold>The SD group showed a significant increase in PI 38:0, PI 40:2, and PI 44:4 levels after treatment. <bold>C</bold> In the PD group, more than half of the PIs exhibited a significant up-regulation after treatment. To evaluate the changes in PI levels, a paired two-tailed Student’s t-test was performed within each group (PR, SD, PD). The comparison was made between the non-treatment group and the group treated with a combination of anlotinib and a PD-1 inhibitor. Statistical significance was determined with a <italic>P</italic> -value of less than 0.05, which was denoted with an asterisk</p></caption></fig>" ]

[ "<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Patients characteristics</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\"> Characteristic</th><th align=\"left\">ICIs plus anlotinib No (%)</th></tr></thead><tbody><tr><td align=\"left\">No of total patients</td><td align=\"left\">30</td></tr><tr><td align=\"left\" colspan=\"2\"><bold>Gender</bold></td></tr><tr><td align=\"left\"> Male</td><td align=\"left\">25(83.33%)</td></tr><tr><td align=\"left\"> Female</td><td align=\"left\">5(16.67%)</td></tr><tr><td align=\"left\" colspan=\"2\"><bold>Age(years)</bold></td></tr><tr><td align=\"left\"> Median age(range)</td><td align=\"left\">63.5(range,36–79)</td></tr><tr><td align=\"left\">  &lt;65</td><td align=\"left\">16(53.33%)</td></tr><tr><td align=\"left\">  ≥ 65</td><td align=\"left\">14(46.67%)</td></tr><tr><td align=\"left\" colspan=\"2\"><bold>Smoking History</bold></td></tr><tr><td align=\"left\"> Non-smoker</td><td align=\"left\">5(16.67%)</td></tr><tr><td align=\"left\"> Former smoker</td><td align=\"left\">25(83.33%)</td></tr><tr><td align=\"left\" colspan=\"2\"><bold>ECOG performance status</bold></td></tr><tr><td align=\"left\"> 0</td><td align=\"left\">10(33.33%)</td></tr><tr><td align=\"left\"> 1</td><td align=\"left\">20(66.67%)</td></tr><tr><td align=\"left\" colspan=\"2\"><bold>Histology</bold></td></tr><tr><td align=\"left\"> Squamous cell carcinoma</td><td align=\"left\">15(50.00%)</td></tr><tr><td align=\"left\"> Adenocarcinoma</td><td align=\"left\">15(50.00%)</td></tr><tr><td align=\"left\" colspan=\"2\"><bold>Stage</bold></td></tr><tr><td align=\"left\"> IIIb-IIIc</td><td align=\"left\">5(16.67%)</td></tr><tr><td align=\"left\"> IV</td><td align=\"left\">25(83.33%)</td></tr><tr><td align=\"left\" colspan=\"2\"><bold>Metastatic</bold></td></tr><tr><td align=\"left\"> Liver</td><td align=\"left\">5(16.67%)</td></tr><tr><td align=\"left\"> Brain</td><td align=\"left\">4(13.33%)</td></tr><tr><td align=\"left\"> Bone</td><td align=\"left\">10(33.33%)</td></tr><tr><td align=\"left\"> Lung</td><td align=\"left\">16(53.33%)</td></tr><tr><td align=\"left\"> Pleural metastasis</td><td align=\"left\">4(13.33%)</td></tr><tr><td align=\"left\" colspan=\"2\"><bold>PD-L1 status test</bold></td></tr><tr><td align=\"left\"> Data unavailable</td><td align=\"left\">9(30.00%)</td></tr><tr><td align=\"left\">  0%</td><td align=\"left\">5(16.67%)</td></tr><tr><td align=\"left\">  1–49%</td><td align=\"left\">6(20.00%)</td></tr><tr><td align=\"left\">  ≥ 50%</td><td align=\"left\">10(33.33%)</td></tr><tr><td align=\"left\" colspan=\"2\"><bold>PFS</bold></td></tr><tr><td align=\"left\"> ≥ 6个月</td><td align=\"left\">15(50.00%)</td></tr><tr><td align=\"left\"> &lt;6个月</td><td align=\"left\">15(50.00%)</td></tr><tr><td align=\"left\" colspan=\"2\"><bold>Efficacy evaluation</bold></td></tr><tr><td align=\"left\"> PR</td><td align=\"left\">6(20.00%)</td></tr><tr><td align=\"left\"> SD</td><td align=\"left\">17(56.67%)</td></tr><tr><td align=\"left\"> PD</td><td align=\"left\">7 (23.33%)</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab2\"><label>Table 2</label><caption><p>Patients characteristics in each response group (PR, SD, and PD)</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" colspan=\"2\">Efficacy evaluation</th><th align=\"left\">PR</th><th align=\"left\">SD</th><th align=\"left\">PD</th></tr></thead><tbody><tr><td align=\"left\" colspan=\"2\">No of total patients</td><td align=\"left\">6</td><td align=\"left\">17</td><td align=\"left\">7</td></tr><tr><td align=\"left\" rowspan=\"2\"><bold>Gender</bold></td><td align=\"left\">Male</td><td align=\"left\">4(66.67%)</td><td align=\"left\">16(94.12%)</td><td align=\"left\">5(71.43%)</td></tr><tr><td align=\"left\">Female</td><td align=\"left\">2(33.33%)</td><td align=\"left\">1(5.88%)</td><td align=\"left\">2(28.57%)</td></tr><tr><td align=\"left\"><bold>Age(years)</bold></td><td align=\"left\"><p><italic>P</italic> = 0.8784 (PR vs. SD)</p><p><italic>P</italic> = 0.7409 (PR vs. PD)</p><p><italic>P</italic> = 0.3373 (SD vs. PD)</p></td><td align=\"left\">59.50 ± 9.73</td><td align=\"left\">62.17 ± 10.18</td><td align=\"left\">54.71 ± 13.91</td></tr><tr><td align=\"left\"><bold>Height(cm)</bold></td><td align=\"left\"><p><italic>P</italic> = 0.1646 (PR vs. SD)</p><p><italic>P</italic> = 0.8785 (PR vs. PD)</p><p><italic>P</italic> = 0.1646 (SD vs. PD)</p></td><td align=\"left\">161.33 ± 9.11</td><td align=\"left\">165.47 ± 5.18</td><td align=\"left\">161.86 ± 5.15</td></tr><tr><td align=\"left\"><bold>Weight(Kg)</bold></td><td align=\"left\"><p><italic>P</italic> = 0.8951 (PR vs. SD)</p><p><italic>P</italic> = 0.5347 (PR vs. PD)</p><p><italic>P</italic> = 0.8651 (SD vs. PD)</p></td><td align=\"left\">60.50 ± 15.24</td><td align=\"left\">59.76 ± 11.93</td><td align=\"left\">56.43 ± 5.99</td></tr><tr><td align=\"left\"><bold>BMI(Kg/m</bold>^{<bold>2</bold>}<bold>)</bold></td><td align=\"left\"><p><italic>P</italic> = 0.4892 (PR vs. SD)</p><p><italic>P</italic> = 0.5287 (PR vs. PD)</p><p><italic>P</italic> = 0.9610 (SD vs. PD)</p></td><td align=\"left\">22.97 ± 5.15</td><td align=\"left\">21.70 ± 3.65</td><td align=\"left\">21.61 ± 2.81</td></tr><tr><td align=\"left\" rowspan=\"2\"><bold>Smoking History</bold></td><td align=\"left\">Non-smoker</td><td align=\"left\">2(33.33%)</td><td align=\"left\">2(11.76%)</td><td align=\"left\">1(14.29%)</td></tr><tr><td align=\"left\">Former smoker</td><td align=\"left\">4(66.67%)</td><td align=\"left\">15(88.24%)</td><td align=\"left\">6(85.71%)</td></tr><tr><td align=\"left\" rowspan=\"2\"><bold>ECOG performance status</bold></td><td align=\"left\">0</td><td align=\"left\">3(50.00%)</td><td align=\"left\">5(29.41%)</td><td align=\"left\">2(28.57%)</td></tr><tr><td align=\"left\">1</td><td align=\"left\">3(50.00%)</td><td align=\"left\">12(70.59%)</td><td align=\"left\">5(71.43%)</td></tr><tr><td align=\"left\" rowspan=\"2\"><bold>Histology</bold></td><td align=\"left\">Squamous cell carcinoma</td><td align=\"left\">3(50.00%)</td><td align=\"left\">7(41.18%)</td><td align=\"left\">5(71.43%)</td></tr><tr><td align=\"left\">Adenocarcinoma</td><td align=\"left\">3(50.00%)</td><td align=\"left\">10(58.82%)</td><td align=\"left\">2(28.57%)</td></tr><tr><td align=\"left\" rowspan=\"2\"><bold>Stage</bold></td><td align=\"left\">IIIb-IIIc</td><td align=\"left\">3(50.00%)</td><td align=\"left\">2(11.76%)</td><td align=\"left\">0(0.00%)</td></tr><tr><td align=\"left\">IV</td><td align=\"left\">3(50.00%)</td><td align=\"left\">15(88.24%)</td><td align=\"left\">7(100.00%)</td></tr><tr><td align=\"left\" rowspan=\"5\"><bold>Metastatic</bold></td><td align=\"left\">Liver</td><td align=\"left\">2(33.33%)</td><td align=\"left\">2(11.76%)</td><td align=\"left\">1(14.29%)</td></tr><tr><td align=\"left\">Brain</td><td align=\"left\">1(16.67%)</td><td align=\"left\">2(11.76%)</td><td align=\"left\">1(14.29%)</td></tr><tr><td align=\"left\">Bone</td><td align=\"left\">2(33.33%)</td><td align=\"left\">5(29.41%)</td><td align=\"left\">3(42.86%)</td></tr><tr><td align=\"left\">Lung</td><td align=\"left\">2(33.33%)</td><td align=\"left\">10(58.82%)</td><td align=\"left\">4(57.14%)</td></tr><tr><td align=\"left\">Pleural metastasis</td><td align=\"left\">0(0.00%)</td><td align=\"left\">3(17.65%)</td><td align=\"left\">1(14.29%)</td></tr><tr><td align=\"left\" rowspan=\"4\"><bold>PD-L1 status test</bold></td><td align=\"left\">Data unavailable</td><td align=\"left\">3(50.00%)</td><td align=\"left\">3(17.65%)</td><td align=\"left\">3(42.86%)</td></tr><tr><td align=\"left\">0%</td><td align=\"left\">1(16.67%)</td><td align=\"left\">2(11.76%)</td><td align=\"left\">2(28.57%)</td></tr><tr><td align=\"left\">1–49%</td><td align=\"left\">1(16.67%)</td><td align=\"left\">3(17.65%)</td><td align=\"left\">2(28.57%)</td></tr><tr><td align=\"left\">≥ 50%</td><td align=\"left\">1(16.66%)</td><td align=\"left\">9(52.94%)</td><td align=\"left\">0(0.00%)</td></tr></tbody></table></table-wrap>" ]

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[ "<supplementary-material content-type=\"local-data\" id=\"MOESM1\"></supplementary-material>" ]

[ "<fn-group><fn><p><bold>Publisher’s Note</bold></p><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn></fn-group>" ]

[ "<graphic xlink:href=\"12944_2023_1960_Fig1_HTML\" id=\"MO1\"/>", "<graphic xlink:href=\"12944_2023_1960_Fig2_HTML\" id=\"MO2\"/>", "<graphic xlink:href=\"12944_2023_1960_Fig3_HTML\" id=\"MO3\"/>", "<graphic xlink:href=\"12944_2023_1960_Fig4_HTML\" id=\"MO4\"/>", "<graphic xlink:href=\"12944_2023_1960_Fig5_HTML\" id=\"MO5\"/>", "<graphic xlink:href=\"12944_2023_1960_Fig6_HTML\" id=\"MO6\"/>" ]

[ "<media xlink:href=\"12944_2023_1960_MOESM1_ESM.docx\"><caption><p><bold>Additional file 1: Supplemental Fig. 1.</bold> The potential mechanism by which abnormal elevations of PG and PI hinder the beneficial effects of combining anlotinib with PD-1/PD-L1 inhibitors for patients. During the analysis of Pearson correlation, a positive correlation between PG and PI was observed in each response group (PR, SD, and PD). This indicates that an increase in PG content corresponds to an increase in PI content, and vice versa. A hypothesis is proposed that the elevation of PG/PI levels could activate the PI3K-AKT pathway. This is because PI can act as a substrate for PIP2, which is phosphorylated by PI3K. Activation of the PI3K-AKT pathway has been linked to promoting tumor growth.</p></caption></media>" ]

{ "acronym": [], "definition": [] }

[ "<title>Background</title>", "<p id=\"Par8\">Medicinal plants refer to plants that have been recognized and utilized by people to treat human and animal diseases. Medicinal plants play a role in protecting the lives and health of ethnic groups living in the remote areas of developing countries [##REF##26762159##1##, ##REF##30390675##2##]. Some of these practices have also been applied in developed areas [##UREF##0##3##, ##REF##30626417##4##]. At least 80% of developing countries rely mainly on local traditional medicine to prevent and treat various diseases in humans and animals [##UREF##1##5##]. Medicinal plants are an important basis for the emergence and development of Chinese medicine [##REF##19857257##6##–##UREF##3##8##]. Ethnoveterinary medicines are generally defined as those used based on folk expertise, beliefs, knowledge, practices, methods related to animals’ health, and to cure various ailments in the ethnic group areas [##UREF##4##9##]. Ethnoveterinary medicine is not only an important part of traditional medicine but also an indispensable part of local animal health and the most basic veterinary services [##UREF##5##10##, ##REF##19941663##11##].</p>", "<p id=\"Par9\">Ethnoveterinary medicine plants (EMPs) are the plants used to prevent and control animal diseases, especially in remote and undeveloped areas where access to medical care is limited or missing. EMPs have a long history of practice, especially in countries with more developed animal husbandry practices [##UREF##6##12##–##REF##35591874##17##]. At the last ten years, the topic of ethnoveterinary has developed a great interest among researchers in China, such as many surveys of ethnoveterinary and EMPs [##REF##35146017##18##, ##REF##36597154##19##]. Traditional low-cost methods for treating animal diseases, rather than synthetic drugs, are often desired.</p>", "<p id=\"Par10\">The Bai people are the ancestral home of Yunnan [##UREF##7##20##]. With a population of 2.09 million, the Bai ethnic group is the 15th largest in China [##UREF##8##21##], mainly distributed in Yunnan, Guizhou, Hunan, and other provinces [##UREF##9##22##]. Most of the Bai people in China reside in the Dali Bai Autonomous Prefecture of Yunnan Province. The Dali Prefecture was the origin and main settler of the Bai people. The Bai nationality has its own language and belongs to the Bai branch of the Tibetan-Burmese family of the Sino-Tibetan language family [##UREF##7##20##]. Based on the regional and national characteristics,  Bai and Chinese bilingual bicultural education are carried out for Bai students. Buddhism and “Benzhu” worship constitute an important part of the Bai religious culture. “Benzhu” worship is a unique religious belief of the Bai nationality, which is generally a hero in local myths and legends, and the Bai people regard “Benzhu” as the local protection god [##UREF##10##23##].</p>", "<p id=\"Par11\">Bai medicine has a long history, and archaeology has been used since the Ming Dynasty. Bai ancestors generally used local herbs or traditional Chinese medicines to treat diseases [##UREF##11##24##]. Bai medicine is an accumulation and summary of the experiences of the Bai people in disease prevention and treatment over generations. Its diagnosis and treatment are characteristic of “Medicine with God.” Treating diseases and praying to gods do not conflict with each other. “Medicine with God” means doctors praying in the healing process. The generation of “Medicine with the God” is bound to primitive religious, cultural, and economic factors [##UREF##12##25##].</p>", "<p id=\"Par12\">Local people have developed special diagnostic and treatment methods, such as spa, moxibustion, rolling egg (roll a shelled, boiled egg on the area of discomfort for relief), steam, and medicinal dietary therapies, and these therapies integrate the medical theories and methods of Han, Yi, Tibetan, and other ethnic groups. They were also good at using single-experience prescriptions [##UREF##13##26##]. Finally, a medical culture with national and regional characteristics was formed, which contributed greatly to the reproduction of the Bai people, including the research on the medicinal culture of Yunnan Bai people [##UREF##14##27##], Yunnan Bai people medicine [##UREF##15##28##], habitual plant medicine of the Bai people [##UREF##16##29##], and illustrated guide of medicinal plants of the Bai people [##UREF##17##30##]. In China, traditional knowledge of ethnoveterinary medicine originates from the daily livestock management of indigenous people and the long history of these practices.</p>", "<p id=\"Par13\">The Yunlong County is located west of Dali Bai Autonomous Prefecture and is a collection of remote mountainous areas, ethnic groups, poor areas, and alpine areas [##UREF##18##31##]. There are five deeply poor townships in Dali Bai Autonomous Prefecture, and there are four in Yunlong County, accounting for 80% of the deeply poor townships in the prefecture. According to the poverty standard line of 2300 yuan (per capita annual net income) established in 2011, Yunlong County had a total of 151,900 poor people in that year (the total population was 207,117) [##UREF##19##32##]. It has a large population in both mountainous and semi-mountainous regions. The Bai people in Yunlong County have a long history of livestock and poultry farming. They are rice farmers with a long history of farming in the plateau region, although the rice-planting area is small in Yunlong County, and the local economy is mainly mountainous agriculture.</p>", "<p id=\"Par14\">There are more than ten ethnic groups, including the Bai, Han, Yi, Miao, Hui, Dai, Lisu, and Achang, with the Bai people, accounting for the largest proportion (72.7%) of the total population [##UREF##11##24##]. Differences in culture, religion, customs, language, dietary habits, and living environments between ethnic groups have prompted the generation of several traditional medicines with distinct regional characteristics [##UREF##20##33##]. According to the results of the seventh national census, the population living in cities and towns in Yunlong County was 51,298, accounting for 28.04% of the total population. The population living in rural areas was 131,679, accounting for 71.96% of the total population [##UREF##21##34##].</p>", "<p id=\"Par15\">Most of the rural population is scattered on mountain tops or hillsides; mountain roads are muddy and potholed, and transportation is inconvenient. These areas are far from urban areas, and agriculture and animal husbandry are the main sources of income. Indigenous people have a long history of raising livestock and poultry, such as black goats, pigs, cattle, donkeys, chickens, and ducks, to meet their needs and as a source of family income. In addition, according to the Circular on the 13th Five-Year Plan for Poverty Alleviation issued by the State Council on November 23, 2016 [##UREF##22##35##], The Yunlong County established several farms in various townships based on the advantages and endowments of natural resources to revitalize the countryside (Fig. ##FIG##0##1##).</p>", "<p id=\"Par16\">The local agricultural department crossbred non-local beef cattle with local breeds to increase meat production. Additionally, it provided policy assistance and economic support to local residents. Veterinarians have used medicinal plants to treat animal diseases, forming a set of unique knowledge systems for local traditional veterinary medicine; however, to date, these have not been systematically studied. Detailed information on the use of traditional ethnoveterinary knowledge by the Bai people in Yunlong County is lacking. In this study, ethnobotanical methods were used to investigate and catalog traditional EMPs in Yunlong County, China. This investigation will contribute to the cataloging of medicinal plants for the treatment of livestock diseases and uncover relevant knowledge of traditional Bai medicine.</p>" ]

[ "<title>Methods</title>", "<title>Study area</title>", "<p id=\"Par17\">The Yunlong County, Dali Bai Autonomous Prefecture, is located in the western part of Yunnan Province, in the longitudinal valley of the Lancang River at the southern end of the Hengduan Mountain, between 98°52′–99°46′ E and 25°28′–26°23′ N. It is located at the junction of Dali Prefecture, Baoshan area, and Nujiang Prefecture, and the total area is 4400.95 km², 90% of which is mountainous [##UREF##23##36##]. The distribution characteristics of the water systems in the territory are clear. The Lancang River and its tributaries run west and in the middle of Yunlong County from north to south, respectively. The riverbed has a large slope and is rich in hydraulic resources. The basic topography is high from east to west, low in the middle, gradually decreasing from north to south, and the elevation is approximately 2000–2500 m. The Yunlong County generally has a continental subtropical plateau monsoon climate with distinct dry and wet seasons, the same season of rain and heat, and the same period of dryness and cooling [##UREF##24##37##].</p>", "<p id=\"Par18\">It is a complex and changeable “compound three-dimensional climate.” The annual average temperature in Yunlong County is 16.1℃, the hottest monthly average temperature is 22.3℃, and the coldest monthly average temperature is 8.4℃. The difference between the annual average temperature at the highest and lowest elevations is 17℃ [##UREF##25##38##]. The local mountains are undulating, the forests are dense, the rivers are vertical and horizontal, the sunshine is sufficient, the rainfall is moderate, and the climate is suitable, which provides superior conditions for the growth and reproduction of all kinds of animals and plants, so it is rich in plant resources and is a natural medicinal resource bank. Yunlong County has jurisdiction over 11 townships, including Miaowei, Guanping, Baofeng, Nuodeng, Gongguoqiao, and Tuanjie. This survey area included six villages (Biaocun, Dalishu, Xiaomaidi, Nuodeng, Gongguoqiao, Tuanjie), three local herbal medicinal markets (Tenlong, Miaowei, Baofeng), three traditional animal breeding farms, and four herbal medicine planting bases (Yunlong County Yuanheng biotechnology development Co., Ltd, Songping, Longze, Yunlong county Canwen) from Yunlong County (Fig. ##FIG##1##2##).</p>", "<title>Data collection</title>", "<p id=\"Par19\">Ethnobotanical investigations were conducted from August 2021 to August 2022, which included structured interviews, participatory observations, semi-structured interviews, and key person interviews combined with field investigations to complete the cataloging of traditional EMPs in Yunlong County (Fig. ##FIG##2##3##). All interviews were conducted by Wei Huang in the local Bai language. A total of 68 local residents were interviewed, including 58 men and 10 women. These were farmers and herbal veterinarians with several years of experience in raising and treating livestock diseases. Local farmers with knowledge of veterinary medicine would collect some herbs (Fig. ##FIG##3##4##a, b) and hang them at home for drying on rainy days (Fig. ##FIG##3##4##c). Herbal veterinarians would prepare the medicine according to common local veterinary diseases and save it for later use (Fig. ##FIG##3##4##d).</p>", "<p id=\"Par20\">The informants were apprised of our purpose before the interview to gain consent and trust so that we could communicate freely and openly with them. The primary content of the interviews consisted of “5W + H” questions (i.e., questions concerning what, when, where, who/whom, why, and how the informants utilized EMPs). The recorded information was shown again to the informant again to avoid errors and tampering.</p>", "<p id=\"Par21\">Plant specimens were collected by the first author and identified using the “Flora of China” and China Digital Plant Museum (<ext-link ext-link-type=\"uri\" xlink:href=\"https://www.cvh.ac.cn/\">https://www.cvh.ac.cn/</ext-link>). The Latin names of the plants were corrected and verified using The Plant List (<ext-link ext-link-type=\"uri\" xlink:href=\"http://www.theplantlist.org/\">http://www.theplantlist.org/</ext-link>). The voucher specimens were stored in the plant specimen room of the Key Laboratory of Ethnic Medicine Resources Chemistry, Yunnan Minzu University, Kunming, China.</p>", "<title>Data analysis</title>", "<p id=\"Par22\">For each plant collected, according to its use report (URs), the UR was defined as the type of disease treated by the plant [##UREF##26##39##]. To analyze the differences in medicinal plant species used by different herbalists in the treatment of a certain type of disease [##UREF##27##40##], the diseases reported by the interviewer were divided into 10 categories. The informant consensus factor (FIC) was calculated as follows:</p>", "<p id=\"Par23\">Nur represents the sum of the number of plant species used by all informants to treat a particular disease and Nt is the number of plant species commonly used by all informants to treat the disease. The FIC value ranges from 0 to 1, and the higher the FIC value, the higher the difference in plant species used to treat a disease; the lower the FIC value, the more concentrated the plant species used in the treatment of disease [##UREF##27##40##].</p>" ]

[ "<title>Results</title>", "<title>Informant characteristics</title>", "<p id=\"Par24\">A total of 68 informants were interviewed, including 58 men (85.3%) and 10 women (14.7%). The informants ranged in age from 30 to 79 years, with most being older than 50 years (50%), and the average age was 52 years (Table ##TAB##0##1##). These included farmers, herbalists, truck drivers, etc. They were localities with several years of experience treating livestock diseases. Most of them had low primary school education levels.</p>", "<title>Ethnoveterinary medicinal plant diversity</title>", "<p id=\"Par25\">A total of 90 plant species belonging to 51 families and 84 genera were recorded. The Asteraceae (12 spp.) family had the highest number of individual species used in ethnoveterinary practices, followed by Fabaceae (4 spp.) and Apiaceae (4 spp.). Figure ##FIG##4##5## shows the EMPs in the Bai region, 7 families had 3 species, 8 families had 2 species, and 33 families had only 1 species.</p>", "<title>Common livestock diseases in Yunlong County</title>", "<p id=\"Par26\">As can be seen from Table ##TAB##1##2##, fall injury is the most common disease of large livestock in Yunlong County, followed by gastrointestinal diseases, respiratory diseases, snake bites, and other diseases. The Yunlong County is characterized by high mountains and steep slopes. Local livestock mostly adopt modes of grazing and free-raising, which makes them prone to infectious diseases caused by traumatic wounds. Hot and humid climatic conditions may lead to wound inflammation and slow healing; moreover, gastrointestinal, respiratory, and parasitic diseases often occur in enclosures with poor sanitary conditions. In recent years, economic development has driven improvements in hygiene levels, and the importance of enclosures for healthy livestock growth is increasingly being recognized. In spite of its simplicity, a traditional breeding farm emphasizes regular cleaning of the enclosure. The sheepfold is usually designed with two layers, with a gap in the boards. Most of the feces fall through the gap to the lower layer, which helps maintain cleanliness of the pen while enabling easy cleaning (Fig. ##FIG##5##6##A). The use of dry straw washers not only protects cattle and other large livestock from falls and injuries but also provides optimal composting conditions and increases nutrients for crops when using the compost of manure mixed with straw (Fig. ##FIG##5##6##B, ##FIG##5##C##). Many local farmers maintain the traditional habit of livestock feeding, which greatly reduces the possibility of parasitic infection and diseases (Fig. ##FIG##5##6##D). The Yunlong County has several retail investors, mostly living on hilltops or hillsides with lush vegetation in the front and back of houses. Livestock may be bitten by snakes during grazing and while in captivity. Reproductive diseases are common in patients with dystocia, persistent placenta, or postpartum weakness.</p>", "<p id=\"Par27\">Foot-and-mouth disease is an occasional infectious disease, the control of which is mainly based on prevention, adhering to the principle of “early detection and early treatment.” Once discovered, the infected animals are immediately killed and buried. Foot-and-mouth disease occurs in many areas of China. According to the announcement of the Ministry of Agriculture and Rural Affairs of the People’s Republic of China, for the relevant livestock breeds throughout the country, according to the local actual situation, the appropriate immune vaccines against foot-and-mouth disease type O and A are selected on the basis of scientific evaluation, and the import of epidemic products from abroad is prohibited [##UREF##28##41##]. The interviewer was blinded to whether Radix Isatidis, Milkvetch Root, Palmatine, and other proprietary Chinese medicines were supplemented to livestock feed for preventing infectious diseases. The interviewers were also blinded to the specific types of diseases that were termed miscellaneous, including fever, nose bleeding, edema, loss of appetite, and abnormal conditions related to various organ systems of the animal. Miscellaneous information is related to the limitations of local residents’ miscellaneous medical knowledge.</p>", "<title>Life forms and parts of plants used for ethnoveterinary purposes</title>", "<p id=\"Par28\">The survey found that among the 90 EMPs (Additinal file ##SUPPL##0##1##: Table 3), herbs accounted for the largest proportion with 70 species (77.78%), followed by eight shrub (8.89%), seven trees (7.78%), and five liana species (5.56%), as shown in Fig. ##FIG##6##7##. This distribution is closely related to local climatic conditions. The Yunlong County has a continental subtropical plateau monsoon climate with abundant shrubs, trees, and herbaceous plant types. Herbaceous plants have a short growth cycle and large growth, which are sufficient to meet the demand and are easy to harvest and process.</p>", "<p id=\"Par29\">The total number of medicinal parts was 111 (some plants contained multiple medicinal parts). Roots were the most frequently used plant parts, constituting 40.54%, followed by whole plants (25.23%), leaves (9.01%), stems (7.20%), and mixed plant parts (18.02%) (Fig. ##FIG##7##8##).</p>", "<p id=\"Par30\">Root and whole plant medicines were used more frequently. These conditions may be the result of screening by local residents combined with local plant resources and traditional ethnoveterinary practices.</p>", "<title>Methods of ethnoveterinary medicine preparation and administration</title>", "<p id=\"Par31\">Different methods were used to prepare medicinal plants for treating livestock diseases. The most commonly used method for preparing medicinal plants was decoction (52.63%), followed by mashing (23.16%), grinding into a powder (11.58%), soaking in liquor (5.26%), and soaking in boiling water (3.16%). A few of the preparations used honey, sugar, and rapeseed oil (Table ##TAB##2##3##).</p>", "<p id=\"Par32\">Medicine administration involved two modes: oral administration (64, 71.11%) and external application (26, 28.89%).</p>", "<p id=\"Par33\">Local veterinarians used auxiliary tools for livestock that posed problems with orally administered medicines; the tools were borrowed from a local veterinary station if required. Most auxiliary tools were modified using common materials and the common auxiliary tools included feeding tools, syringes, and steel needles (Fig. ##FIG##8##9##).</p>" ]

[ "<title>Discussion</title>", "<title>Characteristics of informants and their sources of traditional ethnoveterinary knowledge</title>", "<p id=\"Par34\">Most local veterinarians and herbalists worked part-time, mainly in farming or other jobs, such as driver. They were localities with several years of experience in treating livestock diseases and most had low primary school-level education. Traditionally, in Bai culture, women are responsible for housework and men are the breadwinners of the family. Accordingly, men are responsible for feeding the livestock. Traditional ethnoveterinary practices are mainly passed on from older herbalists to the next male heir or protege. During our investigation, a 79-year-old local herbalist accepted a 40-year-old truck driver from the same village as an apprentice in Tuanjie Town. Truck drivers need to use veterinary knowledge to treat livestock in the process of transportation, and first aid experience enables truck drivers to further accumulate veterinary drug knowledge.</p>", "<p id=\"Par35\">The traditional medical knowledge of herbal veterinarians comes from self-study or the learning practices of older generations. They continue accumulating experience in treating diseases and learning about the pharmacological effects of plants in their lifetime, which are passed down from generation to generation. In general, nobility is a requirement of the Bai community to become a healer. Many local Bai healers treat their patients without expecting anything in return.</p>", "<p id=\"Par36\">In the past, localities who were generally poor did not charge for the treatment of other people’s livestock. This spirit of self-sacrifice in diagnosis and treatment is influenced by Buddhism and legendary tales, and the Legend of the Great Black God, the Legend of the King of Medicine, and the glazed Beast are all myths and legends about “self-sacrifice culture” [##UREF##29##42##, ##UREF##30##43##]. The “self-sacrifice culture” forms the basis of humanistic care of Bai medicine and is evident throughout the medical history of the Bai people.</p>", "<title>Characteristics of Bai Ethnoveterinary medicinal plants in Yunlong County</title>", "<p id=\"Par37\">During the investigation, 90 species of medicinal plants belonging to 51 families and 84 genera were recorded and used to treat livestock diseases. Plants from the Asteraceae family were most widely used by local healers. This may be related to local hot and humid climatic conditions. Plants of the Asteraceae family, one of the largest families of seed plants worldwide, grew readily in local communities. The biomass and population size of Asteraceae e plants are typically extremely large. The Asteraceae medicinal plants are characterized by their heat-clearing and detoxifying, wind dispelling, and dehumidifying antimicrobial properties [##UREF##31##44##–##UREF##33##46##]. The medicinal plants of Asteraceae, Fabaceae, Apiaceae, Ranunculaceae, Euphorbiaceae, Gentianaceae, Lamiaceae, Polygonaceae, and Rosaceae were widely used by the localities, which may be owing to the abundance of wild plant resources in Yunlong County. This is consistent with the results of Yunfang’s investigative study on the diversity of medicinal plant resources and the dominant plant family in Yunlong County [##UREF##34##47##]. The Asteraceae and Fabaceae plants were used the most, and the results were similar to the survey results of many other research areas in southern China [##UREF##35##48##, ##REF##30333030##49##].</p>", "<p id=\"Par38\">Among 33 plant families, only one medicinal plant species was eligible as an EMP. Medicinal plants are abundant in Bai village, and local residents collect diverse medicinal species. Most EMPs are collected from wild habitats; they are dug up from near the mountains and planted in their courtyard or in the front and back of the house. In our study, <italic>Solanum violaceum</italic> Ortega and <italic>Phedimus aizoon</italic> (L.) 't Hart were planted in the courtyard of a farmer’s house, after boiling, and were fed the livestock to clear away heat and detoxify. The life forms of herbs planted in the courtyard are mostly herbs. These are regularly cared for until required during emergencies and also serve to protect endangered medicinal plants [##UREF##36##50##].</p>", "<p id=\"Par39\">Our investigation indicated that herbal veterinarians usually went to various parts of the county to collect the necessary medicinal materials in August, thus avoiding the busy agricultural season and ensuring optimal plant growth.</p>", "<p id=\"Par40\">Most of the harvested medicinal plants were herbs. This is not only herbs are the most used plant part for medicine, but also because they are easy to procure [##REF##30333030##49##, ##REF##18359178##51##]. The roots and rhizomes were the most commonly used parts for medicines, followed by whole grass, and the result is the same as other ethnic groups (Buyi, Yao, Zhuan, and Maonan) in the choice of medicinal parts [##REF##25572933##52##–##UREF##39##56##]. However, this traditional utilization method causes significant damage to the biodiversity of the medicinal plants.</p>", "<p id=\"Par41\">The selection of medicinal parts should be modified to ensure sustainable utilization of medicinal plant resources. Therefore, the resource utilization rate should be improved. The Bais have herbal medicine markets in various townships in Yunlong County. Raw herbs are used to prevent and treat various diseases. The local herbal medicinal market enriches the diversity of medicinal plants and is an important place for the exchange and dissemination of Bai medicinal culture [##UREF##40##57##]. As the education level of the older generation of herbal veterinarians is generally low, their traditional knowledge is derived from previous experiences and daily practice.</p>", "<p id=\"Par42\">Local herbalists are avant-garde and dare to accept and try new things. In our study, we encountered an old herbalist who grafted mistletoe (<italic>Viscum coloratum</italic>) onto a succulent plant (<italic>Euphorbia royleana</italic>) to improve plant growth (Fig. ##FIG##9##10##). He was able to acquire the medicinal plant <italic>Viscum coloratum</italic> by grafting it on the succulent plant near his home.</p>", "<title>Livestock breed management and treatment of livestock diseases</title>", "<p id=\"Par43\">Outbreaks of livestock diseases seriously affect the development of aquaculture and the economic income of residents [##UREF##41##58##, ##REF##30332533##59##]. Livestock breed selection is closely related to disease prevention and economic benefits. According to the interviews, veterinary staff are aware that an improvement in people’s living standards has increased the demand for meat, which the local old breed of beef cattle has been unable to meet. In 1988, local animal husbandry and veterinary management departments began to cross-breed old cattle breeds free of charge to increase the number of beef cattle. The hybrid cattle were strong, disease-resistant, and highly valued. Cross-breeding is now mostly performed by veterinary station staff, which charges 100–300 RMB each time. As older yellow cattle breed are small and rarely get sick, they are more suitable for free breeding in the local mountainous environment; therefore, there is still a large stock in the Yunlong Bai region.</p>", "<p id=\"Par44\">Local veterinarians diagnose livestock diseases based on existing medical knowledge. Common diagnostic methods include observation (e.g., observing the physical manifestations and disease symptoms), listening and smelling (e.g., listening to the sound and breath of animals, sniffing secretions, and excreta), questioning (e.g., asking animal keepers about the appearance or history of the disease), and palpation (e.g., touching or pressing the animal’s body, feeling the pulse, and other viscera), which is similar to traditional Chinese medicine [##UREF##42##60##, ##UREF##43##61##]. Tongue examination is not only an important part of traditional Bai medicine but also an important component of disease diagnosis [##UREF##44##62##].</p>", "<p id=\"Par45\">Red fur is wind-cold, yellow fur indicates excess heat, green fur indicates toxicosis, and white fur indicates collapse. After diagnosing the disease, the local veterinarians begin to prescribe the right medicine for the case. Suitable medicinal plants can be used to prevent and treat diseases owing to their medicinal properties.</p>", "<p id=\"Par46\"><italic>Rodgersia sambucifolia</italic> is widely used by local veterinarians for the treatment of livestock respiratory diseases owing to the plant’s polyphenols, flavonoids, terpenoids, and volatile oils [##UREF##45##63##, ##UREF##46##64##]. Farmers and herbal veterinarians use varied methods to treat their livestock. They often mash<italic> Selaginella moellendorffii </italic>and feed it to the animals to treat a postpartum abdominal cold for livestock postnatal care. Because of the hot and humid climate in Yunlong County, the Asteraceae medicinal plants (<italic>Chrysanthemum indicum</italic> L., <italic>Taraxacum mongolicum</italic> Hand.-Mazz., <italic>Aucklandia costus</italic> Falc., etc.) are often mashed or boiled and fed to livestock for heat-clearing and detoxification.</p>", "<p id=\"Par47\">In addition to using plants to prevent livestock diseases, local veterinarians have developed unique diagnostic and treatment methods. They use gunpowder to wipe the fur of livestock to treat depilation and apply gasoline to the wounds to ward off maggots.</p>", "<p id=\"Par48\">The donkey turned mad and pulled up the long mane on top of its head and put it in with a needle supplemented with cat incense (wildcat secretion) internal administration can cure. The tripe flatulence was inserted directly with a steel needle, turned down, and taken internally with <italic>Rodgersia sambucifolia</italic> Hemsl, <italic>Actaea cimicifuga</italic> L., and other medications after bloodletting and outgassing.</p>", "<title>Prospects and challenges of traditional ethnoveterinary knowledge</title>", "<p id=\"Par49\">Although Chinese traditional medical theory is famous worldwide for its application in human health, it is rarely mentioned in countries other than China. Traditional Chinese medicine has been used in veterinary medicine and human medicine practice in China for thousands of years.</p>", "<p id=\"Par50\">In modern Chinese society, herbs used for the treatment of animal diseases or animal feed are believed to contain fewer residues than traditional medicines [##UREF##47##65##]; moreover, they are believed to reduce bacterial drug resistance and food safety problems caused by modern veterinary drugs [##REF##26242647##66##]. Notices numbers 194 and 246 of the Ministry of Agriculture and Villages of the People’s Republic of China have led to the ban of the addition of antibiotics to veterinary drugs. Conversely, the various standards of traditional Chinese medicine allowing feed additives for both growth promotion and prevention and control have been revised [##UREF##48##67##, ##UREF##49##68##].</p>", "<p id=\"Par51\">Therefore, EMPs will gradually be welcomed in the prevention and control of diseases and the health protection of livestock. In remote and poor areas, EMPs are the first choice for local prevention and treatment of livestock diseases. However, under the influence of the mainstream social economy, an increasing number of people choose to work in cities, which hinders the inheritance of the traditional medicine culture and decreases the traditional animal husbandry and the number of animals in rural areas. In this survey, a number of practicing veterinarians said that children in their families would rather sell tea or work in a factory than learn about veterinary medicine.</p>", "<p id=\"Par52\">Currently, most people with knowledge of traditional medicinal plants and their use are over 50 years of age. They mostly engage in agricultural labor or breeding and rely only on their spare time to acquire traditional veterinary medical knowledge. These results threaten the inheritance of EMPs and traditional medical knowledge.</p>" ]

[ "<title>Conclusion</title>", "<p id=\"Par53\">Traditional veterinary medicine is easy to master and perform and is inexpensive. It plays an important role in the development of local aquaculture and animal husbandry and is the first choice for the prevention and treatment of animal diseases in remote and poor areas.</p>", "<p id=\"Par54\">However, with the passing on of the older generation, traditional knowledge of EMPs may disappear. In this study, we collected and sorted traditional knowledge about medicinal plants used in veterinary practice in Yunlong County. We obtained information on 90 EMPs and their corresponding treatment types for livestock diseases and studied the life form, drug preparation, and mode of administration of EMPs. This study plays an important role in the protection and inheritance of Bai EMPs and their traditional knowledge in Yunlong County.</p>", "<p id=\"Par55\">Traditional knowledge of ethnoveterinary medicine is related to the local social–cultural characteristics of the Bai people and plays a pivotal role in livestock production. Plants are the carriers of traditional culture, and traditional culture nourishes plant culture. Cultural diversity and biodiversity depend on each other. The traditional community has extremely rich traditional knowledge related to the improvement of people’s health and environmental hygiene conditions.</p>" ]

[ "<title>Background</title>", "<p id=\"Par1\">The Bai people in Yunlong County, northwest Yunnan, China, have used medicinal plants and traditional remedies for ethnoveterinary practices. The Bai have mastered ethnoveterinary therapeutic methods in livestock breeding since ancient times. The Bai’s traditional ethnoveterinary knowledge is now facing extinction, and their unique ethnoveterinary practices have rarely been recorded. This study documented animal diseases, EMPs, and related traditional knowledge in Yunlong County, China.</p>", "<title>Methods</title>", "<p id=\"Par2\">Ethnobotanical fieldwork was conducted in six villages and townships of Yunlong County between 2021 and 2022. Data were obtained through semi-structured interviews, participatory observations, and keyperson interviews. A total of 68 informants were interviewed, and the informant consensus factor and use reports (URs) were used to evaluate the current ethnoveterinary practices among the local communities. Information on livestock diseases, medicinal plants, and traditional ethnoveterinary medicine knowledge were also obtained.</p>", "<title>Results</title>", "<p id=\"Par3\">A total of 90 plant species belong to 51 families, 84 genera were recorded as being used as EMPs by the Bai people, and Asteraceae plants are most frequently used. A total of 68 informants were interviewed, including 58 men (85.3%) and 10 women (14.7%). The most commonly used EMPs parts included the roots, whole plants, leaves, and stems, and the common livestock diseases identified in this field investigation included trauma and fracture, gastrointestinal disorders, respiratory disorders, parasitic diseases, miscellaneous, venomous snake bites, reproductive diseases, infectious diseases, skin disease, and urinary diseases. Most of the EMPs are herbs (77.78%). Courtyard is one of the habitats of medicinal plants in Yunlong County.</p>", "<title>Conclusion</title>", "<p id=\"Par4\">Traditional knowledge of ethnoveterinary medicine is related to the local sociocultural characteristics of the Bai. Plants are used in cultural traditions, which, in turn, nourish the plant culture. Cultural diversity and biodiversity are interdependent. This traditional knowledge is at risk of disappearance because of the increasing extension of Western veterinary medicine, lifestyle changes, and mainstream cultural influences. Therefore, it is important to continue research on ethnoveterinary practices.</p>", "<title>Supplementary Information</title>", "<p>The online version contains supplementary material available at 10.1186/s13002-023-00633-0.</p>", "<title>Keywords</title>" ]

[ "<title>Supplementary Information</title>", "<p>\n</p>" ]

[ "<title>Acknowledgements</title>", "<p>Thanks go to the local Bai people in Yunlong County, Yunnan Province, who provided valuable information about traditional ethnoveterinary knowledge. Dr. Qingsong Yang assisted with the identification of plant specimens. Members of the School of Ethnic Medicine at Yunnan Minzu University participated in the field surveys, they are Hui Wang and Jingxian Sun. We would like to give sincere thanks to them for their help in the process of research.</p>", "<title>Author contributions</title>", "<p>HLG, WH, and CYZ performed the field work and collected data. HLG and XY organized the literature, analyzed the data, and drafted the manuscript. YX conceptualized the study, edited the final version, and obtained funding for the study. All authors have approved the final version of the manuscript for submission. </p>", "<title>Funding</title>", "<p>This work was financially supported by the National Natural Science Foundation of China (81760655) and the Open Project Fund of the Key Laboratory of Ethnic Medicine Resource Chemistry of the State Ethnic Affairs Commission and the Ministry of Education, Yunnan Minzu University (MZY2104).</p>", "<title>Availability of data and materials</title>", "<p>Not applicable.</p>", "<title>Declarations</title>", "<title>Ethics approval and consent to participate</title>", "<p id=\"Par56\">Not applicable.</p>", "<title>Consent for publication</title>", "<p id=\"Par57\">Prior and informed consent of local people’s pictures was obtained for publication.</p>", "<title>Competing interests</title>", "<p id=\"Par58\">The authors declare that they have no competing interests.</p>" ]

[ "<fig id=\"Fig1\"><label>Fig. 1</label><caption><p>Tuanjie Township Biao Village Beef cattle Farm (<bold>A</bold>) and Miaowei Township breeding Farm (<bold>B</bold>), and two photographs were taken by Hongli Gao on August 9, 2021</p></caption></fig>", "<fig id=\"Fig2\"><label>Fig. 2</label><caption><p>Location of the Yunlong County in China and elevation map of the townships in study area</p></caption></fig>", "<fig id=\"Fig3\"><label>Fig. 3</label><caption><p>Key person interview (<bold>A</bold>) was taken by local residents on January 14, 2022, in the Guanping township xiao di pang Village; local herbal medicinal markets (<bold>B</bold>) were taken on August 8, 2022 in Baofeng, and two photographs were taken by Hongli Gao</p></caption></fig>", "<fig id=\"Fig4\"><label>Fig. 4</label><caption><p>Fresh herbs (<bold>A</bold>\n<italic>Phedimus aizoon</italic> (L.) 't Hart and <bold>B</bold>\n<italic>Reynoutria japonica</italic> Houtt.), dried herbs (<bold>C</bold>\n<italic>Berberis diaphana</italic> Maxim.) and prepared herbs (<bold>D</bold>\n<italic>Botrychium ternatum</italic> (Thunb.) Sw. And <italic>Lycopodium japonicum</italic> Thunb.), and two photographs were taken by Hongli Gao in Baofeng on September 3, 2021</p></caption></fig>", "<fig id=\"Fig5\"><label>Fig. 5</label><caption><p>Distribution of plant species number on the basis on families</p></caption></fig>", "<fig id=\"Fig6\"><label>Fig. 6</label><caption><p>Local breeding livestock (<bold>A</bold>–<bold>C</bold>) and pig pots (<bold>D</bold>) were taken by Hongli Gao in Gongguoqiao on August 17, 2021</p></caption></fig>", "<fig id=\"Fig7\"><label>Fig. 7</label><caption><p>Ethnoveterinary medicinal plant life form</p></caption></fig>", "<fig id=\"Fig8\"><label>Fig. 8</label><caption><p>Distribution of plants used in the ethnoveterinary practice of the Bai people according to the frequency of plant parts</p></caption></fig>", "<fig id=\"Fig9\"><label>Fig. 9</label><caption><p>Auxiliary tools used by local veterinarians in Yunlong County was taken by Hongli Gao in Xiaomaidi on August 21, 2021</p></caption></fig>", "<fig id=\"Fig10\"><label>Fig. 10</label><caption><p>The old herbalist grafted mistletoe on the whip of <italic>Euphorbia royleana,</italic> was taken by Hongli Gao in Dalishu Village, Baofeng Township on July 20, 2022</p></caption></fig>" ]

[ "<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Characteristics of informants</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Characteristics</th><th align=\"left\">Frequency</th><th align=\"left\">Percentage (%)</th></tr></thead><tbody><tr><td align=\"left\" colspan=\"3\"><italic>Sex</italic></td></tr><tr><td align=\"left\">Male</td><td align=\"left\">58</td><td align=\"left\">85.3</td></tr><tr><td align=\"left\">Female</td><td align=\"left\">10</td><td align=\"left\">14.7</td></tr><tr><td align=\"left\" colspan=\"3\"><italic>Age range (y)</italic></td></tr><tr><td align=\"left\">30–40</td><td align=\"left\">10</td><td align=\"left\">14.7</td></tr><tr><td align=\"left\">41–50</td><td align=\"left\">24</td><td align=\"left\">35.3</td></tr><tr><td align=\"left\"> ≥ 50</td><td align=\"left\">34</td><td align=\"left\">50</td></tr><tr><td align=\"left\" colspan=\"3\"><italic>Occupation</italic></td></tr><tr><td align=\"left\">Farming</td><td align=\"left\">38</td><td align=\"left\">55.9</td></tr><tr><td align=\"left\">Truck driver</td><td align=\"left\">11</td><td align=\"left\">16.2</td></tr><tr><td align=\"left\">Veterinarian</td><td align=\"left\">15</td><td align=\"left\">22.1</td></tr><tr><td align=\"left\">Other</td><td align=\"left\">4</td><td align=\"left\">5.8</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab2\"><label>Table 2</label><caption><p>Common livestock diseases in Yunlong County</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Disease categories</th><th align=\"left\">Number of species</th><th align=\"left\">Citations</th><th align=\"left\">FIC</th></tr></thead><tbody><tr><td align=\"left\">Trauma and fractures</td><td align=\"left\">32</td><td align=\"left\">54</td><td char=\".\" align=\"char\">0.42</td></tr><tr><td align=\"left\">Gastrointestinal disorders</td><td align=\"left\">18</td><td align=\"left\">50</td><td char=\".\" align=\"char\">0.65</td></tr><tr><td align=\"left\">Respiratory disorders</td><td align=\"left\">16</td><td align=\"left\">48</td><td char=\".\" align=\"char\">0.68</td></tr><tr><td align=\"left\">Parasitic diseases</td><td align=\"left\">15</td><td align=\"left\">40</td><td char=\".\" align=\"char\">0.64</td></tr><tr><td align=\"left\">Miscellaneous</td><td align=\"left\">14</td><td align=\"left\">60</td><td char=\".\" align=\"char\">0.78</td></tr><tr><td align=\"left\">Venomous snake bites</td><td align=\"left\">14</td><td align=\"left\">38</td><td char=\".\" align=\"char\">0.63</td></tr><tr><td align=\"left\">Reproductive diseases</td><td align=\"left\">10</td><td align=\"left\">42</td><td char=\".\" align=\"char\">0.78</td></tr><tr><td align=\"left\">Infectious diseases</td><td align=\"left\">8</td><td align=\"left\">50</td><td char=\".\" align=\"char\">0.85</td></tr><tr><td align=\"left\">Skin diseases</td><td align=\"left\">7</td><td align=\"left\">38</td><td char=\".\" align=\"char\">0.84</td></tr><tr><td align=\"left\">Urinary diseases</td><td align=\"left\">5</td><td align=\"left\">12</td><td char=\".\" align=\"char\">0.64</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab3\"><label>Table 3</label><caption><p>Preparation methods of medicinal plants</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Method of preparation</th><th align=\"left\">Frequency</th><th align=\"left\">Percentage (%)</th></tr></thead><tbody><tr><td align=\"left\">Decoction</td><td align=\"left\">50</td><td char=\".\" align=\"char\">52.63</td></tr><tr><td align=\"left\">Crushing</td><td align=\"left\">22</td><td char=\".\" align=\"char\">23.16</td></tr><tr><td align=\"left\">Powdered</td><td align=\"left\">11</td><td char=\".\" align=\"char\">11.58</td></tr><tr><td align=\"left\">Medicinal liquor</td><td align=\"left\">5</td><td char=\".\" align=\"char\">5.26</td></tr><tr><td align=\"left\">Soak in boiling water</td><td align=\"left\">3</td><td char=\".\" align=\"char\">3.16</td></tr><tr><td align=\"left\">Others (add honey, oil, and sugar)</td><td align=\"left\">4</td><td char=\".\" align=\"char\">4.21</td></tr></tbody></table></table-wrap>" ]

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[ "<fn-group><fn><p><bold>Publisher's Note</bold></p><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn></fn-group>" ]

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[ "<media xlink:href=\"13002_2023_633_MOESM1_ESM.docx\"><caption><p><bold>Additional file 1: Table S1.</bold> Plants and their used in ethnoveterinary medicine by Bai people.</p></caption></media>" ]

{ "acronym": [ "FIC", "URs", "EMPs" ], "definition": [ "The informant consensus factor", "Use reports", "Ethnoveterinary medicine plants" ] }

[ "<title>Background</title>", "<p id=\"Par5\">Acute mesenteric ischemia (AMI) is a rare disease. A British study [##REF##21366637##1##] suggested that the incidence was 0.63/100,000/year. A study in Sweden [##REF##20298944##2##] based on autopsy reports showed that the incidence was 12.90/100,000/year. Despite its low incidence, the mortality rate of this disease is as high as 50–69% [##REF##25457233##3##]. AMI is categorized into four subtypes according to its cause: mesenteric artery embolism (EAMI), mesenteric artery thrombosis (TAMI), nonocclusive mesenteric ischemia (NOMI) and mesenteric vein thrombosis (VAMI). EAMI accounts for 25% of cases, TAMI accounts for approximately 40% of cases, VAMI accounts for approximately 15% of cases, and NOMI accounts for approximately 20% of cases [##REF##36261857##4##]. Acute occlusive mesenteric ischemia (AOMI) consists of the EAMI, TAMI and VAMI. The incidence rate of complications ranges from 13.33–61.5% [##REF##33602217##5##–##UREF##0##10##]. The complications include short bowel syndrome (SBS), electrolyte imbalance, intestinal obstruction, intestinal hemorrhage, renal or cardiac dysfunction, intestinal fistula and wound infection. Some of the complications are severe and may lead to death. According to the Clavien‒Dindo score system, patients with the score ≥ 2 have to be readmitted to the hospital, and the cost is high. Will different categories lead to different outcomes? Will early diagnosis and early management improve the outcomes? Will different operation methods decrease the complication rate? In our study, we aimed to identify significant factors that may affect the outcomes.</p>" ]

[ "<title>Methods</title>", "<title>Case selection</title>", "<p id=\"Par7\">Data from patients with AOMI admitted to the Beijing Tsinghua Changguang Hospital surgery emergency department or gastrointestinal surgery department from May 2016 to May 2022 were reviewed retrospectively. All diagnoses were confirmed by computed tomography angiography (CTA). The inclusion criteria were a diagnosis of superior mesenteric artery (SMA) embolism (CTA showed the emboli in SMA and the emboli located at least 3 cm far from the origin of SMA), SMA thrombosis (CTA showed the thrombus in SMA and the thrombus located within 3 cm far from the origin of SMA), or superior mesenteric vein (SMV) thrombosis (CTA showed the thrombus in SMV). The exclusion criteria were as follows: (1) age &lt; 18 years; (2) patients who refused further treatment after diagnosis; or (3) diagnosis of NOMI (CTA showed bowel ischemia with no emboli or thrombus in SMA or SMV).</p>", "<title>Data collection</title>", "<p id=\"Par9\">This study was approved by the Beijing Tsinghua Changgung Hospital Ethics Committee (22003-6-01). Informed consent was waived by the Beijing Tsinghua Changgung Hospital Ethics Committee. All methods were performed in accordance with the Declaration of Helsinki. All the cases were divided into 2 groups according to whether complications(Clavien‒Dindo ≥ 2) occurred within 6 months of the first admission. Cases without complications(Clavien‒Dindo ≥ 2) occurred within 6 months of the first admission were categorized into normal group. The other cases were categorized into complication group. Complications(Clavien‒Dindo ≥ 2) included SBS, electrolyte imbalance, intestinal obstruction, intestinal hemorrhage, renal or cardiac dysfunction, intestinal fistula and death. The following clinical characteristics were examined herein: age, sex, diagnosis, transmural intestinal necrosis (confirmed by the pathology reports), duration from onset to diagnosis, duration from onset to treatment, abdominal pain, abdominal distension, nausea and vomiting, hematemesis and hematochezia, diarrhea, comorbidities (cardiac problem including history of atrial fibrillation, recent myocardial infarction, cardiac thrombi, mitral valve disease, left ventricular aneurysm and endocarditis, previous embolic disease, diffuse atherosclerotic disease, portal hypertension, history of venous thromboembolism, oral contraceptives, estrogen use, thrombophilia pancreatitis), peritonitis, fever, white blood cell count (WBC), C reactive protein (CRP), hemoglobin (HGB), platelet (PLT), percentage of neutrophils (N%), percentage of lymphocytes (L%), neutrophil-to-lymphocyte ratio (NLR), D-dimer, lactate dehydrogenase (LDH), creatine kinase (CK), creatine kinase isoenzyme (CKMB), myoglobin (MYO), cardiac troponin I (CTNI), lactate (LAC), pondus hydrogenii (PH), CTA details (emboli or thrombus in vessel, decreased intestinal wall enhancement, intestinal wall thickening, pneumatosis intestinalis and ascites), surgical approach (endovascular surgery, laparoscopic exploration, open embolectomy and enterostomy), length of necrosis small bowel, length of healthy small bowel, surgical time and intraoperative blood loss.</p>", "<title>Treatment and follow-up</title>", "<p id=\"Par11\">We recommended AOMI cases followed our treatment algorithm as follows (Fig. ##FIG##0##1##).</p>", "<p id=\"Par12\">\n\n</p>", "<p id=\"Par13\">The indications of intestinal necrosis with AOMI were as follows: (1) AOMI with decreased intestinal wall enhancement, intestinal wall thickening, pneumatosis intestinalis, or ascites on CTA; and (2) AOMI with peritonitis.</p>", "<p id=\"Par14\">VAMI cases without indications of intestinal necrosis underwent anticoagulation therapy. EAMI or TAMI cases without indications of intestinal necrosis underwent endovascular procedures for recanalization. Subsequently, laparoscopic exploration was performed to confirm that there was no transmural intestinal necrosis.</p>", "<p id=\"Par15\">AOMI patients with 1 or 2 indications of intestinal necrosis underwent laparoscopic exploration first. Intestinal necrosis was judged by inspection of the color of the intestines and intestinal peristalsis during the operation. Once transmural intestinal necrosis was confirmed, we converted to an open operation. Resection of the necrotic intestine and ostomy were performed. Otherwise, an endovascular procedure was performed for recanalization.</p>", "<p id=\"Par16\">Anticoagulation, antibiotic therapy, rehydration, and nutrition were performed post-operation.</p>", "<p id=\"Par17\">The criteria for discharge were as follows: (1) no remaining intestinal necrosis of the bowel; (2) total enteral nutrition tolerated; and (3) no need for intravenous antibiotics. All cases were followed up for 6 months. The primary endpoint was complications(Clavien‒Dindo ≥ 2) occurred within 6 months of the first admission.</p>", "<title>Statistical analysis</title>", "<p id=\"Par18\">The factors were compared between groups. The results were analyzed by SPSS 25.0 (IBM, USA). A t test was used to compare normally distributed continuous variables, the Mann‒Whitney t test was used to compare non-normally distributed continuous variables. The chi-squared test was used to compare categorical data. Factors with significant difference were listed after t test and chi-squared test. Logistic regression (backward stepwise selection based on the likelihood ratio method) was performed to identify factors that were associated with complications among the listed factors. A receiver operating characteristic(ROC)curve was established for CKMB and surgical time. A statistically significant difference was indicated when <italic>P</italic> &lt; 0.05.</p>" ]

[ "<title>Results</title>", "<p id=\"Par19\">59 patients were enrolled in this study. 23 patients had EAMI, 11 patients had TAMI, and 25 patients had VAMI. The cases didn’t follow our treatment algorithm strictly. Among the 34 patients with EAMI or TAMI, 11 cases had no indications for intestinal necrosis. 1 case refused further surgery and underwent anticoagulation therapy alone, the other 10 cases underwent endovascular surgery and all of them succeeded in recanalization. 4 of 10 cases refused further laparoscopic exploration, and the other 6 of 10 cases underwent laparoscopic exploration. Intestinal necrosis was found in 1 of 6 cases. Open surgery with necrotic small bowel removal and ostomy was performed. 23 cases with EAMI or TAMI had 1 or 2 indications for intestinal necrosis. 2 of 23 cases refused further surgery and underwent anticoagulation therapy alone. 5 of 23 cases underwent endovascular surgery for recanalization and refused further surgery after successful recanalization. 7 of 23 cases underwent open surgery and intestinal necrosis was found in all the cases. Open surgery with necrotic small bowel removal and ostomy was performed. 9 of 23 cases underwent laparoscopic exploration and intestinal necrosis was found in 5 of 9 cases. Open surgery with necrotic small bowel removal and ostomy was performed. The other 4 of 9 cases underwent endovascular surgery and all of them succeeded in recanalization. The management was shown in Fig. ##FIG##1##2##.</p>", "<p id=\"Par20\">\n\n</p>", "<p id=\"Par21\">Among the 25 patients with VAMI, 3 cases had no indications for intestinal necrosis and underwent endovascular surgery. 22 cases had 1 or 2 indications for intestinal necrosis. 3 of 22 cases refused further surgery and underwent anticoagulation therapy alone. 4 of 22 cases underwent endovascular surgery for recanalization and refused further surgery after successful recanalization. 6 of 22 cases underwent open surgery and intestinal necrosis was found in all the cases. Open surgery with necrotic small bowel removal and ostomy was performed. 9 of 22 cases underwent laparoscopic exploration and intestinal necrosis was found in 7 of 9 cases. Open surgery with necrotic small bowel removal and ostomy was performed. The other 2 of 9 cases with no intestinal necrosis refused further surgery and underwent anticoagulation therapy. The management was shown in Fig. ##FIG##2##3##.</p>", "<p id=\"Par22\">\n\n</p>", "<p id=\"Par23\">Severe complications within 6 months after the first admission occurred in 17 cases (12 males and 5 females) aged 67.94 ± 15.89 years. They were divided into the complication group. 2 patients died within 30 days post first management. 4 patients experienced short bowel syndrome and electrolyte imbalance. 2 patients experienced electrolyte imbalance, and 10 patients experienced intestinal obstruction. The other 42 patients were divided into the normal group. Compared to the normal group, the following parameters differed significantly after univariate analysis: the ratio of transmural intestinal necrosis (82.4% vs. 28.6%, <italic>P</italic> &lt; 0.01), peritonitis (64.7% vs. 28.6%, <italic>P</italic> = 0.01), laparoscopic exploration (64.7% vs. 31%, <italic>P</italic> = 0.02), open embolectomy (82.4% vs. 26.2%, <italic>P</italic> &lt; 0.01), and enterostomy (82.4% vs. 21.4%, <italic>P</italic> &lt; 0.01). WBC (14.87 × 10⁹/L, 10.08 vs. 11.49 × 10⁹/L, 7.70, <italic>P</italic> = 0.03), N% (87.6%, 12.02 vs. 82.6%, 14.7, <italic>P</italic> = 0.03), L% (5.65%, 8.13 vs. 9.95%, 12.33, <italic>P</italic> = 0.03), NLR (16.2, 22.54 vs. 8.31, 13.42 <italic>P</italic> = 0.03), LDH (254.5 IU/L, 79.5 vs. 230.5 IU/L, 36.5, <italic>P</italic> = 0.03), CKMB (4.38 ng/ml, 5.39 vs. 1.71 ng/ml, 2.27, <italic>P</italic> = 0.02), CTNI (0.25 ng/ml, 0.09 vs. 0.14 ng/ml, 0.01, <italic>P</italic> = 0.02), length of necrosis small bowel (75 cm, 187.5 vs. 0 cm, 15, <italic>P</italic> &lt; 0.01), length of healthy small bowel (325 cm, 221.5 vs. 400 cm, 0, <italic>P</italic> &lt; 0.01), surgical time (300 min, 94.25 vs.105 min, 182.25, <italic>P</italic> &lt; 0.01), and intraoperative blood loss (50 ml, 68.75 vs. 10 ml, 98, <italic>P</italic> = 0.01) also differed significantly. The results are shown in Table ##TAB##0##1##. Logistic regression (backward LR method) showed that CKMB (OR = 1.415, 95% CI = 1.060–1.888, <italic>P</italic> = 0.02) and surgical time (OR = 1.014, 95% CI = 1.001–1.026, <italic>P</italic> = 0.03) were independent risk factors associated with severe complications, as shown in Table ##TAB##1##2##. Additionally, most of the cases with elevated CKMB had cardiac problem.The ratio of it was much higher than that in cases with normal CKMB(82.4%vs 33.3%). Regarding the prediction of severe complications, ROC curves were drawn, and the area under the curve (AUC) values for CKMB and surgical time were 0.69 (95% CI = 0.533–0.848) and 0.814 (95% CI = 0.707–0.92), respectively, as shown in Figs. ##FIG##3##4## and ##FIG##4##5##. When the cutoff for CKMB was 2.22 ng/ml, the sensitivity and specificity were 82.4% and 66.7%, respectively. When the cutoff for surgical time was 156 min, the sensitivity and specificity were 94.1% and 66.7%, respectively.</p>", "<p id=\"Par24\">\n\n</p>", "<p id=\"Par25\">\n\n</p>", "<p id=\"Par26\">\n\n</p>", "<p id=\"Par27\">\n\n</p>" ]

[ "<title>Discussion</title>", "<p id=\"Par28\">AMI is a rare but lethal disease. The reported mortality within 30 postoperative days ranged from 8.9 to 73.5% during the past decade [##REF##33602217##5##, ##REF##32328096##8##–##REF##33083058##22##]. The incidence rate of complications ranged from 13.33–61.5% [##REF##33602217##5##–##UREF##0##10##]. The complications include SBS, electrolyte imbalance, intestinal obstruction, intestinal hemorrhage, renal or cardiac dysfunction, intestinal fistula and wound infection. Some of the complications lead to readmission and high costs. Previous studies have investigated the predictive factors of transmural intestinal necrosis in AMI. Previous studies have also investigated the predictive factors of in-hospital mortality. However, few studies have focused on prognostic factors for outcome. This study aimed to identify factors that may be associated with complications (Clavien‒Dindo ≥ 2) that require readmission.</p>", "<p id=\"Par29\">When transmural intestinal necrosis occurs, exudation around the bowel presents, and peritonitis appears. The WSES guidelines suggested that prompt laparotomy should be performed for AMI patients with overt peritonitis due to the high possibility of bowel necrosis [##REF##28794797##23##]. This study demonstrated that more peritonitis occurred in the complication group. However, multivariate analysis showed that peritonitis was not an independent risk factor associated with complications. Transmural intestinal necrosis leads to bowel resection. A large amount of small bowel resection leads to inadequate absorption and electrolyte imbalance, even SBS. A previous study reported that the incidence of SBS caused by AMI was 25–30% [##REF##31892505##24##]. In our study, cases in the complication group had a higher ratio of transmural intestinal necrosis, and the necrotic bowel was longer. However, none of them were predictive factors of severe complications. This was mainly because of the 3 points. The healthy bowel left was more than 100 cm, the colon was not involved, and there was late reconstruction of the bowel continuity.</p>", "<p id=\"Par30\">Delayed diagnosis and management are associated with intestinal necrosis and in-hospital mortality [##REF##27181564##25##]. Mikail Cakir et al. reported that irreversible intestinal mucosal necrosis occurred 4 h after occlusion of the superior mesenteric artery in a rat model [##REF##29469635##26##]. The literature reported that the time from diagnosis to management ranged from 27 to 120 h [##REF##30502253##6##, ##REF##32077303##13##, ##REF##32547750##27##]. Mateusz Jagielski et al. reported that the mortality rate was 100% if the time from diagnosis to management exceeded 24 h [##REF##32328096##8##]. In our study, there were no significant differences in the duration from symptom onset to diagnosis or the duration from onset to treatment. It might because that the criteria for grouping were not only 30-day post management mortality but also other complications (Clavien‒Dindo ≥ 2). Most of the cases in this study took more than 24 h from diagnosis to management also might influence the result in our study.</p>", "<p id=\"Par31\">When the patients admitted to the ER department with suspected AMI, most doctors routinely ordered complete blood cell count, biochemistry, D-dimer, and arterial blood gas analysis. The sensitivity and specificity of these biomarkers remain debated. Some studies reported that WBC, CRP, NLR, red blood cell volume distribution width (RDW), total bilirubin, creatinine, lactate, pH and PLT were significantly different between the intestinal necrosis group and the short-term postoperative death group [##REF##27598606##12##, ##REF##33083058##22##]. Other studies reported that a low pH level, low lymphocyte count, low platelet count, high platelet volume distribution width (PDW) level, high platelet-to-lymphocyte ratio and high creatinine level were risk factors associated with intestinal necrosis and short-term postoperative death [##REF##30885018##11##, ##REF##31361977##15##, ##REF##31314768##17##, ##REF##31146693##18##, ##REF##33268989##20##, ##UREF##3##28##, ##REF##32317577##29##]. In contrast, another study reported that routinely used laboratory tests could not predict intestinal necrosis or postoperative death [##REF##32240729##7##, ##REF##32328096##8##, ##REF##30538292##14##, ##REF##31553659##30##]. This study suggested that WBC, N%, L%, NLR, LDH, CKMB, and CTNI differed significantly in the complication group. Few studies have reported that CKMB and CTNI could predict mortality or poor outcomes. Both of them were used to suggest cardiac injury. Most of the cases with elevated CKMB in our study had cardiac problem.The ratio of it was much higher than that in cases with normal CKMB(82.4%vs 33.3%). It would reduce the patient’s tolerance to infection, surgery, and ischemia, and increase the difficulty of postoperative recovery. Our results demonstrated that AOMI with cardiac injury might lead to poor outcome. Logistic regression showed that CKMB was an independent risk factor associated with complications(Clavien‒Dindo ≥ 2) in this study. When the cutoff for CKMB was 2.22 ng/ml, the sensitivity and specificity were 82.4% and 66.7%, respectively.</p>", "<p id=\"Par32\">CTA have become the standard to identify AMI recently. Prasaanthan Gopee-Ramanan et al. reported that the accuracy of CTA for identifying AMI was 92.9% [##REF##31240505##31##]. Decreased intestinal wall enhancement, mesenteric stranding, dilated bowel and ascites pneumatosis intestinalis were reported in CTA with intestinal necrosis [##REF##32077303##13##, ##REF##31361977##15##, ##UREF##1##16##, ##UREF##4##32##]. Mothes, H. et al. reported that the specificities of decreased intestinal wall enhancement, pneumatosis intestinalis, and mesenteric stranding in predicting intestinal necrosis were 88.6%, 98.6% and 77.1%, respectively [##REF##32077303##13##]. Wang, X. et al. reported that pneumatosis intestinalis (OR = 7.08) and ascites (OR = 9.49) were independent risk factors for intestinal necrosis [##REF##31361977##15##]. In our study, none of the CTA findings differed between groups; therefore, proper management could decrease the complication rate even with bowel necrosis.</p>", "<p id=\"Par33\">The surgical approach for AMI needs to achieve 3 goals: revascularization, resection of necrotic bowel and restoration of viable bowel as long as possible. Compared with endovascular surgery, open surgery leads to a higher rate of complications [##REF##35152898##33##]. We selected enterostomy after necrotic bowel removal because of the fear of intestinal fistula after one-stage anastomosis. It was reported that the rate ranged from 23.4–27% [##UREF##5##34##, ##REF##36589248##35##]. Enterostomy leads to electrolyte imbalance due to the loss of a large amount of digestive juice, especially in patients with improper home enteral nutrition. That was why our study revealed a higher ratio of enterostomy in the complication group. Although the heathy bowel length was shorter in the complication group, it failed to be a predictive factor associated with complications. This was because the length of the healthy bowel was more than 100 cm and no colon was involved. Unlike other reports, our study revealed that surgical time was an independent risk factor for complications(Clavien‒Dindo ≥ 2). Prolonged surgical time usually caused by open surgery which led to a higher rate of complications [##REF##35152898##33##] in our study. Prolonged surgical time led to prolonged intestinal ischemia time and prolonged severe infection time, and finally led to poor outcome. When the cutoff for surgical time was 156 min, the sensitivity and specificity were 94.1% and 66.7%, respectively.</p>", "<title>Limitations</title>", "<p id=\"Par34\">Since this study was a retrospective study with a small sample size, the results need to be tested with studies including a larger number of cases.</p>" ]

[ "<title>Conclusions</title>", "<p id=\"Par35\">In our study, AOMI patients with a CKMB level of more than 2.22 ng/mL or a surgical time of more than 156 min are more likely to experience complications’(Clavien‒Dindo ≥ 2) occurrence within 6 months of the first admission.</p>" ]

[ "<title>Background</title>", "<p id=\"Par1\">Acute mesenteric ischemia is a rare but lethal disease. Acute occlusive mesenteric ischemia consists of mesenteric artery embolism, mesenteric artery thrombosis, and mesenteric vein thrombosis. This study aimed to investigate the factors that may affect the outcome of acute occlusive mesenteric ischemia.</p>", "<title>Methods</title>", "<p id=\"Par2\">Data from acute occlusive mesenteric ischemia patients admitted between May 2016 and May 2022 were reviewed retrospectively. Patients were divided into 2 groups according to whether complications(Clavien‒Dindo ≥ 2) occurred within 6 months of the first admission. Demographics, symptoms, signs, laboratory results, computed tomography angiography features, management and outcomes were analyzed.</p>", "<title>Results</title>", "<p id=\"Par3\">59 patients were enrolled in this study. Complications(Clavien‒Dindo ≥ 2) occurred within 6 months of the first admission in 17 patients. Transmural intestinal necrosis, peritonitis, white blood cell count, percentage of neutrophils, percentage of lymphocytes, neutrophil-to-lymphocyte ratio, lactate dehydrogenase, creatine kinase isoenzyme, cardiac troponin I, laparoscopic exploration rate, open embolectomy rate, enterostomy rate, length of necrotic small bowel, length of healthy small bowel, surgical time and intraoperative blood loss differed significantly between groups. Creatine kinase isoenzyme (OR = 1.415, 95% CI: 1.060–1.888) and surgical time (OR = 1.014, 95% CI: 1.001–1.026) were independent risk factors associated with complications(Clavien‒Dindo ≥ 2).</p>", "<title>Conclusions</title>", "<p id=\"Par4\">Our analysis suggests that acute occlusive mesenteric ischemia patients with a creatine kinase isoenzyme level greater than 2.22 ng/mL or a surgical time longer than 156 min are more likely to experience complications’(Clavien‒Dindo ≥ 2) occurrence within 6 months of the first admission.</p>", "<title>Keywords</title>" ]

[]

[ "<title>Acknowledgements</title>", "<p>Not applicable.</p>", "<title>Author contributions</title>", "<p>The study design was contributed by PZ; data acquisition was performed by QZ, TM; statistical analysis was carried out by HZ,YL; manuscript writing was completed by QZ,TM, and PZ. The manuscript was reviewed by all the authors, and final approval was performed by PZ. All authors read and approved the final manuscript.</p>", "<title>Funding</title>", "<p>Self-funded.</p>", "<title>Data availability</title>", "<p>The data used and/or analyzed during the current study are available from the corresponding author upon reasonable request.</p>", "<title>Declarations</title>", "<title>Ethics approval and consent to participate</title>", "<p id=\"Par46\">This study was approved by the Beijing Tsinghua Changgung Hospital Ethics Committee (22003-6-01). Informed consent was waived by the Beijing Tsinghua Changgung Hospital Ethics Committee.</p>", "<title>Consent for publication</title>", "<p id=\"Par47\">Not applicable.</p>", "<title>Competing interests</title>", "<p id=\"Par45\">The authors declare no competing interests.</p>" ]

[ "<fig id=\"Fig1\"><label>Fig. 1</label><caption><p>Management algorithm. AOMI cases treatment algorithm</p></caption></fig>", "<fig id=\"Fig2\"><label>Fig. 2</label><caption><p>EAMI + TAMI cases management. Among the 34 patients with EAMI or TAMI, 11 cases had no indications for intestinal necrosis. 1 case refused further surgery and underwent anticoagulation therapy alone, the other 10 cases underwent endovascular surgery and all of them succeeded in recanalization. 4 of 10 cases refused further laparoscopic exploration, and the other 6 of 10 cases underwent laparoscopic exploration. Intestinal necrosis was found in 1 of 6 cases. Open surgery with necrotic small bowel removal and ostomy was performed. 23 cases with EAMI or TAMI had 1 or 2 indications for intestinal necrosis. 2 of 23 cases refused further surgery and underwent anticoagulation therapy alone. 5 of 23 cases underwent endovascular surgery for recanalization and refused further surgery after successful recanalization. 7 of 23 cases underwent open surgery and intestinal necrosis was found in all the cases. Open surgery with necrotic small bowel removal and ostomy was performed. 9 of 23 cases underwent laparoscopic exploration and intestinal necrosis was found in 5 of 9 cases. Open surgery with necrotic small bowel removal and ostomy was performed. The other 4 of 9 cases underwent endovascular surgery and all of them succeeded in recanalization</p></caption></fig>", "<fig id=\"Fig3\"><label>Fig. 3</label><caption><p>VAMI cases management. Among the 25 patients with VAMI, 3 cases had no indications for intestinal necrosis and underwent endovascular surgery. 22 cases had 1 or 2 indications for intestinal necrosis. 3 of 22 cases refused further surgery and underwent anticoagulation therapy alone. 4 of 22 cases underwent endovascular surgery for recanalization and refused further surgery after successful recanalization. 6 of 22 cases underwent open surgery and intestinal necrosis was found in all the cases. Open surgery with necrotic small bowel removal and ostomy was performed. 9 of 22 cases underwent laparoscopic exploration and intestinal necrosis was found in 7 of 9 cases. Open surgery with necrotic small bowel removal and ostomy was performed. The other 2 of 9 cases with no intestinal necrosis refused further surgery and underwent anticoagulation therapy</p></caption></fig>", "<fig id=\"Fig4\"><label>Fig. 4</label><caption><p>ROC curve for CKMB. ROC curve for CKMB suggested the area under the curve (AUC) values for CKMB was 0.69 (<italic>P</italic> = 0.02) and 0.814 (95% CI = 0.707–0.92)</p></caption></fig>", "<fig id=\"Fig5\"><label>Fig. 5</label><caption><p>ROC curve for surgical time. ROC curve for surgical time suggested the area under the curve (AUC) values for surgical time was 0.814 (<italic>P</italic> &lt; 0.001)</p></caption></fig>" ]

[ "<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Comparison of characteristics and outcomes between the complication groups</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\"/><th align=\"left\">Normal group<break/><italic>n</italic> = 42</th><th align=\"left\">Complication group<break/><italic>n</italic> = 17</th><th align=\"left\">P</th></tr></thead><tbody><tr><td align=\"left\">Sex, male (<italic>n</italic>, %)</td><td align=\"left\">28, 66.70%</td><td align=\"left\">12, 70.60%</td><td char=\".\" align=\"char\">0.77</td></tr><tr><td align=\"left\">Age (year, mean ± SD)</td><td align=\"left\">62.64 ± 16.45</td><td align=\"left\">67.94 ± 15.89</td><td char=\".\" align=\"char\">0.26</td></tr><tr><td align=\"left\">Diagnosis</td><td align=\"left\"/><td align=\"left\"/><td char=\".\" align=\"char\">0.14</td></tr><tr><td align=\"left\"> EAMI (<italic>n</italic>, %)</td><td align=\"left\">13, 31.00%</td><td align=\"left\">10, 58.80%</td><td align=\"left\"/></tr><tr><td align=\"left\"> TAMI (<italic>n</italic>, %)</td><td align=\"left\">9, 21.40%</td><td align=\"left\">2, 11.80%</td><td align=\"left\"/></tr><tr><td align=\"left\"> VAMI (<italic>n</italic>, %)</td><td align=\"left\">20, 47.60%</td><td align=\"left\">5, 29.40%</td><td align=\"left\"/></tr><tr><td align=\"left\">Transmural intestinal necrosis (<italic>n</italic>, %)</td><td align=\"left\">12, 28.60%</td><td align=\"left\">14, 82.40%</td><td char=\".\" align=\"char\">&lt; 0.01</td></tr><tr><td align=\"left\">Duration from onset to diagnosis (hours, Median, IQR)</td><td align=\"left\">84, 148.50</td><td align=\"left\">49, 90.00</td><td char=\".\" align=\"char\">0.74</td></tr><tr><td align=\"left\">Duration from onset to diagnosis categories</td><td align=\"left\"/><td align=\"left\"/><td char=\".\" align=\"char\">0.98</td></tr><tr><td align=\"left\"> ≤ 24 h (<italic>n</italic>, %)</td><td align=\"left\">15, 35.70%</td><td align=\"left\">6, 35.30%</td><td align=\"left\"/></tr><tr><td align=\"left\"> &gt; 24 h (<italic>n</italic>, %)</td><td align=\"left\">27, 64.30%</td><td align=\"left\">11, 64.70%</td><td align=\"left\"/></tr><tr><td align=\"left\">Duration from onset to treatment (hours, Median, IQR)</td><td align=\"left\">98.75, 207.13</td><td align=\"left\">75.75, 140.13</td><td char=\".\" align=\"char\">0.91</td></tr><tr><td align=\"left\">Duration from onset to treatment categories</td><td align=\"left\"/><td align=\"left\"/><td char=\".\" align=\"char\">0.38</td></tr><tr><td align=\"left\"> ≤ 24 h(<italic>n</italic>, %)</td><td align=\"left\">8, 19.00%</td><td align=\"left\">1, 5.90%</td><td align=\"left\"/></tr><tr><td align=\"left\"> &gt; 24 h (<italic>n</italic>, %)</td><td align=\"left\">34, 81.00%</td><td align=\"left\">16, 94.10%</td><td align=\"left\"/></tr><tr><td align=\"left\">Abdominal pain (<italic>n</italic>, %)</td><td align=\"left\">41, 97.60%</td><td align=\"left\">17, 100.00%</td><td char=\".\" align=\"char\">1.00</td></tr><tr><td align=\"left\">Abdominal distension (<italic>n</italic>, %)</td><td align=\"left\">28, 66.70%</td><td align=\"left\">15, 88.20%</td><td char=\".\" align=\"char\">0.17</td></tr><tr><td align=\"left\">Nausea (<italic>n</italic>, %)</td><td align=\"left\">24, 57.10%</td><td align=\"left\">13, 76.50%</td><td char=\".\" align=\"char\">0.16</td></tr><tr><td align=\"left\">Vomiting (<italic>n</italic>, %)</td><td align=\"left\">19, 45.20%</td><td align=\"left\">12, 70.60%</td><td char=\".\" align=\"char\">0.08</td></tr><tr><td align=\"left\">Hematemesis (<italic>n</italic>, %)</td><td align=\"left\">3, 7.10%</td><td align=\"left\">3, 17.60%</td><td char=\".\" align=\"char\">0.46</td></tr><tr><td align=\"left\">Hematochezia (<italic>n</italic>, %)</td><td align=\"left\">14, 33.30%</td><td align=\"left\">7, 41.20%</td><td char=\".\" align=\"char\">0.57</td></tr><tr><td align=\"left\">Diarrhea (<italic>n</italic>, %)</td><td align=\"left\">5, 11.90%</td><td align=\"left\">2, 11.80%</td><td char=\".\" align=\"char\">1.00</td></tr><tr><td align=\"left\">Comorbidities (<italic>n</italic>, %)</td><td align=\"left\">35, 83.30%</td><td align=\"left\">16, 94.10%</td><td char=\".\" align=\"char\">0.50</td></tr><tr><td align=\"left\">Peritonitis (<italic>n</italic>, %)</td><td align=\"left\">12, 28.60%</td><td align=\"left\">11, 64.70%</td><td char=\".\" align=\"char\">0.01</td></tr><tr><td align=\"left\">Fever (<italic>n</italic>, %)</td><td align=\"left\">12, 28.60%</td><td align=\"left\">7, 41.20%</td><td char=\".\" align=\"char\">0.35</td></tr><tr><td align=\"left\">WBC (10⁹/L, Median, IQR)</td><td align=\"left\">11.49, 7.70</td><td align=\"left\">14.87, 10.08</td><td char=\".\" align=\"char\">0.03</td></tr><tr><td align=\"left\">CRP (mg/L, Median, IQR)</td><td align=\"left\">63, 113.37</td><td align=\"left\">47.18, 102.07</td><td char=\".\" align=\"char\">0.87</td></tr><tr><td align=\"left\">PLT(10⁹/L, Median, IQR)</td><td align=\"left\">210.50, 69.75</td><td align=\"left\">202, 83.75</td><td char=\".\" align=\"char\">0.51</td></tr><tr><td align=\"left\">HGB (mg/L, mean ± SD)</td><td align=\"left\">130.98 ± 27.26</td><td align=\"left\">141.56 ± 24.10</td><td char=\".\" align=\"char\">0.13</td></tr><tr><td align=\"left\">N% (%,Median, IQR)</td><td align=\"left\">82.60, 14.70</td><td align=\"left\">87.60, 12.02</td><td char=\".\" align=\"char\">0.03</td></tr><tr><td align=\"left\">L% (%,Median, IQR)</td><td align=\"left\">9.95, 12.33</td><td align=\"left\">5.65, 8.13</td><td char=\".\" align=\"char\">0.03</td></tr><tr><td align=\"left\">NLR (Median, IQR)</td><td align=\"left\">8.31, 13.42</td><td align=\"left\">16.20, 22.54</td><td char=\".\" align=\"char\">0.03</td></tr><tr><td align=\"left\">D-dimer (mg/L, Median, IQR)</td><td align=\"left\">6.72, 11.58</td><td align=\"left\">5.73, 9.36</td><td char=\".\" align=\"char\">0.85</td></tr><tr><td align=\"left\">LDH(IU/L, Median, IQR)</td><td align=\"left\">230.50, 36.50</td><td align=\"left\">254.50, 79.50</td><td char=\".\" align=\"char\">0.03</td></tr><tr><td align=\"left\">CK(IU/L, Median, IQR)</td><td align=\"left\">100.50, 112.50</td><td align=\"left\">194, 248.75</td><td char=\".\" align=\"char\">0.26</td></tr><tr><td align=\"left\">CKMB(ng/mL, Median, IQR)</td><td align=\"left\">1.71, 2.27</td><td align=\"left\">4.38, 5.39</td><td char=\".\" align=\"char\">0.02</td></tr><tr><td align=\"left\">MYO(ng/mL, Median, IQR)</td><td align=\"left\">59.78,106.20</td><td align=\"left\">112.50, 125.84</td><td char=\".\" align=\"char\">0.12</td></tr><tr><td align=\"left\">CTNI(ng/mL, Median, IQR)</td><td align=\"left\">0.14, 0.01</td><td align=\"left\">0.25, 0.09</td><td char=\".\" align=\"char\">0.02</td></tr><tr><td align=\"left\">LAC(mmol/L, Median, IQR)</td><td align=\"left\">1.85, 0.90</td><td align=\"left\">2.20, 1.63</td><td char=\".\" align=\"char\">0.22</td></tr><tr><td align=\"left\">PH(Median, IQR)</td><td align=\"left\">7.40, 0.06</td><td align=\"left\">7.42, 0.06</td><td char=\".\" align=\"char\">0.70</td></tr><tr><td align=\"left\">CTA</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\"> Emboli or thrombus in vessel (<italic>n</italic>, %)</td><td align=\"left\">42,100.00%</td><td align=\"left\">17,100.00%</td><td align=\"left\"/></tr><tr><td align=\"left\"> Decreased intestinal wall enhancement (<italic>n</italic>, %)</td><td align=\"left\">14, 33.30%</td><td align=\"left\">9, 52.90%</td><td char=\".\" align=\"char\">0.16</td></tr><tr><td align=\"left\"> Intestinal wall thickening (<italic>n</italic>, %)</td><td align=\"left\">28, 66.70%</td><td align=\"left\">13, 76.50%</td><td char=\".\" align=\"char\">0.46</td></tr><tr><td align=\"left\"> Pneumatosis intestinalis (<italic>n</italic>, %)</td><td align=\"left\">0, 0.00%</td><td align=\"left\">1, 5.90%</td><td char=\".\" align=\"char\">0.29</td></tr><tr><td align=\"left\"> Ascites (<italic>n</italic>, %)</td><td align=\"left\">16, 38.10%</td><td align=\"left\">9, 52.90%</td><td char=\".\" align=\"char\">0.30</td></tr><tr><td align=\"left\">Surgical approach (<italic>n</italic>, %)</td><td align=\"left\">35, 83.30%</td><td align=\"left\">17,100.00%</td><td char=\".\" align=\"char\">0.18</td></tr><tr><td align=\"left\"> Endovascular surgery (<italic>n</italic>, %)</td><td align=\"left\">21, 50.00%</td><td align=\"left\">6, 35.30%</td><td char=\".\" align=\"char\">0.30</td></tr><tr><td align=\"left\"> Laparoscopic exploration (<italic>n</italic>, %)</td><td align=\"left\">13, 31.00%</td><td align=\"left\">11, 64.70%</td><td char=\".\" align=\"char\">0.02</td></tr><tr><td align=\"left\"> Open embolectomy (<italic>n</italic>, %)</td><td align=\"left\">11, 26.20%</td><td align=\"left\">14, 82.40%</td><td char=\".\" align=\"char\">&lt;0.01</td></tr><tr><td align=\"left\"> Enterostomy (<italic>n</italic>, %)</td><td align=\"left\">9, 21.40%</td><td align=\"left\">14, 82.40%</td><td char=\".\" align=\"char\">&lt;0.01</td></tr><tr><td align=\"left\">Length of necrosis small bowel(cm, Median, IQR)</td><td align=\"left\">0, 15.00</td><td align=\"left\">75, 187.50</td><td char=\".\" align=\"char\">&lt;0.01</td></tr><tr><td align=\"left\">Length of healthy small bowel(cm, Median, IQR)</td><td align=\"left\">400, 0.00</td><td align=\"left\">325, 221.50</td><td char=\".\" align=\"char\">&lt;0.01</td></tr><tr><td align=\"left\">Surgical time(min, Median, IQR)</td><td align=\"left\">105, 182.25</td><td align=\"left\">300, 94.25</td><td char=\".\" align=\"char\">&lt;0.01</td></tr><tr><td align=\"left\">Intraoperative blood loss(ml, Median, IQR)</td><td align=\"left\">10, 98.00</td><td align=\"left\">50, 68.75</td><td char=\".\" align=\"char\">0.01</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab2\"><label>Table 2</label><caption><p>Factors associated with complications after logistic regression</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Factor</th><th align=\"left\">Wald</th><th align=\"left\">P</th><th align=\"left\">OR</th><th align=\"left\">95% CI</th></tr></thead><tbody><tr><td align=\"left\">Transmural intestinal necrosis</td><td char=\".\" align=\"char\">0.000</td><td char=\".\" align=\"char\">1.00</td><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\">Laparoscopic exploration</td><td char=\".\" align=\"char\">2.596</td><td char=\".\" align=\"char\">0.11</td><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\">Enterostomy</td><td char=\".\" align=\"char\">0.000</td><td char=\".\" align=\"char\">1.00</td><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\">CKMB</td><td char=\".\" align=\"char\">5.563</td><td char=\".\" align=\"char\">0.02</td><td char=\".\" align=\"char\">1.415</td><td char=\".\" align=\"char\">1.060–1.888</td></tr><tr><td align=\"left\">Length of necrosis small bowel</td><td char=\".\" align=\"char\">1.490</td><td char=\".\" align=\"char\">0.22</td><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\">Surgical time</td><td char=\".\" align=\"char\">4.756</td><td char=\".\" align=\"char\">0.03</td><td char=\".\" align=\"char\">1.014</td><td char=\".\" align=\"char\">1.001–1.026</td></tr></tbody></table></table-wrap>" ]

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[ "<fn-group><fn><p><bold>Publisher’s Note</bold></p><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn><fn><p>Qian Zhang and Tianyi Ma contributed equally to this work.</p></fn></fn-group>" ]

[ "<graphic xlink:href=\"12893_2024_2310_Fig1_HTML\" id=\"d32e357\"/>", "<graphic xlink:href=\"12893_2024_2310_Fig2_HTML\" id=\"d32e391\"/>", "<graphic xlink:href=\"12893_2024_2310_Fig4_HTML\" id=\"d32e406\"/>", "<graphic xlink:href=\"12893_2024_2310_Fig5_HTML\" id=\"d32e1216\"/>", "<graphic xlink:href=\"12893_2024_2310_Fig6_HTML\" id=\"d32e1230\"/>" ]

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{ "acronym": [ "AMI", "AOMI", "EAMI", "TAMI", "VAMI", "NOMI", "CTA", "WBC", "N%", "L%", "NLR", "LDH", "CKMB", "CTNI", "SBS", "CRP", "HGB", "PLT", "CK", "MYO", "LAC", "PH", "SMA", "SMV", "RDW", "PDW" ], "definition": [ "Acute mesenteric ischemia", "Acute occlusive mesenteric ischemia", "Mesenteric artery embolism", "Mesenteric artery thrombosis", "Mesenteric vein thrombosis", "Nonocclusive mesenteric ischemia", "Computed tomography angiography", "White blood cell count", "Percentage of neutrophils", "Percentage of lymphocytes", "Neutrophil-to-lymphocyte ratio", "Lactate dehydrogenase", "Creatine kinase isoenzyme", "Cardiac troponin I", "Short bowel syndrome", "C reactive protein", "Hemoglobin", "Platelet", "Creatine kinase", "Myoglobin", "Lactate", "Pondus hydrogenii", "Superior mesenteric artery", "Superior mesenteric vein", "Red blood cell volume distribution width", "Platelet volume distribution width" ] }

[ "<title>Introduction</title>", "<p id=\"Par8\">Early Childhood Caries (ECC) is a dental condition that affects young children worldwide. Untreated ECC causes dental pain, infections, nutritional impairments, developmental delays, reduced quality of life, and increased healthcare costs for individuals and societies [##REF##23633832##1##]. Defined as any carious lesion in the primary teeth of children under the age of 6 years, the impact of ECC on wellness and wellbeing is particularly significant among socially disadvantaged populations, thereby exacerbating oral health inequalities [##REF##10649591##2##] With approximately 514 million affected children globally, ECC ranks among the most common childhood diseases [##UREF##0##3##, ##UREF##1##4##]. As global health priorities continue to evolve, addressing ECC within the context of the United Nations’ Sustainable Development Goal 8 (SDG8) becomes crucial, as this goal aims to promote sustained, inclusive, and sustainable economic growth, full and productive employment, and decent work for all. SDG8 emphasizes the importance of labor rights, eradicating modern slavery and child labor, and ensuring equal access to the benefits of entrepreneurship and innovation. In addition, it reiterates the value of the reciprocal links between social, environmental, and economic policies, full employment, and decent work.</p>", "<p id=\"Par9\">Within the framework of SDG8, there is an opportunity to address the issue of untreated ECC using a human rights perspective [##UREF##2##5##, ##REF##37346104##6##]. The high prevalence of ECC among socially disadvantaged children highlights the need to promote ECC management through the lens of social justice, health equity, and human rights [##REF##35726467##7##, ##REF##34873524##8##]. By linking macro-social development with meso- and micro-economic growth, we can potentially achieve a more equitable distribution of wealth and have a direct impact on health, including oral health [##UREF##3##9##]. SDG8 also encourages investments in health systems and infrastructure [##UREF##4##10##]. Incorporating oral health services into health systems and infrastructure can enhance preventive efforts and early intervention for ECC [##REF##36330110##11##, ##REF##21490232##12##]. This integration can lead to a more comprehensive approach to oral health care, aligning with the principles of SDG8 to ensure well-being for all.</p>", "<p id=\"Par10\">SDG8 includes 12 targets, one of which is achieving full and productive employment, decent work for all, and equal pay for work of equal value (SDG8.5). Full and productive employment refers to the availability of quality job opportunities that enable individuals to earn a decent income and contribute to economic growth [##UREF##2##5##]. Decent work improves income stability and economic security, ultimately leading to greater household income and reduced income inequality [##UREF##5##13##]. Achieving equal pay for work of equal value is crucial for addressing gender discrimination in the labor market, which is particularly relevant for ECC since maternal socioeconomic status strongly influences the risk of ECC [##UREF##6##14##, ##REF##31658278##15##]. Accomplishing SDG8.5 can enable households to meet their basic needs, access better healthcare and education, and invest in their future [##UREF##7##16##]. It will also lead to improved living standards, reduced poverty rates, enhanced economic resilience, and the creation of a more inclusive society [##UREF##8##17##, ##UREF##9##18##]. By using a rights-based approach, SDG8 aligns with the goal of achieving equitable access to health, including oral health, for all individuals.</p>", "<p id=\"Par11\">Given that ECC is preventable adequate and timely preventive and prophylactic cost-effective programs, and in some cases, early lesions can be reversed with early detection and available treatment options, it is essential to include the management of untreated ECC on the global disease elimination agenda [##REF##37346104##6##]. Treating dental caries, particularly in young children, can be expensive and time-consuming, leading families to miss work to address their child’s oral health needs, consequently affecting their economic productivity [##UREF##10##19##]. ECC is more prevalent in disadvantaged and vulnerable populations who frequently consume sugar, have poor access to adequate dental care and poor education on oral hygiene practices [##REF##30791128##20##, ##UREF##11##21##]. This oral health disparity can contribute to broader health and well-being inequalities that the goals of SDG8 try to address. Conversely, poor economic development and growth can negatively affect the prevalence and severity of ECC. Poor economic growth and development reduces expenditure on health [##REF##28593509##22##]. yet, higher expenditure on health may be associated with lower prevalence of ECC [##REF##33731081##23##].</p>", "<p id=\"Par12\">By prioritizing the elimination of untreated ECC within the SDG8 framework, we can strive for a more equitable distribution of resources and higher household income. We conceptualized the impact of interventions related to SDG8 on ECC using the Fisher-Owen et al.’s 2007 model [##REF##17766495##24##] depicted in Fig. ##FIG##0##1##. We perceive that at least, five targets of SDG8 could have a direct or indirect community-level, family-level, and child-level influences on the risk of ECC: SDG8.1 (sustainable economic growth), SDG8.3 (promote policies to support job creation and growing enterprises), SDG8.5 (full employment and decent work with equal pay), SDG8.8 (protection of labor rights and promotion of safe working environments), and SDG8.A (universal access to banking, insurance and financial services). The outcomes of SDG8 can indirectly reduce the risk of and global prevalence of ECC. The exploration of the intersection between ECC and SDG8 can help identify opportunities to leverage economic growth and employment opportunities to strengthen oral health systems.</p>", "<p id=\"Par13\">Though there is very little known about the links between SDG8 and ECC, ecological studies suggest that a growth in per-capita gross national income was significantly associated with higher prevalence of ECC in children aged 36 to 71 months [##REF##29927650##25##]. This association was found to be the reverse for children with ECC in European member countries [##REF##33573027##26##] and for children in Serbia though the findings in Serbia was not statistically significant [##UREF##12##27##]. The aim of this scoping review was to map the evidence on the links between ECC and targets of the SDG8, and to identify research gaps to be filled to provide evidence on the link between SDG8 and ECC.</p>" ]

[ "<title>Methods</title>", "<p id=\"Par14\">We conducted this scoping review to explore the connections between ECC and the objectives of SDG8, which encompass economic growth and decent work. To ensure methodological rigor and transparency, we followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guidelines [##UREF##13##28##] during the review process.</p>", "<title>Research questions</title>", "<p id=\"Par15\">The following questions guided this review: What is the existing evidence on the association between decent work and economic growth (sustained economic growth, higher levels of productivity and technological innovation, entrepreneurship, job creation, and efforts to eradicate forced labor, slavery, and human trafficking) and ECC?</p>", "<title>Search strategy</title>", "<p id=\"Par16\">In January 2023, a search was conducted on three electronic databases: PubMed, Web of Science, and Scopus. The search utilized a combination of key terms as shown in Additional file ##SUPPL##0##1##: Appendix 1. The search terms were tailored to meet the specific requirements of each database. The key terms used were for the Pubmed search were: (((((((((“Economic Development”[Mesh]) OR “Sustainable Growth”[Mesh]) OR “Right to Work”[Mesh]) OR “Unemployment”[Mesh]) OR “Small Business”[Mesh]) OR “Human Trafficking”[Mesh]) OR “Labor Unions”[Mesh]) OR “Working Poor”[Mesh]) OR “Resource Allocation”[Mesh]) OR “Banking, Personal”[Mesh]. That for Web of Science search were: (((((((((“Economic Development”[Mesh]) OR “Sustainable Growth”[Mesh]) OR “Right to Work”[Mesh]) OR “Unemployment”[Mesh]) OR “Small Business”[Mesh]) OR “Human Trafficking”[Mesh]) OR “Labor Unions”[Mesh]) OR “Working Poor”[Mesh]) OR “Resource Allocation”[Mesh]) OR “Banking, Personal”[Mesh] and ((((((“Dental Caries”[Mesh]) OR “Tooth Demineralization”[Mesh]) OR (caries[Text Word])) OR (dental decay[Text Word])) OR (dental cavities [Text Word])) OR (tooth cavities[Text Word])) OR (enamel demineralization[Text Word]). Screening of publications was conducted from the inception of the databases up to 2023. The search was completed in July 2023.</p>", "<title>Eligibility criteria and article selection</title>", "<p id=\"Par17\">For inclusion in this review, only English language publications until July 2023 were considered. The selected studies included cross-sectional, case-control, and cohort designs, and they reported findings on the association between decent work, economic growth, related factors, and ECC among children aged six years and below. To maintain the focus of this review on the association between decent work, economic growth-related factors, and ECC, studies that solely examined the prevalence and severity of ECC with no reference to the goals of SDG 8 were excluded. Publications that were not primary studies such as ecological studies and letters to the editors were also excluded.</p>", "<p id=\"Par18\">The literature obtained from the database searches was exported to Zotero version 6, a reference management software. Duplicate publications were identified and removed using the “duplicate items” function in Zotero. Title and abstract screening were carried out by two independent reviewers (IA, AN) who followed the eligibility criteria established for this review. No attempts were made to contact authors or institutions for additional sources of information.</p>" ]

[ "<title>Results</title>", "<p id=\"Par19\">The initial search across three databases, namely PubMed, Web of Science, and Scopus, using the predefined search terms resulted in a total of 761 articles. After removing duplicates and ineligible manuscripts, 84 unique articles remained for further screening. However, none of the identified studies provided data on the association between decent work, economic growth-related factors, and ECC. Figure ##FIG##1##2## shows the details of the search findings.</p>" ]

[ "<title>Discussion</title>", "<p id=\"Par20\">Recognizing the potential impact of socioeconomic development oral health is crucial, as it paves the way for a future where every child can access high-quality oral healthcare and enjoy a healthy and prosperous life. The SDG8 has the potential to contribute to global health and well-being. However, despite the plausible evidence supporting the link between SDG8 and ECC, this scoping review could identify no evidence derived from primary studies supporting this connection. The study finding suggest there is a lacuna of evidence derived from primary studies on the links between SDG8 and ECC.</p>", "<p id=\"Par21\">This study represents the first comprehensive analysis examining the potential association between ECC and SDG8. It highlights the possibility of generating evidence to establish this link through further research. It is important to note that attributing the impact of economic development on ECC to SDG8 may be challenging due to links with other SDGs that can influence the prevalence, burden, and severity of ECC. Nevertheless, this challenge does not negate the potentials for developing new methodologies for assessing the impact of economic development on oral health in children. Perhaps as more countries undertake nationally representative oral health surveys and adopt SDG8 measurements, future investigations of potential interactions are possible.</p>", "<p id=\"Par22\">There are numerous studies on the links between human health on health expenditure, economic activity and growth and the SDG8 [##REF##28593509##22##, ##REF##35431414##29##]. There are, however, fewer studies on the impact of oral health on economic activity and growth. One study suggests that poor oral health causes an indirect global loss worth $144 billion, direct annual cost of oral problems was about $298 billion [##REF##26318590##30##]. There are no specific data on the impact of ECC and ECC expenditure on economic activity and growth despite the recognized economic toll ECC exerts [##REF##19491160##31##]. The absence of specific data can significantly impact the ability of policymakers to establish relevant oral health programs, making it challenging to develop ECC-focused policies and effectively allocate resources for children’s oral health. Concrete data on the economic toll of ECC is crucial for designing sustainable oral health programs and promoting oral health in vulnerable populations.</p>", "<p id=\"Par23\">There is a growing body of literature that explores the relationship between macroeconomic activities, economic growth, and population health [##UREF##14##32##, ##UREF##15##33##]. Economic growth has the potential to positively influence population health by promoting the utilization of preventive health services, improving nutrition, and reducing the risk of health disorders caused by diseases. However, empirical evidence on the impact of economic growth on population health is diverse and lacks a clear consensus [##REF##28069615##34##]. This is reflected in the findings of the ecological studies on the impact of economic growth on the risk for ECC [##REF##33731081##23##, ##REF##29927650##25##–##UREF##12##27##] suggestive of differences in global and country-level findings on the impact of economic development on the risk of ECC.</p>", "<p id=\"Par24\">In addition, a prior ecological study further puts a caveat to the possible impact of economic development on ECC wherein the gross national income per capita for females was associated with lower ECC prevalence [##REF##32503512##35##]: countries with more females living under 50% of median income had higher prevalence of ECC among 3 to 5-year olds [##REF##32066424##36##]; and the gross national income per capita for females had a great effect on ECC prevalence [##REF##32503512##35##]. These studies underscore the need for further research and collaborative efforts among experts to gain a comprehensive understanding of the complex relationship between ECC and the SDG8 to promote population oral health in the context of economic growth. Without a concrete understanding of the relationship between economic growth and health, designing targeted and effective programs to address ECC becomes challenging.</p>", "<p id=\"Par25\">Moreover, the absence of empirical evidence concerning the effective and efficient allocation of additional resources to promote oral health, specifically in preventing untreated ECC, creates a critical gap that requires attention. Without this evidence, there is a risk of misallocating resources and efforts, leading to inefficiencies in oral health programs. Consequently, preventive measures targeting ECC may not receive sufficient support, allowing the condition to persist and worsen [##REF##21623864##37##]. The lack of data-driven insights may result in missed opportunities to implement innovative and effective strategies for ECC prevention. Promising interventions may not undergo adequate investigation, and their potential impact on preventing ECC might not be fully realized, especially when competing with other health priorities. Consequently, ECC prevention efforts may not receive the necessary attention and resources required to make a significant impact on children’s oral health [##REF##21692782##38##].</p>", "<p id=\"Par26\">Understanding this aspect will provide valuable insights for the development and implementation of oral health policies for children. Given the intricate relationship between SDG8 and health [##UREF##16##39##], as well as the close connection between oral health and overall health [##REF##31275387##40##], it is reasonable to assume that SDG8 and oral health are intertwined. Therefore, empirical studies examining the link between economic development, decent workplaces, and the oral health of children are warranted.</p>", "<p id=\"Par27\">The SDG 8 targets creates an opportunity to explore the possible impact of having a healthy workforce with decent work and economic growth. The provision of decent, healthy, and safe oral health workforce will help improve ECC outcomes. To quantify contributory benefits of decent work and economic growth on ECC indicators measuring this impact is needed as this evidence can encourage investments in enhancing working conditions and safeguarding oral health workers to tackle ECC.</p>", "<p id=\"Par28\">In conclusion, although there are plausible links between SDG8 and ECC, there is currently no evidence derivable from primary studies showing these links. Though the evidence on the associations between SDG8 and health are controversial, these findings further substantiate the possibility to generate evidence on the associations between the SDG8 and ECC. Generating evidence on the links between SDG8 and oral health, inclusive of ECC, will help drive investments, policy formulation, and programs linking macrostructural factors to enhance the control of ECC globally.</p>" ]

[]

[ "<title>Background</title>", "<p id=\"Par1\">Early Childhood Caries (ECC) is a prevalent chronic non-communicable disease that affects millions of young children globally, with profound implications for their well-being and oral health. This paper explores the associations between ECC and the targets of the Sustainable Development Goal 8 (SDG 8).</p>", "<title>Methods</title>", "<p id=\"Par2\">The scoping review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guidelines. In July 2023, a search was conducted in PubMed, Web of Science, and Scopus using tailored search terms related to economic growth, decent work sustained economic growth, higher levels of productivity and technological innovation, entrepreneurship, job creation, and efforts to eradicate forced labor, slavery, and human trafficking and ECC all of which are the targets of the SDG8. Only English language publications, and publications that were analytical in design were included. Studies that solely examined ECC prevalence without reference to SDG8 goals were excluded.</p>", "<title>Results</title>", "<p id=\"Par3\">The initial search yielded 761 articles. After removing duplicates and ineligible manuscripts, 84 were screened. However, none of the identified studies provided data on the association between decent work, economic growth-related factors, and ECC.</p>", "<title>Conclusions</title>", "<p id=\"Par4\">This scoping review found no English publication on the associations between SDG8 and ECC despite the plausibility for this link. This data gap can hinder policymaking and resource allocation for oral health programs. Further research should explore the complex relationship between economic growth, decent work and ECC to provide additional evidence for better policy formulation and ECC control globally.</p>", "<title>Supplementary Information</title>", "<p>The online version contains supplementary material available at 10.1186/s12903-023-03766-6.</p>", "<title>Keywords</title>" ]

[ "<title>Supplementary Information</title>", "<p>\n</p>" ]

[ "<title>Authors’ contributions</title>", "<p>M.O.F conceived the study. The Project was managed by M.O.F. Data curating was done by MET, RA, IA, and AN. Data analysis was conducted by MOF, RA and MET. MOF developed the first draft of the document. DD and IGS drew the conceptual framework. RA, AK, IGS, DD, IM, AN, JIV, RMS, AV, OAA_B, BG, TM, RJS and MET read the draft manuscript and made inputs prior to the final draft. All authors approved the final manuscript for submission.</p>", "<title>Funding</title>", "<p>Not applicable.</p>", "<title>Availability of data and materials</title>", "<p>The datasets used and/or analysed for the study are publicly accessible.</p>", "<title>Declarations</title>", "<title>Ethics approval and consent to participate</title>", "<p id=\"Par29\">Not applicable.</p>", "<title>Consent for publication</title>", "<p id=\"Par30\">Not applicable.</p>", "<title>Competing interests</title>", "<p id=\"Par31\">Duangporn Duangthip and Jorma Virtanen are Associated Editors with the BMC Oral Health. Morẹ́nikẹ́ Oluwátóyìn Foláyan, and Maha El Tantawi are Senior Editor Board members with BMC Oral Health. Arthur Kemoli is a member of the Editorial board of BMC Oral Health. All other authors declare no conflict of interest.</p>" ]

[ "<fig id=\"Fig1\"><label>Fig. 1</label><caption><p>The conceptual framework of ECC and decent work and economic growth (SDG8) adaptation from Fisher-Owens Model [##REF##17766495##24##]</p></caption></fig>", "<fig id=\"Fig2\"><label>Fig. 2</label><caption><p>Flow diagram based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020 flowchart template of the search and selected process</p></caption></fig>" ]

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[ "<fn-group><fn><p><bold>Publisher’s Note</bold></p><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn></fn-group>" ]

[ "<graphic xlink:href=\"12903_2023_3766_Fig1_HTML\" id=\"MO1\"/>", "<graphic xlink:href=\"12903_2023_3766_Fig2_HTML\" id=\"MO2\"/>" ]

[ "<media xlink:href=\"12903_2023_3766_MOESM1_ESM.docx\"><caption><p><bold>Additional file 1: Appendix 1.</bold></p></caption></media>" ]

{ "acronym": [ "ECC", "PRISMA-ScR", "SDG" ], "definition": [ "Early Childhood Caries", "Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews guidelines", "Sustainable Development Goal" ] }

[ "<title>Background</title>", "<p id=\"Par5\">Triple-negative breast cancer (TNBC) accounts for around 15% of breast cancers. The prognosis of TNBC is unfavorable due to poor differentiation, strong invasion, and easy recurrence, resulting in 5-year survival less than 30% in metastatic stage [##REF##34451860##1##]. A fraction of mTNBC patients responded to immune checkpoint inhibitors (ICIs) monotherapy or combined treatments. Thus, the selection of ICIs beneficial subgroups and how to improve the efficacy of ICIs in mTNBC are still challenging.</p>", "<p id=\"Par6\">High levels of PD-L1 and stromal tumor-infiltrating lymphocytes (TILs) reflect the potential benefit of ICIs in mTNBC [##REF##32129476##2##, ##REF##32957579##3##]. Mesenchyme TILs enrichment contributed to reduced relapse and longer survival in TNBC [##REF##34853355##4##]. Dieci et al. reported that the five-year overall survival (OS) of high TILs group in neoadjuvant thermotherapy was 91%, in contrast to low TILs group (55%) [##REF##24401929##5##]. However, the distribution of TILs varied significantly, depending on tumor heterogeneity [##REF##34218258##6##]. TILs score access is also limited due to the test availability and high expense in hospital. Peripheral blood indices were reported to predict effects of ICIs in non-small cell lung cancer and early-stage hepatocellular carcinoma, highlighting the need to develop circulating biomarkers to foresee recurrence risk in mTNBC [##REF##30470260##7##, ##REF##35101942##8##].</p>", "<p id=\"Par7\">Our study proved the correlation between circulating blood cells and the therapeutic efficacies of ICIs in mTNBC.</p>" ]

[ "<title>Material and methods</title>", "<title>Patient population</title>", "<p id=\"Par8\">mTNBC patients treated with ICIs in affiliated hospitals of Anhui Medical University from 2018 to 2023 were collected and screened. They were administered with ICIs and/or chemotherapies. Tumor evaluation by CT (computed tomography) scanning was performed after every two cycles of treatment according to RECIST 1.1 (Response Evaluation Criteria in Solid Tumors version 1.1). Our study was approved by the Ethics Committee of Anhui Medical University (Reference number. PJ 2023-11-58).</p>", "<title>Treatment and data collection</title>", "<p id=\"Par9\">A total 83 mTNBC patients were collected and 50 patients treated with ICIs were included. The baseline features are listed in Table ##TAB##0##1##.\n</p>", "<p id=\"Par10\">The peripheral blood cell counts at baseline and prior to second-line treatments included white blood cell (WBC), absolute neutrophil (ANC), absolute lymphocyte (ALC), absolute monocyte (AMC) and blood platelet (PLT). NLR (ANC/ALC ratio), MLR (monocyte/lymphocyte ratio), and PLR (hemocyte/lymphocyte ratio) were calculated.</p>", "<p id=\"Par11\">Tumor evaluation was performed post every two cycles of treatment; Adverse events were assessed with Immune-related Response Evaluation Criteria in Solid Tumors.</p>", "<title>Statistical analysis</title>", "<p id=\"Par12\">Patient features were described via descriptive statistics. Overall survival (OS) and progression-free survival (PFS) were collected and analyzed. The Cox proportional risk model was established with hazard ratios (HRs) and 95% confidence intervals (CIs). The multi-variable death model was modified based on age (initial diagnosis) and therapeutic line numbers (0, 1, 2, 3 and higher). All statistical tests were two-sided with significance threshold (alpha, α) at 0.05.</p>" ]

[ "<title>Results</title>", "<title>Patient characteristics</title>", "<p id=\"Par13\">Among a total of 83 mTNBC patients, 50 cases were selected, which received at least two cycles of ICIs. The baseline features are listed in Table ##TAB##0##1##. The median age was 54 years old and over half (<italic>n</italic> = 26, 52%) had received at least one line of palliative chemotherapy before immunotherapy. Low HER-2 expression is defined by 1 + to 2 + with absence of HER-2 amplification via fluorescence in situ hybridization (FISH). 40% of mTNBC are HER-2 lowly expressed (<italic>n</italic> = 20) and the disease control rate (DCR) by ICIs was 80%. The median OS (mOS) was 226 days and the median PFS (mPFS) was 145 days.</p>", "<title>The baseline and post-ICIs peripheral blood biomarkers in mTNBC</title>", "<p id=\"Par14\">The mean baseline peripheral blood lymphocyte (PBLC) of ICI responding mTNBC subgroup (SD, PR and CR post immunotherapy) was 1.242*10⁹/L (95%CI:1.125–1.359), significantly higher than that in non-responding group, 0.925 *10⁹/L (95%CI: 0.0634–1.215) (<italic>P</italic> = 0.021). After one cycle of ICIs, the mean PBLC values in both groups were 1.258*10⁹/L (95%CI:1.137–1.380) <italic>verus</italic> 0.839*10⁹/L (95%CI: 0.6014–1.077) (<italic>P</italic> = 0.002). The NLR and MLR (2.30 [1.64–3.67] and 0.25 [0.17-0.0.32]) in beneficial group also decreased significantly, in contrast to those in ICI failed group (4.78[2.21–8.88] and 0.37[0.27–0.62]) (NLR: <italic>P</italic> = 0.018 and MLR: <italic>P</italic> = 0.023). The baseline monocyte counts and PLR were not found to significantly correlate with the response of ICIs.</p>", "<title>The correlation of peripheral blood biomarkers with immunotherapy outcomes</title>", "<p id=\"Par15\">Lymphocyte count reduction was defined as &lt; 1.1*10⁹/L [##UREF##0##9##]. High PBLC significantly improved OS and PFS in mTNBC either in ICIs naïve cases or post-ICIs (Fig. ##FIG##0##1##). Based on adjusted treatment lines, age, liver metastasis and HER-2 expression, the baseline lymphocyte in ICI treated mTNBC was associated with OS (HR: 0.280; 95% CI: 0.095–0.823; <italic>p</italic> = 0.021). In the group with baseline lymphocyte over 1.10*10⁹/L (LN-high group), mOS was 520 days (95% CI: 207.8-832.2), and 12-month survival rate was 55.6%. The group with baseline lymphocyte less than 1.10*10⁹/L (LN-low group) showed that mOS was 155 days (95% CI: 117.4-192.6) (HR: 0.482; 95% CI: 0.233–0.999; <italic>p</italic> = 0.049), and the 12-month survival rate was 17.4% (<italic>p</italic> = 0.06) (Fig. ##FIG##1##2##). The 6-month PFS in both groups was 51.9% and 30.4%, but without statistical significance (<italic>p</italic> = 0.126). However, the 6-month PFS rate post one cycle of ICIs in LN-high group (55.2%) significantly exceeded that in LN-low group (23.8%) (<italic>p</italic> = 0.027).</p>", "<p id=\"Par16\">\n\n</p>", "<p id=\"Par17\">\n\n</p>", "<p id=\"Par18\">The cutoff points of NLR and PLR were defined at median values of samples. NLR, other than PLR and MLR, significantly extended survivals of mTNBC when it is over 2.75 (Fig. ##FIG##0##1## and Supplementary Figures ##SUPPL##0##1## and ##SUPPL##1##2##). The treatment lines, age, andHER-2 expression were adjusted accordingly in the multivariable analysis. The baseline NLR was significantly associated with OS (HR:1.150; 95% CI:1.052–1.257; <italic>p</italic> = 0.002) and PFS (HR:1.086; 95% CI:1.002–1.177; <italic>p</italic> = 0.045) (Table ##TAB##1##2##). The cutoff point of NLR was 2.75. The mOS of NLR-high group (≥ 2.75) and NLR-low group was 143 days (95% CI: 92.4-193.6) and 520 days (95% CI: 110.8-929.2) (HR: 2.575; 95% CI: 1.217–5.447; <italic>p</italic> = 0.013) (Fig. ##FIG##1##2##E). The mPFS in both groups was 118 days (95% CI: 77.2-158.8) and 253 days (95% CI: 110.8-929.2) (HR: 2.189; 95% CI: 1.085–4.414; <italic>p</italic> = 0.029) (Fig. ##FIG##1##2##F). The 12-month survival rates were 24% and 52% (<italic>p</italic> = 0.041), while the 6-month PFS rates were 24% and 60% (<italic>p</italic> = 0.01).</p>", "<p id=\"Par19\">The baseline PLR also showed a positive correlation with survival time after immunotherapy (<italic>p</italic> = 0.028) (Table ##TAB##1##2##). However, the inter-group differences were not significant (Fig. ##FIG##1##2## and Supplementary Figures ##SUPPL##0##1## and ##SUPPL##1##2##).\n</p>", "<title>HER-2 expression and anti-tumor therapeutic lines on the survival of ICI-treated mTNBC</title>", "<p id=\"Par20\">HER-2 low expression is defined as HER-2 1 + or 2 + immunohistochemistry without gene amplification. With adjusted therapeutic lines, age, and liver metastasis, HER-2 expression in ICI-treated mTNBC was significantly associated with OS (HR:3.253; 95% CI,1.418–7.460; <italic>p</italic> = 0.005) and PFS (HR:2.710; 95% CI:1.226–5.992; <italic>p</italic> = 0.014) (see Supplementary Figures ##SUPPL##2##3## and ##SUPPL##4##5##). The median OS and 12-month survival rate in HER-2 low subgroup were 343 days and 50% respectively. The median PFS and 6-month survival rate in HER-2 low group were 206 days and 55%, superior to HER-2 zero group (mOS: 161 days; 12-month survival rate: 30%127; mPFS: 127 days; 6-month rate:33%). Metastatic TNBC patients previously treated with less than two anti-tumor therapeutic lines prior to ICIs showed better OS and PFS than those with over one or two lines (Table ##TAB##1##2## and Supplementary Figures ##SUPPL##6##7## and ##SUPPL##7##8##).</p>", "<title>Safety</title>", "<p id=\"Par21\">A total of 13 patients (26%) had grade 3 to 4 immune-related adverse responses, leading to the cease of immunotherapy. No significance was shown in mTNBC with and without adverse immune events (AEs) on baseline demographics.</p>", "<p id=\"Par22\">The most common grade 3 to 4 immune-related AEs (irAEs) included myositis/myocardial damage (<italic>n</italic> = 7, 53.8%), pneumonia (<italic>n</italic> = 3, 15.4%), myelosuppression (<italic>n</italic> = 2, 15.4%), abnormal liver function (<italic>n</italic> = 1, 7.7%), and skin reaction (<italic>n</italic> = 1, 7.7%). ICIs were stopped immediately upon the occurrence of AEs; glucocorticoids and/or immunoglobulins were administrated accordingly. Four cases died of irAEs (three cases of cardiac dysfunction and one case of myelosuppression). The baseline peripheral lymphocyte count, and monocyte count declined AE population (lymphocytes: <italic>p</italic> = 0.042, monocytes: <italic>p</italic> = 0.040).</p>" ]

[ "<title>Discussions</title>", "<p id=\"Par23\">In our study, absolute baseline lymphocyte enrichment improves the survival of mTNBC, which was further reflected even post one cycle of ICIs. In non-small cell lung cancer treated with ICIs, OS was prolonged with higher absolute baseline lymphocyte [##REF##31295966##10##]. The relatively high lymphocyte count in peripheral blood is related to prolonged survival in gynecologic malignancies [##REF##37407184##11##]. Anosheh et al. also reported less death risk in early-staged TNBCs, with higher absolute lymphocyte counts [##REF##29581131##12##]. ICIs reverse the effects of PD-1 on lymphocyte signal conduction via PD-1–PD-L1 axis blockage, which facilitates the production of effective T cells and memory cells, inhibits the differentiation of TEX and T-Reg cells, and strengthens anti-tumor T-cell activation [##REF##28018338##13##]. It is difficult to induce anti-tumor effects in absence of lymphocytes.</p>", "<p id=\"Par24\">Additionally, higher PBLC contributes to better OS. Although baseline lymphocyte counts in ICIs naïve mTNBCs are generally higher than in those exposed to second- or third-line ICIs, the positive correlation between PBLC and OS is significant by statistical adjustment. Mechanistically, advanced metastatic breast cancer shows stronger immune suppression with insufficient TILs in cancer tissues [##REF##30203045##14##, ##REF##32041353##15##]. Previous studies prove a correlation between TILs and absolute lymphocyte count in breast cancer [##REF##30285668##16##].</p>", "<p id=\"Par25\">We also found that NLR negatively correlated with OS and PFS, in contrast to positive correlation with OS by PLR. NLR and PLR are predictive of poor prognosis in many types of tumors [##REF##30048474##17##]. In the latest bioinformatic analysis involving 2,994 patients, TNBC with less genetic NLR were enriched in several immunity-related gene sets; TNBC carrying lower NLR might benefit from ICIs [##REF##34873491##18##]. Tumor derived platelets recruit hemocyte and immune cells for migration and established inflammatory tumor microenvironment at primary and metastatic sites [##REF##37153586##19##]. Inflammatory environment via high NLR impaired the clinical efficacy of ICIs and chemotherapy.</p>", "<p id=\"Par26\">mTNBCs with baseline NLR less than 3.16 showed prolonged OS and PFS post neoadjuvant chemotherapies [##REF##30064200##20##]. In non-small cell lung cancer with baseline NLR &gt; 5.9, the therapeutic effect and long-term prognosis of anti-PD-1 inhibitors fell significantly [##REF##31038558##21##]. In advanced gastric cancer and liver cancer, The NLR cutoff values were 3.23 and 3, respectively [##REF##34993252##22##]. The values of NLR and PLR in our study were 2.75 and 157.28 (median), respectively. There are no commonly recommended thresholds of NLR etc. to predict the immunotherapeutic efficacy in cancer diseases, partially due to varied tumor immune microenvironments. Studies focusing on ICI predictors were mainly limited to non-small cell lung cancer and melanoma [##REF##30012216##23##, ##REF##35060536##24##]. In studies of pan-cancer species, breast cancer patients with high NLR benefited less from ICI clinically. The OS and PFS with low NLR or high tumor mutation loads increased partially post immunotherapy, but without statistical significance and pathological stratification [##REF##33526794##25##]. Peripheral blood PLR and NLR in TNBC may be positively correlated with PD-L1 expression in immune cells, but without available pathological evidence [##REF##36217150##26##]. Thus, the correlation of NLR and PLR with prognosis in breast cancer treated with immunotherapy is still veiled.</p>", "<p id=\"Par27\">The prognostic effect of HER-2 low expression on breast cancer is still controversial. HER-2 low group intends to possess smaller tumor size and lower Ki67 index. In one retrospective study of 3689 breast cancer patients, HER-2 low is not associated with OS in TNBC without tumor proliferation genetic variation. Menopausal status, histological grade, Ki67 scores and percentage of TILs showed no significance difference [##REF##33397968##27##]. In Chinese breast cancer population (<italic>n</italic> = 772), no statistical significance was observed in pCR rate between mTNBC HER-2 low and HER-2 zero groups; however, in non-pCR groups, prognosis was significantly improved in HER-2 low, other than Her-2 zero group, consistent with our data (<italic>n</italic> = 50) [##REF##37365055##28##]. The prognosis of HER-2 will be validated in expanded sample size cohort studies.</p>", "<p id=\"Par28\">In terms of tumor microenvironment (TME), TILs were significantly less expressed in HER-2 low tumor tissue other than HER-2 zero sample, indicating that HER-2 zero group might benefit more from ICIs. Therefore, the TILs levels are not convincing to elaborate prognosis of HER-2 low subgroup. Further investigations on mapped oncogene network in TNBCs were also demanded. The prognostic value of HER-2 low was solely restricted in specific subtypes. T cell exhausting was assumed post multiple lines of chemotherapies, partially contributing to inferior efficacy of ICIs. Our study also proved better survival when ICIs were initiated as the first or second anti-tumor therapy line.</p>", "<p id=\"Par29\">Our study was limited by small sample size in the retrospective scale, mainly in Han population. Moreover, different combination of chemotherapies with immunotherapies might affect the exhaustion of bone marrow and PBLCs at baseline. mTNBCs with lowly expressed HER-2 showed longer survivals by immunotherapy than HER-2 negative subgroup. None was treated with anti-HER-2 antibodies or ADC drugs.</p>" ]

[ "<title>Conclusions</title>", "<p id=\"Par30\">Our data suggested that the baseline PBLC, NLR, MLR and absolute lymphocyte counts post ICIs clinically predict efficacies of anti-PD-1 antibodies in mTNBCs. HER-2 low expression and early ICI involvement also improve the survivals of mTNBCs. The access to whole blood sample from TNBC are easier and mor convenient in clinical practice. These findings may assist ICI related risk stratification and prevent unnecessary toxicities for those benefiting less from ICIs. However, the further investigation is demanded in a large scale and prospective view.</p>" ]

[ "<title>Background</title>", "<p id=\"Par1\">Immune checkpoint inhibitors (ICIs) can improve survivals of metastatic triple negative breast cancer (mTNBC); however, we still seek circulating blood biomarkers to predict the efficacy of ICIs.</p>", "<title>Materials and methods</title>", "<p id=\"Par2\">In this study, we analyzed the data of ICIs treated mTNBC collected in Anhui Medical University affiliated hospitals from 2018 to 2023. The counts of lymphocytes, monocytes, platelets, and ratio indexes (NLR, MLR, PLR) in peripheral blood were investigated via the Kaplan-Meier curves and the Cox proportional-hazards model.</p>", "<title>Results</title>", "<p id=\"Par3\">The total of 50 mTNBC patients were treated with ICIs. High level of peripheral lymphocytes and low level of NLR and MLR at baseline and post the first cycle of ICIs play the predictable role of immunotherapies. Lymphocytes counts (HR = 0.280; 95% CI: 0.095–0.823; <italic>p</italic> = 0.021) and NLR (HR = 1.150; 95% CI: 1.052–1.257; <italic>p</italic> = 0.002) are significantly correlated with overall survival. High NLR also increases the risk of disease progression (HR = 2.189; 95% CI:1.085–4.414; <italic>p</italic> = 0.029). When NLR at baseline ≥ 2.75, the hazard of death (HR = 2.575; 95% CI:1.217–5.447; <italic>p</italic> = 0.013) and disease progression (HR = 2.189; 95% CI: 1.085–4.414; <italic>p</italic> = 0.029) significantly rise. HER-2 expression and anti-tumor therapy lines are statistically correlated with survivals.</p>", "<title>Conclusions</title>", "<p id=\"Par4\">Before the initiation of ICIs, enriched peripheral lymphocytes and poor neutrophils and NLR contribute to the prediction of survivals.</p>", "<title>Supplementary Information</title>", "<p>The online version contains supplementary material available at 10.1186/s12905-023-02871-6.</p>", "<title>Keywords</title>" ]

[ "<title>Supplementary Information</title>", "<p>\n</p>", "<p>\n</p>", "<p>\n</p>", "<p>\n</p>", "<p>\n</p>", "<p>\n</p>", "<p>\n</p>", "<p>\n</p>" ]

[ "<title>Acknowledgements</title>", "<p>The authors would like to thank Cheng Zhou for his academic assistance.</p>", "<title>Authors’ contributions</title>", "<p>X.Y.L., M.Y. and Y.D. collected and interpreted the clinical data. Y.Y.Z, C.Z. and W.T.X. completed the statistical analysis. D.A.S.M. and J.L.A.R. revised the manuscript. H.W. and X.L.H. wrote the manuscript and the graphical illustrations. All authors critically reviewed and approved the manuscript.</p>", "<title>Funding</title>", "<p>This work was supported by the grant from Anhui Natural Science Foundation Youth Program (2008085QH424) and Basic and Applied Basic Research Fund of Guangdong Province (2019A1515011331). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.</p>", "<title>Availability of data and materials</title>", "<p>All the data we used in this study were available as described in the “<xref rid=\"Sec2\" ref-type=\"sec\">Material and methods</xref>” section.</p>", "<title>Declarations</title>", "<title>Ethics approval and consent to participate</title>", "<p id=\"Par31\">All procedures performed in this study were in accordance with the ethical standards of the Helsinki declaration. The approved number by the Institutional Review Board in the First Affiliated Hospital of Anhui Medical University is PJ 2023-11-58. Informed consent was obtained from all individuals.</p>", "<title>Consent for publication</title>", "<p id=\"Par32\">Not applicable.</p>", "<title>Competing interests</title>", "<p id=\"Par33\">The authors declare no competing interests.</p>" ]

[ "<fig id=\"Fig1\"><label>Fig. 1</label><caption><p>Kaplan-Meier survival curves for OS and PFS in mTNBC treated with ICIs. OS is plotted in (<bold>A</bold>, <bold>C</bold>, <bold>E</bold>, <bold>G</bold>, <bold>I</bold>) and PFS is plotted in (<bold>B</bold>, <bold>D</bold>, <bold>F</bold>, <bold>H</bold>, <bold>J)</bold>. Time is represented in days from initiation of ICIs. <bold>A</bold> and <bold>B</bold> patients are stratified by PBLC (prior to ICIs). Blue lines: PBLC (prior to ICIs) &lt; 1.10*10⁹/L; red lines: PBLC (prior to ICIs) ≥ 1.10*10⁹/L. <bold>C</bold> and <bold>D</bold> patients are stratified by PBLC (post ICIs). Blue lines: PBLC (post ICIs) &lt; 1.10*10⁹/L; red lines: PBLC (post ICIs) ≥ 1.10*10⁹/L. <bold>E</bold> and <bold>F</bold> patients are stratified by ANC/ALC ratio (NLR). Blue lines: NLR &lt; 2.75; red lines: NLR ≥ 2.75. <bold>G</bold> and <bold>H</bold> patients are stratified by MLR. Blue lines: MLR &lt; 0.294; red lines: MLR ≥ 0.294. <bold>I</bold> and <bold>J</bold> patients are stratified by PLR. Blue lines: PLR &lt; 157.28; red lines: PLR ≥ 157.2</p></caption></fig>", "<fig id=\"Fig2\"><label>Fig. 2</label><caption><p>Cox proportional hazards model for OS and PFS of TNBC treated with ICIs. OS (<bold>A</bold>, <bold>C</bold>, <bold>E</bold>, <bold>G</bold>, <bold>I</bold>) and PFS (<bold>B</bold>, <bold>D</bold>, <bold>F</bold>, <bold>H</bold>, <bold>J</bold>) were plotted by COX proportional model in mTNBC. Time is presented as days from the start of immunotherapy. <bold>A</bold> and <bold>B</bold> patients are stratified by PBLC (prior to ICIs). Blue lines: PBLC (prior to ICIs) &lt; 1.10*10⁹/L; red lines, PBLC (prior to ICIs) ≥ 1.10*10⁹/L. <bold>C</bold> and <bold>D</bold> patients are stratified by PBLC (post ICIs). Blue lines: PBLC (post ICIs) &lt; 1.10*10⁹/L; red lines: PBLC (post ICIs) ≥ 1.10*10⁹/L. <bold>E</bold> and <bold>F</bold> patients are stratified by NLR. Blue lines: NLR &lt; 75; red lines: NLR ≥ 2.75. <bold>G</bold> and <bold>H</bold> patients are stratified by MLR. Blue lines: MLR &lt; 0.294 and red lines: MLR ≥ 0.294. <bold>I</bold> and <bold>J</bold> patients are stratified by PLR. Blue lines: PLR &lt; 157.28 and red lines: PLR ≥ 157.28</p></caption></fig>" ]

[ "<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Baseline characteristics</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Patient Characteristics</th><th align=\"left\">Total (<italic>N</italic> = 50)</th></tr></thead><tbody><tr><td align=\"left\">Age, median(range)</td><td align=\"left\">54(38–72)</td></tr><tr><td align=\"left\" colspan=\"2\">Prior chemotherapy lines, No.(%)</td></tr><tr><td align=\"left\"> 0</td><td align=\"left\">24(48%)</td></tr><tr><td align=\"left\"> 1</td><td align=\"left\">6(12%)</td></tr><tr><td align=\"left\"> ≥ 2</td><td align=\"left\">20(40%)</td></tr><tr><td align=\"left\" colspan=\"2\">Optimal therapeutic effect, NO(%)</td></tr><tr><td align=\"left\"> PD</td><td align=\"left\">10(20%)</td></tr><tr><td align=\"left\"> SD</td><td align=\"left\">26(52%)</td></tr><tr><td align=\"left\"> PR</td><td align=\"left\">11(22%)</td></tr><tr><td align=\"left\"> CR</td><td align=\"left\">3(6%)</td></tr><tr><td align=\"left\" colspan=\"2\">HER-2 expression.NO(%)</td></tr><tr><td align=\"left\"> 0</td><td align=\"left\">30(60%)</td></tr><tr><td align=\"left\"> 1+/2+</td><td align=\"left\">20(40%)</td></tr><tr><td align=\"left\" colspan=\"2\">Hepatic metastases.NO(%)</td></tr><tr><td align=\"left\"> Yes</td><td align=\"left\">12(24%)</td></tr><tr><td align=\"left\"> No</td><td align=\"left\">36(76%</td></tr><tr><td align=\"left\" colspan=\"2\">Brain metastases.NO(%)</td></tr><tr><td align=\"left\"> Yes</td><td align=\"left\">7(14%)</td></tr><tr><td align=\"left\"> No</td><td align=\"left\">43(86%)</td></tr><tr><td align=\"left\" colspan=\"2\">Lung metastasis.NO(%)</td></tr><tr><td align=\"left\"> Yes</td><td align=\"left\">28(56%)</td></tr><tr><td align=\"left\"> No</td><td align=\"left\">22(44%)</td></tr><tr><td align=\"left\" colspan=\"2\">Chest wall metastasis.NO(%)</td></tr><tr><td align=\"left\"> Yes</td><td align=\"left\">24(48%)</td></tr><tr><td align=\"left\"> No</td><td align=\"left\">26(52%)</td></tr><tr><td align=\"left\" colspan=\"2\">Bone metastases.NO(%)</td></tr><tr><td align=\"left\"> Yes</td><td align=\"left\">23(46%)</td></tr><tr><td align=\"left\"> No</td><td align=\"left\">27(54%)</td></tr><tr><td align=\"left\" colspan=\"2\">Immune adverse effects.NO(%)</td></tr><tr><td align=\"left\"> Yes</td><td align=\"left\">13(26%)</td></tr><tr><td align=\"left\"> No</td><td align=\"left\">37(74%)</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab2\"><label>Table 2</label><caption><p>Multivariable models of OS and PFS adjusted to prognostic factors</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Biomarkers HR (95% CI), <italic>n</italic>  = 50</th><th align=\"left\">Multivariable model of OS</th><th align=\"left\"><italic>P</italic></th><th align=\"left\">Multivariable model of PFS</th><th align=\"left\"><italic>P</italic></th></tr></thead><tbody><tr><td align=\"left\">PBLC (prior to ICIs)</td><td align=\"left\">0.280(0.095–0.823)</td><td align=\"left\">0.021</td><td align=\"left\">0.413(0.141–1.213)</td><td align=\"left\">0.108</td></tr><tr><td align=\"left\">Adjusted PBLC (prior to ICIs)</td><td align=\"left\">0.482(0.233–0.999)</td><td align=\"left\">0.049</td><td align=\"left\">0.693(0.337–1.426)</td><td align=\"left\">0.319</td></tr><tr><td align=\"left\">PBLC (post treatment)</td><td align=\"left\">0.459(0.148–1.422)</td><td align=\"left\">0.177</td><td align=\"left\">0.791(0.285–2.422)</td><td align=\"left\">0.681</td></tr><tr><td align=\"left\">NLR</td><td align=\"left\">1.150(1.052–1.257)</td><td align=\"left\">0.002</td><td align=\"left\">1.086(1.002–1.177)</td><td align=\"left\">0.045</td></tr><tr><td align=\"left\">Adjusted NLR</td><td align=\"left\">2.575(1.217–5.447)</td><td align=\"left\">0.013</td><td align=\"left\">2.189(1.085–4.414)</td><td align=\"left\">0.029</td></tr><tr><td align=\"left\">MLR</td><td align=\"left\">3.880(0.665–22.626)</td><td align=\"left\">0.132</td><td align=\"left\">3.433(0.610-19.327)</td><td align=\"left\">0.162</td></tr><tr><td align=\"left\">Adjusted MLR</td><td align=\"left\">1.802(0.858–3.787)</td><td align=\"left\">0.120</td><td align=\"left\">1.619(0.802–3.271)</td><td align=\"left\">0.179</td></tr><tr><td align=\"left\">PLR</td><td align=\"left\">1.004(1.000-1.008)</td><td align=\"left\">0.028</td><td align=\"left\">1.002(0.999–1.006)</td><td align=\"left\">0.161</td></tr><tr><td align=\"left\">Adjusted PLR</td><td align=\"left\">1.596(0.714–3.568)</td><td align=\"left\">0.255</td><td align=\"left\">1.596(0.709–3.593)</td><td align=\"left\">0.259</td></tr><tr><td align=\"left\">ICIs lines</td><td align=\"left\">1.929(1.289–2.887)</td><td align=\"left\">0.001</td><td align=\"left\">2.778(1.819–4.241)</td><td align=\"left\">0.00</td></tr><tr><td align=\"left\">Age</td><td align=\"left\">1.007(0.959–1.057)</td><td align=\"left\">0.784</td><td align=\"left\">1.075(1.011–1.143)</td><td align=\"left\">0.021</td></tr></tbody></table></table-wrap>" ]

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[ "<supplementary-material content-type=\"local-data\" id=\"MOESM1\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM2\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM3\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM4\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM5\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM6\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM7\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM8\"></supplementary-material>" ]

[ "<table-wrap-foot><p>1. Age, HER-2 expression, and number of treatment lines and liver metastases were adjusted in PBLC, MLR and PLR multivariable models. 2. Age, HER-2 expression, and treatment lines were adjusted in NLR multivariable model. 3. Age, HER-2 expression, and liver metastasis were adjusted in ICIs lines multivariable models. 4. HER-2 expression, number of treatment lines, liver metastasis were adjusted in Age multivariable model</p></table-wrap-foot>", "<fn-group><fn><p><bold>Publisher’s Note</bold></p><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn></fn-group>" ]

[ "<graphic xlink:href=\"12905_2023_2871_Fig1_HTML\" id=\"d32e634\"/>", "<graphic xlink:href=\"12905_2023_2871_Fig2_HTML\" id=\"d32e717\"/>" ]

[ "<media xlink:href=\"12905_2023_2871_MOESM1_ESM.pptx\"><caption><p><bold>Additional file 1: Supplementary Figure 1.</bold> Forest plot of the prognostic effect of relevant variables on OS. HR are calculated using Cox proportional hazards regression models and presented with 95% CIs. </p></caption></media>", "<media xlink:href=\"12905_2023_2871_MOESM2_ESM.pptx\"><caption><p><bold> Additional file 2: Supplementary Figure 2.</bold> Forest plot of the prognostic effect of relevant variables on PFS. HR are derived using Cox proportional hazards regression models and presented with 95% CIs.</p></caption></media>", "<media xlink:href=\"12905_2023_2871_MOESM3_ESM.pptx\"><caption><p><bold> Additional file 3: Supplementary Figure 3.</bold> Cox proportional hazards model for OS TNBC treated with ICIs. OS was plotted by Cox proportional model in mTNBC. Time is presented as days from the start of immunotherapy. Patients are stratified by HER-2. Blue lines: HER-2 (-); red lines, HER-2 (1+/2+).</p></caption></media>", "<media xlink:href=\"12905_2023_2871_MOESM4_ESM.pptx\"><caption><p><bold> Additional file 4: Supplementary Figure 4.</bold> Hazards model for OS TNBC treated with ICIs. OS was plotted by hazards model in mTNBC. Time is presented as days from the start of immunotherapy. Patients are stratified by HER-2. Blue lines: HER-2 (-); red lines, HER-2 (1+/2+).</p></caption></media>", "<media xlink:href=\"12905_2023_2871_MOESM5_ESM.pptx\"><caption><p><bold> Additional file 5: Supplementary Figure 5.</bold> Cox proportional hazards model for PFS TNBC treated with ICIs. PFS was plotted by Cox proportional hazards model in mTNBC. Time is presented as days from the start of immunotherapy. Patients are stratified by HER-2. Blue lines: HER-2 (-); red lines, HER-2 (1+/2+).</p></caption></media>", "<media xlink:href=\"12905_2023_2871_MOESM6_ESM.pptx\"><caption><p><bold> Additional file 6: Supplementary Figure 6.</bold> Hazards model for PFS TNBC treated with ICIs. PFS was plotted by hazards model in mTNBC. Time is presented as days from the start of immunotherapy. Patients are stratified by HER-2. Blue lines: HER-2 (-); red lines, HER-2 (1+/2+).</p></caption></media>", "<media xlink:href=\"12905_2023_2871_MOESM7_ESM.pptx\"><caption><p><bold> Additional file 7: Supplementary Figure 7.</bold> Cox proportional hazards model for OS in TNBC treated with ICIs. OS was plotted by Cox proportional hazards model in mTNBC. Time is presented as days from the start of immunotherapy. Patients are stratified by ICI lines. Blue lines: 1st line; red lines: 2nd line; green line: ≥ 3rd line.</p></caption></media>", "<media xlink:href=\"12905_2023_2871_MOESM8_ESM.pptx\"><caption><p><bold> Additional file 8: Supplementary Figure 8.</bold> Cox proportional hazards model for PFS in TNBC treated with ICIs. PFS was plotted by Cox proportional hazards model in mTNBC. Time is presented as days from the start of immunotherapy. Patients are stratified by ICI lines. Blue lines: 1st line; red lines: 2nd line; green line: ≥3rd line.</p></caption></media>" ]

{ "acronym": [], "definition": [] }

[ "<title>Background</title>", "<p id=\"Par15\">Breast Cancer (BC) is the most common type of cancer among women worldwide. Even though BC incidence is higher in high-income countries in comparison to low- and middle-income countries (LMICs), the majority of deaths actually occur in LMIC settings [##UREF##0##1##] The higher mortality rates observed in LMICs compared to high-income countries (HICs) are thought to be a consequence of late detection and limited access to standard quality treatment [##UREF##0##1##].</p>", "<p id=\"Par16\">In Mexico, BC is the most frequent cancer and the main cause of cancer mortality among women since 2006 [##UREF##1##2##]. The majority of BC cases (65%) are diagnosed at advanced stages (IIB to IV) and the estimated overall survival is 72% [##UREF##2##3##]. Although the burden of BC disease is usually higher in urban populations, incidence has been also increasing in Mexico’s rural populations [##UREF##3##4##]. Evidence shows that women living in marginalized areas have a higher risk of dying from preventable cancer deaths than other populations due to a combination of vulnerabilities that often result in late detection and delayed or incomplete treatment [##UREF##4##5##–##REF##19967276##7##].</p>", "<p id=\"Par17\">Indigenous minorities in Mexico compound several vulnerabilities: they tend to have lower socioeconomic status, less access to education, and more commonly live in small rural communities that lack access to many services, including healthcare [##REF##19967276##7##–##UREF##6##10##]. Gender and ethnicity interact and indigenous women in Mexico face a double social vulnerability: that of being women in a society where power structures favor men and that of belonging to a minority ethnic group that has suffered systemic discrimination for more than 200 years [##REF##27117482##11##, ##UREF##7##12##]. Indigenous women in Mexico experience the greatest lags in health (e.g., the lowest life expectancy at birth and the highest maternal and infant mortality ratios), and face the greatest barriers to accessing health services including discrimination at healthcare facilities [##REF##32520480##13##, ##UREF##8##14##]. The superimposition or intersectionality of these social factors of vulnerability configure the systematic inequalities that determine the subordinate position of indigenous women in the social structure [##UREF##9##15##].</p>", "<p id=\"Par18\">Several barriers have been described in the international literature for early BC diagnosis among minority populations living in HICs, like immigrants and, afro-descendants [##REF##30117005##16##–##REF##31601730##20##]. However, there is a dearth of studies related with barriers and facilitators of BC early diagnosis in indigenous populations worldwide. In Mexico, the scarce existing literature on barriers and facilitators for early detection of cancer among indigenous women has been limited to understanding their participation in cervical cancer screening [##UREF##10##21##–##UREF##12##23##]. Therefore, we undertook this qualitative study to explore barriers and facilitators for early BC diagnosis as perceived by <italic>otomí</italic> women living in the suburbs of an urban city in central Mexico.</p>" ]

[ "<title>Methods</title>", "<title>Design</title>", "<p id=\"Par19\">An exploratory and descriptive qualitative study was conducted [##UREF##13##24##, ##UREF##14##25##] with <italic>Otomí</italic> women living in Jiquipilco, State of Mexico. The study received approval from the National Cancer Institute of Mexico’s institutional review boards (021/041/IBI) (CEI/1592/21).</p>", "<title>Study setting</title>", "<p id=\"Par20\">The State of Mexico is a neighbor state of Mexico City. Jiquipilco is located approximately 45 km from Toluca, the capital of the state, and has a population of 69,031 habitants, of which 23.2% identify as indigenous [##UREF##15##26##]. It has an urban central area surrounded by rural areas, and its main economic activity is agriculture.</p>", "<p id=\"Par21\">The <italic>Otomí</italic> people are one of the original ethnic groups of Mexico and live across different regions of the country [##UREF##16##27##]. In the State of Mexico, the <italic>Otomí</italic> population concentrates in 21 municipalities, and Jiquipilco is one of them. In Jiquipilco, the <italic>Otomí</italic> people tend to concentrate in the rural outskirts of the municipality, living in conditions of poverty, and limited access to services, education and employment opportunities [##UREF##6##10##, ##UREF##16##27##, ##UREF##17##28##]. The <italic>Otomí</italic> people of the State of Mexico tend to work in agriculture activities part of the year, mainly in the cultivation of corn, beans, wheat, oats and maguey [##UREF##16##27##]. In the months when there is no agricultural activity, they migrate from rural communities to the Metropolitan Areas of Toluca and Mexico City where they are most employed as domestic workers, peddlers or as construction workers [##UREF##16##27##, ##UREF##18##29##]. These occupations are in the informal sector of the economy, and therefore most <italic>Otomí</italic> people are not covered by social security health insurance, which is provided through formal employment in Mexico. National Guidelines for BC Control in Mexico recommend: monthly breast self-examination starting at age 20, annual clinical breast examination (CBE) starting at age 25, and screeningmammography every 2 years starting at age 40 and up to 69 years [##UREF##19##30##]. Access to CBE and screening mammography varies according to women’s health insurance coverage and their capacity to pay for private services. At the time of this study, approximately 40% of the National population was covered by a social security health insurance scheme and only 3% had private insurance. For the uninsured, the state offers health services through its own infrastructure. Mammography units closest to Jiquipilco are in the state capital (Toluca), which is approximately 45 km from Jiquipilco.</p>", "<title>Study participants</title>", "<p id=\"Par22\">We used intentional non-probability sampling to find adult Otomí women -in Mexico, legally adulthood starts at 18 years of age- native of and currently living in Jiquipilco who could speak Spanish, and had no personal history of breast cancer [##UREF##13##24##]. The main objective of intentional sampling is to elicit different perspectives from people who represent the opinion of their group of reference [##UREF##20##31##, ##REF##10490670##32##]. The vast majority of indigenous people who live in Jiquipilco speak Spanish. According to data from the National Census, in Jiquipilco 8.0% of residents speak an indigenous language, and only 0.1% of the population speaks an indigenous language and no Spanish [##UREF##21##33##]. The State’s Council for the Integral Development of Indigenous Peoples (CEDIPIEM for its acronym in Spanish) helped us establish contact with the community to facilitate the invitation of potential participants. CEDIPIEM is a decentralized public entity whose purpose is to define, execute and evaluate policies directed to improve the lives of the State of Mexico’s indigenous population [##UREF##16##27##]. Even though, inviting participants through an official organization could have increased the risk of selection bias, this was the best alternative we found to identify and invite otomí women of the region who would trust our invitation. We explained the study to all of those invited, emphasizing that participation was voluntary and that there would be no repercussions on health care or social benefits if they refused to participate. Written informed consent was signed by all participants previous to their participation in the focus group interviews. We included all who were willing to participate.</p>", "<title>Conceptual framework</title>", "<p id=\"Par23\">Our study was guided by a conceptual framework that integrates the Social Ecological Model [##UREF##22##34##], the Health Belief Model [##UREF##23##35##] and the Institute of Medicine’s Healthcare Quality framework [##UREF##24##36##]. Figure ##FIG##0##1## illustrates how we integrated these three theoretical perspectives to guide our interview analyses in the identification of the Otomí women’s perceived barriers and facilitators for early diagnosis of BC.</p>", "<p id=\"Par24\">The Social Ecological Model (SEM) is a useful framework to identify the full range of factors that can influence health and health behavior. These factors can be located at different levels: individual, interpersonal, institutional or organizational, community, and public policy levels. The SEM framework emphasizes the interaction and interdependence between factors within and across all these levels [##UREF##22##34##, ##UREF##25##37##, ##UREF##26##38##]. It has been used to study diverse social problems and health behaviors [##UREF##27##39##–##UREF##31##45##].</p>", "<p id=\"Par25\">The SEM can be used to integrate components of other theories. We used the Health Belief Model (HBM) to strengthen the analysis of individual level factors that exert an influence on <italic>Otomí</italic> women’s help seeking behavior, and the Healthcare Quality (HCQ) framework to strengthen our analysis of the organizational level factors (our participants’ perception of the quality of services for BC early diagnosis: primary care clinics and breast imaging services).</p>", "<p id=\"Par26\">The HBM stipulates that the following groups of factors influence the likelihood of a person taking a recommended preventive health action: demographic variables -age, race, socioeconomic level-; psychological variables and knowledge of disease (in this case, BC); perceptions of the disease (perceived susceptibility and perceived seriousness of BC); perceptions of the health behavior of interest (perceived benefits and perceived barriers to act on the recommended health behavior) and cues to action [##UREF##23##35##]. Perceived susceptibility refers to a person’s subjective perception of their own risk of developing BC. Perceived severity includes assessments of severity and the medical and social consequences of getting BC. Perceived barriers refer to the possible negative effects of the preventive or health behavior such as its costs, secondary adverse effects, and time required. Perceived benefits refer to the individual’s perception of the effectiveness of the health behavior. The main health behavior we were interested in understanding was timely seeking of medical care for breast symptoms, but we also assessed the study participants’ perceived benefits of breast self-examination, screening clinical breast examination and screening mammography. Finally, cues to action are events or things that trigger people to act or perform a certain health behavior (e.g., medical recommendation, mass media messages, etc.) [##REF##6392204##46##]. Over time this model evolved to include self-efficacy as an important determinant in health behavior [##UREF##32##47##]. Self-efficacy is understood as the conviction of people in their own capability to successfully perform a certain behavior [##UREF##33##48##].</p>", "<p id=\"Par27\">Finally, we used the Health Care Quality (HCQ) framework to strengthen our analysis of the health system (organizational level) factors. According to the HCQ framework, quality healthcare should be: 1) safe, avoiding harm to patients from the care that is intended to help them, 2) effective, providing services based on scientific knowledge to all who could benefit and refraining from providing services to those not likely to benefit, 3) patient-centered: providing care that is respectful of and responsive to individual patient preferences, needs, and values and ensuring that patient values guide all clinical decisions, 4) timely: reducing waits and sometimes harmful delays for both those who receive and those who give care, 5) efficient: avoiding waste, including waste of equipment, supplies, ideas, and energy, and 6) equitable: providing care that does not vary in quality because of personal characteristics such as gender, ethnicity, geographic location, and socioeconomic status [##UREF##24##36##]. Even though we did not analyze patient nor services outcomes, using this framework we were able to identify our participants’ perceptions on HCQ dimensions based on their previous interactions with health services. In this study, we were particularly interested in our participants’ previous experiences with health services and their perceptions regarding patient-centeredness, as this can be especially challenging in the context of care for women that belong to a historically marginalized and discriminated social group.</p>", "<title>Data collection</title>", "<p id=\"Par28\">We conducted three focus group interviews with 19 women in November 2021 [##UREF##34##49##]. Focus group interviews are recognized as a useful tool to obtain information about collective points of view and their meanings, and to generate a rich understanding of the experiences and beliefs of the participants [##UREF##35##50##]. MST moderated the interviews. She is a woman, psychologist and qualitative researcher with no previous relationship with the Otomí community at Jiquipilco nor with the CEDIPIEM.</p>", "<p id=\"Par29\">The interviews were conducted using a semi-structured interview guide with open-ended questions to ask participants about their perceptions of barriers and facilitators, knowledge, attitudes and beliefs about cancer early detection in general and more specifically about early BC diagnosis. We developed our interview guides based on our conceptual framework and key findings from the existing literature on barriers and facilitators for early detection of BC among underserved populations.</p>", "<p id=\"Par30\">Each focus group interview lasted approximately 60 minutes and the number of participants in the groups ranged between 4 and 8. All interviews were audio-recorded. Data saturation was achieved with the last focus group and, therefore, no more participants were recruited. We decided saturation was reached when no new codes appeared and each of the codes had been applied to a sufficient amount of data [##UREF##34##49##, ##UREF##36##51##]. We also collected descriptive demographic data from all the participants including age, marital status, occupation, years of school education and family income.</p>", "<title>Data analysis</title>", "<p id=\"Par31\">Participants’ responses were transcribed verbatim and all transcripts were de-identified prior to analysis. Transcripts and field notes were organized using Atlas.ti 8 software to aid the analysis. We used a pragmatic approach for data interpretation, using both deductive and inductive data analysis to explain findings. These type of analytical processes that engage both deductive and inductive strategies have shown to help researchers apply concepts from the literature and theory, which can in turn support the trustworthiness and applicability of the study [##UREF##37##52##]. We identified barriers and facilitators for early BC diagnosis guided by our conceptual framework (Fig. ##FIG##0##1##) which integrates theoretical perspectives of the Social Ecological Model, the Health Belief Model and the Institute of Medicine’s HealthCare Quality Framework [##UREF##38##53##, ##UREF##39##54##]. But data was also coded using the constant comparison method. The constant comparison method is an iterative and inductive process of reducing the data through constant recoding to assure that all data are systematically compared to all other data in the data set [##UREF##40##55##, ##UREF##41##56##]. Using this strategy we continually compared data to other data within a single interview, between interviews within the same group and between interviews from different groups [##UREF##42##57##]. We read all interview transcripts carefully several times in order to identify the codes through the participants’ narratives. To enhance trustworthiness and rigor, we used triangulation for coding of the data. Data were coded by two different researchers: MST who is psychologist with postgraduate studies in health psychology and KUS is a medical doctor and health systems researcher. The coding results were then reviewed for cases with differing results, reaching consensus between the two coders to establish the final codes.</p>" ]

[ "<title>Results</title>", "<p id=\"Par32\">Nineteen Otomí women participated in the study. To keep the confidentiality agreement we made with all of our participants, the names used in this paper are pseudonyms. Participant sociodemographic characteristics are shown in Table ##TAB##0##1##<bold>.</bold>\n</p>", "<p id=\"Par33\">Figure ##FIG##1##2## summarizes the perceived barriers and facilitators for BC early diagnosis that we identified in the interviews, and organizes them at the different levels of the Social Ecological Model (SEM). The Health Belief Model constructs were used to code the barriers and facilitators identified at the individual level of the SEM, and the Healthcare Quailty framework was used for the Health Services Organization level. The arrow crossing through all levels represents gender and ethnicity as the key social processes that act at every level of the SEM to influence individual women’s help-seeking behaviors for breast symptoms and timely access to quality medical care for BC early diagnosis.</p>", "<title>Perceived barriers to early BC diagnosis</title>", "<title>Health policy barriers</title>", "<p id=\"Par34\">Our study participants perceived the elimination of the social program “<italic>Progresa-Oportunidades-Prospera</italic>” (POP) as an access barrier to healthcare services. This was a federal program that gave conditional cash transfers to families living in poverty to improve their access to nutritional food, healthcare and education. The program operated for 20 years and was terminated in 2019 by the current government [##UREF##38##53##]. The POP program provided basic health services free of charge, in addition to health promotion actions under three modalities: self-care promotion; individualized guidance and counseling during medical consultations; and health promotion messages aimed at the families of beneficiaries [##UREF##43##58##].</p>", "<p id=\"Par35\">Our interviewees reported that through <italic>POP</italic> they had access to special health programs, health information and better access to health care. They perceive that, as a result of its elimination, people seek less care at health centers, as they report experiences of not receiving medical attention at the health center when they need it, and they feel “lost” regarding where to seek medical attention.</p>", "<title>Social and cultural context barriers</title>", "<title>Cultural gender norms</title>", "<p id=\"Par39\">Gender issues constantly emerged in the participants’ narratives. Women spoke about cultural gender norms and men’s attitudes towards sexuality as a barrier to BC early diagnosis. They referred to men as being “<italic>machista</italic>”, trying to control their female partners’ behavior. They explained that in their community it was prohibited for women to talk about their breasts, to examine their own breasts, and to get general check-ups with male doctors.</p>", "<p>\n</p>", "<p>Our participants described that this “sexual taboo” limits them to talk about their bodies, their breasts and breast diseases because they feel embarrassed. They said that they do not know their own bodies and that they don’t explore their breasts because of shame and fear of being judged.</p>", "<p>Due to the assigned gender roles in the community, girls receive less school education than boys. Our participants reported that once girls finish the mandatory 6 years of elementary school education in Mexico, they are considered to be ready for marriage. These low levels of schooling not only have a negative impact in indigenous women’s health literacy and awareness of different health problems, like cancer, but also in their own empowerment to fight for their rights within their families, their communities and in their exchanges with healthcare services.</p>", "<p>Additionally, as part of their gender roles, women in the community are expected to take care of their children, spouse, and other family members, prioritize the care of others over their self-care, and are also responsible for all the housework (buying food, cooking, cleaning, washing clothes, etc.). They usually have several children as they are not empowered to negotiate birth control with their partners. More and more women are also working outside the house, in search for better economic conditions, but the gender roles of taking care of others and the household are still in place. Our participants referred that they hardly have any time to take care of themselves and this makes it very difficult to seek healthcare when they feel ill and even more so for preventive activities.</p>", "<title>Myths and beliefs about illness in general and about cancer</title>", "<p id=\"Par48\">Beliefs about illness in general were also perceived as barriers for early diagnosis of BC by our participants. For example, they believe that if they think about a certain disease, they can attract it and then fall ill. For this reason, people in the community tend not to talk or think about diseases, as they believe that this way they will avoid getting sick. This makes it very difficult for people in the community to be willing to get health information and to participate in preventive and early detection behaviors.</p>", "<p>\n</p>", "<p>Another common belief about illness in this community, as described by our participants, is that they only perceive themselves to be ill when they feel that their life is in danger. Additionally, they seek medical care only if they feel ill or interpret their symptoms as being life-threatening.</p>", "<title>Cancer stigma</title>", "<p id=\"Par53\">Cancer stigma was perceived as a barrier to seeking medical care. Some participants reported reluctance to talk about cancer in their community and commented that women with BC generally do not reveal their diagnosis even to their own families. They believe this is because of the common belief that cancer is a consequence of having misbehaved, “having been bad”. They see cancer as a divine punishment, so people avoid sharing their diagnosis because of fear of feeling judged by their family members and friends.</p>", "<p>\n</p>", "<p>Additionally, in regard to BC, they spoke about the stigma in relation to mastectomy and “being a woman without breasts”.</p>", "<p>Cancer in general is viewed as a fatal disease, which they associate with death, pain, suffering and aggressive treatments. Therefore, if they think their symptoms are related with cancer, they are likely to postpone seeking medical care in order to avoid what they see as aggressive unnecessary treatments. This belief is further confirmed once people seek care very late and so in fact receive aggressive treatments and nevertheless die soon.</p>", "<title>Traditional medicine use</title>", "<p id=\"Par59\">Also, our participants said that sometimes people in their community prefer using traditional medicine and postpone seeking medical care, or interrupt medical treatment in favor of traditional medicine treatment.</p>", "<p id=\"Par60\">\n<italic>“…Well, a neighbor of my community was going with a “healer” who is, according to her, very famous for healing people with cancer…She was being treated in a hospital in Mexico City, but she abandoned her treatment and instead went to see the healer. She died a year later…”</italic> (Cecilia, 28 years old).</p>", "<title>Fear of COVID</title>", "<p id=\"Par61\">Our participants reported that during the pandemic they avoided going to healthcare facilities because of fear of getting infected and dying of COVID. In addition to this postponement of health service utilization due to fear of COVID, they also reported difficulties to access health services due to the reconfiguration of healthcare services to prioritize attention for COVID. Those who tried seeking care faced even longer waiting times than usual to get consultations and tests.</p>", "<p>\n</p>", "<title>Health services organization</title>", "<p id=\"Par65\">The majority of the perceived barriers for BC early diagnosis described by our participants were at the level of the health system. According to the HCQ framework, quality healthcare should be safe, effective, patient-centered, timely, efficient and equitable. Our focus groups participants perceived quality problems in the public health services that they are entitled to use, and the problems they described were mainly related with disrespectful (instead of patient-centered), untimely, and inequitable care.</p>", "<title>Discrimination/Mistreatment by health care personnel</title>", "<p id=\"Par66\">Our study participants reported experiences of disrespectful and even discriminatory treatment in their interactions with healthcare personnel in public services. They shared several personal experiences of abuse by healthcare personnel in public services. They questioned the reasons for this, and explained that they think it is due to a combination of their low levels of education, being women and being indigenous.</p>", "<p>\n</p>", "<title>Lack of trust in health personnel</title>", "<p id=\"Par69\">Many of our participants expressed a lack of trust in doctors and healthcare personnel in general due to these past personal negative experiences as well as stories they have heard from other people in their community. For this reason, they try to seek care in private services which they perceive as better quality. The problem is that they often can’t afford it.</p>", "<p id=\"Par70\">In more extreme cases, our participants described being denied healthcare. The health workers would tell them to return to their homes without giving them care. They would be told that it was due to administrative issues, or lack of time, or insufficient doctors, or sometimes without any explanation. This was perceived by our participants as “unfair” treatment.</p>", "<p>\n</p>", "<title>Language barriers</title>", "<p id=\"Par73\">They also commented that language is a barrier for indigenous people who don’t speak Spanish. This mainly affects the elderly. Our participants expressed that healthcare personnel get angry when women don’t speak Spanish.</p>", "<p>\n</p>", "<title>Long waiting times/Difficulties in making an appointment</title>", "<p id=\"Par76\">Our participants described long times to get medical appointments at the local health center, long times to get referred to specialists, to receive test results and long hours waiting at the clinics to receive medical attention. They also described very complex administrative procedures to receive care, like having to arrive very early in the morning to the clinic and then stand in line for several hours in an attempt to get a medical consultation, without guarantee that they would succeed.</p>", "<p>\n</p>", "<title>Costs/Distance to health services</title>", "<p id=\"Par80\">Our participants described that financial barriers also limit their access to healthcare services, even if the consultations at public services are available without cost to the patient. Having to cover costs of medical care is not only a barrier for private service use. Our participants described that even if they manage to get a consultation in public services without having to pay, they often can’t cover the costs of the medicines that are prescribed.</p>", "<p>\n</p>", "<p>In addition to direct medical costs, there are costs related to transportation and time. Some participants explained that the people in the community have to travel long distances and take several means of transportation to get to medical services, especially if they need specialized care.</p>", "<title>Interpersonal barriers</title>", "<title>Influenced by peers</title>", "<p id=\"Par85\">At this level of the Social Ecological Model, the influence of peers and family came up as very relevant in the decision of whether or not to seek care, when to seek care, and what type of care to seek: whether traditional medicine, or the local public health center, or even private services. It was reported that when women are ill, instead of going to the doctor, family and friends recommend treating with natural remedies, even one participant reported that a woman in the community with BC abandoned cancer treatment for traditional medicine on the recommendation of her husband.</p>", "<p>\n</p>", "<title>Individual barriers</title>", "<title>Lack of cancer awareness</title>", "<p id=\"Par88\">There was in general low cancer awareness among our participants. Even though they had heard about BC, they recognized they did not have enough information about the disease, its risk factors and how to diagnose it early.</p>", "<p>\n</p>", "<title>Low risk perception of breast cancer</title>", "<p id=\"Par92\">Although participants know other people who have been affected with BC, or have heard about it, some participants perceived themselves as not being at risk of developing BC. The fact of thinking that BC is mainly transmitted through family inheritance, makes them feel at low risk of developing it. In words of a participant <italic>“I am certain that I will not develop that disease because no one in my family has had it”.</italic> (Patricia, 48 years old).</p>", "<title>Fear of cancer</title>", "<p id=\"Par93\">Among our participants, fear of having cancer was perceived as an important barrier to seek care. They described that the fear of having the diagnosis confirmed could cause women in their community to postpone health care-seeking for breast symptoms. This fear is related with their fatalistic attitudes towards cancer.</p>", "<p>\n</p>", "<title>Perceived facilitators to early BC diagnosis</title>", "<title>Social cultural level</title>", "<title>Information by media</title>", "<p id=\"Par96\">One of the elements that were found within the cues to action dimension were the messages and information received through the media (radio and television commercials), social networks, and screening mammography promotion activities done in their communities. They perceived all this information as facilitators for early breast cancer diagnosis. They find informative posters in the community and community health workshops very useful to keep themselves informed and to inform younger people on the importance of taking care of their health.</p>", "<title>Health services organization</title>", "<title>Respectful patient-centered medical care</title>", "<p id=\"Par97\">One of the main perceived facilitators that women emphasized would facilitate early cancer diagnosis and medical attention of any health problem was receiving respectful, empathetic care with good attitudes of healthcare personnel and effective communication between doctors and patients. This was more aspirational than actual experiences of the participants.</p>", "<p>\n</p>", "<title>Information by doctors and promoters</title>", "<p id=\"Par100\">Women reported receiving information about BC by health care personnel in public and private services. They had heard about breast self-examination mainly in public primary care clinics through nurses “We need a lot of talks, but I would like to include young people because they are beginning to take care of themselves so that they know the care they should have is very important” (Verónica, 46 years old).</p>", "<title>Interpersonal facilitators</title>", "<title>Social support</title>", "<p id=\"Par101\">\n<italic>Social support</italic> from other women and from their family members, especially their partners was reported as a potential key facilitator. Women shared that hearing experiences of women who had cancer could be a strong motivator for them to check themselves and go to the doctor. They also commented that the support of other women is key, especially in two ways: by accompanying them to the health center and by being able to share and discuss these issues with them.</p>", "<p>\n</p>", "<title>Individual facilitators</title>", "<title>Perceived severity of breast cancer</title>", "<p id=\"Par104\">The fact that women perceived BC as a serious disease that begins without symptoms, progresses over time if women do not receive medical attention, and that can spread to other parts of the body and cause death, can motivate them to look for medical care.</p>", "<p>\n</p>", "<title>Perceived benefits of early BC diagnosis</title>", "<p id=\"Par108\">Almost all participants were aware of the importance of cancer early detection. They mentioned that early detection increases the chances of cure, and that this motivates them to keep themselves informed and to talk about it with their peers.</p>" ]

[ "<title>Discussion</title>", "<p id=\"Par109\">This is the first study to explore perceived barriers and facilitators to timely healthcare seeking and access for early diagnosis of BC among Otomí indigenous women in Mexico. The results reveal barriers and facilitators at different levels of the Social Ecological Model that may inform interventions to improve early diagnosis of BC in this vulnerable population. Among the most salient barriers were: the elimination of well-established social programs that facilitated access to healthcare, fatalistic cultural beliefs about cancer, cultural gender roles related with prioritization of the care of other people, sexual taboos that can interfere with self-detection and healthcare seeking for breast symptoms, lack of trust in healthcare providers due to past experiences of mistreatment and discrimination, and access barriers for use of healthcare services.</p>", "<p id=\"Par110\">One of the most striking findings of this study are the participants’ descriptions of mistreatment by healthcare personnel that they have experienced when using medical services. These seem to be a consequence of healthcare ethnic and gender discrimination. Although until recently healthcare racism towards indigenous people was overlooked, both in academia and public policy [##UREF##44##59##], there is emerging scientific evidence that identifies various forms of discrimination as a structural determinant of the lack of access to healthcare for these populations [##REF##30601726##60##–##UREF##46##62##]. Healthcare personnel may hold unconscious biases and heuristics based on gender and ethnic stereotypes [##UREF##47##63##], that can negatively impact patient care [##REF##31155275##64##]. These biases have been found to be further compounded when healthcare providers are faced with patients who are not only women but are additionally poor, from a rural community, and belong to a marginalized ethnic group [##REF##28501223##65##]. The lack of physician cultural competency and implicit bias by clinicians toward ethnic, racial and gender minorities have been shown to result in the provision of unequal healthcare and disparities in cancer outcomes [##REF##31670752##66##]. In turn, these experiences of mistreatment and discrimination, damage the patients’ trust in healthcare providers, and thus, can act thereafter as barriers to participation in screening, timely healthcare seeking for cancer symptoms and adherence to treatment [##UREF##48##67##–##REF##21388729##71##].</p>", "<p id=\"Par111\">The preference of traditional medicine over formal medical care services that is described by some of the study participants could be related, in addition to cultural health beliefs, to the mistreatment that indigenous people often experience when seeking medical care. The use of traditional medicine and home remedies has been described in other studies as a barrier to healthcare seeking of formal medical services and cancer awareness in other indigenous populations in Mexico and Ghana [##UREF##50##72##–##UREF##51##74##]. It has been described that they usually consult a traditional healer as a first point of contact, they believe that traditional healers have supernatural powers they have inherited from their ancestors, which cements their authority in the community [##REF##24474424##73##]. Indigenous people have more trust in traditional medicine and traditional healers than in modern western medicine and medical care providers [##REF##25566790##75##–##REF##8719975##77##], although anthropological evidence allows us to recognize that they also value and make use of allopathic medicine [##REF##37988570##78##]. Indigenous populations throughout the world have used traditional medicine for many generations, and many communities perceive it as valuable, affordable, and more acceptable as it aligns with their sociocultural beliefs [##UREF##52##79##–##UREF##53##81##]. In contrast, indigenous population have described western medicine as very impersonal with very short consultations, little space and opportunity to express their concerns, and almost no explanations of their illness [##REF##8719975##77##]. However, criticism is directed primarily at how they are treated by healthcare personnel, not at the effectiveness of western medicine therapeutic resources. Also, access to traditional healers is easier for indigenous people both in terms of geographic proximity and waiting times to get a consultation. In addition, within these relationships with traditional healers there are no forms of discrimination and racism based on ethnic differences [##REF##25566790##75##, ##UREF##54##82##].</p>", "<p id=\"Par112\">Our participants described that they perceived easier access to formal healthcare services when the social program POP (Progresa-Oportundiades-Prospera) was in place. That program was coupled to health promotion and prevention activities that took place in health centers. After the elimination of this program in 2019, our participants describe that they lost the direct link they had to healthcare facilities where they could seek care. The elimination of successful social and health programs has also been described as an access barrier for use of reproductive health services by indigenous women in other studies [##UREF##55##83##]. The COVID-19 pandemic was a global health crisis that generated uncertainty and fear around the world. Learning and social interaction are factors that help us to understand how risk awareness and fear are generated in the presence of a pandemic [##REF##31905206##84##]. Our results show that fear of becoming infected with COVID-19 acted as a barrier to approaching health centers. This is consistent with other studies, where COVID-19 fatality rates were higher in indigenous population in comparison to the rest of the Mexican population [##UREF##56##85##]. Additionally, due to the pandemic, many health centers were converted to only attend COVID-19 cases, while others were saturated, and this complicated access to the early diagnosis and treatment of cancer worldwide [##REF##34217412##86##–##UREF##57##88##].</p>", "<p id=\"Par113\">Another group of salient barriers identified in this study were cultural beliefs and roles: fatalistic cultural beliefs about illness, cancer stigma, gender roles related with prioritization of the care of other people, and sexual taboos that can interfere with the detection of breast symptoms and healthcare seeking for symptoms. The study participants described a widespread cultural belief among <italic>otomíes</italic> of cancer being seen as a divine punishment for “bad behavior”. Similar beliefs have been reported for African American and Hispanic women residing in the USA [##REF##15712779##89##]. Seeking for healthcare is likely to be postponed if a person doesn’t believe there is much she can do to influence her health [##REF##15712779##89##, ##UREF##58##90##]. Our study participants also described that a commonly shared belief in their community is that if a person thinks about an illness, he/she may attract such illness. This can also act as a barrier to preventive and healthcare seeking behaviors, as people opt to avoid thinking about any diseases in order to avoid being affected by them. To our knowledge, this belief has not been described in previous studies, but given its relevance, should be intentionally explored in future studies.</p>", "<p id=\"Par114\">Another salient cultural belief that our participants described as having an impact on health behavior of women in particular is that of sexual taboos and embarrassment to touch their own bodies or have healthcare professionals examine their bodies. They see the touching of their own breasts as a sexual behavior that is disapproved in their community, especially by the men. In the same line, the male partners disapprove of their wives having their breasts or sexual organs examined by a doctor, especially if it is a man. These sexual taboos and male control over their female partners’ health and sexuality can act as barriers for early discovery of breast symptoms as well as for early seeking of medical care, as it has been reported for other populations [##UREF##59##91##, ##REF##16430393##92##]. Embarrassment to be seen or touched by healthcare personnel has also been reported in the literature as a barrier for not participating in BC screening programs in other countries [##REF##25921163##93##].</p>", "<p id=\"Par115\">Finally, the main barriers identified at the individual level were limited cancer awareness -with misinformation about the disease, its risk factors and how to detect it early- and fear of being diagnosed with BC. Limited cancer awareness has been documented as a major barrier to seeking care, using medical services, as well as late detection and poor outcomes [##REF##30117005##16##, ##REF##24692336##17##, ##REF##31601730##20##, ##REF##11109689##94##, ##REF##28192444##95##]. To increase individuals’ knowledge, awareness, risk perception and motivation to seek healthcare, educational interventions can be effective [##UREF##60##96##]. But, if they are to be effective in specific indigenous populations, the design of these educational interventions need to be tailored according to the needs, beliefs and cosmovision of the indigenous population towards which they will be directed to [##UREF##61##97##]. In addition, interventions directed to increase the perception of severity of the disease should simultaneously increase the perception of benefits of early diagnosis so that fear does not stop women from seeking care.</p>", "<p id=\"Par116\">This study has some limitations. Due to our qualitative design and purposeful sampling strategy, our findings are not generalizable to the entire <italic>otomí</italic> population, not even that residing in Jiquipilco. Also, even though our study participants were instructed to speak on behalf of cultural views that would be representative of their communities, they may also have provided personal views. However, we believe this information is valuable as personal views are often a reflection of shared cultural values.</p>" ]

[ "<title>Conclusions</title>", "<p id=\"Par117\">This study identified barriers and facilitators for early diagnosis of BC as perceived by <italic>otomí</italic> indigenous women. Healthcare providers and policy makers should take notice of indigenous women’s beliefs, access barriers and healthcare discrimination experiences in the design of programs that aim to facilitate early BC diagnosis and treatment for these vulnerable populations. It is urgent to improve the quality of care and access to public healthcare services available in Mexico for the poor, especially for health problems where access to early diagnosis and treatment is key for good outcomes as is the case of cancer. Indigenous women, in addition to often being poor, too frequently face discrimination by healthcare providers due to their gender and ethnicity. Thus, beyond cultural differences, discriminatory treatment stands as a structural barrier to otomí women’s access to BC screening services. This is a characteristic shared by other Amerindian indigenous groups of people. Measures to prevent and eradicate all forms of mistreatment and discrimination in healthcare services are imperative.</p>" ]

[ "<title>Background</title>", "<p id=\"Par1\">Literature on barriers and facilitators for early detection of Breast Cancer (BC) among indigenous women is very scarce. This study aimed to identify barriers and facilitators for BC early diagnosis as perceived by women of the <italic>otomí</italic> ethnic group in Mexico.</p>", "<title>Methods</title>", "<p id=\"Par2\">We performed an exploratory qualitative study. Data was collected in 2021 through three focus group interviews with 19 <italic>otomí</italic> women. The interview transcripts were analyzed using the constant comparison method and guided by a conceptual framework that integrates the Social Ecological Model (SEM), the Health Belief Model and the Institute of Medicine’s Healthcare Quality Framework.</p>", "<title>Results</title>", "<p id=\"Par3\">Barriers and facilitators were identified at several levels of the SEM. Among the main barriers reported by the study participants were: beliefs about illness, cancer stigma, cultural gender norms, access barriers to medical care, and mistreatment and discrimination by health care personnel. Our participants perceived as facilitators: information provided by doctors, social support, perceived severity of the disease and perceived benefits of seeking care for breast symptoms.</p>", "<title>Conclusions</title>", "<p id=\"Par4\">Healthcare policies need to be responsive to the particular barriers faced by indigenous women in order to improve their participation in early detection and early help-seeking of care for breast symptoms. Measures to prevent and eradicate all forms of discrimination in healthcare are required to improve the quality of healthcare provided and the trust of the indigenous population in healthcare practitioners.</p>", "<title>Keywords</title>" ]

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[ "<title>Acknowledgements</title>", "<p>We want to thank the Center for the Development of the Indigenous People of the State of Mexico (CEDIPIEM) for their support in participant recruitment and provision of adequate spaces for the interviews. We are grateful to all the participants for allowing us to hear their voices and for sharing their experiences.</p>", "<title>Authors’ contributions</title>", "<p>Study conception and design: Saldaña-Téllez, M. and Unger-Saldaña K. Data collection: Saldaña-Téllez, M. and Cano-Garduño, L. Data analysis: Saldaña-Téllez, M. and Unger-Saldaña, K. Drafting of the manuscript: Saldaña-Téllez M., Meneses-Navarro, S. and Unger-Saldaña K. Writing review and editing: All authors. All authors read and approved the final manuscript.</p>", "<title>Funding</title>", "<p>MST was supported by Council of Science and Technology of State of Mexico (COMECYT) to carry out this project.</p>", "<title>Availability of data and materials</title>", "<p>The data (de-identified interview transcripts in Spanish) that support the findings of this study are available on request from the corresponding author [KUS].</p>", "<title>Declarations</title>", "<title>Ethics approval and consent to participate</title>", "<p id=\"Par118\">The study received approval from the National Cancer Institute of Mexico’s institutional review boards (021/041/IBI) (CEI/1592/21). All methods have been performed in accordance with the Declaration of Helsinki. Informed consent was obtained from all individual participants included in the study.</p>", "<title>Consent for publication</title>", "<p id=\"Par119\">The possibility of publication of de-identified data was explained to the participants in the written informed consent forms.</p>", "<title>Competing interests</title>", "<p id=\"Par120\">The authors declare no competing interests.</p>" ]

[ "<fig id=\"Fig1\"><label>Fig. 1</label><caption><p>Conceptual framework that guided our Interview Analysis</p></caption></fig>", "<fig id=\"Fig2\"><label>Fig. 2</label><caption><p>Perceived barriers and facilitators at different levels of the SEM</p></caption></fig>" ]

[ "<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Sociodemographic characteristics</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th colspan=\"2\">Characteristics</th><th>No.</th></tr></thead><tbody><tr><td colspan=\"2\">Age <italic>(Mean, range)</italic></td><td><italic>37 (18–61)</italic></td></tr><tr><td>Marital status</td><td>In a relationship</td><td>6</td></tr><tr><td/><td>Not in a relationship</td><td>13</td></tr><tr><td>Occupation</td><td>Unemployed</td><td>10</td></tr><tr><td/><td>Employed</td><td>8</td></tr><tr><td/><td>No response</td><td>1</td></tr><tr><td>Education</td><td>6 years or less</td><td>3</td></tr><tr><td/><td>7 to 9 years</td><td>7</td></tr><tr><td/><td>10 years or more</td><td>9</td></tr><tr><td>Otomí speakers</td><td>Yes</td><td>9</td></tr><tr><td/><td>No</td><td>10</td></tr><tr><td>Health insurance</td><td>Yes</td><td>9</td></tr><tr><td/><td>No</td><td>10</td></tr><tr><td>Monthly family income^a</td><td>&lt; 1 minimum salary</td><td>14</td></tr><tr><td/><td>1- &lt; 3 minimum salaries</td><td>2</td></tr><tr><td/><td>≥3 minimum salaries</td><td>3</td></tr></tbody></table></table-wrap>" ]

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[ "<disp-quote><p id=\"Par36\"><italic>“…When we had the program, well, that program worked in conjunction with the health center, so the nurses gave us health talks and workshops and taught us how to examine ourselves. Yes, in fact that program was very good, because they also taught us things like healthy eating, how to exercise…”</italic> (María 52 years old).</p></disp-quote>", "<disp-quote><p id=\"Par37\">“<italic>Since the program disappeared… we no longer have the same attention and in fact, I feel proud of those years, when that program existed…because in those years I went with the doctors and they provided medical care, when they realized that the pain did not decrease, they gave me a referral to see a specialist, a gynecologist, and that’s where my myomas were diagnosed…”</italic> (Sandra, 41 years old).</p></disp-quote>", "<disp-quote><p id=\"Par38\"><italic>“…Everything changed, now we feel like we’re lost, like we don’t even know where to go, with what doctor we can go. Before, when the program existed, there were members here in the community that organized people and would take them to health workshops and to get medical attention in health centers…”</italic> (Teresa, 47 years old).</p></disp-quote>", "<disp-quote><p id=\"Par41\">\n<italic>“…If the husbands find them touching their breasts...they question them “why are you doing that? you can’t do that, why are you touching yourself?” I feel like that is machismo… You cannot touch and explore your breasts without being sexualized…”</italic> (Cecilia, 28 years old).</p></disp-quote>", "<disp-quote><p id=\"Par42\">\n<italic>“…Yes, oh yes, there are many men who do not let women go to the doctor, that kind of men predominate here. That’s because in our community there is machismo…”</italic> (Teresa, 47 years old).</p></disp-quote>", "<disp-quote><p id=\"Par44\"><italic>“…So, the context has a lot of influence here, I feel that this community is very conservative, for example, I don’t talk about those things (breast topics) because it is frowned upon, and I know it could be misunderstood ...”</italic> (Margarita, 38 years old).</p></disp-quote>", "<disp-quote><p id=\"Par47\"><italic>“...I feel that we always have time for everything, except for our health, for example I invited some women to a health talk and they did not go… I think we don’t take care of ourselves, we don’t go to the doctor, always the family first, always the children, always the house, always! What about us? We are always last…”</italic> (Verónica, 46 years old).</p></disp-quote>", "<disp-quote><p id=\"Par50\">\n<italic>“… Sometimes we psychologically call disease, so we better not think about it, we better think that it is far away and is not going to touch us, so we don’t get sick…”</italic> (Nancy, 50 years old).</p></disp-quote>", "<disp-quote><p id=\"Par52\"><italic>“…The people of my community, well, no, they don’t go to the doctor, well I think we all go to the doctor until we feel a lot of pain, as we said, when the problem is already very advanced. I had a neighbor who got sick with cancer, the cancer attacked several systems, organs and she died because nothing could be done, not even with chemo, she went to the doctor too late…”.</italic> (Cecilia, 28 years old).</p></disp-quote>", "<disp-quote><p id=\"Par55\">\n<italic>“…Sometimes, we are embarrassed to say we have a disease, we don’t want our neighbors or other people to know. We think that they will judge us, that people will say “if she’s sick, it’s because she surely did something bad and God punished her”…That’s why people don’t say anything when they feel ill... About women with BC or with cervical cancer, many times people say: “she did something wrong, God punished her for that”... It’s better we don’t talk, it’s better we don’t say we’re sick...”</italic> (Margarita, 38 years old).</p></disp-quote>", "<disp-quote><p id=\"Par57\"><italic>“…It is complicated because cancer “eats you from the inside”, to the point that the entire breast has to be amputated, they have to remove the breast. No, the word is not removal, it is amputation, they amputate the entire breast, which is sometimes hard and difficult to assimilate, imagine a woman without breasts…”</italic> (Nancy, 50 years old).</p></disp-quote>", "<disp-quote><p id=\"Par63\">\n<italic>“…Now with the COVID pandemic it is more difficult going to the health center, people ask you “what are you going for?... you will get infected”.</italic> (Patricia, 48 years old).</p></disp-quote>", "<disp-quote><p id=\"Par64\">“…<italic>So, they gave me the appointment a year and a half later, it took a year and a half for me to see the specialist. When I went a year and a half later, they told me “You need recent studies” and then they sent me to do a tomography a month later, then with the COVID pandemic and restrictions they have not given me the results of the tomography, and until know I’m still waiting to get attention…”</italic> (Cecilia, 28 years old).</p></disp-quote>", "<disp-quote><p id=\"Par68\">\n<italic>“There are many times that I don’t know if it’s because of their profession or because they feel superior to us, they treat us badly. I mean, I have felt abuse, we all have experienced that, doctors even make fun of us…for example, when my children were born, they examined me, but it was a horrible examination, I mean they put their hands inside, they laughed…they even made fun of me. So I ask myself why? Why do they treat us like that?”</italic> (Margarita, 38 years old).</p></disp-quote>", "<disp-quote><p id=\"Par72\">\n<italic>“I went for a consultation and they told me “come back in 8 days” so, the truth is, I was really upset because I really needed the service. But no, they told me to return in 8 days, so I wanted to report them for the bad attention, not just for me, but for the others because I have heard other people’s experiences. The truth is that it is unfair, they should work and serve with joy because they receive a salary”</italic> (Nancy, 50 years old).</p></disp-quote>", "<disp-quote><p id=\"Par75\">\n<italic>“In the health center they don’t speak Otomí, if you speak in Otomí, they get angry, because they don’t understand…for example, there are elderly people who speak perfect Otomi, and they have to go with someone to translate, doctors say “Oh I didn’t understand you, a family member must come with you to translate”. They get angry”</italic> (Patricia, 48 years old).</p></disp-quote>", "<disp-quote><p id=\"Par78\">\n<italic>“You have to go very early to get a voucher so that you can receive a consultation and you have to see if there are enough vouchers, because sometimes they just give a limited amount and if you don’t get one you have to go the next day and the next day to try to get one”</italic> (Sonia, 42 years old).</p></disp-quote>", "<disp-quote><p id=\"Par79\">\n<italic>“…I had to go to the emergency room and that’s when they treated me. Then they told me “no, you have to go to your health center, and they have to give you a referral pass so that we can continue treating you”. So, I went back to my health center and they gave me the referral to the specialist a year and a half later…”</italic> (Cecilia, 28 years old).</p></disp-quote>", "<disp-quote><p id=\"Par82\">\n<italic>“I wanted to go back to the hospital, make my appointment again, but many commented that now you have to pay, many are commenting that now you have to pay even for the medicine”</italic> (Alejandra, 42 years old).</p></disp-quote>", "<disp-quote><p id=\"Par84\"><italic>“To get there we go in public transport, in a community taxi, for example to get to the Jiquipilco hospital, we have to go up the hill and from the hill we have to transfer to another community)</italic> (Nancy, 50 years old).</p></disp-quote>", "<disp-quote><p id=\"Par87\">“<italic>And sometimes, as we said before, the opinion of the husbands, of the family, influences the women too much, it really influences them a lot… maybe they want to go to the doctor, but if they are told “oh, don’t go , there is a neighbor who was cured with such thing (natural medicine), take this, go with a “healer”” so I think they don’t go to the doctor because of that”</italic> (Yolanda, 26 years old).</p></disp-quote>", "<disp-quote><p id=\"Par90\">\n<italic>“I think that although we have heard about BC, we need a lot of information, especially in BC, because for example, the test for cervical cancer is much more feasible, we know about the Pap smear”</italic> (Alejandra, 42 years old).</p></disp-quote>", "<disp-quote><p id=\"Par91\">\n<italic>“They have told us to explore our breasts ourselves, but how do we have to check them? We practically don’t know, maybe we touch a deformity but we don’t know if it is dangerous or not”</italic> (Teresa, 47 years old).</p></disp-quote>", "<disp-quote><p id=\"Par95\">\n<italic>“Because yes, fear usually paralyzes you, right? You say “Oh no, maybe I feel something and I’m imagining the worst…you don’t want to know…there are many people who, despite the fact that maybe it is something very simple, find it very difficult to go to the doctor. Yes, going for treatment, going for a check-up, maybe it is not serious, but maybe they are already thinking that it is something fatal”</italic> (Margarita, 50 years old).</p></disp-quote>", "<disp-quote><p id=\"Par99\">\n<italic>“It is very important that when we go to the health center we can be served, and that the doctors tell us what we should do, and it is also very important to be treated nicely so that we can trust doctors, and we are able to talk to them about what we feel or need.”</italic> (Alejandra, 42 years old).</p></disp-quote>", "<disp-quote><p id=\"Par103\">\n<italic>“It is important for women who have had it (cancer) that they talk about their experience because sometimes we see it on television but it is not the same, but if you know someone who had it and she talks about their story, how they lived it, that makes us more aware”</italic> (Maribel, 35 years old).</p></disp-quote>", "<disp-quote><p id=\"Par106\">“<italic>Um, cancer are tumors that are in your body, something dark that grows inside you and can contaminate your entire body”</italic> (Participant, 47 years old)</p><p id=\"Par107\">“<italic>You have to go to the doctor, because we always leave it for later, but then, sometimes, with the passage of time, and when you want to go to the doctor, well... it’s too late, it turns too complicated for you and that’s it”</italic> (María, 52 years old).</p></disp-quote>" ]

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[ "<table-wrap-foot><p>^a1 minimum monthly salary ~ 207 USD as defined by the Mexican government in 2021</p></table-wrap-foot>", "<fn-group><fn><p><bold>Publisher’s Note</bold></p><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn></fn-group>" ]

[ "<graphic xlink:href=\"12905_2023_2875_Fig1_HTML\" id=\"MO1\"/>", "<graphic xlink:href=\"12905_2023_2875_Fig2_HTML\" id=\"MO2\"/>" ]

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{ "acronym": [ "BC", "LMICs", "HICs", "CEDIPIEM", "SEM", "HBM", "HCQ", "POP", "MST", "KUS" ], "definition": [ "BC", "Low- and middle-income countries", "High-income countries", "The State Council for the Integral Development of Indigenous People (for its acronym in spanish)", "Social Ecological Model", "Health Belief Model", "Healthcare Quality", "“Progresa-Oportunidades-Prospera” (A social program in Mexico)", "Minerva Saldaña Téllez", "Karla Unger Saldaña" ] }

[ "<title>Background</title>", "<p id=\"Par53\">Thyroid eye disease (TED), also known as thyroid-associated ophthalmopathy (TAO) or Graves’ orbitopathy (GO), is the most common autoimmune orbital disease that affects 25–40% of patients with Graves’ disease and other thyroid disorders [##REF##34297684##1##, ##REF##20181974##2##].Based on the immune status and disease duration, the pathogenesis of TED can be divided into two phases: an active phase and an inactive phase [##REF##34297684##1##, ##REF##20181974##2##]. With TED progression, lesions develop in all orbital soft tissues [##REF##31889140##3##]. Additionally, TED could be classified into mild, moderate-to-severe, or sight-threatening, based on the evaluation of its clinical manifestations, such as visual acuity, proptosis, and upper eyelid retraction [##REF##34297684##1##]. Intravenous glucocorticoid (IVGC) therapy is the routinely recommended first-line treatment for active and moderate-to-severe TED, offering potent anti-inflammatory effects that could alleviate extraocular muscles (EOMs) edema and orbital lipid hyperplasia [##REF##34297684##1##, ##UREF##0##4##, ##REF##29162310##5##]. However, the therapy inevitably brings about risks and can result in side effects, such as hypertension, hyperglycemia, and osteoporosis [##REF##24128430##6##–##REF##27346786##8##]. Therefore, proper implementation of IVGC therapy is crucial to achieve maximum benefit and avoid ineffectiveness.</p>", "<p id=\"Par54\">The clinical activity score (CAS) has been used to classify the activity of TED in patients and prescribe IVGC therapy in those with an active status (CAS ≥ 3) [##REF##34297684##1##, ##REF##20181974##2##, ##UREF##0##4##]. However, CAS does not provide precise prediction, since it is only the record of an ocular inflammatory manifestation and the conceived painfulness, but the pathologic lesions in the posterior orbit are overlooked. In a previous study, based on the application of CAS as a criterion, 38·46% active TED patients (CAS ≥ 3) were determined to be unresponsive to IVGC, whereas 45·45% of the inactive patients (CAS &lt; 3) turned out responsive [##UREF##1##9##]. Due to its ability to reveal alterations throughout the orbital soft tissues, magnetic resonance imaging (MRI) has been increasingly utilized for TED examination, which effectively contributes markedly to disease activity assessment and therapy response prediction [##REF##31412229##10##, ##REF##11952043##11##]. T2-weighted imaging (T2WI) is a commonly used MRI sequence in clinical applications that provides anatomical and metabolic information of soft tissues [##UREF##2##12##, ##REF##2772184##13##]. The pathogenesis of the orbital tissues in TED could be clearly revealed on T2WI, characterized by inflammatory edema, chronic fibrosis, and fatty degeneration [##UREF##2##12##, ##REF##2772184##13##]. Despite the certain predictive value of signal intensity ratio (SIR) or other simple metrics on T2WI for IVGC therapy response, its effectiveness was found to be limited due to insufficient exploitation of images [##REF##35092642##14##]. Therefore, conventional semiquantitative measurements may not ideally meet the requirement of therapy response prediction.</p>", "<p id=\"Par55\">In recent years, radiomics analysis has emerged as a promising solution to this issue by extracting high-throughput quantitative features for further analysis and model construction [##REF##32060219##15##]. It is widely utilized in the field of oncology for the prediction of macrovascular invasion and recurrence [##REF##34917908##16##, ##REF##34927034##17##]. It was first applied in orbital disease by Duron et al. [##REF##32932375##18##] in 2021 to construct an MRI-derived radiomics model in differentiating benign from malignant orbital lesions. Hu et al. [##REF##35092642##14##] have constructed a radiomics model for IVGC response prediction based on the features extracted from EOMs bellies on T2WI, which behaved better than conventional semiquantitative imaging model (AUC = 0·916 vs. 0·745). However, the potential of radiomics for TED therapy response prediction could be further enhanced. Despite EOMs, other vital structures in the orbit, such as lacrimal gland (LG) [##REF##35997966##19##], orbital fat (OF) [##UREF##3##20##], and optic nerve (ON) [##UREF##4##21##], also undergo distinct changes in the pathogenesis of TED. The predictive value of these structures has been confirmed in several imaging studies [##REF##28845157##22##–##REF##32504380##24##]. Therefore, our investigation takes a step further in orbital radiomics analysis by integrating all orbital soft tissues to construct a more accurate and robust radiomics prediction model.</p>", "<p id=\"Par56\">Interestingly, similar strategies, namely multi-regional radiomics, have been explored in other human structures and diseases, which performed superior to single-regional radiomics [##REF##36300676##25##, ##UREF##5##26##]. To the best of our knowledge, no such techniques have been applied for investigations of the ocular orbit. Indeed, fine segmentation of orbital structures on MRI imaging is a challenging task due to its complicacy and considerable time cost. Hence, our study pioneered in this attempt. In order to process high-throughput data from complex segmentation, various machine learning (ML) algorithms were adopted in our study. Ultimately, we established whole-orbit radiomics (WOR) models for the prediction of IVGC response of patients with active, moderate-to-severe TED and attained satisfactory prediction results.</p>" ]

[ "<title>Methods</title>", "<title>Patients and clinical evaluations</title>", "<p id=\"Par57\">This manuscript adheres to STROBE guidelines. This retrospective study was approved by our Institutional Review Board (SH9H-2021-T246-2), and the requirement for informed consent was waived. Clinical and radiological data of 127 patients with clinically confirmed active and moderate-to-severe TED who had undergone MRI scans before IVGC treatment were collected from the hospital between June 2017 and June 2021. The inclusion criteria were as follows: (1) Patients aged 18–75 years, without complex systemic disease or other orbital disease; (2) High quality of MRI adequate for radiomics analysis; (3) Bilateral manifestation of TED; (4) Disease duration less than 18 months; (5) No previous orbital decompression surgery or radiotherapy, or administration of IVGC ≥ 1·0 g before MRI scans; (6) Patients received IVGC schedule according to standard EUGOGO guidelines (4·5 g, 12 weeks).</p>", "<p id=\"Par58\">The disease activity was evaluated by seven-point CAS, including: (1) Spontaneous retrobulbar pain; (2) pain on attempted up or down gaze; (3) redness of the eyelids; (4) redness of the conjunctiva; (5) swelling of the eyelids; (6) inflammation of the caruncle and/or plica; and (7) conjunctival edema. Patients with CAS &lt; 3 and inactive orbital MRI were categorized as inactive TED, and those with CAS ≥ 3 and active orbital MRI were categorized as active TED. If the indicated activity of CAS and MRI contradicted, an orbital disease specialist with 20 years of experience made a final judgment. The disease severity was assessed according to EUGOGO guidelines. Moderate-to-severe refers to those who met two or more of the following criteria: (1) lid retraction ≥ 2 mm; (2) moderate or severe soft-tissue involvement; (3) exophthalmos ≥ 3 mm above normal for race and gender; (4) inconstant or constant diplopia; without signs of sight-threatening conditions. Ophthalmic assessments for each eye were performed prior to and after the IVGC treatment schedule, including: (1) evaluation of CAS; (2) lid aperture; (3) exophthalmos assessment with a Hertel exophthalmometer; (4) best corrected visual acuity (BCVA); (5) intraocular pressure (IOP); (6) diplopia score. Thyroid-stimulating hormone receptor antibodies (TRAb) was measured before IVGC treatment. Restoration of euthyroidism was recorded if the thyroid-stimulating hormone, free triiodothyronine, and free thyroxine were within the normal range.</p>", "<p id=\"Par59\">Therapy response of IVGC treatment was assessed within three months after the last administration of IVGC. The definition of “responsive” and “unresponsive” was based on the standard proposed by Bartalena et al. [##REF##34297684##1##] The responsive group included those with an improvement of at least two of the following in one eye after treatment: (1) Reduction of lid aperture ≥ 2 mm; (2) Reduction of exophthalmos ≥ 3 mm; (3) Eye motility with an increase of ≥ 8°; (4) Reduction in five-item CAS (not including spontaneous or gaze-evoked pain) of ≥ 1 point; without concomitant deterioration in the other eye. Deterioration was defined by the occurrence of dysthyroid optic neuropathy (DON) or worsening of at least two of the four components mentioned above. The unresponsive group was composed of those who did not meet the aforementioned criteria.</p>", "<p id=\"Par60\">All patients included were allocated to a training cohort and a test cohort with a proportion of 8:2 using a stratified random splitting method. The flowchart of patient enrollment and the scheme for analysis is presented in Additional file ##SUPPL##0##1## Fig. S1.</p>", "<title>Orbital MRI acquisition</title>", "<p id=\"Par61\">Before the IVGC treatment schedule began, patients were examined using a 3·0 T MRI system (Ingenia CX, Philips Medical Systems) with a 32-channel head coil. During the scan, the patients were placed in the supine position with their eyes closed. Coronal T2-weighted Turbo Spin-Echo with 90° Flip-Back Pulse (T2-DRIVE) imaging was acquired, with the following parameters: repetition time/echo time, 3000/90 ms; field of view, 133·3 133·3 mm²; slice thickness, 3·5 mm; slices, 20; gap, 3·85 mm; acquisition matrix, 320 224. Figure ##FIG##0##1## depicts the workflow of the radiomics procedure.</p>", "<title>Radiomics analysis</title>", "<title>ROI segmentation</title>", "<p id=\"Par62\">Regions of interest (ROIs) were manually segmented on coronal T2WI using the ITK-SNAP software (v. 3.6.0; <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.itksnap.org\">www.itksnap.org</ext-link>). Two methods of ROI segmentation were employed (Fig. ##FIG##1##2##). The first approach, multi-organ segmentation (MOS) was applied to eight orbital structures, including LG, OF, ON, and separate EOMs: superior rectus (SR), inferior rectus (IR), medial rectus (MR), lateral rectus (LR), and superior oblique (SO). These ROIs were individually contoured using different labels. The contours of each ROIs were drawn slice-by-slice from the emergence of OF in the anterior orbit to the vanish of EOMs in the posterior orbit. Subsequently, four single-regional radiomics (SRR) models were constructed based on different structures (EOMs, LG, OF, and ON), and the dataset comprising all eight labels was later used to develop the multi-regional radiomics (MRR) model. The second approach, namely fused-organ segmentation (FOS) strategy using one single label was also implemented, which regarded all structures including EOMs, LG, OF, and ON as a cohesive unit. A fused-regional radiomics (FRR) model was later built on this basis. For all manual segmentation work, an experienced orbital radiologist (reader 1) viewed each MRI and conducted ROIs segmentations without knowing the disease status of the participants. Each segmented contour was further reviewed by an orbital radiology expert for accuracy. Discussions were held for any disagreement until a consensus on the final decision was reached.</p>", "<title>Feature extraction</title>", "<p id=\"Par63\">Radiomics features were extracted from ROIs using an in-house feature analysis program implemented in Pyradiomics (<ext-link ext-link-type=\"uri\" xlink:href=\"http://pyradiomics.readthedocs.io\">http://pyradiomics.readthedocs.io</ext-link>) for all radiomics models (SRR, MRR, and FRR models). Orbital structures from bilateral orbits of the same patient were considered as a unit, and the features were extracted in the meantime. All features were categorized into three groups: (1) geometry features, which described the three-dimensional shape characteristics of the ROIs; (2) intensity features, which described the first-order statistical distribution of the voxel intensities within the ROIs; and (3) texture features, which described the patterns or the second- and higher-order spatial distributions of the intensities. Specifically, to extract texture features, various methods were employed, including the gray-level co-occurrence matrix (GLCM), gray-level run length matrix (GLRLM), gray-level size zone matrix (GLSZM), and neighborhood gray-tone difference matrix (NGTDM) methods.</p>", "<title>Feature selection</title>", "<p id=\"Par64\">After feature extraction, reproducibility analysis, Mann–Whitney U-test, Spearman's rank correlation, max-relevance, min-redundancy (mRMR), and least absolute shrinkage and selection operator (LASSO) regression were consecutively performed to reduce the feature dimension for the different radiomics models. Initially, 40 cases were randomly chosen (20 of responsive and 20 of unresponsive), and their orbital MRI were segmented by reader 2 in the same manner as reader 1. Inter-reader variation of radiomics features was evaluated by calculating intraclass correlation coefficients (ICC) between the results from reader 1 and reader 2. Only features with ICC &gt; 0·75 were subjected to further analysis. Afterwards, a Mann–Whitney U-test was then employed to identify significant features between responsive and unresponsive groups, only those with a p-value &lt; 0·05 were kept. Then, the Spearman’s rank correlation coefficient was used to identify highly correlated features (Spearman’s correlation coefficient &gt; 0·9), with one of them randomly retained to avoid redundancy. To depict features to the greatest extent, greedy recursive deletion was applied for feature filtering, where the feature with the most redundancy in the current set was deleted each time. Subsequently, to avoid over-fitting and maximizing the correlation between features and target variables, the mRMR algorithm was implemented to select the top eight features for each label. Eventually, the LASSO regression model with tenfold cross test supported by Onekey AI platform was used for signature construction (Fig. ##FIG##2##3##a, b). The retained features with nonzero coefficients were used for regression model fitting and combined into a radiomics signature (Fig. ##FIG##2##3##c). The detailed rad score formulae of the models are provided in Additional file ##SUPPL##0##1##: Table S1.</p>", "<title>Radiomics signature construction</title>", "<p id=\"Par65\">SRR, MRR, and FRR models were individually constructed based on the datasets derived from corresponding ROIs as stated above. For all radiomics models, the final selected features were inputted into six robust classification algorithms supported by Onekey AI platform, including logistic regression (LR), NaiveBayes, support vector machines (SVM), extremely randomized trees (ExtraTrees), extreme gradient boosting (XGBoost), and light gradient boosting machine (LightGBM). A five-fold cross-validation was implemented to obtain the final radiomics signatures.</p>", "<title>Semiquantitative measurements and model construction</title>", "<p id=\"Par66\">Semiquantitative measurements on T2WI involved all eight orbital structures, including EOMs, LG, OF, and ON. Two radiologists (reader 1 and reader 2) independently implemented measurements without knowing the disease status of study participants. The signal intensity (SI) of EOMs, LG, and OF was measured by placing polygonal ROIs separately on EOM bellies, LG, and OF, locating the maximum cross-section on the coronal T2WI. The corresponding SI on the anterior and posterior layers of the selected layer of each region of these seven structures were also measured. For measurement of the SI of ON, ROIs were manually segmented on three consecutive layers behind the eyeball, and the surrounding cerebrospinal fluid signal was carefully avoided. For each orbital structure, the maximum, mean, and minimum of SI over the ROI were all extracted, and the final SI_max, SI_mean, SI_min were recorded as the mean value of SI derived from three consecutive layers. Later, they were normalized to SIR_max, SIR_mean, and SIR_min using the formula SIR = SI_EOM/SI_{brain white matter}.</p>", "<p id=\"Par67\">Inter-observer variation of measurements between the two observers was assessed by ICC. Then univariate analysis was adopted to test the difference of SIR_max, SIR_mean, and SIR_min between the responsive and the unresponsive groups. After screening features with P &lt; 0·05, identical six ML algorithms were employed to construct semiquantitative imaging models (SIR models) through five-fold cross-validation.</p>", "<title>Assessment and comparison of different prediction models</title>", "<p id=\"Par68\">The diagnostic performances of the radiomics and semiquantitative imaging models based on different ML algorithms were assessed using their receiver operating characteristic (ROC) curves. For each model, metrics including area under curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated. Internal validation of the prediction models was performed using an independent set. To compare the largest prediction capacity of different models, the ML algorithm with the highest AUC was finally selected for each model subset for further assessment and comparisons. DeLong’s test was applied to test the difference of diagnostic performance among different models. The calibration curves were depicted to assess the calibration of the prediction models. Decision curve analysis (DCA) was performed to evaluate the clinical usefulness of different models by calculating the net benefits at different threshold probabilities.</p>", "<title>Statistical analyses</title>", "<p id=\"Par69\">All statistical analyses were conducted using Python programming language (version 3.7.6) with the use of SciPy library (1.4.1) and Statsmodels module (v0.11.1). Statistical significance was set at a two-tailed P-value &lt; 0·05. For categorical data, the chi-squared test or Fisher’s exact test was applied to compare the difference between two groups. For numeric data, independent-sample t-test or Mann–Whitney U-test was implemented. Other statistical tools employed for analysis are specified above.</p>" ]

[ "<title>Results</title>", "<title>Clinical characteristics</title>", "<p id=\"Par70\">Of the 127 enrolled patients, 56 were identified as responsive to IVGC treatment, whereas 71 patients were unresponsive. The clinical characteristics of both groups are presented in Table ##TAB##0##1##, showing no significant differences in sex, age, or duration time. Univariate analysis revealed significant differences in smoking (P-value = 0·016), diplopia score (P-value = 0·031), CAS (P-value = 0·002), and lid aperture (P-value = 0·031) between the two groups.</p>", "<title>Radiomics model construction</title>", "<title>Single-regional radiomics (SRR) models</title>", "<p id=\"Par71\">Through MOS strategy, 1906 features were respectively extracted from the ROIs of EOMs, LG, OF, and ON. After feature selection, five, eight, five, and seven were finally retained, respectively. For each structure, the corresponding SRR models based on different ML algorithms performed diversely (Fig. ##FIG##3##4##). For each ML algorithm, EOM radiomics model and OF radiomics model had the best performance. The highest AUC of individual SRR models were achieved by XGBoost on the EOM radiomics model (AUC = 0·766), NaiveBayes on the LG radiomics model (AUC = 0·727), LR on the OF radiomics model (AUC = 0·766), and NaiveBayes on the ON radiomics model (AUC = 0·669), respectively. Details of diagnostic performance of SRR models can be found in Additional file ##SUPPL##0##1##: Table S2.</p>", "<title>Multi-regional radiomics (MRR) models</title>", "<p id=\"Par72\">For the construction of MRR models based on MOS strategy, 15,248 features were extracted from eight independent structures, and 35 were finally retained. Notably, the SVM model achieved remarkable performance, with the highest AUC value of 0·961 in the test cohort. The other models achieved good to excellent AUC values, with LR achieving 0·916, NaiveBayes achieving 0·893, and LightGBM achieving 0·883 (Fig. ##FIG##4##5##a, b).</p>", "<title>Fused-regional radiomics (FRR) models</title>", "<p id=\"Par73\">Through FOS strategy, 1906 features were extracted from the cohesive unit of orbital soft tissues and eight were included in the FRR models. All models achieved moderate to good AUC values, with LR achieving 0·916, NaiveBayes achieving 0·896, SVM achieving 0·903 (Fig. ##FIG##4##5##c, d).</p>", "<title>Semiquantitative model construction</title>", "<p id=\"Par74\">The inter-reader variation of semiquantitative SIRs was found to be good to excellent, with ICCs ranging from 0·766 to 0·893. Results of semiquantitative measurement were shown in Table ##TAB##1##2##. Models yielded moderate to good results, with most AUC values ranging from below 0·7 to a maximum of 0·760 achieved by the NaiveBayes algorithm (Additional file ##SUPPL##0##1##: Fig. S2).</p>", "<title>Comparison of different prediction models</title>", "<p id=\"Par75\">As is shown in Fig. ##FIG##5##6##a, radiomics models significantly outperformed semiquantitative imaging model. The WOR models in this study, including MRR (highest AUC = 0·961, SVM) and FRR models (highest AUC = 0·916, LR), had superior performance over all the SRR models, including the formally reported EOM radiomics model (AUC = 0.766) (Fig. ##FIG##5##6##a). The calibration curves and DCA provided additional supporting evidence to such conclusion (Fig. ##FIG##5##6##b, c). The MRR model based on SVM had the best performance as regards AUC, calibration, and net benefit. However, further analysis using Delong's test showed that the best performing MRR model based on SVM, and the best performing FRR model based on LR, did not have a significant difference in diagnostic performance (Fig. ##FIG##5##6##d). Considering the influence of ML algorithms, the comparison of multiple parameters of MRR models and FRR models utilizing the same ML algorithm is shown in Fig. ##FIG##6##7##. In most cases, the area of the radar chart of MRR is slightly larger than FRR. However, when utilizing NaiveBayes or ExtraTrees, the AUC of FRR is larger than that of MRR.</p>" ]

[ "<title>Discussion</title>", "<p id=\"Par76\">The preliminary application of radiomics analysis in orbital MRI offers a promising solution to the prediction of IVGC therapy response in TED. Nevertheless, radiomics is still underdeveloped in orbital diseases like TED with deficiency in methodology and practice. In this work, we established the WOR models as a credible and efficient tool to predict IVGC therapy response. The MOS strategy was applied to orbital MRI processing, which included all structures potentially affected in TED. An MRR model (AUC = 0·961) was constructed based on this strategy, reaching a predictive value much superior to SRR models (highest AUC = 0·766) and a conventional semiquantitative imaging model (AUC = 0·760). Besides, we proposed a FOS strategy and constructed an FRR model, as a feasible alternative mode of the WOR models and also achieved a satisfactory result (highest AUC = 0·916). To process high-throughput data, a series of ML algorithms were employed to construct different prediction models and the best was finally chosen. It is highly probable that WOR models will substantially benefit clinical decision-making of TED patients, and that MOS and FOS strategies might bring a new prospect for radiomics research for orbital disease and other disease models.</p>", "<p id=\"Par77\">The MOS strategy has emerged as a highly effective approach in radiomics analysis, as evidenced by a large number of previous studies. For instance, a recent investigation utilized the similar strategy to construct an MRR model that accurately assessed muscle invasion in bladder cancer, with an impressive AUC of 0·931 [##REF##36300676##25##]. Similarly, in cervical cancer, Shi et al. [##UREF##5##26##] partitioned tumors into two intratumoral subregions to create an MRR model, which were confirmed to be superior to the model based on the whole tumor (AUC = 0·817 vs. 0·562). However, the MOS segmentation is challenging, particularly in the orbital region due to the anatomical complexity. In our study, we incorporated the whole orbital soft tissues associated with the pathogenesis of TED on T2WI. By employing MOS strategy, the MRR model outperformed SRR models that solely relied on a single orbital structure. It serves as another promising application of MOS strategy in radiomics analysis, and the first attempt in orbital setting.</p>", "<p id=\"Par78\">Of the different SRR models, the EOM radiomics model and the OF radiomics model showed relatively good predictive performance, with the highest AUC values of 0·766 for both. As previous studies have suggested, the mechanism of TED pathogenesis is complicated since it affects multiple orbital structures [##REF##20181974##2##]. The pathogenesis of TED is primarily characterized by enlarged and edematous EOMs, making them the major affected structure. Multiple studies revealed that patients who respond well to IVGC have more homogeneous edema within their EOMs, while unresponsive patients exhibit greater tissue complexity and more fibrotic compounds [##REF##35092642##14##, ##REF##32504380##24##, ##UREF##6##27##, ##REF##24821102##28##]. Similarly, a previous TED radiomics study also constructed the model based on EOMs to predict IVGC therapy response [##REF##35092642##14##]. It is also worth noting that significant differences existed in SIR value of MR and IR between responsive and unresponsive groups in our study. These results prove again that MR and IR are the two primary rectus muscles altered during TED pathogenesis. Nevertheless, they fail to alter the fact that SIR models performed poorly in response prediction compared to MRR and SRR models. Despite EOMs, OF is also a vital morbid structure in the orbits of TED. The majority of patients have enlargement of EOMs or OF, with predominance of one or the other in some [##REF##20181974##2##]. The expansion of OF volume is caused by the accumulation of glycosaminoglycans and adipocytes, which is also the main therapeutic target of IVGC [##REF##18552385##29##, ##UREF##7##30##]. Previous MRI studies of TED had focused relatively less attention on OF, whereas our earlier studies added evidence to its predictive value in IVGC therapy response [##REF##28845157##22##]. Interestingly, SIR of OF showed no significant difference between the responsive and unresponsive groups, but it is under the premise that the SIR value concentrated on the value determined from a specific point on the structure. However, radiomics model took into account a wider spectrum of features, encompassing geometry, intensity, and texture features. With deeper investigation, detailed information of OF can be extracted and exploited for IVGC response prediction of TED, which was proved to be powerful.</p>", "<p id=\"Par79\">Apart from EOMs and OF, other structures including LG and ON were also of certain predictive value. The highest AUC value of the ON radiomics model was 0·727, while that of the LG radiomics model was 0·675, which was inferior to EOMs and OF. In TED, LG is also affected by immunological disorders in the orbit, characterized by multifocal infiltration of lymphocytes and hyperplasia of adipose tissue [##REF##18953897##31##]. These typical alterations of LG in TED are manifested on T2WI as increased volume and hyperintensity [##REF##27446267##32##]. The herniation of LG has been established to be associated with therapy response of IVGC, demonstrating its contribution to the predictive models [##REF##32504380##24##]. ON is mainly related to visual acuity, concerning the emergence of DON. Interestingly, a retrospective study detected an increased ON T2 value in TED compared with healthy controls [##REF##34642808##33##]. Other studies also indicated a potential correlation between ON and the severity and prognosis of TED. In this investigation, ON was also evidenced to be of predictive value of the IVGC response. Currently, the majority of the studies on activity assessment and response prediction have been focused on EOMs solely, neglecting other affected orbital soft tissues. This has probably attributed to the cognitive deficit, measurability limitations, and time cost. Although the orbital pathologies of different structures are not fully elucidated, and their correlation with radiomics features are scarcely uncovered, we revealed that involving multiple morbid structures in the orbit greatly enhanced the performance of our radiomics models.</p>", "<p id=\"Par80\">By including multiple structures, the MRR model achieved excellent predictive results, but its considerable segmentation efforts may limit the universal application due to the significant time cost. Compared with the EOM radiomics model (total average time, 15·8 min), the performance of the MRR model took much longer (total average time, 25·4 min) for each MRI sample. With the relatively low time cost (total average time, 10·2 min), the semiquantitative imaging model had a moderate predictive value, which was better than those of the ON and LG radiomics models (AUC = 0·760 vs. 0·727 and 0·675, respectively). This outcome was presumably attributable to the incorporation of the whole orbital soft tissue offering more conducive information compared with the single structures. However, the AUC value of semiquantitative imaging model was much inferior to MRR model, which cannot satisfy the requirement of accurate prediction. Therefore, we put forward an alternative WOR model, namely FRR model, which was based on the FOS strategy. When utilizing the same ML algorithms, the performance of FRR model and MRR model was approximate and MRR seemed slightly superior, with the highest AUC value of 0·916 and 0·961 (P-value = 0·468 on DeLong’s test) (Figs. ##FIG##3##4##a–f, ##FIG##5##6##d). It is reasonable that MRR outperformed FRR, in that fine segmentation according to priori knowledge is beneficial to image analysis. A recent radiomics investigation revealed that without segmentation masks, feature descriptors encompassed the entire image, which limited their effectiveness in focusing on ROI and leveraging the available prognostic information [##REF##35696469##34##]. This limitation, compounded by noise and the loss of local information regarding size, shape, and location, may have contributed to the slightly lower performance observed in the FRR models. Nevertheless, due to the limited sample size applied in this research, further validation with larger samples is necessary to determine whether the MRR model outperforms the FRR model. However, it is worth noting that the segmentation time cost of the FRR model (total average time of 9·6 min) was only 37·8% of that of the MRR model. This shows the potential of applying the FRR model for IVGC response prediction with higher efficiency. Future explorations of the automatic segmentation of different orbital structures might be of great value to resolving this issue.</p>", "<p id=\"Par81\">In the construction of the prediction models, the ML algorithms played a crucial role for achieving high accuracy and efficiency. However, it is important to consider the suitability of ML algorithms for the input dataset. Our research revealed a shift in the best performing algorithm types from SRR to MRR models. Simpler algorithms such as LR and NaiveBayes worked better in cases of straightforward mapping relationships in ON (Highest AUC = 0·669, NaiveBayes), OF (Highest AUC = 0·766, LR), and LG (Highest AUC = 0·675, NaiveBayes). On the other hand, the XGBoost algorithm showed the highest performance in the EOM dataset (AUC = 0·766) due to its ability to prevent overfitting through shrinkage and generalization features in datasets with multiple labels [##UREF##8##35##]. Notably, the SVM algorithm attained remarkable results with the highest AUC value of 0·961 in the MRR model. This was due to the fact that SVM was able to recognize and fit valuable underlying mapping effectively when more information was included in the feature datasets [##UREF##9##36##]. However, the high learning capacity of SVM also made it susceptible to overfitting, leading to poor performance in the semiquantitative imaging model and moderate performance in several SRR models. A deeper investigation of the application of ML algorithms in orbital MRI would provide more solid evidence by using larger datasets, which shall be explored in the future.</p>", "<p id=\"Par82\">Compared with other reported prediction models for IVGC response in TED, the accuracy of our models still needs to be improved. In addition to the potential drawbacks of radiomics analysis, this issue might be attributed to the disunity of the standards for patient enrollment and therapy response evaluations among different studies. The management of TED involves multidisciplinary effort, while many aspects of the diagnosis and treatment are unclear and controversial. For example, the patients in our cohorts met the comprehensive criteria of activity assessment considering CAS and orbital MRI. That is to say, patients with CAS lower than 3 but with actively altered orbital MRI were advised to receive IVGC therapy in our center but were excluded in other centers. This significantly affected the treatment outcome. In addition, the determination of “responsive” or “unresponsive” to anti-inflammatory treatment in TED varied markedly from one study to another. In the present investigation, we adopted a well-recognized evaluation standard proposed by Bartalena et al. [##REF##34297684##1##], integrating four important items of clinical presentations in a composite index. In former studies, usually an eye is perceived as a research object, while in our study, a patient with bilateral eyes were perceived as a research object. This makes our results more feasible for clinical practice. TED clinical management and research work urgently need standardization of evaluation, diagnosis, and treatment.</p>", "<p id=\"Par83\">The present study is a novel attempt to implement the concept of MOS/FOS and MRR/FRR in orbital MRI processing. However, it is only a preliminary exploration and further improvements are needed. First, the sample size of this retrospective study was relatively small, despite being the maximum in TED radiomics research works published to date. Thus, a larger sample size is expected to augment the reliability. Second, our models lack external validation. As TED management is highly complicated, the judgement of the activity of patients varies widely among centers, with different parameters for clinical measurements and MRI data acquisition, which makes it extremely challenging to integrate. This could potentially be tackled in the future by conducting a multicenter prospective study with unified metrics. While our study provides a new strategy for future research in this area, it is important to consider these limitations when interpreting our results.</p>" ]

[ "<title>Conclusions</title>", "<p id=\"Par84\">The results of this study revealed that radiomics models based on the whole orbital structures can accurately predict the response to IVGC in TED patients with the highest AUC of 0·961. Therefore, the MRR model is a reliable and effective tool for outcome prediction. The FRR model performed very well in reducing the time consumption of segmentation while preserving a rather satisfactory prediction value; thus, it can be applied as an alternative. The findings of our study could considerably contribute to the accurate prediction of responsive or unresponsive TED patients and allow for individualized management and therapy decisions, leading to improved patient prognosis and quality of life. In the meantime, the WOR strategy can be generalized to the application of other orbital diseases.</p>" ]

[ "<title>Background</title>", "<p id=\"Par1\">Radiomics analysis of orbital magnetic resonance imaging (MRI) shows preliminary potential for intravenous glucocorticoid (IVGC) response prediction of thyroid eye disease (TED). The current region of interest segmentation contains only a single organ as extraocular muscles (EOMs). It would be of great value to consider all orbital soft tissues and construct a better prediction model.</p>", "<title>Methods</title>", "<p id=\"Par2\">In this retrospective study, we enrolled 127 patients with TED that received 4·5 g IVGC therapy and had complete follow-up examinations. Pre-treatment orbital T2-weighted imaging (T2WI) was acquired for all subjects. Using multi-organ segmentation (MOS) strategy, we contoured the EOMs, lacrimal gland (LG), orbital fat (OF), and optic nerve (ON), respectively. By fused-organ segmentation (FOS), we contoured the aforementioned structures as a cohesive unit. Whole-orbit radiomics (WOR) models consisting of a multi-regional radiomics (MRR) model and a fused-regional radiomics (FRR) model were further constructed using six machine learning (ML) algorithms.</p>", "<title>Results</title>", "<p id=\"Par3\">The support vector machine (SVM) classifier had the best performance on the MRR model (AUC = 0·961). The MRR model outperformed the single-regional radiomics (SRR) models (highest AUC = 0·766, XGBoost on EOMs, or LR on OF) and conventional semiquantitative imaging model (highest AUC = 0·760, NaiveBayes). The application of different ML algorithms for the comparison between the MRR model and the FRR model (highest AUC = 0·916, LR) led to different conclusions.</p>", "<title>Conclusions</title>", "<p id=\"Par4\">The WOR models achieved a satisfactory result in IVGC response prediction of TED. It would be beneficial to include more orbital structures and implement ML algorithms while constructing radiomics models. The selection of separate or overall segmentation of orbital soft tissues has not yet attained its final optimal result.</p>", "<title>Supplementary Information</title>", "<p>The online version contains supplementary material available at 10.1186/s12967-023-04792-2.</p>", "<title>Keywords</title>" ]

[ "<title>Supplementary Information</title>", "<p>\n</p>" ]

[ "<title>Acknowledgements</title>", "<p>We would like to express our gratitude to the technical professionals from Shanghai Medoo Tech Company. We also extend our gratitude to Ms. Qingwen Tang and Ms. Qi Zheng for their help in data collation.</p>", "<title>Author contributions</title>", "<p>HYZ, HFZ, XQF, and XFS contributed to the overall conception and design development. HYZ, MDJ, LZ, XFT, YWL, and JS were responsible for data collection and interpretation. HCC, HJZ, JSX, and YTL proofread the data. HYZ, MDJ, HCC, HJZ, DJX, and LZ performed data analysis. HYZ, HCC, HJZ, and JSX completed the manuscript drafting. MDJ, YTL, LZ, XFT, DJX, LZ, YWL, JS, XFS, XQF, and HFZ edited and reviewed the manuscript. All authors read, discussed, and approved the final version of the manuscript. All authors had full access to the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis, as well as the decision to submit this manuscript for publication.</p>", "<title>Funding</title>", "<p>This work was supported by the National Natural Science Foundation of China (81930024, and 82271122); the Science and Technology Commission of Shanghai (20DZ2270800); Shanghai Key Clinical Specialty, Shanghai Eye Disease Research Center (2022ZZ01003); Clinical Acceleration Program of Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine (JYLJ202202); and Cross disciplinary Research Fund of Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine (JYJC202115).</p>", "<title>Availability of data and materials</title>", "<p>The datasets generated and analyzed during the current study are available by the corresponding author Huifang Zhou upon reasonable request.</p>", "<title>Declarations</title>", "<title>Ethics approval and consent to participate</title>", "<p id=\"Par85\">This retrospective study was approved by our Institutional Review Board (SH9H-2021-T246-2), and the requirement for informed consent was waived.</p>", "<title>Consent for publication</title>", "<p id=\"Par86\">All authors have approved the manuscript for submission.</p>", "<title>Competing interests</title>", "<p id=\"Par87\">The authors declare no potential competing interests related to this work.</p>" ]

[ "<fig id=\"Fig1\"><label>Fig. 1</label><caption><p>Radiomics workflow</p></caption></fig>", "<fig id=\"Fig2\"><label>Fig. 2</label><caption><p>Illustration of the two segmentation strategies on the T2WI. The MOS strategy for the construction of multiple SRR and MRR models is presented in plot <bold>a</bold>, and the FOS strategy for the construction of FRR model is depicted in plot <bold>b</bold></p></caption></fig>", "<fig id=\"Fig3\"><label>Fig. 3</label><caption><p>Feature screening for MRR model. Plot shows the coefficients <bold>a</bold> and MSE <bold>b</bold> of LASSO regression model and features selected for model construction <bold>c</bold></p></caption></fig>", "<fig id=\"Fig4\"><label>Fig. 4</label><caption><p>Color maps demonstrating the diagnostic performance of different SRR models (EOM, OF, LG, or ON radiomic models) when utilizing different ML algorithms <bold>a–f</bold>. Colors depicted on each structure represent the AUC of corresponding SRR model based on a specific ML algorithm</p></caption></fig>", "<fig id=\"Fig5\"><label>Fig. 5</label><caption><p>Predictive performance of the MRR (Multi-regional radiomics) and FRR (Fused-regional radiomics) models in the training and test cohorts. The ROC curves of MRR model in training cohort <bold>a</bold> and test cohort <bold>b</bold>; the ROC curves of FRR model in training cohort <bold>c</bold> and test cohort <bold>d</bold></p></caption></fig>", "<fig id=\"Fig6\"><label>Fig. 6</label><caption><p>The result and evaluation of prediction models in the test cohort. <bold>a</bold> The ROC curves of different radiomics models and SIR model based on the machine learning algorithms that achieved the highest AUC value. <bold>b</bold> DeLong’s test comparing the diagnostic performance (AUC) of different models. Calibration curves <bold>c</bold> and DCA <bold>d</bold> of different models. <italic>MRR</italic> Multi-regional radiomics, <italic>FRR</italic> Fused-regional radiomics</p></caption></fig>", "<fig id=\"Fig7\"><label>Fig. 7</label><caption><p>Radar chart of the performance of MRR (Multi-regional radiomics) models and FRR (Fused-regional radiomics) models by using different machine learning algorithms (<bold>a</bold>–<bold>f</bold>)</p></caption></fig>" ]

[ "<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Demographic and clinical characteristics of TED patients and controls</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Characteristics</th><th align=\"left\">Responsive</th><th align=\"left\">Unresponsive</th><th align=\"left\">P-value</th></tr></thead><tbody><tr><td align=\"left\" colspan=\"3\">Sex</td><td char=\".\" align=\"char\">0.093</td></tr><tr><td align=\"left\"> Male</td><td align=\"left\">28 (22.4%)</td><td align=\"left\">25 (20.0%)</td><td char=\".\" align=\"char\"/></tr><tr><td align=\"left\"> Female</td><td align=\"left\">28 (22.4%)</td><td align=\"left\">46 (36.8%)</td><td char=\".\" align=\"char\"/></tr><tr><td align=\"left\"> Age (year)</td><td align=\"left\">47.10 ± 10.32</td><td align=\"left\">44.89 ± 11.24</td><td char=\".\" align=\"char\">0.891</td></tr><tr><td align=\"left\">Disease duration (month)</td><td align=\"left\">6.00 (3.00, 12.00)</td><td align=\"left\">6.00 (3.50, 14.50)</td><td char=\".\" align=\"char\">0.140</td></tr><tr><td align=\"left\" colspan=\"3\">Smoking</td><td char=\".\" align=\"char\">0.016*</td></tr><tr><td align=\"left\"> Yes</td><td align=\"left\">21 (16.8%)</td><td align=\"left\">13 (10.4%)</td><td char=\".\" align=\"char\"/></tr><tr><td align=\"left\"> No</td><td align=\"left\">33 (26.4%)</td><td align=\"left\">55 (44.0%)</td><td char=\".\" align=\"char\"/></tr><tr><td align=\"left\" colspan=\"3\">Restoration of euthyroidism</td><td char=\".\" align=\"char\">0.542</td></tr><tr><td align=\"left\"> Yes</td><td align=\"left\">30 (24.0%)</td><td align=\"left\">41 (32.8%)</td><td char=\".\" align=\"char\"/></tr><tr><td align=\"left\"> No</td><td align=\"left\">23 (18.4%)</td><td align=\"left\">25 (20.0%)</td><td char=\".\" align=\"char\"/></tr><tr><td align=\"left\"> TRAb (IU/L)</td><td align=\"left\">10.41 ± 12.73</td><td align=\"left\">10.82 ± 11.16</td><td char=\".\" align=\"char\">0.460</td></tr><tr><td align=\"left\"> CAS</td><td align=\"left\">2.50 (1.50, 3.00)</td><td align=\"left\">1.00 (1.00, 3.00)</td><td char=\".\" align=\"char\">0.002*</td></tr><tr><td align=\"left\"> Diplopia score</td><td align=\"left\">1.59 ± 1.14</td><td align=\"left\">1.30 ± 1.12</td><td char=\".\" align=\"char\">0.034*</td></tr><tr><td align=\"left\"> Exophthalmos (mm)</td><td align=\"left\">19.10 ± 2.53</td><td align=\"left\">18.24 ± 2.80</td><td char=\".\" align=\"char\">0.066</td></tr><tr><td align=\"left\"> Lid aperture (mm)</td><td align=\"left\">10.04 ± 1.46</td><td align=\"left\">9.36 ± 1.61</td><td char=\".\" align=\"char\">0.031*</td></tr><tr><td align=\"left\"> IOP (mmHg)</td><td align=\"left\">18.41 ± 3.55</td><td align=\"left\">18.25 ± 3.18</td><td char=\".\" align=\"char\">0.612</td></tr><tr><td align=\"left\"> BCVA</td><td align=\"left\">0.86 ± 0.24</td><td align=\"left\">0.87 ± 0.23</td><td char=\".\" align=\"char\">0.849</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab2\"><label>Table 2</label><caption><p>Semiquantitative SIR values of different orbital soft tissues in TED patients</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Characteristics</th><th align=\"left\">Responsive</th><th align=\"left\">Unresponsive</th><th align=\"left\">P-value</th></tr></thead><tbody><tr><td align=\"left\">LG-SIR_mean</td><td char=\".\" align=\"char\">1.66 ± 0.27</td><td char=\".\" align=\"char\">1.61 ± 0.3</td><td char=\".\" align=\"char\">0.30</td></tr><tr><td align=\"left\">LG-SIR_max</td><td char=\".\" align=\"char\">2.03 ± 0.39</td><td char=\".\" align=\"char\">1.93 ± 0.44</td><td char=\".\" align=\"char\">0.22</td></tr><tr><td align=\"left\">LG-SIR_min</td><td char=\".\" align=\"char\">1.34 ± 0.25</td><td char=\".\" align=\"char\">1.31 ± 0.26</td><td char=\".\" align=\"char\">0.57</td></tr><tr><td align=\"left\">LR-SIR_mean</td><td char=\".\" align=\"char\">1.21 ± 0.29</td><td char=\".\" align=\"char\">1.19 ± 0.23</td><td char=\".\" align=\"char\">0.43</td></tr><tr><td align=\"left\">LR-SIR_max</td><td char=\".\" align=\"char\">1.45 ± 0.30</td><td char=\".\" align=\"char\">1.43 ± 0.29</td><td char=\".\" align=\"char\">0.59</td></tr><tr><td align=\"left\">LR-SIR_min</td><td char=\".\" align=\"char\">0.97 ± 0.22</td><td char=\".\" align=\"char\">0.95 ± 0.19</td><td char=\".\" align=\"char\">0.47</td></tr><tr><td align=\"left\">SR-SIR_mean</td><td char=\".\" align=\"char\">1.37 ± 0.41</td><td char=\".\" align=\"char\">1.39 ± 0.37</td><td char=\".\" align=\"char\">0.91</td></tr><tr><td align=\"left\">SR-SIR_max</td><td char=\".\" align=\"char\">1.70 ± 0.44</td><td char=\".\" align=\"char\">1.70 ± 0.44</td><td char=\".\" align=\"char\">0.82</td></tr><tr><td align=\"left\">SR-SIR_min</td><td char=\".\" align=\"char\">1.05 ± 0.40</td><td char=\".\" align=\"char\">1.06 ± 0.35</td><td char=\".\" align=\"char\">0.93</td></tr><tr><td align=\"left\">MR-SIR_mean</td><td char=\".\" align=\"char\">1.42 ± 0.40</td><td char=\".\" align=\"char\">1.28 ± 0.28</td><td char=\".\" align=\"char\">0.01*</td></tr><tr><td align=\"left\">MR-SIR_max</td><td char=\".\" align=\"char\">1.66 ± 0.44</td><td char=\".\" align=\"char\">1.52 ± 0.31</td><td char=\".\" align=\"char\">0.02*</td></tr><tr><td align=\"left\">MR-SIR_min</td><td char=\".\" align=\"char\">1.20 ± 0.39</td><td char=\".\" align=\"char\">1.06 ± 0.29</td><td char=\".\" align=\"char\">0.01*</td></tr><tr><td align=\"left\">IR-SIR_mean</td><td char=\".\" align=\"char\">1.51 ± 0.39</td><td char=\".\" align=\"char\">1.39 ± 0.33</td><td char=\".\" align=\"char\">0.03*</td></tr><tr><td align=\"left\">IR-SIR_max</td><td char=\".\" align=\"char\">1.80 ± 0.45</td><td char=\".\" align=\"char\">1.66 ± 0.39</td><td char=\".\" align=\"char\">0.05</td></tr><tr><td align=\"left\">IR-SIR_min</td><td char=\".\" align=\"char\">1.22 ± 0.37</td><td char=\".\" align=\"char\">1.12 ± 0.29</td><td char=\".\" align=\"char\">0.04*</td></tr><tr><td align=\"left\">SO-SIR_mean</td><td char=\".\" align=\"char\">1.35 ± 0.28</td><td char=\".\" align=\"char\">1.36 ± 0.31</td><td char=\".\" align=\"char\">0.74</td></tr><tr><td align=\"left\">SO-SIR_max</td><td char=\".\" align=\"char\">1.81 ± 1.29</td><td char=\".\" align=\"char\">1.72 ± 0.39</td><td char=\".\" align=\"char\">0.42</td></tr><tr><td align=\"left\">SO-SIR_min</td><td char=\".\" align=\"char\">0.97 ± 0.26</td><td char=\".\" align=\"char\">0.99 ± 0.29</td><td char=\".\" align=\"char\">0.06</td></tr><tr><td align=\"left\">ON-SIR_mean</td><td char=\".\" align=\"char\">1.25 ± 0.21</td><td char=\".\" align=\"char\">1.20 ± 0.21</td><td char=\".\" align=\"char\">0.13</td></tr><tr><td align=\"left\">ON-SIR_max</td><td char=\".\" align=\"char\">1.69 ± 1.08</td><td char=\".\" align=\"char\">1.59 ± 0.8</td><td char=\".\" align=\"char\">0.86</td></tr><tr><td align=\"left\">ON-SIR_min</td><td char=\".\" align=\"char\">0.96 ± 0.20</td><td char=\".\" align=\"char\">0.91 ± 0.18</td><td char=\".\" align=\"char\">0.08</td></tr><tr><td align=\"left\">OF-SIR_mean</td><td char=\".\" align=\"char\">2.61 ± 0.35</td><td char=\".\" align=\"char\">2.60 ± 0.43</td><td char=\".\" align=\"char\">0.92</td></tr><tr><td align=\"left\">OF-SIR_max</td><td char=\".\" align=\"char\">2.95 ± 0.54</td><td char=\".\" align=\"char\">2.96 ± 0.50</td><td char=\".\" align=\"char\">0.48</td></tr><tr><td align=\"left\">OF-SIR_min</td><td char=\".\" align=\"char\">2.27 ± 0.36</td><td char=\".\" align=\"char\">2.27 ± 0.45</td><td char=\".\" align=\"char\">0.95</td></tr></tbody></table></table-wrap>" ]

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[ "<supplementary-material content-type=\"local-data\" id=\"MOESM1\"></supplementary-material>" ]

[ "<table-wrap-foot><p>Continuous variables are presented as the mean (± standard deviation) or as the median (interquartile range). Categorical variables are presented as the number (%) and counts</p><p>*P-value &lt; 0.05</p></table-wrap-foot>", "<table-wrap-foot><p>Continuous variables are presented as the mean (± standard deviation)</p><p>*P-value &lt; 0.05</p></table-wrap-foot>", "<fn-group><fn><p><bold>Publisher's Note</bold></p><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn><fn><p>Haiyang Zhang and Mengda Jiang have contributed equally to this work and share first authorship.</p></fn></fn-group>" ]

[ "<graphic xlink:href=\"12967_2023_4792_Fig1_HTML\" id=\"MO1\"/>", "<graphic xlink:href=\"12967_2023_4792_Fig2_HTML\" id=\"MO2\"/>", "<graphic xlink:href=\"12967_2023_4792_Fig3_HTML\" id=\"MO3\"/>", "<graphic xlink:href=\"12967_2023_4792_Fig4_HTML\" id=\"MO4\"/>", "<graphic xlink:href=\"12967_2023_4792_Fig5_HTML\" id=\"MO5\"/>", "<graphic xlink:href=\"12967_2023_4792_Fig6_HTML\" id=\"MO6\"/>", "<graphic xlink:href=\"12967_2023_4792_Fig7_HTML\" id=\"MO7\"/>" ]

[ "<media xlink:href=\"12967_2023_4792_MOESM1_ESM.pdf\"><caption><p><bold>Additional file 1: </bold><bold>Fig. S1.</bold> The flowchart of patient enrollment and scheme for analysis. <bold>Fig. S2. </bold>Performances of SIR models using six machine learning algorithms in the test cohort were evaluated and compared through ROC curves. <bold>Table S1.</bold> The Rad score formula used in model performance. <bold>Table S2.</bold> Diagnostic performance of different SRR models.</p></caption></media>" ]

{ "acronym": [ "AUC", "BCVA", "CAS", "DCA", "DON", "EOMs", "ExtraTrees", "FOS", "FRR", "GLCM", "GLRLM", "GLSZM", "GO", "ICC", "IOP", "IR", "IVGC", "LASSO", "LG", "LightGBM", "LR", "ML", "MOS", "MR", "MRI", "mRMR", "MRR", "NGTDM", "NPV", "OF", "ON", "PPV", "ROC", "ROIs", "SI", "SIR", "SO", "SR", "SRR", "SVM", "T2-DRIVE", "T2WI", "TAO", "TED", "TRAb", "WOR", "XGBoost" ], "definition": [ "Area under curve", "Best corrected visual acuity", "Clinical activity score", "Decision curve analysis", "Dysthyroid optic neuropathy", "Extraocular muscles", "Extremely randomized trees", "Fused-organ segmentation", "Fused-regional radiomics", "Gray-level co-occurrence matrix", "Gray-level run length matrix", "Gray-level size zone matrix", "Graves’ orbitopathy", "Intraclass correlation coefficients", "Intraocular pressure", "Inferior rectus", "Intravenous glucocorticoid", "Least absolute shrinkage and selection operator", "Lacrimal gland", "Light gradient boosting machine", "Logistic regression", "Machine learning", "Multi-organ segmentation", "Medial rectus", "Magnetic resonance imaging", "Max-relevance and min-redundancy", "Multi-regional radiomics", "Neighborhood gray-tone difference matrix", "Negative predictive value", "Orbital fat", "Optic nerve", "Positive predictive value", "Receiver operating characteristic", "Regions of interest", "Signal intensity", "Signal intensity ratio", "Superior oblique", "Superior rectus", "Single-regional radiomics", "Support vector machines", "Coronal T2-weighted Turbo Spin-Echo with 90° Flip-Back Pulse", "T2-weighted imaging", "Thyroid-associated ophthalmopathy", "Thyroid eye disease", "Thyroid-stimulating hormone receptor antibodies", "Whole-orbit radiomics", "Extreme gradient boosting" ] }

[ "<title>Background</title>", "<p id=\"Par17\">Breast fibroadenomas are common, benign fibro-epithelial lesions [##REF##29248888##1##], which are frequently encountered in adolescent girls and young women [##REF##14553851##2##–##UREF##1##4##]. About 10% of women have such symptoms in their lifetime, accounting for 67–94% of all breast biopsies in women under the age of 20 years [##REF##17572539##5##, ##REF##10384810##6##]. Although mammography is the gold standard in the detection and evaluation of masses in the breast, sonography has become an indispensable imaging modality because of its technical advantages of non-ionization, low cost, mobility, and real-time diagnosis [##REF##35138938##7##]. However, the low spatial resolution and image quality of sonography make it hard to extract tissue morphological features accurately and reliably. Breast sonography is thus highly operator-dependent and has a high inter-observer variation rate [##REF##29107353##8##]. To reduce the burden for the radiologist in reviewing hundreds of clinical images and improve the accuracy of diagnosis, computer-assisted image segmentation becomes valuable.</p>", "<p id=\"Par18\">Deep learning approaches are increasingly being used for medical image segmentation and quantitative information regarding the morphology and textural features of lesions [##REF##33936986##9##]. Several neural network architectures developed from convolutional neural networks (CNNs) have shown satisfactory segmentation performance. However, breast ultrasound (BUS) image segmentation is still challenging due to high speckle noise, a low contrast, blurry boundaries, and intensity inhomogeneity in sonography [##REF##28070777##10##]. Therefore, precisely segmenting breast fibroadenoma in sonography requires extensive investigation, and deep learning models with the capability of processing more complex textures, focusing on the most important features, increasing robustness, and having noise immunity are preferred.</p>", "<p id=\"Par19\">There have also been various attempts to incorporate domain knowledge into neural networks in medical image analysis, such as diagnosis, detection, and segmentation [##REF##33588117##11##]. Some notable approaches include transfer learning, teacher–student course learning, and combined attention maps. Transfer learning involves leveraging knowledge from natural images to guide medical image analysis. By using a pre-trained network as a fixed feature extractor, knowledge can be transferred between image domains. Although these approaches are promising, simulating the natural learning process observed in humans may be another strategy in deep learning. Humans typically break down complex information into smaller, manageable chunks during learning and then integrate them according to their inherent relationships, which facilitates the learning of large-scale information more effectively. Thus, we propose a human instinct learning paradigm that involves feature fragmentation and information aggregation as a guide for neural network learning to enhance the segmentation performance of breast fibroadenoma in sonography. The workflow of our proposed learning paradigm is shown in Fig. ##FIG##0##1##.</p>", "<p id=\"Par20\">In this study, we propose an efficient paradigm that emulates the intuitive human learning mechanisms within an artificial neural network to guide the segmentation of breast fibroadenomas in sonography. Feature fragmentation attention modules (Focus, BottleneckCSP, and C3ECA) and information aggregation modules (LogSparse Attention, C3CBAM, and ProbSparse Attention) were selected and specifically tailored to the characteristics of ultrasound images. A dataset of breast ultrasound images of Asian women at Suining Central Hospital, China was constructed, and then the validation and performance of our proposed lightweight model were tested and evaluated on both local and public datasets. Furthermore, our approach was compared with other state-of-the-art (SOTA) methods to confirm its superior segmentation performance.</p>", "<title>Related works</title>", "<title>Deep learning-based network</title>", "<p id=\"Par21\">U-Net is one of the most popular and outstanding networks [##UREF##2##12##]. However, it cannot learn global and long-range semantic information interaction well due to the locality of the convolution operation. The self-attention mechanism and sequence-to-sequence design of transformers work effectively in the global extraction of contextual information, being extensively successful in natural language processing (NLP) [##UREF##3##13##]. Cao et al. proposed a pure transformer-based U-shaped encoder–decoder model for medical image segmentation [##UREF##4##14##]. Furthermore, the CNN–transformer hybrid network demonstrates great segmentation performance. Schlemper et al. integrated the attention gate module into the encoder–decoder design of the U-shaped architecture [##REF##30802813##15##]. In addition, TransUNet combines the best features of CNN in processing high-dimensional data with the transformer’s ability to capture location and contextual information [##UREF##5##16##]. This model can hold more than 100B trainable parameters, but at a significantly increased computational burden [##UREF##6##17##, ##UREF##7##18##].</p>", "<title>Breast ultrasound image segmentation</title>", "<p id=\"Par22\">Deep CNNs have been applied to lesion segmentation in BUS images [##REF##33631497##19##, ##UREF##8##20##]. A fuzzy CNN model incorporating data enhancement as well as fine-tuning post-processing is proposed by Huang et al. for BUS image segmentation [##UREF##9##21##]. Xue et al. designed a neural network with a global guidance module as well as a breast lesion boundary detection module, whose outcomes are further optimized by pre-defined regularization conditions to improve the segmentation accuracy [##UREF##10##22##]. Similarly, Lei et al. introduced boundary regularization into deep convolutional encoder–decoder networks to reduce the influences of noise and other factors on BUS images [##UREF##11##23##]. Abdelali et al. presented an automated CAD system for breast cancer detection and classification in mammography utilizing multiple instance learning (MIL) algorithms in decision-making [##UREF##12##24##]. Furthermore, suspicious regions were assessed in screening mammography at an impressive sensitivity of 98.60% using a modified K-means algorithm for region segmentation and bi-dimensional empirical mode decomposition (BEMD) for feature extraction [##UREF##13##25##].</p>", "<title>Attention mechanisms</title>", "<p id=\"Par23\">Attention mechanisms that emulate human perception have recently been introduced to neural networks [##UREF##14##26##] to select the more critical features from an extremely large amount of information by classifying feature maps channel-by-channel [##UREF##15##27##]. There are two main classes in the practice. The first class chunks the feature information channel-by-channel and then reassembles it. The Focus module, derived from Yolo v5 [##UREF##16##28##], reconstructs low-resolution images by selecting pixels from the original one and stacking adjacent pixels using dilated convolution [##UREF##17##29##]. This approach is also adopted by the Concentration-Comprehensive Convolution (C3) block [##UREF##18##30##], which enhances the network depth while reducing the computational complexity. For further optimization, a Bottleneck module replaces a single large-sized convolution with multiple small-sized convolutions [##UREF##19##31##]. Combining it with the cross-stage partial network (CSPNet) gives rise to the BottleneckCSP module to improve memory consumption and learning efficiency [##UREF##20##32##]. These findings suggest that piecewise learning of feature information can enhance model efficiency.</p>", "<p id=\"Par24\">The second class of attention mechanisms aims to enhance the extraction of semantic information by encoding the feature map’s location information for contextual perception. The Efficient Channel Attention (ECA) module employs a local cross-channel interaction strategy to facilitate information interaction [##UREF##21##33##]. The LogSparse Transformer addresses the prediction accuracy of time series with fine-grained and strong long-term dependencies within memory constraints [##UREF##22##34##]. The Convolutional Block Attention Module (CBAM) provides attention weights in both channel and spatial dimensions, aiding in the extraction of effective target features [##UREF##23##35##]. Additionally, a new formulation of attention through the kernel lens provides a deeper understanding of attention components and enhances the dynamics and utilization of the transformer's multi-headed self-attention mechanism [##UREF##19##31##]. Despite the promising performance of these attention modules, their application in medical imaging is still limited.</p>", "<title>Knowledge-based methods</title>", "<p id=\"Par25\">Maicas et al. proposed a teacher–student curriculum learning strategy that mimics more challenging tasks, such as breast image classification for dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) [##UREF##24##36##]. Emulating this process in the neural network training improved the classification performance by 5.88% compared to the baseline, DenseNet. An attention-based CNN for glaucoma detection, AG-CNN, combined attention maps during the supervised training process and simulates the physicians’ focus on regions of interest [##UREF##25##37##]. This method allows the network to learn from the attention patterns of medical professionals and enhance its ability to identify the most relevant features. Hsu et al. incorporated the existing BI-RADS (Breast Imaging Reporting and Data System) score [##REF##19945040##38##] as the knowledge to guide the neural network in learning the texture and intensity features of BUS images [##REF##30617720##39##]. A learning paradigm that goes beyond the confines of medical knowledge to guide neural networks in medical image processing and uses human learning paradigm may provide a more versatility.</p>" ]

[ "<title>Methods</title>", "<title>Image data set</title>", "<p id=\"Par38\">The dataset consists of clinical breast ultrasound images, including both our local dataset and a supplementary one using publicly available sources. The local dataset was collected retrospectively at Suining Central Hospital in Sichuan, China. The images were acquired from patients who underwent breast ultrasound examinations between January and July 2022. During the image acquisition, a sonographer systematically scanned the outer lower, outer upper, inner upper, and inner lower quadrants of the breast in a clockwise manner. Suspected lesions were analyzed, and the periareolar area and armpit were examined to determine the location and size of the lesions at both sagittal and cross-sectional viewing angles. Multiple ultrasound images were acquired for each fibroadenoma case according to the standard protocols. The image acquisition was performed by professional sonographers using a DC-80S system (Mindray Medical, Shenzhen, China). Overall, our local dataset comprises 600 breast ultrasound images obtained from 30 patients. Table ##TAB##4##5## summarizes the basic information of all patients included in our local dataset. To supplement our local dataset and further validate the validity and robustness of our model, a professional sonographer selected benign fibroadenoma images from publicly available datasets, Dataset_BUSI [##UREF##25##37##] and DatasetB [##REF##19945040##38##], and excluded those with ambiguous performance. Finally, 207 images from Dataset_BUSI and 39 images from DatasetB with their corresponding labels were merged as a public dataset. To ensure proper segmentation evaluation, all images on both local and public datasets are randomly divided into training and test sets in a 5:1 ratio in our experiments.</p>", "<title>Network architecture</title>", "<p id=\"Par39\">To implement the segmentation of breast fibroadenomas in sonography, we designed a model based on the framework of TransUNet, utilizing an encoder–decoder architecture (see Fig. ##FIG##7##8##). The process begins by reshaping the input image into a series of 2D patches using the patch partition parts. These patches are then vectorized and mapped to an embedding space using a trainable linear projection while preserving the positional information. Patch merging and expanding are responsible for downsampling and upsampling tasks, respectively. In Fig. ##FIG##7##8##, the dimensional changes of the input feature map are annotated. During each downsampling, the width and height are halved, while the number of channels is doubled (from to ). Conversely, in the upsampling process, the dimensional changes occur in the opposite direction. Typically, feature maps do not change in dimensions after being processed by the transformer layer. However, incorporation of the human learning paradigm within the transformer layer introduces dimension transformations based on the operations of different fragmentation and aggregation modules. Figure ##FIG##8##9## provides an explanation of the feature map's transformations. The transformed patches are subsequently passed through the transformer layers, where the hidden layer features are extracted using the multi-head self-attention mechanism (MSA) and multi-layer perceptron (MLP).</p>", "<p id=\"Par40\">At the decoder block, the image undergoes multiple layers of upsampling, and feature fusion is performed to generate the final prediction. This U-shaped network structure enables the model to capture and preserve the underlying characteristics of the image through skip connections, which are often overlooked. In our study, a fragmentation module and an aggregation module are integrated within the MLP of the transformer layer, which is designed to mimic knowledge paradigms inspired by human brain learning patterns, thereby facilitating improved information acquisition and processing. A more comprehensive exposition of these modifications is given in the section of transformers layer and the multi-layer perceptron for an easy understanding of our approach.</p>", "<title>Transformers layer and multi-layer perceptron</title>", "<p id=\"Par41\">The transformer layer, a crucial component of the network architecture, consists of the MSA module and the MLP module. The MSA module operates on the entire embedded sequence, extracting potential features, generating valuable features, and eliminating irrelevant noises. It focuses on the most important parts to enhance the quality of the extracted features. The resulting features are then passed to the MLP module for further processing. Within the MLP module, there are two linear projections to transform the features and one dropout layer to prevent overfitting. Here, the Focus and LogSparse modules are used as examples to describe the feature map dimension transformations within the transformer layer. Starting with an input vector in the dimension of , and after a series of data transformations, including layer normalization and MSA, the dimensions remain unchanged. The Focus module divides the feature map into four contiguous blocks, effectively increasing the number of channels while reducing the length and width to half, thus the dimension will become . On the other hand, the LogSparse attention module calculates the attention using a mechanism similar to the transformer, preserving the tensor's dimensions without altering them. The BottleneckCSP module conducts two consecutive convolutions, keeping the height and width unchanged while quadrupling the number of channels (). To maintain the consistency, the C3ECA module also quadruples the number of channels. Both ProbSparse attention and LogSparse attention achieve information aggregation through attention computation, keeping the dimensions of the feature map unchanged. The C3CBAM module has the ability to simultaneously control channel and spatial attention. To bring the feature map back to its initial size, one more convolution is conducted at the end of the computation. Finally, the human brain-inspired learning paradigm implemented in the MLP module is illustrated in Fig. ##FIG##0##1##.</p>", "<p id=\"Par42\">The MLP module incorporates the fragmentation and aggregation modules as shown in Fig. ##FIG##8##9##. Although the original MLP module has a simple structure, it can be modified to adapt to specific requirements and improve its performance. Since the hidden layer features are extracted early in the MSA module, the feature maps passed into the MLP module contain a large amount of information, which may introduce ambiguity. To address it, the high-dimensional information is first fragmented, which effectively enhances the information learning rate and reduces the computational complexity from to by replacing cumulative multiplication with cumulative addition. The fragmented information retains the essential correlations between features, enabling more efficient information processing. Subsequently, the information aggregation module operates on the fragmented information and leverages the strong correlations between them. It aggregates the information according to its correlation and completes the construction of the entire learning paradigm. Such integration improves information utilization while minimizing the loss of valuable data, which is the superiority of the human learning paradigm. By incorporating the MSA and MLP modules within the transformer layer, the network architecture has the benefits of feature extraction, noise reduction, fragmentation, and information integration, which contribute to the overall performance and effectiveness of the proposed network.</p>", "<p id=\"Par43\">Further explanations for the core illustrations of the technical methods mentioned above are given to clarify their relationships. Figure ##FIG##0##1## elucidates the introduced human learning paradigm and outlines its knowledge framework. Figure ##FIG##7##8## depicts the framework of the baseline model with our learning paradigm embedded in the model’s transformer layer. The overall module of integrating the paradigm within the MLP section of the transformer layer is shown in Fig. ##FIG##8##9##. Altogether, these depictions aim to clarify how the human learning paradigm is implemented within the MLP in the transformer layer.</p>", "<title>Fragmentation module</title>", "<p id=\"Par44\">The Focus module periodically extracts pixels from a high-resolution image and then rebuilds them into a low-resolution image by stacking four neighbors to map the width- and height-dimensional information into the c-channel, enhancing each pixel's perceptual field and minimizing the information loss. In short, the Focus module performs fragmentation operations by proportionally dividing the feature map along three dimensions: length, width, and height. The hardswish activation function in Eq. ##FORMU##8##1## was employed in the original Focus module [##UREF##32##49##]. In our model, it was replaced by the SiLU activation function in Eq. ##FORMU##9##2## because the pixel values of breast fibroadenomas in sonography do not require many boundary constraints [##UREF##33##50##]:</p>", "<p id=\"Par45\">The BottleneckCSP module consists of a bottleneck block and the main module from CSPNet. The feature map is divided into two parts before entering BottleneckCSP in both length and width dimensions. One part is computed by a series of convolution blocks (1 × 1), and the other is directly fused with the original features by a shortcut. Finally, the fused feature map will be resized to the initial channel dimension using a 1 × 1 convolution block. This module efficiently reduces memory usage and computational bottlenecks because of its lightweight design and strong feature extraction capabilities. The activation functions used in the BottleneckCSP for object detection are Hardswish and LeakyRelu:</p>", "<p id=\"Par46\">Since the boundary delineation of the activation function is less required in sonography segmentation, we replaced the activation function with SiLU.</p>", "<p id=\"Par47\">The C3ECA module consists of the C3 and ECABottleneck modules and the concept of the slice, processing each block by slicing the pixels of each channel into a defined size. Upon entering the C3ECA module, the feature map undergoes a sequence of three consecutive convolution blocks (1 × 1, 3 × 3, and 1 × 1) for feature slicing. Subsequently, it proceeds through an average pooling layer to capture pertinent information. Finally, a 1 × 1 convolution and an activation function are applied to resize the channel to the initial dimension and ensure feature usability, respectively. To increase the performance without sacrificing the information, a skip connection is added to the C3 module to fuse the feature map before slicing and after processing. The feature fusion can ensure that the feature information after slicing still maintains trustworthiness.</p>", "<title>Aggregation module</title>", "<p id=\"Par48\">We applied the convolutional self-attention mechanism which is capable of convolutionally transforming input into queries/keys in the network [##UREF##22##34##]. In comparison to the original transformer design, its location-aware capability can accurately match the most relevant input elements, and LogSparse Attention can read more contextual information to enhance the internal location and sonography perception (see Fig. ##FIG##9##10##). Most significantly, the network is able to integrate and purify information from each slice for lesion awareness.</p>", "<p id=\"Par49\">The CBAM's spatial and channel attention mechanisms have been shown to improve the network performance in Yolo v5 but with more computational complexity [##UREF##23##35##]. Therefore, we combined the C3 module, the convolutional block attention module (CBAM), and the Bottleneck module as the C3CBAM Bottleneck module to keep the computation under control. With the integration of spatial and channel attention mechanisms, the meaningful part of the fragmented information is effectively selected, and each piece of information is successfully located to assure its validity.</p>", "<p id=\"Par50\">According to [##UREF##34##51##], we derived a probabilistic formal approach for convolutional kernel smoothing. Equations ##FORMU##11##4## and ##FORMU##12##5## describe the MSA mechanism [##UREF##3##13##] and the modified attention mechanism, respectively:where is a sparse matrix of the same size as <italic>Q,</italic> and it only contains the Top-u queries under the sparsity measurement <italic>M</italic>(<italic>q</italic>, <italic>K</italic>). It is expected that ProbSparse Attention using the adaptive convolutional kernel approach may perform better in terms of location retention and lengthy sequence prediction, and the addition of the sparse attention coefficients can improve the capture ability of scattered pixels. Information fragmentation may make the image features more confusing, and ProbSparse Attention is able to control the semantic information extraction through smoothly stretched convolutional kernels, which improves the utilization of semantic information and reduces the interference caused by scattered information in the network simultaneously.</p>", "<title>Training and data augmentation</title>", "<p id=\"Par51\">Python 3.7 and PyTorch 1.11.0 were used for the compilation. To decrease the potential for overfitting and regularize the network better, several data augment strategies were applied. With a maximum center and a random offset of 20% from the original image, 128 pixels in each dimension were randomly cropped. Additionally, during data improvement, more images were added to the training dataset by rotating the cropped images with a 20% probability up to and mirroring them with a 50% probability. Stochastic gradient descent (SGD) was utilized as the optimizer for the training model with an initial learning rate of 0.01, a momentum of 0.9, a weight decay factor of 1e-4, and a default batch size of 24 for 150 epochs. The training was carried out using a single Nvidia Tesla V100 32 GB GPU.</p>", "<title>Evaluation metrics</title>", "<p id=\"Par52\">The similarity between the ground truth and the segmentation is assessed by employing several comparison metrics. Dice similarity coefficient (DSC) was used to compare the areas based on their overlap, and Hausdorff distance <bold>(</bold>HD) was defined as the distance between the boundaries of the ground truth and the segmentation result [##UREF##35##52##]. The DSC is a widely accepted measure for assessing the overlap between the predicted and ground truth segmentation masks, providing insight into the accuracy of the segmentation process. Additionally, the HD metric quantifies the maximum distance between the contours of the predicted and ground truth regions, offering valuable information about boundary localization accuracy:where <italic>I</italic>_{<italic>gt</italic>} is the ground truth mask, <italic>I</italic>_{<italic>pt</italic>} is the predicted mask, <italic>i</italic> and <italic>j</italic> are points belonging to different sets, and <italic>d</italic> represents the distance between <italic>i</italic> and <italic>j</italic>.</p>" ]

[ "<title>Results</title>", "<p id=\"Par26\">We utilized both local and public datasets to validate the effectiveness of our proposed learning paradigm and conducted ablation experiments to explore its interpretability.</p>", "<title>Performance assessment</title>", "<p id=\"Par27\">Table ##TAB##0##1## lists the outcomes of the proposed CNN–transformer hybrid network using different fragmentation and aggregation modules on the local dataset. Appropriate network architectures show improvements in the evaluation metrics as well as a reduction in the training time. The most significant results came from combining the C3ECA and LogSparse Attention modules, 0.876 in DSC and 5.82 mm in HD, respectively. Compared with the baseline model (TransUNet), the corresponding improvements are 0.76% in DSC and 3.51% in HD, respectively. The computation time is reduced by 1.25 h. The impact of incorporating different fragmentation modules on segmentation performance and training time is also illustrated. Notably, the model utilizing C3ECA as the fragmentation module achieves the overall shortest training time within the range of 2.25–2.75 h.</p>", "<p id=\"Par28\">To assess whether the human learning paradigm introduces overfitting issues during training, we analyzed the training losses of the model incorporating the LogSparse Attention module (see Fig. ##FIG##1##2##). The results clearly indicate that the inclusion of C3ECA module achieves the fastest fitting and convergence and the lowest loss with gradually diminishing loss jitters even after only 250 iterations. In contrast, the model incorporating BottleneckCSP exhibits higher loss jitters around the 700th iteration (i.e., up to 0.128). Overall, it shows that the neural network guided by the human learning paradigm is capable of finding the local optimal solutions more efficiently at faster convergence in the training process.</p>", "<title>Visualizations</title>", "<p id=\"Par29\">A visual comparison of the segmentation results for three representative cases: Case I, a conventional fibroadenoma; Case II, a fibroadenoma with an overall longer cross-section; and Case III, a fibroadenoma with an overall larger area and the corresponding DSC metrics are shown in Figs. ##FIG##2##3## and ##FIG##3##4##, respectively. Our findings reveal that the LogSparse-related models demonstrate successful segmentation of all breast fibroadenomas with various pathological characteristics, resulting in segmentation contours that closely match the ground truth. However, the C3CBAM- and ProbSparse-related models exhibit some misclassifications, particularly in Case I with incorrect expansions in the segmented lesion boundaries. In Case II, the network model containing Focus and LogSparse modules performs the best. However, in Case III, the model combining C3ECA and LogSparse modules excels in predicting the conspicuous lower right part of the fibroadenoma. These results suggest that the model incorporating the human learning paradigm, especially the combined C3ECA and LogSparse Attention modules, demonstrates improvements over the baseline model.</p>", "<title>Comparison to state-of-the-art methods</title>", "<p id=\"Par30\">To further evaluate the performance of our work, the best-performing model (the combined C3ECA and LogSparse Attention modules) was compared with the SOTA models using a local dataset. Table ##TAB##1##2## and Fig. ##FIG##4##5## show that our proposed network enhances DSC and HD metrics by 6.1% and 4.3 mm and 3.82% and 3.46 mm, respectively, as compared to the U-net and DeepLab V3 + , which may be due to the emphasis on semantic information interaction. In comparison to U-net which has the least training time among all tested SOTA models, our approach reduces the training time by another 0.25 h.</p>", "<title>Robustness on the public dataset</title>", "<p id=\"Par31\">To further validate the robustness of our learning paradigm, we conducted tests on the publicly available dataset (Dataset_BUSI and DatasetB) to compare with the SOTA model. Table ##TAB##2##3## and Fig. ##FIG##5##6## show that our network is also applicable to the public dataset quite well, outperforming TransUNet by 0.42% in DSC and 5.13 mm in HD and DeepLab V3 + by 1.43 mm in HD, respectively. However, the training time optimization embodied in our lightweight model for the local dataset becomes less pronounced here, which may be due to the smaller sample size (500 vs. 207). Because of the more complicated structure of artificial neural networks than these traditional linear ones [##UREF##27##41##], the training time is not proportional to the sample size.</p>", "<title>Ablation study</title>", "<p id=\"Par32\">To investigate the effect of the fragmentation module (C3ECA) and aggregation module (LogSparse) on the neural network in improving breast fibroadenoma segmentation, these two modules were integrated into the baseline model individually. Each hyperparameter in the experiment and clinical dataset was maintained consistently to ensure the fairness. The network containing the fragmentation module exhibits a significant decrease in training time but a minor improvement in DSC, while the inclusion of the aggregation module improves DSC by 0.56% and HD by 0.17 mm (see Table ##TAB##3##4##). More importantly, the performance of the combined fragmentation and aggregation modules is much better than that of the individuals (i.e., by 3.38 mm and 2.97 mm in HD, respectively). Therefore, such a combination is synergistic in the learning paradigm.</p>" ]

[ "<title>Discussion</title>", "<p id=\"Par33\">In this study, we present a novel approach to enhance the segmentation of breast fibroadenomas in sonography through the utilization of an artificial neural network with a human learning paradigm. Our method is inspired by the learning mechanisms of the human brain, and the new paradigm combines feature fragmentation modules (Focus, BottleneckCSP, and C3ECA) and information aggregation modules (C3CBAM, LogSparse Attention, and ProbSparse Attention) to effectively guide the neural network's learning process. To validate its effectiveness, we conducted a comprehensive set of experiments using both local and public datasets. The quantitative evaluation demonstrates the superiority of our model over state-of-the-art models in terms of Dice similarity coefficient (DSC) and Hausdorff distance (HD) metrics, while also significantly reducing training time. Remarkably, the network employing C3ECA and LogSparse Attention mechanisms showcased the most exceptional performance on both datasets, improving DSC and HD by 0.76% and 3.51% on the local dataset and by 0.42% and 12.59% on the public dataset compared to the TransUNet model, respectively. Altogether, our study introduces an artificial neural network framework augmented by a human-inspired learning paradigm that effectively enhances the segmentation of breast fibroadenomas.</p>", "<p id=\"Par34\">Considering the variations in breast densities and anatomical features among populations, particularly between Asian and European-American women, our study holds clinical relevance for early breast fibroadenoma diagnosis. It is also important to note that the image quality of sonography varies greatly due to the operating equipment. In our data collection, ultrasound images were acquired from local women in Suining, a representative small to medium-sized city in China, using the sonographic equipment of the Mindray DC-80S. Their qualities are found poorer compared to those in the public datasets that consist of the BUS images in Europe and North America (see Fig. ##FIG##6##7##). However, the consistently outstanding performance (i.e., robustness and reliability) of our models across diverse datasets showcases their potential and value for clinical applications.</p>", "<p id=\"Par35\">The future of computer vision research is likely to focus on targeted and guided feature learning. Self-attention mechanisms, derived from transformers, are competitive, and many variants have been developed. While attention mechanisms improve contextual information extraction, their performance in sonography, which contains uniformly distributed complex patterns, is unsatisfactory. Filipczuk et al. used a k-means based hybrid method for beast fibroadenomas segmentation, but at an average classification accuracy of only 77.20% [##UREF##28##42##]. In our work, inspiration from the human’s learning pattern led to devising a fragmentation–aggregation learning paradigm and then incorporating it with a feature segmentation method. This method involves partitioning and allocating feature maps to distinct channels, culminating in the comprehensive acquisition of information and the emulation of the human’s learning trajectory, and progressing from surface-level to in-depth understanding and from local to global comprehension. Rather than indiscriminately adding modules to the neural network, focusing on specific scenarios to optimize performance seems more effective. Here, this learning paradigm was adapted into a mechanism encompassing both fragmentation focus and information aggregation for improved segmentation and streamlined architectures. Ablation experiments have validated its synergistic benefits.</p>", "<p id=\"Par36\">This study has some limitations. Firstly, the size of our dataset is modest compared to other publicly available medical image datasets (i.e., MRI and CT). However, we plan to continuously collect more breast ultrasound images (i.e., 300 ones over the next four months), which will significantly enhance the dataset's size and diversity. Although a promising DSC of 0.875815 was achieved here, there is still ample room for further investigation and improvement. Future studies should focus on exploring novel techniques and continuously refining the learning paradigm to enhance the accuracy and effectiveness of sonography segmentation. We will combine the online transfer learning strategy with the CNN–transformer hybrid network model [##UREF##29##43##], apply the feature-based transfer learning method [##UREF##30##44##], and migrate the SOTA methods of medical image segmentation, such as fuzzy c-means (FCM), Gaussian mixture model (GMM) [##REF##28622675##45##], and the topology-preserving approach [##REF##19766196##46##]. Furthermore, specific segmentation requirements will be explored. Ding et al. suggested that the segmentation of the brachial plexus could be transformed into a segmentation of the nerve as well as the surrounding tissues [##UREF##31##47##]. Therefore, blood flow and tissue elasticity signals may assist in segmenting the breast fibroadenomas in sonography (see Fig. ##FIG##6##7##). Finally, image segmentation will be evaluated in line with clinical practice and the physician’s intuitive judgment. DSC and HD illustrate only the geometric differences without considering the clinical implications. The smooth lesion boundaries and their tendency toward concavity can influence the physician's assessment of the tumor's benignity and malignancy in clinical diagnosis. Under- or over-contouring tumors with similar DSC and HD measures may lead to significantly different diagnosis results. A medical similarity index (MSI) that involves a user-defined medical consideration function (MCF) derived from an asymmetric Gaussian function will be used for evaluating the segmentation accuracy since the MCF shape shows the anatomical position and characteristics of a particular tissue, organ, or tumor type [##REF##26127054##48##]. A subjective evaluation will also be applied. Although human evaluation is very cumbersome and time-consuming, it provides more clinical insights into the tumors. And the acceptance by experienced radiologists of the image segmentation results is critical in the clinical applications. Accurate medical image segmentation is a complex and challenging task due to significant variations in image quality, artifacts, and anatomical structures among patients. Thus, further investigation is required for the technical development and its translation to the clinics.</p>" ]

[ "<title>Conclusion</title>", "<p id=\"Par37\">Although sonography is preferred in the diagnosis of breast masses, segmentation of the tumors in sonography is unsatisfactory because of the inherent limitations of this imaging modality, low image quality and the presence of artifacts (i.e., speckles and scattering). Utilizing prior knowledge to guide neural network learning can lead to improved performance in specified medical image segmentation tasks. In this paper, we applied a paradigm inspired by human learning patterns to an artificial neural network for the segmentation of breast fibroadenomas in sonography. Our research findings indicate that aggregating high-dimensional information into cohesive modules can enhance the model's information perception ability while reducing training costs. By introducing three fragmentation attention modules and three information aggregation modules, we successfully implemented this learning paradigm and guided the neural network's learning process. Improvements in performance metrics across various network structures are found in comparison to the baseline network. Among all combinations, the C3ECA and LogSparse Attention modules showed the best overall segmentation performance in DSC, HD, and training time. Additionally, our approach demonstrated robust advantages over other state-of-the-art methods on both local and public breast ultrasound image datasets. This study underscores the immense potential of a modular learning paradigm inspired by the human brain within the realm of image processing. Although our findings are promising, there exists ample room for further exploration and refinement of this approach. Additional images will be incorporated into the dataset for a more comprehensive evaluation of our method's capabilities. Ultrasonic elastography may be utilized for capturing intricate mechanical features of breast masses. This strategic augmentation holds the promise of achieving more precise lesion segmentation.</p>" ]

[ "<title>Background</title>", "<p id=\"Par1\">Breast fibroadenoma poses a significant health concern, particularly for young women. Computer-aided diagnosis has emerged as an effective and efficient method for the early and accurate detection of various solid tumors. Automatic segmentation of the breast fibroadenoma is important and potentially reduces unnecessary biopsies, but challenging due to the low image quality and presence of various artifacts in sonography.</p>", "<title>Methods</title>", "<p id=\"Par2\">Human learning involves modularizing complete information and then integrating it through dense contextual connections in an intuitive and efficient way. Here, a human learning paradigm was introduced to guide the neural network by using two consecutive phases: the feature fragmentation stage and the information aggregation stage. To optimize this paradigm, three fragmentation attention mechanisms and information aggregation mechanisms were adapted according to the characteristics of sonography. The evaluation was conducted using a local dataset comprising 600 breast ultrasound images from 30 patients at Suining Central Hospital in China. Additionally, a public dataset consisting of 246 breast ultrasound images from Dataset_BUSI and DatasetB was used to further validate the robustness of the proposed network. Segmentation performance and inference speed were assessed by Dice similarity coefficient (DSC), Hausdorff distance (HD), and training time and then compared with those of the baseline model (TransUNet) and other state-of-the-art methods.</p>", "<title>Results</title>", "<p id=\"Par3\">Most models guided by the human learning paradigm demonstrated improved segmentation on the local dataset with the best one (incorporating C3ECA and LogSparse Attention modules) outperforming the baseline model by 0.76% in DSC and 3.14 mm in HD and reducing the training time by 31.25%. Its robustness and efficiency on the public dataset are also confirmed, surpassing TransUNet by 0.42% in DSC and 5.13 mm in HD.</p>", "<title>Conclusions</title>", "<p id=\"Par4\">Our proposed human learning paradigm has demonstrated the superiority and efficiency of ultrasound breast fibroadenoma segmentation across both public and local datasets. This intuitive and efficient learning paradigm as the core of neural networks holds immense potential in medical image processing.</p>", "<title>Keywords</title>" ]

[]

[ "<title>Acknowledgements</title>", "<p>The authors would like to express their thanks to Dr. Cai Zhang and Miss Hong Liu for the collection of sonography and valuable discussion.</p>", "<title>Author contributions</title>", "<p>YG and YZ were responsible for the conception of the work. MC, LY, HY, and HY acquired the image data for the research. The written paper was drafted by YG and substantively revised by YZ. All authors read and approved the final manuscript.</p>", "<title>Funding</title>", "<p>This work is financially supported by the Chongqing Medical University (Future Innovation Program, 2022-W0063).</p>", "<title>Availability of data and materials</title>", "<p>The datasets generated and/or analyzed during the current study are not publicly available due to security of research data concerns but are available from the corresponding author upon reasonable request.</p>", "<title>Declarations</title>", "<title>Ethics approval and consent to participate</title>", "<p id=\"Par53\">The dataset used in this work was recorded at Suining Central Hospital in Sichuan, China, and the requirement for informed consent was waived.</p>", "<title>Consent for publication</title>", "<p id=\"Par54\">Not applicable.</p>", "<title>Competing interests</title>", "<p id=\"Par55\">The authors declare that they have no competing interests.</p>" ]

[ "<fig id=\"Fig1\"><label>Fig. 1</label><caption><p>A workflow that mimics the human learning paradigm, and the red curves at the information aggregation stage are the relationships between each slice</p></caption></fig>", "<fig id=\"Fig2\"><label>Fig. 2</label><caption><p>Comparison of loss curves produced by the baseline model (TransUNet) and CNN–transformer hybrid network containing the modules of BottleneckCSP + LogSparse, C3ECA + LogSparse, and Foucs + LogSparse with uniform coordinate offsets of 0.13</p></caption></fig>", "<fig id=\"Fig3\"><label>Fig. 3</label><caption><p>Comparison of <bold>a</bold> the breast ultrasonic image, <bold>b</bold> the ground truth masks, and the predicted masks using <bold>c</bold> baseline, <bold>d</bold> C3ECA and C3CBAM, <bold>e</bold> C3ECA and probSparse, <bold>f</bold> BottleneckCSP and LogSparse, <bold>g</bold> Focus and LogSparse, and <bold>h</bold> C3ECA and LogSparse for three representative cases of breast fibroadenoma I (top row): a conventional fibroadenoma; II (middle row): a fibroadenoma with an overall longer cross-section; III (bottom row): a fibroadenoma with an overall larger area</p></caption></fig>", "<fig id=\"Fig4\"><label>Fig. 4</label><caption><p>Quantitative comparison of the segmentation DSC metrics of the breast fibroadenomas using different networks</p></caption></fig>", "<fig id=\"Fig5\"><label>Fig. 5</label><caption><p>Visual comparison of <bold>a</bold> three representative ultrasound breast fibroadenoma images from the local dataset, <bold>b</bold> ground truth, and segmentation results using <bold>c</bold> our proposed model and state-of-the-art methods of <bold>d</bold> TransUNet, <bold>e</bold> U-net, and <bold>f</bold> DeepLab V3 + </p></caption></fig>", "<fig id=\"Fig6\"><label>Fig. 6</label><caption><p>Visual comparison of <bold>a</bold> three representative ultrasound breast fibroadenoma images from the public dataset, <bold>b</bold> ground truth, and segmentation results using <bold>c</bold> our proposed model and state-of-the-art methods of <bold>d</bold> TransUNet, <bold>e</bold> U-net, and <bold>f</bold> DeepLab V3 + </p></caption></fig>", "<fig id=\"Fig7\"><label>Fig. 7</label><caption><p>Representative ultrasound image with <bold>a</bold> the arterial blood flow signal, <bold>b</bold> B-mode sonography on the left and elastography on the right in the local dataset, and <bold>c</bold> B-mode sonography in the public dataset_BUSI</p></caption></fig>", "<fig id=\"Fig8\"><label>Fig. 8</label><caption><p>Architecture of the TransUNet used for segmenting the breast fibroadenomas in sonography</p></caption></fig>", "<fig id=\"Fig9\"><label>Fig. 9</label><caption><p>The structure of the transformer layer and multi-layer perceptron (MLP)</p></caption></fig>", "<fig id=\"Fig10\"><label>Fig. 10</label><caption><p>The self-attention convolution mechanism in the LogSparse Attention module</p></caption></fig>" ]

[ "<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Comparison of the segmentation performances on the local dataset using the CNN–transformer hybrid network containing different modules with the best results in bold</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" colspan=\"2\">Methods</th><th align=\"left\" rowspan=\"2\">DSC ↑</th><th align=\"left\" rowspan=\"2\">HD (mm) ↓</th><th align=\"left\" rowspan=\"2\">Time (h)↓</th></tr><tr><th align=\"left\">Fragmentation module</th><th align=\"left\">Aggregation module</th></tr></thead><tbody><tr><td align=\"left\">NA</td><td align=\"left\">NA</td><td char=\".\" align=\"char\">0.869240</td><td char=\".\" align=\"char\">8.963769</td><td align=\"left\">4</td></tr><tr><td align=\"left\" rowspan=\"3\">Focus</td><td align=\"left\">LogSparse</td><td char=\".\" align=\"char\">0.870811</td><td char=\".\" align=\"char\">8.026729</td><td align=\"left\">4</td></tr><tr><td align=\"left\">C3CBAM</td><td char=\".\" align=\"char\">0.867615</td><td char=\".\" align=\"char\">7.836283</td><td align=\"left\">3.75</td></tr><tr><td align=\"left\">ProbSparse</td><td char=\".\" align=\"char\">0.869836</td><td char=\".\" align=\"char\">8.212088</td><td align=\"left\">3</td></tr><tr><td align=\"left\" rowspan=\"3\">BottleneckCSP</td><td align=\"left\">LogSparse</td><td char=\".\" align=\"char\">0.874429</td><td char=\".\" align=\"char\">7.846115</td><td align=\"left\">3.25</td></tr><tr><td align=\"left\">C3CBAM</td><td char=\".\" align=\"char\">0.855873</td><td char=\".\" align=\"char\">10.848110</td><td align=\"left\"><bold>2.25</bold></td></tr><tr><td align=\"left\">ProbSparse</td><td char=\".\" align=\"char\">0.854521</td><td char=\".\" align=\"char\">9.704177</td><td align=\"left\">3.25</td></tr><tr><td align=\"left\" rowspan=\"3\">C3ECA</td><td align=\"left\">LogSparse</td><td char=\".\" align=\"char\"><bold>0.875815</bold></td><td char=\".\" align=\"char\"><bold>5.820322</bold></td><td align=\"left\">2.75</td></tr><tr><td align=\"left\">C3CBAM</td><td char=\".\" align=\"char\">0.870480</td><td char=\".\" align=\"char\">8.873851</td><td align=\"left\"><bold>2.25</bold></td></tr><tr><td align=\"left\">ProbSparse</td><td char=\".\" align=\"char\">0.863999</td><td char=\".\" align=\"char\">9.969885</td><td align=\"left\">2.75</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab2\"><label>Table 2</label><caption><p>Comparison of the segmentation performances of three SOTA networks and ours on the local ultrasound breast images dataset with the best results in bold</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Network</th><th align=\"left\">DSC ↑</th><th align=\"left\">HD (mm) ↓</th><th align=\"left\">Time (h)↓</th></tr></thead><tbody><tr><td align=\"left\">TransUNet</td><td char=\".\" align=\"char\">0.869240</td><td char=\".\" align=\"char\">8.963769</td><td align=\"left\">4</td></tr><tr><td align=\"left\">U-Net [##UREF##2##12##]</td><td char=\".\" align=\"char\">0.825394</td><td char=\".\" align=\"char\">10.121881</td><td align=\"left\">3</td></tr><tr><td align=\"left\">DeepLab V3 + [##UREF##26##40##]</td><td char=\".\" align=\"char\">0.843596</td><td char=\".\" align=\"char\">9.281189</td><td align=\"left\">3.25</td></tr><tr><td align=\"left\">Proposed</td><td char=\".\" align=\"char\"><bold>0.875815</bold></td><td char=\".\" align=\"char\"><bold>5.820322</bold></td><td align=\"left\"><bold>2.75</bold></td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab3\"><label>Table 3</label><caption><p>Comparison of the segmentation performances using three SOTA networks and ours on the public ultrasound breast dataset with the best results in bold</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Network</th><th align=\"left\">DSC ↑</th><th align=\"left\">HD (mm) ↓</th><th align=\"left\">Time (hr)↓</th></tr></thead><tbody><tr><td align=\"left\">TransUNet</td><td char=\".\" align=\"char\">0.849274</td><td char=\".\" align=\"char\">38.664333</td><td align=\"left\">2.5</td></tr><tr><td align=\"left\">U-Net</td><td char=\".\" align=\"char\">0.832526</td><td char=\".\" align=\"char\">37.099123</td><td align=\"left\">2.25</td></tr><tr><td align=\"left\">DeepLab V3 + </td><td char=\".\" align=\"char\">0.852061</td><td char=\".\" align=\"char\">35.231811</td><td align=\"left\"><bold>2</bold></td></tr><tr><td align=\"left\">Proposed</td><td char=\".\" align=\"char\"><bold>0.852804</bold></td><td char=\".\" align=\"char\"><bold>33.796840</bold></td><td align=\"left\">2.25</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab4\"><label>Table 4</label><caption><p>Ablation analysis of different components in the proposed network with the best results in bold</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Network</th><th align=\"left\">DSC ↑</th><th align=\"left\">HD (mm) ↓</th><th align=\"left\">Time (h)↓</th></tr></thead><tbody><tr><td align=\"left\">baseline</td><td char=\".\" align=\"char\">0.869240</td><td char=\".\" align=\"char\">8.963769</td><td align=\"left\">4</td></tr><tr><td align=\"left\">baseline + C3ECA</td><td char=\".\" align=\"char\">0.873358</td><td char=\".\" align=\"char\">9.295890</td><td align=\"left\">2.5</td></tr><tr><td align=\"left\">baseline + LogSparse</td><td char=\".\" align=\"char\">0.874087</td><td char=\".\" align=\"char\">8.790277</td><td align=\"left\">4.75</td></tr><tr><td align=\"left\">baseline + C3ECA + LogSparse</td><td char=\".\" align=\"char\"><bold>0.875815</bold></td><td char=\".\" align=\"char\"><bold>5.820322</bold></td><td align=\"left\"><bold>2.75</bold></td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab5\"><label>Table 5</label><caption><p>Basic information of the patients and their breast fibroadenomas in the local dataset</p></caption><table frame=\"hsides\" rules=\"groups\"><tbody><tr><td align=\"left\" colspan=\"2\">Number of patients</td><td align=\"left\">30</td></tr><tr><td align=\"left\" colspan=\"2\">Age</td><td align=\"left\">27–59, 41.978 9.029</td></tr><tr><td align=\"left\" rowspan=\"2\">Tumor distribution</td><td align=\"left\">Single</td><td align=\"left\">21</td></tr><tr><td align=\"left\">Multiple</td><td align=\"left\">9</td></tr></tbody></table></table-wrap>" ]

[ "<inline-formula id=\"IEq10\"><alternatives><tex-math id=\"M1\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\pm$$\\end{document}</tex-math><mml:math id=\"M2\"><mml:mo>±</mml:mo></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq1\"><alternatives><tex-math id=\"M3\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\frac{W}{4}\\, \\times \\,\\frac{H}{4}\\, \\times \\,C$$\\end{document}</tex-math><mml:math id=\"M4\"><mml:mrow><mml:mfrac><mml:mi>W</mml:mi><mml:mn>4</mml:mn></mml:mfrac><mml:mspace width=\"0.166667em\"/><mml:mo>×</mml:mo><mml:mspace width=\"0.166667em\"/><mml:mfrac><mml:mi>H</mml:mi><mml:mn>4</mml:mn></mml:mfrac><mml:mspace width=\"0.166667em\"/><mml:mo>×</mml:mo><mml:mspace width=\"0.166667em\"/><mml:mi>C</mml:mi></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq2\"><alternatives><tex-math id=\"M5\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\frac{W}{8}\\, \\times \\,\\frac{H}{8}\\, \\times \\,C$$\\end{document}</tex-math><mml:math id=\"M6\"><mml:mrow><mml:mfrac><mml:mi>W</mml:mi><mml:mn>8</mml:mn></mml:mfrac><mml:mspace width=\"0.166667em\"/><mml:mo>×</mml:mo><mml:mspace width=\"0.166667em\"/><mml:mfrac><mml:mi>H</mml:mi><mml:mn>8</mml:mn></mml:mfrac><mml:mspace width=\"0.166667em\"/><mml:mo>×</mml:mo><mml:mspace width=\"0.166667em\"/><mml:mi>C</mml:mi></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq3\"><alternatives><tex-math id=\"M7\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\frac{W}{16}\\, \\times \\,\\frac{H}{16}\\, \\times \\,4C$$\\end{document}</tex-math><mml:math id=\"M8\"><mml:mrow><mml:mfrac><mml:mi>W</mml:mi><mml:mn>16</mml:mn></mml:mfrac><mml:mspace width=\"0.166667em\"/><mml:mo>×</mml:mo><mml:mspace width=\"0.166667em\"/><mml:mfrac><mml:mi>H</mml:mi><mml:mn>16</mml:mn></mml:mfrac><mml:mspace width=\"0.166667em\"/><mml:mo>×</mml:mo><mml:mspace width=\"0.166667em\"/><mml:mn>4</mml:mn><mml:mi>C</mml:mi></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq4\"><alternatives><tex-math id=\"M9\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\frac{W}{32}\\, \\times \\,\\frac{H}{32}\\, \\times \\,16C$$\\end{document}</tex-math><mml:math id=\"M10\"><mml:mrow><mml:mfrac><mml:mi>W</mml:mi><mml:mn>32</mml:mn></mml:mfrac><mml:mspace width=\"0.166667em\"/><mml:mo>×</mml:mo><mml:mspace width=\"0.166667em\"/><mml:mfrac><mml:mi>H</mml:mi><mml:mn>32</mml:mn></mml:mfrac><mml:mspace width=\"0.166667em\"/><mml:mo>×</mml:mo><mml:mspace width=\"0.166667em\"/><mml:mn>16</mml:mn><mml:mi>C</mml:mi></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq5\"><alternatives><tex-math id=\"M11\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\frac{W}{16}\\, \\times \\,\\frac{H}{16}\\, \\times \\,16C$$\\end{document}</tex-math><mml:math id=\"M12\"><mml:mrow><mml:mfrac><mml:mi>W</mml:mi><mml:mn>16</mml:mn></mml:mfrac><mml:mspace width=\"0.166667em\"/><mml:mo>×</mml:mo><mml:mspace width=\"0.166667em\"/><mml:mfrac><mml:mi>H</mml:mi><mml:mn>16</mml:mn></mml:mfrac><mml:mspace width=\"0.166667em\"/><mml:mo>×</mml:mo><mml:mspace width=\"0.166667em\"/><mml:mn>16</mml:mn><mml:mi>C</mml:mi></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq6\"><alternatives><tex-math id=\"M13\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$o\\left( \\cdot \\right)$$\\end{document}</tex-math><mml:math id=\"M14\"><mml:mrow><mml:mi>o</mml:mi><mml:mfenced close=\")\" open=\"(\"><mml:mo>·</mml:mo></mml:mfenced></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq7\"><alternatives><tex-math id=\"M15\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$O\\,\\left( + \\right)$$\\end{document}</tex-math><mml:math id=\"M16\"><mml:mrow><mml:mi>O</mml:mi><mml:mspace width=\"0.166667em\"/><mml:mfenced close=\")\" open=\"(\"><mml:mo>+</mml:mo></mml:mfenced></mml:mrow></mml:math></alternatives></inline-formula>", "<disp-formula id=\"Equ1\"><label>1</label><alternatives><tex-math id=\"M17\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\text{hardswish}}({\\text{x}})=\\left\\{\\begin{array}{cc}0,&amp; {\\text{x}}\\le -3\\\\ {\\text{x}},&amp; {\\text{x}}\\ge 3\\\\ \\frac{{\\text{x}}({\\text{x}}+3)}{6},&amp; {\\text{otherwise}}\\end{array},\\right.$$\\end{document}</tex-math><mml:math id=\"M18\" display=\"block\"><mml:mrow><mml:mtext>hardswish</mml:mtext><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mtext>x</mml:mtext><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mo>=</mml:mo><mml:mfenced open=\"{\"><mml:mrow><mml:mtable><mml:mtr><mml:mtd><mml:mrow><mml:mn>0</mml:mn><mml:mo>,</mml:mo></mml:mrow></mml:mtd><mml:mtd><mml:mrow><mml:mtext>x</mml:mtext><mml:mo>≤</mml:mo><mml:mo>-</mml:mo><mml:mn>3</mml:mn></mml:mrow></mml:mtd></mml:mtr><mml:mtr><mml:mtd><mml:mrow><mml:mrow/><mml:mtext>x</mml:mtext><mml:mo>,</mml:mo></mml:mrow></mml:mtd><mml:mtd><mml:mrow><mml:mtext>x</mml:mtext><mml:mo>≥</mml:mo><mml:mn>3</mml:mn></mml:mrow></mml:mtd></mml:mtr><mml:mtr><mml:mtd><mml:mrow><mml:mrow/><mml:mfrac><mml:mrow><mml:mtext>x</mml:mtext><mml:mo stretchy=\"false\">(</mml:mo><mml:mtext>x</mml:mtext><mml:mo>+</mml:mo><mml:mn>3</mml:mn><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mn>6</mml:mn></mml:mfrac><mml:mo>,</mml:mo></mml:mrow></mml:mtd><mml:mtd><mml:mtext>otherwise</mml:mtext></mml:mtd></mml:mtr></mml:mtable></mml:mrow><mml:mo>,</mml:mo></mml:mfenced></mml:mrow></mml:math></alternatives></disp-formula>", "<disp-formula id=\"Equ2\"><label>2</label><alternatives><tex-math id=\"M19\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\text{SiLU}}\\left({\\text{x}}\\right)={\\text{x}}\\cdot {\\text{sigmoid}}\\left({\\text{x}}\\right)=\\frac{{\\text{x}}}{1+{{\\text{e}}}^{-{\\text{x}}}}.$$\\end{document}</tex-math><mml:math id=\"M20\" display=\"block\"><mml:mrow><mml:mtext>SiLU</mml:mtext><mml:mfenced close=\")\" open=\"(\"><mml:mtext>x</mml:mtext></mml:mfenced><mml:mo>=</mml:mo><mml:mtext>x</mml:mtext><mml:mo>·</mml:mo><mml:mtext>sigmoid</mml:mtext><mml:mfenced close=\")\" open=\"(\"><mml:mtext>x</mml:mtext></mml:mfenced><mml:mo>=</mml:mo><mml:mfrac><mml:mtext>x</mml:mtext><mml:mrow><mml:mn>1</mml:mn><mml:mo>+</mml:mo><mml:msup><mml:mrow><mml:mtext>e</mml:mtext></mml:mrow><mml:mrow><mml:mo>-</mml:mo><mml:mtext>x</mml:mtext></mml:mrow></mml:msup></mml:mrow></mml:mfrac><mml:mo>.</mml:mo></mml:mrow></mml:math></alternatives></disp-formula>", "<disp-formula id=\"Equ3\"><label>3</label><alternatives><tex-math id=\"M21\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$LeakyRelu\\left( x \\right)\\, = \\,\\left\\{ \\begin{gathered} 0.01x,\\quad x &lt; \\,\\,0 \\hfill \\\\ x,\\quad x\\,\\, \\ge \\,0 \\hfill \\\\ \\end{gathered} \\right.$$\\end{document}</tex-math><mml:math id=\"M22\" display=\"block\"><mml:mrow><mml:mi>L</mml:mi><mml:mi>e</mml:mi><mml:mi>a</mml:mi><mml:mi>k</mml:mi><mml:mi>y</mml:mi><mml:mi>R</mml:mi><mml:mi>e</mml:mi><mml:mi>l</mml:mi><mml:mi>u</mml:mi><mml:mfenced close=\")\" open=\"(\"><mml:mi>x</mml:mi></mml:mfenced><mml:mspace width=\"0.166667em\"/><mml:mo>=</mml:mo><mml:mspace width=\"0.166667em\"/><mml:mfenced open=\"{\"><mml:mrow><mml:mtable><mml:mtr><mml:mtd><mml:mrow><mml:mn>0.01</mml:mn><mml:mi>x</mml:mi><mml:mo>,</mml:mo><mml:mspace width=\"1em\"/><mml:mi>x</mml:mi><mml:mo>&lt;</mml:mo><mml:mspace width=\"0.166667em\"/><mml:mspace width=\"0.166667em\"/><mml:mn>0</mml:mn></mml:mrow></mml:mtd></mml:mtr><mml:mtr><mml:mtd><mml:mrow><mml:mrow/><mml:mi>x</mml:mi><mml:mo>,</mml:mo><mml:mspace width=\"1em\"/><mml:mi>x</mml:mi><mml:mspace width=\"0.166667em\"/><mml:mspace width=\"0.166667em\"/><mml:mo>≥</mml:mo><mml:mspace width=\"0.166667em\"/><mml:mn>0</mml:mn></mml:mrow></mml:mtd></mml:mtr><mml:mtr><mml:mtd><mml:mrow/></mml:mtd></mml:mtr></mml:mtable></mml:mrow></mml:mfenced></mml:mrow></mml:math></alternatives></disp-formula>", "<disp-formula id=\"Equ4\"><label>4</label><alternatives><tex-math id=\"M23\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$Attention\\,\\left( {Q,\\,K,\\,V} \\right)\\, = \\,{\\text{softmax}}\\,\\left( {\\frac{{QK^{T} }}{{\\sqrt {d_{k} } }}} \\right)\\,V,$$\\end{document}</tex-math><mml:math id=\"M24\" display=\"block\"><mml:mrow><mml:mi>A</mml:mi><mml:mi>t</mml:mi><mml:mi>t</mml:mi><mml:mi>e</mml:mi><mml:mi>n</mml:mi><mml:mi>t</mml:mi><mml:mi>i</mml:mi><mml:mi>o</mml:mi><mml:mi>n</mml:mi><mml:mspace width=\"0.166667em\"/><mml:mfenced close=\")\" open=\"(\"><mml:mrow><mml:mi>Q</mml:mi><mml:mo>,</mml:mo><mml:mspace width=\"0.166667em\"/><mml:mi>K</mml:mi><mml:mo>,</mml:mo><mml:mspace width=\"0.166667em\"/><mml:mi>V</mml:mi></mml:mrow></mml:mfenced><mml:mspace width=\"0.166667em\"/><mml:mo>=</mml:mo><mml:mspace width=\"0.166667em\"/><mml:mtext>softmax</mml:mtext><mml:mspace width=\"0.166667em\"/><mml:mfenced close=\")\" open=\"(\"><mml:mfrac><mml:mrow><mml:mi>Q</mml:mi><mml:msup><mml:mi>K</mml:mi><mml:mi>T</mml:mi></mml:msup></mml:mrow><mml:msqrt><mml:msub><mml:mi>d</mml:mi><mml:mi>k</mml:mi></mml:msub></mml:msqrt></mml:mfrac></mml:mfenced><mml:mspace width=\"0.166667em\"/><mml:mi>V</mml:mi><mml:mo>,</mml:mo></mml:mrow></mml:math></alternatives></disp-formula>", "<disp-formula id=\"Equ5\"><label>5</label><alternatives><tex-math id=\"M25\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$Attention\\,\\left( {Q,\\,K,\\,V} \\right)\\, = \\,{\\text{softmax}}\\,\\left( {\\frac{{\\overline{Q} K^{T} }}{\\sqrt d }} \\right)\\,V,$$\\end{document}</tex-math><mml:math id=\"M26\" display=\"block\"><mml:mrow><mml:mi>A</mml:mi><mml:mi>t</mml:mi><mml:mi>t</mml:mi><mml:mi>e</mml:mi><mml:mi>n</mml:mi><mml:mi>t</mml:mi><mml:mi>i</mml:mi><mml:mi>o</mml:mi><mml:mi>n</mml:mi><mml:mspace width=\"0.166667em\"/><mml:mfenced close=\")\" open=\"(\"><mml:mrow><mml:mi>Q</mml:mi><mml:mo>,</mml:mo><mml:mspace width=\"0.166667em\"/><mml:mi>K</mml:mi><mml:mo>,</mml:mo><mml:mspace width=\"0.166667em\"/><mml:mi>V</mml:mi></mml:mrow></mml:mfenced><mml:mspace width=\"0.166667em\"/><mml:mo>=</mml:mo><mml:mspace width=\"0.166667em\"/><mml:mtext>softmax</mml:mtext><mml:mspace width=\"0.166667em\"/><mml:mfenced close=\")\" open=\"(\"><mml:mfrac><mml:mrow><mml:mover><mml:mi>Q</mml:mi><mml:mo>¯</mml:mo></mml:mover><mml:msup><mml:mi>K</mml:mi><mml:mi>T</mml:mi></mml:msup></mml:mrow><mml:msqrt><mml:mi>d</mml:mi></mml:msqrt></mml:mfrac></mml:mfenced><mml:mspace width=\"0.166667em\"/><mml:mi>V</mml:mi><mml:mo>,</mml:mo></mml:mrow></mml:math></alternatives></disp-formula>", "<inline-formula id=\"IEq8\"><alternatives><tex-math id=\"M27\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\overline{Q}$$\\end{document}</tex-math><mml:math id=\"M28\"><mml:mover><mml:mi>Q</mml:mi><mml:mo>¯</mml:mo></mml:mover></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq9\"><alternatives><tex-math id=\"M29\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$60^{ \\circ }$$\\end{document}</tex-math><mml:math id=\"M30\"><mml:msup><mml:mn>60</mml:mn><mml:mo>∘</mml:mo></mml:msup></mml:math></alternatives></inline-formula>", "<disp-formula id=\"Equ6\"><label>6</label><alternatives><tex-math id=\"M31\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$DSC\\,\\left( {I_{gt} ,\\,I_{pt} } \\right)\\, = \\,\\,\\frac{{2\\,\\,\\left| {I_{gt} \\cap I_{pt} } \\right|}}{{\\left| {I_{gt} } \\right| + \\left| {I_{pt} } \\right|}},$$\\end{document}</tex-math><mml:math id=\"M32\" display=\"block\"><mml:mrow><mml:mi>D</mml:mi><mml:mi>S</mml:mi><mml:mi>C</mml:mi><mml:mspace width=\"0.166667em\"/><mml:mfenced close=\")\" open=\"(\"><mml:mrow><mml:msub><mml:mi>I</mml:mi><mml:mrow><mml:mi mathvariant=\"italic\">gt</mml:mi></mml:mrow></mml:msub><mml:mo>,</mml:mo><mml:mspace width=\"0.166667em\"/><mml:msub><mml:mi>I</mml:mi><mml:mrow><mml:mi mathvariant=\"italic\">pt</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mfenced><mml:mspace width=\"0.166667em\"/><mml:mo>=</mml:mo><mml:mspace width=\"0.166667em\"/><mml:mspace width=\"0.166667em\"/><mml:mfrac><mml:mrow><mml:mn>2</mml:mn><mml:mspace width=\"0.166667em\"/><mml:mspace width=\"0.166667em\"/><mml:mfenced close=\"|\" open=\"|\"><mml:mrow><mml:msub><mml:mi>I</mml:mi><mml:mrow><mml:mi mathvariant=\"italic\">gt</mml:mi></mml:mrow></mml:msub><mml:mo>∩</mml:mo><mml:msub><mml:mi>I</mml:mi><mml:mrow><mml:mi mathvariant=\"italic\">pt</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mfenced></mml:mrow><mml:mrow><mml:mfenced close=\"|\" open=\"|\"><mml:msub><mml:mi>I</mml:mi><mml:mrow><mml:mi mathvariant=\"italic\">gt</mml:mi></mml:mrow></mml:msub></mml:mfenced><mml:mo>+</mml:mo><mml:mfenced close=\"|\" open=\"|\"><mml:msub><mml:mi>I</mml:mi><mml:mrow><mml:mi 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{ "acronym": [ "DSC", "HD", "CNNs", "BUS", "NLP", "C3", "CSPNet", "ECA", "CBAM", "DCE-MRI", "MSA", "MLP" ], "definition": [ "Dice similarity coefficient", "Hausdorff distance", "Convolution neural networks", "Breast ultrasound", "Natural language processing", "Concentrated-comprehensive convolutions", "Cross-stage partial network", "Efficient channel attention", "Convolutional block attention module", "Dynamic contrast-enhanced magnetic resonance imaging", "Multi-head self-attention mechanism", "Multi-layer perceptron" ] }

[ "<title>Background</title>", "<p id=\"Par5\">One of the most prevalent vector-borne infections is dengue fever with an estimated annual global burden of 390 million infections, of which 96 million present clinically [##REF##23563266##1##]. The disease is caused by dengue virus principally transmitted by <italic>Aedes</italic> mosquitoes which are commonly found in tropical and sub-tropical regions. In addition, dengue has surpassed other infectious diseases such as malaria to be the most prominent vector-borne disease globally in terms of morbidity and cost of treatment [##REF##22556068##2##]. The impact of dengue is a great burden on public health costs in South-East Asia and the burden of this infection in Thailand is among the highest in the world [##UREF##0##3##].</p>", "<p id=\"Par7\">Dengue infection is commonly asymptomatic but when clinical manifestations occur, they can vary from mild to severe and life-threatening. Severe dengue, in particular dengue hemorrhagic fever (DHF) and dengue shock syndrome (DSS), is an important cause of hospitalization and death in Thailand [##REF##15964964##4##]. The mild form of infection may be infectious and spread the virus in the community. The only available vaccine for dengue has limited efficacy and can only be administered to people who have previously been infected with challenges of pre-vaccination screening and suboptimal test performance [##REF##31365115##5##]. Due to these limitations and the absence of any specific treatment, vector control has remained a focus of public health interventions to interrupt the infection cycle. Estimating when and where an outbreak will occur is an important goal to effectively allocate prevention and control resources. Therefore, efficient and reliable notification systems are vital to monitor dengue incidence including spatial and temporal distributions to detect outbreaks in order to initiate timely and effective control measures.</p>", "<p id=\"Par8\">Effective communicable disease surveillance systems are a prerequisite to ensure early detection of health threats and their timely control. Delay in infectious disease reporting might hamper timely outbreak interventions. In general, public health surveillance of diseases relies on the notification system which is a result of a chain of events from infection through reporting to public health services, be they local, regional or national. The general flow of surveillance information in Thailand is depicted in Fig. ##FIG##0##1##. Delays in the system arise at different stages: different health-seeking behaviors (community), laboratory and follow-up tests (health care facility), the reporting system, and communications between different health providers (surveillance response), including hospitals, the district officer and the insecticide sprayer operatives, as well as people in targeted areas. Dengue surveillance in many countries including Thailand relies on passive reporting which is susceptible to delays. The lag in the surveillance system is therefore a vital issue for disease control planning as incomplete and delayed information can undermine any efforts to deliver early warning and real-time outbreak detection required to trigger an effective response to public health threats.</p>", "<p id=\"Par9\">Influenced by healthcare provider adherence and patient access, lagged reports exhibit variations across locations. Recent methodologies (examples [##REF##31292995##6##–##REF##32251464##10##]) aim to estimate current disease incidence by addressing notification lags, primarily focusing on systematic delays. However, these approaches overlook cluster detection, a crucial aspect in the decision-making process for disease outbreak control. While a prior effort offered a valuable framework for reporting delay correction in dengue control in Thailand [##REF##32013994##11##], the correction alone falls short of the ultimate surveillance goal: informing public health actions to reduce morbidity and mortality [##UREF##2##12##]. Consequently, in this study, we went beyond delay correction, also implementing and comparing the performance of cluster detection methods with case nowcasting.</p>", "<p id=\"Par6\">&gt;</p>", "<p id=\"Par10\">Reporting system time lags hinder timely cluster identification, impeding the initiation of effective disease control interventions. Therefore, we introduced an integrated two-step methodology for spatiotemporal real-time cluster detection, specifically tailored to correct reporting delays. The first step involved adopting space-time nowcasting modeling to account for reporting system lags. Subsequently, anomaly detection methods assessed adverse risks, demonstrated using weekly dengue surveillance data in Thailand. We also further evaluated effectiveness with various metrics compared different methods, revealing similarities and differences among detection techniques with optimal thresholds. This advancement offers valuable insights for informing additional public health actions to reduce dengue morbidity and mortality in Thailand.</p>" ]

[ "<title>Methods</title>", "<title>Dengue surveillance data</title>", "<p id=\"Par11\">In this study, we analyzed dengue case data obtained from the routine surveillance system of the Bureau of Epidemiology, Thai Ministry of Public Health. The dataset consisted of reported cases from various healthcare facilities, including governmental hospitals, clinics under the universal health coverage scheme, and private hospitals, all of which reported cases to district health surveillance data centers. To examine the influence of reporting delays and outbreaks, our study focused specifically on the data collected from the 50 districts of the Bangkok metropolitan area. The years 2010–2011 were chosen as they presented a significant and illustrative case study for our research objectives. During this period, widespread dengue outbreaks were observed across the country, with particular intensity in Bangkok. Notably, the response to these outbreaks exhibited notable delays. Therefore, this timeframe serves as a relevant case study to investigate the impact of reporting delays and outbreak occurrences.</p>", "<p id=\"Par12\">The dengue case types considered in our analysis encompassed dengue fever, dengue hemorrhagic fever, and dengue shock syndrome. Our primary goal was to achieve real-time detection, enabling prompt identification of dengue infection clusters and facilitating timely intervention to prevent further disease transmission. Consequently, we combined the number of cases across all dengue types in our analysis. Figure ##FIG##1##2## illustrates the temporal trend of dengue incidence in Bangkok during the years 2010–2011. Notably, reporting delays tended to increase during the high season, which corresponds to the rainy period, potentially leading to substantial delays in the availability of data. Such delays can hinder the early detection of possible outbreaks, underscoring the significance of improving the timeliness of surveillance systems to enhance outbreak response capabilities.</p>", "<p id=\"Par13\">\n\n</p>", "<title>Ethics declarations</title>", "<p id=\"Par14\">Ethics Committee of the Faculty of Tropical Medicine, Mahidol University waived for informed consent of participants. This study was approved by the Ethics Committee of the Faculty of Tropical Medicine, Mahidol University. The submission number was TMEC 22–054 and the number of ethical approval certificate was MUTM 2022-057-01. All methods were carried out in accordance with relevant guidelines and regulations.</p>", "<title>Nowcasting for lagged reporting</title>", "<p id=\"Par15\">A key challenge for infectious disease surveillance in countries with developing infrastructure including Thailand is the time lag before reports are delivered at different levels in the notification system. The report structure of surveillance data with reporting lags can be seen as the lag triangle presented in Fig. ##FIG##2##3##. As described in [##REF##32013994##11##], let be the number of disease incidence which occurred during calendar week <italic>t</italic> in district <italic>i</italic> (<italic>i</italic> = 1,…, <italic>I</italic> = 50) but arrived in the surveillance database in week <italic>d</italic> (<italic>d</italic> = 1,…, <italic>D</italic>) weeks after the onset date. This signifies the problem that cases have been recorded but have not yet been entered into the database. Note that the event that the cases were in the surveillance system in the same week as the date of diagnosis was denoted as <italic>d</italic> = 1. The current time point of interest is indexed as <italic>t</italic> = T and the maximum possible delay that can happen in the surveillance system is labelled as <italic>D</italic>, i.e., full data were delivered into the system from <italic>T</italic> + <italic>D</italic> weeks onwards. Thencan be defined as the estimated number of dengue cases that truly occurred by summing predicted reporting lag fractions happening at week <italic>t</italic>,, over the possible lag range. The goal here was to correct the reported cases by nowcasting the actual weekly fractions of dengue cases for each district, , in a real-time manner.</p>", "<p id=\"Par16\">\n\n</p>", "<p id=\"Par17\">To address spatiotemporal reporting lags, a frequently adopted approach in small area health studies is to model case counts as conditionally independent Poisson variates. The likelihood function for this is defined as</p>", "<p id=\"Par18\">where the mean and variance are both equal to. That is, for our modeling, we assumed i.e.wherewas the relative dengue case risk adjusted for the offset,, as the baseline level at risk. There are a number of ways to adjust for the baseline (see examples [##REF##27389668##13##–##UREF##3##15##]), however a common practice for disease mapping [##UREF##4##16##] is to calculate the expected rate as , whereandare the true number of disease incidence and population at risk for each location and time. Since we performed the analysis at a weekly scale, the number of populations was assumed to be constant over the study period. Then the expected rate used in the analysis was computed as . Another main parameter of interest is and the most common approach to model this is to assume a logarithmic link to a linear combination of space-time random effects. First, we structured the model-based lag reporting correction by using information across neighboring districts and time periods to incorporate spatiotemporal smoothing. The convolution model (see examples [##UREF##3##15##–##REF##30611213##18##]) was employed to capture spatially correlated and unstructured extra variation in the model. Both structured and unstructured random effects were included to capture various forms of unobserved confounding. The uncorrelated random effect is described by a zero mean Gaussian prior distribution. The spatially correlated effect is assumed to have the intrinsic conditional autoregressive model [##UREF##5##19##]. To capture the time series trend, the first-order random walk model was applied. All random interaction terms among space, time and delay dimension were specified by a Gaussian distribution with zero mean. All precision (reciprocal of variance) parameters were assumed as a Log-Gamma distribution with hyperparameters as 1 and 0.0005, and 1 and 0.00005 for the conditional autoregressive model, and for uncorrelated and random walk random effects.</p>", "<p id=\"Par19\">To address the variability in dengue incidence, the Negative Binomial distribution, which incorporates an overdispersion parameter, can be considered as an alternative to the Poisson likelihood. Typically, issues of dispersion can be tackled through models like Negative Binomial and Quasi-Poisson, both having an equal number of parameters and suitability for overdispersed count data [##REF##18051645##20##]. In our exploration of modeling choices for reporting lags in this study, we also considered the Generalized Poisson model as an alternative base count distribution. This model not only accommodates dispersion but also possesses a heavier tail with the same first two moments, offering increased flexibility for a broader range of data compared to the Negative Binomial [##UREF##6##21##].</p>", "<p id=\"Par20\">The Generalized Poisson model can be seen as a characterization, operating as an alternative Poisson mixture model to the Negative Binomial distribution for overdispersed count data, as emphasized in a study cited in our original submission [##UREF##6##21##]. Moreover, another study suggests that generalized Poisson regression models can serve as viable alternatives to negative binomial regression [##UREF##7##22##]. Despite the typical preference for the Negative Binomial distribution when evidence of dispersion is present relative to the Poisson, a Negative Binomial model had previously shown similar performance to the Poisson in a scenario involving delay correction with mild overdispersion [##REF##32013994##11##]. Additionally, during our extended study period, we noted similarities in temporal patterns and magnitudes compared to the previous study period. Consequently, we chose to compare only Poisson and Generalized Poisson models in this study.</p>", "<p id=\"Par21\">The generalized Poisson distribution used in this study follows the form introduced in previous works [##UREF##8##23##, ##UREF##9##24##], represented as.</p>", "<p id=\"Par22\">Given, we have the mean and variance equal toand. When, the generalized Poisson approaches the Poisson distribution with mean and variance equal to. The mean is also linked to the linear predictor with the logarithm function as in the Poisson.</p>", "<title>Space-time cluster diagnostics</title>", "<p id=\"Par23\">Space-time cluster diagnostics in epidemiology often employ scan statistics and various refinements of scan statistics have been proposed (for example [##UREF##10##25##–##UREF##12##27##]), including the version implemented in SatScan software [##UREF##13##28##]. However, a fundamental challenge lies in interpreting p-values and establishing a threshold for defining ‘significance’ [##UREF##14##29##]. Therefore, we alternatively based our approaches in this study to cluster detection within the model-based framework.</p>", "<p id=\"Par24\">In the context of this framework, it becomes crucial to define what constitutes a cluster. In infectious disease surveillance, it is important to effectively identify localized case anomaly that deviate from expected baseline patterns in both space and time, prompting further investigation. This concept is akin to anomaly detection, where we employ the goodness of fit of a model to quantify unusual events within a set of space-time observations. Measures of goodness of fit help summarize the differences between observed local case counts and the values expected under the model or baseline for each location and time. In our study, we thus explored and compared various model-based measures for anomaly detection, including exceedance probability, information criteria, and leave-one-out cross-validation.</p>", "<title>Exceedance probability</title>", "<p id=\"Par25\">A number of diagnostic tools are available to evaluate the local anomalies. However, it is a natural idea to consider a cluster as any isolated locations or geographically-bounded regions that display an excess of disease risk or incidence in a particular time. The excess of disease risk can be examined by comparison with the expected rate previously described. So, an approach for space-time anomaly detection is to calculate , exceedance probability (EXC), from the number of estimates in the posterior sample which exceed a threshold [##REF##16453377##30##, ##UREF##15##31##]. Usually the limit is assumed to be <italic>a</italic>= 1 which means we apply the level of the expected rate as the baseline.</p>", "<title>Information criteria</title>", "<p id=\"Par26\">An aim of diagnostic checking is to compare observed data with the fitted model in such a way that it is possible to detect any discrepancies. Forms of model assessment involve measuring the goodness-of-fit (GOF) to evaluate whether the particular data in space and time provide an adequate fit to the model. A set of common GOF measures is the information criteria. The deviance information criterion (DIC) [##UREF##16##32##] has been widely used for overall model fit in the Bayesian setting generalized from the Akaike information criterion (AIC) in the Frequentist framework. Another is the widely applicable or Watanabe-Akaike information criterion (WAIC) [##UREF##17##33##] which can be viewed as an improvement on DIC. WAIC is fully Bayesian in which this measure applies the entire use of posterior distribution. Unlike DIC, WAIC is robust to different parametrizations and is also valid for singular models [##UREF##18##34##].</p>", "<p id=\"Par27\">While the global information criteria have been primarily used as an overall measure of model fit, they can be partitioned into contributions from individual observations in space and time to provide finer details of model discrepancies [##REF##27566768##35##, ##REF##21243121##36##]. The partitioning of the DIC for the observed data, local DIC, can be written as [##REF##21243121##36##] whereis the mean deviance for nowcasted cases at district <italic>i</italic> and week <italic>t</italic> and is the effective number of parameters, amount of information used for the particular observation for each location and time. Likewise, local WAIC, which is a direct result of pointwise predictive density, can be defined as [##UREF##18##34##] where (log pointwise predictive density) = andcalculated over the posterior sample. Since the range of information criteria is on the positive real line, we adopted the transformed values on a unit interval as 1-and 1- . This similar transformation was also utilized as model probability in model selection and averaging [##REF##21243121##36##, ##REF##28034176##37##].</p>", "<title>Leave-one-out cross-validation</title>", "<p id=\"Par28\">Another set of metrics widely used to estimate the model fit error is cross validation. In a general setting of cross-validation, the data are repeatedly divided into a training set and a test set. Then the model is fitted using the training set and the cross-validation error is calculated from the test set. However, we restricted our attention here to leave-one-out cross-validation (LOO-CV), the special case with all partitions in which each test set represents a single data point. Among LOO-CV methods, the conditional predictive ordinate (CPO) [##UREF##19##38##] and probability integral transform (PIT) [##UREF##20##39##] are commonly used to detect extreme observations in statistical modeling. The CPO detection in our case for the delay-corrected dengue incidence at district <italic>i</italic> during week <italic>t</italic> can be computed as . For each observed case, its CPO is the posterior probability of observing that dengue case when the model is fit using all data except . Large CPO values imply a good fit of the model to observed data, while small values suggest a worse fitting of the model to that observed data point and, perhaps, that it should be further explored.</p>", "<p id=\"Par29\">On the other hand, PIT measures the probability of a new value to be less than the actual observed value: where is the observation vector with the <italic>it-</italic>th component omitted. This procedure is performed in cross-validation mode meaning that in each step of the validation process the ensuing leave-one-out posterior predictive distribution is calculated. However, in our data which are discrete (disease count) data, the estimate was adjusted as, and unusually large or small values of PIT indicate possible outliers or surprising observations not supported by the model under consideration [##REF##22214542##40##].</p>", "<title>Evaluation and computation of anomaly diagnostic methods</title>", "<p id=\"Par30\">Surveillance systems for infectious diseases must strike a balance between outbreak detection accuracy and the efficient allocation of disease control resources. The concepts of optimal criteria, accuracy (Acc), sensitivity (Se), specificity (Sp), positive predictive value (PPV), and negative predictive value (NPV) serve as valuable metrics for comparing and assessing the validity of cluster detection methods. In this study, these five evaluation metrics were employed for method comparison and performance evaluation. An anomaly was considered alarmed when the anomaly diagnostic value from space-time cluster diagnostics, computed for each case count, exceeded a predefined cutoff. We then systematically evaluated the performance of the cluster diagnostics across different threshold values.</p>", "<p id=\"Par31\">The key evaluation components are defined as follows. The true positive (TP) was calculated as instances where a method correctly indicates the presence of a disease anomaly. True negative (TN) was the count where a method correctly indicates the absence of a disease anomaly. False positive (FP) was the count of cases where a method incorrectly suggests the presence of an anomaly. False negative (FN) was the count of instances where a method incorrectly indicates the absence of an anomaly. Then sensitivity, specificity, and predictive values are expressed as follows: sensitivity = TP / (TP + FN); specificity = TN / (FP + TN); positive predictive value = TP / (TP + FP); negative predictive value = TN / (TN + FN); accuracy is defined as the proportion of correct detections among the total number of detections, i.e., Acc = (TP + TN) / (TP + TN + FP + FN).</p>", "<p id=\"Par32\">In order to efficiently apply this methodology in real surveillance situations, one essential characteristic that should be considered in real-time surveillance systems is computational practicability. Using all the data history is perhaps unnecessary while the most recent information might be adequate to capture the disease pattern needed to detect an outbreak. To reduce computing resource, we partitioned the surveillance data into sliding windows to optimize computational competence of the system. Rather than the full likelihood, the working likelihood was partitioned as where <italic>w</italic> is the length of sliding window. The sliding window technique then investigates only the most recent <italic>w</italic> and hence the surveillance might be more efficient and practical for real-time applications. However, the partition can be a trade-off between computing efficiency and estimation of precision. We then also examined the effect of different window sizes in the case study.</p>", "<p id=\"Par33\">Estimates derived from the models and diagnostic methods are typically computed from converged posterior samples using sampling-based algorithms like Markov Chain Monte Carlo (McMC). However, real-time estimation in infectious disease surveillance requires timeliness. With the setup of a multidimensional model and accumulating surveillance data over time, the parameter space can rapidly expand, demanding exponential computational resources. To address this, a more efficient approach for inferring parameters is the Integrated Nested Laplace Approximation (INLA) [##UREF##21##41##]. This method is particularly suitable for the rapid estimation of parameters in a real-time context. The proposed model was implemented using the numerical Laplace approximation within the R-INLA package, available at <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.r-inla.org\">www.r-inla.org</ext-link>. All computations were conducted using RStudio version 2020.07.0. Computing information using INLA with R code was provided in supplementary document ##SUPPL##0##S1##.</p>" ]

[ "<title>Results</title>", "<p id=\"Par34\">The data employed to demonstrate anomaly detection consisted of weekly dengue incidence in Bangkok, the location with the highest annual incidence in the country. Results, averaged across study areas and detection thresholds, are presented in Table ##TAB##0##1##, detailing estimates of sensitivity, specificity, accuracy, and their corresponding predictive values for anomaly detection. Without delay correction, the accuracy of detection methods under both likelihood assumptions ranged from 0.4791 using PIT to 0.6092 using WAIC. DIC and EXC performed best under a General Poisson model while WAIC and EXC had the best outcome with a Poisson model. The highest accuracy with reporting delay was the Poisson model with WAIC. With nowcasting correcting for reporting lags, EXC performed best across the evaluation metrics with accuracies of 0.7221 and 0.6916 under both Poisson and Generalized Poisson models. The accuracies with corrected delays using the proposed spatiotemporal nowcasting technique were improved about 22.7% and 17.52% under Poisson and Generalized Poisson assumptions respectively.</p>", "<p id=\"Par35\">\n\n</p>", "<p id=\"Par36\">\n\n</p>", "<p id=\"Par37\">\n\n</p>", "<p id=\"Par38\">We further examined the optimal threshold and effect of different window sizes in order to apply the cluster detection in real situations. The focus was limited to the test characteristics of EXC since the detection had the best performance across the evaluation measures and likelihood assumptions. The best threshold was defined as the cut-off value with the maximum accuracy. Table ##TAB##1##2## shows the cut-off points with the highest accuracy using different computing window lengths. These comparisons were computed on a Dell computer with 64-bit Windows system, 8GB RAM and Intel i5-3570 S CPU @ 3.10 GHz. The optimal threshold varied in a range of 0.95–0.99 for Poisson and 0.93–0.99 for Generalized Poisson models with the maximum accuracy of approximately 72%. The computing times ranged from 0.5376 min per calculation with 5-week window size to 48.6852 min per calculation with 30-week window size under Poisson model, however the accuracy increased less than 1%. On the other hand, the Generalized Poisson model required slightly more computing time of 0.5487 min for 5-week and 53.2669 min for 30-week window sizes. The improved accuracy was also similarly small at less than 1%. The posterior summary of overdispersion parameters with their corresponding credible intervals (CrI) for both delay correction and anomaly detection indicated a mild overdispersion in the observed data with posterior means of 0.0861–0.0937 (95% CrI: 0.041–0.167) and 0.1466–0.1636 (95% CrI: 0.041–0.384). These implied that the Poisson likelihood assumption with space-time random effects might be adequate to capture the case variability in our data set.</p>", "<p id=\"Par39\">Figure ##FIG##3##4## compares dengue incidence, standardized incidence and exceedance probability at week 102 during the high season in year 2011. Note that the result of other periods (weeks 96–104) was provided in supplementary document ##SUPPL##0##S2##. The complete (true) incidence depicted in the left column showed a possible disease cluster in the southwest of Bangkok and hot spots in the center. Exceedance probabilities also revealed the same pattern of high-risk areas using complete and nowcasted data. In contrast, those clusters and hot spots did not appear in data with reporting delays. The reporting lags are crucial for infectious disease surveillance as the infection can actually spread during the lag period while anomaly detection with nowcasting could accurately recover and detect potential outbreaks in the case study. The developed methodology hence demonstrated an advantage in revealing the true disease pattern properly for real-time public health intervention planning.</p>" ]

[ "<title>Discussion</title>", "<p id=\"Par40\">Efficient surveillance is paramount for early infectious disease outbreak detection, particularly for diseases like dengue with no effective vaccines or specific treatments. As vector control remains the primary intervention, timely outbreak detection is crucial. In this study, we devised an integrated approach to assess risks while addressing reporting lags, comparing anomaly detection measures in a dengue surveillance case study in Thailand. Unlike prior efforts that often focus solely on delay correction, we extended our investigation to include and compare cluster detection methods, augmenting the decision-making process for disease outbreak control.</p>", "<p id=\"Par41\">Spatiotemporal cluster detection typically necessitates complex models, especially when modeling specific localized space-time behaviors. Real-time infectious disease surveillance requires effective clustering methods capable of promptly detecting deviations from normal background variation. To accommodate space-time reporting variations, we modeled dengue case counts using a count likelihood with a spatiotemporal latent random-effect structure. While a Poisson distribution is a common choice, our investigation also included a Generalized Poisson assumption, offering flexibility for a wider range of data compared to the negative binomial [##UREF##6##21##].</p>", "<p id=\"Par42\">The dispersion parameter, indicative of data variability, demonstrated mild dispersion across scenarios and window sizes. The use of a Generalized Poisson model, known for its flexibility in handling dispersion, proved effective in capturing complex multidimensional correlations, though at the expense of increased computing time. Considering the real-time surveillance context, the feasibility of model computation should be a key consideration. Experiments with different moving window lengths revealed marginal improvements in accuracy, suggesting that small sliding windows can yield reasonably good performance, capturing data variation adequately within the model specification.</p>", "<p id=\"Par43\">A number of measures of adverse risks were compared and investigated. The exceedance probability outperformed followed by information criteria and leave-one-out cross validation. PIT had the lowest overall performance but higher specificity than information criteria. Information criteria and CPO appeared to have high sensitivity but low PPV. This may imply that PIT yielded conservative detection while CPO and information criteria may produce more false positives. EXC appeared to have highest specificity and PPV without lag nowcasting and had the best values across evaluation metrics with correction for delays. Although WAIC has been suggested lately as an alternative to DIC, which has a long historical development in Bayesian statistics, in our case study both WAIC and DIC had very similar results and performance in various assessment measures. The choice of the most appropriate measure should consider the specific requirements and objectives of the surveillance system.</p>", "<p id=\"Par44\">Timeliness is a critical aspect of real-time surveillance. One of the key advantages of our proposed framework is its minimal data requirement, as it solely relies on past surveillance data on incidence reporting using a sliding window partition. This flexibility allows the system to be readily adaptable to various disease systems, particularly in cases where other variables such as climatic or clinical confounders are not available in real-time for inclusion in the model. Nevertheless, our unified approach has been designed to accommodate the inclusion of such covariates through the link function, providing a comprehensive framework for capturing additional factors.</p>", "<p id=\"Par45\">Despite its advancements, it is important to acknowledge several limitations in this study. Firstly, the developed methodology does not explicitly include prediction, which is a significant aspect of disease surveillance and planning. However, to support real-time disease control activities, our development effectively complements existing disease prediction efforts. The incorporation of lag-corrected nowcasting into forecasting can enhance the effectiveness of surveillance in disease control activities.</p>", "<p id=\"Par46\">Another limitation is the exclusive testing of the developed platform using dengue data from Thailand. Generalizing its applicability to other diseases and settings may require further validation. Nevertheless, the developed platform demonstrates potential for a broad spectrum of applications, extending beyond dengue clustering scenarios to address challenges in infectious or emerging disease surveillance. The versatility and robustness of our approach render it applicable to various disease surveillance problems, providing public health practitioners with an effective tool for enhancing real-time monitoring, control, and prediction of infectious diseases.</p>" ]

[ "<title>Conclusions</title>", "<p id=\"Par47\">Effective disease surveillance systems are crucial for timely detection and control of health threats. However, reporting lags in infectious disease surveillance systems can hinder the prompt implementation of outbreak control measures. Existing methods for estimating disease incidence often overlook anomaly detection in the presence of reporting delays. In this study, we introduced an integrated approach that addresses this challenge by enabling accurate real-time cluster detection, even in the presence of reporting delays. While further research and collaboration are necessary to enhance the methodology and its development, our approach offers flexibility by relaxing disease-specific assumptions, making it adaptable to various disease settings. By incorporating anomaly detection, our method can effectively identify disease clusters in real-time, contributing to timely initiation of disease control activities. Furthermore, the efforts made in this study can complement existing surveillance systems and forecasting methods. By integrating our approach into the existing infrastructure, we can enhance the overall surveillance effectiveness and facilitate the timely implementation of disease control measures.</p>" ]

[ "<title>Background</title>", "<p id=\"Par1\">Dengue infection ranges from asymptomatic to severe and life-threatening, with no specific treatment available. Vector control is crucial for interrupting its transmission cycle. Accurate estimation of outbreak timing and location is essential for efficient resource allocation. Timely and reliable notification systems are necessary to monitor dengue incidence, including spatial and temporal distributions, to detect outbreaks promptly and implement effective control measures.</p>", "<title>Methods</title>", "<p id=\"Par2\">We proposed an integrated two-step methodology for real-time spatiotemporal cluster detection, accounting for reporting delays. In the first step, we employed space-time nowcasting modeling to compensate for lags in the reporting system. Subsequently, anomaly detection methods were applied to assess adverse risks. To illustrate the effectiveness of these detection methods, we conducted a case study using weekly dengue surveillance data from Thailand.</p>", "<title>Results</title>", "<p id=\"Par3\">The developed methodology demonstrated robust surveillance effectiveness. By combining space-time nowcasting modeling and anomaly detection, we achieved enhanced detection capabilities, accounting for reporting delays and identifying clusters of elevated risk in real-time. The case study in Thailand showcased the practical application of our methodology, enabling timely initiation of disease control activities.</p>", "<title>Conclusion</title>", "<p id=\"Par4\">Our integrated two-step methodology provides a valuable approach for real-time spatiotemporal cluster detection in dengue surveillance. By addressing reporting delays and incorporating anomaly detection, it complements existing surveillance systems and forecasting efforts. Implementing this methodology can facilitate the timely initiation of disease control activities, contributing to more effective prevention and control strategies for dengue in Thailand and potentially other regions facing similar challenges.</p>", "<title>Supplementary Information</title>", "<p>The online version contains supplementary material available at 10.1186/s12874-024-02141-5.</p>", "<title>Keywords</title>", "<p>Open access funding provided by Mahidol University</p>" ]

[ "<title>Electronic supplementary material</title>", "<p>Below is the link to the electronic supplementary material.</p>", "<p>\n\n</p>" ]

[ "<title>Acknowledgements</title>", "<p>We would like to thank Nattwut Ekapirat and Naraporn Khuanyoung for assistance with the epidemiological data.</p>", "<title>Author contributions</title>", "<p>All authors contributed to the conceptual design of the study. CR designed and developed the statistical methodology, completed analyses, and drafted the manuscript. DA assisted with the epidemiological interpretation and data. RJM and DA were responsible for clinical revision and improvements of the manuscript. All authors have read and approved the final manuscript.</p>", "<title>Funding</title>", "<p>This research was funded in part by the Faculty of Tropical Medicine, Mahidol University, and the Wellcome Trust [Grant number 220211]. For the purpose of open access, the authors have applied a CC BY public copyright license to any Author Accepted Manuscript version arising from this submission. The funding body had no role in the design or analysis of the study, interpretation of results, or writing of the manuscript.</p>", "<p>Open access funding provided by Mahidol University</p>", "<title>Data availability</title>", "<p>The data that support the findings of this study were obtained from the Thai Bureau of Epidemiology, Ministry of Public Health, but restrictions apply to the availability of these data, which were used with permission for the current study, and are therefore not publicly available. For data requests related to this study, please contact the corresponding author, Dr. Chawarat Rotejanaprasert, at [email protected]. Data may be available from the authors upon a reasonable request and with permission of the Thai Bureau of Epidemiology.</p>", "<title>Declarations</title>", "<title>Ethics approval and consent to participate</title>", "<p id=\"Par50\">Ethics Committee of the Faculty of Tropical Medicine, Mahidol University waived for informed consent of participants. This study was approved by the Ethics Committee of the Faculty of Tropical Medicine, Mahidol University. The submission number was TMEC 22–054 and the number of ethical approval certificate was MUTM 2022-057-01. All methods were carried out in accordance with relevant guidelines and regulations.</p>", "<title>Consent to publish</title>", "<p id=\"Par49\">Not applicable.</p>", "<title>Competing interests</title>", "<p id=\"Par48\">The authors declare no competing interests.</p>" ]

[ "<fig id=\"Fig1\"><label>Fig. 1</label><caption><p>Flow chart of disease surveillance system with possible reporting delays in different parts of the system</p></caption></fig>", "<fig id=\"Fig2\"><label>Fig. 2</label><caption><p>Plot of weekly dengue incidence in Bangkok, Thailand, 2010–2011. Grey lines represent reported dengue incidence for each district, while black and red lines depict true (no delays, black) and reported (with delays, red) dengue cases averaged over all districts for specific weeks</p></caption></fig>", "<fig id=\"Fig3\"><label>Fig. 3</label><caption><p>Surveillance reporting lag format. The blue cells represent completely observed data in the system for each district at week <italic>t</italic> and partially observed cases are in green cells. The yellow cells represent the unobserved data. <italic>d</italic> is the lag index with <italic>D</italic> maximum delays, i.e. delays beyond <italic>D</italic> were not considered</p></caption></fig>", "<fig id=\"Fig4\"><label>Fig. 4</label><caption><p>Maps of crude incidence (top row), standardized incidence (middle row), and cluster detection (bottom row) using exceedance probability of complete dengue reported cases in Bangkok. Left column: data with nowcasting. Middle column: data without nowcasting. Right column: data with nowcasting during week 102 of the study period</p></caption></fig>" ]

[ "<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Comparison of model-based cluster detection methods with and without nowcasting for reporting lags under evaluation metrics and likelihood assumptions. The bold numbers represent the highest value in each category</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Likelihood</th><th align=\"left\">Delay</th><th align=\"left\">Cluster</th><th align=\"left\" colspan=\"5\">Evaluation metric</th></tr><tr><th align=\"left\">model</th><th align=\"left\">correction</th><th align=\"left\">detection</th><th align=\"left\">Se</th><th align=\"left\">Sp</th><th align=\"left\">NPV</th><th align=\"left\">PPV</th><th align=\"left\">Acc</th></tr></thead><tbody><tr><td align=\"left\"/><td align=\"left\"/><td align=\"left\">EXC</td><td align=\"left\">\n<bold>0.8723</bold>\n</td><td align=\"left\">\n<bold>0.6123</bold>\n</td><td align=\"left\">\n<bold>0.8394</bold>\n</td><td align=\"left\">\n<bold>0.6531</bold>\n</td><td align=\"left\">\n<bold>0.7221</bold>\n</td></tr><tr><td align=\"left\"/><td align=\"left\"/><td align=\"left\">CPO</td><td align=\"left\">0.8241</td><td align=\"left\">0.2324</td><td align=\"left\">0.6895</td><td align=\"left\">0.4863</td><td align=\"left\">0.5237</td></tr><tr><td align=\"left\"/><td align=\"left\">Yes</td><td align=\"left\">PIT</td><td align=\"left\">0.4025</td><td align=\"left\">0.5579</td><td align=\"left\">0.5291</td><td align=\"left\">0.4308</td><td align=\"left\">0.4874</td></tr><tr><td align=\"left\"/><td align=\"left\"/><td align=\"left\">DIC</td><td align=\"left\">0.8611</td><td align=\"left\">0.2313</td><td align=\"left\">0.6671</td><td align=\"left\">0.4822</td><td align=\"left\">0.5172</td></tr><tr><td align=\"left\"/><td align=\"left\"/><td align=\"left\">WAIC</td><td align=\"left\">0.8661</td><td align=\"left\">0.2326</td><td align=\"left\">0.6929</td><td align=\"left\">0.4869</td><td align=\"left\">0.5247</td></tr><tr><td align=\"left\">Poisson</td><td align=\"left\"/><td align=\"left\">EXC</td><td align=\"left\">0.1135</td><td align=\"left\">\n<bold>0.9833</bold>\n</td><td align=\"left\">0.5716</td><td align=\"left\">\n<bold>0.8502</bold>\n</td><td align=\"left\">0.5885</td></tr><tr><td align=\"left\"/><td align=\"left\"/><td align=\"left\">CPO</td><td align=\"left\">0.7063</td><td align=\"left\">0.4141</td><td align=\"left\">\n<bold>0.7201</bold>\n</td><td align=\"left\">0.5336</td><td align=\"left\">0.5921</td></tr><tr><td align=\"left\"/><td align=\"left\">No</td><td align=\"left\">PIT</td><td align=\"left\">0.4146</td><td align=\"left\">0.5804</td><td align=\"left\">0.5439</td><td align=\"left\">0.4511</td><td align=\"left\">0.5051</td></tr><tr><td align=\"left\"/><td align=\"left\"/><td align=\"left\">DIC</td><td align=\"left\">0.7562</td><td align=\"left\">0.4672</td><td align=\"left\">0.6975</td><td align=\"left\">0.5413</td><td align=\"left\">0.5984</td></tr><tr><td align=\"left\"/><td align=\"left\"/><td align=\"left\">WAIC</td><td align=\"left\">\n<bold>0.7778</bold>\n</td><td align=\"left\">0.4689</td><td align=\"left\">0.7175</td><td align=\"left\">0.5491</td><td align=\"left\">\n<bold>0.6092</bold>\n</td></tr><tr><td align=\"left\"/><td align=\"left\"/><td align=\"left\">EXC</td><td align=\"left\">\n<bold>0.8611</bold>\n</td><td align=\"left\">\n<bold>0.5021</bold>\n</td><td align=\"left\">\n<bold>0.8291</bold>\n</td><td align=\"left\">\n<bold>0.6296</bold>\n</td><td align=\"left\">\n<bold>0.6916</bold>\n</td></tr><tr><td align=\"left\"/><td align=\"left\"/><td align=\"left\">CPO</td><td align=\"left\">0.8477</td><td align=\"left\">0.1951</td><td align=\"left\">0.6467</td><td align=\"left\">0.5116</td><td align=\"left\">0.5325</td></tr><tr><td align=\"left\"/><td align=\"left\">Yes</td><td align=\"left\">PIT</td><td align=\"left\">0.3862</td><td align=\"left\">0.4337</td><td align=\"left\">0.4779</td><td align=\"left\">0.4403</td><td align=\"left\">0.4618</td></tr><tr><td align=\"left\"/><td align=\"left\"/><td align=\"left\">DIC</td><td align=\"left\">0.8589</td><td align=\"left\">0.1928</td><td align=\"left\">0.6464</td><td align=\"left\">0.5112</td><td align=\"left\">0.5319</td></tr><tr><td align=\"left\">Generalized</td><td align=\"left\"/><td align=\"left\">WAIC</td><td align=\"left\">0.8577</td><td align=\"left\">0.1951</td><td align=\"left\">0.6466</td><td align=\"left\">0.5116</td><td align=\"left\">0.5325</td></tr><tr><td align=\"left\">Poisson</td><td align=\"left\"/><td align=\"left\">EXC</td><td align=\"left\">0.0161</td><td align=\"left\">\n<bold>0.9889</bold>\n</td><td align=\"left\">0.5716</td><td align=\"left\">\n<bold>0.8502</bold>\n</td><td align=\"left\">0.5885</td></tr><tr><td align=\"left\"/><td align=\"left\"/><td align=\"left\">CPO</td><td align=\"left\">0.7771</td><td align=\"left\">0.4281</td><td align=\"left\">0.6691</td><td align=\"left\">0.5634</td><td align=\"left\">0.5981</td></tr><tr><td align=\"left\"/><td align=\"left\">No</td><td align=\"left\">PIT</td><td align=\"left\">0.3129</td><td align=\"left\">0.6369</td><td align=\"left\">0.4939</td><td align=\"left\">0.4501</td><td align=\"left\">0.4791</td></tr><tr><td align=\"left\"/><td align=\"left\"/><td align=\"left\">DIC</td><td align=\"left\">0.7777</td><td align=\"left\">0.4286</td><td align=\"left\">\n<bold>0.6699</bold>\n</td><td align=\"left\">0.5638</td><td align=\"left\">\n<bold>0.5987</bold>\n</td></tr><tr><td align=\"left\"/><td align=\"left\"/><td align=\"left\">WAIC</td><td align=\"left\">\n<bold>0.7779</bold>\n</td><td align=\"left\">0.4281</td><td align=\"left\">0.6691</td><td align=\"left\">0.5634</td><td align=\"left\">0.5981</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab2\"><label>Table 2</label><caption><p>Detection characteristics and parameters with different sliding window sizes and likelihood assumptions</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Likelihood</th><th align=\"left\">Detection</th><th align=\"left\" colspan=\"6\">Window size (weeks)</th><th align=\"left\" colspan=\"1\"/></tr><tr><th align=\"left\">model</th><th align=\"left\">characteristic</th><th align=\"left\">5</th><th align=\"left\">10</th><th align=\"left\">15</th><th align=\"left\">20</th><th align=\"left\">25</th><th align=\"left\" colspan=\"2\">30</th></tr></thead><tbody><tr><td align=\"left\"/><td align=\"left\">Max accuracy</td><td align=\"left\">0.7143</td><td align=\"left\">0.7214</td><td align=\"left\">0.7175</td><td align=\"left\">0.7234</td><td align=\"left\">0.7209</td><td align=\"left\" colspan=\"2\">0.7202</td></tr><tr><td align=\"left\">Poisson</td><td align=\"left\">Cut-off (percentile)</td><td align=\"left\">0.97</td><td align=\"left\">0.98</td><td align=\"left\">0.95</td><td align=\"left\">0.93</td><td align=\"left\">0.98</td><td align=\"left\" colspan=\"2\">0.98</td></tr><tr><td align=\"left\"/><td align=\"left\">Time</td><td align=\"left\">0.5376</td><td align=\"left\">2.1435</td><td align=\"left\">6.5293</td><td align=\"left\">14.6618</td><td align=\"left\">28.5325</td><td align=\"left\" colspan=\"2\">48.6852</td></tr><tr><td align=\"left\"/><td align=\"left\">Max accuracy</td><td align=\"left\">0.7081</td><td align=\"left\">0.7158</td><td align=\"left\">0.7145</td><td align=\"left\">0.7214</td><td align=\"left\">0.7227</td><td align=\"left\" colspan=\"2\">0.7222</td></tr><tr><td align=\"left\"/><td align=\"left\">Cut-off (percentile)</td><td align=\"left\">0.93</td><td align=\"left\">0.95</td><td align=\"left\">0.94</td><td align=\"left\">0.95</td><td align=\"left\">0.98</td><td align=\"left\" colspan=\"2\">0.98</td></tr><tr><td align=\"left\">Generalized</td><td align=\"left\">Time (min)</td><td align=\"left\">0.5487</td><td align=\"left\">2.2986</td><td align=\"left\">6.7418</td><td align=\"left\">15.8412</td><td align=\"left\">30.8942</td><td align=\"left\" colspan=\"2\">53.2669</td></tr><tr><td align=\"left\">Poisson</td><td align=\"left\">Overdispersion delay</td><td align=\"left\">0.0862</td><td align=\"left\">0.0848</td><td align=\"left\">0.0937</td><td align=\"left\">0.0861</td><td align=\"left\">0.0923</td><td align=\"left\" colspan=\"2\">0.0918</td></tr><tr><td align=\"left\"/><td align=\"left\">95% CrI</td><td align=\"left\">(0.041, 0.124)</td><td align=\"left\">(0.051, 0.135)</td><td align=\"left\">(0.055, 0.167)</td><td align=\"left\">(0.094, 0.191)</td><td align=\"left\">(0.092, 0.148)</td><td align=\"left\" colspan=\"2\">(0.091, 0.129)</td></tr><tr><td align=\"left\"/><td align=\"left\">Overdispersion cluster</td><td align=\"left\">0.158</td><td align=\"left\">0.1636</td><td align=\"left\">0.1551</td><td align=\"left\">0.1534</td><td align=\"left\">0.1478</td><td align=\"left\" colspan=\"2\">0.1466</td></tr><tr><td align=\"left\"/><td align=\"left\">95% CrI</td><td align=\"left\">(0.045, 0.374)</td><td align=\"left\">(0.046, 0.384)</td><td align=\"left\">(0.042, 0.371)</td><td align=\"left\">(0.042, 0.364)</td><td align=\"left\">(0.042, 0.353)</td><td align=\"left\" colspan=\"2\">(0.041, 0.351)</td></tr></tbody></table></table-wrap>" ]

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id=\"M5\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$f(y|\\mu )=\\frac{{{\\mu ^y}}}{{y!}}\\exp \\left( { - \\mu } \\right)$$\\end{document}</tex-math></alternatives></disp-formula>", "<inline-formula id=\"IEq5\"><alternatives><tex-math id=\"M6\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\mu$$\\end{document}</tex-math></alternatives></inline-formula>", "<inline-formula id=\"IEq6\"><alternatives><tex-math id=\"M7\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${y_{itd}}\\sim Poisson({e_{it}}{\\theta _{itd}})$$\\end{document}</tex-math></alternatives></inline-formula>", "<inline-formula id=\"IEq7\"><alternatives><tex-math id=\"M8\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\theta _{itd}}$$\\end{document}</tex-math></alternatives></inline-formula>", "<inline-formula id=\"IEq8\"><alternatives><tex-math id=\"M9\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${e_{it}}$$\\end{document}</tex-math></alternatives></inline-formula>", "<inline-formula id=\"IEq9\"><alternatives><tex-math id=\"M10\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${e_{it}}=\\frac{{\\sum\\nolimits_{i} {\\sum\\nolimits_{t} {{n_{it}}} } }}{{\\sum\\nolimits_{i} {\\sum\\nolimits_{t} {po{p_{it}}} } }}po{p_{it}}$$\\end{document}</tex-math></alternatives></inline-formula>", "<inline-formula id=\"IEq10\"><alternatives><tex-math id=\"M11\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${n_{it}}$$\\end{document}</tex-math></alternatives></inline-formula>", "<inline-formula id=\"IEq11\"><alternatives><tex-math id=\"M12\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$po{p_{it}}$$\\end{document}</tex-math></alternatives></inline-formula>", "<inline-formula id=\"IEq12\"><alternatives><tex-math id=\"M13\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${e_{it}}={e_i}=\\frac{{\\sum\\nolimits_{i} {\\sum\\nolimits_{t} {{n_{it}}} } }}{{\\sum\\nolimits_{i} {\\sum\\nolimits_{t} {po{p_i}} } }}po{p_i}\\,,\\forall t$$\\end{document}</tex-math></alternatives></inline-formula>", "<inline-formula id=\"IEq13\"><alternatives><tex-math id=\"M14\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\theta _{itd}}$$\\end{document}</tex-math></alternatives></inline-formula>", "<disp-formula id=\"Equ2\"><label>2</label><alternatives><tex-math id=\"M15\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$f(y|\\mu ,\\varphi ,\\delta )=\\frac{{\\mu {{(\\mu +\\varphi {\\mu ^{\\delta - 1}}y)}^{y - 1}}}}{{{{(1+\\varphi {\\mu ^{\\delta - 1}})}^y}y!}}\\exp \\left( { - \\frac{{\\mu +\\varphi {\\mu ^{\\delta - 1}}y}}{{1+\\varphi {\\mu ^{\\delta - 1}}}}} \\right)$$\\end{document}</tex-math></alternatives></disp-formula>", "<inline-formula id=\"IEq14\"><alternatives><tex-math id=\"M16\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\delta =1$$\\end{document}</tex-math></alternatives></inline-formula>", "<inline-formula id=\"IEq15\"><alternatives><tex-math id=\"M17\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\mu$$\\end{document}</tex-math></alternatives></inline-formula>", "<inline-formula id=\"IEq16\"><alternatives><tex-math id=\"M18\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\mu {(1+\\varphi )^2},\\varphi &gt;0$$\\end{document}</tex-math></alternatives></inline-formula>", "<inline-formula id=\"IEq17\"><alternatives><tex-math id=\"M19\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\varphi \\to 0$$\\end{document}</tex-math></alternatives></inline-formula>", "<inline-formula id=\"IEq18\"><alternatives><tex-math id=\"M20\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\mu$$\\end{document}</tex-math></alternatives></inline-formula>", "<inline-formula id=\"IEq19\"><alternatives><tex-math id=\"M21\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$P({\\theta _{it}}&gt;a)$$\\end{document}</tex-math></alternatives></inline-formula>", "<inline-formula id=\"IEq21\"><alternatives><tex-math id=\"M22\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$DI{C_{it}}=\\bar {D}({\\varvec{\\theta}_{it}})+p{D_{it}}$$\\end{document}</tex-math></alternatives></inline-formula>", "<inline-formula id=\"IEq22\"><alternatives><tex-math id=\"M23\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\bar {D}({\\varvec{\\theta}_{it}})$$\\end{document}</tex-math></alternatives></inline-formula>", "<inline-formula id=\"IEq23\"><alternatives><tex-math id=\"M24\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$p{D_{it}}$$\\end{document}</tex-math></alternatives></inline-formula>", "<inline-formula id=\"IEq24\"><alternatives><tex-math id=\"M25\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$WAI{C_{it}}=lpp{d_{it}}+pWAI{C_{it}}$$\\end{document}</tex-math></alternatives></inline-formula>", "<inline-formula id=\"IEq25\"><alternatives><tex-math id=\"M26\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$lpp{d_{it}}$$\\end{document}</tex-math></alternatives></inline-formula>", "<inline-formula id=\"IEq26\"><alternatives><tex-math id=\"M27\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$- 2\\log \\left( {\\bar {f}(y{*_{it}}|{\\varvec{\\theta}_{it}})} \\right)$$\\end{document}</tex-math></alternatives></inline-formula>", "<inline-formula id=\"IEq27\"><alternatives><tex-math id=\"M28\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$pWAI{C_{it}}=2\\operatorname{var} \\left( {\\log \\left( {f(y{*_{it}}|{\\varvec{\\theta}_{it}})} \\right)} \\right)$$\\end{document}</tex-math></alternatives></inline-formula>", "<inline-formula id=\"IEq28\"><alternatives><tex-math id=\"M29\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${e^{ - DIC}}$$\\end{document}</tex-math></alternatives></inline-formula>", "<inline-formula id=\"IEq29\"><alternatives><tex-math id=\"M30\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${e^{ - WAIC}}$$\\end{document}</tex-math></alternatives></inline-formula>", "<inline-formula id=\"IEq30\"><alternatives><tex-math id=\"M31\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$CP{O_{it}}=\\int {f(y{*_{it}}|} {\\varvec{y}}{*_{ - it}},{\\varvec{\\theta}_{it}})\\pi ({\\varvec{\\theta}_{it}}|{\\varvec{y}}{*_{ - it}})d{\\varvec{\\theta}_{it}}$$\\end{document}</tex-math></alternatives></inline-formula>", "<inline-formula id=\"IEq31\"><alternatives><tex-math id=\"M32\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$y{*_{it}}$$\\end{document}</tex-math></alternatives></inline-formula>", "<inline-formula id=\"IEq32\"><alternatives><tex-math id=\"M33\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$PIT{}_{{it}}=\\pi (y_{{it}}^{{new}} \\leqslant {y_{it}}|{{\\varvec{y}}_{ - it}})$$\\end{document}</tex-math></alternatives></inline-formula>", "<inline-formula id=\"IEq33\"><alternatives><tex-math id=\"M34\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${{\\varvec{y}}_{ - it}}$$\\end{document}</tex-math></alternatives></inline-formula>", "<inline-formula id=\"IEq34\"><alternatives><tex-math id=\"M35\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$PIT_{{it}}^{{adjust}}=PI{T_{it}} - 0.5 \\times CP{O_{it}}$$\\end{document}</tex-math></alternatives></inline-formula>", "<inline-formula id=\"IEq35\"><alternatives><tex-math id=\"M36\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\Pi _{t = T - w + 1}^T\\Pi _{d = 1}^D\\Pi _{i = 1}^If({y_{itd}}|{\\theta _{itd}})$$\\end{document}</tex-math></alternatives></inline-formula>" ]

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[ "<media xlink:href=\"12874_2024_2141_MOESM1_ESM.docx\"><caption><p>Supplementary Material 1</p></caption></media>" ]

{ "acronym": [ "DIC", "EXC", "CPO", "PIT", "WAIC", "CrI", "TP", "TN", "FP", "FN", "Se", "Sp", "PPV", "NPV", "Acc" ], "definition": [ "Deviance Information Criterion", "Exceedance probability", "Conditional predictive ordinate", "Probability integral transform", "Watanabe-Akaike information criterion", "Credible interval", "True positive", "True negative", "False positives", "False negatives", "Sensitivity", "Specificity", "Positive predictive value", "Negative predictive value", "Accuracy" ] }

[ "<title>Introduction</title>", "<p id=\"Par7\">Prominent facial deformity, a prevalent malocclusion in orthodontic clinical practice, significantly impacts facial aesthetics. To enhance the lateral appearance in cases of dental or mild bony protrusions, optimal results can be achieved by extracting the first premolar and utilizing a fixed appliance or clear aligner for maximizing internal retraction of the anterior teeth. Fixed orthodontic maxillary micro-implant anchorage structures provide effective and safe treatment for cases of protrusion [##REF##25386516##1##]. In contrast, achieving precise control over the three-dimensional movement of teeth using clear aligners necessitates a combination of mini-screws, power ridges, overtreatment, or power arms to optimize anterior torque control and ensure posterior anchorage during anterior retraction [##REF##30920875##2##–##REF##35543236##9##]. However, both clear aligners and fixed orthotics currently possess several limitations including potential trauma associated with micro-implant, aesthetic concerns, possible increase in unnecessary reciprocating motion, and treatment uncertainty [##REF##30920875##2##, ##REF##34061964##7##, ##REF##19121497##10##–##REF##36801091##13##].</p>", "<p id=\"Par8\">To enhance the aesthetic appeal, minimize invasiveness, and optimize efficiency in retracting anterior teeth during clear aligner therapy, we have developed two novel design models for clear aligner retraction. The first modification involves a palatal plate-shaped clear aligner, which can now be directly printed using 3D-printing technology. This advancement improves the design parameters of aligners, including configuration, strength, elasticity, and thickness [##REF##35466087##14##–##REF##33250102##17##], thereby enhancing their therapeutic efficacy. The second one is a Lingual Retractor that utilizes advanced 3D-printing technology to create a compound structure specifically designed for seamless integration with clear aligners. Recently, our research group has developed patient-specific attachments utilizing 3D printing technology that have been validated through finite element analysis to exhibit superior anterior tooth anchorage in comparison to alternative attachments during maxillary molar distalization [##REF##36435687##18##]. Several studies have documented that successful treatment of patients requiring anterior retraction can be achieved by combining a Double J retractor with a fixed appliance [##REF##34996660##19##, ##REF##31149609##20##]. Additionally, the bracket re-bonding procedure, which is a complex operation, may also be necessary. Moreover, the utilization of a palatal micro-implant remains indispensable. The incorporation of clear aligners in conjunction with tongue retractors is expected to enhance the convenience and efficacy of anterior tooth retraction.</p>", "<p id=\"Par9\">Orthodontic clear aligners can be fabricated from either traditional thermoplastic materials or light-cured shape memory resins. The development of innovative materials has played a pivotal role in enhancing the effectiveness of clear aligners. Currently, there is an abundance of research available on clear aligner materials, with more comprehensive investigations accessible in scholarly articles authored by Ning and Naohisa [##REF##23035832##21##–##REF##18249289##24##]. It is worth noting that the meticulous design of clear aligner morphology and its composite force system structure holds paramount importance. For instance, in the case of anterior internal retraction, a power ridge was incorporated into the clear aligner design to effectively control maxillary anterior teeth torque [##REF##35909188##25##]. However, it has been observed that the utilization of a power ridge frequently results in dislocation of clear aligners, subsequently exerting an impact on orthodontic outcomes [##REF##37349701##26##]. Additionally, micro-implant anchorage composite force systems have been explored for anterior teeth retraction; however, many patients are reluctant to undergo this invasive treatment modality [##REF##34061964##7##]. Despite these challenges, there remains a lack of effective noninvasive and aesthetic anterior teeth retraction using clear aligners. Recently, we employed simulation methodology to investigate the biomechanical characteristics and retraction effects of our innovative designs for two non-invasive and aesthetically pleasing models using clear aligners. Nonetheless, comprehensive comparative and biomechanical analyses regarding the clinical efficacy of anterior teeth retractions versus fixed appliances are still insufficient.</p>", "<p id=\"Par10\">Therefore, the purpose of this study was to compare and evaluate the differences among various design of clear aligners, as well as to assess the disparities between the clear aligner model and the fixed appliance. The study encompasses five distinct clear aligner retraction models and one fixed appliance retraction model (Model C0 Control, Model C1 Posterior Micro-implant, Model C2 Anterior Micro-implant, Model C3 Palatal Plate and Model C4 Lingual Retractor, and Model F0 Fixed Appliance). In this study, employing numerical modeling, we conducted an analysis and comparison of the therapeutic efficacy of various orthodontic appliances as well as the biomechanical response of dental and periodontal ligament structures in orthodontics.</p>" ]

[ "<title>Materials and methods</title>", "<title>Acquisition of medical image data</title>", "<p id=\"Par11\">A patient with permanent dentition and maxillary bone protrusion requiring extraction of the first premolar was selected from the Department of Orthodontics at Affiliated Stomatological Hospital of Chongqing Medical University. The present study was granted ethical approval by the Stomatological Hospital of Chongqing Medical University (2023) 056. Cone-beam computed tomography (CBCT) with specific parameters (120 kVp; 5 mA; voxel size of 0.4 mm; Kava, Biberach, Germany) and 3D intraoral scanning were employed to acquire DICOM (Digital Imaging and Communications in Medicine) data.</p>", "<p id=\"Par12\">Inclusion criteria for the study were as follows: (a) Complete development of the jaw and presence of all teeth, excluding third molars; (b) Adult patients with maxillary protrusion, ANB＞4°, U1-SN &lt; 105°, and extraction of the maxillary first premolar for orthodontic treatment [##REF##21406002##27##]; (c) Healthy dentition without extensive fillings, no history of root canal treatment, and absence of restoration crowns or dental implants; (d) Periodontal and temporomandibular joints exhibited normal conditions; (e) Complete cone-beam computed tomography (CBCT) and intraoral scan data were available.</p>", "<p id=\"Par13\">Exclusion criteria: (a) The clinical crown height on the palatal side of the maxillary posterior teeth is insufficient, measuring less than 4 mm; (b) The root length of the maxillary posterior teeth is inadequate, with a root to crown ratio (R/C) ≤ 1 [##REF##11902614##28##]; (c) Patients with a history of maxillary surgery, trauma, or tumor are included; (d) Developmental deformities affecting the integrity and structure of the jaw, such as severe asymmetry and cleft palate in the maxilla.</p>", "<title>The construction of orthodontic model</title>", "<p id=\"Par14\">The DICOM data was imported into the Mimics system (Materialize, Belgium). The threshold range was adjusted based on grayscale differences to segment preliminary 3D models of the maxilla and dentition. Geomagic Studio software (Geomagic, USA) was used for surface fine-tuning and smoothing, followed by generating CAD models through autosurfacing. By utilizing the Boolean operation and offset functions in 3-matic software, we established a PDL with an average thickness of 0.2 mm and cortical bone of 2.0 mm, considering cancellous bone as residual material. The extraction dentition model was created by removing the first premolars and their PDL. We obtained a model of anterior tooth retraction of 0.2 mm using six retraction approaches (five clear aligner approaches and one fixed appliance approach), as shown in Fig. ##FIG##0##1## [##REF##31683382##29##]. The clear aligner was developed by applying an external offset on the post-retraction model with a thickness of 0.75 mm [##REF##35188858##30##].</p>", "<p id=\"Par26\">\n\n</p>", "<p id=\"Par16\">One of the clear aligner retraction models combined a clear aligner with a 3D printed lingual retraction hook and a 3D printed palatal plate. In this simulation, the anterior teeth were considered as a retraction unit. The lingual retractor and palatal plate were bonded to the tooth surface through the base plate [##REF##33393828##31##]. The thickness of both the lingual retractor and the palatal plate was 0.5 mm (Supplementary Fig. ##SUPPL##0##1##). The center of resistance (CR) is considered the fundamental reference point for controlled tooth movement, and the height of the lingual retraction hook was determined based on the center of resistance (CR) of the retraction unit. The retraction unit models were assigned the property of rigidity. The mesial-distal truncated surfaces of the maxilla were firmly constrained (Fig. ##FIG##1##2##, A). In order to ascertain the vertical position of the center of resistance (CR) for the retraction unit, a 100 g horizontal force was exerted in close proximity to the median sagittal plane and parallel to the occlusal plane, inducing lingual retraction (Fig. ##FIG##1##2##, A). In addition, the point of force application (level 0) was precisely positioned on the alveolar ridge roof of the posterior teeth, at a distance of 7.69 mm from the incisal edge (Fig. ##FIG##1##2##, B). Commencing from level 0, it was incrementally advanced towards the root in perpendicular alignment with the occlusal plane at intervals of 1 mm up to level 7, which corresponded closely to the apex of the anterior teeth. during anterior retraction. All components were imported into finite element (FE) software for calculations. The difference between the displacement of the root tip and crown displacement was defined as the crown-root differential displacement. The center of resistance (CR) level is defined as the point where the differential displacements of anchorage units are close to 0. After step-by-step subdivision of the loading calculation, we determined that the vertical position of the center of resistance (CR) is at 4.85 mm. Our clear aligner force system consists of a lingual retraction hook and a clear aligner, which shifts the center of resistance (CR) position towards the root due to force exerted on the crown section. we selected a position 6 mm above the CR as the length for lingual retraction hook (i.e., 18.54 mm above occlusal plane) (Supplementary file 1, Supplementary Fig. ##SUPPL##0##2##), which was close to the hard palate. (Fig. ##FIG##2##3##, B). A posterior traction site was designed using a 3D printed device, uniting six posterior teeth for anchorage (Fig. ##FIG##2##3##, A). Additionally, the traction points can be customized based on clinical needs.</p>", "<p id=\"Par21116\">\n\n</p>", "<p id=\"Par2622\">\n\n</p>", "<p id=\"Par19\">The construction of five types of clear aligner retraction models (including the Model C0 Control, Model C1 Posterior Micro-implant, Model C2 Anterior Micro-implant, Model C3 Palatal Plate and Model C4 Lingual Retractor) and one Fixed retraction model (Model F0 Fix Appliance) is illustrated in Fig. ##FIG##2##3##. The Model C0 served as the control group for the clear aligner model, consisting solely of clear aligners. In Model C1, a micro-implant was positioned between the second premolar and first molar, 5 mm above the alveolar ridge’s highest point, at an angle of 60° to the maxillary occlusal plane, with an intraosseous length measuring 8 mm. The force of 150 g was applied [##UREF##3##32##, ##UREF##4##33##]. In Model C2, a micro-implant was positioned between the central incisors to apply the force of 150 g by directing it towards the lingual side through the precision cut [##REF##34061964##7##]. Model C3 incorporated a palatal lateral plate that seamlessly integrated with the clear aligner in terms of thickness and material, which could be obtained through cutting. Additionally, Model C4 combined a clear aligner with a 3D printed lingual retraction device. Furthermore, the 3D printed device was generated using Mimics software. The lingual retraction hook was positioned 6 mm above the center of resistance (CR) (18.54 mm above the occlusal plane) and was designed and modeled using computer-aided design software SolidWorks (Dassault, France) (Fig. ##FIG##2##3##, A). Buccal surfaces of the canine featured vertical rectangular attachments measuring 3*2*1mm, while horizontal rectangular attachments of the same dimensions were designed on the buccal surfaces of both second premolar and first molar in all clear aligner models. Model F0, a commonly used clinical retraction system, comprised a relatively rigid rectangular archwire (0.018 × 0.025inch), a posterior micro-implant, and an anterior retraction hook with a height of 7 mm [##REF##19852600##34##]. Additionally, a retraction force of 150 g was applied [##REF##25085299##35##, ##REF##30663006##36##].</p>", "<title>Material properties and meshing</title>", "<p id=\"Par20\">The models were assembled and imported into ABAQUS software (SIMULIA, France). Each study subject was assumed to possess continuous homogeneity, isotropy, and a linear elastic material constitutive model. The material properties of the components, obtained from previous studies, are summarized in Table ##TAB##0##1## [##REF##31683382##29##, ##REF##21195258##37##–##REF##36277381##45##]. The meshing of the three-dimensional models was performed using the C3D10M element type, also known as a modified tetrahedral quadratic element that is particularly suitable for contact calculations. The number of nodes and mesh is approximately presented in Table ##TAB##0##1##.</p>", "<p id=\"Par21\">\n\n</p>", "<title>Boundary constraints and contact conditions</title>", "<p id=\"Par22\">The base of the maxilla was constrained to prevent any rotation or displacement from occurring. The contact relationships between the cortical and cancellous bone, alveolar bone and periodontal ligament (PDL), teeth and PDL, attachment and corresponding teeth, micro-implant and jaws, 3D printed lingual retractor and corresponding teeth, archwire and anterior teeth, as well as power arm and archwire were defined as bonded connections. The outer surface of the crown and the inner surface of the clear well as the attachment’s outer surface and the clear aligner’s inner surface, are considered non-linear face-to-face contacts. The tangential direction between these two contact surfaces is set to frictional with a coefficient of 0.2 [##REF##25181252##38##, ##REF##28253487##46##]. The coefficient of friction between bracket slots and archwire is assumed to be 0.2 [##REF##1763840##47##–##REF##9573878##49##]. The y-axis of the global coordinate system represents the vertical direction, with positive values defined as perpendicular to the occlusal plane towards the root. The local coordinate system is established for each tooth due to variations in mesiodistal and buccolingual directions. The x-axis represents the mesiodistal direction, where the x-value is defined as the distal direction and positive values are assigned to this direction. The z-axis represents the buccopalatal direction, with positive values defined for the palatal direction. Reference points were selected at the incisal midpoint and root apex of the incisors, cusp tip and root apex of the canines, buccal cusp tip and lingual cusp tip of second premolar, mesial buccal cusp tip, distal buccal cusp tip, mesial lingual cusp tip and distal lingual cusp tip of first molar, and mesial buccal cusp tip, distal buccal cusp tip and lingual cusp tip of second molar.</p>", "<title>Calculation and analysis</title>", "<p id=\"Par23\">Due to the bilateral symmetry of the model employed in this study, we specifically selected the right maxillary tooth and periodontal ligament (PDL) for meticulous analysis. Nonlinear iterative calculations were conducted using ABAQUS software (SIMULIA, France), yielding comprehensive results encompassing displacement of teeth and aligners, as well as von-Mises equivalent stress experienced by both PDL and aligners.</p>" ]

[ "<title>Results</title>", "<title>Determining the center of resistance</title>", "<p id=\"Par24\">The displacement distribution and crown-root displacement differences of the six anterior teeth were illustrated in Fig. ##FIG##1##2##. As the center of resistance (CR) vertical position approached, the sagittal crown-root displacement difference tended to approach zero. Specifically, at level 4, the central incisor, lateral incisor, and canine exhibited positive crown-root displacement differences of 9.65E-05 mm, 4.96E-05 mm, and 1.20E-05 mm respectively. However, at level 5, these values became negative with respective crown-root displacement differences of -1.49E-05 mm for the central incisor, -1.24E-05 mm for the lateral incisor, and − 1.66E-05 mm for the canine. The force level axis from level 4.0 to 5.0 was meticulously sectioned at intervals of 0.2 mm for the various points of force application, as depicted in Fig. ##FIG##1##2##, D. At level 4.8, the crown-root displacement differences of the central incisor, lateral incisor, and canine were positive: 7.43E-06 mm, 7.79E-06 mm, and 1.07E-05 mm respectively. At level 5.0, the crown-root displacement differences of these teeth were negative with values consistent with those previously described. Subsequently, the force level axis from level 4.8 to 5 was meticulously sectioned every increment of 0.05 mm for the different points of force application shown in Fig. ##FIG##1##2##, E. At level 4.85 (Fig. ##FIG##1##2##, E), the difference in crown-root displacement between, lateral incisor and canine approached zero; specifically measuring at approximately: 1.86E-06 mm (central incisor), 2.75E-06 mm (lateral incisor) and 3.89E-06 mm (canine). Therefore, we considered this position as representing the vertical height of the center of resistance (CR).</p>", "<title>Comparison of the maximum displacements of the central incisor, lateral incisor, and canine in sagittal dimension</title>", "<p id=\"Par216\">The sagittal movement patterns of the central incisor, lateral incisor, and canine were found to be similar under the loading conditions of all five clear aligner models, as depicted in Fig. ##FIG##3##4##. Notably, these movements exhibited a consistent inclination of the crown towards the lingual side and the root towards the labial side. However, in the fixed appliance model, both the crown and root of the central incisor exhibited buccal movement. The crown of the lateral incisor had buccal movement, while the root moved lingually. Additionally, the canines displayed an opposite trend to that of the lateral incisor. Furthermore, it is worth noting that tooth displacement was significantly lower in the fixed appliance model compared to clear aligner models. Table ##TAB##1##2## demonstrates that Model C3 had the smallest crown-root displacement difference for the central incisor at 6.30E-02 mm. For Model C4, the smallest differences were observed for both lateral incisors and canines at 7.47E-02 mm and 6.31E-02 mm respectively. In contrast, in the fixed appliance model, these differences were measured at 1.34E-04 mm for central incisors, 1.43E-02 mm for lateral incisors, and 5.55E-03 mm for canines respectively (Fig. ##FIG##3##4##). The sagittal retraction of central incisors, lateral incisors and canines under different retraction models was visually demonstrated through a series of figures depicting their initial positions as well as post-retraction positions using both clear aligners and fixed appliances (Fig. ##FIG##4##5##). For a better understanding of the displacement of the teeth, these movements were magnified 50 times.</p>", "<p id=\"Par2226\">\n\n</p>", "<p id=\"Par22006\">\n\n</p>", "<p id=\"Par29\">\n\n</p>", "<title>Comparison of the maximum displacements of the central incisor, lateral incisor, and canine in vertical dimension</title>", "<p id=\"Par31\">As depicted in Fig. ##FIG##5##6##, displacement tendencies were compared for the central incisor, lateral incisor, and canine in terms of crown and root displacement along the vertical dimension. In Table ##TAB##2##3##, among the clear aligner models, Model C3 exhibited the smallest crown displacement for the central incisor (-3.08E-02 mm). Similarly, Model C4 showed the smallest displacements for both lateral incisor (-3.65E-02 mm) and canine (-2.27E-02 mm). In contrast, within the fixed appliance model, crown displacements were measured as 5.26E-04 mm for central incisors, 9.77E-03 mm for lateral incisors, and − 4.69E-03 mm for canines. Vertically speaking, all five clear aligner models demonstrated a tendency towards extrusion of anterior teeth; whereas in the fixed appliance model, there was an inclination towards intrusion of central and lateral incisors alongside extrusion of canines.</p>", "<p id=\"Par2202206\">\n\n</p>", "<p id=\"Par33\">\n\n</p>", "<title>Comparison of the maximum displacements of the second premolar, first molar, and second molar in sagittal and vertical dimension</title>", "<p id=\"Par35\">As shown in Fig. ##FIG##6##7##, sagittally, the movement trend of the posterior teeth was similar in the five clear aligner models, all showed an inclined movement trend of the crown toward the mesial and the root toward the distal. In the fixed appliance model, the crown of the posterior teeth showed a tendency to move distally in the sagittal direction (Fig. ##FIG##6##7##, A). As shown in Table ##TAB##3##4##, in the clear aligner models, the smallest displacement of the crown of second premolar and second molar in sagittal dimension were observed in Model C3, which were − 2.72E-02 mm and − 1.72E-02 mm. The smallest displacement of the crown of first molar was observed in Model C4, and were − 2.21E-02 mm. The displacement of the crown of second premolar, first molar and second molar in the fixed appliance model were 7.24E-04 mm, 1.05E-03 mm, and 1.78E-03 mm, respectively. Vertically, the movement trend of the posterior was similar in the clear aligner models. The second premolar showed a tendency to intrude, and the first molar was intrusive except for Model C4. The second molar had a tendency to extrude. In the fixed appliance model, the second premolar showed a tendency to intrude, while the first molar and second molar showed a tendency to extrude (Fig. ##FIG##6##7##, C). In the clear aligner models, the smallest displacement of the crown of second premolar and second molar in vertical dimension were observed in Model C3, which were 6.98E-03 mm -3.08E-02 mm and − 1.50E-03 mm. The displacement of the crown of second premolar, first molar and second molar in the fixed appliance model were 1.23E-03 mm, -5.82E-04 mm, and − 6.25E-05 mm, respectively (Table ##TAB##4##5##).</p>", "<p id=\"Par845\">\n\n</p>", "<p id=\"Par37\">\n\n</p>", "<p id=\"Par38\">\n\n</p>", "<title>Comparison of the maximum displacements and von mises stress in the clear aligners and fixed appliance</title>", "<p id=\"Par40\">The maximum von mises of the clear aligner was 693.733 MPa, 772.713 MPa, 754.77 MPa, 717.365 MPa, and 784.445 MPa, respectively. The maximum von mises of the fixed appliance was 68668.1 MPa (Fig. ##FIG##7##8##, B). The stress distribution in the clear aligner model was similar, with stresses concentrated at the aligners corresponding to the canine, first premolar, and second premolar teeth, especially at the teeth adjacencies. The stress of the fixed appliance was located primarily on the archwire and the brackets corresponding to the first molar. (Fig. ##FIG##7##8##, A). The maximum displacement of the five clear aligner models was 0.304961 mm, 0.283423 mm, 0.295801 mm, 0.298634 mm, 0.292909 mm, respectively. The maximum displacement of the fixed appliance was 0.022658 mm. The displacement trends of the clear aligners in the clear aligner models were similar, with a tendency to move buccally and toward the occlusal direction. In the fixed appliance model, there was a tendency for the corresponding position of the lateral incisors of the archwire to be deformed towards the root and lingual side. Moreover, the corresponding position of the second premolar of the arch wire had a trend of buccal dislocation. Since the archwire corresponding to the position of the first molar was constrained by the buccal canal, the deformation and extrusion were obvious under the retraction force.</p>", "<p id=\"Par945\">\n\n</p>", "<title>Comparison of von mises stress in the PDL of the central incisor, lateral incisor, canine, second premolar, first molar, and second molar</title>", "<p id=\"Par43\">As depicted in Fig. ##FIG##8##9##, the average von mises and stress distribution of PDL in six retraction models were compared. The stress magnitude and stress distribution on PDL were similar in the five clear aligner models. Among the five clear aligner models, the lowest stress of the PDL of the central incisor, lateral incisor, canine, second premolar and second molar appeared in Model C3, which were 0.025871 MPa, 0.030915 MPa, 0.041213 MPa, 0.021395 MPa and 0.011692 MPa, respectively. Model C4 had the lowest PDL stress in the first molar, which was 0.013860 MPa. The stress on the PDL of the central incisor, lateral incisor, canine, second premolar, first molar, and second molar in the fixed appliance retraction model were 0.00256 MPa, 0.012276 MPa, 0.006295 MPa, 0.003738 MPa, 0.001902 MPa, and 0.001394 MPa, respectively. The PDL stress distribution was obviously different between the clear aligner model and the fixed appliance model. In the clear aligner models, the stress was mainly located in the anterior teeth and the second premolar, and the PDL stress of the first molar and the second molar decreased significantly. In the fixed appliance model, the stress was mainly concentrated on the lateral incisor, canine and second premolar. In the clear aligner models, the stress on the PDL of the central incisors, lateral incisors and canines was located on the buccolingual side and concentrated mainly in the cervical position. The stress on the PDL of the second premolar was mainly distributed in the cervical of the mesial and distal of the root surface. The stress of PDL on the first and second molars was concentrated in the cervical region of the mesial and distal of the root surface. In the fixed appliance model, the stress of PDL of the lateral incisor was mainly located on the buccal side, with a concentration in the apical and lingual cervical locations. For canine, the PDL stress was mainly located on the buccal side and distributed more uniformly. The PDL stress of the second premolar was mainly concentrated on the buccolingual cervical region.</p>", "<p id=\"Par9945\">\n\n</p>" ]

[ "<title>Discussion</title>", "<p id=\"Par45\">In this study, we conducted numerical simulations to investigate the process of anterior retraction in different orthodontic designs and compared the biomechanical differences among various invisible orthodontic devices during anterior retraction. Additionally, we compared the clear aligner retraction model with the fixed appliance retraction model. The results showed minimal biomechanical disparities among different clear aligner models. The additional force systems did not alter the trend of tooth movement in clear aligner models but rather adjusted both anterior and posterior teeth displacement during retraction. Model C3 demonstrated superior torque control and provided enhanced protection for posterior anchorage teeth compared to other four clear aligners. The clear aligner and fixed appliance exhibited distinct biomechanical properties, with the latter showing superior anterior torque control and posterior anchorage tooth protection compared to the former.</p>", "<p id=\"Par46\">The clear aligner models consistently demonstrated lingual tipping and extrusion in the anterior teeth, as well as a similar movement pattern in the posterior teeth with their crowns tilting towards the mesial side, consistent with the findings reported by Wang et al. [##REF##37749548##50##, ##REF##37024337##51##]. Retraction of the anterior teeth using clear aligners leads to a roller-coaster effect of tooth movement [##REF##35219555##5##, ##REF##37749548##50##, ##REF##36372824##52##, ##REF##36964108##53##]. The additional force systems in the study did not change the observed trend of tooth movement in the model, but they did introduce some variation in the displacement magnitude of both anterior and posterior teeth. In Liu et al.‘s study, the utilization of anterior mini-screws and elastics demonstrated their efficacy in achieving incisor intrusion and palatal root torquing [##REF##34061964##7##]. Consistent with their findings, our experimental group Model C2 also exhibited superior control over the anterior teeth in terms of torque and vertical control when compared to Models C0 and C1. However, the observed trend was not as pronounced, potentially due to variations in force magnitude and application method. Liu’s study revealed that longer anterior teeth experienced less tipping [##REF##36964108##53##], which aligns with the results obtained from our control group Model C0. Furthermore, our experimental group Model C3 deviated from this trend by showcasing a smaller displacement tendency for central incisors with shorter roots compared to canines. Additionally, all anterior teeth displayed a decreasing sagittal tipping displacement trend. The results indicate that Model C3 exhibited the most precise torque and vertical control for central incisors, as evidenced by its minimal crown-root displacement difference and vertical displacement. This phenomenon can be attributed to the stabilizing and cushioning effect of the palatal plate structure during the retraction process. The displacement of the posterior teeth in the sagittal and vertical directions was effectively minimized, indicating optimal protection for posterior retention. This was related to the role of the palatal plate in combining with the posterior teeth to form a stronger anchorage unit. The Model C4 had the best torque control and vertical control for lateral incisor and canine, which was due to the role of the lingual retractor.</p>", "<p id=\"Par47\">The initial displacement tendency of teeth in the fixed appliance model was significantly different from that in the clear aligners. The fixed appliance had the most pronounced effect on the lateral incisor, causing a labial tipping with intrusion of the lateral incisor. The reason for this was the proximity of the traction point to the lateral incisors and the fact that the lateral incisors exhibited a relatively smaller periodontium compared to other anterior teeth in general condition [##REF##8195437##54##, ##REF##28460250##55##]. Moreover, the posterior teeth showed a tendency to move distally, due to the backward frictional force exerted by the archwire on the posterior teeth when closing the gap. On the other hand, the displacement magnitude of the teeth in the fixed appliance model was significantly less than in the clear aligner models. This was consistent with previous studies that clear aligner was not as good as fixed appliance in controlling tooth torque and posterior anchorage protection [##REF##30674307##56##, ##REF##31651082##57##]. We explored the reasons for this by comparing the stress and displacement of clear aligners with fixed appliance. From Fig. ##FIG##6##7##, A. it can be seen that the clear aligners had greater stress at the joint of adjacent teeth and a tendency to fall off in the occlusion direction, which was in agreement with the findings of Meng et al. [##REF##31683382##29##]. However, the maximum von mises stress of clear aligner was still significantly less than that of fixed appliance. When fixed appliances were subjected to forces, most of the forces were carried by the fix appliances themselves, so the forces transmitted to the teeth were significantly reduced. However, when clear aligners were deformed, the force acted directly on the tooth surface and there was no force decay process. Moreover, Fig. ##FIG##6##7##, B showed that the deformation of the clear aligners was significantly greater than that of the fixed appliance, about fifteen times greater. The greater the deformation of the clear aligner the greater the force applied to the tooth. In agreement with Danilee K. B et al., clear aligner was not stiff enough to maintain the tipping tendency compared to fixed appliance, which can lead to a significant roller-coaster effect [##REF##18538247##58##]. The clear aligner approach and the fixed appliance approach still exhibit a disparity; nevertheless, this study offered a developmental direction and established a theoretical foundation for future non-invasive, aesthetically pleasing, comfortable, and efficient modalities of clear aligner treatment. Improvements in materials, design refinements, and 3D printing technology have made it possible to create clear aligner with better orthodontic capabilities by improving design parameters such as aligner configuration, strength, elasticity, or thickness [##REF##33950232##16##, ##REF##33250102##17##, ##REF##35842359##59##].</p>", "<p id=\"Par48\">Root absorption can result from excessive stress concentration, and it has been reported that 91% of teeth underwent some degrees of root resorption after orthodontic treatment [##REF##21919826##60##]. Stress distribution of PDL was consistent with the trend of tooth movement [##REF##35188858##30##]. Since the five clear aligner models had the same trend of movement, the stress distribution in PDL was also roughly the same. For the clear aligner models, the stress of the central incisors, lateral incisors and canines was mainly concentrated on the cervical of the buccal and lingual root surfaces and apical regions, which was consistent with the findings of Liu [##REF##34061964##7##]. In addition, the stress of the second premolar, first molar and second molar was mainly concentrated on the cervical of the mesial and distal root surfaces. The root surfaces of central and lateral incisors are smaller than those of premolars and molars, making them more susceptible to root resorption [##REF##36277381##45##]. In Model C3, the PDL stress of anterior teeth was smaller than that in the other clear aligner models, and the stress distribution area was also smaller. The results suggested that the modified palatal plate clear aligner helped reduce the risk of root resorption during anterior retraction. In the fixed appliance model, the lateral incisor was subjected to the greatest stress, and the stress mainly concentrated on the buccal surface, the root tip and the cervical of the lingual surface. However, the stress was still smaller than that in the clear aligner models. Consistent with Tang et al., the stress of the PDL in the fixed appliance model was significantly less than that in the clear aligner models [##UREF##7##61##]. Accordingly, this may be an obvious risk factor for root resorption caused by clear aligner therapy.</p>", "<p id=\"Par49\">However, it is imperative for this study to acknowledge its potential limitations. The limitations of this simulated study remain, as it can only explain the initial effects of stress distribution and displacement patterns on teeth when analyzing orthodontic appliance force systems. Simplification and assumption pose evident limitations in the context of finite element analysis. Frequently, more intricate anatomical structures are disregarded during the modeling phase. Another concern arises when attempting to accurately represent not only the anatomy but also the morphology of tested tissues, where simplifications are commonly employed [##UREF##8##62##]. As digital simulation technology advances, our next endeavor is to achieve a more precise and comprehensive simulation of the orthodontic process. Additionally, replicating exactly the same living substance in a mechanical model proves virtually impossible; hence further investigation into finite element analysis through extensive clinical studies is necessary to quantitatively validate our findings. Moreover, combining FE analysis with clinical studies for mutual validation will enhance the significance of this study, which represents our subsequent step. The modified palatal plate clear aligner we designed is too monolithic, but this study provides direction for future research. Moreover, we will further improve the configuration, strength, elasticity, thickness and other design parameters of the clear aligner to explore the modified clear aligner with better efficacy.</p>" ]

[ "<title>Conclusions</title>", "<p id=\"Par50\">After conducting preliminary research, we have arrived at the following conclusions:</p>", "<p id=\"Par51\">\n<list list-type=\"order\"><list-item><p id=\"Par52\">The teeth movement pattern remained consistent across all five clear aligners, characterized by lingual tipping and extrusion of anterior teeth, as well as mesial tipping of posterior teeth during anterior retraction.</p></list-item><list-item><p id=\"Par53\">Fixed appliances exhibit superior control over torque in anterior teeth and provide better protection against anchorage loss in posterior teeth compared to invisible appliances.</p></list-item><list-item><p id=\"Par54\">The implementation of an additional force system in clear aligners did not alter the observed trend of tooth movement, but it did exert an influence on the magnitude of tooth displacement. Specifically, modified palatal plate structure clear aligner Model C3 demonstrated enhanced torsional control and improved preservation of posterior dental anchorage.</p></list-item></list>\n</p>" ]

[ "<title>Background</title>", "<p id=\"Par1\">The aim of this study is to conduct a comparative evaluation of different designs of clear aligners and examine the disparities between clear aligners and fixed appliances.</p>", "<title>Methods</title>", "<p id=\"Par2\">3D digital models were created, consisting of a maxillary dentition without first premolars, maxilla, periodontal ligaments, attachments, micro-implant, 3D printed lingual retractor, brackets, archwire and clear aligner. The study involved the creation of five design models for clear aligner maxillary anterior internal retraction and one design model for fixed appliance maxillary anterior internal retraction, which were subsequently subjected to finite element analysis. These design models included: (1) Model C0 Control, (2) Model C1 Posterior Micro-implant, (3) Model C2 Anterior Micro-implant, (4) Model C3 Palatal Plate, (5) Model C4 Lingual Retractor, and (6) Model F0 Fixed Appliance.</p>", "<title>Results</title>", "<p id=\"Par3\">In the clear aligner models, a consistent pattern of tooth movement was observed. Notably, among all tested models, the modified clear aligner Model C3 exhibited the smallest differences in sagittal displacement of the crown-root of the central incisor, vertical displacement of the central incisor, sagittal displacement of the second premolar and second molar, as well as vertical displacement of posterior teeth. However, distinct variations in tooth movement trends were observed between the clear aligner models and the fixed appliance model. Furthermore, compared to the fixed appliance model, significant increases in tooth displacement were achieved with the use of clear aligner models.</p>", "<title>Conclusions</title>", "<p id=\"Par4\">In the clear aligner models, the movement trend of the teeth remained consistent, but there were variations in the amount of tooth displacement. Overall, the Model C3 exhibited better torque control and provided greater protection for posterior anchorage teeth compared to the other four clear aligner models. On the other hand, the fixed appliance model provides superior anterior torque control and better protection of the posterior anchorage teeth compared to clear aligner models. The clear aligner approach and the fixed appliance approach still exhibit a disparity; nevertheless, this study offers a developmental direction and establishes a theoretical foundation for future non-invasive, aesthetically pleasing, comfortable, and efficient modalities of clear aligner treatment.</p>", "<title>Supplementary Information</title>", "<p>The online version contains supplementary material available at 10.1186/s12903-023-03704-6.</p>", "<title>Keywords</title>" ]

[ "<title>Electronic supplementary material</title>", "<p>Below is the link to the electronic supplementary material.</p>", "<p>\n\n</p>" ]

[ "<title>Acknowledgements</title>", "<p>We gratefully thank the National Natural Science Foundation of China (Grant No. 12072055, 11872135, U20A20390), Natural Science Foundation of Beijing (Grant No. L 212063) and the Fundamental Research Funds for the Central Universities, the 111 Project (No. B 13003), CAMS Innovation Fund for Medical Sciences (CIFMS) under Grant 2019-I2M-5-016. Chongqing Stomatological Association Zhengya Orthodontic Clinical Research Scientific Research Fund Project (CQSA-ZY2021-01). Project of Chongqing Graduate Tutor Team (dstd201903), Chongqing Young and Middle-Aged Medical Excellence Team.</p>", "<title>Author Contributions</title>", "<p>QX and WXW performed the experiments, analyzed the data, and wrote the manuscript. CJW involved in conceptualization and methodology. GF contributed to the interpretation of the results. CW involved in conceptualization, provided manuscript writing assistance, and critically revised the manuscript for important intellectual content. JLS contributed to supervision, project administration, and funding acquisition. YBF contributed to conceptualization and supervision. All authors read and approved the final manuscript.</p>", "<title>Funding</title>", "<p>This work was supported by the National Natural Science Foundation of China (Grant No. 12072055, 11872135, U20A20390), Natural Science Foundation of Beijing (Grant No. L 212063) and the Fundamental Research Funds for the Central Universities, the 111 Project (No. B 13003), CAMS Innovation Fund for Medical Sciences (CIFMS) under Grant 2019-I2M-5-016. Chongqing Stomatological Association Zhengya Orthodontic Clinical Research Scientific Research Fund Project (CQSA-ZY2021-01). Project of Chongqing Graduate Tutor Team (dstd201903), Chongqing Young and Middle-Aged Medical Excellence Team. Open Subjects of Shanxi Key Laboratory of Prevention and treatment of Oral Disease and New Materials (KF2020-01). Chongqing Education Commission “Chengdu-Chongqing area twin city economic Circle Construction” science and technology innovation project (KJCX2020017). Technology Innovation and Application Development Specialized for Population Health (CSTB2023TIAD-KPX0054).</p>", "<title>Data Availability</title>", "<p>The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.</p>", "<title>Declarations</title>", "<title>Ethics approval and consent to participate</title>", "<p id=\"Par56\">Ethical approval was granted by the ethical committee of Stomatological Hospital of Chongqing Medical University and the ethics number was (2023) 056. The patients provided their written informed consent to participate in this study.</p>", "<title>Consent for publication</title>", "<p id=\"Par57\">Not applicable.</p>", "<title>Competing interests</title>", "<p id=\"Par55\">The authors declare no competing interests.</p>" ]

[ "<fig id=\"Fig1\"><label>Fig. 1</label><caption><p>3D finite element model design of anterior teeth retraction approach</p></caption></fig>", "<fig id=\"Fig2\"><label>Fig. 2</label><caption><p>Design models for determining the center of resistance: (<bold>A</bold>) Boundary condition and force loading of retraction units; (<bold>B</bold>) The approximate force level axis; (<bold>C</bold>), Comparison of the maximum initial displacements (blue to red reflects lower to higher displacement) of the teeth (level 0–7); (<bold>D</bold>) Comparison of the maximum initial displacements of the teeth (level 4.0–5.0); (<bold>E</bold>), Comparison of the maximum initial displacements of the teeth (level 4.80–4.95)</p></caption></fig>", "<fig id=\"Fig3\"><label>Fig. 3</label><caption><p>(<bold>A</bold>) Grouping about retraction treatment: Model C0 Control, Model C1 Posterior MI, Model C2 Anterior MI, Model C3 Palatal Plate, Model C4 Lingual Retractor, Model F0 Fix Appliance. Red arrow represents the applied force loading (150 g) from precision cutting or hook to Micro-implants. Black arrow represents the same activation (0.2 mm retraction) of anterior teeth. (<bold>B</bold>) Details of Model C1, Model C2, Model C3 and Model C4, the distance from the traction point to the occlusion plane is 18.54 mm</p></caption></fig>", "<fig id=\"Fig4\"><label>Fig. 4</label><caption><p><bold>A</bold>) Displacement tendencies of central incisor, lateral incisor, and canine in sagittal dimension. (<bold>B</bold>) Crown-root displacement difference of central incisor, lateral incisor, and canine in sagittal dimension</p></caption></fig>", "<fig id=\"Fig5\"><label>Fig. 5</label><caption><p>Retraction of central incisor, lateral incisor, and canine in sagittal dimension with different Models. The original place represents the initial position of the anterior teeth in the global coordinate system, and low point represents the incisal midpoint of the anterior and high point represents the root tip of the anterior</p></caption></fig>", "<fig id=\"Fig6\"><label>Fig. 6</label><caption><p>(<bold>A</bold>) Displacement tendencies of central incisor, lateral incisor, and canine in vertical dimension. (<bold>B</bold>) Crown and root displacement of central incisor, lateral incisor, and canine in vertical dimension</p></caption></fig>", "<fig id=\"Fig7\"><label>Fig. 7</label><caption><p>(<bold>A</bold>) Displacement tendencies of second premolar, first molar, second molar in sagittal dimension. (<bold>B</bold>) Crown displacement of second premolar, first molar, second molar in sagittal dimension. (<bold>C</bold>) Displacement tendencies of second premolar, first molar, second molar in vertical dimension. (<bold>D</bold>) Crown displacement of second premolar, first molar, second molar in vertical dimension</p></caption></fig>", "<fig id=\"Fig8\"><label>Fig. 8</label><caption><p>(<bold>A</bold>) The von Mises distribution (blue to gray reflects lower to higher stress) of clear aligners and fixed appliance. (<bold>B</bold>) Stress value for maximum von Mises of the clear aligners and fixed appliance. (<bold>C</bold>) Displacement tendencies of clear aligners and fixed appliance. (<bold>D</bold>) The maximum displacement of clear aligner and fixed appliance</p></caption></fig>", "<fig id=\"Fig9\"><label>Fig. 9</label><caption><p>(<bold>A</bold>) Distribution of von Mises stresses in the PDL of the central incisor, lateral incisor, canine, second premolar, first molar and second molar. (<bold>B</bold>) Stress value for average von Mises in the PDL of the central incisor, lateral incisor, canine, second premolar, first molar and second molar</p></caption></fig>" ]

[ "<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Material properties and element number</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Component</th><th align=\"left\">Young’s modulus (MPa)</th><th align=\"left\">Poisson’s ratio</th><th align=\"left\">Nodes</th><th align=\"left\">Elements</th></tr></thead><tbody><tr><td align=\"left\">Teeth</td><td align=\"left\">18,600</td><td char=\".\" align=\"char\">0.31</td><td align=\"left\">232,722</td><td align=\"left\">130,616</td></tr><tr><td align=\"left\">PDL</td><td align=\"left\">0.68</td><td char=\".\" align=\"char\">0.48</td><td align=\"left\">125,028</td><td align=\"left\">63,489</td></tr><tr><td align=\"left\">Cortical bone</td><td align=\"left\">13,700</td><td char=\".\" align=\"char\">0.3</td><td align=\"left\">217,757</td><td align=\"left\">120,647</td></tr><tr><td align=\"left\">Cancellous bone</td><td align=\"left\">1370</td><td char=\".\" align=\"char\">0.3</td><td align=\"left\">111,373</td><td align=\"left\">60,362</td></tr><tr><td align=\"left\">Clear aligner</td><td align=\"left\">816.31</td><td char=\".\" align=\"char\">0.3</td><td align=\"left\">123,155–162,229</td><td align=\"left\">64,300–67,937</td></tr><tr><td align=\"left\">Attachment</td><td align=\"left\">12,500</td><td char=\".\" align=\"char\">0.36</td><td align=\"left\">5850</td><td align=\"left\">2683</td></tr><tr><td align=\"left\">Power arm</td><td align=\"left\">200,000</td><td char=\".\" align=\"char\">0.3</td><td align=\"left\">5265</td><td align=\"left\">2537</td></tr><tr><td align=\"left\">3D printed attachment</td><td align=\"left\">235,000</td><td char=\".\" align=\"char\">0.33</td><td align=\"left\">52,082</td><td align=\"left\">24,438</td></tr><tr><td align=\"left\">Archwire</td><td align=\"left\">200,000</td><td char=\".\" align=\"char\">0.3</td><td align=\"left\">24,109</td><td align=\"left\">10,536</td></tr><tr><td align=\"left\">Bracket</td><td align=\"left\">210,000</td><td char=\".\" align=\"char\">0.3</td><td align=\"left\">30,317</td><td align=\"left\">14,254</td></tr><tr><td align=\"left\">Micro-implant</td><td align=\"left\">114,000</td><td char=\".\" align=\"char\">0.34</td><td align=\"left\">4871</td><td align=\"left\">2709</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab2\"><label>Table 2</label><caption><p>Displacement of crown and root of the maxillary anterior teeth under different loading models in sagittal direction (mm)</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" rowspan=\"2\"/><th align=\"left\"/><th align=\"left\" colspan=\"2\">Central incisor</th><th align=\"left\"/><th align=\"left\" colspan=\"2\">Lateral incisor</th><th align=\"left\"/><th align=\"left\">Canine</th><th align=\"left\"/></tr><tr><th align=\"left\">Crown</th><th align=\"left\">Root</th><th align=\"left\">Difference</th><th align=\"left\">Crown</th><th align=\"left\">Root</th><th align=\"left\">Difference</th><th align=\"left\">Crown</th><th align=\"left\">Root</th><th align=\"left\">Difference</th></tr></thead><tbody><tr><td align=\"left\">Model C0</td><td align=\"left\">6.39E-02</td><td align=\"left\">-1.40E-02</td><td align=\"left\">7.79E-02</td><td align=\"left\">6.71E-02</td><td align=\"left\">-1.88E-02</td><td align=\"left\">8.59E-02</td><td align=\"left\">6.13E-02</td><td align=\"left\">-2.11E-02</td><td align=\"left\">8.24E-02</td></tr><tr><td align=\"left\">Model C1</td><td align=\"left\">6.22E-02</td><td align=\"left\">-1.36E-02</td><td align=\"left\">7.57E-02</td><td align=\"left\">6.94E-02</td><td align=\"left\">-1.96E-02</td><td align=\"left\">8.90E-02</td><td align=\"left\">6.04E-02</td><td align=\"left\">-2.02E-02</td><td align=\"left\">8.07E-02</td></tr><tr><td align=\"left\">Model C2</td><td align=\"left\">6.08E-02</td><td align=\"left\">-1.25E-02</td><td align=\"left\">7.33E-02</td><td align=\"left\">6.51E-02</td><td align=\"left\">-1.81E-02</td><td align=\"left\">8.32E-02</td><td align=\"left\">6.21E-02</td><td align=\"left\">-2.12E-02</td><td align=\"left\">8.33E-02</td></tr><tr><td align=\"left\">Model C3</td><td align=\"left\">5.19E-02</td><td align=\"left\">-1.11E-02</td><td align=\"left\">6.30E-02</td><td align=\"left\">5.91E-02</td><td align=\"left\">-1.67E-02</td><td align=\"left\">7.59E-02</td><td align=\"left\">5.45E-02</td><td align=\"left\">-1.87E-02</td><td align=\"left\">7.31E-02</td></tr><tr><td align=\"left\">Model C4</td><td align=\"left\">6.44E-02</td><td align=\"left\">-1.54E-02</td><td align=\"left\">7.98E-02</td><td align=\"left\">5.86E-02</td><td align=\"left\">-1.50E-02</td><td align=\"left\">7.36E-02</td><td align=\"left\">4.50E-02</td><td align=\"left\">-1.59E-02</td><td align=\"left\">6.09E-02</td></tr><tr><td align=\"left\">Model F0</td><td align=\"left\">-1.12E-04</td><td align=\"left\">-2.46E-04</td><td align=\"left\">1.34E-04</td><td align=\"left\">-1.01E-02</td><td align=\"left\">4.22E-03</td><td align=\"left\">1.43E-02</td><td align=\"left\">4.09E-03</td><td align=\"left\">-1.46E-03</td><td align=\"left\">5.55E-03</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab3\"><label>Table 3</label><caption><p>Displacement of crown and root of the maxillary anterior teeth under different loading models in vertical direction (mm)</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" rowspan=\"2\"/><th align=\"left\"/><th align=\"left\">Central incisor</th><th align=\"left\"/><th align=\"left\">Lateral incisor</th><th align=\"left\"/><th align=\"left\">Canine</th></tr><tr><th align=\"left\">Crown</th><th align=\"left\">Root</th><th align=\"left\">Crown</th><th align=\"left\">Root</th><th align=\"left\">Crown</th><th align=\"left\">Root</th></tr></thead><tbody><tr><td align=\"left\">Model C0</td><td align=\"left\">-3.93E-02</td><td align=\"left\">1.13E-02</td><td align=\"left\">-4.44E-02</td><td align=\"left\">1.38E-02</td><td align=\"left\">-3.00E-02</td><td align=\"left\">2.37E-02</td></tr><tr><td align=\"left\">Model C1</td><td align=\"left\">-3.71E-02</td><td align=\"left\">1.19E-02</td><td align=\"left\">-4.61E-02</td><td align=\"left\">1.42E-02</td><td align=\"left\">-2.98E-02</td><td align=\"left\">2.47E-02</td></tr><tr><td align=\"left\">Model C2</td><td align=\"left\">-3.53E-02</td><td align=\"left\">1.17E-02</td><td align=\"left\">-4.07E-02</td><td align=\"left\">1.44E-02</td><td align=\"left\">-2.96E-02</td><td align=\"left\">2.33E-02</td></tr><tr><td align=\"left\">Model C3</td><td align=\"left\">-3.08E-02</td><td align=\"left\">9.66E-03</td><td align=\"left\">-3.92E-02</td><td align=\"left\">1.28E-02</td><td align=\"left\">-2.75E-02</td><td align=\"left\">2.06E-02</td></tr><tr><td align=\"left\">Model C4</td><td align=\"left\">-4.14E-02</td><td align=\"left\">1.03E-02</td><td align=\"left\">-3.65E-02</td><td align=\"left\">1.45E-02</td><td align=\"left\">-2.27E-02</td><td align=\"left\">2.28E-02</td></tr><tr><td align=\"left\">Model F0</td><td align=\"left\">5.26E-04</td><td align=\"left\">7.59E-04</td><td align=\"left\">9.77E-03</td><td align=\"left\">8.37E-04</td><td align=\"left\">-4.69E-03</td><td align=\"left\">-1.29E-03</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab4\"><label>Table 4</label><caption><p>Displacement of crown of the maxillary posterior teeth under different loading models in sagittal direction (mm)</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" rowspan=\"2\"/><th align=\"left\">Second premolar</th><th align=\"left\">First molar</th><th align=\"left\">Second molar</th></tr><tr><th align=\"left\">Crown</th><th align=\"left\">Crown</th><th align=\"left\">Crown</th></tr></thead><tbody><tr><td align=\"left\">Model C0</td><td align=\"left\">-3.31E-02</td><td align=\"left\">-2.37E-02</td><td align=\"left\">-2.13E-02</td></tr><tr><td align=\"left\">Model C1</td><td align=\"left\">-3.34E-02</td><td align=\"left\">-2.41E-02</td><td align=\"left\">-2.05E-02</td></tr><tr><td align=\"left\">Model C2</td><td align=\"left\">-3.40E-02</td><td align=\"left\">-2.46E-02</td><td align=\"left\">-2.15E-02</td></tr><tr><td align=\"left\">Model C3</td><td align=\"left\">-2.72E-02</td><td align=\"left\">-2.22E-02</td><td align=\"left\">-1.72E-02</td></tr><tr><td align=\"left\">Model C4</td><td align=\"left\">-3.27E-02</td><td align=\"left\">-2.21E-02</td><td align=\"left\">-1.73E-02</td></tr><tr><td align=\"left\">Model F0</td><td align=\"left\">7.24E-04</td><td align=\"left\">1.05E-03</td><td align=\"left\">1.78E-03</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab5\"><label>Table 5</label><caption><p>Displacement of crown of the maxillary posterior teeth under different loading models in vertical direction (mm)</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" rowspan=\"2\"/><th align=\"left\">Second premolar</th><th align=\"left\">First molar</th><th align=\"left\">Second molar</th></tr><tr><th align=\"left\">Crown</th><th align=\"left\">Crown</th><th align=\"left\">Crown</th></tr></thead><tbody><tr><td align=\"left\">Model C0</td><td align=\"left\">7.57E-03</td><td align=\"left\">8.19E-04</td><td align=\"left\">-2.01E-03</td></tr><tr><td align=\"left\">Model C1</td><td align=\"left\">8.57E-03</td><td align=\"left\">5.35E-04</td><td align=\"left\">-2.28E-03</td></tr><tr><td align=\"left\">Model C2</td><td align=\"left\">8.64E-03</td><td align=\"left\">5.89E-04</td><td align=\"left\">-2.31E-03</td></tr><tr><td align=\"left\">Model C3</td><td align=\"left\">6.98E-03</td><td align=\"left\">3.58E-04</td><td align=\"left\">-1.50E-03</td></tr><tr><td align=\"left\">Model C4</td><td align=\"left\">1.05E-02</td><td align=\"left\">-5.49E-05</td><td align=\"left\">-2.61E-03</td></tr><tr><td align=\"left\">Model F0</td><td align=\"left\">1.23E-03</td><td align=\"left\">-5.82E-04</td><td align=\"left\">-6.25E-05</td></tr></tbody></table></table-wrap>" ]

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[ "<supplementary-material content-type=\"local-data\" id=\"MOESM1\"></supplementary-material>" ]

[ "<fn-group><fn><p><bold>Publisher’s Note</bold></p><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn><fn><p>Qian Xia and Weixu Wang contributed equally to this work and shared first authorship.</p></fn></fn-group>" ]

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[ "<media xlink:href=\"12903_2023_3704_MOESM1_ESM.docx\"><caption><p><bold>Supplementary Material 1: Additional file 1:</bold> Supplementary Figure 1. Different views of the Model C4: (A) Occlusal view of Model C4, red arrow represents the applied force loading (150 g) from hook to the 3D printed palatal plate. (B) Palatal view of Model C4. (C) Occlusal view of the 3D printed lingual retraction hook and the 3D printed palatal plate. (D) Sagittal view of Model C4, the distance from the traction point to the occlusion plane is 18.54 mm. <bold>Additional file 2:</bold> Supplementary file 1. Determination of the center of resistance (CR) and Determination of the height of lingual retraction hook. <bold>Additional file 3:</bold> Supplementary Figure 2. (A) Displacement tendencies of central incisor, lateral incisor, and canine in sagittal dimension. (B) Crown-root displacement difference of central incisor, lateral incisor, and canine in sagittal dimension</p></caption></media>" ]

{ "acronym": [], "definition": [] }

[ "<title>Background</title>", "<title>Introduction</title>", "<p id=\"Par42\">Tremor is characterized as an involuntary, rhythmic, oscillatory movement of a body part [##REF##29193359##1##], and it can manifest as a symptom of various neurological diseases, including essential tremor (ET), Parkinson’s disease (PD), and multiple sclerosis (MS). The categorization of tremors is based on clinical factors such as anatomical distribution, activation conditions, amplitude, frequency, and underlying etiology. Within the scope of this review, tremors will be classified according to their activation condition and corresponding neurological symptoms and diseases.</p>", "<p id=\"Par43\">Tremor can be classified into two main categories: rest tremor [##REF##18383537##2##], characterized by nonvoluntary activation that occurs when the individual is attempting to rest and is commonly observed in people with PD. In contrast, action tremor [##REF##29193359##1##] involves voluntary movement. Action tremor can be further classified into two subtypes: postural tremor, which occurs when the subject maintains a position against gravity, and kinetic tremor, which is associated with any voluntary movement that can be constant (simple kinetic), specific to a particular activity, such as writing (task-specific), or that increases as the individual approaches a goal or visual target (intention tremor). Intention tremor refers to a rise in the amplitude of tremors when visually guided movements are made toward a target, especially when nearing it. This type of tremor can also be coupled with task-specific tremor as the individual performs targeted movements, for example, during drawing (Archimedes Spiral tests). Intention tremor is believed to be correlated with cerebellar pathology, its connected pathways, or both, and it is a common symptom in people with, for example, MS [##REF##11287372##3##]. It is estimated that 25–60% of people with MS experience postural and intention tremor [##REF##17318714##4##], which typically occurs in the upper limbs at a frequency of 3–4 Hz [##REF##11287372##3##]. However, other types of tremors, such as rest, simple kinetic, and task-specific tremors, are not frequently observed in MS [##UREF##0##5##].</p>", "<p id=\"Par44\">Assessing tremors in patients with neurological diseases is crucial for determining disease progression and the effectiveness of medical treatments. Traditionally, clinicians use various clinical tests to identify tremor type and severity in patients. However, with the advancement of wearable technologies, such as smartphones, smartwatches, and sophisticated muscle sensors, there are now quantifiable ways to measure movement and tremor. Although wearable technology is a promising approach for quantifying tremors, identifying relevant features for each type of tremor is necessary for practical use. Recent research has shown that analyzing tremor amplitude and frequency makes it possible to differentiate between different movement disorders such as ET and PD versus healthy controls, classify tremor severity, and correlate it with traditional qualitative-scored neurological tests [##UREF##1##6##]. However, the changing nature of intention tremors, whose amplitude depends on the movement intention of the patient, makes it difficult to quantify this type of tremor and extract valuable features using the current approaches.</p>", "<p id=\"Par45\">Identifying and analyzing intention tremors can greatly aid disease progression monitoring and intervention efficacy assessment. This review examines the advancement of upper limb tremor assessment technology, methodology, and future directions for algorithm and sensor development to improve quantification of tremor in general and intention tremor specifically.</p>", "<title>Neurological tests for tremor assessment correlation and comparison</title>", "<p id=\"Par46\">Researchers evaluate tremor assessment technologies by performing specific tasks that amplify the targeted tremor type. These tasks are based on tests used in clinical practice to assess upper limb impairments. Table ##TAB##0##1## displays the most common clinical tests used to correlate or as a reference for evaluating assessment technologies. The Fahn-Tolosa-Marin Tremor Scale (FTMRS) [##UREF##2##7##] and the Essential Tremor Rating Assessment Scale (TETRAS) [##UREF##3##8##] are frequently used to quantify rest, postural, and kinetic tremor, including tremor during activities of daily living (ADLs). When the technology is tailored for a single population, e.g., people with PD, a more disease-specific test such as the Movement Disorder Society Unified Parkinson’s Disease Rating Scale, Part III Motor Examination (UPDRS-III) [##REF##19025984##9##] is used for correlation purposes.</p>", "<p id=\"Par47\">Another example of a disease-specific test is the Scale for the Assessment and Rating of Ataxia (SARA) test [##REF##17516493##10##], which focuses on cerebellar ataxia. SARA includes the finger to nose test (FTN) and the finger chase test, which specifically evaluates intention tremor.</p>", "<p id=\"Par48\">In summary, clinical tests include different tasks assessing tremor severity depending on their type (see Fig. ##FIG##0##1##):<list list-type=\"bullet\"><list-item><p id=\"Par49\"><italic>Rest tremor</italic>: Sitting with fully supported arms against gravity.</p></list-item><list-item><p id=\"Par50\"><italic>Postural tremor</italic>: Maintaining a specific posture against gravity, for example, stretching arms to the front so that the subject maintains their elbows stretched against gravity; or shoulder abduction with elbows flexed and hands held in a pronated position resembling a 'wing-beating' posture.</p></list-item><list-item><p id=\"Par51\"><italic>Kinetic tremor</italic>: Simple kinetic and task-specific tremors are evaluated using tasks such as handwriting, Archimedes spirals drawings, and finger tapping (FT), as well as ADLs involving whole-body movement, such as pouring drinks, eating, and dressing. Intention tremor severity can be measured using the finger to nose test (FTN). In this test, the subject touches their nose and then the examiner’s finger, with the tremor amplitude expected to increase as the hand approaches the finger. Intention tremor can also be assessed using the finger chase test, where the examiner performs sudden fast pointing movements in a frontal plane. At the same time, the subject follows with their finger as quickly and accurately as possible.</p></list-item></list></p>", "<title>Literature search and data extraction</title>", "<p id=\"Par52\">This review was primarily conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) scoping review checklist (see Additional file ##SUPPL##1##2##). In this review, we were interested in finding studies examining quantifiable upper limb tremor assessment strategies accessible to clinicians and patients without highly specialized equipment. To determine the criteria for inclusion and exclusion, we conducted a comprehensive search on PubMed and Scopus with the following title/abstract terms (\"tremor\") AND (\"assessment\" OR \"measurement\" OR \"evaluation\" OR \"detection\" OR \"quantification\" OR \"monitoring\" OR \"correlation\" OR \"estimation\" or \"discrimination\" OR \"analysis\" OR \"differentiation\" OR \"classification\") AND (\"technology\" OR \"sensor\" OR \"device\" OR \"quantification\") (last search date: 10 July 2023) (see Additional file ##SUPPL##4##5## for the detailed search strings). Further publications were identified from the list of references of relevant papers and relevant review papers found in our search [##UREF##1##6##, ##REF##36959763##11##, ##REF##34434161##12##]. After screening the articles for relevance and eligibility, we excluded studies that (1) did not focus on upper limb impairment, (2) focused on upper limb symptoms that explicitly excluded tremor, (3) only used clinical tests and clinician evaluation without any sensor or any automated tool, (4) the type of technology is not portable or usable outside of specialized rooms (e.g., functional magnetic resonance (fMRI) or magnetoencephalography (MEG)) or are invasive, (5) only evaluated healthy subjects, (6) interventional studies using damping tools, such as exoskeletons or functional electrical stimulation (FES), (7) preprints, prospective studies, and not peer-reviewed, and (8) not written in English. The remaining studies, 243 publications (see the details on data extraction in Additional file ##SUPPL##2##3##), were analyzed to identify common themes and establish criteria based on the type of sensors, number of subjects, technology, methodology, purpose, and year of publication. According to our screened papers, tremor assessment technologies can be classified into three distinct types, as depicted in Fig. ##FIG##1##2## and classified in the table of Additional file ##SUPPL##0##1## and the database found in Additional file ##SUPPL##3##4##:<list list-type=\"simple\"><list-item><label>i.</label><p id=\"Par53\"><italic>Activity level: Based on tools and digitized tasks</italic>, using smartphones or tablets, assessment is made through manipulanda or touch-based games.</p></list-item><list-item><label>ii.</label><p id=\"Par54\"><italic>Based on physiological sensors,</italic> physiological measurements are used to detect and differentiate tremors using surface electromyography (EMG) sensors, muscle activation following motor unit recruitment, and electroencephalogram (EEG) measuring the brain's electrical activity from the scalp.</p></list-item><list-item><label>iii.</label><p id=\"Par55\"><italic>Body function level: Movement based on motion capture systems</italic>, the tremor and the posture of the subject’s upper limbs are captured using accelerometers, gyroscopes, inertial measurement units (IMUs), electromagnetic tracking, or camera systems, with or without markers.</p></list-item></list></p>" ]

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[ "<title>Conclusions: future avenues to assess intention tremor</title>", "<p id=\"Par77\">Of all the collected studies, 52 (21% of the total) assessed intention tremor tasks. Furthermore, 37% of these studies [##UREF##11##36##, ##REF##14582776##37##, ##REF##32259798##56##, ##REF##17113154##65##, ##REF##29940199##66##, ##REF##11514245##71##, ##REF##26421829##84##, ##REF##1151405##115##, ##UREF##27##122##, ##UREF##28##124##, ##REF##26040012##183##, ##REF##30243994##193##, ##REF##30813963##210##, ##REF##32528140##241##, ##UREF##75##251##–##REF##31419976##253##, ##REF##10908187##257##, ##REF##33266481##265##] (less than 8% from all studies) focus on pwMS, ataxia, or cerebellar disease, who tend to exhibit intention tremor more clearly. The findings indicate that assessment technologies measuring intention tremor should design tasks that elicit intention tremor and involve individuals who exhibit relevant symptoms.</p>", "<p id=\"Par78\">Although digitized drawings have been examined in people with intention tremor [##REF##36563127##14##, ##REF##35178527##55##, ##REF##32259798##56##, ##REF##34259588##58##, ##REF##17113154##65##, ##REF##29940199##66##], further comparison with other intention tremor tasks is needed, such as the SARA scale and the FTN or finger chase tasks. Moreover, the effectiveness of digitized drawings in eliciting intention tremor and their association with task-specific tremors require more investigation.</p>", "<p id=\"Par79\">Regarding physiological sensors, EMG has been used in pwMS [##REF##26421829##84##, ##REF##35834887##95##]. Still, only one study has explored its application in intention tremor [##REF##26421829##84##], yet their findings did not provide conclusive evidence concerning the relationship between accelerometry and EMG. The understanding of muscle activity in intention tremor remains incomplete, necessitating a more comprehensive analysis. For instance, conducting tasks specifically designed to elicit intention tremor in individuals with cerebellar pathology would facilitate an in-depth investigation of motor conduction times and activation patterns [##REF##35378656##62##].</p>", "<p id=\"Par80\">EEG could help to differentiate movement intention from tremor, as previously suggested by Gallego and Ibáñez et al. [##UREF##23##98##, ##UREF##68##238##] in their analysis of tremor in ET. Examining patients' brain activity with intention tremors may shed light on how cortical or cerebellar activities change during motor control tasks. From computational neuroanatomy and neuroimaging studies, the premotor, primary motor, parietal regions of the cortex, and cerebellum are believed to be involved in motor control [##REF##18251019##271##] and tremorous movements [##REF##22293134##101##, ##REF##18823037##272##]. Assessing cerebellar activity during motor control and intention tremor tasks could be valuable, especially for patients with cerebellar pathology [##REF##34755280##107##, ##REF##32278092##273##, ##REF##36070135##274##]. For example, recent studies observed heightened cerebellar activity through cerebellar EEG recordings of ET patients [##REF##31941824##105##] with only one study, to the best of the authors’ knowledge, using an intention tremor task [##REF##34341893##106##]. Additionally, the interaction between the motor, parietal, and cerebellar regions could be analyzed during motor execution and intention tremor tasks. A past study investigated the functional interaction (using EEG modular functional connectivity) of the somatomotor system and higher-order processing systems during a motor task [##REF##36443387##275##].</p>", "<p id=\"Par81\">Motion capture algorithms could be one of the best ways to assess intention tremors due to their easy integration with wearable technologies for intervention, such as tremor-damping exoskeletons. The valuable research conducted by Morgan et al. [##REF##1151405##115##] and Deuschl et al. [##REF##10908187##257##], investigating intention tremor during activities that induce this type of tremor, can now be easily replicated using markerless pose estimation software, as done by Pang et al. in PD [##REF##31923452##269##]. On the other hand, IMU sensors have become practical and effective for tremor detection but require sensor fusion algorithms and signal processing techniques for reliable analysis [##REF##24930942##90##, ##REF##26040012##183##, ##UREF##70##242##]. Another study was performed by Carpinella et al. [##REF##26040012##183##] effectively employed the combined capabilities of EMD and HHT to accurately detect minute variations in intention tremor tasks. They accomplished automatic classification and distinction between HS and pwMS and detected subtle tremors from voluntary movement in MS. Furthermore, Tran et al. [##UREF##75##251##, ##UREF##76##252##] used ballistic tracking (an intention tremor task analogous to the finger chase test) with an IMU and a Kinect camera to distinguish between ataxia and HS successfully. These outcomes present promising prospects for the automated detection and assessment of intention tremors. In addition to facilitating such analysis, this technique could also provide valuable insight into developing intention detection algorithms for individuals with neurological conditions such as pwMS, thereby enabling wearable technologies to function not only as assessment tools but also as sensors for interventions and assistive technologies in daily life.</p>", "<p id=\"Par82\">This review examined the utilization of sensor technology in evaluating tremors across various neurological conditions. Some limitations of our review include manuscripts with unclear terminology related to tremor, e.g., studies not differentiating between the different types of kinetic tremor, and studies with imprecise methodology, especially on sensor fusion with IMUs. Nevertheless, in this review, we tried to the best of our abilities to systematically infer those missing fields using the information in other parts of the manuscripts, e.g., experimental protocol and patient population, to infer tremor type and results and conclusions to infer sensor fusion modalities.</p>", "<p id=\"Par83\">While most research has focused on assessing tremor in PD and ET, intentional tremors observed in patients with lesions in the cerebellum could be better understood. This challenge can be approached by targeting intention tremors and leveraging existing technology (see Fig. ##FIG##2##3##). First and foremost, a technical contribution is needed to make better intention tremor assessments beyond the current tests. Furthermore, analyzing muscle activation and brain activity through EMG and EEG can provide insights into the underlying causes of intentional tremors. Regarding motion capture, it is crucial to optimize IMUs through sensor fusion algorithms that utilize the strengths of each sensor (accelerometer, gyroscope, magnetometer) to obtain an accurate limb position to extract tremorous movements using time–frequency analysis.</p>", "<p id=\"Par84\">Additionally, using markerless pose estimation would offer a more straightforward and flexible means of capturing data without requiring specialized equipment, enabling assessments to be conducted on more subjects exhibiting intention tremors, for example, at home. Distinguishing between voluntary and involuntary movement remains a challenge for the technologies discussed. Therefore, it is essential to use and further develop signal processing techniques that focus on separating different movement components, such as EMD or DWT, to enhance the detection of the distinct aspects of tremorous movements, their onset, and their differentiation from voluntary movements.</p>" ]

[ "<title>Background</title>", "<p id=\"Par1\">Tremors are involuntary rhythmic movements commonly present in neurological diseases such as Parkinson's disease, essential tremor, and multiple sclerosis. Intention tremor is a subtype associated with lesions in the cerebellum and its connected pathways, and it is a common symptom in diseases associated with cerebellar pathology. While clinicians traditionally use tests to identify tremor type and severity, recent advancements in wearable technology have provided quantifiable ways to measure movement and tremor using motion capture systems, app-based tasks and tools, and physiology-based measurements. However, quantifying intention tremor remains challenging due to its changing nature.</p>", "<title>Methodology &amp; Results</title>", "<p id=\"Par2\">This review examines the current state of upper limb tremor assessment technology and discusses potential directions to further develop new and existing algorithms and sensors to better quantify tremor, specifically intention tremor. A comprehensive search using PubMed and Scopus was performed using keywords related to technologies for tremor assessment. Afterward, screened results were filtered for relevance and eligibility and further classified into technology type. A total of 243 publications were selected for this review and classified according to their type: body function level: movement-based, activity level: task and tool-based, and physiology-based. Furthermore, each publication's methods, purpose, and technology are summarized in the appendix table.</p>", "<title>Conclusions</title>", "<p id=\"Par3\">Our survey suggests a need for more targeted tasks to evaluate intention tremors, including digitized tasks related to intentional movements, neurological and physiological measurements targeting the cerebellum and its pathways, and signal processing techniques that differentiate voluntary from involuntary movement in motion capture systems.</p>", "<title>Supplementary Information</title>", "<p>The online version contains supplementary material available at 10.1186/s12984-023-01302-9.</p>", "<title>Keywords</title>", "<p>Open Access funding enabled and organized by Projekt DEAL.</p>" ]

[ "<title>Technologies for tremor assessment</title>", "<p id=\"Par56\">The following sections will discuss the different assessment technologies and algorithms to quantify tremors. The studies in this section have been classified in detail according to sensor type, patient population, and tremor type in Additional files 1 and 4. We encourage the readers to consider this chapter together with those additional files. Table ##TAB##1##2## presents an overview of the tools discussed in this chapter and the main type of tremor assessed with them.</p>", "<title>Signal processing to quantify and analyze tremors</title>", "<p id=\"Par57\">Tremor assessment technologies measure physical parameters and transform them into electronic signals. For instance, accelerometers placed on the subject’s hand analyze the frequency components of arm acceleration to detect tremors. Signal processing techniques are necessary to remove noise and measure various movement features. The publications in our review employ different algorithms and feature extraction methods based on signal processing techniques for tremor detection. To detect tremors, measurements are typically transformed from the time domain to the frequency domain, focusing on tremor frequencies (2–10 Hz) compared to regular movement. Fast Fourier transform (FFT) and power spectral distribution (PSD) analysis are commonly used. The FFT provides information about the amplitude and phase of individual frequency components in a signal, while the PSD offers insights into the power distribution across different frequency bands. The PSD is especially suitable for comparing signals of varying lengths because it focuses on the frequency distribution regardless of the signal length. In contrast, the FFT is dependent on the signal length.</p>", "<p id=\"Par58\">In addition to the FFT and PSD, decomposing electronic signals in both time and frequency is advantageous, particularly for analyzing changes in frequency strength over time. The discrete wavelet transform (DWT) and Hilbert-Huang transform (HHT) [##UREF##4##16##] can be helpful for this. The DWT decomposes a signal into wavelets of different frequencies, scales, and orientations, making it more efficient to simultaneously analyze both frequency and time information, more robust to noise, and computationally efficient. On the other hand, the HHT decomposes a signal into its intrinsic mode functions (IMFs) using empirical mode decomposition (EMD) [##UREF##5##17##] and is better suited for analyzing nonstationary signals with precise time–frequency information. However, it may require more processing power. Thus, DWT and EMD are valuable tools to decompose voluntary and involuntary movement.</p>", "<title>Manipulanda and technical tools to quantify tremors</title>", "<p id=\"Par59\">One approach for assessing tremors involves using tools with embedded sensors that can measure the direction, speed, and force of movement [##UREF##6##18##–##UREF##8##24##]. Researchers have utilized tools such as pens [##REF##33076436##25##–##REF##33776080##30##] with embedded IMUs and load cells to quantify tremor amplitude while users hold it, attach it to their hands, or write with it. An advantage of embedded sensor tools is their ability to identify different features in virtual tasks [##REF##31991705##31##–##REF##34851839##33##]. For example, the Virtual Peg Insertion Test (VPIT), based on the 9HPT [##REF##5551515##34##] test, employs a manipulandum with force sensors in a virtual game environment and serves as a digital health metric for predicting the response to neurorehabilitation interventions in neurological disorders.</p>", "<p id=\"Par60\">Kanzler et al. [##REF##35224896##13##, ##REF##34822120##35##] identified several features and studied their correlation to clinical tests. They found a high correlation between the SARA test and velocity and path length features in relation to intention tremor. Manipulanda have also been used to elicit intention tremor during goal-directed movements; for example, Feys et al. [##UREF##11##36##, ##REF##14582776##37##] conducted studies involving people with MS (pwMS) and intention tremors, where they observed more significant target overshoot and unsteady eye fixation during goal-directed movement tasks.</p>", "<p id=\"Par61\">Overall, pens with embedded IMUs have shown promise in measuring different types of tremors, particularly during task-specific movements such as writing or drawing [##REF##36846115##28##]. However, wearable sensors may be more suitable and sensitive for measuring steady tremors than tools. On the other hand, analyzing digital features in addition to traditional completion time in tests such as the 9HPT could provide further insight into the characteristics of intention tremor. However, focused symptom testing is necessary to determine the effectiveness of these digital features in measuring intention tremor. Therefore, studies that specifically focus on it, using manipulanda in tasks similar to the finger chase test [##UREF##11##36##–##REF##8327136##38##], would be advantageous; however, a quantification of intensity and its test correlation would still be required for future studies.</p>", "<title>From measuring the duration of completion to quantifying the drawn lines</title>", "<p id=\"Par62\">Digitized drawing tests, such as writing or drawing shapes on tablets or smartphones, offer advantages over traditional methods of assessing tremors. These tests allow for the quantification of drawn lines in terms of time and extraction of different features. The assessment of digitized drawings often involves calculating the power spectral density (PSD) of the drawing position, velocity, or acceleration to determine the frequency ranges of the movement. This can help distinguish subjects with tremors, who are expected to have distinguishable spectra at higher frequencies (&gt; 2 Hz), from those without tremors. Digitizing tablets have been used to assess tremor by analyzing writing and drawing shapes and AS [##REF##8771070##39##–##REF##36966002##49##], as well as combining it with FT [##UREF##14##50##–##REF##29028217##53##]. Studies have shown that the frequency spectrum of velocity profiles in digitized Archimedes spirals drawings is a reliable measure of tremor intensity and more accurate than traditional visual rating methods [##REF##21714004##54##].</p>", "<p id=\"Par63\">Smartphone apps offer greater accessibility and flexibility for at-home testing compared to tablets since individuals are more likely to possess a smartphone than a tablet. Furthermore, the choice between smartphones and tablets can affect the reproducibility and intravariability of results, and more straightforward tests may be preferred for smartphone-based MS assessment [##REF##35178527##55##]. This could be advantageous, especially in using small screens where drawings are limited due to space. These approaches include drawing simpler shapes than Archimedes spirals [##REF##36563127##14##, ##REF##32259798##56##–##REF##34259588##58##], tilting a smartphone to maintain an objective in position using the smartphone accelerometers [##REF##31191424##59##–##REF##33242017##61##], and finger tapping (FT) to assess upper limb impairment [##REF##35378656##62##, ##REF##29582075##63##].</p>", "<p id=\"Par64\">Regarding intention tremor, Erasmus et al. [##REF##11459615##64##] pioneered this method for quantification of ataxic symptoms in MS. They tested it in a large cohort of 342 pwMS where they drew an’8’ shape in a tablet. Consequently, Feys et al. [##REF##17113154##65##] investigated the validity and reliability of drawing regular and squared Archimedes spirals on a tablet as a test for tremor severity. They successfully differentiate pwMS with intention tremor from pwMS with no tremor and healthy subjects (HS) by comparing the radial and tangential velocity PSD in the 3–5 Hz frequencies with FTMRS scores. Archimedes spirals drawings have also proven to be a good measure to identify the presence of intention tremor in pwMS by comparing it with FTN, 9HPT, and BBT [##REF##29940199##66##]. Measuring the segment rate, i.e., the number of times the pen changes from the upward to the downward direction, is the feature that correlates more to visually inspected intention tremor. The advantage of this metric is probably related to the fact that the segment rate increases as the frequency of the movement increases, suggesting that intention tremor could also be detected by analyzing the PSD of the Archimedes spirals movement, as proven by Creagh et al. [##REF##32259798##56##] during the DaS test.</p>", "<p id=\"Par65\">In summary, digitized drawings and app-based games are accessible tools to quantify tremors that could be used in clinics and at home. Tasks such as Archimedes spirals are very effective in eliciting tremors in various neurological diseases. However, it is still unclear how this task is related to intention tremor. Further analysis and correlation to intention tremor tasks, for example, using it in combination with the SARA test, would provide a deeper understanding of its relation to intentional movements.</p>", "<title>Physiological measurements: discriminating between different neurological diseases</title>", "<p id=\"Par66\">Surface electromyography (EMG), measuring muscle electrical activity, and mechanomyography (MMG), measuring surface oscillations produced by motor units, are used to analyze muscle activation patterns in upper limb tremors. In the 80–90s, EMG was used to detect tremors using FFT and PSD in subjects with neurological disorders [##REF##2464489##67##–##UREF##16##69##]. EMG has been used to distinguish muscle activation depending on the neurological disease [##UREF##17##70##–##REF##15922033##72##]; for example, Nisticò and Vescio et al. [##REF##21071257##73##, ##REF##33573076##74##] showed that during rest tremor, the activation of antagonist muscles is synchronous in subjects with ET and alternating in those with PD. EMG and accelerometer/IMU combinations [##REF##23658233##75##–##REF##9827600##83##] have been extensively used to discriminate PD, ET [##REF##26421829##84##–##UREF##21##89##], physiological tremor (PH) [##REF##24930942##90##, ##REF##32280070##91##], psychogenic tremor [##REF##16092105##92##, ##REF##12939436##93##], advanced ET [##REF##35034234##94##], and MS [##REF##35834887##95##] from each other by using ML techniques on DWT and HT signal decomposition during, in its majority, stretch and steady positions. MMG [##REF##32305925##96##] was recently used with EMG, force sensors, and IMUs to detect tremor differences in PD after deep brain stimulation [##UREF##22##97##].</p>", "<p id=\"Par67\">Electroencephalogram (EEG) measures the brain's electrical activity from the scalp, providing excellent temporal resolution. However, its low spatial resolution poses a challenge in precisely identifying activity in different brain structures. Despite this drawback, EEG is a valuable tool for evaluating motor tasks [##UREF##23##98##], as long as the influence of movement artifacts is carefully considered. EEG has been used to explore the involvement of the cerebellum in conditions such as spinocerebellar and cerebellar AT [##REF##31417491##99##, ##REF##10480276##100##], as well as ET in comparison with PD [##REF##22293134##101##, ##REF##29701820##102##], HS [##REF##28754506##103##], and people with age-related tremors (ART) [##REF##26347194##104##]. These studies consistently demonstrate a strong involvement and oscillations of cerebellar activity in ET and PD. Excessive oscillations in cerebellar EEG have been correlated with tremor intensity in ET [##REF##31941824##105##, ##REF##34341893##106##], while increased oscillations in the theta band of cerebellar EEG have been observed in PD [##REF##34755280##107##]. EEG has also been employed to assess the effects of transcranial magnetic stimulation (TMS) therapy in individuals with multiple system atrophy cerebellar subtypes (MSA-C) [##REF##33085647##108##], showing higher cerebello-frontal connectivity and a negative correlation to SARA.</p>", "<p id=\"Par68\">EMG and MMG measurements have effectively been used to differentiate tremor pattern activations in different neurological conditions, even when the subjects perform the same type of activity. These results suggest that muscular activity could be a powerful tool to understand how tremor is propagated and where it is localized. On the other hand, the mentioned studies have emphasized the importance of EEG in studying the involvement of the cerebellum in movement disorders, which could provide valuable insights into the underlying pathophysiology of intention tremor and potential treatment strategies.</p>", "<title>Inertial-based recordings using acceleration, orientation, and sensor fusion algorithms</title>", "<p id=\"Par69\">Inertial measurement units (IMUs), consisting of accelerometers, gyroscopes, and magnetometers, measure linear acceleration, angular velocity, and magnetic field strength, respectively. As these signals vary depending on the orientation of the sensor, IMUs have become increasingly prevalent in modern technology applications. These sensors can be positioned on different parts of the limbs, such as the wrist, hand, or fingers, to analyze movement by measuring the acceleration, velocity, and orientation of the limbs. Furthermore, suppose multiple IMUs are used on each limb segment, i.e., hand, forearm, upper arm, and trunk. In that case, it is possible to extract the limb's position relative to the trunk and measure additional features such as range of motion and movement synergy.</p>", "<p id=\"Par70\">In the past, accelerometers, gyroscopes, and magnetometers were available as separate components, and smartphones typically only included accelerometers due to cost considerations. At the end of the last century, accelerometers were used to detect tremors [##REF##3612150##109##–##REF##9509754##113##], quantify medication efficacy [##REF##8665557##114##] in PD, and analyze intention tremors in patients with cerebellar pathology [##REF##1151405##115##]. Accelerometers attached to the hands or wrist either in single form [##UREF##24##116##–##UREF##39##144##] or in the form of a smartwatch [##REF##29736737##145##–##REF##28360883##156##] or smartphone [##REF##28841694##157##–##UREF##41##165##] have been extensively used to quantify tremors in different neurological diseases [##UREF##42##166##, ##UREF##43##167##], either by analyzing acceleration frequency [##UREF##20##88##, ##REF##33614109##168##] and amplitude [##REF##31860947##169##] or by using machine learning methods to classify measurements according to tremor type [##UREF##44##170##–##REF##19846382##173##]. Gyroscopes can detect changes in angular velocity and measure the angular movement of a body part. Analogous to accelerometers, gyroscopes have also been used individually [##UREF##46##174##–##UREF##49##178##], in smartphones [##UREF##50##179##] and smartwatches [##REF##31569335##180##–##REF##31005763##182##] to decompose tremorous and voluntary movement using different signal processing techniques such as EMD, HHT [##REF##26040012##183##, ##REF##16937193##184##], WFLC, and EKF [##REF##22294919##185##]. Other types of motion detection sensors, such as force transducers [##REF##28726022##186##–##REF##17566934##188##] or electromagnetic sensors [##UREF##51##189##–##UREF##53##194##], have been proposed to track tremors in ET, PD, and MS.</p>", "<p id=\"Par71\">The miniaturization of IMUs has enabled the direct measurement of tremors on distal limbs using a single chip. Although some studies have utilized both accelerometers and gyroscopes [##REF##32305925##96##, ##UREF##22##97##, ##UREF##54##195##–##REF##25393786##230##] to gain insight into tremorous movements, only a portion of them have employed sensor fusion algorithms to integrate these data and improve measurement reliability [##UREF##32##131##, ##UREF##66##231##–##UREF##78##255##]. Sensor fusion filters are used in IMUs to combine data from multiple sensors and improve the accuracy and reliability of the measurements. Their output is no longer angular velocity or acceleration but the IMU orientation relative to a predefined reference. Popular filters include the Madgwick filter and extended Kalman filter (EKF). The Madgwick filter is computationally efficient, using quaternions to combine accelerometer, gyroscope, and magnetometer data for orientation estimation. In contrast, the EKF employs a mathematical model and Bayesian inference to estimate the system state by fusing data from multiple sensors.</p>", "<p id=\"Par72\">Overall, measuring acceleration and angular velocity, using electromagnetic tracking to track upper limb movement, or using a combination of sensors embedded in IMUS has proven to be a popular and straightforward method for measuring tremors. To achieve a more accurate and comprehensive understanding of tremorous movement, future research should use sensor fusion algorithms, which are currently underutilized (less than 39% of the studies using IMUs). This approach would enable researchers to calculate limb position, velocity, and acceleration without the noise drawbacks from accelerometers and gyroscopes to characterize tremor movements. Additionally, this approach would benefit understanding movement synergies and tremor propagation.</p>", "<title>Movement prediction with video recordings</title>", "<p id=\"Par73\">Marker-based motion capture uses optical 3D motion analysis systems to track reflective markers placed strategically on the body during movement analysis. It uses infrared cameras to capture marker movement, which is then used to calculate various spatiotemporal, kinematic, and kinetic gait parameters through software calculations [##UREF##79##256##]. In particular, Deutschl et al. [##REF##10908187##257##] used marker pose estimation to observe whether people with ET showed intention tremors by instructing the participants to grasp a target. The researchers identified the presence of intention tremors similar to that seen in MS and ataxia.</p>", "<p id=\"Par74\">Leap motion systems use multiple cameras and infrared sensors to analyze hand motions within their field of view. While highly accurate, their range of motion is limited [##UREF##80##258##, ##UREF##81##259##]. Chen et al. [##REF##27058772##260##] and Khwaounjoo et al. [##REF##35746395##261##] used a leap motion sensor to quantify ET and PD postural tremor by measuring the finger tremor amplitude and frequency. Although their results were less accurate than using IMUs, they showed a strong correlation with respect to them; they localized the best positions for tremor identification and achieved high accuracy at lower frequencies.</p>", "<p id=\"Par75\">Markerless pose estimation is a new technique used to estimate the position and movement of human body joints without using physical markers. Using standard video, it utilizes computer vision and machine learning algorithms to analyze movement in real-time. The technique involves detecting and recognizing key body landmarks, constructing a skeletal model, and estimating joint position and movement over time. Markerless pose estimation software is user-friendly and flexible. Still, it has limitations, including lower accuracy than marker-based systems, difficulty tracking occluded or partially visible body parts, and sensitivity to environmental factors. Nonetheless, ongoing advances in computer vision and machine learning are enhancing the accuracy and robustness of these techniques [##UREF##82##262##–##UREF##85##267##], making them potentially valuable for tremor characterization—for example, Park et al. [##REF##36707402##15##] utilized Mediapipe [##UREF##86##268##] to analyze its feasibility in telemedicine for PD. Although the study involved healthy subjects, the findings suggested that movement tracking accuracy was hindered by poor video quality. Nevertheless, the researchers proposed that the software could be effectively utilized with better video setup and equipment. Furthermore, Pang et al. [##REF##31923452##269##] used OpenPose [##REF##31331883##270##], a real-time body pose estimation library using deep learning, to successfully track tremors and bradykinesia in PD using DWT to detect finger motion changes in the frequency domain.</p>", "<p id=\"Par76\">In summary, marker-based estimation technologies capture tremors, but their setup and costs limit their evaluation in large patient cohorts or clinical practice. However, with advancements in computer vision based on deep learning algorithms, markerless pose estimators have the potential to become widely adopted for easy tremor analysis using simple setups such as phone cameras.</p>", "<title>Supplementary Information</title>", "<p>\n</p>" ]

[ "<title>Acknowledgements</title>", "<p>We gratefully acknowledge the funding and support from the Institute for Advanced Study (IAS)—Technical University of Munich.</p>", "<title>Author contributions</title>", "<p>NP was involved in the conception, organization, and execution of the research project and the design, execution, review, and critique of the statistical analysis. NP also played a role in writing the first draft of the manuscript and provided input during its review and critique. DU participated in the statistical analysis, provided feedback during the manuscript preparation, and contributed to its review and critique. KD contributed to the manuscript's preparation and writing during the review and critique process. NT was involved in organizing the research project and the review and critique of the manuscript. GC was involved in the research project's conception and organization, took part in the design, execution, and review of the statistical analysis, and contributed to the review and critique of the manuscript. All authors read and approved the final manuscript.</p>", "<title>Funding</title>", "<p>Open Access funding enabled and organized by Projekt DEAL. This work was supported by the Hans Fischer Senior Fellowship from the Institute for Advanced Study (TUM-IAS).</p>", "<title>Availability of data and materials</title>", "<p>The datasets supporting the conclusions of this article are included within the article and its additional files.</p>", "<title>Declarations</title>", "<title>Ethics approval and consent to participate</title>", "<p id=\"Par85\">The authors confirm that the approval of an institutional review board or ethics committee was not required for this work. Informed patient consent was not necessary for this work. We confirm that this manuscript aligns with the guidelines of the Journal's stance on ethical publication matters.</p>", "<title>Consent for publication</title>", "<p id=\"Par86\">Not applicable.</p>", "<title>Competing interests</title>", "<p id=\"Par87\">The authors declare that they have no competing interests. Author’s financial disclosures for the previous 12 months: NP is supported by the Institute for Cognitive Systems (TUM-ICS) and the Institute for Advance Studies from the Technical University of Munich (TUM-IAS). DU is supported by the Department of Neurology, Klinikum rechts der Isar of the Technical University of Munich. KD is supported by the U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research under award number DE-SC0022150. NT is a co-founder of Infinite Biomedical Technologies and Vigilant Medical Technologies and serves on their board as a scientific advisor. His intellectual property has been licensed to Vasopatic Medical and Phantom Robotics, although he has not received any royalties. GC is a shareholder of intouch-robotics GmbH. This study is not related to the company.</p>" ]

[ "<fig id=\"Fig1\"><label>Fig. 1</label><caption><p>Rest tremors are evaluated using supported positions, postural tremors with no support, kinetic tremors through tasks such as writing, finger tapping (FT), and Activities of Daily Living (ADLs), and intention tremors using tasks such as finger to nose test (FTN) and finger chase (FC) tests</p></caption></fig>", "<fig id=\"Fig2\"><label>Fig. 2</label><caption><p>Types of tremor assessment technologies include activity level tasks and tools such as tablets and smartphones for drawing, physiological technologies such as surface electromyography (EMG) and electroencephalogram (EEG), and body function level movement-based technologies such as inertial measurement units (IMUs) and camera systems for measuring upper limb pose and movement. Figures adapted from [##REF##35224896##13##, ##REF##36563127##14##] used under CC BY 4.0 and from [##REF##36707402##15##] used under granted copyright by CCC RightsLink</p></caption></fig>", "<fig id=\"Fig3\"><label>Fig. 3</label><caption><p>Intention tremor can be further studied through technology and specialized tasks, which isolate and amplify it. EMG and EEG provide insights into source localization and connectivity. Motion capture technologies and algorithms such as EMD reveal details about voluntary and involuntary actions. The figure is adapted from [##REF##32272463##276##] and used under granted copyright by CCC RightsLink</p></caption></fig>" ]

[ "<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Common neurological tests and tasks used in clinical practice to assess tremor</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" rowspan=\"2\">Name</th><th align=\"left\" rowspan=\"2\">Tremor type/function</th><th align=\"left\" colspan=\"9\">Tasks</th></tr><tr><th align=\"left\">Rest</th><th align=\"left\">Posture against gravity</th><th align=\"left\">Write</th><th align=\"left\">Archimedes spiral</th><th align=\"left\">FT</th><th align=\"left\">FTN</th><th align=\"left\">Finger chase</th><th align=\"left\">ADLs</th><th align=\"left\">Other</th></tr></thead><tbody><tr><td align=\"left\">FTMRS [##UREF##2##7##]</td><td align=\"left\">Rest, postural, kinetic</td><td align=\"left\">X</td><td align=\"left\">X</td><td align=\"left\">X</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\">X</td><td align=\"left\"/><td align=\"left\">X</td><td align=\"left\"/></tr><tr><td align=\"left\">TETRAS [##UREF##3##8##]</td><td align=\"left\">Postural, kinetic</td><td align=\"left\"/><td align=\"left\">X</td><td align=\"left\">X</td><td align=\"left\">X</td><td align=\"left\"/><td align=\"left\">X</td><td align=\"left\"/><td align=\"left\">X</td><td align=\"left\">X</td></tr><tr><td align=\"left\">UPDRS-III [##REF##19025984##9##]</td><td align=\"left\">Rest, postural, kinetic</td><td align=\"left\">X</td><td align=\"left\">X</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\">X</td><td align=\"left\">X</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\">SARA [##REF##17516493##10##]</td><td align=\"left\">Postural, kinetic, ataxia</td><td align=\"left\"/><td align=\"left\">X</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\">X</td><td align=\"left\">X</td><td align=\"left\"/><td align=\"left\">X</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab2\"><label>Table 2</label><caption><p>Summary of type of technology and main targeted tremor discussed in Chapter 2</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Section</th><th align=\"left\">Type</th><th align=\"left\">Main targeted tremor</th></tr></thead><tbody><tr><td align=\"left\">2.1 Algorithms</td><td align=\"left\">Fourier transform (FFT), power spectral analysis (PSD), wavelet decomposition (DWT), and Hilbert-Huang transform (HHT)</td><td align=\"left\">All types</td></tr><tr><td align=\"left\">2.2 Tool based</td><td align=\"left\">Smart pens and manipulanda with embedded IMUs and force sensors</td><td align=\"left\">Task-specific tremor</td></tr><tr><td align=\"left\">2.3 Task based</td><td align=\"left\">Digitized drawings such as Archimedes Spirals, and different types of shapes. Smartphone games where the subject maintain objects in equilibrium</td><td align=\"left\">Task-specific and intention tremor</td></tr><tr><td align=\"left\">2.4 Physiology based</td><td align=\"left\">Muscle activity using EMG and MMG</td><td align=\"left\">Rest and postural tremor, discrimination between ET and PD</td></tr><tr><td align=\"left\">2.4 Physiology based</td><td align=\"left\">Brain activity using EEG</td><td align=\"left\">Rest and postural tremor</td></tr><tr><td align=\"left\">2.5 Movement based</td><td align=\"left\">Accelerometers, gyroscopes, magnetometers and IMUs (sensor fusion)</td><td align=\"left\">Rest, postural, and intention tremor</td></tr><tr><td align=\"left\">2.6 Movement based</td><td align=\"left\">Cameras</td><td align=\"left\">Rest, postural, and intention tremor</td></tr></tbody></table></table-wrap>" ]

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[ "<supplementary-material content-type=\"local-data\" id=\"MOESM1\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM2\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM3\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM4\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM5\"></supplementary-material>" ]

[ "<table-wrap-foot><p><italic>FTMRS</italic> Fahn-Tolosa-Marin Tremor Scale, <italic>TETRAS</italic> Essential Tremor Rating Assessment Scale, <italic>UPDRS-III</italic> Movement Disorder Society Unified Parkinson’s Disease Rating Scale, Part III Motor Examination, <italic>SARA</italic> Scale for the Assessment and Rating of Ataxia, <italic>FT</italic> Finger Tapping, <italic>FTN</italic> Finger To Nose test, <italic>ADLs</italic> Activities of Daily Living</p></table-wrap-foot>", "<table-wrap-foot><p><italic>EMG</italic> electromyography, <italic>MMG</italic> mechanomyography, <italic>PD</italic> Parkinson’s disease, <italic>ET</italic> essential tremor, <italic>IMUs</italic> inertial measurement units</p></table-wrap-foot>", "<fn-group><fn><p><bold>Publisher's Note</bold></p><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn></fn-group>" ]

[ "<graphic xlink:href=\"12984_2023_1302_Fig1_HTML\" id=\"MO1\"/>", "<graphic xlink:href=\"12984_2023_1302_Fig2_HTML\" id=\"MO2\"/>", "<graphic xlink:href=\"12984_2023_1302_Fig3_HTML\" id=\"MO3\"/>" ]

[ "<media xlink:href=\"12984_2023_1302_MOESM1_ESM.docx\"><caption><p><bold>Additional file 1.</bold> Studies using tremor assessment technologies classified according to its type. This additional file is a table including all studies considered in this review. The table categorizes the studies into assessment type, number and type of patients, technology used, method, purpose, and year.</p></caption></media>", "<media xlink:href=\"12984_2023_1302_MOESM2_ESM.docx\"><caption><p><bold>Additional file 2.</bold> PRISMA checklist for scoping reviews. This checklist structures the reporting items of our scoping review by providing the page number where each section can be found.</p></caption></media>", "<media xlink:href=\"12984_2023_1302_MOESM3_ESM.docx\"><caption><p><bold>Additional file 3.</bold> Results of literature search and data extraction. This flow diagram shows the number of sources of evidence screened and assessed for eligibility and the number of studies excluded at each stage of the data extraction process.</p></caption></media>", "<media xlink:href=\"12984_2023_1302_MOESM4_ESM.xlsx\"><caption><p><bold>Additional file 4.</bold> Database of selected studies. This table shows the database of the selected studies. It provides additional information than Additional file ##SUPPL##0##1##, such as the type of IMU sensors, and disseminates the data to automatically analyze it.</p></caption></media>", "<media xlink:href=\"12984_2023_1302_MOESM5_ESM.txt\"><caption><p><bold>Additional file 5.</bold> Database search strings. This text document contains the search strings used in PubMed and Scopus for data retrieval.</p></caption></media>" ]

{ "acronym": [ "ET", "PD", "MS", "HS", "ADLs", "FTMRS", "TETRAS", "SARA", "FTN", "ARAT", "9HPT", "BBT", "FT", "PRISMA", "fMRI", "MEG", "FES", "EMG", "EEG", "FFT", "PSD", "DWT", "HHT", "IMF", "EMD", "VPIT", "pwMS", "MMG", "PH", "ART", "TMS", "MSA-C", "IMU", "EKF", "IAS", "WFLC", "SVM", "CNN" ], "definition": [ "Essential tremor", "Parkinson’s disease", "Multiple sclerosis", "Healthy subjects", "Activities of daily living", "Fahn-Tolosa-Marin Tremor Scale", "Essential Tremor Rating Assessment Scale", "Scale for the Assessment and Rating of Ataxia", "Finger to nose test", "Action Research Arm Test", "9 Hole Peg Test", "Box and Blocks Test", "Finger tapping", "Preferred Reporting Items for Systematic Reviews and Meta-Analyses", "Functional magnetic resonance", "Magnetoencephalography", "Functional electrical stimulation", "Electromyography", "Electroencephalogram", "Fast Fourier transform", "Power spectral density", "Discrete wavelet transform", "Hilbert-Huang transform", "Intrinsic model functions", "Empirical mode decomposition", "Virtual Peg Insertion Test", "People with MS", "Mechanomyography", "Physiological tremor", "Age-related tremors", "Transcranial magnetic stimulation", "Multiple system atrophy cerebellar subtype", "Inertial measurement units", "Extended Kalman filter", "Institute for Advanced Study", "Weighted frequency Fourier linear combiner", "Support vector machine", "Convolutional neural network" ] }

[ "<title>Background</title>", "<p id=\"Par21\">Tooth development is a time- and space-specific process including the initiation, bud, cap, and bell stages. In the past few decades, the molecular pathways and regulating mechanisms underlying tooth morphogenesis have been widely explored [##REF##24009032##1##]. In tooth germ, the intimate interactivities between dental epithelium and mesenchyme tissues are sequentially controlled by multiple cytokines/signaling molecules, including bone morphogenetic proteins (BMPs), Wnt, and Shh [##REF##32111038##2##–##REF##31980484##4##]. During the bell stage, the inner enamel epithelial cells differentiate into enamel-secreting ameloblasts, while the adjacent dental papilla mesenchymal cells polarize and differentiate into odontoblasts to secrete dentin matrix [##REF##30894046##5##]. Subsequently, the dental papilla mesenchyme encompassed by accumulative dentin matrix forms dental pulp. The outside enamel and dentin are hard component of tooth, protecting the dental pulp tissues.</p>", "<p id=\"Par22\">The dentin and dental pulp are together called dental-pulp complex because of their close relationships in biological development and physiological structure. The dental-pulp complex is crucial for the life of tooth, not only because of the commonly physiologic functions of pulp, but also the regulating effects on pulp homeostasis. After severe pulp injury, odontoblasts differentiated from dental pulp stem cells (DPSCs) form reparative dentinogenesis.</p>", "<p id=\"Par23\">Human dental pulp stem cells (hDPSCs) are isolated from adult dental pulp tissues and positive for mesenchymal stem cells markers [##REF##27240827##6##]. As multipotent progenitors, hDPSCs are able to self-renewal and differentiate into dentin-forming odontoblasts [##REF##17316462##7##]. Multiple growth factors and complex molecular signal pathways are related to odontogenic differentiation of hDPSCs and dentinogenesis, including BMPs, insulin-like growth factor, vascular endothelial growth factor and platelet-derived growth factor [##REF##33459973##8##]. Reasonably, DPSCs are considered to be a promising and suitable source for in vivo and in vitro studies of tertiary dentin formation and dental pulp regeneration [##REF##11087820##9##–##REF##32899877##12##].</p>", "<p id=\"Par24\">Numerous studies have explored biomolecular capping materials to promote the repair of injured pulp tissue [##REF##34434262##13##, ##REF##31454111##14##]. Among these, microRNAs (miRNAs) are promising molecules due to their epigenetic regulatory role in multiple biological processes like osteo/odontogenic differentiation [##REF##25807941##15##–##REF##22980176##20##]. By binding to the 3′UTR of messenger RNAs, some microRNAs are proved to influence the odontogenic differentiation of DPSCs in post-transcriptional level by negatively regulate the target genes like krüpple-like factor 4, bone morphogenetic protein receptor type II, osterix and glycoprotein non-metastatic melanomal protein B [##REF##33256846##16##, ##REF##30670091##21##–##REF##29127007##23##]. Furthermore, studies have revealed that some microRNAs can epigenetically regulate other epigenetic factors like DNA methyltransferases and histone modification enzymes, functioning as epigenetic-microRNAs (epi-miRNAs) [##REF##20493980##24##]. After odontogenic induction of hDPSCs, the differentially expressed miRNAs have been analyzed [##REF##22980176##20##]. However, whether these miRNAs can work as epi-miRNA and the underlying regulation pattern still need to be explored.</p>", "<p id=\"Par25\">In addition to the epigenetic regulation by miRNA, posttranslational modifications of histone proteins are also closely associated with odontoblast differentiation and tooth development [##REF##25394593##25##–##REF##24158144##28##]. In bell stage of mice tooth germ, the lysine 27 trimethylation on histone 3 (H3K27me3) marks of dental papilla showed a spatiotemporal pattern and decreased from early to late bell stage. During the odontogenic differentiation process of human dental papilla cells, the dynamic levels of H3K27me3 marks accompanied by the elevated trend of specific histone demethylase KDM6B [##REF##28880717##29##]. Besides, the KDM6B was found to remove the H3K27me3 marks from the promoter region of <italic>BMP2</italic> to promote odontogenic differentiation [##REF##33149742##30##, ##REF##33759287##31##]. These modifications at histone level represent a complicated and dynamic process. Studies have analyzed the miRNAs that differentially expressed during tooth development and odontogenic differentiation of DPSCs, however, if these miRNAs could interplay with these epigenetic modifiers and further influence the tooth development in histone modification levels still need further studies.</p>", "<p id=\"Par26\">Our previous study analyzed the miRNAs in human tooth germs during bell stage and found significantly varied expression of miR-93-5p [##REF##23226240##32##]. As a member of miR-106b-25 cluster, miR-93-5p had been proved to play a functional role in osteoarthritis by affecting anti-inflammation and associate to the recovery of sepsis related acute kidney injury by targeting to KDM6B [##REF##26243299##19##, ##REF##25309979##33##–##REF##32679234##36##]. In present study, the potential functions of miR-93-5p working as an epi-miRNA in dentin formation of tooth development and odontogenic differentiation were investigated.</p>" ]

[ "<title>Methods</title>", "<title>Oligonucleotide transfection</title>", "<p id=\"Par27\">HDPSCs were cultured as reported method [##REF##31387241##37##] and approval from the Medical Ethics Committee of West China Hospital of Stomatology, Sichuan University (WCHSIRB-CT-2021-243). Hsa-miR-93-5p mimic (miR-10000093-1-5, Ribobio), mimic NC (miR-1N0000001-1-5, Ribobio), hsa-miR-93-5p inhibitor (miR-20000093-1-5, Ribobio) and inhibitor NC (miR-2N0000001-1-5, Ribobio) were synthesized. Lipofectamine 3000 reagents (Invitrogen) was used for oligonucleotide transfection. The final concentrations in the transfection system were 50 nM. The mimic/inhibitor NC were controls.</p>", "<title>Dual-luciferase reporter assay</title>", "<p id=\"Par28\">Synthetic KDM6B-WT-3′UTR (wild type) and KDM6B-MUT-3′UTR (mutant) gene fragment were cloned into pGLO vectors separately, and then co-transfected with 293T cells and miR-93-5p/NC mimic in virtue of Lipofectamine 3000 Reagents. Cell suspension was collected and luciferase activities of samples were detected.</p>", "<title>qRT-PCR and western blotting</title>", "<p id=\"Par29\">Total RNAs including miRNAs in hDPSCs were prepared by RNeasy Plus Mini Kit (74134, Qiagen) and mice tooth tissues were prepared by RNeasy Plus Micro Kit (74034, Qiagen). After reverse transcription, samples were processed for quantitative polymerase chain reaction (qPCR). Tables ##TAB##0##1## and ##TAB##1##2## listed the primers.</p>", "<p id=\"Par30\">Total proteins were extracted according to the protocol (KeyGEN). The primary antibodies were β-actin (1:1000, GB11001, Servicebio), Histone 3 (1:1000, GB11102, Servicebio), H3K27me3 (1:700, 6002, Abcam) and KDM6B (1:700, ab169197, Abcam). Secondary antibodies of goat anti-rabbit/mouse IgG-HRP (1:5000, L3012-2/L3032-2, Signal way Antibody) were used.</p>", "<title>Alizarin Red S and ALP staining</title>", "<p id=\"Par31\">Base medium (NC) and odontogenic induction medium (OM) for cells culture were compounded as previous description [##REF##32460893##38##]. After odontogenic induction for 3, 7 and 14 days, hDPSCs were fixed and dyed by Alkaline Phosphatase (ALP) Assay Kit (Biyotime). Besides, mineral nodules were stained and observed by Alizarin Red S solution (Solarbio). Images were acquired with inverted light microscopy (Olympus, Japan).</p>", "<title>Chromatin immunoprecipitation (ChIP) assays</title>", "<p id=\"Par32\">Cells were harvested after transfected with miR-93-5p mimic and odontogenic induction for 7 days. Enzymatically processed chromatin was obtained by EZ-Zyme Chromatin Prep Kit (17-375, Millipore). EZ-Magna ChIP HiSens kit (17-10461, Millipore) and antibodies of rabbit anti-H3K27me3 (9733, Cell Signaling Technology), rabbit anti-KDM6B (ab16917, Abcam), normal rabbit IgG (CS200581, Millipore) were used for ChIP assay. DNA samples were acquired and then quantified by real-time PCR. Table ##TAB##2##3## listed the primers.</p>", "<title>Animals</title>", "<p id=\"Par33\">The animal studies had approval from the Medical Ethics Committee of West China Hospital of Stomatology, Sichuan University (WCHSIRB-D-2021-321). Embryos and newborn mice at embryonic day 17.5, postnatal day 0 and 3 (E17.5, P0 and P3) were obtained by time-mated pregnant C57BL/6 mice (Chengdu Dossy Experimental animals Co., Ltd.). After mice were euthanized, dental papilla and enamel organ tissues of mandibular first molars were separated under transmitted light microscope.</p>", "<p id=\"Par34\">Five-weeks-old male Sprague Dawley rats (Chengdu Dossy Experimental animals Co., Ltd.) were used for pulpotomy. Rats were randomly separated into six groups (5 rats for per group), including group without pulpotomy (Control) and capping groups: Vitapex (Morita, Japan), lentivirus-scramble (GeneCopoeia), KDM6B-overexpression (pEZ-Lv105 lentivirus vector, GeneCopoeia), AAV-scramble (5′-CGCTGAGTACTTCGAAATGTC-3′, Genechemand AAV-miR-93-5p inhibitor (5′-ACCGCTACCTGCACGAACAGCACTTTGTTTTT-3′, GV479 vector, Genechem). Rats were anesthetized and the cavities on occlusal surfaces of maxillary first molars were prepared by 1/4-inch burs under water cooling. The dentin debris on pulp wound was flushed away by sterile saline. After the pulp surface were cleaned and covered by fresh blood, aseptic cotton pellet soaked in sterile saline was pressured on the pulp surface to stop bleeding. After the hemorrhage was under control, gelatin sponges were used to deliver capping agents. The cavities were protected with a thin layer of glass-ionomer cement and closely restored by composite resin at last. After 2 and 4 weeks, all rats were euthanized and the maxillae were fixed in 4% paraformaldehyde. For observing enhanced green fluorescent protein, the samples were embedded by Tissue-Tek O.C.T. Compound (Sakura). The tissue sections (6 μm) were obtained and photographed.</p>", "<title>MicroCT</title>", "<p id=\"Par35\">The rats’ maxillae were collected for microCT analysis before decalcification. Tertiary dentin was analyzed by micro-CT scanner (μCT50, SCANCO MEDICAL AG) in a scanning resolution of 8 μm pixel size under the following settings: 70 kVp, 200 μA, AL 0.5 mm, 1 × 300 ms.</p>", "<title>Histologic and immunologic staining</title>", "<p id=\"Par36\">The decalcified samples were embedded by paraffin for cutting into slices. Hematoxylin and eosin (Beyotime) staining was performed according to the instruction. For immunohistochemistry staining, the antibodies were BMP2 (AF5163, 1:200, Affinity Biosciences). For immunofluorescence staining, the antibodies were H3K27me3 (9733, 1:200, Cell Signaling Technology), KDM6B (ab169197, 1:250, abcam) and FITC conjugated secondary antibodies (1:400, Santa Cruz Biotechnology). Images were acquired by microscopy (Olympus, Japan).</p>", "<title>Statistical analysis</title>", "<p id=\"Par37\">Relative mRNA levels were normalized with GAPDH<italic>.</italic> Relative microRNA levels were normalized with U6. Numerical data were presented as mean ± SD. GraphPad Prism 7 was used for data analysis. Student’s <italic>t</italic> test or ANOVA followed by Tukey’s test was used to evaluate statistical significance. <italic>P</italic> values &lt; 0.05 were considered statistically significant.</p>" ]

[ "<title>Results</title>", "<title>MicroRNA-93-5p downregulation is paralleled with KDM6B upregulation in tooth development and odontogenic differentiation of DPSCs</title>", "<p id=\"Par38\">In our previous study [##REF##23226240##32##], the expression of miRNAs from human tooth germ in bell stages were analyzed by miRNA microarray. The differentially expressed miRNAs were listed in the heat map (Additional file ##SUPPL##0##1##: Fig. S1A), suggesting that miR-93-5p of human tooth germ was significantly decreased during bell stage. Previous study confirmed that specific demethylase KDM6B of H3K27me3 marks was a key epigenetic regulator during dental papilla development and dynamically expressed in a spatiotemporal pattern [##REF##28880717##29##]. For further investigated the epi-miRNAs interact with histone modification in dentin formation, we then searched the miRNA databases including TargetScanHuman7.2, miRbase Target and miRDB for miRNAs that not only differentially express during the bell stage, but also probably target on KDM6B. Afterwards, miR-93-5p was predicted to target with KDM6B (Additional file ##SUPPL##0##1##: Fig. S1B).</p>", "<p id=\"Par39\">Moreover, the dynamic expression trend of miR-93-5p in mice tooth germ from early to late bell stages was investigated. Dental papilla from mice first molar germs of embryonic 17.5 days (E17.5), postnatal 0 and 3 days (P0, P3) were separated under light microscope (Fig. ##FIG##0##1##A). The dental epithelial organ tissues expressed specific epithelial marker cytokeratin 14 (<italic>Ck14</italic>) while the dental papilla tissues significantly expressed specific mesenchymal marker <italic>Vimentin</italic> (Fig. ##FIG##0##1##B, C). In developing mouse dental papilla tissues, the expression of odontogenic genes collagen type-1α (<italic>Col-1α</italic>) and osterix (<italic>Osx</italic>) were upregulated from E17.5 to P3 (Fig. ##FIG##0##1##D). Along with the odontogenic differentiation process of mice dental papilla mesenchymal cells, miR-93-5p was down regulated (Fig. ##FIG##0##1##E) while <italic>Kdm6b</italic> showed an up-regulated trend (Fig. ##FIG##0##1##F).</p>", "<p id=\"Par40\">To further delineate the expression pattern of KDM6B and miR-93-5p in odontogenic differentiation of adult dental mesenchymal cells, hDPSCs were cultured under odontogenic condition. MiR-93-5p was down-regulated when hDPSCs differentiated into odontoblasts (Fig. ##FIG##0##1##G). Furthermore, the expression of <italic>KDM6B</italic> was up-regulated in both mRNA and protein levels (Fig. ##FIG##0##1##H, I).</p>", "<title>MicroRNA-93-5p regulates odontogenic differentiation of hDPSCs</title>", "<p id=\"Par41\">MicroRNA-93-5p mimic/inhibitor was transfected into hDPSCs effectively (Figs. ##FIG##1##2##A, ##FIG##2##3##A). After miR-93-5p mimic transfection and odontogenic induction, the activity of ALP was significantly suppressed while the mineralized nodule was reduced in hDPSCs (Fig. ##FIG##1##2##B, C). The mRNA expression of odontogenic genes <italic>OSX</italic>, <italic>ALP</italic>, osteocalcin (<italic>OCN</italic>) and <italic>COL-1α</italic> were significantly suppressed (Fig. ##FIG##1##2##D–G). In contrast, miR-93-5p inhibitor treatment promoted the ALP activity and mineralized nodule formation in hDPSCs, accordingly, mineralization indicators above were upregulated (Fig. ##FIG##2##3##B–G). Above data together indicating the miR-93-5p functionally regulating the odontogenic differentiation of hDPSCs.</p>", "<title>MicroRNA-93-5p targets KDM6B and influences H3K27me3 marks of BMP2</title>", "<p id=\"Par42\">During the odontogenic differentiation of hDPSCs, the H3K27me3 marks was down-regulated (Fig. ##FIG##3##4##A). After miR-93-5p mimic treatment, H3K27me3 marks in hDPSCs were significantly enriched (Fig. ##FIG##3##4##B, C). The KDM6B was targeted by miR-93-5p and down-regulated, the bonding site on KDM6B was also validated (Fig. ##FIG##3##4##D–G). The H3K27me3 methylases including EZH2, SUZ12, and EED were no different expression after miR-93-5p mimic treatment (Fig. ##FIG##3##4##F). BMP2 was further detected to be down-regulated in hDPSCs after miR-93-5p mimic transfected (Fig. ##FIG##3##4##H). To examined how miR-93-5p functioned on the <italic>BMP2</italic> transcription, ChIP-qPCR assays were conducted. After odontogenic induction for 7 days, the KDM6B affinity on <italic>BMP2</italic> promoters was decreased (Fig. ##FIG##4##5##A). Accordingly, increased H3K27me3 marks on <italic>BMP2</italic> promoters mirrored the loss of KDM6B occupancy (Fig. ##FIG##4##5##D). The different levels of KDM6B affinities on promoter regions of <italic>OSX</italic> and <italic>OCN</italic> had no significant effects on H3K27me3 marks (Fig. ##FIG##2##3##B, C, E, F). Above results suggested that miR-93-5p could influence H3K27me3 marks in <italic>BMP2</italic> promoter regions by targeting KDM6B, therefore epigenetically regulated the odontogenic differentiation of hDPSCs.</p>", "<title>MicroRNA-93-5p inhibitor induces dentin formation in rat pulpotomy model</title>", "<p id=\"Par43\">The pulpotomy was performed on rats’ maxillary first molars and the pulp cutting surfaces were capped by gelatin sponges with agents (Additional file ##SUPPL##0##1##: Fig. S2A–H). Fluorescence observation revealed that the capping agents with lentivirus and AAV vector were successfully transfected into the residual pulp of rats’ molars (Additional file ##SUPPL##0##1##: Fig. S2I). For MicroCT analysis, KDM6B-overexpression and miR-93-5p inhibitor treatment effectively promoted the formation of dentin bridges over the opening of tooth root canals after 4 weeks (Fig. ##FIG##5##6##A). In rat’s dental pulp, KDM6B-overexpression and miR-93-5p inhibitor treatment upregulated KDM6B accompanying with the downregulation of H3K27me3 marks, which is accordance with results in cultured hDPSCs (Fig. ##FIG##5##6##B, C). H&amp;E staining showed that the necrotic pulp without tertiary dentin formation was in the lentivirus-scramble and AAV-scramble groups, while the KDM6B<italic>-</italic>overexpression and miR-93-5p inhibitor treatment induced the tertiary dentin formation above the pulp surfaces and protected the residual pulp tissues from inflammation (Fig. ##FIG##5##6##D). Accordingly, KDM6B-overexpression and miR-93-5p inhibitor treatment upregulated the expression of BMP2 in residual pulp tissues (Fig. ##FIG##6##7##).</p>" ]

[ "<title>Discussion</title>", "<p id=\"Par44\">MicroRNAs play an important role in organ development and pathological changes, not only via directly targeting on gene mRNAs, but also via their complicate interactions with other epigenetic factors. Some miRNAs work as epi-miRNAs to create controlled feedbacks by interacting with DNA methylation or histone modification marks. The epi-miRNAs related to the differentiation and proliferation of embryonic pluripotent stem cells are highly valued as potential molecular drugs for disease management and tissue regeneration [##REF##19915669##39##].</p>", "<p id=\"Par45\">As small molecular epigenetic factors, miRNAs have been confirmed to regulate the multiple signaling molecules underlying the whole process of odontogenesis by targeting various genes especially associated with cell differentiation [##REF##22980176##20##, ##REF##27835837##40##]. During the odontogenesis, the miR-34a can indirectly regulate the expression of ALP and promote odontogenic differentiation of dental apical papilla cells by inhibiting the Notch pathway [##REF##23226240##32##, ##REF##24710391##41##]. The miRNA-27 and miR-338-3p can promote odontoblast differentiation by activating Wnt/β-catenin signaling and directly suppress RUNX2 [##REF##24487055##42##, ##REF##23380982##43##]. As an epi-miRNA, miR-720 can suppress NANOG by DNMT3A and DNMT3B, accordingly regulate the proliferation and odontogenic differentiation of DPSCs [##REF##24386225##44##].</p>", "<p id=\"Par46\">Trimethylated of H3K27 is a repressive epigenetic mark and is crucial for relevant genes expression during tooth development. Studies demonstrated that the specific demethylase KDM6B was able to activate the expression of odontogenesis-associated genes <italic>OSX</italic>, <italic>OCN</italic> and <italic>BMP2</italic> in dental mesenchymal stem cells by regulating H3K27me3 marks [##REF##24158144##28##, ##REF##22770241##45##]. Since the bell stage of tooth development is critical period for dentin formation, the key factors and epigenetic machinery involved in this process should be well studied for exploring innovative therapies for dentin generation. In our previous study, the marks of H3K27me3 changed in a spatiotemporal trend during bell stage of tooth development. Considering the complex and multi-level relationship between epigenetic factors, we further analyzed the miRNAs express in human tooth germ between early and late bell stages by microarray analysis. After quering the miRNA databases (TargetScanHuman7.2, miRbase Target and miRDB), miR-93-5p was identified as the only candidate miRNA differentially expressed during the process of dentin formation and targeted to KDM6B. In addition, other H3K27me3 methylases including EZH2, SUZ12, and EED was not the target gene of miR-93-5p. The expression of EZH2, SUZ12, and EED showed no significant difference after the treatment of miR-93-5p mimics and inhibitors. These results suggested that miR-93-5p influences H3K27me3 by targeting to KDM6B, but not H3K27me3 methylases.</p>", "<p id=\"Par47\">In a study of acute kidney treatment, the regulatory axis of KDM6B/H3K27me3/TNF-α was confirmed and the targeting site of miR-93-5p on KDM6B was identified by dual-luciferase reporter assay [##REF##32679234##36##]. However, the expression pattern and underlying interaction between miR-93-5p and demethylase KDM6B in odontogenesis especially in dentinogenesis have not been reported. In present study, up-regulation of miR-93-5p suppressed the odontogenic differentiation while inhibition of miR-93-5p promoted hDPSCs differentiation into odontoblasts. These results suggested that miR-93-5p can work as an epi-miRNA and effectively regulate the odontogenic differentiation of hDPSCs in a multi-level epigenetic mechanism.</p>", "<p id=\"Par48\">The dentinogenesis and osteogenesis are analogous process of synthesizing the extracellular matrix for hard tissue formation and share similar mineralization genes of <italic>OSX</italic>, <italic>OCN</italic> and <italic>BMP2</italic>. Previous studies have confirmed that <italic>KDM6B</italic> depletion can suppress the expression of <italic>OSX</italic>, <italic>OCN</italic> and <italic>BMP2</italic>, as well as the secretion of mineral matrix [##REF##24158144##28##, ##REF##22770241##45##, ##REF##27286573##46##]. These results were consistent with our present results. The ChIP-qPCR data showed miR-93-5p suppressed the specific recruitment of KDM6B to the promoter region of <italic>BMP2</italic>, and consequently inhibited BMP2 expression by influencing the H3K27me3 marks on promoter region. The H3K27me3 marks with affinities of KDM6B on the promoter regions of <italic>OSX</italic> and <italic>OCN</italic> showed no significant alteration after miR-93-5p mimic treatment, suggesting the existence of complex and finer mechanisms underlying the regulation of <italic>OSX</italic> and <italic>OCN</italic> to maximize the benefit in varied tissue microenvironments. As reported, RUNX2 and OSX are early stages markers of osteo/odontoblastic differentiation, however, OCN mainly occurs late [##REF##31015840##47##]. Studies have reported that in dental mesenchymal stem cells, KDM6B knockdown significantly altered the expression of downstream target gene DLX2 which is important for biomineralization by regulating the extracellular matrix proteins including OCN [##REF##24158144##28##, ##REF##10750557##48##]. After odontoblastic induction, the overexpression of lysine acetyltransferase p300 enriches H3K9ac mark on promoter regions and increase the expression of OCN [##REF##25007265##49##]. During the odontogenic differentiation, OSX is in the downstream of IGF-I and MAPK signaling pathway in addition to the BMP-2/Smad/Runx2 axis [##REF##19783797##50##, ##REF##15786511##51##]. Besides, the suppressive epigenetic marks of H3K9me3 and H3K27me3 show a bivalent modification mode and locate predominantly on OSX during odontogenic differentiation of dental mesenchymal progenitors [##REF##23379639##52##]. Additionally, under mineralized induction, the modification of active H3K4me3 marks on matrix-related genes <italic>OCN</italic>, <italic>OSX</italic>, <italic>DMP1</italic> and <italic>DSPP</italic> effectively promote odontogenic differentiation of hDPSCs [##REF##32208897##27##]. All these studies provide further interpretations for the multiple regulatory mechanisms underlying the expression of <italic>OSX</italic> and <italic>OCN</italic>, explaining our relevant results to some extent.</p>", "<p id=\"Par49\">Although microRNAs have been reported to function in the odontogenesis of hDPSCs through BMP2 pathway and subsequently regulating odontoblast markers DSP and DMP-1 [##REF##33256846##16##], our study firstly proved miR-93-5p can work as an epi-miR by leading an innovative epigenetic network of BMP2 signals. As the BMP2 pathway severely influences the odontogenic differentiation of hDPSCs, miR-93-5p showed an effective impact on tertiary dentin formation by regulating KDM6B/H3K27me3/BMP2. In current study, we observed pulp capping agents that either elevated KDM6B expression or inhibited miR-93-5p significantly induced the formation of dentin bridge in rat pulpotomy model. Our results enriched the interaction between epigenetic factors, additionally, the underlying epigenetic regulation mechanism of miR-93-5p may be a prospective target to dentin regeneration and vital pulp therapy.</p>", "<p id=\"Par50\">As a promising small biomolecular drug for pulp regeneration, the treatment effects of miRNAs are dependent on the mechanisms underlying hDPSCs proliferation, odontogenic differentiation, and inflammatory response [##REF##26131314##53##, ##REF##31800747##54##]. MiR-143-5p was reported to regulate the odontogenic differentiation by targeting MAPK14, and thus participated in the p38 MAPK signaling pathways [##REF##30362514##55##]. Wnt1 was found to be a target of miR-140-5p, and the down-regulation of miR-140-5p promoted the odontogenic differentiation of DPSCs by activating Wnt1/β-catenin signaling pathway [##REF##31358066##56##]. For inflamed human dental pulp cells stimulate by lipopolysaccharide, miR-146a and basic fibroblast growth factor worked cooperatively to promote the cell proliferation and odontogenic differentiation [##REF##27057540##57##]. Besides, miRNAs also play a role in tissue defense and repair by regulating inflammation related genes. MiR-125a-3p has shown odonto-immunomodulatory properties by inhibiting NF-κΒ and TLR signaling [##REF##33256846##16##]. Mesenchymal stem cell-derived exosomes miR-27b can inhibit sepsis by suppressing KDM6B and NF-κB signaling pathway [##REF##33413595##58##]. Interestingly, miR-93-5p has also been proved to attenuate lipopolysaccharide-induced chondrocyte inflammation by targeting TLR4 and further inhibiting the NF-κB signaling [##REF##30569118##34##]. The function of miR-93-5p in regulating inflammation also suggesting the miR-93-5p may have a potential advantage for vital pulp therapy.</p>" ]

[ "<title>Conclusions</title>", "<p id=\"Par51\">MiR-93-5p can target KDM6B and regulate H3K27me3 marks in the promoter region of <italic>BMP2</italic>, thus modulating the odontoblastic differentiation of hDPSCs and the formation of tertiary dentin. Our findings may not only advance our knowledge on the epigenetic regulation on the repair of pulp injury, but also provide a potential therapeutic measure to promote the success of vital pulp therapy and regenerative endodontics.</p>" ]

[ "<title>Background</title>", "<p id=\"Par1\">Epigenetic factors influence the odontogenic differentiation of dental pulp stem cells and play indispensable roles during tooth development. Some microRNAs can epigenetically regulate other epigenetic factors like DNA methyltransferases and histone modification enzymes, functioning as epigenetic-microRNAs. In our previous study, microarray analysis suggested microRNA-93-5p (miR-93-5p) was differentially expressed during the bell stage in human tooth germ. Prediction tools indicated that miR-93-5p may target lysine-specific demethylase 6B (KDM6B). Therefore, we explored the role of miR-93-5p as an epi-miRNA in tooth development and further investigated the underlying mechanisms of miR-93-5p in regulating odontogenic differentiation and dentin formation.</p>", "<title>Methods</title>", "<p id=\"Par2\">The expression pattern of miR-93-5p and KDM6B of dental pulp stem cells (DPSCs) was examined during tooth development and odontogenic differentiation. Dual luciferase reporter and ChIP-qPCR assay were used to validate the target and downstream regulatory genes of miR-93-5p in human DPSCs (hDPSCs). Histological analyses and qPCR assays were conducted for investigating the effects of miR-93-5p mimic and inhibitor on odontogenic differentiation of hDPSCs. A pulpotomy rat model was further established, microCT and histological analyses were performed to explore the effects of KDM6B-overexpression and miR-93-5p inhibition on the formation of tertiary dentin.</p>", "<title>Results</title>", "<p id=\"Par3\">The expression level of miR-93-5p decreased as odontoblast differentiated, in parallel with elevated expression of histone demethylase KDM6B. In hDPSCs, miR-93-5p overexpression inhibited the odontogenic differentiation and vice versa. MiR-93-5p targeted 3′ untranslated region (UTR) of KDM6B, thereby inhibiting its protein translation. Furthermore, KDM6B bound the promoter region of <italic>BMP2</italic> to demethylate H3K27me3 marks and thus upregulated BMP2 transcription. In the rat pulpotomy model, KDM6B-overexpression or miR-93-5p inhibition suppressed H3K27me3 level in DPSCs and consequently promoted the formation of tertiary dentin.</p>", "<title>Conclusions</title>", "<p id=\"Par4\">MiR-93-5p targets epigenetic regulator KDM6B and regulates H3K27me3 marks on <italic>BMP2</italic> promoters, thus modulating the odontogenic differentiation of DPSCs and dentin formation.</p>", "<title>Supplementary Information</title>", "<p>The online version contains supplementary material available at 10.1186/s12967-024-04862-z.</p>", "<title>Keywords</title>" ]

[ "<title>Supplementary Information</title>", "<p>\n</p>" ]

[ "<title>Acknowledgements</title>", "<p>Not applicable.</p>", "<title>Author contributions</title>", "<p>YZ and SW contributed to conception and design, acquisition, analysis and interpretation of data, drafted and critically revised the manuscript. SG and SH contributed to data acquisition and analysis. MW, XZ and XZ contributed to conception and design. LZ and XX contributed to conception and design, drafted and critically revised the manuscript. All authors approved the author list and agreed to be accountable for all aspects of the work.</p>", "<title>Funding</title>", "<p>The design of the study and collection, analysis, and interpretation of data and in writing the manuscript work were supported by the Sichuan Science and Technology Program (2022NSFSC1358), National Natural Science Foundation of China (81800927, 82170921, 82370947, 81870754), Health Commission of Sichuan Province (21PJ058), and West China Hospital of Stomatology (LCYJ2019-4).</p>", "<title>Availability of data and materials</title>", "<p>The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.</p>", "<title>Declarations</title>", "<title>Ethics approval and consent to participate</title>", "<p id=\"Par52\">The present study is approved by the Ethical Committee of the West China School of Stomatology, Sichuan University and State Key Laboratory of Oral Diseases (WCHSIRB-D-2021-243, WCHSIRB-D-2021-321).</p>", "<title>Consent for publication</title>", "<p id=\"Par53\">Not applicable.</p>", "<title>Competing interests</title>", "<p id=\"Par54\">The authors declare that they have no completing of interests.</p>" ]

[ "<fig id=\"Fig1\"><label>Fig. 1</label><caption><p>The expression pattern of miR-93-5p and KDM6B in tooth development and odontogenic differentiation of hDPSCs. <bold>A</bold> Dental papilla and enamel organ tissues of the first molar of mouse dissected under transmitted light microscope. <bold>B</bold>, <bold>C</bold> The expression levels of Ck14 and Vimentin in dissected enamel organ and dental papilla tissues of mouse, respectively. <bold>D</bold> The expression levels of Col-1α and Osx during the bell stage in mouse dental papilla tissues. <bold>E</bold>, <bold>F</bold> The expression levels of miR-93-5p and Kdm6b during the bell stage of mouse dental papilla, respectively. <bold>G</bold> The expression level of miR-93-5p during the odontogenic differentiation of hDPSCs. <bold>H</bold>, <bold>I</bold> The expression of KDM6B in hDPSCs during odontogenic induction at mRNA and proteins levels, respectively. *<italic>P</italic> &lt; 0.05, **<italic>P</italic> &lt; 0.01, ***<italic>P</italic> &lt; 0.001 and ****<italic>P</italic> &lt; 0.0001</p></caption></fig>", "<fig id=\"Fig2\"><label>Fig. 2</label><caption><p>Upregulation of miR-93-5p suppresses the odontogenic differentiation of hDPSCs. <bold>A</bold> The miR-93-5p mimic was effectively transfected into hDPSCs. <bold>B</bold> MiR-93-5p mimic suppressed the ALP activity of hDPSCs during odontogenic differentiation. <bold>C</bold> MiR-93-5p mimic suppressed the mineralized nodule formation during the odontogenic differentiation of hDPSCs. <bold>D</bold>–<bold>G</bold> MiR-93-5p mimic decreased the mineralization indicators genes <italic>OSX</italic>, <italic>ALP</italic>, <italic>OCN</italic> and <italic>COL-1α</italic> when hDPSCs differentiated into odontoblasts. ns, not significant. *<italic>P</italic> &lt; 0.05, **<italic>P</italic> &lt; 0.01, ***<italic>P</italic> &lt; 0.001 and ****<italic>P</italic> &lt; 0.0001</p></caption></fig>", "<fig id=\"Fig3\"><label>Fig. 3</label><caption><p>Downregulation of miR-93-5p promotes the odontogenic differentiation of hDPSCs. <bold>A</bold> The miR-93-5p inhibitor was effectively transfected into hDPSCs. <bold>B</bold> MiR-93-5p inhibitor increased the ALP activity of hDPSCs during odontogenic differentiation. <bold>C</bold> MiR-93-5p inhibitor induced mineralized nodule formation during the odontogenic differentiation of hDPSCs. <bold>D</bold>–<bold>G</bold> MiR-93-5p inhibitor increased the mineralization indicators genes <italic>OSX</italic>, <italic>ALP</italic>, <italic>OCN</italic> and <italic>COL-1α</italic> when hDPSCs differentiated into odontoblasts. ns, not significant. *<italic>P</italic> &lt; 0.05, **<italic>P</italic> &lt; 0.01, ***<italic>P</italic> &lt; 0.001 and ****<italic>P</italic> &lt; 0.0001</p></caption></fig>", "<fig id=\"Fig4\"><label>Fig. 4</label><caption><p>MiR-93-5p targets on KDM6B and influences H3K27me3 marks. <bold>A</bold> The H3K27me3 mark was down-regulated during the odontogenic differentiation of hDPSCs. <bold>B</bold>–<bold>E</bold> MiR-93-5p mimic decreased KDM6B and induced H3K27me3 marks during the odontogenic differentiation of hDPSCs. <bold>F</bold> After miR-93-5p mimic treatment, H3K27me3 demethylases were suppressed while H3K27me3 methylases were no significantly different. <bold>G</bold> Dual-luciferase assay confirmed that miR-93-5p targeted on the 3’ UTR of KDM6B. <bold>H</bold> MiR-93-5p mimic treatment down-regulated the expression level of <italic>BMP2</italic> in hDPSCs after odontogenic induction. ns, not significant. *<italic>P</italic> &lt; 0.05, **<italic>P</italic> &lt; 0.01 and ***<italic>P</italic> &lt; 0.001</p></caption></fig>", "<fig id=\"Fig5\"><label>Fig. 5</label><caption><p>MiR-93-5p influences the KDM6B and H3K27me3 affinity on <italic>BMP2</italic> gene. <bold>A</bold>–<bold>C</bold> MiR-93-5p mimic treatment reduced the KDM6B affinity in promoter regions of <italic>BMP2</italic>, <italic>OSX</italic> and <italic>OCN</italic>, respectively. <bold>D</bold> MiR-93-5p mimic treatment increased the H3K27me3 marks in <italic>BMP2</italic> promoter regions. <bold>E</bold>, <bold>F</bold> MiR-93-5p mimic treatment had no significant effects on H3K27me3 marks in promoter regions of <italic>OSX</italic> and <italic>OCN</italic>. ns, not significant. *<italic>P</italic> &lt; 0.05, **<italic>P</italic> &lt; 0.01 and ***<italic>P</italic> &lt; 0.001</p></caption></fig>", "<fig id=\"Fig6\"><label>Fig. 6</label><caption><p>MiR-93-5p inhibitor promotes dentin formation in pulpotomy model. <bold>A</bold> MicroCT showed that KDM6B-overexpression and miR-93-5p inhibitor induced tertiary dentin formation in rat pulpotomy model after 4 weeks treatment (yellow arrows: tertiary dentin). <bold>B</bold>, <bold>C</bold> Immunofluorescence staining showed that KDM6B was upregulated while H3K27me3 marks were downregulated after miR-93-5p inhibitor or KDM6B-overexpression treatment (TD: tertiary dentin, NT: necrosis tissues, red arrows: positive cells). <bold>D</bold> H&amp;E staining illustrated the tertiary dentin over the opening of tooth root canals (green arrows: odontoblasts)</p></caption></fig>", "<fig id=\"Fig7\"><label>Fig. 7</label><caption><p>MiR-93-5p inhibitor upregulates the expression of BMP2 in pulp tissues. <bold>A</bold> Immunohistochemical staining of BMP2 (TD: tertiary dentin, NT: necrosis tissues, green arrows: positive cells). <bold>B</bold> Quantitative analysis of BMP2 positive area for each group. **<italic>P</italic> &lt; 0.01</p></caption></fig>" ]

[ "<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>The primer sequences for qRT-PCR of mice tooth tissues</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Primers</th><th align=\"left\" colspan=\"2\">Sequences</th></tr></thead><tbody><tr><td align=\"left\" rowspan=\"2\"><italic>Gapdh</italic></td><td align=\"left\">F</td><td align=\"left\">ACTGAGGACCAGGTTGTC</td></tr><tr><td align=\"left\">R</td><td align=\"left\">TGCTGTAGCCGTATTCATTG</td></tr><tr><td align=\"left\" rowspan=\"2\"><italic>Ck14</italic></td><td align=\"left\">F</td><td align=\"left\">ACATTAAAATGCCAAGCCCCA</td></tr><tr><td align=\"left\">R</td><td align=\"left\">TGATCCCGCATCTCGTTCAG</td></tr><tr><td align=\"left\" rowspan=\"2\"><italic>Vimentin</italic></td><td align=\"left\">F</td><td align=\"left\">TACATCGACAAGGTGCGCTT</td></tr><tr><td align=\"left\">R</td><td align=\"left\">CACGCTTTCATACTGCTGGC</td></tr><tr><td align=\"left\" rowspan=\"2\"><italic>Osx</italic></td><td align=\"left\">F</td><td align=\"left\">AGTGGGAACAAGAGTGAGCTG</td></tr><tr><td align=\"left\">R</td><td align=\"left\">TAGTGAGCTTCTTCCTGGGT</td></tr><tr><td align=\"left\" rowspan=\"2\"><italic>Col-1α</italic></td><td align=\"left\">F</td><td align=\"left\">GCGCTAAAGGTGCCAATG</td></tr><tr><td align=\"left\">R</td><td align=\"left\">AGCACCAGGTTCACCACTG</td></tr><tr><td align=\"left\" rowspan=\"2\"><italic>Kdm6b</italic></td><td align=\"left\">F</td><td align=\"left\">CAATCCCCGCAGAGCTTACC</td></tr><tr><td align=\"left\">R</td><td align=\"left\">TTCTACTGGAGGTGGTGCATT</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab2\"><label>Table 2</label><caption><p>The primer sequences for qRT-PCR of hDPSCs</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Primers</th><th align=\"left\" colspan=\"2\">Sequences</th></tr></thead><tbody><tr><td align=\"left\" rowspan=\"2\"><italic>GAPDH</italic></td><td align=\"left\">F</td><td align=\"left\">CGGACCAATACGACCAAATCCG</td></tr><tr><td align=\"left\">R</td><td align=\"left\">AGCCACATCGCTCAGACACC</td></tr><tr><td align=\"left\" rowspan=\"2\"><italic>OCN</italic></td><td align=\"left\">F</td><td align=\"left\">ATTGTGGCTCACCCTCCATC</td></tr><tr><td align=\"left\">R</td><td align=\"left\">CCAGCCTCCAGCACTGTTTA</td></tr><tr><td align=\"left\" rowspan=\"2\"><italic>OSX</italic></td><td align=\"left\">F</td><td align=\"left\">TCTGCGGGACTCAACAACTC</td></tr><tr><td align=\"left\">R</td><td align=\"left\">TAGCATAGCCTGAGGTGGGT</td></tr><tr><td align=\"left\" rowspan=\"2\"><italic>ALP</italic></td><td align=\"left\">F</td><td align=\"left\">CTATCCTGGCTCCGTGCTCC</td></tr><tr><td align=\"left\">R</td><td align=\"left\">GTTAACTGATGTTCCAATCCTGCG</td></tr><tr><td align=\"left\" rowspan=\"2\"><italic>COL-1α</italic></td><td align=\"left\">F</td><td align=\"left\">AGGGACACAGAGGTTTCAGT</td></tr><tr><td align=\"left\">R</td><td align=\"left\">AGCACCATCATTTCCACGAG</td></tr><tr><td align=\"left\" rowspan=\"2\"><italic>BMP-2</italic></td><td align=\"left\">F</td><td align=\"left\">GTCAACTCGATGCTGTACCTTGACG</td></tr><tr><td align=\"left\">R</td><td align=\"left\">CAACCCTCCACAACCATGTCC</td></tr><tr><td align=\"left\" rowspan=\"2\"><italic>KDM6A</italic></td><td align=\"left\">F</td><td align=\"left\">GGCAGTGGAACGGTACGAAT</td></tr><tr><td align=\"left\">R</td><td align=\"left\">TCCTGCAGCAATGAGAGCTT</td></tr><tr><td align=\"left\" rowspan=\"2\"><italic>KDM6B</italic></td><td align=\"left\">F</td><td align=\"left\">CCTGAGGGTGAGCAACTCC</td></tr><tr><td align=\"left\">R</td><td align=\"left\">GGGGGTGAAGGTCTGTGTTTT</td></tr><tr><td align=\"left\" rowspan=\"2\"><italic>SUZ12</italic></td><td align=\"left\">F</td><td align=\"left\">CCTGGAAGTCCTGCTTGTGA</td></tr><tr><td align=\"left\">R</td><td align=\"left\">ACTGGAAACTGCAAGGGACG</td></tr><tr><td align=\"left\" rowspan=\"2\"><italic>EED</italic></td><td align=\"left\">F</td><td align=\"left\">AAATCCACCCTGGGATTCGG</td></tr><tr><td align=\"left\">R</td><td align=\"left\">TGGCGAATGGAAAGTACCCG</td></tr><tr><td align=\"left\" rowspan=\"2\"><italic>EZH2</italic></td><td align=\"left\">F</td><td align=\"left\">GGGACTCAGAAGGCAGTGG</td></tr><tr><td align=\"left\">R</td><td align=\"left\">TGCACAGGCTGTATCCTTCG</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab3\"><label>Table 3</label><caption><p>The primer sequences for ChIP-qPCR</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Primers</th><th align=\"left\" colspan=\"2\">Sequences</th></tr></thead><tbody><tr><td align=\"left\" rowspan=\"2\"><italic>BMP2</italic></td><td align=\"left\">F</td><td align=\"left\">CGTCTAGTATTTTGGCATAGCATAGACG</td></tr><tr><td align=\"left\">R</td><td align=\"left\">ACTCAATTTCCAGCCTGCTGTTT</td></tr><tr><td align=\"left\" rowspan=\"2\"><italic>OSX</italic></td><td align=\"left\">F</td><td align=\"left\">AAGATGAAAGGGGCCGAAGG</td></tr><tr><td align=\"left\">R</td><td align=\"left\">AATCCTCCAGCGGTGTTCAG</td></tr><tr><td align=\"left\" rowspan=\"2\"><italic>OCN</italic></td><td align=\"left\">F</td><td align=\"left\">GCTGGGATGTTCTGTACCGT</td></tr><tr><td align=\"left\">R</td><td align=\"left\">CCCTTCCCTGTGTCCTTAGC</td></tr></tbody></table></table-wrap>" ]

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[ "<supplementary-material content-type=\"local-data\" id=\"MOESM1\"></supplementary-material>" ]

[ "<fn-group><fn><p><bold>Publisher's Note</bold></p><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn></fn-group>" ]

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[ "<media xlink:href=\"12967_2024_4862_MOESM1_ESM.docx\"><caption><p><bold>Additional file 1: Figure S1.</bold> MiR-93-5p is downregulated in bell stage of human tooth germ and predicted to target on 3′UTR of KDM6B. (<bold>A</bold>) Heatmap of differentially expressed miRNAs during bell stage of human tooth germ (<italic>P</italic> &lt; 0.05). (<bold>B</bold>) MiR-93-5p was predicted to target on KDM6B in databases of TargetScanHuman7.2, miRbase Target and miRDB. <bold>Figure S2.</bold> Rat pulpotomy model. (<bold>A</bold>–<bold>H</bold>) The pulpotomy on rats’ maxillary first molars. (<bold>I</bold>) The observation of green fluorescence protein in rats’ molars identified the transfection of agents was effective.</p></caption></media>" ]

{ "acronym": [ "miRNAs", "KDM6B", "H3K27me3", "DPSCs", "hDPSCs", "3′ UTR", "BMP", "ALP", "ARS", "OSX", "Col-1α", "OCN", "qRT-PCR", "ChIP", "AAV", "FITC" ], "definition": [ "MicroRNAs", "Lysine-specific demethylase 6B", "Lysine 27 trimethylation on histone 3", "Dental pulp stem cells", "Human dental pulp stem cells", "3′ Untranslated region", "Bone morphogenetic protein", "Alkaline phosphatase", "Alizarin red S", "Osterix", "Collagen type I alpha", "Osteocalcin", "Quantitative reverse translation polymerase chain reaction", "Chromatin immunoprecipitation", "Adreno-associated virus", "Fluorescein isothiocyanate" ] }

[ "<title>Background</title>", "<p id=\"Par4\">Uterine leiomyosarcoma (ULMS) is a rare and very aggressive mesenchymal tumour with poor 5-year survival rates, accounting for 1.3% of all uterine malignancies [##UREF##0##1##]. ULMS metastasizes most frequently to the lung, peritoneum, bone, and liver [##UREF##1##2##], whereas metastasis to the heart is very uncommon. ULMS diagnosis can definitely be established only after histopathological analysis because symptoms and signs of ULMS resemble benign uterine myomas. However, in patients with early surgical intervention including hysterectomy and adnexectomy, the prognosis is beneficial. In cases of ULMS metastasis to the heart, there are certain treatment challenges that should be considered in a multidisciplinary team. Hereby, we report our experience in the case of successful treatment of solitary ULMS metastasis to the visceral layer of the pericardium.</p>", "<title>Case presentation</title>", "<p id=\"Par5\">Our patient was a 49-year-old female referred to the Department of Cardiac Surgery for scheduled surgery of pericardial neoplasia. Three years prior, the patient underwent a hysterectomy and adnexectomy owing to the ULMS. Considering the fact that the tumour was limited to the uterus, further chemotherapy or radiotherapy was not needed. Three months before admission to our institution, regular follow-up included magnetic resonance imaging (MRI) of the abdomen and pelvis and a computed tomography (CT) scan of the chest. The MRI of the abdomen and pelvis discovered neoplasia in the diaphragmic portion of the pericardium, whereas a CT chest scan showed the tumour mass adjacent to the apex of the heart. No other signs of primary disease relapse or metastases were found. The patient was asymptomatic, in good general condition, and the physical exam was unremarkable. After admission to our institution further workup included a heart MRI that confirmed the finding of the tumour between two layers of the pericardium (Fig. ##FIG##0##1##a) and adjacent to the apex of the heart, whereas on the performed PET/CT scan, besides the intrapericardial tumour, the presence of other metastases was excluded. Coronary angiography revealed tumour vascular supply from the left anterior descending coronary artery (LAD) (Fig. ##FIG##0##1##b). The multidisciplinary team concluded that the patient was a candidate for surgery. Surgery included diastolic cardiac arrest achievement and resection of the tumour with macroscopically healthy edges width of 5–8 mm. Macroscopically, a parietal layer of the pericardium was completely free from the tumour that invaded only the apical myocardium of the left ventricle (Fig. ##FIG##1##2##a and b). Intraoperative histopathology showed resected edges free of tumour cells. The defect of the left ventricle was reconstructed with a polyester patch and polypropylene sutures placed around the defect (Fig. ##FIG##1##2##c and d).</p>", "<p id=\"Par6\">\n\n</p>", "<p id=\"Par7\">\n\n</p>", "<p id=\"Par8\">Intra- and the postoperative course went uneventfully. Completed histopathology confirmed the diagnosis of leiomyosarcoma (Fig. ##FIG##1##2##e and f) with positive immunohistochemical stains for oestrogen and progesterone receptors confirming the uterine origin of the tumour.</p>", "<p id=\"Par9\">The patient was discharged from the hospital after 13 days. The control CT scans of the chest, abdomen and pelvis performed two, six and twelve months later did not show any relapse of the primary disease. Three months after the cardiac surgery, the patient received adjuvant chemotherapy with doxorubicin and dacarbazine. Consecutive control echocardiographs showed a left ventricular ejection fraction of 55% and no pericardial effusion. One year after surgery, there are no signs of new metastases, the Eastern Cooperative Oncology Group (ECOG) performance status is grade 0, whereas New York Heart Association (NYHA) functional class is I.</p>" ]

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[ "<title>Discussion</title>", "<p id=\"Par10\">ULMS metastases to the heart are very rare and require thorough deliberation of medical treatment. Even though ULMS metastases are related to the advanced stages of the disease, [##UREF##1##2##] our case showed that after adequate treatment in the early stages, late ULMS metastases are possible, even to very uncommon sites, such as pericardium. Malignant tumours may reach the heart via hematogenous or lymphatic spread, transvenous extension or direct invasion. The malignancies that spread through the lymphatics often seed the pericardium or epicardium, whereas myocardial and endocardial metastases generally rise from the hematogenous spread [##UREF##2##3##]. The majority of reported ULMS metastases to the heart were intracavitary [##UREF##3##4##–##UREF##7##9##]. In our case, the major portion of the metastasis was epicardial, whereas only a smaller part invaded the myocardium without relation to the cardiac chambers. Although ULMS metastases to the heart are rare, we should always pay attention to this possibility in patients with ULMS. In the context of the past medical history positive for ULMS, and the fact that primary leiomyosarcomas of the heart are extremely rare and constitute less than 0.25% of all cardiac tumours, [##UREF##8##10##] it was more likely that our patient had metastatic disease rather than primary leiomyosarcoma of the heart. However, considering the unpredictable nature of malignant diseases, these two entities should be distinguished. We performed immunohistochemical staining for oestrogen and progesterone receptors that confirmed the uterine origin of the tumour.</p>", "<p id=\"Par11\">In our case, regular follow-up ensured early detection of ULMS cardiac metastasis and early treatment before any symptoms developed. Our multidisciplinary team opted for surgery and adjuvant chemotherapy because the patient had solitary metastasis, was in good general condition without any symptoms, and was very motivated for the treatment. Surgery is generally recommended only in selected conditions - in patients with intracavitary metastases resulting in significant hemodynamic complications and in patients with solitary cardiac disease when the primary tumour is controlled, and a beneficial prognosis is expected [##UREF##2##3##]. From a surgical point of view, it is very challenging to assess the width of the resection edges to be radical enough because leiomyosarcoma is a very aggressive tumour and the remaining tumour cells along resection edges could result in tumour growth and the need for repetitive surgery. Moreover, the surgery should be sufficiently conservative at the same time to ensure the satisfactory function of the remaining ventricle postoperatively. Therefore, intraoperative histopathology might be very helpful and should always be performed to avoid excessive resection and recurrent tumour growth after surgery as well. Although our patient had late and solitary metastasis of ULMS which was completely resected, according to the current recommendations [##UREF##9##11##] the stage of the disease required adjuvant chemotherapy. This approach resulted in the absence of any new metastases in the one-year follow-up. However, current evidence for adjuvant chemotherapy in oligometastatic ULMS is weak [##UREF##10##12##, ##UREF##11##13##]. Therefore, we do not know whether chemotherapy contributed to the 1-year relapse-free survival after surgery in our patient and further studies are certainly needed to clarify this issue.</p>" ]

[ "<title>Conclusion</title>", "<p id=\"Par12\">Notwithstanding the ULMS by itself and ULMS metastases to the heart, especially to the visceral layer of the pericardium, are very rare, there is a certain possibility for such an appearance. Our case demonstrated that strict surveillance of patients with ULMS even after successful treatment of the early stage of the disease is of utmost importance to reveal metastatic disease to the heart in a timely manner and to treat it with beneficial outcomes. Such cases should always be carefully discussed in a multidisciplinary team in a tertiary centre, and surgery with adjuvant chemotherapy might be a good approach in patients with beneficial prognosis. Considering the surgical challenges of epicardial metastasis of ULMS, the main aim should be to ensure radical resection to avoid repetitive tumour growth and satisfactory function of the remaining myocardium. Therefore, intraoperative histopathology might contribute to surgical decision-making and is strongly recommended.</p>" ]

[ "<title>Background</title>", "<p id=\"Par1\">Uterine leiomyosarcoma is a rare and aggressive tumour with a poor prognosis. Its metastases to the heart are even rarer, especially to the epicardium. The majority of reported cardiac metastases of uterine leiomyosarcoma were in the cardiac chambers or intramyocardial. Surgical resection of the uterine leiomyosarcoma in the early stages is the only definitive treatment for this disease. However, in the cases of cardiac metastasis, surgery is recommended only in emergencies and patients with expected beneficial outcomes.</p>", "<title>Case presentation</title>", "<p id=\"Par2\">Our patient was a 49-year-old female referred to the Department of Cardiac Surgery for scheduled surgery of pericardial neoplasia. The patient underwent a hysterectomy and adnexectomy three years prior owing to the uterine leiomyosarcoma. A regular follow-up magnetic resonance imaging of the abdomen and pelvis discovered neoplasia in the diaphragmic portion of the pericardium. No other signs of primary disease relapse or metastases were found. The patient was asymptomatic. The multidisciplinary team concluded that the patient is a candidate for surgery. Surgery included diastolic cardiac arrest achievement and resection of the tumour. Macroscopically, a parietal layer of the pericardium was completely free from the tumour that invaded only the apical myocardium of the left ventricle. Completed histopathology confirmed the diagnosis of leiomyosarcoma of the uterine origin. Three months after surgery, the patient received adjuvant chemotherapy with doxorubicin and dacarbazine. One year after surgery, there are no signs of new metastases.</p>", "<title>Conclusions</title>", "<p id=\"Par3\">Strict surveillance of patients with uterine leiomyosarcoma after successful treatment of the early stage of the disease is of utmost importance to reveal metastatic disease to the heart in a timely manner and to treat it with beneficial outcomes. Surgery with adjuvant chemotherapy might be a good approach in patients with a beneficial prognosis. From a surgical point of view, it is challenging to assess the appropriate width of the resection edges to be radical enough and, at the same time, sufficiently conservative to ensure the satisfactory postoperative function of the remaining myocardium and avoid repetitive tumour growth. Therefore, intraoperative histopathology should always be performed.</p>", "<title>Keywords</title>" ]

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[ "<title>Author contributions</title>", "<p>The authors that contributed significantly to the conception and design of the manuscript are K.K., A.L., and I.S. Authors that contributed significantly to the analysis and interpretation of data are K.K., A.L., V.R.L., D.M., I.I., L.S., Z.S.D., H.G., B.B. and I.S. Authors that significantly contributed to drafting the manuscript are K.K., A.L., I.I., and I.S. Authors that significantly contributed to critical revise of the manuscript for important intellectual content are K.K., A.L., V.R.L., D.M., I.I., L.S., Z.S.D., H.G., B.B. and I.S. All authors read and approved the final manuscript.</p>", "<title>Funding</title>", "<p>None.</p>", "<title>Data availability</title>", "<p>Not applicable.</p>", "<title>Declarations</title>", "<title>Ethics approval and consent to participate</title>", "<p id=\"Par14\">Not applicable.</p>", "<title>Consent for publication</title>", "<p id=\"Par15\">Written Informed Consent was obtained from the patient for the publication of Case report and accompanying images.</p>", "<title>Competing interests</title>", "<p id=\"Par13\">The authors declare no competing interests.</p>" ]

[ "<fig id=\"Fig1\"><label>Fig. 1</label><caption><p>MRI of the suspected cardiac metastasis of the ULMS. <bold>a</bold>) A cardiac MRI shows an intrapericardial tumour (white arrow) adjacent to the apex of the left ventricle. LV—left ventricle; RV—right ventricle. <bold>b</bold>) Coronary artery angiography shows tumour vascular supply from the left anterior descending artery (black arrow)</p></caption></fig>", "<fig id=\"Fig2\"><label>Fig. 2</label><caption><p>Macroscopic features of the suspected ULMS metastasis to the epicardium, surgical technique and histopathology of the tumour. <bold>a</bold>) Intraoperative finding - the tumour (interrupted line) is on the apex of the heart and completely free from the parietal layer of the pericardium (arrows); <bold>b</bold>) tumour mass after resection; <bold>c</bold>) defect of the left ventricle after resection of the tumour with polypropylene sutures placed around the defect; <bold>d</bold>) defect of the ventricle reconstructed with a polyester patch; <bold>e</bold>) histopathology - tumour cells (asterisk) infiltrate myocardium but not endocardium (HE staining, x2); <bold>f</bold>) histopathology - tumour cells are spindle-shape, pleomorphic with high mitotic activity (HE staining, x40)</p></caption></fig>" ]

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[ "<fn-group><fn><p><bold>Publisher’s Note</bold></p><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn></fn-group>" ]

[ "<graphic xlink:href=\"12872_2023_3689_Fig1_HTML\" id=\"d32e301\"/>", "<graphic xlink:href=\"12872_2023_3689_Fig2_HTML\" id=\"d32e331\"/>" ]

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{ "acronym": [], "definition": [] }

[ "<title>Introduction</title>", "<p id=\"Par5\">Although cervical cancer is largely preventable through effective screening and vaccination for human papillomavirus (HPV), it still remains a major public health burden in much of the world [##UREF##0##1##]. Approximately 90% of cervical cancer incidence and mortality occur in resource-limited settings where the lack of prevention is compounded by limited treatment options [##REF##25220842##2##]. In much of East Africa, including Kenya, cervical cancer is the most frequent cause of cancer-related death among women [##UREF##1##3##–##UREF##3##5##]. While Kenya has adopted the World Health Organization (WHO) recommendations for simplified HPV-based screening strategies, there are major gaps in implementation and substantial loss-to-follow-up after screening [##UREF##4##6##, ##UREF##5##7##]. Reasons for loss-to-follow-up include transportation costs and distance to treatment facilities, stigma, lack of social support, and low levels of personal risk perception or knowledge about HPV and cervical cancer [##UREF##6##8##, ##UREF##7##9##].</p>", "<p id=\"Par6\">Offering self-collected HPV-testing in the community in Western Kenya has been shown to be an effective screening strategy. However, while community-based screening can substantially improve screening rates over baseline, and is more cost-effective than facility-based testing [##UREF##8##10##, ##UREF##9##11##], a crucial limitation is the loss to follow-up of women who tested positive for HPV. Novel strategies to increase attendance at both screening and follow-up include visit navigators, transportation vouchers, treatment incentives, and health messaging via mobile phones (mHealth). Several programs have evaluated the combination of reminder telephone calls and travel incentives, which were shown to improve follow-up [##UREF##10##12##, ##REF##10343430##13##]. However, this combination of interventions is labor intensive, costly, and places additional burdens on health facility staff. mHealth strategies utilizing text messages have the potential to reach large numbers of people through automated messaging about health conditions and services, while requiring relatively low costs and administrative burdens [##UREF##11##14##]. One possible solution would be to use text messages as a way to delivering cervical cancer screening results, health messaging and logistical information about follow-up [##UREF##12##15##].</p>", "<p id=\"Par7\">mHealth solutions may be particularly suited to Kenya, where 78% of households own or have access to mobile phones [##UREF##13##16##]—more than those who have access to public water and sanitation services—and many use their mobile phones frequently, as evidenced by over $108 million in cash transfers carried out through mobile phones daily. Mobile phones have been shown to be effective in educating patients about sensitive health-related issues that require confidentiality in various health domains in Kenya, such as HIV prevention [##REF##22788357##17##, ##REF##31142546##18##], family planning [##REF##31752872##19##], and sexually transmitted infections [##REF##24433348##20##]. Text messages, in particular, have been found useful for reminding patients about medication adherence [##REF##24433348##20##, ##REF##21323532##21##] and increasing preventive health visits and outpatient clinic attendance in many low- and middle-income countries (LMICs) [##REF##24205809##22##, ##REF##25361730##23##].</p>", "<p id=\"Par8\">As part of a two-phase trial of implementation strategies for cervical cancer screening in western Kenya, our team introduced text messaging to deliver HPV test results and follow-up plans to women [##REF##31246553##24##, ##REF##35671089##25##]. In the first phase, while text messaging was a popular and efficient method of results delivery, it did not result in higher rates of treatment uptake when compared with notification through phone calls or home visits. From individual interviews at the time of treatment, we found that women wanted more clear and personalized information when receiving their results. Therefore, in the second phase, we sought to develop and evaluate an intensified mHealth strategy with enhanced text-messaging to improve rates of follow-up with treatment after a positive HPV test through improved understanding of HPV, treatment logistics and information to share with their partners. This paper describes the modifications made to the content of text messages informed by feedback from focus group discussions. It further examines the acceptability of enhanced text messages and their impact on treatment uptake by comparing two different community arms in the second phase: one employing standard text messaging, and the other utilizing enhanced text messaging.</p>" ]

[ "<title>Methods</title>", "<p id=\"Par9\">This study was part of a two-phase cluster-randomized trial evaluating community-based cervical cancer prevention strategies using HPV self-sampling in Migori County, Kenya (<ext-link ext-link-type=\"uri\" xlink:href=\"http://clinicaltrials.gov\">ClinicalTrials.gov</ext-link> identifier: NCT02124252–28/04/2014). The two-phase design allowed the study team, including community partners, to collect feedback and evaluate uptake data to iteratively improve the implementation strategy, with a focus on improving follow-up, between phases. Results from the first phase showed that the community-based HPV testing model had higher uptake and lower program costs compared to screening in health facilities [##UREF##8##10##, ##UREF##9##11##, ##REF##29197067##26##]. This paper presents a mixed-method sub-study within the single-arm, second phase of the randomized trial, exploring the development, acceptability, and impact of an enhanced mHealth strategy on cryotherapy uptake among women who test positive for HPV. In the second phase, we offered the more effective community-based screening coupled with optimized linkage to treatment strategies in six communities. The enhanced linkage strategy included decentralization of treatment sites, increasing from one to four; increased provider training and supervision, and texts tailored to provide further education on cervical cancer and reminders for treatment. The study activities described below were nested within the second phase.</p>", "<title>Participants and setting</title>", "<p id=\"Par10\">Participants included women between the ages of 25 and 65 years, with an intact uterus and cervix, who resided within the six study communities in Migori County, Kenya. Study communities were defined by the sub-locations assigned to one government health facility, with an overall population of approximately 5000. To avoid spillover, we identified communities with non-adjacent borders that had not participated in the first phase of the study. Prior to carrying out the community heath campaigns (CHCs), the study team conducted door-to-door enumeration to characterize the study communities more accurately, which is presented in detail elsewhere [##REF##29197067##26##].</p>", "<title>Structure of community health campaign with standard text messaging</title>", "<p id=\"Par11\">The process of education and self-collection of specimens for HPV testing at the CHCs is described in detail elsewhere [##REF##29197067##26##]. After collection, women provided their preference for receiving HPV test results (text message, phone call, and home visit). We used the <italic>care</italic>HPV test (QIAGEN, Germantown, MD) to collect and process samples, with a goal of providing results to participants within 2 weeks. Text notification provided through the Frontline SMS™ program (<ext-link ext-link-type=\"uri\" xlink:href=\"https://www.frontlinesms.com/\">https://www.frontlinesms.com/</ext-link>). Receipt of HPV test results via text was considered successful if the program confirmed the transmission of text message, meaning the participant’s phone was on and the SIM card was valid or phone line was active [##REF##31246553##24##].</p>", "<p id=\"Par12\">The text-based HPV test results notification took into consideration participants’ HIV status and HPV test results: 1) HPV negative and HIV positive; 2) HPV negative and HIV negative; 3) HPV positive; and 4) inconclusive HPV test result (Table ##TAB##0##1##). Based on their test result, participants also received guidance regarding their next cervical cancer screening and any necessary treatment following a positive HPV test.</p>", "<p id=\"Par13\">Women who opted to receive their HPV test result notification via text in the standard group received one text message. For those who did not follow-up for treatment, second and third attempts for results notification for women were completed by phone call or home visit. Phone call and home visit strategies were deemed successful if the participant was reached and was given their results directly by study staff. Our study staff attempted up to four phone calls or three home visits before determining that a participant was unable to be reached.</p>", "<title>Development of the enhanced text messaging strategy</title>", "<p id=\"Par14\">After the fourth CHC, in July 2018, we conducted two semi-structured focus group discussions (FGDs) with female participants who had been previously screened during the first phase of the trial and had opted for a cell phone-based strategy (text of phone call) for their results notification. These participants were identified and recruited by community health volunteers for their ability to actively engage and provide valuable feedback. Each FGD consisted of 10 participants and lasted for 2.5 hours. We explored myths and misperceptions related to HPV found in qualitative data from the first phase, such as misunderstanding of how HPV is treated, what causes HPV, and meaning of a positive result. We also examined women’s preferences for sharing information with their partners, barriers to accessing treatment, and the rationale behind their choice of text or phone call for receiving HPV test results. FGD participants were asked to help identify appropriate content and wording to develop messages that would most resonate with women.</p>", "<p id=\"Par15\">Given the straightforward nature of participants’ responses to our research topics, we employed structural coding to synthesize our findings. In response to the FGD feedback, the enhanced text messaging strategy included changes in the timing, number, and content of messages (Fig. ##FIG##0##1##). Messages were developed to be more clear, concise, and specific to the patient (Table ##TAB##0##1##). To ensure understanding, women who opted to receive their results via texts were shown examples of texts at the time of HPV screening. Messages were sent out more frequently; in addition to a text with their results, women received a brief message thanking them for screening and additional treatment reminders if they tested positive. Treatment reminder text messages were tailored to address common barriers in accessing treatment, which may include providing location of clinics, time of appointment, and a possible description of transport options.</p>", "<title>Evaluation of the enhanced text messaging strategy</title>", "<p id=\"Par16\">The enhanced text messaging strategy was deployed in the last two study communities. We collected information about participants through a structured questionnaire administered at three time points: at the CHCs prior to HPV testing (pre-test), immediately after HPV testing (post-test), and after treatment for those who screened positive (follow-up). Our primary outcomes for this study were receipt of HPV test results and treatment uptake. Prior to screening, we collected information of sociodemographic characteristic, clinical information, and behavior regarding their phone use, frequency, and barriers to phone ownership, access, or use, if any. At the follow up, we asked participants about their acceptability of text messaging and the role of receiving text messages in their decision to access treatment.</p>", "<title>Treatment</title>", "<p id=\"Par17\">Women who tested HPV positive were referred for evaluation for cryotherapy at local health facilities. Treatment was provided by trained clinic providers, and the study staff kept track of participants who successfully received treatment and their date of treatment. Treatment was available for up to 3 months at each health facility after participants who tested HPV positive were notified of their result in their respective community. To calculate time to treatment, we only included women who accessed treatment through April, 2019.</p>", "<title>Statistical analysis</title>", "<p id=\"Par18\">Descriptive statistics were used to compare the baseline characteristics of the women in the first four communities and the last two communities, as well as standard and enhanced text groups. To test bivariate relationships between treatment uptake and categorical demographic characteristic variables, we performed chi-squared tests. We used Kruskal-Wallis test to evaluate continuous variables and the median time and interquartile range in days between screening and notification, notification and treatment access, and screening to treatment access. <italic>P</italic> values of &lt; 0.05 were considered statistically significant. All analyses were performed using STATA version 16 (College Station, TX: StataCorp LP).</p>" ]

[ "<title>Results</title>", "<title>Focus groups</title>", "<title>What FGD participants liked and disliked about results notification via text messaging</title>", "<p id=\"Par19\">Participants highlighted several key benefits of receiving their results via text, including convenience, privacy, and control over information sharing. Some women felt there was a lower chance of missing their results when delivered via text. They liked the flexibility of receiving information at any time, even if their phone was off, as it could be accessed later at their convenience, unlike phone calls with a specific place and time. One 26-year-old participant noted, “Even if my phone was off by the time they are sending the message, I will still get the message.” Another 27-year-old participant expressed, “I am not always with my phone all the time. They [study team] can call and at that time I don’t have it, and they may not call again. That means I will miss it, that’s why I prefer texting to a phone call.” Furthermore, some participants appreciated that they could refer back to the text at a later time. “With text, you’ll have that information as long as you want it, that’s why I preferred text to phone call.” (A 27-year-old participant).</p>", "<p id=\"Par20\">Participants also expressed a sense of comfort when receiving sensitive information via text due to the privacy it offered. One 26-year-old stated, “I’ll use my own password to read the text…no other person will have access to it.” Several participants specifically noted the increased privacy with text messages compared to other result notification strategies. A 42-year-old participant mentioned, “I chose texting because it has privacy. You have to shout when making or receiving a phone call that even those whom you don’t want to have your information will have it. With text, you read it on your own.” Another participant shared, “I had the opportunity for them [study team] to do a home visit, but I chose not to because people in our village will talk a lot and make statements.” (A 52-year-old participant).</p>", "<p id=\"Par21\">An important aspect of privacy was control over how they share their information. They valued having information readily accessible, giving them the autonomy to decide when and with whom to share it. A 42-year-old participant explained, “I’ll receive a text on my phone and I’m the one who will read it. If I want to share it, that will be up to me.” Another woman noted, “I will not be able to explain everything well to him (my husband), but with the text, he can read and get full information.” (A 26-year-old participant).</p>", "<p id=\"Par22\">For one participant, the extent of control over sharing information was tied to the specific HPV test result they received. This 50-year-old participant explained, “I preferred text because it allowed me to review the information first before sharing the good news of my negative test result with my husband.” However, with a positive HPV result, a few participants held a contrasting view and preferred not to receive their HPV results via text due to the emotional distress it may cause. One 31-year-old participant mentioned being “stressed to death [if they tested positive for HPV]”, while a 25-year-old participant expressed a desire for privacy as they “didn’t want anybody to know and wanted to avoid stigma [related to HPV].” As a result, they preferred to receive their results through a phone call.</p>", "<p id=\"Par23\">In addition to the fear around receiving a positive HPV diagnosis via text, there were other disadvantages to receiving results and other health information in this manner, including inability to ask follow-up questions, communication barriers, and possible unfamiliarity with technology. Despite being provided with contact information for the clinical team, a primary concern was the lack of immediate access to a knowledgeable provider to answer questions or provide more counseling and information about treatment. A 35-year-old woman who opted for phone calls said, “If I had any questions, I could not get my answers right there,” while a 25-year-old participant shared that “you can be counseled through a phone call which is not possible with text. It [phone call] gives you an opportunity to ask questions.”</p>", "<p id=\"Par24\">Some participants felt that there would be communication barriers over text. One 40-year-old participant stated, “I preferred phone call because I will have to talk to the person in a language that I understand well.” Although women were asked about their language preference, they remained uncertain about whether the message would be easily comprehensible, suggesting underlying concerns about the complexity of the information provided. A 25-year-old participant reported, “I didn’t know the type of language they were going to use in sending that text.” For one participant, communication barriers would potentially be compounded by lack of familiarity with text messaging. This 32-year-old participant explained, “In case I would test positive, I will know when and where to go for treatment. Through a text, I cannot even ask that. I don’t know how to text.”</p>", "<title>Ideas to improve text messaging reported by FGD participants</title>", "<p id=\"Par25\">Women suggested the messages be simple, short, personalized, and the information conveyed in the messages should be educational to the recipient as well as the recipient’s family. Some women commented that texts should be concise because it would make the readers lose interest and that texts should address participants by their names. They also recommended that the messages be sent frequently. One woman reported that notification should be sent 3–4 days prior to actual treatment and should include the specific date and time of when each woman should visit the clinic for treatment. Based in these results, we developed the strategy described above with the content shown in Table ##TAB##0##1##.</p>", "<title>Pilot of the enhanced text messaging strategy</title>", "<p id=\"Par26\">Between February and November 2018, 3303 women participated in cervical cancer screening with self-collected HPV tests offered through CHCs in six communities (Table ##TAB##1##2##). Of the 2368 women who underwent cervical cancer screening in the first four communities, almost half (49.4%) chose to receive HPV test results via phone call and less than one-quarter (23.9%) opted for text, making it the least acceptable notification method. In the last two communities, where enhanced text messaging notification was offered, over half (51.2%) of the 935 screened women opted for phone call, followed by more than one-quarter (28.2%) opting for text. Among all participants, 555 (16.8%) tested HPV positive, and 257 (46.3%) of the HPV-positive women accessed treatment. HPV rates (15.9% vs. 15.5%; <italic>p</italic> = 0.943) and treatment uptake (53.3% vs. 53.7%; <italic>p</italic> = 0.928) did not vary between standard and enhanced text groups.</p>", "<p id=\"Par27\">Compared to women in the first four communities, women in the last two communities were younger (37.1 years vs. 38.6 years; <italic>p</italic> = 0.004), had fewer children (4.5 vs. 5; <italic>p</italic> = 0.005), had higher rates of cervical cancer screening prior to the CHCs (20.7% vs. 12.9%; <italic>p</italic> &lt; 0.001), with higher proportion of women having completed HPV testing in the past (26.9% vs. 12.5%; <italic>p</italic> &lt; 0.001), and were more likely to report a positive HIV status (34.9% vs. 20.3%; p &lt; 0.001) and engage in family planning (43.7% vs. 38.9%; <italic>p</italic> = 0.008). The similar differences were also observed between standard and enhanced text groups. More women had undergone cervical cancer screening prior to the CHCs (25.8% vs. 18.4%; <italic>p</italic> &lt; 0.05) and reported living with HIV (32.6% vs. 20.2%; <italic>p</italic> &lt; 0.001) in the enhanced text group than those in the standard text group.</p>", "<p id=\"Par28\">Among all women who attended CHCs, 2749 (83.2%) women reported using cell phones daily (Table ##TAB##2##3##). More women who opted for texts reported owning their own phone (92.5%) and being comfortable with reading and writing texts and receiving sensitive information via text than those who opted for phone calls or home visit (<italic>p</italic> &lt; 0.001). However, women were less likely to share their positive HPV test result with their partners if they opted for texts compared to those who opted for phone calls and home visit. For those who opted for texts, 12.6% requested their results via phone call or 2.1% home visit in the event of a positive test result.</p>", "<p id=\"Par29\">There was a significant difference in notification of results at first attempt across the text, phone call, and home visit categories (<italic>p</italic> &lt; 0.001) (Table ##TAB##2##3##). All women who opted for text received their test result at first attempt, followed by those who opted for home visit (86.8%) and phone calls (54.5%). For those who opted for text and accessed treatment, most (82.5%; <italic>p</italic> &lt; 0.001) did so after receiving first text notification while significantly fewer women sought treatment after second (10.9%) and third text notifications (6.6%).</p>", "<p id=\"Par30\">The median time it took from screening to notification of test results varied by notification method, with text messaging strategy delivering the results most efficiently (16 days; p &lt; 0.001), followed by home visit (20 days) and phone calls (31 days) (Table ##TAB##3##4##). HPV positive women who opted for text messaging took the longest time to access treatment after receiving their test results (25 days) while those who opted for phone calls had the shortest (7 days).</p>" ]

[ "<title>Discussion</title>", "<p id=\"Par31\">We sought to develop an enhanced text messaging strategy to increase completion of the cervical cancer screening cascade in a community-based HPV screening program in partnership with women in western Kenya. We found that, although providing women various options for notification was valued, the chosen notification modality had no effect on treatment uptake, which remained around 50%. Treatment uptake did not improve after incorporating an in-person review of text content, increased frequency, and enhanced text messaging, which was clearer, more concise, and more personalized.</p>", "<p id=\"Par32\">Besides the enhanced text messaging strategy, our team also aimed to make cervical cancer prevention services more accessible and reduce structural barriers by increasing treatment sites and providing additional training and supervision for medical providers in cervical cancer treatment. Overall, treatment uptake did not differ across notification methods. However, women in the communities where the enhancement measures were implemented accessed cervical cancer treatment sooner than women in other communities after receiving their positive HPV result by phone call or home visit. This decrease in time from HPV result notification to treatment may be explained by the enhanced linkage to care strategies and in-person contact with study staff, rather than via text, to counsel women or address their apprehension toward treatment. Similar to our study, one study in Tanzania found that one-way text messages had no effect on the follow-up screening rate among HPV-positive women and instead suggested that provider-initiated phone calls to educate women on the importance of rescreening may be more effective [##REF##32238335##27##].</p>", "<p id=\"Par33\">While we did not observe a greater treatment uptake with the text messaging strategy compared to phone calls or home visits—in fact, time before accessing treatment was longest in the text messaging group—it is important to highlight that the text messaging strategy was also not associated with a lower treatment uptake. The delay in accessing treatment among women who received enhanced text messages and tested positive for HPV in our study differed from a study in Argentina, where text messages served as a cue to action for women to visit the health center to obtain their HPV test results [##REF##37353835##28##]. In their study, 69% visited a health center within 7 days of receiving the text, including 7.5% on the same day. Notably, their study area primarily consisted of urban populations, whereas our study focused on rural communities in Kenya. The limited impact of the text enhancements in our study suggests that there are higher structural barriers to treatment acquisition in this setting that are difficult to offset by enhanced text messaging strategy alone. Such barriers include a long travel distance to the clinic, transportation costs, and a misalignment between work and clinic hours. Similar to our study, a pilot study of community-based HPV self-sampling in rural Uganda, which used SMS for result notifications, found that only 22% of women with positive HPV results attended the clinic for follow-up, identifying transportation challenges as a significant barrier [##REF##35003710##29##]. However, one study based in Tanzania showed that women who received a transportation voucher via text to return to the clinic for cervical cancer screening, as well as 15 texts promoting behavioral change, were 1.53 times more likely to attend screening than those who only received the texts [##REF##31645991##30##]. Although the overall screening uptake was relatively low in the study, their findings highlight the potential impact of mHealth in reducing socioeconomic and systemic barriers for women to access cervical cancer services, especially in rural areas.</p>", "<p id=\"Par34\">Most women felt comfortable receiving either test result via text. However, it is notable that some women chose to receive results via text in the case of negative HPV test results, but via phone calls or home visits if the results were positive. These findings are critical for understanding the gaps in the cervical cancer care continuum. One study in South Africa suggested that in case of abnormal Pap smear results, a text should instruct the women to come to the clinic where the results are then shared during face-to-face discussions with a medical provider [##UREF##10##12##], given the concerns around privacy of texts and fear of stigma—an important consideration when women may not have their own phone and may share it with their family. Another study based in Argentina used a text messaging strategy to connect women with triage Pap post-HPV testing and to inform women about their HPV test result availability while replacing the term “HPV-testing” with the term “self-collection.” The authors hypothesized that this is one of the ways that helped women reduce concerns related to privacy and increased clinic attendance rates in their study [##REF##37353835##28##, ##UREF##14##31##]. Although our study team informed women of their positive HPV test result via text and used the term “HPV,” we attempted to reduce stigma toward HPV and ensure confidentiality by asking women to choose their preferred results notification method (phone call, text, or home visit) depending on their HPV test result (positive or negative), making this process as individualized as possible. Nonetheless, more research should be conducted to develop a culturally tailored text intervention for improving treatment uptake.</p>", "<p id=\"Par35\">The challenges inherent in text messaging highlight the advantages of and potential need for greater individual interaction via phone calls or home visits to provide education and link women to treatment. In fact, in our FGDs, women reported that the inability to ask follow-up questions was a negative aspect of receiving test results via text. Two-way messaging, which has been shown to be more effective in various behavior change interventions compared to one-way interventions [##REF##31645991##30##, ##REF##25785892##32##], could mitigate these challenges and allow women to actively engage in cervical cancer education and services, especially those in resource-limited settings. One study based in Portugal found that adding more than one communication method was more effective than sending only written invitation letters in increasing cervical cancer screening uptake [##REF##30936001##33##, ##REF##35969450##34##]. Their study included a 3-step invitation to screening, in which an automated reminder via text or phone call (step 1), manual phone call (step 2), and face-to-face interview (step 3) were applied sequentially and demonstrated that screening uptake was increased by 17% among women who received the invitation through step 3 compared to those receiving the standard invitation letter. The similar multistep, multimodal system that integrates HPV test result notification via text, phone call, and home visit could be applied in western Kenya to optimize linkage to care.</p>", "<p id=\"Par36\">Our study had several limitations. First, we asked whether enhanced text messaging helped women understand why they needed treatment or how they could access treatment. We relied on self-reporting and did not require participants to share what their understanding was (they simply indicated “yes” or “no”). Second, we only included survey items about the effect of text notifications on the decision-making process with the use of enhanced text notification, and not the use of standard text notification. Therefore, we were not able to accurately compare the varying effects of standard text and enhanced text messaging on treatment acquisition. Third, the measurement of results notification timing for text messages may not be completely accurate, as receipt of the test results was recorded after a message was sent and registered in an active phone; the actual reading of the message was not confirmed by the women. This part of the data collection relied on the transmission of text messages through the Frontline SMS program, in which we did not require a confirmation text to avoid data costs for the women. Fourth, our study did not explore the acceptability of the content in enhanced text messages for participants who sought treatment and those who did not. In contrast, a similar study also developed text messages for women during focus groups but validated the content through interviews with health providers and women, considering both perspectives [##REF##32032940##35##]. Their findings emphasized personalized and persuasive language with a professional tone to encourage women to visit the clinic for their HPV test results. In our study, although we assessed the impact of enhanced text messaging on treatment uptake, we did not directly investigate the effect of the message content or wording on the participants who received these messages. Understanding the impact of specific language and content on women’s decisions to access treatment, their privacy experiences with text-based test results, whether positive or negative, and their perception of sender legitimacy, a crucial element in the context of mobile-based interventions, would have been of immense value. Last, we encountered delays in HPV test kit availability in the middle of the study due to slow customs clearance of the test kits, leading to delays in planned CHCs. This may have contributed to the low uptake of screening and treatment among women. It is also an example of one of the external logistical barriers faced by women in this rural area for which the study could not control.</p>" ]

[ "<title>Conclusion</title>", "<p id=\"Par37\">In this cohort of women undergoing community-based HPV testing, over three quarters of the participants preferred a cell phone-based strategy (phone call or text messaging) for results delivery. There was no difference in treatment uptake rates between standard and enhanced text groups, even after the text messaging strategy was enhanced with increased messages and adapted content. This enhanced text strategy is one attempt to address low linkage to care in cervical cancer amidst the overall poor transportation, education, and supply resources in Kenya. While enhanced text messaging did not garner higher treatment uptake, reflecting the multiple factors impacting ability to complete the care cascade in in Kenya, it did not result in lower treatment rates or a negative experience for women. As cell phone ownership increases, these results may help programs to provide different options for results notification, though there remains a need to address the structural and logistical barriers that may inhibit women’s decision or ability to follow up with treatment. Future programs could therefore offer multiple results notification methods, including a combination of cell phone-based strategy and home visit, to ensure that they meet the needs of their populations.</p>" ]

[ "<title>Background</title>", "<p id=\"Par1\">Mobile health (mHealth) has become an increasingly popular strategy to improve healthcare delivery and health outcomes. Communicating results and health education via text may facilitate program planning and promote better engagement in care for women undergoing human papillomavirus (HPV) screening. We sought to develop and evaluate an mHealth strategy with enhanced text messaging to improve follow-up throughout the cervical cancer screening cascade.</p>", "<title>Methods</title>", "<p id=\"Par2\">Women aged 25–65 participated in HPV testing in six community health campaigns (CHCs) in western Kenya as part of a single arm of a cluster-randomized trial. Women received their HPV results via text message, phone call, or home visit. Those who opted for text in the first four communities received “standard” texts. After completing the fourth CHC, we conducted two semi-structured focus group discussions with women to develop an “enhanced” text strategy, including modifying the content, number, and timing of texts, for the subsequent two communities. We compared the overall receipt of results and follow-up for treatment evaluation among women in standard and enhanced text groups.</p>", "<title>Results</title>", "<p id=\"Par3\">Among 2368 women who were screened in the first four communities, 566 (23.9%) received results via text, 1170 (49.4%) via phone call, and 632 (26.7%) via home visit. In the communities where enhanced text notification was offered, 264 of the 935 screened women (28.2%) opted for text, 474 (51.2%) opted for phone call, and 192 (20.5%) for home visit. Among 555 women (16.8%) who tested HPV-positive, 257 (46.3%) accessed treatment, with no difference in treatment uptake between the standard text group (48/90, 53.3%) and the enhanced text group (22/41, 53.7%). More women in the enhanced text group had prior cervical cancer screening (25.8% vs. 18.4%; <italic>p</italic> &lt; 0.05) and reported living with HIV (32.6% vs. 20.2%; <italic>p</italic> &lt; 0.001) than those in the standard text group.</p>", "<title>Conclusions</title>", "<p id=\"Par4\">Modifying the content and number of texts as an enhanced text messaging strategy was not sufficient to increase follow-up in an HPV-based cervical cancer screening program in western Kenya. A one-size approach to mHealth delivery does not meet the needs of all women in this region. More comprehensive programs are needed to improve linkage to care to further reduce structural and logistical barriers to cervical cancer treatment.</p>", "<title>Keywords</title>" ]

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[ "<p>We would like to thank the participants as well as the research assistants and health care providers for their support of and contributions to this study.</p>", "<title>Authors’ contributions</title>", "<p>All authors (YC, SI, LP, EAB, and MJH) were involved in the preparation, review, and editing of the final manuscript. MJH and YC were responsible for the conception and design of the manuscript. YC, SI, LP, and MJH carried out data analysis and interpretation. YC and MJH drafted the manuscript with contributions from the other authors.</p>", "<title>Funding</title>", "<p>This research was funded from by the National Institutes of Health (R01 CA188248). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.</p>", "<title>Availability of data and materials</title>", "<p>Per the Kenya Medical Research Institute (KEMRI) guidelines, the data will be made available upon reasonable requests to the corresponding author.</p>", "<title>Declarations</title>", "<title>Ethics approval and consent to participate</title>", "<p id=\"Par38\">This study received ethical approval from Duke University School of Medicine (IRB No. Pro00077442) and the Kenya Medical Research Institute Scientific and Ethical Review Unit (SERU No. 2918). All methods were carried out in accordance with relevant institutional and national guidelines and regulations. All participants provided written informed consent. To ensure confidentiality and anonymity, data were deidentified. The study team’s contact information was provided to participants during and after the consent process to address any questions related to the study.</p>", "<title>Consent for publication</title>", "<p id=\"Par39\">Not applicable.</p>", "<title>Competing interests</title>", "<p id=\"Par40\">The authors declare no competing interests.</p>" ]

[ "<fig id=\"Fig1\"><label>Fig. 1</label><caption><p>Flowchart of the results notification strategies</p></caption></fig>" ]

[ "<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p> Examples of the standard and enhanced texts</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th>Standard text</th><th>Enhanced text</th></tr></thead><tbody><tr><td><p><bold>Post-screening text</bold></p><p>N/A</p></td><td><p><bold>Post-screening text</bold></p><p>Thank you for screening, you will receive results soon! Remember, HPV causes cervical cancer, but HPV doesn’t mean you have cancer. Free treatment is available.</p></td></tr><tr><td><p><bold>Treatment</bold></p><p>HPV−/HIV+ client</p><p>Hallo (recipient name). Thank you for taking cervical cancer screen test! Your result was Negative! Visit your nearest clinic after 1 year (2019) for another test. Please call or flash (Study Phone) if you have questions.</p><p>HPV−/HIV- client</p><p>Hallo (recipient name). Thank you for taking cervical cancer screen test! Your result was Negative! Visit your nearest clinic after five years (2023) for another test. Please call or flash (Study Phone) if you have questions.</p><p>HPV+ client</p><p>Hallo (recipient name). Thank you for taking cervical cancer screen test! Your results showed that you have HPV. Please come to (facility) to talk about treatment options. Treatment will be available until (Date). Call or flash (Study Phone) if you have questions.</p><p>Indeterminate HPV test</p><p>Hallo (recipient name). Thank you for taking cervical cancer screen test! Sorry we were not able to evaluate your sample. We would like to collect another sample from you. Please call or flash (Study Phone) to discuss plan for repeat testing.</p></td><td><p><bold>Treatment (same as previous)</bold></p><p>HPV−/HIV+ client</p><p>Hallo (recipient name). Thank you for taking cervical cancer screen test! Your result was Negative! Visit your nearest clinic after 1 year (2019) for another test. Please call or flash (Study Phone) if you have questions.</p><p>HPV−/HIV- client</p><p>Hallo (recipient name). Thank you for taking cervical cancer screen test! Your result was Negative! Visit your nearest clinic after five years (2023) for another test. Please call or flash (Study Phone) if you have questions.</p><p>HPV+ client</p><p>Hallo (recipient name). Thank you for taking cervical cancer screen test! Your results showed that you have HPV. Please come to (facility) to talk about treatment options. Treatment will be available until (Date). Call or flash (Study Phone) if you have questions.</p><p>Indeterminate HPV test</p><p>Hallo (recipient name). Thank you for taking cervical cancer screen test! Sorry we were not able to evaluate your sample. We would like to collect another sample from you. Please call or flash (Study Phone) to discuss plan for repeat testing.</p></td></tr><tr><td><p><bold>Follow-up text</bold></p><p>N/A</p></td><td><p><bold>Treatment Reminders</bold></p><p>Your treatment for HPV will be on XXX at (facility). Come between XX and XX. We encourage you to bring your partner or relative. (Study Phone) for questions.</p></td></tr><tr><td><p><bold>Post-treatment text</bold></p><p>N/A</p></td><td><p><bold>Post-treatment text (One day after treatment)</bold></p><p>The treatment you had is important to your health. To allow healing and prevent re-infection, please avoid intercourse for 6 weeks. (Study Phone) for questions.</p></td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab2\"><label>Table 2</label><caption><p>Demographic and clinical characteristics of participants who participated in HPV-based cervical cancer screening by notification method and communities</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th>Variable</th><th>First four communities <italic>n</italic> = 2368</th><th>Last two communities <italic>n</italic> = 935</th><th><italic>p</italic>-value</th><th>Standard text group <italic>n</italic> = 566</th><th>Enhanced text group <italic>n</italic> = 264</th><th><italic>p</italic>-value</th></tr></thead><tbody><tr><td>Age, mean (sd)</td><td>38.6 (11.5)</td><td>37.1 (10.7)</td><td>0.004</td><td>34.2 (8.7)</td><td>33.1 (7.4)</td><td>0.238</td></tr><tr><td>Marital status</td><td/><td/><td>0.551</td><td/><td/><td>0.400</td></tr><tr><td> Married</td><td>1808 (76.4)</td><td>704 (75.3)</td><td/><td>472 (83.4)</td><td>206 (78.8)</td><td/></tr><tr><td> Separated/divorced</td><td>31 (1.3)</td><td>8 (0.9)</td><td/><td>4 (0.7)</td><td>2 (0.8)</td><td/></tr><tr><td> Single</td><td>35 (1.5)</td><td>13 (1.4)</td><td/><td>9 (1.6)</td><td>7 (2.7)</td><td/></tr><tr><td> Widowed</td><td>494 (20.9)</td><td>210 (22.5)</td><td/><td>81 (14.3)</td><td>47 (17.8)</td><td/></tr><tr><td>Education</td><td/><td/><td>0.488</td><td/><td/><td>0.518</td></tr><tr><td> None/Some primary</td><td>1475 (62.3)</td><td>573 (61.3)</td><td/><td>190 (33.6)</td><td>101 (38.3)</td><td/></tr><tr><td> Completed primary</td><td>536 (22.6)</td><td>198 (21.2)</td><td/><td>180 (31.8)</td><td>81 (30.7)</td><td/></tr><tr><td> Some secondary</td><td>166 (7.0)</td><td>74 (7.9)</td><td/><td>85 (15.0)</td><td>30 (11.4)</td><td/></tr><tr><td> Completed secondary</td><td>122 (5.2)</td><td>57 (6.1)</td><td/><td>67 (11.8)</td><td>29 (11.0)</td><td/></tr><tr><td> College and beyond</td><td>69 (2.9)</td><td>33 (3.5)</td><td/><td>44 (7.8)</td><td>23 (8.7)</td><td/></tr><tr><td>Occupation</td><td/><td/><td>&lt; 0.001</td><td/><td/><td>&lt; 0.001</td></tr><tr><td> Unemployed</td><td>250 (10.6)</td><td>79 (8.5)</td><td/><td>64 (11.3)</td><td>21 (8.0)</td><td/></tr><tr><td> Business</td><td>884 (37.3)</td><td>576 (61.6)</td><td/><td>218 (38.5)</td><td>155 (58.7)</td><td/></tr><tr><td> Farming</td><td>919 (38.8)</td><td>202 (21.6)</td><td/><td>188 (33.2)</td><td>52 (19.7)</td><td/></tr><tr><td> Other</td><td>315 (13.3)</td><td>78 (8.3)</td><td/><td>96 (17.0)</td><td>36 (13.6)</td><td/></tr><tr><td>Number of children, mean (sd)</td><td>5.0 (2.9)</td><td>4.5 (2.5)</td><td>0.005</td><td>4.2 (2.4)</td><td>4.0 (2.3)</td><td>0.382</td></tr><tr><td>Number of children under 13, mean (sd)</td><td>2.0 (1.6)</td><td>2.1 (1.6)</td><td>0.072</td><td>2.3 (1.5)</td><td>2.3 (1.5)</td><td>0.719</td></tr><tr><td>Prior cervical cancer screening, n (%)</td><td>305 (12.9)</td><td>193 (20.7)</td><td>&lt; 0.001</td><td>104 (18.4)</td><td>68 (25.8)</td><td>0.015</td></tr><tr><td>Type of cervical cancer screening received</td><td/><td/><td>&lt; 0.001</td><td/><td/><td>0.023</td></tr><tr><td> VIA/VILI</td><td>253 (83.2)</td><td>131 (67.9)</td><td/><td>85 (82.5)</td><td>43 (63.2)</td><td/></tr><tr><td> Pap smear</td><td>9 (3.0)</td><td>4 (2.1)</td><td/><td>1 (1.0)</td><td>0</td><td/></tr><tr><td> HPV</td><td>38 (12.5)</td><td>52 (26.9)</td><td/><td>15 (14.6)</td><td>22 (32.4)</td><td/></tr><tr><td> Don’t know</td><td>4 (1.3)</td><td>6 (3.1)</td><td/><td>2 (1.9)</td><td>3 (4.4)</td><td/></tr><tr><td>Prior cervical cancer screening result</td><td/><td/><td>0.239</td><td/><td/><td>0.187</td></tr><tr><td> Positive</td><td>5 (1.6)</td><td>8 (4.2)</td><td/><td>2 (1.9)</td><td>3 (4.4)</td><td/></tr><tr><td> Negative</td><td>269 (88.2)</td><td>167 (86.5)</td><td/><td>95 (91.4)</td><td>64 (94.1)</td><td/></tr><tr><td> Don’t know</td><td>31 (10.2)</td><td>18 (9.3)</td><td/><td>7 (6.7)</td><td>1 (1.5)</td><td/></tr><tr><td>Prior cervical cancer treatment</td><td/><td/><td>0.196</td><td/><td/><td>0.095</td></tr><tr><td> Yes</td><td>3 (1.0)</td><td>6 (3.1)</td><td/><td>0</td><td>3 (4.4)</td><td/></tr><tr><td> No</td><td>297 (96.7)</td><td>185 (95.4)</td><td/><td>102 (98.1)</td><td>64 (94.1)</td><td/></tr><tr><td> Don’t know</td><td>7 (2.3)</td><td>3 (1.6)</td><td/><td>2 (1.9)</td><td>1 (1.47)</td><td/></tr><tr><td>Prior HIV testing</td><td>2283 (96.4)</td><td>906 (96.9)</td><td>0.329</td><td>555 (98.1)</td><td>259 (98.1)</td><td>0.312</td></tr><tr><td>HIV Status</td><td/><td/><td>&lt; 0.001</td><td/><td/><td>&lt; 0.001</td></tr><tr><td> Positive</td><td>459 (20.3)</td><td>315 (34.9)</td><td/><td>112 (20.2)</td><td>84 (32.6)</td><td/></tr><tr><td> Negative</td><td>1806 (79.7)</td><td>587 (65.1)</td><td/><td>443 (79.8)</td><td>174 (67.4)</td><td/></tr><tr><td>Currently enrolled in HIV Care</td><td>458 (99.8)</td><td>313 (99.4)</td><td>0.359</td><td>111 (99.1)</td><td>83 (98.8)</td><td>0.837</td></tr><tr><td>Family planning</td><td/><td/><td>0.008</td><td/><td/><td>0.933</td></tr><tr><td> Yes</td><td>921 (38.9)</td><td>409 (43.7)</td><td/><td>300 (53.1)</td><td>141 (53.4)</td><td/></tr><tr><td> No</td><td>1415 (59.8)</td><td>508 (54.3)</td><td/><td>265 (46.9)</td><td>123 (46.6)</td><td/></tr><tr><td> Not sexually active</td><td>29 (1.2)</td><td>18 (1.9)</td><td/><td>0</td><td>0</td><td/></tr><tr><td>Tested positive for HPV through this study</td><td>403 (17.0)</td><td>152 (16.3)</td><td>0.594</td><td>90 (15.9)</td><td>41 (15.5)</td><td>0.943</td></tr><tr><td>Completed treatment</td><td>182 (45.2)</td><td>75 (49.3)</td><td>0.378</td><td>48 (53.3)</td><td>22 (53.7)</td><td>0.928</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab3\"><label>Table 3</label><caption><p>Phone usage experience and preference by notification method</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th>Variable</th><th>Overall <italic>n</italic> = 3303</th><th>Phone call <italic>n</italic> = 1650</th><th>Text <italic>n</italic> = 829</th><th>Home visit <italic>n</italic> = 824</th><th><italic>p</italic>-value</th></tr></thead><tbody><tr><td>Use mobile phone</td><td>2749 (83.2)</td><td>1597 (96.8)</td><td>811 (97.8)</td><td>341 (41.4)</td><td>&lt; 0.001</td></tr><tr><td>Own phone</td><td/><td/><td/><td/><td>&lt; 0.001</td></tr><tr><td> Self</td><td>2355 (86.0)</td><td>1408 (88.3)</td><td>750 (92.5)</td><td>197 (58.8)</td><td/></tr><tr><td> Family (spouse, child)</td><td>359 (13.1)</td><td>175 (11.0)</td><td>59 (7.3)</td><td>125 (37.3)</td><td/></tr><tr><td> Friends, neighbors</td><td>26 (1.0)</td><td>11 (0.7)</td><td>2 (0.3)</td><td>13 (3.9)</td><td/></tr><tr><td>Preferred method of receiving negative test result</td><td/><td/><td/><td/><td>&lt; 0.001</td></tr><tr><td> Text</td><td>859 (26.0)</td><td>30 (1.8)</td><td>823 (99.3)</td><td>6 (0.7)</td><td/></tr><tr><td> Phone call</td><td>1638 (49.6)</td><td>1620 (98.2)</td><td>6 (0.7)</td><td>12 (1.5)</td><td/></tr><tr><td> Home visit</td><td>806 (24.4)</td><td>0</td><td>0</td><td>806 (97.8)</td><td/></tr><tr><td>Preferred method of receiving positive test result</td><td/><td/><td/><td/><td>&lt; 0.001</td></tr><tr><td> Text</td><td>745 (22.6)</td><td>37 (2.2)</td><td>708 (85.4)</td><td>0</td><td/></tr><tr><td> Phone call</td><td>1670 (50.6)</td><td>1566 (94.9)</td><td>104 (12.6)</td><td>0</td><td/></tr><tr><td> Home visit</td><td>888 (26.9)</td><td>47 (2.9)</td><td>17 (2.1)</td><td>824 (100)</td><td/></tr><tr><td>Comfort with reading texts</td><td/><td/><td/><td/><td>&lt; 0.001</td></tr><tr><td> Very comfortable</td><td>651 (19.8)</td><td>330 (20.1)</td><td>277 (33.4)</td><td>44 (5.4)</td><td/></tr><tr><td> Comfortable</td><td>1455 (44.3)</td><td>774 (47.0)</td><td>509 (61.4)</td><td>172 (21.3)</td><td>`</td></tr><tr><td> Not comfortable</td><td>295 (9.0)</td><td>131 (8.0)</td><td>19 (2.3)</td><td>145 (17.9)</td><td/></tr><tr><td> Not comfortable at all</td><td>68 (2.1)</td><td>42 (2.6)</td><td>2 (0.2)</td><td>24 (3.0)</td><td/></tr><tr><td> Cannot read</td><td>815 (24.8)</td><td>369 (22.4)</td><td>22 (2.7)</td><td>424 (52.4)</td><td/></tr><tr><td>Comfort with writing texts</td><td/><td/><td/><td/><td>&lt; 0.001</td></tr><tr><td> Very comfortable</td><td>645 (19.7)</td><td>323 (20.0)</td><td>281 (34.1)</td><td>41 (5.1)</td><td/></tr><tr><td> Comfortable</td><td>1277 (39.0)</td><td>657 (40.0)</td><td>475 (57.6)</td><td>145 (18.0)</td><td/></tr><tr><td> Not comfortable</td><td>372 (11.4)</td><td>179 (10.9)</td><td>33 (4.0)</td><td>160 (19.8)</td><td/></tr><tr><td> Not comfortable at all</td><td>68 (2.1)</td><td>44 (2.7)</td><td>1 (0.1)</td><td>23 (2.9)</td><td/></tr><tr><td> Cannot read</td><td>913 (27.9)</td><td>440 (26.8)</td><td>35 (4.2)</td><td>438 (54.3)</td><td/></tr><tr><td>Comfort with receiving confidential information via text</td><td/><td/><td/><td/><td>&lt; 0.001</td></tr><tr><td> Very comfortable</td><td>671 (21.1)</td><td>331 (20.1)</td><td>298 (36.0)</td><td>42 (5.7)</td><td/></tr><tr><td> Comfortable</td><td>1285 (40.5)</td><td>660 (40.9)</td><td>483 (58.4)</td><td>142 (19.4)</td><td/></tr><tr><td> Not comfortable</td><td>893 (28.1)</td><td>477 (29.5)</td><td>38 (4.6)</td><td>378 (51.5)</td><td/></tr><tr><td> Not comfortable at all</td><td>327 (10.3)</td><td>147 (9.1)</td><td>8 (1.0)</td><td>172 (23.4)</td><td/></tr><tr><td>Notified on first attempt</td><td>2443 (74.0)</td><td>899 (54.5)</td><td>829 (100)</td><td>715 (86.8)</td><td>&lt; 0.001</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab4\"><label>Table 4</label><caption><p>Impact of actual notification strategies on treatment uptake and time to treatment</p></caption><table frame=\"hsides\" rules=\"groups\"><tbody><tr><td align=\"left\"><bold>Phone calls</bold></td><td align=\"left\"><bold>First four communities </bold><bold><italic>n</italic></bold><bold> = 1168</bold></td><td align=\"left\"><bold>Last two communities </bold><bold><italic>n</italic></bold><bold> = 481</bold></td><td align=\"left\"><bold><italic>p</italic></bold>-<bold>value</bold></td></tr><tr><td align=\"left\">HPV Positive, n (%)</td><td align=\"left\">195 (16.7)</td><td align=\"left\">84 (17.5)</td><td align=\"left\">0.705</td></tr><tr><td align=\"left\">Completed treatment n (%)</td><td align=\"left\">83 (42.6)</td><td align=\"left\">40 (47.6)</td><td align=\"left\">0.435</td></tr><tr><td align=\"left\">Time duration, median days, (Q1, Q3)</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\">Screening to Notification (<italic>n</italic> = 1649)</td><td align=\"left\">30 (21, 55)</td><td align=\"left\">35 (14, 62)</td><td align=\"left\">0.087</td></tr><tr><td align=\"left\">Notification to Treatment (<italic>n</italic> = 123)</td><td align=\"left\">9 (4, 34)</td><td align=\"left\">3 (2, 14.5)</td><td align=\"left\">0.002</td></tr><tr><td align=\"left\">Screening to Treatment (<italic>n</italic> = 123)</td><td align=\"left\">34 (27, 67)</td><td align=\"left\">29 (13.5, 59)</td><td align=\"left\">0.068</td></tr><tr><td align=\"left\"><bold>Text messaging</bold></td><td align=\"left\"><bold>First four communities with standard text </bold><bold><italic>n</italic></bold> <bold>= 567</bold></td><td align=\"left\"><bold>Last two communities with enhanced text </bold><bold><italic>n</italic></bold> <bold>= 262</bold></td><td align=\"left\"><bold><italic>p</italic></bold><bold>-value</bold></td></tr><tr><td align=\"left\">HPV Positive, n (%)</td><td align=\"left\">90 (15.9)</td><td align=\"left\">40 (15.3)</td><td align=\"left\">0.824</td></tr><tr><td align=\"left\">Completed treatment n (%)</td><td align=\"left\">48 (53.3)</td><td align=\"left\">22 (55.0)</td><td align=\"left\">0.860</td></tr><tr><td align=\"left\">Time duration, median days, (Q1, Q3)</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\">Screening to Notification (<italic>n</italic> = 829)</td><td align=\"left\">18 (13, 21)</td><td align=\"left\">12 (10, 50)</td><td align=\"left\">0.015</td></tr><tr><td align=\"left\">Notification to Treatment (<italic>n</italic> = 70)</td><td align=\"left\">25 (9, 71)</td><td align=\"left\">24.5 (10, 55)</td><td align=\"left\">0.894</td></tr><tr><td align=\"left\">Screening to Treatment (<italic>n</italic> = 70)</td><td align=\"left\">52 (22, 96)</td><td align=\"left\">61.5 (25, 77)</td><td align=\"left\">0.690</td></tr><tr><td align=\"left\"><bold>Home visits</bold></td><td align=\"left\"><bold>First four communities </bold><bold><italic>n</italic></bold> <bold>= 632</bold></td><td align=\"left\"><bold>Last two communities </bold><bold><italic>n</italic></bold> <bold>= 192</bold></td><td align=\"left\"><bold><italic>p</italic></bold><bold>-value</bold></td></tr><tr><td align=\"left\">HPV Positive, n (%)</td><td align=\"left\">118 (18.7)</td><td align=\"left\">28 (14.6)</td><td align=\"left\">0.194</td></tr><tr><td align=\"left\">Completed treatment, n (%)</td><td align=\"left\">51 (43.2)</td><td align=\"left\">13 (46.4)</td><td align=\"left\">0.758</td></tr><tr><td align=\"left\">Time duration, median days, (Q1, Q3)</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\">Screening to Notification (<italic>n</italic> = 824)</td><td align=\"left\">20 (15, 30)</td><td align=\"left\">17 (12, 46)</td><td align=\"left\">0.491</td></tr><tr><td align=\"left\">Notification to Treatment (<italic>n</italic> = 64)</td><td align=\"left\">11 (6, 26)</td><td align=\"left\">6 (3, 14)</td><td align=\"left\">0.063</td></tr><tr><td align=\"left\">Screening to Treatment (<italic>n</italic> = 64)</td><td align=\"left\">34 (26, 67)</td><td align=\"left\">58 (17, 63)</td><td align=\"left\">0.683</td></tr></tbody></table></table-wrap>" ]

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[ "<title>Introduction</title>", "<p id=\"Par5\">Endometriosis is a chronic gynecological disorder characterized by the presence of endometrial-like tissue outside the uterus, most commonly in the pelvic cavity [##REF##29276652##1##]. It affects approximately 10% of women of reproductive age and is associated with debilitating symptoms such as pelvic pain, dysmenorrhea, dyspareunia, and infertility [##REF##29276652##1##]. The pathogenesis of endometriosis remains poorly understood, and there is a need for reliable biomarkers that can aid in its diagnosis and management [##UREF##0##2##].</p>", "<p id=\"Par6\">Brain-derived neurotrophic factor (BDNF) is a neurotrophin that plays a crucial role in the development, survival, and plasticity of neurons in the central nervous system [##REF##26788077##3##]. It has been implicated in various physiological processes, including neuronal growth, synaptic plasticity, and pain modulation [##REF##26788077##3##, ##UREF##1##4##]. BDNF is primarily synthesized in the brain, but emerging evidence suggests that it is also expressed in peripheral tissues, including the reproductive system [##REF##36528281##5##].</p>", "<p id=\"Par7\">Recent studies have proposed a potential association between BDNF and endometriosis, highlighting BDNF as a promising candidate biomarker for this condition [##UREF##2##6##, ##REF##35300713##7##]. Elevated levels of BDNF have been reported in the peritoneal fluid, serum, and endometrial tissue of women with endometriosis compared to healthy controls [##REF##32192291##8##–##REF##28954602##10##]. These findings suggest that BDNF may be involved in the pathogenesis of endometriosis and could potentially serve as a diagnostic or prognostic marker [##REF##35300713##7##, ##REF##36415495##11##]. However, the existing literature on the association between BDNF and endometriosis is still limited and characterized by inconsistencies in findings. Therefore, a comprehensive evaluation of the available evidence is warranted to clarify the role of BDNF in endometriosis. The aim of this systematic review and meta-analysis is to evaluate the existing evidence on the association between BDNF levels and endometriosis.</p>" ]

[ "<title>Methods</title>", "<p id=\"Par8\">The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines was followed for conducting the present study. More details about PRISMA can be found in Supplementary File Table ##TAB##0##1##. The protocol of this study is registered in PROSPERO with the code CRD42023439147.</p>", "<p id=\"Par9\">\n\n</p>", "<title>Search strategy</title>", "<p id=\"Par10\">A systematic search was performed in four international bibliometric databases, including Scopus, Embase, PubMed, and Web of Science from the inception up to 12 June 2023, with the goal of identifying any published article which evaluated the altered levels of BDNF in endometriosis. Regarding our systematic search strategy, we categorized the keywords into two different groups, including the endometriosis group and the BDNF group. In the endometriosis group, we used any possible keyword related to endometriosis, including endometriosis, adenomyosis, or abnormal uterine tissue. In the BDNF group, we used all possible keywords related to BDNF, such as BDNF, brain-derived neurotrophic factor, or brain-derived neurotrophic factor. We used “OR” between the keywords in each group and utilized “AND” between the groups. Supplementary Table 2 represents the search string for each database in detail.</p>", "<title>Eligibility criteria</title>", "<p id=\"Par11\">We included studies that evaluated the levels of BDNF in endometriosis using enzyme-linked immunoassays (ELISA) or any other methods. The exclusion criteria included animal studies, in-vitro studies, meta-analyses, review articles, letters to editors, case reports, and congress abstracts. We did not impose any language restriction regarding the original language of the identified articles.</p>", "<title>Data extraction and quality assessment</title>", "<p id=\"Par12\">The initial screening of the identified studies, based on their titles and abstracts was performed by two independent reviewers, in order to exclude irrelevant studies. Then, the full texts of the remained articles were evaluated for extracting their data. Two independent reviewers performed the data extraction, based on an Excel sheet, containing the first author’s names, country of origin, year of publication, type of endometriosis, the stage of the endometriosis, source of the BDNF, age of the patients, and sample sizes of the studies. Moreover, two independent reviewers assessed the quality of the included studies, using Newcastle-Ottawa Scale (NOS) tool.</p>", "<title>Data synthesis and meta-analysis</title>", "<p id=\"Par13\">The meta-analysis utilized a random-effects model to determine the combined effect size and evaluate its statistical significance. The standardized mean difference (SMD) and its corresponding 95% confidence intervals (95% CIs) were employed to present the pooled effect sizes. Sensitivity analysis was performed by including only the studies that assessed blood levels of BDNF. Assessment of publication bias was conducted through the implementation of funnel plots and Egger’s regression test.</p>" ]

[ "<title>Results</title>", "<title>Study selection</title>", "<p id=\"Par14\">A systematic search of electronic databases yielded a total of 192 articles. After removing duplicates and applying the inclusion and exclusion criteria which was done by two reviewers (A.S &amp; S.R), a final set of 12 articles were included in this systematic review and meta-analysis [##UREF##2##6##, ##REF##32192291##8##–##UREF##6##18##] The characteristic information of included studies is in Table ##TAB##0##1##. The inclusion criteria were as follows: (1) patient population: women of reproductive age after being diagnosed with endometriosis; (2) Intervention: evaluating level of BDNF in serum or plasma; (3) Comparison: healthy women ; (4) Outcome: impact on the BDNF level; (5) Setting/Time: All and (6) study design: randomized controlled trial, retrospective studies, and prospective studies. Studies that were conducted on animals or have not met our inclusion criteria or were designed as case reports, case series, and non-English articles were excluded.</p>", "<p id=\"Par15\">The selection process is illustrated in Fig. ##FIG##0##1##.</p>", "<p id=\"Par16\">\n\n</p>", "<title>Characteristics of included studies</title>", "<title>Quality assessment</title>", "<p id=\"Par17\">The quality assessment of the included studies was performed using the Newcastle-Ottawa Scale (NOS) for observational studies (Table ##TAB##1##2##). The overall quality of the studies ranged from moderate to high, with most studies scoring 6 or higher on the NOS. Only two studies had poor quality [##REF##32192291##8##, ##UREF##4##14##].</p>", "<p id=\"Par18\">\n\n</p>", "<title>Meta-analysis results</title>", "<p id=\"Par19\">The meta-analysis of the included studies revealed a significant association between BDNF levels and endometriosis. The pooled standardized mean difference (SMD) of BDNF levels between women with endometriosis and controls was 0.87 (95% confidence interval [CI] 0.34 to 1.39, <italic>p</italic> = 0.001; I2 = 93%), indicating higher BDNF levels in women with endometriosis compared to controls. The forest plot depicting the individual study results and the overall pooled effect is presented in Fig. ##FIG##1##2##.</p>", "<p id=\"Par20\">\n\n</p>", "<title>Publication bias</title>", "<p id=\"Par21\">Publication bias was assessed using funnel plots and Egger’s test. The funnel plot appeared symmetrical, indicating no significant publication bias. Egger’s test also confirmed the absence of publication bias (<italic>p</italic> = 0.15) (Fig. ##FIG##2##3##).</p>", "<p id=\"Par22\">\n\n</p>", "<title>Sensitivity analysis</title>", "<p id=\"Par23\">A sensitivity analysis was conducted by studies that assessed blood levels of BDNF. The results showed that blood levels of BDNF are significantly higher in endometriosis patients (SMD: 1.13 95% CI 0.54 to 1.73, <italic>p</italic> = 0.0002; I2 = 93%) (Fig. ##FIG##3##4##).</p>", "<p id=\"Par24\">\n\n</p>" ]

[ "<title>Discussion</title>", "<p id=\"Par25\">The result of the present systematic review and meta-analysis indicates that BDNF levels significantly increase in patients diagnosed with endometriosis compared to healthy controls. The result of the sensitive analysis showed a significant increase in BDNF levels in both plasma and serum in endometriosis.</p>", "<p id=\"Par26\">Evidence showed that BDNF level varies during a healthy menstrual cycle, and it is reported that BDNF significantly increases during the Luteal phase in comparison with the follicular phase [##REF##17251358##19##]. It is also mentioned that BDNF is significantly lower in Amenorrhoeic subjects, as well as postmenopausal women [##REF##17251358##19##]. Taken together, all this evidence shows that estradiol and progesterone might have an impact on BDNF circulation, and also literature showed a positive correlation between BDNF and E (2) and progesterone in fertile women [##REF##17251358##19##].</p>", "<p id=\"Par27\">Results of a study done by Bucci et al. revealed a significantly higher level of estradiol and progesterone among patients with stage 1 and 2 endometriosis compared to healthy controls [##UREF##3##12##]. It can therefore be assumed that BDNF can increase in patients diagnosed with endometriosis.</p>", "<p id=\"Par28\">This study produced results that corroborate the findings of a great deal of the previous work in this field. Giannini et al. found that the level of BDNF in plasma was significantly higher in comparison with healthy controls in the follicular phase, also the results of a study done by Browne et al. are consistent with Giannini et al. study and showed a higher level of BDNF in patients diagnosed with endometriosis [##REF##22717347##9##, ##UREF##4##14##]. However, the findings of the Ding et al. and De Arellano et al. studies do not support the results of the studies mentioned earlier, they revealed no significant difference between healthy controls and women with endometriosis in the level of BDNF [##REF##28954602##10##, ##REF##23545214##13##]. A systematic review done by Chow et al. indicates that Pro-BDNF is expressed in the endometrium, and BDNF expression in the endometrium is significantly higher in patients with endometriosis [##REF##32378708##20##]. These findings may be a possible explanation for the results of Browne et al. study which showed that although BDNF concentration was higher in women with endometriosis, three months after surgical removal of endometriotic lesions, no difference was found in the level of BDNF between healthy controls and women with endometriosis [##REF##22717347##9##]. Wessels et al. compared BDNF levels in patients who received treatment for endometriosis with patients who did not, the results showed a significantly decreased BDNF level in the treated group [##UREF##2##6##]. Although BDNF was significantly higher in endometriosis compared with healthy controls, no significant changes were reported between different stages of endometriosis [##UREF##2##6##, ##REF##36415495##11##]. However, BDNF expression in eutopic endometrium is positively correlated with stages of endometriosis [##REF##35300713##7##]. A study done by Rocha et al. showed that although BDNF is higher in plasma among patients with ovarian endometrioma and can be used as a diagnostic marker, it is not helpful for the diagnosis of other forms of endometriosis including peritoneal or deep infiltrating endometriosis [##REF##28290209##21##].</p>", "<p id=\"Par29\">BDNF expression plays an essential role in female reproductivity by affecting placental function, oocyte maturation, embryo development, follicle development, and oogenesis, therefore dysregulation of BDNF can lead to several serious complications in women such as endometriosis, intra-uterine growth restriction (IUGR), preeclampsia and cancers [##REF##32378708##20##]. A positive correlation is reported between estrogen and BDNF, and the interaction of inflammatory factors [Interleukin-1β (IL-1β)] and estradiol (E2) with their receptors leads to increased extracellular signal-regulated kinase 1/2 (ERK1/2) expression, within transcription factor phosphorylation, cAMP response element binding protein (CREB) causes synthesis of BDNF in the endometrium [##REF##28954602##10##]. Capillary blood vessels formed around endometriosis tissue would help this increased amount of BDNF reach the peripheral circulation.</p>", "<p id=\"Par30\">To the best of our knowledge, the present systematic review and meta-analysis is the very first study that investigates the level of BDNF in patients with endometriosis and evaluates the diagnostic value of BDNF in endometriosis. Also, our study has extended the results of previous studies on this topic by including 12 studies. Additionally, in our sensitive analysis, we have compared BDNF levels in serum and plasma separately, which can lead to a better vision for utilizing the BDNF as a novel biomarker for endometriosis. However, with a small sample size, caution must be applied, as findings might not be transferable to all the patients who are diagnosed with endometriosis. Only 50% of the included studies have evaluated the level of BDNF in either serum or plasma, since it is easier for both health workers and patients to evaluate BDNF in blood samples, more studies are required to investigate BDNF levels in blood.</p>", "<p id=\"Par31\">Number of limitations should be considered for current study. Several confounding factors are able to make changes in BDNF level in individuals such as socioeconomic status which can lead to escalating rate of depression, different type of mental disorders and administration of number of medicines including Analgesics. [##UREF##7##22##] Included studies in our meta-analysis have not considered mentioned factor in their participants, therefore evaluated BDNF level in these studies can be effected by confounding factors. Other limitation for our study is number od included articles and participants, for considering BDNF as a diagnostic value for endometriosis, more studies should be included and determined.</p>", "<p id=\"Par32\">Considerably more work will need to be done to determine the correlation between BDNF level and endometriosis and to evaluate the diagnostic value of BDNF. These would help health workers with earlier diagnosis, more efficient treatment, and controlling the adverse effect of endometriosis such as pain and infertility. As mentioned earlier, since BDNF increases in both serum and plasma, it can be utilized as an accessible, fast, non-invasive, and inexpensive method for not only diagnosis but also evaluating the severity and treatment respond in women with endometriosis.</p>", "<p id=\"Par33\">In conclusion, our study revealed that BDNF level is significantly higher in patients with endometriosis compared to healthy control. Further investigation and experimentation into the correlation between BDNF and endometriosis is strongly recommended.</p>" ]

[]

[ "<title>Background</title>", "<p id=\"Par1\">The existing literature on the association between BDNF protein levels and endometriosis presents inconsistent findings. This systematic review and meta-analysis aim to synthesize the available evidence and evaluate the possible relationship between BDNF protein levels and endometriosis.</p>", "<title>Methods</title>", "<p id=\"Par2\">Electronic databases (PubMed, Embase, Scopus, PsycINFO, and Web of Science) were used to conduct a comprehensive literature search from inception to June 2023. The search strategy included relevant keywords and medical subject headings (MeSH) terms related to BDNF, endometriosis, and protein levels. A random-effects model was used for the meta-analysis, and to explore heterogeneity subgroup analyses were performed. funnel plots and statistical tests were used for assessing the publication bias.</p>", "<title>Results</title>", "<p id=\"Par3\">A total of 12 studies were included. The pooled standardized mean difference (SMD) of BDNF levels between women with endometriosis and controls was 0.87 (95% confidence interval [CI] 0.34 to 1.39, <italic>p</italic> = 0.001; I2 = 93%). The results showed that blood levels of BDNF are significantly higher in endometriosis patients (SMD: 1.13 95% CI 0.54 to 1.73, <italic>p</italic> = 0.0002; I2 = 93%). No significant publication bias was observed based on the results of Egger’s regression test ((<italic>p</italic> = 0.15).</p>", "<title>Conclusion</title>", "<p id=\"Par4\">This study revealed a significant difference between patients diagnosed with endometriosis and healthy control in the level of BDNF. The results indicate that women with endometriosis have higher levels of BDNF. Further studies are needed to be undertaken to investigate the role of BDNF in endometriosis pathophysiology and the diagnostic value of BDNF in endometriosis.</p>", "<title>Supplementary Information</title>", "<p>The online version contains supplementary material available at 10.1186/s12905-023-02877-0.</p>", "<title>Keywords</title>" ]

[ "<title>Electronic supplementary material</title>", "<p>Below is the link to the electronic supplementary material.</p>", "<p>\n\n</p>" ]

[ "<title>Acknowledgements</title>", "<p>The authors would like to acknowledge the Clinical Research Development Unit of Imam Ali Hospital, Karaj, Iran.</p>", "<title>Author contributions</title>", "<p><bold>A.S., S.P, R.B:</bold> Conceptualization, Project Administration, Data curation, Writing- Original Draft, Writing ? Review &amp; Editing, Visualization</p>", "<p><bold>K.J, A.S; M.B, F.S, M.A:</bold> Validation, Resources, Methodology, Software, Formal analysis, Writing ? Original Draft</p>", "<p><bold>I.M, E.M.:</bold> Writing- Original Draft</p>", "<p><bold>S.R.:</bold> Data curation</p>", "<title>Funding</title>", "<p>This study did not receive funding, grant, or sponsorship from any individuals or organizations.</p>", "<title>Data availability</title>", "<p>All data generated or analyzed during this study are included in this published article [and its supplementary information files].</p>", "<title>Code availability</title>", "<p>Not applicable.</p>", "<title>Declarations</title>", "<title>Ethics approval</title>", "<p id=\"Par35\">Not applicable.</p>", "<title>Consent to participate</title>", "<p id=\"Par37\">Not applicable.</p>", "<title>Ethical statement</title>", "<p id=\"Par38\">Not applicable.</p>", "<title>Consent for publication</title>", "<p id=\"Par39\">Not applicable.</p>", "<title>Conflict of interest</title>", "<p id=\"Par36\">The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript.</p>", "<title>IRB</title>", "<p id=\"Par40\">Not required for meta-analysis or a systematic review and commentaries.</p>" ]

[ "<fig id=\"Fig1\"><label>Fig. 1</label><caption><p>PRISMA flow diagram</p></caption></fig>", "<fig id=\"Fig2\"><label>Fig. 2</label><caption><p>Results of meta-analysis for the level of Brain-Derived Neurotrophic Factor (BDNF) levels in patients with endometriosis</p></caption></fig>", "<fig id=\"Fig3\"><label>Fig. 3</label><caption><p>Funnel plot</p></caption></fig>", "<fig id=\"Fig4\"><label>Fig. 4</label><caption><p>Results of sensitivity analysis</p></caption></fig>" ]

[ "<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Characteristics of the included studies</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Author</th><th align=\"left\">Country</th><th align=\"left\">Year</th><th align=\"left\">Endometriosis<break/>type</th><th align=\"left\">Endometriosis<break/>stage</th><th align=\"left\">BDNF<break/>source</th><th align=\"left\">Age</th><th align=\"left\">Sample<break/>size (case/control)</th><th align=\"left\">Quality</th></tr></thead><tbody><tr><td align=\"left\">de Arellano</td><td align=\"left\">Germany</td><td align=\"left\">2013</td><td align=\"left\">Peritoneal endometriotic lesions</td><td align=\"left\">NA</td><td align=\"left\">Peritoneal fluid</td><td align=\"left\">NA</td><td align=\"left\">40 (20/20)</td><td align=\"left\">Fair</td></tr><tr><td align=\"left\">Bucci</td><td align=\"left\">Italy</td><td align=\"left\">2011</td><td align=\"left\">NA</td><td align=\"left\">Stages 1 and 2</td><td align=\"left\">Plasma</td><td align=\"left\"><p>case = 28.36 ± 3.9</p><p>control = 26.81 ± 4.53</p></td><td align=\"left\">22 (11/11)</td><td align=\"left\">Fair</td></tr><tr><td align=\"left\">Browne</td><td align=\"left\">USA</td><td align=\"left\">2012</td><td align=\"left\">NA</td><td align=\"left\">NA</td><td align=\"left\">Eutopic endometrial biopsy</td><td align=\"left\"><p>case = 34 ± 7</p><p>control = 34 ± 6</p></td><td align=\"left\">33 (18/15)</td><td align=\"left\">Good</td></tr><tr><td align=\"left\">Herranz-Blanco</td><td align=\"left\">Spain</td><td align=\"left\">2023</td><td align=\"left\"><p>superficial peritoneal lesions = 54 (39.7%)</p><p>ovarian endometriomas = 26 (19.1%)</p><p>deep infiltrating endometriosis = 29 (21.3%)</p><p>deep infiltrating endometriosis and ovarian endometriomas = 25 (18.4%)</p><p>Unclassified = 2 (1.5%)</p></td><td align=\"left\"><p>rASRM classification</p><p>I–II = 68 (50%)</p><p>III–IV = 68 (50%)</p></td><td align=\"left\">serum samples</td><td align=\"left\"><p>case = 35.6 ± 6.42</p><p>control = 33.5 ± 5.96</p></td><td align=\"left\">204 (136/68)</td><td align=\"left\">Fair</td></tr><tr><td align=\"left\">Giannini</td><td align=\"left\">Italy</td><td align=\"left\">2010</td><td align=\"left\">NA</td><td align=\"left\">stage I and II</td><td align=\"left\">plasma and follicular fluid</td><td align=\"left\"><p>case = 29.8 ± 4.13</p><p>control = 27.7 ± 4.7</p></td><td align=\"left\">56 (26/30)</td><td align=\"left\">Poor</td></tr><tr><td align=\"left\">Dwiningsih</td><td align=\"left\">Indonesia</td><td align=\"left\">2022</td><td align=\"left\"><p>Ovarian endometriosis n = 32 (88.9)</p><p>Peritoneal endometriosis n = 4 (11.1)</p></td><td align=\"left\"><p>rASRM classification</p><p>I = 3 (8.3)</p><p>II = 1 (2.8)</p><p>III = 11 (30.6)</p><p>IV = 21 (58.3)</p></td><td align=\"left\">Serum</td><td align=\"left\"><p>case = 31.47 ± 6.5</p><p>control = 38.14 ± 4.4</p></td><td align=\"left\">50 (36/14)</td><td align=\"left\">Good</td></tr><tr><td align=\"left\">Ding</td><td align=\"left\">People’s Republic of China</td><td align=\"left\">2017</td><td align=\"left\">Ovarian endometrioma</td><td align=\"left\"><p>Revised American Fertility Society</p><p>scoring system</p><p>I–II = 32 (53.3%)</p><p>III–IV = 28 (46.7%)</p></td><td align=\"left\">Serum and peritoneal fluid</td><td align=\"left\"><p>case = 35.3 ± 0.9</p><p>control = 35.6 ± 1.4</p></td><td align=\"left\">98 (60/38)</td><td align=\"left\">Good</td></tr><tr><td align=\"left\">Yu</td><td align=\"left\">USA</td><td align=\"left\">2023</td><td align=\"left\">NA</td><td align=\"left\">NA</td><td align=\"left\">Peritoneal fluid</td><td align=\"left\"><p>Cases = 38.0 ± 6.0</p><p>Control = 43.0 ± 4.5</p></td><td align=\"left\">40 (14/26)</td><td align=\"left\">Good</td></tr><tr><td align=\"left\">Wessels</td><td align=\"left\">Canada</td><td align=\"left\">2016</td><td align=\"left\">NA</td><td align=\"left\"><p>I: 10</p><p>II: 9</p><p>III: 10</p><p>IV: 64</p></td><td align=\"left\">Plasma</td><td align=\"left\"><p>Cases = 34.7 ± 7.0</p><p>Control = 29.9 ± 8.5</p></td><td align=\"left\">104 (68/36)</td><td align=\"left\">Fair</td></tr><tr><td align=\"left\">Stefani</td><td align=\"left\">Brazil</td><td align=\"left\">2019</td><td align=\"left\">NA</td><td align=\"left\">NA</td><td align=\"left\">serum</td><td align=\"left\">NA</td><td align=\"left\">53 (36/17)</td><td align=\"left\">Fair</td></tr><tr><td align=\"left\">Perricos</td><td align=\"left\">Austria</td><td align=\"left\">2018</td><td align=\"left\"><p>22 superficial peritoneal</p><p>3 deep infiltrating</p><p>12 endometrioma</p><p>32 combination of two</p><p>7 combination of three</p></td><td align=\"left\"><p>I:21</p><p>II: 14</p><p>III: 20</p><p>IV: 20</p></td><td align=\"left\">serum</td><td align=\"left\"><p>Cases = 33.7 ± 6.04</p><p>Controls = 34.8 ± 6.9</p></td><td align=\"left\">128 (77/51)</td><td align=\"left\">Good</td></tr><tr><td align=\"left\">Liang</td><td align=\"left\">China</td><td align=\"left\">2020</td><td align=\"left\">NA</td><td align=\"left\">NA</td><td align=\"left\">serum</td><td align=\"left\">NA</td><td align=\"left\">157 (82/75)</td><td align=\"left\">Poor</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab2\"><label>Table 2</label><caption><p>Results of quality assessments</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Author</th><th align=\"left\">Year</th><th align=\"left\">Is the case definition adequate?</th><th align=\"left\">Representativeness of the cases</th><th align=\"left\">Selection of Controls</th><th align=\"left\">Definition of Controls</th><th align=\"left\">Comparability</th><th align=\"left\">Ascertainment of exposure</th><th align=\"left\">Same method of ascertainment for cases and controls</th><th align=\"left\">Non-Response rate</th><th align=\"left\">Total</th></tr></thead><tbody><tr><td align=\"left\">Maria Luisa Barcena de Arellano</td><td align=\"left\">2013</td><td align=\"left\">1</td><td align=\"left\">1</td><td align=\"left\">0</td><td align=\"left\">1</td><td align=\"left\">0</td><td align=\"left\">1</td><td align=\"left\">1</td><td align=\"left\">1</td><td align=\"left\">Fair</td></tr><tr><td align=\"left\">Fiorella Bucci</td><td align=\"left\">2011</td><td align=\"left\">1</td><td align=\"left\">1</td><td align=\"left\">1</td><td align=\"left\">0</td><td align=\"left\">2</td><td align=\"left\">1</td><td align=\"left\">0</td><td align=\"left\">0</td><td align=\"left\">Fair</td></tr><tr><td align=\"left\">Aimee S. Browne</td><td align=\"left\">2012</td><td align=\"left\">1</td><td align=\"left\">1</td><td align=\"left\">0</td><td align=\"left\">1</td><td align=\"left\">2</td><td align=\"left\">1</td><td align=\"left\">1</td><td align=\"left\">0</td><td align=\"left\">Good</td></tr><tr><td align=\"left\">Bárbara Herranz-Blanco</td><td align=\"left\">2023</td><td align=\"left\">1</td><td align=\"left\">0</td><td align=\"left\">0</td><td align=\"left\">1</td><td align=\"left\">2</td><td align=\"left\">1</td><td align=\"left\">1</td><td align=\"left\">0</td><td align=\"left\">Fair</td></tr><tr><td align=\"left\">Andrea Giannini</td><td align=\"left\">2010</td><td align=\"left\">1</td><td align=\"left\">0</td><td align=\"left\">0</td><td align=\"left\">0</td><td align=\"left\">2</td><td align=\"left\">1</td><td align=\"left\">0</td><td align=\"left\">0</td><td align=\"left\">Poor</td></tr><tr><td align=\"left\">Sri Ratna Dwiningsih</td><td align=\"left\">2022</td><td align=\"left\">1</td><td align=\"left\">1</td><td align=\"left\">0</td><td align=\"left\">1</td><td align=\"left\">2</td><td align=\"left\">1</td><td align=\"left\">1</td><td align=\"left\">0</td><td align=\"left\">Good</td></tr><tr><td align=\"left\">Shaojie Ding</td><td align=\"left\">2017</td><td align=\"left\">1</td><td align=\"left\">1</td><td align=\"left\">0</td><td align=\"left\">1</td><td align=\"left\">2</td><td align=\"left\">1</td><td align=\"left\">1</td><td align=\"left\">0</td><td align=\"left\">Good</td></tr><tr><td align=\"left\">Yu</td><td align=\"left\">2023</td><td align=\"left\">1</td><td align=\"left\">1</td><td align=\"left\">0</td><td align=\"left\">1</td><td align=\"left\">2</td><td align=\"left\">1</td><td align=\"left\">1</td><td align=\"left\">0</td><td align=\"left\">Good</td></tr><tr><td align=\"left\">Wessels</td><td align=\"left\">2016</td><td align=\"left\">1</td><td align=\"left\">1</td><td align=\"left\">0</td><td align=\"left\">0</td><td align=\"left\">2</td><td align=\"left\">1</td><td align=\"left\">0</td><td align=\"left\">1</td><td align=\"left\">Fair</td></tr><tr><td align=\"left\">Stefani</td><td align=\"left\">2019</td><td align=\"left\">1</td><td align=\"left\">1</td><td align=\"left\">1</td><td align=\"left\">0</td><td align=\"left\">2</td><td align=\"left\">1</td><td align=\"left\">0</td><td align=\"left\">0</td><td align=\"left\">Fair</td></tr><tr><td align=\"left\">Perricos</td><td align=\"left\">2018</td><td align=\"left\">1</td><td align=\"left\">1</td><td align=\"left\">0</td><td align=\"left\">1</td><td align=\"left\">2</td><td align=\"left\">1</td><td align=\"left\">1</td><td align=\"left\">0</td><td align=\"left\">Good</td></tr><tr><td align=\"left\">Liang</td><td align=\"left\">2020</td><td align=\"left\">1</td><td align=\"left\">1</td><td align=\"left\">0</td><td align=\"left\">0</td><td align=\"left\">1</td><td align=\"left\">1</td><td align=\"left\">0</td><td align=\"left\">0</td><td align=\"left\">Poor</td></tr></tbody></table></table-wrap>" ]

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[ "<supplementary-material content-type=\"local-data\" id=\"MOESM1\"></supplementary-material>" ]

[ "<fn-group><fn><p><bold>Publisher’s Note</bold></p><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn></fn-group>" ]

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[ "<media xlink:href=\"12905_2023_2877_MOESM1_ESM.docx\"><caption><p>Supplementary Material 1</p></caption></media>" ]

{ "acronym": [], "definition": [] }

[ "<title>Introduction\n</title>", "<p id=\"Par5\">As the world’s second-largest economy, China is also grappling with the intricate challenge of rapid ageing [##REF##34727357##1##]. According to a recent national survey (2020) based on the scale assessment for activities of daily living (ADLs) and instrumental activities of daily living (IADLs) [##REF##36423656##2##], three levels were categorised based on the severity of dependency and older adults’ requirements for care. The study estimated that more than 20 million Chinese older adults were in need of minimal assistance with daily living activities, such as meal preparation and basic hygiene (level 1 dependency), 36 million needed moderate assistance with daily tasks, including cooking, shopping, and medication management (level 2 dependency), and 45 million were largely dependent on others for their daily living activities, requiring continuous supervision and assistance, such as those with severe cognitive or physical impairments (level 3 dependency), respectively. The one-child policy has directly impacted the availability of family caregivers, compounding the issue of inadequate care for Chinese older adults in their later years [##UREF##0##3##]. For the majority of older adults, dependency on assistance for daily living activities and cognitive impairments has become a significant life event, and these aspects lead to an increasing demand for nursing homes [##UREF##1##4##]. However, the quality of care provided in Chinese nursing homes is primarily influenced by policies, often falling short of meeting the demands of older adults in terms of having skilled caregivers, real-time monitoring, and continuous health assessment [##UREF##2##5##].</p>", "<p id=\"Par6\">As sustainable strategies for promoting care for the ageing population, the use of smart technologies can address the escalating unmet healthcare needs of older adults and offset the inadequacy of medical resources to effectively improve the current healthcare system [##REF##36427766##6##]. In hospital settings, smart technologies are used to enhance clinical decision-making [##REF##28189116##7##], while in home-based care, they help with self-management and the remote monitoring of chronic diseases [##UREF##3##8##, ##UREF##4##9##]. In nursing home settings, technologies are predominantly implemented to provide person-centred care services and integrate medical services from remote hospitals [##REF##34979941##10##]. The use of smart technologies holds the potential to support a substantial number of older adults in both home-based and nursing home-based care [##UREF##5##11##]. In 2014, the Ministry of Civil Affairs of the People’s Republic of China, the supervisory department for geriatric care, initiated the ‘Smart Elderly Internet of Things (IoT) Pilot Project’ to enhance the operation of SNHs [##UREF##6##12##]. In 2015, the Chinese government introduced the ‘Internet Plus’ plan to encourage technological innovation [##UREF##7##13##], encompassing projects related to IoT or Artificial Intelligence (AI) in safety monitoring, fall prevention, and disease detection for older adults. However, the concept of a SNH and the availability of smart technologies in nursing home settings remain ambiguous. Moreover, many older adults have a negative attitude towards smart technologies, perceiving them as challenging to use and being expensive [##UREF##8##14##]. Exploring the expectations and acceptability of SNHs within a defined service scope and associated technologies [##REF##34979941##10##] among stakeholders, particularly older adults, will provide a better understanding of the future development and implementation of SNH models. Expectations, in this context, generally encompass the desires of consumers regarding what they expect a SNH to provide [##UREF##9##15##], while acceptability refers to the intention to use services when they are available and meet the criteria of target users willing to adopt SNHs [##UREF##10##16##].</p>", "<p id=\"Par7\">Previous studies have often defined a SNH as either a smart building equipped with IoT networks [##UREF##11##17##], or the isolated application of smart technology within nursing home environment [##UREF##12##18##–##UREF##15##21##]. Specifically, a precise definition of SNHs and the comprehensive implementation of functional technologies is needed. A comprehensive scoping review has defined a SNH as characterised by the incorporating of functional information technologies, encompassing the IoT, digital health, big data, AI, cloud computing technologies, and information management system (IMS) that enable the monitoring of abnormal events, provision of remote clinical services, establishment of health information databases, enhancement of decision making processes, analysis of clinical data, and facilitation of activities of daily living for older residents [##REF##34979941##10##]. It may integrate medical services from remote hospitals or healthcare experts, using telemedicine, mHealth, and other electronic clinical information, to manage complex health conditions among their residents and ensure their overall well-being within a safe and cost-effective environment [##REF##34979941##10##]. Previous studies have investigated the willingness and associated factors of Chinese older adults to the conventional nursing homes [##UREF##16##22##, ##REF##32075825##23##]. However, there is a lack of studies that have examined the expectations and acceptability of SNHs. It is crucial to thoroughly investigate the perspective of Chinese older adults regarding SNHs. This is necessary to ensure the successful development of innovative geriatric care models that meet the healthcare demands of China’s ageing population and are widely embraced.</p>", "<title>Research questions\n</title>", "<p id=\"Par8\">Drawing upon the defined SNH model [##REF##34979941##10##], the following research inquiries were devised: 1) What factors are important to assess the expectations and acceptability of SNHs, and their psychometric property as a tool? 2) To what extent are Chinese older adults inclined to embrace the evidence-based SNH model? 3) What are the levels of expectation and acceptability exhibited by Chinese older adults towards the SNH model? 4) Is there an association between the sociodemographic characteristics of Chinese older adults and their levels of expectations and acceptability concerning SNHs?</p>" ]

[ "<title>Methods\n</title>", "<p id=\"Par9\">In this study, an exploratory sequential mixed method (Fig. ##FIG##0##1##) was used to answer the research questions. There were no similar instruments or pre-existing questionnaires available to measure the expectations and acceptability towards SNHs. Hence, a newly developed instrument was designed based on the results of a qualitative study to assess the levels of expectation and acceptability of SNHs among Chinese older adults. Subsequently, a survey was conducted in four Chinese cities. The sociodemographic factors associated with expectations and acceptability of SNHs were also explored and examined. Guidelines for conducting and reporting mixed research in the field of counseling and beyond guided results reporting [##UREF##17##24##] (Additional file ##SUPPL##0##1##). In the mixed method approach, qualitative insights were derived from a developed questionnaire assessing the expectations and acceptability. Both quantitative and qualitative data were combined in the final analysis to enhance the depth of findings. The study protocol, a scoping review and the preceding qualitative study have been previously published [##REF##34979941##10##, ##REF##34424931##25##, ##UREF##18##26##].</p>", "<p id=\"Par10\">\n</p>", "<title>Questionnaire development and validation</title>", "<p id=\"Par11\">The questionnaire was developed as a measurement tool building on the conceptual framework (Fig. ##FIG##1##2##) derived from the ‘smart technology adoption behaviors of older consumers theory’ proposed by Golant [##REF##28918822##27##], a scoping review [##REF##34979941##10##], as well as the results of a qualitative study which has been published elsewhere [##UREF##18##26##]. According to the conceptual framework, the adoption of SNH emerges in response to unmet healthcare needs, resulting in unfulfilled expectations among older adults. The decision to embrace SNHs is underpinned by appraisals of information and technology. Older adults’ choices are influenced by their prior experiences with smart technologies and external sources of persuasiveness, including public media, friends, family members, and healthcare professionals (HCPs). The determinants shaping their technology appraisal encompass perceived efficaciousness, positive or negative usability, and the potential collateral damage associated with adopting smart technologies. Simultaneously, attributes specific to older adults, such as their resilience towards smart technologies, are linked to their acceptability of SNHs.</p>", "<p id=\"Par12\">\n</p>", "<p id=\"Par13\">A qualitative case study was conducted using the snowball sampling method to collect data from a total of 34 participants until data saturation was achieved. Of these participants, 28 were older adults aged 60–75, residing in Hainan and Dalian, China, during the winter season. They were selected from six provinces to ensure a diverse representation of older adults. Additionally, six adult children were included in the study to explore their expectations and acceptability of SNHs. Semi-structured in-depth interviews and focus group discussions were conducted for data collection. Data were imported and managed using ATLAS.ti8 software. A framework method [##REF##24047204##28##] was employed using inductive and deductive approaches to analyse the textual data. Furthermore, data were coded and categorised into themes. All items in the new questionnaire were derived from the interviews and previous scoping review through the mentioned analytical strategy. The questionnaire item design incorporated direct quotes from the qualitative data to ensure that the latter survey aligns authentically with the perspectives of the Chinese ageing population. Meanwhile, the concept of SNHs, captured from the scoping review was stated before the questionnaire to assist the respondents in sharing their perspectives on the expectation and acceptability of mart nursing homes. It included an explanation of it as a care model that provide continuous monitoring of its residents through information technologies, connect them with their remote HCPs, and integrate medical resources to satisfy the care needs of older residents. Additionally, information on sociodemographic characteristics, including age, place of residence, gender, health condition, income, type of insurance, educational attainment, number of children, and living partners, was collected from the respondents. Three items were included to measure respondents’ resilience to smart technologies, comprising familiarity with technologies, openness to new technology, and self-efficacy in applying smart technologies [##REF##28918822##27##].</p>", "<p id=\"Par14\">An expert panel, which included two statisticians, two family physicians, one public health physician, one nursing home operator, one business stakeholder, and three older adults, was invited to assess the content validity using the content validity index (CVI) for 49-item of the questionnaire [##UREF##19##29##]. This was done in line with the Consensus-based Standards for the selection of health status Measurement Instruments (COSMIN checklist) guideline [##REF##20053272##30##], which evaluates the relevance, comprehensibility, and comprehensiveness of a newly developed questionnaire. Subsequently, cognitive debriefing was conducted among ten older adults [##UREF##20##31##]. Of those, eight were selected from Dalian and two from Hainan community groups. Considering the diverse characteristics of the intended respondents, three participants with primary school education were recruited, three with junior or high school education, and remaining had university education. The research team organised an online group discussion where they introduced the purpose of the study and explained the concept of SNHs, along with the content of each item in the questionnaire. Participants were instructed to provide insights into their understanding of the questions, any ambiguous terms, and potential areas of confusion. The investigator (ZYY) recorded and clarified the responses for each question. For example, the investigator used a fixed probe to ask the participants, ‘Is this a correct choice that can reflect your response? Can you paraphrase this item in your own words based on your understanding? Can you elaborate on why you chose this answer?’. The frequency of problems encountered for each question would be gathered, such as difficulties in understanding and ambiguity of wording, and adjustments would be made accordingly. One session was carried out with a duration of approximately 2–3 h.</p>", "<p id=\"Par15\">Structural validity was established through exploratory factor analysis (EFA), based on data collected from the survey respondents. The eigenvalue was set above 1, and items with a loading value below 0.40, as well as cross-loadings greater than 0.40 were dropped [##UREF##21##32##]. Subsequently, structural equation modelling (SEM) was utilised to evaluate model fit with the SPSS AMOS software. Internal consistency was assessed using Cronbach’s alpha. A Cronbach’s alpha exceeding 0.70 is considered indicative of good internal consistency for the questionnaire [##UREF##22##33##].</p>", "<p id=\"Par16\">Construct validity (hypothesis-testing) was assessed by comparing responses towards the expectations and acceptability of SNHs with a single item regarding willingness to move to a nursing home (Yes or No) [##UREF##16##22##]. The expectations and acceptability scores were categorised into tertiles. The hypothesis posited that the highest tertile of expectations would exhibit an association with the willingness to move to a nursing home as evidenced by an a priori odds ratio of at least 2.0, while the highest tertile of acceptability would be linked to the willingness to move to a nursing home, reflecting a priori odds ratio of at least 3.0 [##REF##30509522##34##]. It was also hypothesised that expectations and acceptability would be positively correlated, with a correlation coefficient <italic>r</italic> value of &gt; 0.4.</p>", "<p id=\"Par17\">A one-month intra-rater test–retest was performed among participants who answered and returned the second completed questionnaire. The participants were recruited from those who had the willingness to participate in the test–retest and provided their telephone numbers when they answered the questionnaire for the first time.</p>", "<title>Quantitative study (survey)</title>", "<title>Study setting\n</title>", "<p id=\"Par18\">Quantitative data using surveys were collected in four major cities namely Xi’an, Nanjing, Shenyang, and Xiamen, representing the west, east, north, and south of China. In Xi’an, Nanjing, and Shenyang, the estimated older population comprises 18%, 22%, and 26%, respectively [##UREF##23##35##–##UREF##25##37##]. Meanwhile, the government of Xiamen has actively promoted smart healthcare initiatives to assist older adults in their activities of daily living [##UREF##26##38##].</p>", "<title>Participants and sample size estimation</title>", "<p id=\"Par19\">The selected older adults were within the age range of 60–75 years. Individuals residing in nursing homes, receiving palliative care, or experiencing cognitive impairment were excluded. Sample size calculation was conducted using PASS software. Based on an expected 10% level of acceptance of nursing homes among Chinese older adults [##UREF##16##22##], a 95% confidence level with a two-sided and 5% margin of error, the minimum required sample size was 139. However, for this study, a target sample size of 300 was set with inflation for non-response and incompletion rates. The data was collected from older adults who usually gather in public parks for group activities, such as morning or post-dinner exercise.</p>", "<title>Data collection</title>", "<p id=\"Par20\">A stratified random sampling method was used to identify participants. Eight enumerators (two in each city) recruited participants and asked them to suggest the ten most popular parks or communities where local older adults participate in physical activities. Subsequently, they recruited older adults from randomly selected public parks or community centres. In China, older adults typically visit public parks for collective activities, such as physical exercise and morning routines, or post-dinner dancing. Different age groups can be easily identified by the types of activities they engage in. For example, older adults aged 60–70 years usually join dancing groups, while older individuals prefer playing chess or engaging in conversations with others. Additionally, respondents were encouraged to provide their telephone numbers to enhance research credibility and facilitate participant recruitment for the intra-rater test-retest. During data collection, enumerators explained the concept of SNHs, which was stated on the questionnaire and checked the completeness of the questionnaires when all respondents returned them.</p>", "<title>Data analysis</title>", "<p id=\"Par21\">The IBM Statistical Package for Social Sciences (SPSS 26) software was used for data management and analysis. Qualitative variables were presented as frequencies and percentages. The expectations and acceptability of SNHs were categorised into tertiles.Chi-square tests were used to examine the associations among the sociodemographic factors, expectations and acceptability of SNHs, and the willingness to move to a nursing home. Multiple logistic regression models were utilised to analyse the association between the independent variables, including sociodemographic characteristics and older adults’ resilience to smart technologies, on expectations and acceptability of SNHs. Variables from the univariable regression analysis with a <italic>p</italic>-value &lt; 0.20 in the expectation and acceptability domains were included in the multinomial logistic regression analysis. In all analyses, the significance level was set at 0.05. Statistical strategies to multicollinearity, data normality, and assumptions of the final model were checked.</p>" ]

[ "<title>Results</title>", "<title>Questionnaire development and validation</title>", "<p id=\"Par22\">The initial version of the questionnaire was crafted by synthesising qualitative data obtained from a scoping review and qualitative case study using both deductive and inductive analysis approaches, incorporating themes, codes, and subcodes [##REF##34979941##10##, ##UREF##18##26##] (Additional file ##SUPPL##1##2##, A2-1). It comprised 24 items for the expectation domain, and 25 items pertaining to the acceptability domain. Among the 24 items in the expectation domain, five codes (subdomains) were identified from the qualitative phase. The subdomains are ‘quality of care supported by governments and societies’ with five items; ‘smart technology applications’ with seven items; ‘presence of a skilled HCP team’ with three items; ‘access and scope of basic medical services’ with six items; and ‘integration of medical services’ with two items. In the 25-item acceptability domain, six codes (subdomains) were identified, which encompass ‘perceived efficaciousness’ of SNHs with four items; ‘perceived positive usability’ with nine items; ‘perceived negative usability’ with two items; ‘perceived collateral damages’ with four items; ‘persuasiveness of external information’ with four items; and ‘persuasiveness of internal information’ with two items. Each item was measured on a 5-point Likert scale, where a response of 1 indicated the lowest levels of expectations or acceptability of SNHs, while a response of 5 indicated the highest levels of expectations or acceptability.</p>", "<p id=\"Par23\">The CVI scores for relevance, comprehensibility, and comprehensiveness were 0.97, 0.96, and 0.95, respectively (Additional file ##SUPPL##1##2##, A2-2). These results were considered highly valuable [##UREF##19##29##]. The second version of the questionnaire had been reduced to 40 items from the initial 49 items (Additional file ##SUPPL##1##2##, A2-3) and named the Expectation and Acceptability of Smart Nursing Homes questionnaire (EASNH-Q). The item on willingness to move to a nursing home was moved to the sociodemographic characteristics section and all items were renumbered. All participants in the cognitive debriefing agreed with the item description and scale design for these 40 items without any problems. After undergoing the process of face and content validity, structural validity, internal consistency tests, one-month intra-rater test–retest, and construct validity were conducted using the data obtained from the latter survey among 264 respondents.</p>", "<p id=\"Par24\">EFA identified three subdomains (three factors) for the underlying structure of expectations and these three factors were renamed as nursing care, medical services, and government and social support in relation to the service categories. EFA also identified three subdomains (three factors) for the acceptability structure and the three factors were categorised as perceived usability, perceived efficaciousness, and perceived collateral damages and negative usability (Additional file ##SUPPL##1##2##, A2-4). In confirmatory factor analysis (CFA), single-factor models indicated the presence of 24 remaining items. Of which, 10 items in the expectation domain and 14 items in the acceptability domain were considered adequate (Table ##TAB##0##1##; Fig. ##FIG##2##3##) (Additional file ##SUPPL##1##2##, A2-5). Cronbach’s alpha was 0.87 in the expectation domain, and it was 0.92 in the acceptability domain.</p>", "<p id=\"Par25\">\n</p>", "<p id=\"Par26\">\n</p>", "<p id=\"Par27\">Construct validity indicated by the strong correlation between the expectations and acceptability of SNHs Pearson’s coefficient of 0.85 (<italic>p</italic>&lt; 0.01). Among the 264 respondents, 84 (31.8%) were unwilling to move to nursing homes, while 180 (68.2%) expressed a willingness to move (Table ##TAB##1##2##). Type of insurance, education, the degree of familiarity with technology, openness to technology, and self-efficacy in applying smart technologies were significantly associated with the willingness to move to nursing homes. The binary logistic regression analysis for expectations and acceptability in relation to the willingness to move to nursing homes presented that the odds of older adults in the higher tertiles of expectations for SNHs towards moving to nursing homes were higher compared to those with the lowest tertile scores (OR of 1.99, 95% CI 1.01–3.93 for the middle tertile and OR of 3.02, 95% CI: 1.18–7.73 for the highest tertile) (Table ##TAB##2##3##). Similarly, the odds of older adults with the higher tertiles of acceptability for SNHs towards moving to nursing homes were higher compared to those with the lowest tertile scores. (OR of 2.36, 95% CI 1.13–4.91 for the middle terile and OR of 2.43, 95% CI: 1.11–5.39 for the highest tertile).</p>", "<p id=\"Par28\">\n</p>", "<p id=\"Par29\">\n</p>", "<p id=\"Par30\">In the test-retest reliability analysis, 52 participants (13 in each city) answered and returned the second completed EASNH-Q. More than half of them were women, the majority were aged 60–70. Five did not have a pension, two had no insurance, four had a primary school education, six had three or more children, and four lived alone without partners (Additional file ##SUPPL##1##2##, A2-6). The intraclass correlation coefficients (ICC) values for expectation and acceptability factors were 0.90 and 0.81, respectively (Additional file ##SUPPL##1##2##, A2-7).</p>", "<title>Quantitative study (survey)\n</title>", "<p id=\"Par31\">In total, 264 respondents completed the questionnaires, resulting in a response rate of 70%. The demographic characteristics of the respondents are presented in Table ##TAB##3##4##. The number of respondents in each age group (60–64 years old, 65–70 years old, and 71–75 years old) was similar. Among these respondents, over 60% reported having one or more chronic diseases. More than 90% had insurance coverage and 68.1% had a high school or university education. In addition, 56.8% had one child and only 9% lived alone. Approximately one-quarter (24.2%) of the respondents were familiar with technology, 71.2% had openness to technologies, and 63.6% had self-efficacy in applying smart technologies. The overall means (SD) for expectations and acceptability were 4.0 (0.60) (Min-Max: 2.0–5.0) and 4.0 (0.60) (Min-Max: 1.6–4.9), respectively. The associations between sociodemographic characteristics and expectations and acceptability of SNHs presented that the younger age, having insurance, a university level of education, openness to technology, and self-efficacy in applying smart technologies were significantly associated with expectations (Table ##TAB##4##5##). Older age, living with partners and children, openness to technology, and self-efficacy in applying smart technologies were significantly associated with acceptability (Table ##TAB##4##5##). Table ##TAB##5##6## displays the comparisons between the highest tertile of the expectation group and the lowest tertile of the expectation group. Older adults with self-efficacy in applying smart technologies were 28 times more likely to have the highest tertile of expectation (OR: 28.02, 95% CI: 5.92-132.66), and those with willingness to move to a nursing home were 3 times more likely to have the highest tertile of expectation (OR: 2.98, 95% CI: 1.06–8.37). Meanwhile, older adults with self-efficacy in applying smart technologies were 14 times more likely to be in the highest tertile of acceptability compared between the highest tertile of the acceptability group and the lowest tertile group (OR: 13.80, 95% CI: 4.33–43.95). The multinomial logistic regression models revealed that 41.7% (Nagelkerke R² = 0.417) and 32.2% (Nagelkerke R² = 0.322) of the variances in the expectation domain and the acceptability domains, respectively.</p>", "<p id=\"Par32\">\n</p>", "<p id=\"Par33\">\n</p>", "<p id=\"Par34\">\n</p>" ]

[ "<title>Discussion</title>", "<p id=\"Par35\">This is the first study in which an instrument was developed to assess the expectations and acceptability of SNHs among mainland Chinese older adults, both in general and in particular. It aims to examine their levels of expectations and acceptability towards SNHs, as well as to determine the sociodemographic factors associated with different categories of expectations and acceptability. The exploratory sequential mixed methods study design integrates various data sources offering strength to confirmatory results [##REF##22167325##39##]. The study began with a qualitative phase, which explored the expectations and acceptability of a SNH model in general, and specifically among Chinese older adults and their family members. The qualitative phase mapped the knowledge bases for the development and validation of a 24-item EASNH-Q [##REF##24279835##40##], and continued with a cross-sectional study in four major cities in China involving 264 respondents. Data integration was achieved through a data-building approach, in which the results from the qualitative phase and the survey were analysed and compared to understand complex phenomena, measure changes, and examine the hypothesis [##UREF##17##24##, ##REF##24279835##40##]. The results from both qualitative and quantitative phases aligned with study design principles, variables exploration and analysis, and data interpretation. Many concordant findings, rather than discordant ones, were noted between the two phases. The former phase indicated the highest acceptance of moving to nursing homes as an alternative and a high level of agreeableness with external information persuasiveness for receiving healthcare benefits, such as media. A few discordant results in the later phase were related to a lower acceptance of moving to a nursing home and the family-oriented culture in healthcare decision-making as the trustworthy persuasiveness. Additionally, three items were generated from the emerging codes during the scoping review and content validity, including SNHs can provide better services to improve healthcare accessibility and availability, the preference of “human-centric” designs for the smart devices, and hospice care, were highly expected by the participants (Additional file ##SUPPL##2##3##).</p>", "<p id=\"Par36\">In China, many similar questionnaires commonly focus on older adults’ willingness to move to conventional nursing homes. Two of these studies had larger samples, with 670 and 1003 Chinese older adults [##UREF##16##22##, ##REF##32075825##23##], and more than half of their respondents were in aged 60–70, very similar to the main sample of this study. Additionally, more than half of the other studies’ respondents had a primary school education or lower in contrast to this study that had &lt; 10%. In one study [##UREF##16##22##], data from an urban community showed that half of the respondents had a higher economic status which is similar to the respondents in this study (monthly pension: 1000–4000 CNY, $138–555). Regarding the proportion of willingness to move to a nursing home among Chinese older adults, this study had a higher acceptance rate (68.2%) compared to the other two previous studies (45.4–11.9%) [##UREF##16##22##, ##REF##32075825##23##]. The higher acceptance rate reflects the increased demand for moving to a nursing home, particularly when older adults consider their disabilities [##UREF##27##41##]. It has been reported that older adults may choose to transition from home-based care to nursing homes with intensive supervision and more professional services due to the decline in bodily functions and the obstacles faced by family members who are unable to devote themselves to necessary or additional care [##UREF##28##42##]. As an alternative, nursing homes can provide 24-hour formal care and some medical services for older adults who require daily assistance and have complex health demands [##REF##25704126##43##]. Moreover, the purpose of developing the EASNH-Q was to explore the expectations and acceptability of SNHs, making it a novel contribution. The item design of the EASNH-Q demonstrated good levels of relevance, comprehensibility, and comprehensiveness in assessing the expectations and acceptability of SNHs [##REF##29942800##44##–##UREF##30##46##].</p>", "<p id=\"Par37\">The expectations and acceptability of SNHs were explored among Chinese older adults who were interviewed in the qualitative phase. These expectations and acceptability were examined through a survey in the subsequent quantitative phase, providing empirical evidence of high levels. The survey sites selected from four different regions of mainland China represent the major group of the Chinese ageing population according to their family structures, health status, long-term care needs, and insurance schemes [##UREF##31##47##]. There were small variances in different cities when respondents answered the EASNH-Q (effect size: 0.34 − 0.32) (Additional file ##SUPPL##3##4##). The results showed that expectations were highly correlated with the acceptability of SNHs. Older adults from Nanjing, in the east of China, had the highest expectations of SNHs, and they also had the highest acceptability of SNHs. In contrast, older adults from Xiamen, in the south of China, had the lowest expectations and the lowest acceptability. These geographic differences among older adults may be attributed to their sociodemographic characteristics. For example, urban older adults living in environments more sustainable for an ageing population, with fewer children, higher income, and higher education have a better acceptability of nursing homes than those in rural areas who have more children, limited income, and lower education [##REF##28880887##48##, ##REF##35440452##49##].</p>", "<p id=\"Par38\">In addition, the in-depth analysis of the response distribution for each item revealed that most of the questions had a ceiling effect (&gt; 15%), except item for Q11, ‘persuasiveness of public media increases the acceptability of SNHs’ (3.8%). This reflects the report of Chinese older adults’ social network type to receive healthcare benefits, indicating that the media has less impact on appraising their health [##REF##29554546##50##]. Meanwhile, the floor effect of each item was small (&lt; 15%). The assessments of ceiling and floor effect indicate the ability of a questionnaire to distinguish among respondents at the extreme ends of the scale [##REF##17161752##51##]. High ceiling effects, as observed in many of the items, may suggest a limited instrument range, measurement inaccuracy, or response bias [##UREF##32##52##]. However, no previous research has reported on the ceiling and floor effects on the expectations and acceptability of nursing homes in China. Nevertheless, the high ceiling and floor effects reflected and examined the results from the qualitative phase that all participants had a positive attitude towards SNHs [##UREF##18##26##].</p>", "<p id=\"Par39\">It is believed that IoT, big data, and internet networks can provide quality services [##REF##33149759##53##]. This belief was reflected in the responses to items Q1-5, particularly in real-time monitoring, disease prediction, electronic health records, and customised services. It is important to note that technology is not the primary reason for people deciding to move to nursing homes. Instead, technology acts as an assistant to the functions and care practices provided in nursing homes [##REF##35355808##54##]. In China, more than half of older adults wish for nursing homes to provide medical services at a hospital level [##UREF##16##22##]. This study observed that many respondents had high expectations for collaboration between hospitals and SNHs to integrate medical services with remote hospitals. Moreover, Chinese older adults expected medical staff to be available at conventional nursing homes, as many nursing home residents are moderately dependent and at risk of fatal diseases [##UREF##16##22##, ##UREF##33##55##]. There were also high expectations of having trained caregivers, such as nurses and doctors in SNHs. Additionally, more than half of the respondents had high expectations of hospice care in SNHs because it is an essential part of all healthcare systems. This might be due to the general perception of the limited services and lack of accessibility of hospice care in the current nursing homes. For example, only 30.8% of nursing homes in Hebei province provided hospice care services [##UREF##34##56##].</p>", "<p id=\"Par40\">Chinese older adults are influenced by the family-oriented culture when it comes to receiving and appraising information about their health [##REF##29554546##50##]. The results were indicative of the same path that trustworthy health-related resources were typically found within family members, doctors, friends, and public media, as well as influenced by personal demands. Respondents showed a high acceptability of SNHs when they perceived the benefits and efficaciousness of using smart technologies. This perceived efficaciousness of technology generally involves a comparison between two options and the benefits received, such as comparing the quality of care and cost-effectiveness in SNHs versus conventional ones [##REF##28918822##27##, ##REF##30509522##34##]. Moreover, it has been commonly reported in previous studies that many older adults had negative attitudes towards adopting smart technologies due to the additional cost or the need to purchase expensive devices [##UREF##8##14##, ##UREF##35##57##, ##UREF##36##58##]. However, the high scores of items Q19-22 in the EASNH-Q confirmed that certain features of SNHs could increase older adults’ positive attitude and their consideration of adopting smart technologies. These features include the perceived necessity for health, ease of use, user-friendliness, convenience, and the “human-centric” design of smart solutions.</p>", "<p id=\"Par41\">The final adjusted multivariable analysis showed that only self-efficacy among three items for testing the older adults’ resilience to smart technologies, including familiarity with technology and openness to technology [##REF##28918822##27##], was more likely to influence the information and technology appraisals among Chinese older adults. The direct users of smart technologies designed and applied in nursing home settings have been revealed through the previous scoping review [##REF##34979941##10##]. These users are nursing home residents (81%) and their HCPs (19%), such as nursing home staff and doctors in remote hospitals. Self-efficacy refers to an individual’s belief in their ability to successfully use smart technologies and older adults with self-efficacy in applying smart technologies may increase their willingness to adopt new solutions [##UREF##37##59##].</p>", "<p id=\"Par42\">For other sociodemographic factors, such as age, income, and educational attainment, were not found to be significantly associated with the different categories of expectations and acceptability towards SNHs among Chinese older adults. These factors were previously reported in other studies to be directly associated with Chinese older adults’ willingness to move to a nursing home [##UREF##16##22##, ##REF##32075825##23##], and the willingness to move to a nursing home was examined to be significantly associated with the highest tertile of expectations in this study.</p>", "<p id=\"Par43\">This study employed several strategies to ensure research accuracy and credibility. Firstly, semi-structured, in-depth interviews, focus group discussions, and member checking were used for data collection in the qualitative study phase to ensure study credibility. A team of five investigators participated in data auditing, analysis, and coding discussions to authenticate the findings, ensuring the reliability of the study. In the quantitative phase, the survey sites chosen for data collection were selected to represent the west, east, north, and south of China. Eight onsite enumerators underwent training and were provided with a detailed study procedure to standardise the recruitment of participants and improve data quality. Data accuracy was cross-checked by the research team. However, this study has some potential limitations. Firstly, the concept of SNHs stated on the EASNH-Q was developed based on the informative literature, of which, most of the study population were from middle-income and high-income countries that may not be applicable to resource-challenged or low-income countries, as well as countries with limited internet access. Secondly, selection biases might have occurred, with qualitative study participants being Chinese older adults who were flown into Hainan and Dalian during the winter season, and quantitative study respondents coming from the four major cities [##UREF##18##26##]. This approach might not have captured all the essential factors necessary to measure the expectations and acceptability of SNHs among the entire Chinese ageing population, including other regions and rural areas in China, taking into consideration their multimorbidity and cultural differences. The findings should be generalised with caution to older adults residing in rural areas as they may have a lower acceptance of moving to a nursing home [##UREF##16##22##]. Moreover, the survey respondents in this study were selected among outdoors and able older adults, potentially missing specific groups of older people with limited mobility, economic disadvantages, or those who fall ill at home but still intend to move to nursing homes. In addition, the participants may find it difficult to answer the questions related to the acceptability of SNHs as a whole due to the non-existence of a SNH to refer to or a lack of experience using smart technologies for healthcare.</p>" ]

[ "<title>Conclusion\n</title>", "<p id=\"Par44\">The significance of this study lies in the exploration of the expectations and acceptability of SNHs among Chinese older adults, through both qualitative and quantitative evidence leading to the 24-item EASNH-Q that demonstrated commendable validity, reliability, and stability. The rigorous development process establishes it as a reliable tool for measuring the levels of expectations and acceptability of SNHs. Self-efficacy in applying smart technologies links to the high expectations and acceptability of SNHs. The willingness to relocate to a nursing home increases the high expectations of SNHs.</p>", "<p id=\"Par45\">A feasible SNH model presents a promising solution for addressing the challenges posed by the rapidly ageing society in China. The study results hold relevance for a wide range of stakeholders and audience with an interest in SNHs, including older adults, their family members, healthcare providers, nursing home personnel, policy-makers, and entrepreneurs in the smart device industry. Furthermore, the potential applicability of these findings extends beyond China, encompassing both developed and developing nations. Subsequent research efforts should aim to quantify the expectations and acceptability of SNHs within a larger and more diverse Chinese population considering various societal strata and potentially different countries. Gaining insights from a more extensive population base will enable a more comprehensive assessment of the determinants influencing expectations and acceptability of SNHs. This, in turn, will contribute to the development of a more effective SNH model that aligns with local settings and stakeholders’ requirements.</p>" ]

[ "<title>Background</title>", "<p id=\"Par1\">Smart nursing homes (SNHs) integrate advanced technologies, including IoT, digital health, big data, AI, and cloud computing to optimise remote clinical services, monitor abnormal events, enhance decision-making, and support daily activities for older residents, ensuring overall well-being in a safe and cost-effective environment. This study developed and validated a 24-item Expectation and Acceptability of Smart Nursing Homes Questionnaire (EASNH-Q), and examined the levels of expectations and acceptability of SNHs and associated factors among older adults in China.</p>", "<title>Methods\n</title>", "<p id=\"Par2\">This was an exploratory sequential mixed methods study, where the qualitative case study was conducted in Hainan and Dalian, while the survey was conducted in Xi’an, Nanjing, Shenyang, and Xiamen. The validation of EASNH-Q also included exploratory and confirmatory factor analyses. Multinomial logistic regression analysis was used to estimate the determinants of expectations and acceptability of SNHs.</p>", "<title>Results</title>", "<p id=\"Par3\">The newly developed EASNH-Q uses a Likert Scale ranging from 1 (strongly disagree) to 5 (strongly agree), and underwent validation and refinement from 49 items to the final 24 items. The content validity indices for relevance, comprehensibility, and comprehensiveness were all above 0.95. The expectations and acceptability of SNHs exhibited a strong correlation (<italic>r =</italic> 0.85, <italic>p &lt;</italic> 0.01<italic>)</italic>, and good test-retest reliability for expectation (0.90) and acceptability (0.81). The highest tertile of expectations (X^{<italic>2</italic>}<italic>=</italic>28.89, <italic>p</italic> &lt; 0.001) and acceptability (X^{<italic>2</italic>}<italic>=</italic>25.64, <italic>p</italic> &lt; 0.001) towards SNHs were significantly associated with the willingness to relocate to such facilities. Older adults with self-efficacy in applying smart technologies (OR: 28.0) and those expressing a willingness to move to a nursing home (OR: 3.0) were more likely to have the highest tertile of expectations compared to those in the lowest tertile. Similarly, older adults with self-efficacy in applying smart technologies were more likely to be in the highest tertile of acceptability of SNHs (OR: 13.8).</p>", "<title>Conclusions</title>", "<p id=\"Par4\">EASNH-Q demonstrated commendable validity, reliability, and stability. The majority of Chinese older adults have high expectations for and accept SNHs. Self-efficacy in applying smart technologies and willingness to relocate to a nursing home associated with high expectations and acceptability of SNHs.</p>", "<title>Supplementary Information</title>", "<p>The online version contains supplementary material available at 10.1186/s12912-023-01676-0.</p>", "<title>Keywords</title>" ]

[ "<title>Supplementary Information</title>", "<p>\n</p>", "<p>\n</p>", "<p>\n</p>", "<p>\n</p>" ]

[ "<title>Acknowledgements</title>", "<p>Not applicable.</p>", "<title>Authors’ contributions</title>", "<p>ZYY formulated and assumed overall responsibility for the study’s conduct. FKR, SGS and BHC participated in the research’s design phase. SJ engaged in both qualitative data collection and statistical analysis. FZR, an expert in gerontechnology, served as one of the investigators contributing to the evaluation and appraisal of the technical aspects. KC oversaw the statistical analysis, while SGS and BHC validated the study’s qualitative and quantitative data, methodological design, and provided supervision throughout the research process. All authors have made significant intellectual contributions to the study’s development and have granted their approval for the final manuscript’s submission to the journal.</p>", "<title>Funding</title>", "<p>The author(s) received no financial support for the research, authorship, and publication of this article.</p>", "<title>Availability of data and materials</title>", "<p>The dataset supporting the results and conclusions of this article is included within the article and its additional files.</p>", "<title>Declarations</title>", "<title>Ethics approval and consent to participates</title>", "<p id=\"Par46\">Ethical approvals for this study have been obtained from the Ethics Committee for Research Involving Human Subjects, Universiti Putra Malaysia, Malaysia (UPM/TNCPI/RMC/JKEUPM/1.4.18.2, 28/11/2020) and Hainan Medical University, China (IYLIJ-2020-021, 03/09/2020). The respondent’s Information Sheet was provided, and Informed Consent Form completed before participation in this study. All methods were performed in accordance with the Declaration of Helsinki and other relevant guidelines and regulations.</p>", "<title>Consent for publication</title>", "<p id=\"Par47\">Not applicable.</p>", "<title>Competing interests</title>", "<p id=\"Par48\">The authors declare no competing interests.</p>" ]

[ "<fig id=\"Fig1\"><label>Fig. 1</label><caption><p>Exploratory sequential mixed methods study design</p></caption></fig>", "<fig id=\"Fig2\"><label>Fig. 2</label><caption><p>The coping process of Chinese older adults towards smart solutions in nursing homes</p></caption></fig>", "<fig id=\"Fig3\"><label>Fig. 3</label><caption><p>The assessment of model fit using the structural equation modelling (SEM)</p></caption></fig>" ]

[ "<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Results from confirmatory factor analyses</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Domains</th><th align=\"left\">Model 1</th><th align=\"left\">Χ²\n</th><th align=\"left\">Df^b\n</th><th align=\"left\">CMIN/DF^c\n</th><th align=\"left\">CFI^d\n</th><th align=\"left\">RMSEA^e\n<break/>(90% CI)</th><th align=\"left\">SRMR^f\n</th></tr></thead><tbody><tr><td align=\"left\" rowspan=\"3\">Expectations</td><td align=\"left\"><p>Three-factor model</p><p>(15 items)</p></td><td align=\"left\">186.232*^a\n</td><td align=\"left\">87</td><td align=\"left\">2.14</td><td align=\"left\">0.92</td><td align=\"left\"><p>0.07</p><p>(0.05–0.08)</p></td><td align=\"left\">0.04</td></tr><tr><td align=\"left\"><p>Two-factor model</p><p>(10 items)</p></td><td align=\"left\">82.902*</td><td align=\"left\">34</td><td align=\"left\">2,44</td><td align=\"left\">0.95</td><td align=\"left\"><p>0.07</p><p>(0.05–0.09)</p></td><td align=\"left\">0.04</td></tr><tr><td align=\"left\"><p>One-factor model</p><p>(10 items)</p></td><td align=\"left\">67.947*</td><td align=\"left\">34</td><td align=\"left\">2.00</td><td align=\"left\">0.96</td><td align=\"left\"><p>0.06</p><p>(0.04–0.08)</p></td><td align=\"left\">0.03</td></tr><tr><td align=\"left\" rowspan=\"3\">Acceptability</td><td align=\"left\"><p>Three-factor model</p><p>(21 items)</p></td><td align=\"left\">490.235</td><td align=\"left\">186</td><td align=\"left\">2.64</td><td align=\"left\">0.87</td><td align=\"left\"><p>0.08</p><p>(0.07–0.09)</p></td><td align=\"left\">0.05</td></tr><tr><td align=\"left\"><p>Two-factor model</p><p>(11items)</p></td><td align=\"left\">109.034*</td><td align=\"left\">41</td><td align=\"left\">2.66</td><td align=\"left\">0.95</td><td align=\"left\"><p>0.08</p><p>(0.06-0,10)</p></td><td align=\"left\">0.04</td></tr><tr><td align=\"left\"><p>One-factor model</p><p>(14 items)</p></td><td align=\"left\">185.450*</td><td align=\"left\">74</td><td align=\"left\">2.51</td><td align=\"left\">0.94</td><td align=\"left\"><p>0.08</p><p>(0.06–0.09)</p></td><td align=\"left\">0.05</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab2\"><label>Table 2</label><caption><p>The Association between the socioeconomic characteristics and the willingness to move to a nursing home (<italic>n</italic> = 264)</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Variable</th><th align=\"left\">Total<break/><italic>n</italic> (%)</th><th align=\"left\">No willingness to move to a NH^a, n (%)</th><th align=\"left\">Having willingness to move to a NH, n (%)</th><th align=\"left\"><italic>X</italic>^{<italic>2</italic>}<break/>(<italic>P</italic> value)</th></tr></thead><tbody><tr><td align=\"left\">Total</td><td align=\"left\">264 (100.0)</td><td align=\"left\">84 (31.8)</td><td align=\"left\">180 (68.2)</td><td align=\"left\"/></tr><tr><td align=\"left\" colspan=\"5\"><bold>Expectations of smart NHs, 10 items (mean: 4.0, mean range: 2.0–5.0)</bold></td></tr><tr><td align=\"left\"> Lowest tertile (≤ 3.90)</td><td align=\"left\">93 (35.2)</td><td align=\"left\">47 (50.5)</td><td align=\"left\">46 (49.5)</td><td align=\"left\" rowspan=\"3\"><p>28.89</p><p>&lt; 0.001</p></td></tr><tr><td align=\"left\"> Middle tertile (3.91–4.40)</td><td align=\"left\">101 (38.3)</td><td align=\"left\">29 (28.7)</td><td align=\"left\">72 (71.3)</td></tr><tr><td align=\"left\"> Highest tertile (≥ 4.41)</td><td align=\"left\">70 (26.5)</td><td align=\"left\">8 (11.4)</td><td align=\"left\">62 (88.6)</td></tr><tr><td align=\"left\" colspan=\"5\"><bold>Acceptability of smart NHs, 14 items (mean: 4.0, mean range: 1.6–4.9)</bold></td></tr><tr><td align=\"left\"> Lowest tertile (≤ 3.93)</td><td align=\"left\">94 (35.6)</td><td align=\"left\">48 (51.1)</td><td align=\"left\">46 (48.9)</td><td align=\"left\" rowspan=\"3\"><p>25.65</p><p>&lt; 0.001</p></td></tr><tr><td align=\"left\"> Middle tertile (3.94–4.29)</td><td align=\"left\">87 (33.0)</td><td align=\"left\">21 (24.1)</td><td align=\"left\">66 (75.9)</td></tr><tr><td align=\"left\"> Highest tertile (≥ 4.30)</td><td align=\"left\">83 (31.4)</td><td align=\"left\">15 (18.1)</td><td align=\"left\">68 (81.9)</td></tr><tr><td align=\"left\" colspan=\"5\"><bold>Age</bold></td></tr><tr><td align=\"left\"> 60–64 yo</td><td align=\"left\">88 (33.3)</td><td align=\"left\">24 (27.3)</td><td align=\"left\">64 (72.7)</td><td align=\"left\" rowspan=\"3\"><p>2.20</p><p>0.333</p></td></tr><tr><td align=\"left\"> 65–70 yo</td><td align=\"left\">88 (33.3)</td><td align=\"left\">33 (37.5)</td><td align=\"left\">55 (62.5)</td></tr><tr><td align=\"left\"> 71–75 yo</td><td align=\"left\">88 (33.3)</td><td align=\"left\">27 (30.7)</td><td align=\"left\">61 (69.3)</td></tr><tr><td align=\"left\" colspan=\"5\"><bold>Gender</bold></td></tr><tr><td align=\"left\"> Male</td><td align=\"left\">130 (49.2)</td><td align=\"left\">40 (30.8)</td><td align=\"left\">90 (69.2)</td><td align=\"left\" rowspan=\"2\"><p>0.13</p><p>0.719</p></td></tr><tr><td align=\"left\"> Female</td><td align=\"left\">134 (50.8)</td><td align=\"left\">44 (32.8)</td><td align=\"left\">90 (67.2)</td></tr><tr><td align=\"left\" colspan=\"5\"><bold>Health status</bold></td></tr><tr><td align=\"left\"> Healthy</td><td align=\"left\">98 (37.1)</td><td align=\"left\">36 (36.7)</td><td align=\"left\">62 (63.3)</td><td align=\"left\" rowspan=\"3\"><p>1.74</p><p>0.419</p></td></tr><tr><td align=\"left\"> One chronic disease</td><td align=\"left\">101 (38.3)</td><td align=\"left\">29 (28.7)</td><td align=\"left\">72 (71.3)</td></tr><tr><td align=\"left\"> Two or more chronic diseases</td><td align=\"left\">65 (24.6)</td><td align=\"left\">19 (29.2)</td><td align=\"left\">46 (70.8)</td></tr><tr><td align=\"left\" colspan=\"5\"><bold>Income per month</bold></td></tr><tr><td align=\"left\"> No pension</td><td align=\"left\">30 (11.4)</td><td align=\"left\">11 (36.7)</td><td align=\"left\">19 (63.3)</td><td align=\"left\" rowspan=\"4\"><p>2.81</p><p>0.421</p></td></tr><tr><td align=\"left\"> 1000–2000 CNY</td><td align=\"left\">59 (22.3)</td><td align=\"left\">14 (23.7)</td><td align=\"left\">45 (76.3)</td></tr><tr><td align=\"left\"> 2000–4000 CNY</td><td align=\"left\">89 (33.7)</td><td align=\"left\">32 (36.0)</td><td align=\"left\">57 (64.0)</td></tr><tr><td align=\"left\"> More than 4000 CNY</td><td align=\"left\">86 (32.6)</td><td align=\"left\">27 (31.4)</td><td align=\"left\">59 (68.6)</td></tr><tr><td align=\"left\" colspan=\"5\"><bold>Type of insurance</bold></td></tr><tr><td align=\"left\"> No insurance or with NRCMI^b</td><td align=\"left\">22 (8.3)</td><td align=\"left\">11 (50.0)</td><td align=\"left\">11 (50.0)</td><td align=\"left\" rowspan=\"4\"><p>14.85</p><p>0.002</p></td></tr><tr><td align=\"left\"> URBMI^c</td><td align=\"left\">30 (11.4)</td><td align=\"left\">3 (10.0)</td><td align=\"left\">27 (90.0)</td></tr><tr><td align=\"left\"> UEBMI^d</td><td align=\"left\">181 (68.6)</td><td align=\"left\">65 (35.9)</td><td align=\"left\">116 (64.1)</td></tr><tr><td align=\"left\"> Other commercial insurance</td><td align=\"left\">31 (11.7)</td><td align=\"left\">5 (16.1)</td><td align=\"left\">26 (83.9)</td></tr><tr><td align=\"left\" colspan=\"5\"><bold>Education</bold></td></tr><tr><td align=\"left\"> Primary school degree or lower</td><td align=\"left\">16 (6.1)</td><td align=\"left\">4 (25.0)</td><td align=\"left\">12 (75.0)</td><td align=\"left\" rowspan=\"4\"><p>8.09</p><p>0.044</p></td></tr><tr><td align=\"left\"> Junior school degree</td><td align=\"left\">68 (25.8)</td><td align=\"left\">19 (27.9)</td><td align=\"left\">49 (72.1)</td></tr><tr><td align=\"left\"> High school degree</td><td align=\"left\">139 (52.6)</td><td align=\"left\">54 (38.8)</td><td align=\"left\">85 (61.2)</td></tr><tr><td align=\"left\"> University degree or higher</td><td align=\"left\">41 (15.5)</td><td align=\"left\">7 (17.1)</td><td align=\"left\">34 (82.9)</td></tr><tr><td align=\"left\" colspan=\"5\"><bold>Number of children</bold></td></tr><tr><td align=\"left\"> 1 Child or no child</td><td align=\"left\">150 (56.8)</td><td align=\"left\">39 (26.0)</td><td align=\"left\">111 (74.0)</td><td align=\"left\" rowspan=\"3\"><p>5.79</p><p>0.055</p></td></tr><tr><td align=\"left\"> 2 Children</td><td align=\"left\">96 (36.4)</td><td align=\"left\">39 (40.6)</td><td align=\"left\">57 (59.4)</td></tr><tr><td align=\"left\"> 3 or more than 3 children</td><td align=\"left\">18 (6.8)</td><td align=\"left\">6 (33.3)</td><td align=\"left\">12 (66.7)</td></tr><tr><td align=\"left\" colspan=\"5\"><bold>Living with whom</bold></td></tr><tr><td align=\"left\"> Alone</td><td align=\"left\">24 (9.1)</td><td align=\"left\">5 (20.8)</td><td align=\"left\">19 (79.2)</td><td align=\"left\" rowspan=\"4\"><p>3.89</p><p>0.274</p></td></tr><tr><td align=\"left\"> With partner or housemaid</td><td align=\"left\">125 (47.3)</td><td align=\"left\">44 (39.8)</td><td align=\"left\">81 (85.2)</td></tr><tr><td align=\"left\"> With child or children</td><td align=\"left\">42 (15.9)</td><td align=\"left\">16 (38.1)</td><td align=\"left\">26 (61.9)</td></tr><tr><td align=\"left\"> With partner and Children</td><td align=\"left\">73 (27.7)</td><td align=\"left\">19 (26.0)</td><td align=\"left\">54 (74.0)</td></tr><tr><td align=\"left\" colspan=\"5\"><bold>Familiarity with technology</bold></td></tr><tr><td align=\"left\"> Not familiar with technology</td><td align=\"left\">54 (20.5)</td><td align=\"left\">34 (63.0)</td><td align=\"left\">20 (37.0)</td><td align=\"left\" rowspan=\"3\"><p>31.44</p><p>&lt; 0.001</p></td></tr><tr><td align=\"left\"> Neutral</td><td align=\"left\">146 (55.3)</td><td align=\"left\">38 (26.0)</td><td align=\"left\">108 (74.0)</td></tr><tr><td align=\"left\"> Familiar with technology</td><td align=\"left\">64 (24.2)</td><td align=\"left\">12 (18.8)</td><td align=\"left\">52 (81.2)</td></tr><tr><td align=\"left\" colspan=\"5\"><bold>Openness to technology</bold></td></tr><tr><td align=\"left\"> No (not open to technology)</td><td align=\"left\">76 (28.8)</td><td align=\"left\">50 (65.8)</td><td align=\"left\">26 (34.2)</td><td align=\"left\" rowspan=\"2\"><p>56.77</p><p>&lt; 0.001</p></td></tr><tr><td align=\"left\"> Yes (open to technology)</td><td align=\"left\">188 (71.2)</td><td align=\"left\">34 (18.1)</td><td align=\"left\">154 (81.9)</td></tr><tr><td align=\"left\" colspan=\"5\"><bold>Self-efficacy in applying smart technologies</bold></td></tr><tr><td align=\"left\"> No</td><td align=\"left\">96 (36.4)</td><td align=\"left\">56 (58.3)</td><td align=\"left\">40 (41.7)</td><td align=\"left\" rowspan=\"2\"><p>48.89</p><p>&lt; 0.001</p></td></tr><tr><td align=\"left\"> Yes</td><td align=\"left\">168 (63.6)</td><td align=\"left\">28 (16.7)</td><td align=\"left\">140 (83.3)</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab3\"><label>Table 3</label><caption><p>The association of the highest tertile expectation and acceptability of smart nursing homes and other determinants of the willingness to move to a nursing home</p></caption><table frame=\"hsides\" rules=\"groups\"><tbody><tr><td align=\"left\">\n<bold>Domain</bold>\n</td><td align=\"left\">\n<bold>Tertiles</bold>\n</td><td align=\"left\">\n<bold>B</bold>\n</td><td align=\"left\">\n<bold>S.E.</bold>\n</td><td align=\"left\">\n<bold>Wald</bold>\n</td><td align=\"left\">\n<bold>Sig.</bold>\n</td><td align=\"left\">\n<bold>OR</bold>\n</td><td align=\"left\">\n<bold>95% C.I.</bold>\n</td></tr><tr><td align=\"left\" rowspan=\"3\">Expectations</td><td align=\"left\">Lowest</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\">6.707</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\">Middle</td><td align=\"left\">0.688</td><td align=\"left\">0.347</td><td align=\"left\">3.932</td><td align=\"left\">0.047</td><td align=\"left\">1.990</td><td align=\"left\">1.008–3.930</td></tr><tr><td align=\"left\">Highest</td><td align=\"left\">1.104</td><td align=\"left\">0.480</td><td align=\"left\">5.297</td><td align=\"left\">0.021</td><td align=\"left\">3.017</td><td align=\"left\">1.178–7.725</td></tr><tr><td align=\"left\">\n<bold>Domain</bold>\n</td><td align=\"left\">\n<bold>Variables</bold>\n</td><td align=\"left\">\n<bold>B</bold>\n</td><td align=\"left\">\n<bold>S.E.</bold>\n</td><td align=\"left\">\n<bold>Wald</bold>\n</td><td align=\"left\">\n<bold>Sig.</bold>\n</td><td align=\"left\">\n<bold>OR</bold>\n</td><td align=\"left\">\n<bold>95% C.I.</bold>\n</td></tr><tr><td align=\"left\" rowspan=\"3\">Acceptability</td><td align=\"left\">Lowest</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\">7.015</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\">Middle</td><td align=\"left\">0.858</td><td align=\"left\">0.374</td><td align=\"left\">5.269</td><td align=\"left\">0.022</td><td align=\"left\">2.359</td><td align=\"left\">1.134–4.909</td></tr><tr><td align=\"left\">Highest</td><td align=\"left\">0.889</td><td align=\"left\">0.406</td><td align=\"left\">4.803</td><td align=\"left\">0.028</td><td align=\"left\">2.433</td><td align=\"left\">1.099–5.391</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab4\"><label>Table 4</label><caption><p>Socioeconomic characteristics of the respondents(<italic>n</italic> = 264)</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\"/><th align=\"left\"><bold>N(%)</bold></th></tr></thead><tbody><tr><td align=\"left\"><bold>Age</bold></td><td align=\"left\"/></tr><tr><td align=\"left\"> 60–64</td><td align=\"left\">88 (33.3)</td></tr><tr><td align=\"left\"> 65–70</td><td align=\"left\">88 (33.3)</td></tr><tr><td align=\"left\"> 71–75</td><td align=\"left\">88 (33.3)</td></tr><tr><td align=\"left\" colspan=\"2\"><bold>Gender</bold></td></tr><tr><td align=\"left\"> Male</td><td align=\"left\">130 (49.2)</td></tr><tr><td align=\"left\"> Female</td><td align=\"left\">134 (50.8)</td></tr><tr><td align=\"left\" colspan=\"2\"><bold>Health status</bold></td></tr><tr><td align=\"left\"> Healthy</td><td align=\"left\">98 (37.1)</td></tr><tr><td align=\"left\"> One chronic disease</td><td align=\"left\">101 (38.3)</td></tr><tr><td align=\"left\"> Two or more chronic diseases</td><td align=\"left\">65 (24.6)</td></tr><tr><td align=\"left\" colspan=\"2\"><bold>Income per month</bold></td></tr><tr><td align=\"left\"> No pension</td><td align=\"left\">30 (11.4)</td></tr><tr><td align=\"left\"> 1000–2000 CNY</td><td align=\"left\">59 (22.3)</td></tr><tr><td align=\"left\"> 2000–4000 CNY</td><td align=\"left\">89 (33.7)</td></tr><tr><td align=\"left\"> More than 4000 CNY</td><td align=\"left\">86 (32.6)</td></tr><tr><td align=\"left\" colspan=\"2\"><bold>Type of insurance</bold></td></tr><tr><td align=\"left\"> No insurance or with NRCMI^b</td><td align=\"left\">22 (8.3)</td></tr><tr><td align=\"left\"> URBMI^c</td><td align=\"left\">30 (11.4)</td></tr><tr><td align=\"left\"> UEBMI^d</td><td align=\"left\">181 (68.6)</td></tr><tr><td align=\"left\"> Other commercial insurance</td><td align=\"left\">31 (11.7)</td></tr><tr><td align=\"left\" colspan=\"2\"><bold>Education</bold></td></tr><tr><td align=\"left\"> Primary school degree or lower</td><td align=\"left\">16 (6.1)</td></tr><tr><td align=\"left\"> Junior school degree</td><td align=\"left\">68 (25.8)</td></tr><tr><td align=\"left\"> High school degree</td><td align=\"left\">139 (52.6)</td></tr><tr><td align=\"left\"> University degree or higher</td><td align=\"left\">41 (15.5)</td></tr><tr><td align=\"left\" colspan=\"2\"><bold>Number of children</bold></td></tr><tr><td align=\"left\"> 1 Child or no child</td><td align=\"left\">150 (56.8)</td></tr><tr><td align=\"left\"> 2 Children</td><td align=\"left\">96 (36.4)</td></tr><tr><td align=\"left\"> 3 or more than 3 children</td><td align=\"left\">18 (6.8)</td></tr><tr><td align=\"left\" colspan=\"2\"><bold>Living with whom</bold></td></tr><tr><td align=\"left\"> Alone</td><td align=\"left\">24 (9.1)</td></tr><tr><td align=\"left\"> With partner or housemaid</td><td align=\"left\">125 (47.3)</td></tr><tr><td align=\"left\"> With child or children</td><td align=\"left\">42 (15.9)</td></tr><tr><td align=\"left\"> With partner and Children</td><td align=\"left\">73 (27.7)</td></tr><tr><td align=\"left\" colspan=\"2\"><bold>Familiarity with technology</bold></td></tr><tr><td align=\"left\"> Not familiar with technology</td><td align=\"left\">54 (20.5)</td></tr><tr><td align=\"left\"> Neutral</td><td align=\"left\">146 (55.3)</td></tr><tr><td align=\"left\"> Familiar with technology</td><td align=\"left\">64 (24.2)</td></tr><tr><td align=\"left\" colspan=\"2\"><bold>Openness to technology</bold></td></tr><tr><td align=\"left\"> No (not open to technology)</td><td align=\"left\">76 (28.8)</td></tr><tr><td align=\"left\"> Yes (open to technology)</td><td align=\"left\">188 (71.2)</td></tr><tr><td align=\"left\" colspan=\"2\"><bold>Self-efficacy in applying smart technologies</bold></td></tr><tr><td align=\"left\"> No</td><td align=\"left\">96 (36.4)</td></tr><tr><td align=\"left\"> Yes</td><td align=\"left\">168 (63.6)</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab5\"><label>Table 5</label><caption><p>Sociodemographic characteristics of respondents according to the different categories of expectations and acceptability of smart nursing homes (<italic>n</italic> = 264)</p></caption><table frame=\"hsides\" rules=\"groups\"><tbody><tr><td align=\"left\">\n<bold>Domain</bold>\n</td><td align=\"left\">\n<bold>Variable</bold>\n</td><td align=\"left\"><p>\n<bold>Total</bold>\n</p><p>\n<bold>n (%)</bold>\n</p></td><td align=\"left\">\n<bold>Lowest tertile of expectations (Mean ≤ 3.90), n (%)</bold>\n</td><td align=\"left\"><p>\n<bold>Middle tertile of expectations (Means: 3.91–4.40),</bold>\n</p><p>\n<bold>n (%)</bold>\n</p></td><td align=\"left\">\n<bold>Highest tertile of expectations (Means ≥ 4.41), n (%)</bold>\n</td><td align=\"left\"><p>\n<bold><italic>X</italic></bold>^{<bold><italic>2</italic></bold>}\n</p><p><bold>(</bold><bold><italic>P</italic></bold> <bold>value)</bold></p></td></tr><tr><td align=\"left\" rowspan=\"49\">Expectations</td><td align=\"left\">Total</td><td align=\"left\">264 (100.0)</td><td align=\"left\">93 (35.2)</td><td align=\"left\">101 (38.3)</td><td align=\"left\">70 (26.5)</td><td align=\"left\"/></tr><tr><td align=\"left\" colspan=\"6\">\n<bold>Willingness to move to a NH</bold>\n^a\n</td></tr><tr><td align=\"left\"> No</td><td align=\"left\">84 (31.8)</td><td align=\"left\">47 (56.0)</td><td align=\"left\">29 (34.5)</td><td align=\"left\">8 (9.5)</td><td align=\"left\" rowspan=\"2\"><p>28.885</p><p>(&lt; 0.001)</p></td></tr><tr><td align=\"left\"> Yes</td><td align=\"left\">180 (68.2)</td><td align=\"left\">46 (25.6)</td><td align=\"left\">72 (40.0)</td><td align=\"left\">62 (34.4)</td></tr><tr><td align=\"left\" colspan=\"6\">\n<bold>Age</bold>\n</td></tr><tr><td align=\"left\"> 60–64</td><td align=\"left\">88 (33.3)</td><td align=\"left\">21 (23.9)</td><td align=\"left\">37 (42.0)</td><td align=\"left\">30 (34.1)</td><td align=\"left\" rowspan=\"3\"><p>14.642</p><p>(0.006)</p></td></tr><tr><td align=\"left\"> 65–70</td><td align=\"left\">88 (33.3)</td><td align=\"left\">44 (50.0)</td><td align=\"left\">28 (31.8)</td><td align=\"left\">16 (18.2)</td></tr><tr><td align=\"left\"> 71–75</td><td align=\"left\">88 (33.3)</td><td align=\"left\">28 (31.8)</td><td align=\"left\">36 (40.9)</td><td align=\"left\">24 (27.3)</td></tr><tr><td align=\"left\" colspan=\"6\">\n<bold>Gender</bold>\n</td></tr><tr><td align=\"left\"> Male</td><td align=\"left\">130 (49.2)</td><td align=\"left\">44 (33.8)</td><td align=\"left\">53 (40.8)</td><td align=\"left\">33 (25.4)</td><td align=\"left\" rowspan=\"2\"><p>0.684</p><p>(0.710)</p></td></tr><tr><td align=\"left\"> Female</td><td align=\"left\">134 (50.8)</td><td align=\"left\">49 (36.6)</td><td align=\"left\">48 (35.8)</td><td align=\"left\">37 (27.6)</td></tr><tr><td align=\"left\" colspan=\"6\">\n<bold>Health status</bold>\n</td></tr><tr><td align=\"left\"> Healthy</td><td align=\"left\">98 (37.1)</td><td align=\"left\">43 (43.9)</td><td align=\"left\">33 (33.7)</td><td align=\"left\">22 (22.4)</td><td align=\"left\" rowspan=\"3\"><p>5.446</p><p>(0.245)</p></td></tr><tr><td align=\"left\"> One chronic disease</td><td align=\"left\">101 (38.3)</td><td align=\"left\">32 (31.7)</td><td align=\"left\">40 (39.6)</td><td align=\"left\">29 (28.7)</td></tr><tr><td align=\"left\"> Two or more chronic diseases</td><td align=\"left\">65 (24.6)</td><td align=\"left\">18 (27.7)</td><td align=\"left\">28 (43.1)</td><td align=\"left\">19 (29.2)</td></tr><tr><td align=\"left\" colspan=\"6\">\n<bold>Income per month</bold>\n</td></tr><tr><td align=\"left\"> No pension</td><td align=\"left\">30 (11.4)</td><td align=\"left\">13 (43.3)</td><td align=\"left\">9 (30.0)</td><td align=\"left\">8 (26.7)</td><td align=\"left\" rowspan=\"4\"><p>4.882</p><p>(0.559)</p></td></tr><tr><td align=\"left\"> 1000–2000 CNY</td><td align=\"left\">59 (22.3)</td><td align=\"left\">17 (43.3)</td><td align=\"left\">24 (30.0)</td><td align=\"left\">18 (26.7)</td></tr><tr><td align=\"left\"> 2000–4000 CNY</td><td align=\"left\">89 (33.7)</td><td align=\"left\">37 (41.6)</td><td align=\"left\">32 (36.0)</td><td align=\"left\">20 (36.0)</td></tr><tr><td align=\"left\"> More than 4000 CNY</td><td align=\"left\">86 (32.6)</td><td align=\"left\">26 (30.2)</td><td align=\"left\">36 (41.9)</td><td align=\"left\">24 (27.9)</td></tr><tr><td align=\"left\" colspan=\"4\">\n<bold>Type of insurance</bold>\n</td><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\"> No insurance or with NRCMI^a\n</td><td align=\"left\">22 (8.3)</td><td align=\"left\">9 (40.9)</td><td align=\"left\">11 (50.0)</td><td align=\"left\">2 (9.1)</td><td align=\"left\" rowspan=\"4\"><p>21.412</p><p>(0.002)</p></td></tr><tr><td align=\"left\"> URBMI^b\n</td><td align=\"left\">30 (11.4)</td><td align=\"left\">12 (40.0)</td><td align=\"left\">7 (23.3)</td><td align=\"left\">11 (36.7)</td></tr><tr><td align=\"left\"> UEBMI^c\n</td><td align=\"left\">181 (68.6)</td><td align=\"left\">65 (35.9)</td><td align=\"left\">76 (42.0)</td><td align=\"left\">40 (22.1)</td></tr><tr><td align=\"left\"> Other commercial insurance</td><td align=\"left\">31 (11.7)</td><td align=\"left\">7 (22.6)</td><td align=\"left\">7 (22.6)</td><td align=\"left\">17 (54.8)</td></tr><tr><td align=\"left\" colspan=\"6\">\n<bold>Education</bold>\n</td></tr><tr><td align=\"left\"> Primary school degree or lower</td><td align=\"left\">16 (6.1)</td><td align=\"left\">8 (50.0)</td><td align=\"left\">3 (18.8)</td><td align=\"left\">5 (31.3)</td><td align=\"left\" rowspan=\"4\"><p>13.388</p><p>(0.037)</p></td></tr><tr><td align=\"left\"> Junior school degree</td><td align=\"left\">68 (25.8)</td><td align=\"left\">22 (32.4)</td><td align=\"left\">24 (35.3)</td><td align=\"left\">22 (32.4)</td></tr><tr><td align=\"left\"> High school degree</td><td align=\"left\">139 (52.6)</td><td align=\"left\">51 (36.7)</td><td align=\"left\">62 (44.6)</td><td align=\"left\">26 (18.7)</td></tr><tr><td align=\"left\"> University degree or higher</td><td align=\"left\">41 (15.5)</td><td align=\"left\">12 (29.3)</td><td align=\"left\">12 (29.3)</td><td align=\"left\">17 (41.5)</td></tr><tr><td align=\"left\" colspan=\"6\">\n<bold>Number of children</bold>\n</td></tr><tr><td align=\"left\"> 1 Child or no child</td><td align=\"left\">150 (56.8)</td><td align=\"left\">51 (34.0)</td><td align=\"left\">53 (35.3)</td><td align=\"left\">46 (30.7)</td><td align=\"left\" rowspan=\"3\"><p>5.571</p><p>(0.234)</p></td></tr><tr><td align=\"left\"> 2 Children</td><td align=\"left\">96 (36.4)</td><td align=\"left\">35 (36.5)</td><td align=\"left\">43 (44.8)</td><td align=\"left\">18 (18.8)</td></tr><tr><td align=\"left\"> 3 or more than 3 children</td><td align=\"left\">18 (6.8)</td><td align=\"left\">7 (38.9)</td><td align=\"left\">5 (27.8)</td><td align=\"left\">6 (33.3)</td></tr><tr><td align=\"left\" colspan=\"6\">\n<bold>Living with whom</bold>\n</td></tr><tr><td align=\"left\"> Alone</td><td align=\"left\">24 (9.1)</td><td align=\"left\">10 (41.7)</td><td align=\"left\">6 (25.0)</td><td align=\"left\">8 (33.3)</td><td align=\"left\" rowspan=\"4\"><p>9.906</p><p>(0.129)</p></td></tr><tr><td align=\"left\"> With partner or housemaid</td><td align=\"left\">125 (47.3)</td><td align=\"left\">41 (32.8)</td><td align=\"left\">57 (45.6)</td><td align=\"left\">27 (21.6)</td></tr><tr><td align=\"left\"> With child or children</td><td align=\"left\">42 (15.9)</td><td align=\"left\">19 (45.2)</td><td align=\"left\">14 (33.3)</td><td align=\"left\">9 (21.4)</td></tr><tr><td align=\"left\"> With partner and Children</td><td align=\"left\">73 (27.7)</td><td align=\"left\">23 (31.5)</td><td align=\"left\">24 (32.9)</td><td align=\"left\">26 (35.6)</td></tr><tr><td align=\"left\" colspan=\"6\">\n<bold>Familiarity with technology</bold>\n</td></tr><tr><td align=\"left\"> Not familiar with technology</td><td align=\"left\">54 (20.5)</td><td align=\"left\">26 (48.1)</td><td align=\"left\">25 (46.3)</td><td align=\"left\">3 (5.6)</td><td align=\"left\" rowspan=\"3\"><p>16.212</p><p>(0.003)</p></td></tr><tr><td align=\"left\"> Neutral</td><td align=\"left\">146 (55.3)</td><td align=\"left\">49 (33.6)</td><td align=\"left\">51 (34.9)</td><td align=\"left\">46 (31.5)</td></tr><tr><td align=\"left\"> Familiar with technology</td><td align=\"left\">64 (24.2)</td><td align=\"left\">18 (28.1)</td><td align=\"left\">25 (39.1)</td><td align=\"left\">21 (32.8)</td></tr><tr><td align=\"left\" colspan=\"6\">\n<bold>Openness to technology</bold>\n</td></tr><tr><td align=\"left\"><p> No</p><p>(not open to technology)</p></td><td align=\"left\">76 (28.8)</td><td align=\"left\">48 (63.2)</td><td align=\"left\">26 (34.2)</td><td align=\"left\">2 (2.6)</td><td align=\"left\" rowspan=\"2\"><p>47.051</p><p>(&lt; 0.001)</p></td></tr><tr><td align=\"left\"><p> Yes</p><p>(open to technology)</p></td><td align=\"left\">188 (71.2)</td><td align=\"left\">45 (23.9)</td><td align=\"left\">75 (39.9)</td><td align=\"left\">68 (36.2)</td></tr><tr><td align=\"left\" colspan=\"6\">\n<bold>Self-efficacy in applying smart technologies</bold>\n</td></tr><tr><td align=\"left\"> No</td><td align=\"left\">96 (36.4)</td><td align=\"left\">64 (66.7)</td><td align=\"left\">29 (30.2)</td><td align=\"left\">3 (3.1)</td><td align=\"left\"><p>76.011</p><p>(&lt; 0.001)</p></td></tr><tr><td align=\"left\"> Yes</td><td align=\"left\">168 (63.6)</td><td align=\"left\">29 (17.3)</td><td align=\"left\">72 (42.9)</td><td align=\"left\">67 (39.9)</td><td align=\"left\"/></tr><tr><td align=\"left\">\n<bold>Domain</bold>\n</td><td align=\"left\"> Variable</td><td align=\"left\"><p>Total</p><p>n (%)</p></td><td align=\"left\">Lowest tertile of acceptability (Mean ≤ 3.93), n (%)</td><td align=\"left\"><p>Middle tertile of acceptability (Means: 3.94–4.29),</p><p>n (%)</p></td><td align=\"left\">Highest tertile of acceptability (Means ≥ 4.30), n (%)</td><td align=\"left\"><p>\n<bold><italic>X</italic></bold>^{<bold><italic>2</italic></bold>}\n</p><p>\n<bold><italic>P</italic></bold>\n<bold>value</bold>\n</p></td></tr><tr><td align=\"left\" rowspan=\"48\">\n<bold>Acceptability</bold>\n</td><td align=\"left\"> Total</td><td align=\"left\">264 (100.0)</td><td align=\"left\">94 (35.6)</td><td align=\"left\">87 (33.0)</td><td align=\"left\">83 (31.4)</td><td align=\"left\"/></tr><tr><td align=\"left\" colspan=\"6\">\n<bold>Willingness to move to a nursing home</bold>\n</td></tr><tr><td align=\"left\"> No</td><td align=\"left\">84 (31.8)</td><td align=\"left\">48 (57.1)</td><td align=\"left\">21 (25.0)</td><td align=\"left\">15 (17.9)</td><td align=\"left\" rowspan=\"2\"><p>25.644</p><p>(&lt; 0.001)</p></td></tr><tr><td align=\"left\"> Yes</td><td align=\"left\">180 (68.2)</td><td align=\"left\">46 (25.6)</td><td align=\"left\">66 (36.7)</td><td align=\"left\">68 (37.8)</td></tr><tr><td align=\"left\" colspan=\"6\">\n<bold>Age</bold>\n</td></tr><tr><td align=\"left\"> 60–64</td><td align=\"left\">88 (33.3)</td><td align=\"left\">23 (26.1)</td><td align=\"left\">32 (36.4)</td><td align=\"left\">33 (37.5)</td><td align=\"left\" rowspan=\"3\"><p>9.617</p><p>(0.047)</p></td></tr><tr><td align=\"left\"> 65–70</td><td align=\"left\">88 (33.3)</td><td align=\"left\">42 (47.7)</td><td align=\"left\">25 (28.4)</td><td align=\"left\">21 (23.9)</td></tr><tr><td align=\"left\"> 71–75</td><td align=\"left\">88 (33.3)</td><td align=\"left\">29 (33.0)</td><td align=\"left\">30 (34.1)</td><td align=\"left\">29 (33.0)</td></tr><tr><td align=\"left\" colspan=\"6\">\n<bold>Gender</bold>\n</td></tr><tr><td align=\"left\"> Male</td><td align=\"left\">130 (49.2)</td><td align=\"left\">38 (29.2)</td><td align=\"left\">46 (35.4)</td><td align=\"left\">46 (35.4)</td><td align=\"left\" rowspan=\"2\"><p>4.651</p><p>(0.098)</p></td></tr><tr><td align=\"left\"> Female</td><td align=\"left\">134 (50.8)</td><td align=\"left\">56 (41.8)</td><td align=\"left\">41 (30.6)</td><td align=\"left\">37 (27.6)</td></tr><tr><td align=\"left\" colspan=\"6\">\n<bold>Health status</bold>\n</td></tr><tr><td align=\"left\"> Healthy</td><td align=\"left\">98 (37.1)</td><td align=\"left\">44 (44.9)</td><td align=\"left\">29 (29.6)</td><td align=\"left\">25 (25.5)</td><td align=\"left\" rowspan=\"3\"><p>7.400</p><p>(0.116)</p></td></tr><tr><td align=\"left\"> One chronic disease</td><td align=\"left\">101 (38.3)</td><td align=\"left\">33 (32.7)</td><td align=\"left\">32 (31.7)</td><td align=\"left\">36 (35.6)</td></tr><tr><td align=\"left\"> Two or more chronic diseases</td><td align=\"left\">65 (24.6)</td><td align=\"left\">17 (26.2)</td><td align=\"left\">26 (40.0)</td><td align=\"left\">22 (33.8)</td></tr><tr><td align=\"left\" colspan=\"6\">\n<bold>Income per month</bold>\n</td></tr><tr><td align=\"left\"> No pension</td><td align=\"left\">30 (11.4)</td><td align=\"left\">12 (40.0)</td><td align=\"left\">7 (23.3)</td><td align=\"left\">11 (36.7)</td><td align=\"left\" rowspan=\"4\"><p>7.772</p><p>(0.255)</p></td></tr><tr><td align=\"left\"> 1000–2000 CNY</td><td align=\"left\">59 (22.3)</td><td align=\"left\">18 (30.5)</td><td align=\"left\">25 (42.4)</td><td align=\"left\">16 (27.1)</td></tr><tr><td align=\"left\"> 2000–4000 CNY</td><td align=\"left\">89 (33.7)</td><td align=\"left\">32 (36.0)</td><td align=\"left\">23 (25.8)</td><td align=\"left\">34 (38.2)</td></tr><tr><td align=\"left\"> More than 4000 CNY</td><td align=\"left\">86 (32.6)</td><td align=\"left\">32 (37.2)</td><td align=\"left\">32 (37.2)</td><td align=\"left\">22 (25.6)</td></tr><tr><td align=\"left\" colspan=\"4\">\n<bold>Type of insurance</bold>\n</td><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\"> No insurance or with NRCMI^a\n</td><td align=\"left\">22 (8.3)</td><td align=\"left\">11 (50.0)</td><td align=\"left\">7 (31.8)</td><td align=\"left\">4 (18.2)</td><td align=\"left\" rowspan=\"4\"><p>6.523</p><p>(0.367)</p></td></tr><tr><td align=\"left\"> URBMI^b\n</td><td align=\"left\">30 (11.4)</td><td align=\"left\">13 (43.3)</td><td align=\"left\">6 (20.0)</td><td align=\"left\">11 (36.7)</td></tr><tr><td align=\"left\"> UEBMI^c\n</td><td align=\"left\">181(68.6)</td><td align=\"left\">62 (34.3)</td><td align=\"left\">63 (34.8)</td><td align=\"left\">56 (30.9)</td></tr><tr><td align=\"left\"> Other commercial insurance</td><td align=\"left\">31 (11.7)</td><td align=\"left\">8 (25.8)</td><td align=\"left\">11 (35.5)</td><td align=\"left\">12 (38.7)</td></tr><tr><td align=\"left\" colspan=\"6\">\n<bold>Education</bold>\n</td></tr><tr><td align=\"left\"> Primary school degree or lower</td><td align=\"left\">16 (6.1)</td><td align=\"left\">7 (43.8)</td><td align=\"left\">4 (25.0)</td><td align=\"left\">5 (31.3)</td><td align=\"left\" rowspan=\"4\"><p>3.364</p><p>(0.762)</p></td></tr><tr><td align=\"left\"> Junior school degree</td><td align=\"left\">68 (25.8)</td><td align=\"left\">22 (32.4)</td><td align=\"left\">21 (30.9)</td><td align=\"left\">25 (36.8)</td></tr><tr><td align=\"left\"> High school degree</td><td align=\"left\">139 (52.6)</td><td align=\"left\">53 (38.1)</td><td align=\"left\">45 (32.4)</td><td align=\"left\">41 (29.5)</td></tr><tr><td align=\"left\"> University degree or higher</td><td align=\"left\">41 (15.5)</td><td align=\"left\">12 (29.3)</td><td align=\"left\">17 (41.5)</td><td align=\"left\">12 (29.3)</td></tr><tr><td align=\"left\" colspan=\"6\">\n<bold>Number of children</bold>\n</td></tr><tr><td align=\"left\"> 1 Child or no child</td><td align=\"left\">150 (56.8)</td><td align=\"left\">50 (33.3)</td><td align=\"left\">46 (30.7)</td><td align=\"left\">54 (36.0)</td><td align=\"left\" rowspan=\"3\"><p>5.208</p><p>(0.267)</p></td></tr><tr><td align=\"left\"> 2 Children</td><td align=\"left\">96 (36.4)</td><td align=\"left\">36 (37.5)</td><td align=\"left\">37 (38.5)</td><td align=\"left\">23 (24.0)</td></tr><tr><td align=\"left\"> 3 or more than 3 children</td><td align=\"left\">18 (6.8)</td><td align=\"left\">8 (44.4)</td><td align=\"left\">4 (22.2)</td><td align=\"left\">6 (33.3)</td></tr><tr><td align=\"left\" colspan=\"6\">\n<bold>Living with whom</bold>\n</td></tr><tr><td align=\"left\"> Alone</td><td align=\"left\">24 (9.1)</td><td align=\"left\">8 (33.3)</td><td align=\"left\">10 (41.7)</td><td align=\"left\">6 (25.0)</td><td align=\"left\" rowspan=\"4\"><p>14.149</p><p>(0.028)</p></td></tr><tr><td align=\"left\"> With partner or housemaid</td><td align=\"left\">125 (47.3)</td><td align=\"left\">43 (34.4)</td><td align=\"left\">45 (36.0)</td><td align=\"left\">37 (29.6)</td></tr><tr><td align=\"left\"> With child or children</td><td align=\"left\">42 (15.9)</td><td align=\"left\">22 (52.4)</td><td align=\"left\">13 (31.0)</td><td align=\"left\">7 (16.7)</td></tr><tr><td align=\"left\"> With partner and Children</td><td align=\"left\">73 (27.7)</td><td align=\"left\">21 (28.8)</td><td align=\"left\">19 (26.0)</td><td align=\"left\">33 (45.2)</td></tr><tr><td align=\"left\" colspan=\"6\">\n<bold>Familiarity with technology</bold>\n</td></tr><tr><td align=\"left\"> Not familiar with technology</td><td align=\"left\">54 (20.5)</td><td align=\"left\">28 (51.9)</td><td align=\"left\">18 (33.3)</td><td align=\"left\">8 (14.8)</td><td align=\"left\" rowspan=\"3\"><p>11.442</p><p>(0.022)</p></td></tr><tr><td align=\"left\"> Neutral</td><td align=\"left\">146 (55.3)</td><td align=\"left\">48 (32.9)</td><td align=\"left\">47 (32.2)</td><td align=\"left\">51 (34.9)</td></tr><tr><td align=\"left\"> Familiar with technology</td><td align=\"left\">64 (24.2)</td><td align=\"left\">18 (28.1)</td><td align=\"left\">22 (34.4)</td><td align=\"left\">24 (37.5)</td></tr><tr><td align=\"left\" colspan=\"6\">\n<bold>Openness to technology</bold>\n</td></tr><tr><td align=\"left\"><p> No</p><p>(not open to technology)</p></td><td align=\"left\">76 (28.8)</td><td align=\"left\">50 (65.8)</td><td align=\"left\">18 (23.7)</td><td align=\"left\">8 (10.5)</td><td align=\"left\" rowspan=\"2\"><p>44.936</p><p>(&lt; 0.001)</p></td></tr><tr><td align=\"left\"><p> Yes</p><p>(open to technology)</p></td><td align=\"left\">188 (71.2)</td><td align=\"left\">44 (23.4)</td><td align=\"left\">69 (36.7)</td><td align=\"left\">75 (39.9)</td></tr><tr><td align=\"left\" colspan=\"6\">\n<bold>Self-efficacy in applying smart technologies</bold>\n</td></tr><tr><td align=\"left\"> No</td><td align=\"left\">96 (36.4)</td><td align=\"left\">64 (66.7)</td><td align=\"left\">23 (24.0)</td><td align=\"left\">9 (9.4)</td><td align=\"left\"><p>67.940</p><p>(&lt; 0.001)</p></td></tr><tr><td align=\"left\"/><td align=\"left\"> Yes</td><td align=\"left\">168 (63.6)</td><td align=\"left\">30 (17.9)</td><td align=\"left\">64 (38.1)</td><td align=\"left\">74 (44.0)</td><td align=\"left\"/></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab6\"><label>Table 6</label><caption><p>The multinomial logistic regression analysis of sociodemographic factors on different categories of expectations and acceptability of smart nursing homes</p></caption><table frame=\"hsides\" rules=\"groups\"><tbody><tr><td align=\"left\">\n<bold>Level of expectations</bold>^{<bold>a</bold>}\n</td><td align=\"left\">\n<bold>Variables</bold>\n</td><td align=\"left\">\n<bold>Subgroups of variables</bold>\n</td><td align=\"left\">\n<bold>B</bold>\n</td><td align=\"left\">\n<bold>Std. Error</bold>\n</td><td align=\"left\">\n<bold>Wald</bold>\n</td><td align=\"left\">\n<bold>Sig.</bold>\n</td><td align=\"left\">\n<bold>OR</bold>\n</td><td align=\"left\" colspan=\"2\">\n<bold>95% Confidence Interval for Exp(B) (Lower -upper bound)</bold>\n</td></tr><tr><td align=\"left\" rowspan=\"16\">Middle tertile of expectations (mean: 3.91–4.40)</td><td align=\"left\" rowspan=\"3\">Health status</td><td align=\"left\">Two or more chronic diseases</td><td align=\"left\">0.661</td><td align=\"left\">0.459</td><td align=\"left\">2.071</td><td align=\"left\">0.150</td><td align=\"left\">1.936</td><td align=\"left\">0.787</td><td align=\"left\">4.762</td></tr><tr><td align=\"left\">One chronic disease</td><td align=\"left\">0.287</td><td align=\"left\">0.379</td><td align=\"left\">0.573</td><td align=\"left\">0.449</td><td align=\"left\">1.332</td><td align=\"left\">0.634</td><td align=\"left\">2.800</td></tr><tr><td align=\"left\">Healthy (reference group)</td><td align=\"left\">0^b\n</td><td align=\"left\">.</td><td align=\"left\">.</td><td align=\"left\">.</td><td align=\"left\">.</td><td align=\"left\">.</td><td align=\"left\">.</td></tr><tr><td align=\"left\" rowspan=\"4\">Type of insurance</td><td align=\"left\">UEBMI^c\n</td><td align=\"left\">-0.436</td><td align=\"left\">0.581</td><td align=\"left\">0.562</td><td align=\"left\">0.453</td><td align=\"left\">0.647</td><td align=\"left\">0.207</td><td align=\"left\">2.021</td></tr><tr><td align=\"left\">URBMI^d\n</td><td align=\"left\">-1.895</td><td align=\"left\">0.786</td><td align=\"left\">5.814</td><td align=\"left\">0.016</td><td align=\"left\">0.150</td><td align=\"left\">0.032</td><td align=\"left\">0.701</td></tr><tr><td align=\"left\">Other commercial insurance</td><td align=\"left\">-0.983</td><td align=\"left\">0.833</td><td align=\"left\">1.392</td><td align=\"left\">0.238</td><td align=\"left\">0.374</td><td align=\"left\">0.073</td><td align=\"left\">1.915</td></tr><tr><td align=\"left\"><p>No insurance or with NRCMI^e\n</p><p>(reference group)</p></td><td align=\"left\">0^b\n</td><td align=\"left\">.</td><td align=\"left\">.</td><td align=\"left\">.</td><td align=\"left\">.</td><td align=\"left\">.</td><td align=\"left\">.</td></tr><tr><td align=\"left\" rowspan=\"3\">Familiar with technology</td><td align=\"left\">Familiar with technology</td><td align=\"left\">-1.453</td><td align=\"left\">0.637</td><td align=\"left\">5.204</td><td align=\"left\">0.023</td><td align=\"left\">0.234</td><td align=\"left\">0.067</td><td align=\"left\">0.815</td></tr><tr><td align=\"left\">Neutral</td><td align=\"left\">-1.001</td><td align=\"left\">0.481</td><td align=\"left\">4.326</td><td align=\"left\">0.038</td><td align=\"left\">0.368</td><td align=\"left\">0.143</td><td align=\"left\">0.944</td></tr><tr><td align=\"left\">Not familiar with technology (reference group)</td><td align=\"left\">0^b\n</td><td align=\"left\">.</td><td align=\"left\">.</td><td align=\"left\">.</td><td align=\"left\">.</td><td align=\"left\">.</td><td align=\"left\">.</td></tr><tr><td align=\"left\" rowspan=\"2\">Openness to technology</td><td align=\"left\">Yes</td><td align=\"left\">0.027</td><td align=\"left\">0.501</td><td align=\"left\">0.003</td><td align=\"left\">0.957</td><td align=\"left\">1.028</td><td align=\"left\">0.385</td><td align=\"left\">2.742</td></tr><tr><td align=\"left\"><p>No</p><p>(reference group)</p></td><td align=\"left\">0^b\n</td><td align=\"left\">.</td><td align=\"left\">.</td><td align=\"left\">.</td><td align=\"left\">.</td><td align=\"left\">.</td><td align=\"left\">.</td></tr><tr><td align=\"left\" rowspan=\"2\">Self-efficacy in applying smart technologies</td><td align=\"left\">Yes</td><td align=\"left\">2.272</td><td align=\"left\">0.504</td><td align=\"left\">20.353</td><td align=\"left\">&lt; 0.001</td><td align=\"left\">9.694</td><td align=\"left\">3.613</td><td align=\"left\">26.007</td></tr><tr><td align=\"left\"><p>No</p><p>(reference group)</p></td><td align=\"left\">0^b\n</td><td align=\"left\">.</td><td align=\"left\">.</td><td align=\"left\">.</td><td align=\"left\">.</td><td align=\"left\">.</td><td align=\"left\">.</td></tr><tr><td align=\"left\" rowspan=\"2\">Willingness to move to a nursing home</td><td align=\"left\">Yes</td><td align=\"left\">0.735</td><td align=\"left\">0.377</td><td align=\"left\">3.787</td><td align=\"left\">0.052</td><td align=\"left\">2.085</td><td align=\"left\">0.995</td><td align=\"left\">4.369</td></tr><tr><td align=\"left\"><p>No</p><p>(reference group)</p></td><td align=\"left\">0^b\n</td><td align=\"left\">.</td><td align=\"left\">.</td><td align=\"left\">.</td><td align=\"left\">.</td><td align=\"left\">.</td><td align=\"left\">.</td></tr><tr><td align=\"left\" rowspan=\"16\">Highest tertile of expectations (mean: &gt;4.41)</td><td align=\"left\" rowspan=\"3\">Health status</td><td align=\"left\">Two or more chronic diseases</td><td align=\"left\">0.216</td><td align=\"left\">0.575</td><td align=\"left\">0.141</td><td align=\"left\">0.707</td><td align=\"left\">1.241</td><td align=\"left\">0.402</td><td align=\"left\">3.834</td></tr><tr><td align=\"left\">One chronic disease</td><td align=\"left\">− 0.046</td><td align=\"left\">0.463</td><td align=\"left\">0.010</td><td align=\"left\">0.921</td><td align=\"left\">0.955</td><td align=\"left\">0.385</td><td align=\"left\">2.366</td></tr><tr><td align=\"left\">Healthy (reference group)</td><td align=\"left\">0^b\n</td><td align=\"left\">.</td><td align=\"left\">.</td><td align=\"left\">.</td><td align=\"left\">.</td><td align=\"left\">.</td><td align=\"left\">.</td></tr><tr><td align=\"left\" rowspan=\"4\">Type of insurance</td><td align=\"left\">UEBMI</td><td align=\"left\">0.013</td><td align=\"left\">0.981</td><td align=\"left\">0.000</td><td align=\"left\">0.989</td><td align=\"left\">1.013</td><td align=\"left\">0.148</td><td align=\"left\">6.931</td></tr><tr><td align=\"left\">URBMI</td><td align=\"left\">-0.545</td><td align=\"left\">1.090</td><td align=\"left\">0.251</td><td align=\"left\">0.617</td><td align=\"left\">0.580</td><td align=\"left\">0.068</td><td align=\"left\">4.905</td></tr><tr><td align=\"left\">Other commercial insurance</td><td align=\"left\">0.959</td><td align=\"left\">1.123</td><td align=\"left\">0.729</td><td align=\"left\">0.393</td><td align=\"left\">2.609</td><td align=\"left\">0.289</td><td align=\"left\">23.591</td></tr><tr><td align=\"left\"><p>No insurance or with NRCMI</p><p>(reference group)</p></td><td align=\"left\">0^b\n</td><td align=\"left\">.</td><td align=\"left\">.</td><td align=\"left\">.</td><td align=\"left\">.</td><td align=\"left\">.</td><td align=\"left\">.</td></tr><tr><td align=\"left\" rowspan=\"3\">Familiar with technology</td><td align=\"left\">Familiar with technology</td><td align=\"left\">-1.336</td><td align=\"left\">0.934</td><td align=\"left\">2.046</td><td align=\"left\">0.153</td><td align=\"left\">0.263</td><td align=\"left\">0.042</td><td align=\"left\">1.640</td></tr><tr><td align=\"left\">Neutral</td><td align=\"left\">-0.393</td><td align=\"left\">0.820</td><td align=\"left\">0.230</td><td align=\"left\">0.631</td><td align=\"left\">0.675</td><td align=\"left\">0.135</td><td align=\"left\">3.363</td></tr><tr><td align=\"left\">Not familiar with technology (reference group)</td><td align=\"left\">0^b\n</td><td align=\"left\">.</td><td align=\"left\">.</td><td align=\"left\">.</td><td align=\"left\">.</td><td align=\"left\">.</td><td align=\"left\">.</td></tr><tr><td align=\"left\" rowspan=\"2\">Openness to technology</td><td align=\"left\">Yes</td><td align=\"left\">1.250</td><td align=\"left\">0.947</td><td align=\"left\">1.742</td><td align=\"left\">0.187</td><td align=\"left\">3.491</td><td align=\"left\">0.545</td><td align=\"left\">22.351</td></tr><tr><td align=\"left\"><p>No</p><p>(reference group)</p></td><td align=\"left\">0^b\n</td><td align=\"left\">.</td><td align=\"left\">.</td><td align=\"left\">.</td><td align=\"left\">.</td><td align=\"left\">.</td><td align=\"left\">.</td></tr><tr><td align=\"left\" rowspan=\"2\">Self-efficacy of in applying smart technologies</td><td align=\"left\">Yes</td><td align=\"left\">3.333</td><td align=\"left\">0.793</td><td align=\"left\">17.645</td><td align=\"left\">&lt; 0.001</td><td align=\"left\">28.015</td><td align=\"left\">5.916</td><td align=\"left\">132.661</td></tr><tr><td align=\"left\"><p>No</p><p>(reference group)</p></td><td align=\"left\">0^b\n</td><td align=\"left\">.</td><td align=\"left\">.</td><td align=\"left\">.</td><td align=\"left\">.</td><td align=\"left\">.</td><td align=\"left\">.</td></tr><tr><td align=\"left\" rowspan=\"2\">Willingness to move to a nursing home</td><td align=\"left\">Yes</td><td align=\"left\">1.090</td><td align=\"left\">0.528</td><td align=\"left\">4.265</td><td align=\"left\">0.039</td><td align=\"left\">2.975</td><td align=\"left\">1.057</td><td align=\"left\">8.374</td></tr><tr><td align=\"left\"><p>No</p><p>(reference group)</p></td><td align=\"left\">0^b\n</td><td align=\"left\">.</td><td align=\"left\">.</td><td align=\"left\">.</td><td align=\"left\">.</td><td align=\"left\">.</td><td align=\"left\">.</td></tr><tr><td align=\"left\">\n<bold>Level of acceptability</bold>^{<bold>f</bold>}\n</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\">\n<bold>B</bold>\n</td><td align=\"left\">\n<bold>Std. Error</bold>\n</td><td align=\"left\">\n<bold>Wald</bold>\n</td><td align=\"left\">\n<bold>Sig.</bold>\n</td><td align=\"left\">\n<bold>OR</bold>\n</td><td align=\"left\" colspan=\"2\">\n<bold>95% Confidence Interval for Exp(B)</bold>\n</td></tr><tr><td align=\"left\" rowspan=\"15\">Moderate tertile of acceptability (mean: 3.94–4.29)</td><td align=\"left\" rowspan=\"3\">Health status</td><td align=\"left\">Two or more chronic diseases</td><td align=\"left\">0.616</td><td align=\"left\">0.459</td><td align=\"left\">1.796</td><td align=\"left\">0.180</td><td align=\"left\">1.851</td><td align=\"left\">0.752</td><td align=\"left\">4.553</td></tr><tr><td align=\"left\">One chronic disease</td><td align=\"left\">0.064</td><td align=\"left\">0.387</td><td align=\"left\">0.028</td><td align=\"left\">0.868</td><td align=\"left\">1.066</td><td align=\"left\">0.499</td><td align=\"left\">2.277</td></tr><tr><td align=\"left\">Healthy (reference group)</td><td align=\"left\">0^b\n</td><td align=\"left\">.</td><td align=\"left\">.</td><td align=\"left\">.</td><td align=\"left\">.</td><td align=\"left\">.</td><td align=\"left\">.</td></tr><tr><td align=\"left\" rowspan=\"3\">Number of children</td><td align=\"left\">3 or more than 3 children</td><td align=\"left\">-0.517</td><td align=\"left\">0.742</td><td align=\"left\">0.486</td><td align=\"left\">0.486</td><td align=\"left\">0.596</td><td align=\"left\">0.139</td><td align=\"left\">2.555</td></tr><tr><td align=\"left\">2 Children</td><td align=\"left\">0.278</td><td align=\"left\">0.372</td><td align=\"left\">0.558</td><td align=\"left\">0.455</td><td align=\"left\">1.321</td><td align=\"left\">0.637</td><td align=\"left\">2.741</td></tr><tr><td align=\"left\">1 Child or no child (reference group)</td><td align=\"left\">0^b\n</td><td align=\"left\">.</td><td align=\"left\">.</td><td align=\"left\">.</td><td align=\"left\">.</td><td align=\"left\">.</td><td align=\"left\">.</td></tr><tr><td align=\"left\" rowspan=\"3\">Familiar with technology</td><td align=\"left\">Familiar with technology</td><td align=\"left\">-1.214</td><td align=\"left\">0.648</td><td align=\"left\">3.514</td><td align=\"left\">0.061</td><td align=\"left\">0.297</td><td align=\"left\">0.083</td><td align=\"left\">1.057</td></tr><tr><td align=\"left\">Neutral</td><td align=\"left\">-0.783</td><td align=\"left\">0.493</td><td align=\"left\">2.525</td><td align=\"left\">0.112</td><td align=\"left\">0.457</td><td align=\"left\">0.174</td><td align=\"left\">1.201</td></tr><tr><td align=\"left\">Not familiar with technology (reference group)</td><td align=\"left\">0^b\n</td><td align=\"left\">.</td><td align=\"left\">.</td><td align=\"left\">.</td><td align=\"left\">.</td><td align=\"left\">.</td><td align=\"left\">.</td></tr><tr><td align=\"left\" rowspan=\"2\">Openness to technology</td><td align=\"left\">Yes</td><td align=\"left\">0.508</td><td align=\"left\">0.513</td><td align=\"left\">0.981</td><td align=\"left\">0.322</td><td align=\"left\">1.661</td><td align=\"left\">0.608</td><td align=\"left\">4.538</td></tr><tr><td align=\"left\"><p>No</p><p>(reference group)</p></td><td align=\"left\">0^b\n</td><td align=\"left\">.</td><td align=\"left\">.</td><td align=\"left\">.</td><td align=\"left\">.</td><td align=\"left\">.</td><td align=\"left\">.</td></tr><tr><td align=\"left\" rowspan=\"2\">Self-efficacy in applying smart technologies</td><td align=\"left\">Yes</td><td align=\"left\">1.759</td><td align=\"left\">0.498</td><td align=\"left\">12.495</td><td align=\"left\">&lt; 0.001</td><td align=\"left\">5.808</td><td align=\"left\">2.190</td><td align=\"left\">15.406</td></tr><tr><td align=\"left\"><p>No</p><p>(reference group)</p></td><td align=\"left\">0^b\n</td><td align=\"left\">.</td><td align=\"left\">.</td><td align=\"left\">.</td><td align=\"left\">.</td><td align=\"left\">.</td><td align=\"left\">.</td></tr><tr><td align=\"left\" rowspan=\"2\">Willingness to move to a nursing home</td><td align=\"left\">Yes</td><td align=\"left\">0.710</td><td align=\"left\">0.387</td><td align=\"left\">3.377</td><td align=\"left\">0.066</td><td align=\"left\">2.035</td><td align=\"left\">0.954</td><td align=\"left\">4.341</td></tr><tr><td align=\"left\"><p>No</p><p>(reference group)</p></td><td align=\"left\">0^b\n</td><td align=\"left\">.</td><td align=\"left\">.</td><td align=\"left\">.</td><td align=\"left\">.</td><td align=\"left\">.</td><td align=\"left\">.</td></tr><tr><td align=\"left\" rowspan=\"15\">Highest tertile of acceptability (mean: &gt;4.30)</td><td align=\"left\" rowspan=\"3\">Health status</td><td align=\"left\">Two or more chronic diseases</td><td align=\"left\">0.797</td><td align=\"left\">0.510</td><td align=\"left\">2.444</td><td align=\"left\">0.118</td><td align=\"left\">2.218</td><td align=\"left\">0.817</td><td align=\"left\">6.022</td></tr><tr><td align=\"left\">One chronic disease</td><td align=\"left\">0.308</td><td align=\"left\">0.411</td><td align=\"left\">0.564</td><td align=\"left\">0.453</td><td align=\"left\">1.361</td><td align=\"left\">0.609</td><td align=\"left\">3.043</td></tr><tr><td align=\"left\">Healthy (reference group)</td><td align=\"left\">0^b\n</td><td align=\"left\">.</td><td align=\"left\">.</td><td align=\"left\">.</td><td align=\"left\">.</td><td align=\"left\">.</td><td align=\"left\">.</td></tr><tr><td align=\"left\" rowspan=\"3\">Number of children</td><td align=\"left\">3 or more than 3 children</td><td align=\"left\">-0.309</td><td align=\"left\">0.740</td><td align=\"left\">0.174</td><td align=\"left\">0.676</td><td align=\"left\">0.734</td><td align=\"left\">0.172</td><td align=\"left\">3.133</td></tr><tr><td align=\"left\">2 Children</td><td align=\"left\">-0.329</td><td align=\"left\">0.417</td><td align=\"left\">0.621</td><td align=\"left\">0.431</td><td align=\"left\">0.720</td><td align=\"left\">0.318</td><td align=\"left\">1.631</td></tr><tr><td align=\"left\">1 Child or no child (reference group)</td><td align=\"left\">0^b\n</td><td align=\"left\">.</td><td align=\"left\">.</td><td align=\"left\">.</td><td align=\"left\">.</td><td align=\"left\">.</td><td align=\"left\">.</td></tr><tr><td align=\"left\" rowspan=\"3\">Familiar with technology</td><td align=\"left\">Familiar with technology</td><td align=\"left\">-1.010</td><td align=\"left\">0.733</td><td align=\"left\">1.900</td><td align=\"left\">0.168</td><td align=\"left\">0.364</td><td align=\"left\">0.087</td><td align=\"left\">1.532</td></tr><tr><td align=\"left\">Neutral</td><td align=\"left\">-0.405</td><td align=\"left\">0.604</td><td align=\"left\">0.449</td><td align=\"left\">0.503</td><td align=\"left\">0.667</td><td align=\"left\">0.204</td><td align=\"left\">2.179</td></tr><tr><td align=\"left\">Not familiar with technology (reference group)</td><td align=\"left\">0b</td><td align=\"left\">.</td><td align=\"left\">.</td><td align=\"left\">.</td><td align=\"left\">.</td><td align=\"left\">.</td><td align=\"left\">.</td></tr><tr><td align=\"left\" rowspan=\"2\">Openness to technology</td><td align=\"left\">Yes</td><td align=\"left\">0.566</td><td align=\"left\">0.635</td><td align=\"left\">0.795</td><td align=\"left\">0.373</td><td align=\"left\">1.761</td><td align=\"left\">0.508</td><td align=\"left\">6.109</td></tr><tr><td align=\"left\"><p>No</p><p>(reference group)</p></td><td align=\"left\">0b</td><td align=\"left\">.</td><td align=\"left\">.</td><td align=\"left\">.</td><td align=\"left\">.</td><td align=\"left\">.</td><td align=\"left\">.</td></tr><tr><td align=\"left\" rowspan=\"2\">Self-efficacy in applying smart technologies</td><td align=\"left\">Yes</td><td align=\"left\">2.625</td><td align=\"left\">0.591</td><td align=\"left\">19.721</td><td align=\"left\">&lt; 0.001</td><td align=\"left\">13.801</td><td align=\"left\">4.333</td><td align=\"left\">43.954</td></tr><tr><td align=\"left\"><p>No</p><p>(reference group)</p></td><td align=\"left\">0b</td><td align=\"left\">.</td><td align=\"left\">.</td><td align=\"left\">.</td><td align=\"left\">.</td><td align=\"left\">.</td><td align=\"left\">.</td></tr><tr><td align=\"left\" rowspan=\"2\">Willingness to move to a nursing home</td><td align=\"left\">Yes</td><td align=\"left\">0.617</td><td align=\"left\">0.430</td><td align=\"left\">2.060</td><td align=\"left\">0.151</td><td align=\"left\">1.853</td><td align=\"left\">0.798</td><td align=\"left\">4.300</td></tr><tr><td align=\"left\"><p>No</p><p>(reference group)</p></td><td align=\"left\">0^b\n</td><td align=\"left\">.</td><td align=\"left\">.</td><td align=\"left\">.</td><td align=\"left\">.</td><td align=\"left\">.</td><td align=\"left\">.</td></tr></tbody></table></table-wrap>" ]

[]

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[]

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[ "<supplementary-material content-type=\"local-data\" id=\"MOESM1\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM2\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM3\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM4\"></supplementary-material>" ]

[ "<table-wrap-foot><p>\n^a*<italic>p</italic> &lt; 0.05., Χ² = Chi squared\n</p><p>\n^b\n<italic>Df </italic>Degrees of freedom\n</p><p>\n^c\n<italic>CMIN/DF </italic>Discrepancy divided by degree of freedom\n</p><p>\n^d\n<italic>CFI </italic>Comparative Fit Index\n</p><p>\n^e\n<italic>RMSEA </italic>Root mean square error of approximation, 90% CI = 90% confidence intervals,\n</p><p>\n^f\n<italic>SRMR </italic>Standardised root mean square residual\n</p></table-wrap-foot>", "<table-wrap-foot><p>^a\n<italic>NH </italic>Nursing home</p><p>^b\n<italic>NRCMI </italic>New rural cooperative medical insurance</p><p>^c\n<italic>URBMI </italic>Urban resident medical insurance</p><p>^d\n<italic>UEBMI </italic>Urban employee basic medical insurance</p></table-wrap-foot>", "<table-wrap-foot><p>^a\n<italic>NH </italic>Nursing home</p><p>^b\n<italic>NRCMI </italic>New rural cooperative medical insurance</p><p>^c\n<italic>URBMI </italic>Urban resident medical insurance</p><p>^d\n<italic>UEBMI </italic>Urban employee basic medical insurance</p></table-wrap-foot>", "<table-wrap-foot><p>\n^a\n<italic>NRCMI </italic>New rural cooperative medical insurance\n</p><p>\n^b\n<italic>URBMI </italic>Urban resident medical insurance\n</p><p>\n^c\n<italic>UEBMI </italic>Urban employee basic medical insurance\n</p></table-wrap-foot>", "<table-wrap-foot><p>\n^aThe reference category is the ‘lowest tertile of expectation (mean: ≤ 3.90)’\n</p><p>\n^b\n<italic>0 </italic>This parameter is set to zero because it is redundant\n</p><p>\n^c\n<italic>UEBMI </italic>Urban employee basic medical insurance\n</p><p>\n^d\n<italic>URBMI </italic>Urban resident medical insurance\n</p><p>\n^e\n<italic>NRCMI </italic>The new rural cooperative medical insurance\n</p><p>\n^fThe reference category is the ‘lowest tertile of acceptability (mean: ≤ 3.93)’\n</p></table-wrap-foot>", "<fn-group><fn><p><bold>Publisher’s Note</bold></p><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn></fn-group>" ]

[ "<graphic xlink:href=\"12912_2023_1676_Fig1_HTML\" id=\"d32e407\"/>", "<graphic xlink:href=\"12912_2023_1676_Fig2_HTML\" id=\"d32e432\"/>", "<graphic xlink:href=\"12912_2023_1676_Fig3_HTML\" id=\"d32e762\"/>" ]

[ "<media xlink:href=\"12912_2023_1676_MOESM1_ESM.docx\"><caption><p><bold>Additional file 1.</bold> The Checklist of Guidelines for Conducting and Reporting Mixed Research for Counselor Researchers.</p></caption></media>", "<media xlink:href=\"12912_2023_1676_MOESM2_ESM.docx\"><caption><p><bold>Additional file 2.</bold> Questionnaire Development and Validation Process.</p></caption></media>", "<media xlink:href=\"12912_2023_1676_MOESM3_ESM.docx\"><caption><p><bold>Additional file 3.</bold> The Combination and Comparison among Qualitative and Quantitative Data.</p></caption></media>", "<media xlink:href=\"12912_2023_1676_MOESM4_ESM.docx\"><caption><p><bold>Additional file 4.</bold> Comparing the Variances in Cities.</p></caption></media>" ]

{ "acronym": [], "definition": [] }

[ "<title>Background</title>", "<p id=\"Par12\">Drug overdose has been the leading cause of injury death in the USA over the past decade [##REF##35853029##1##], inflicting a devastating toll on families and communities across the country. The overdose epidemic, a public health crisis, has claimed the lives of roughly one million Americans since 1999 [##REF##36455852##2##], with sharp, unprecedented increases since 2019 due to the emergence—and proliferation—of synthetic opioids, namely fentanyl and its analogues [##REF##35853029##1##, ##REF##28735776##3##, ##UREF##0##4##]. The potency and ubiquity of synthetic opioids in the drug supply have shifted the risk environment for people who use drugs (PWUD) [##REF##28735776##3##], as stimulants and opioids adulterated with fentanyl have become increasingly pervasive, heightening concerns and anxieties related to overdose risk among PWUD [##REF##36000570##5##, ##REF##36106770##6##]. Recently, the increasing presence of xylazine, a veterinary anesthetic, in drug overdose deaths presents an emergent threat, leading to severe soft tissue damage and potentially heightened overdose risk [##REF##35247724##7##, ##REF##35830662##8##]. Additionally, novel benzodiazepines have emerged in the unregulated drug supply in North America [##REF##33627302##9##, ##REF##34437520##10##], raising concerns about heightened overdose risk.</p>", "<p id=\"Par13\">Amidst notable supply shifts observed over the course of the COVID-19 pandemic [##REF##36971722##11##], drug checking services have been proposed as a crucial public health response to the overdose epidemic in the USA [##UREF##1##12##–##REF##30292493##16##]. Drug checking services offer a promising strategy to improve knowledge and agency among PWUD navigating the opaque drug market [##REF##34996466##17##, ##REF##32769054##18##]. Fentanyl test strips (FTS), which are used to detect the presence of fentanyl, are widely used among PWUD to make informed decisions about use and mitigate risks [##REF##35344879##19##]. Existing evidence demonstrates that FTS may modify how individuals intend to use, prompting individuals to discard their sample or practice harm reduction techniques [##UREF##1##12##], such as using a tester shot, using less, using in the presence of others, using more slowly, or ensuring naloxone is accessible [##REF##32187173##14##, ##REF##30292493##16##, ##REF##33713964##20##–##REF##30200991##22##].</p>", "<p id=\"Par14\">FTS and other rapid immunoassay test strips (e.g., benzodiazepine test strips) are commercially available and distributed by many syringe service programs (SSPs) and other harm reduction organization across the USA [##REF##36910306##23##]. Xylazine test strips are currently sold by BTNX, motivated by the needs expressed by PWUD and clinicians alike [##REF##35830662##8##, ##REF##36846574##24##], while critically important tools, rapid immunoassay test strips have noteworthy limitations, suffering from low limits of detection and interferences from adulterants [##REF##36910306##23##]. In providing a binary result (positive or negative), rapid immunoassay test strips provide no information on concentration, which is important for dosing, especially in a market saturated with fentanyl [##REF##32769054##18##]. PWUD have shared that fentanyl is ubiquitous and difficult to avoid [##REF##32769054##18##], thereby limiting the utility of tests to screen for the presence of fentanyl without knowing the concentration of fentanyl in the sample. Additionally, test strips are specific to one substance, or to several compounds of the same class [##REF##35344879##19##]. In other words, an individual wanting to test their sample for fentanyl and benzodiazepines would have to use two strips: one for fentanyl and one for benzodiazepines.</p>", "<p id=\"Par15\">To address these limitations and to offer more detailed analytical data, various harm reduction organizations have piloted the use of Raman spectroscopy and Fourier-transformed infrared (FTIR) spectrometers for drug checking [##REF##34996466##17##, ##REF##30551090##25##, ##REF##31951925##26##]. These devices can be optimized to provide information on the presence and approximations of the amount of multiple compounds simultaneously but typically require users to employ spectral libraries for accurate, routine analysis and are less sensitive than rapid immunoassay test strips [##REF##34996466##17##, ##REF##30551090##25##, ##REF##31951925##26##]. To offset limitations of each analytical method [##REF##36966319##27##], some harm reduction programs use integrated approaches (e.g., using rapid immunoassay test strips in combination with FTIR) [##REF##31951925##26##, ##REF##32438280##28##].</p>", "<p id=\"Par16\">Advances in drug checking are underway, providing potentially life-saving services for PWUD [##REF##30551090##25##], by enhancing market monitoring capacity. Results from drug checking services are often shared within social networks to share information about drug quality with peers but can also feed into public health data systems [##REF##37964286##29##], aiding in the detection of novel adulterants in the supply [##REF##34729849##21##, ##UREF##2##30##, ##REF##34645480##31##].</p>", "<p id=\"Par17\">In this manuscript, we cast attention to the requirements and considerations of drug checking services for supply-level monitoring. This work was informed by the ongoing collaborations between academic institutions, SSPs, and community partners, and we begin with an overview of the various methodologies proposed, followed by a set of guiding principles that emerged from our discussions of implementation. While drug checking services are implemented across Europe, Australia, and Canada [##REF##34729849##21##, ##REF##32535605##32##, ##REF##21898860##33##], the considerations presented herein were focused on implementation in the US context, particularly within SSPs. The overarching aim is to describe how drug checking services at harm reduction organizations can be used for supply-level monitoring amidst rapid shifts in the drug landscape without compromising individual-level information for PWUD, and in this way, inform public health interventions for the worsening overdose crisis in the USA</p>" ]

[ "<title>Methods</title>", "<p id=\"Par18\">As a group of public health researchers, analytical chemists, evaluators, and harm reductionists, we used a semi-structured guide to facilitate discussion on key priorities for drug checking services, considering implementation, data, and public health significance. Four possible methodologies were discussed, each of which would be integrated into a SSP. Following the discussion, we conducted a thematic analysis to identify salient themes. These findings were contextualized with extant literature and were further validated by all members of this collaborative and other harm reductionists and public health professionals in Ohio.</p>" ]

[]

[]

[ "<title>Conclusions</title>", "<p id=\"Par40\">Drug checking services are potentially life-saving interventions, promoting agency among PWUD to mitigate risks in an unpredictable environment. Augmenting existing drug checking programs to facilitate supply-level monitoring has the potential to detect emerging threats in the drug supply, and in this way, public health agencies can proactively respond to supply shifts and tailor interventions to curb the toll of the overdose epidemic.</p>" ]

[ "<title>Background</title>", "<p id=\"Par1\">Shifts in the US drug supply, including the proliferation of synthetic opioids and emergence of xylazine, have contributed to the worsening toll of the overdose epidemic. Drug checking services offer a critical intervention to promote agency among people who use drugs (PWUD) to reduce overdose risk. Current drug checking methods can be enhanced to contribute to supply-level monitoring in the USA, overcoming the selection bias associated with existing supply monitoring efforts and informing public health interventions.</p>", "<title>Methods</title>", "<p id=\"Par2\">As a group of analytical chemists, public health researchers, evaluators, and harm reductionists, we used a semi-structured guide to facilitate discussion of four different approaches for syringe service programs (SSPs) to offer drug checking services for supply-level monitoring. Using thematic analysis, we identified four key principles that SSPs should consider when implementing drug checking programs.</p>", "<title>Results</title>", "<p id=\"Par3\">A number of analytical methods exist for drug checking to contribute to supply-level monitoring. While there is likely not a one-size-fits-all approach, SSPs should prioritize methods that can (1) provide immediate utility to PWUD, (2) integrate seamlessly into existing workflows, (3) balance individual- and population-level data needs, and (4) attend to legal concerns for implementation and dissemination.</p>", "<title>Conclusions</title>", "<p id=\"Par4\">Enhancing drug checking methods for supply-level monitoring has the potential to detect emerging threats in the drug supply and reduce the toll of the worsening overdose epidemic.</p>", "<title>Keywords</title>" ]

[ "<title>Overview of low-barrier methodologies</title>", "<p id=\"Par19\">Drug checking devices, such as the TruNarc Raman spectrometer and Bruker Alpha FTIR [##REF##31951925##26##], provide detailed information for PWUD, but widespread implementation is constrained by legal complexities as well as additional cost and labor requirements for already-stretched harm reduction organizations [##REF##35344879##19##, ##REF##30551090##25##]. All methodologies discussed (Fig. ##FIG##0##1##) were low-barrier methods, in the sense that minimal materials, costs, and labor would be required for implementation. In this community-academic collaborative, drug checking services would be implemented at the SSP, and with prepaid shipping materials, SSP staff would send completed test materials to the research partner, who would perform all analyses using liquid chromatography with tandem mass spectrometry (LC–MS/MS), a highly selective and sensitive analytical tool for pharmaceutical and illicit drug analysis [##REF##36910306##23##]. Evaluation partners in this collaborative would be responsible for dissemination, feeding results into data streams used by PWUD and public health agencies alike; this is discussed in greater detail in the subsequent section.</p>", "<p id=\"Par20\">The first test makes use of the illicit drug paper analytical device (idPAD) [##REF##32227600##34##], a paper test card developed for the analysis of solid illicit drug samples. To use the cards, solid sample is applied to the card, and the card placed in water to run twelve colorimetric tests, each designed for detecting different functional groups of compounds present in illicit drugs [##REF##32227600##34##]. At present, the idPAD is a useful tool for the analysis of bulk (percent-level) composition of illicit drugs, though it is unable to offer immediate information on drug content to non-trained users. Refinements of the idPAD are ongoing, and a mobile app is now available. The ultimate goal of this app is to capture idPAD images and use a trained neural network to detect the presence of various compounds, adulterants, and cutting agents to provide immediate information on drug content without the need for a trained user [##UREF##3##35##]. In addition to these developments in progress, the idPAD has been shown to be a useful tool for the collection and analysis of small quantities of illicit drugs for downstream (LC–MS/MS) analyses [##REF##36910306##23##].</p>", "<p id=\"Par21\">The second test takes the same approach as the idPAD but requires minimal time and sample. Individuals press a small mass of sample (10 mg) on an absorbent paper dot with a wax-printed boundary that helps localize and keep the sample in place during transit. Upon receipt of the paper dot, the testing laboratory can extract the solid drug from the paper dot for downstream analysis methods. In the third approach, the same sample mass (10 mg) is placed into a liquid-filled tube containing an aqueous solution of Bitrex, a non-toxic, bittering agent commonly used to prevent ingestion of cleaning products by children. The sample can be directly analyzed with LC–MS/MS. Each of these approaches yields quantitative information (i.e., concentration) after analysis but provides no information for PWUD at the point-of-use.</p>", "<p id=\"Par22\">The final proposed testing method allows for both the generation of rapid data at the point-of-use and for downstream analysis by making use of the commonly employed rapid immunoassay strips (e.g., FTS). With this approach, individuals use fentanyl or benzodiazepine test strips as normal, receiving a rapid dichotomous result (positive or negative). Rather than discarding the used strip, however, it would be sent for downstream analysis, by extraction of illicit drugs from the paper test card [##UREF##4##36##].</p>", "<title>Key principles</title>", "<p id=\"Par23\">In weighing the strengths and limitations of each testing method, our interdisciplinary team reached a consensus on four guiding principles, or considerations, for selecting a method and implementing drug checking services for supply-level monitoring: (1) immediate utility to PWUD, (2) integration into SSP workflow, (3) balancing individual- and population-level data needs, and (4) attention to the legal context, each of which is described in further detail. Overall, the selected approach should align with the needs and concerns expressed by PWUD.</p>", "<title>Immediate utility to PWUD</title>", "<p id=\"Par24\">Of the four tests discussed, only one method, the rapid immunoassay test strips, provides immediate results to the participant. This was deemed to be of utmost importance because supply-level data cannot come at the expense of individual-level information, especially when such information can be used to inform decision-making related to use and, ultimately, reduce overdose risk [##UREF##1##12##, ##REF##32187173##14##, ##REF##30292493##16##, ##REF##33713964##20##, ##REF##34729849##21##]. In the final three tests, small amounts (10 mg) of sample are required. The idPAD, in contrast, requires much larger amounts (20 mg), presenting a significant barrier to implementation. Demonstration of immediate benefit to PWUD will be key in building trust among prospective participants.</p>", "<title>Integration into SSP workflow</title>", "<p id=\"Par25\">Considerations of the operational context were critical in thinking about the feasibility of implementation at the SSP. The time required for the idPAD would interfere with the existing SSP workflow, as there are often space constraints and lines of people waiting to enter during operating hours, although resources and structures vary widely between SSPs [##REF##35977459##37##, ##REF##24422784##38##]. The processes for the second test using paper dots were cumbersome, often requiring assistance and a flat surface. The ease of the third test, in which individuals simply placed a scoop of sample into a liquid vial or tube, made it a feasible option. Similarly, FTS are portable, meaning they are already distributed by SSPs for use off-site, causing no changes to existing processes.</p>", "<p id=\"Par26\">Since FTS are already distributed by most SSPs, no disruptions would be made to SSP operations. Additionally, advancements in harm reduction are underway in Ohio with the installation of public health vending machines (PHVMs) [##REF##36168975##39##]. PHVMs are stocked with a range of essential supplies for PWUD to mitigate drug-related harms, including but not limited to sterile injection equipment, HIV test kits, condoms, sharps containers, naloxone, and FTS [##REF##36168975##39##]. FTS included in PHVMs could include prepaid mailing materials and information about the testing service, where rather than discarding the used strip, individuals submit the strip for analysis to contribute to supply-level monitoring [##REF##36910306##23##].</p>", "<p id=\"Par27\">As an example of a potential downstream analytical method, the Lieberman group has developed sensitive tandem LC–MS/MS analysis for 22 common drugs and drug metabolites [##REF##36910306##23##]. The limit of detection for all analytes is below 0.07 ng/mL, and preliminary results show that a wide range of illicit compounds can be recovered from used FTS using this method (Fig. ##FIG##1##2##). All 21 drugs were recovered above the limit of detection, demonstrating the potential to obtain much more detailed information about the community drug supply than the result that FTS provide at the point-of-use. The current drug market has been characterized by fentanyl ubiquity [##REF##32769054##18##], and thus, there will likely be shifts in demand for alternative test strips (e.g., xylazine test strips), as opposed to FTS. The method described herein is not limited to FTS, meaning used xylazine test strips could also be used for downstream analysis, but further work is needed to assess how drug-specific antibodies (e.g., fentanyl-specific antibody on FTS) affect the recovery of different drugs. Additionally, future studies should assess how long different drugs can be stored on used immunoassay test strips, how effectively and consistently they can be removed for analysis, and whether other drugs or cutting agents interfere with recovery or downstream analysis.</p>", "<p id=\"Par28\">Besides used test strips, other drug paraphernalia (e.g., cookers, cottons, bags) could be analyzed by extracting residue, but PWUD would receive no information at the point-of-use. This may be a beneficial approach for SSPs and harm reduction organizations that have working relationships with local law enforcement and prosecutors for safe disposal of syringes. For example, when law enforcement officials in St. Joseph County, Indiana, find used drug paraphernalia (e.g., syringes, cookers) in the community, they contact employees from the local harm reduction organization to safely collect and dispose of such materials. Paraphernalia collected for disposal, with the exception of syringes, could be submitted for analysis to contribute to supply-level monitoring. While there are previous studies where syringes were used for analysis [##REF##33932743##40##], this approach requires safeguards for safe transport and handling of biohazardous materials. Additionally, submitting used syringes would limit analyses to substances consumed by injection, whereas collecting test strips or paraphernalia other than syringes accommodates testing of substances that were consumed through various routes of administration. This is an important consideration, as snorting has become increasingly common in the synthetic opioid era [##REF##34662847##41##–##REF##30676198##43##].</p>", "<title>Balancing individual- and population-level data needs</title>", "<p id=\"Par29\">Members of this collaborative discussed the importance of utilizing existing infrastructure for dissemination of results to ensure that, even if there is a data lag, the results are useful and relevant to PWUD in the community. For example, results can feed into “bad batch alerts” systems. The SOAR (Safety, Outreach, Autonomy, Respect) Initiative in Ohio has developed a mobile application, modeled after a text messaging service in Baltimore [##REF##35775468##44##, ##UREF##5##45##], that alerts PWUD when overdoses have surged and when fentanyl has been detected and reported in multiple batches in a particular geographic area. Feeding results into a data stream that is trusted and used by PWUD maximizes the utility of data. Beyond bad batch alerts, this information can be used by SSP staff to share information with participants, effectively tailoring information to current supply trends. Similarly, public health departments often manage dashboards to monitor and evaluate overdose data; such dashboards can be complemented by overlaying overdose trends with supply-level trends (Fig. ##FIG##2##3##), facilitating the detection of emergent shifts and threats.</p>", "<p id=\"Par30\">While aggregate data can provide important information for supply-level monitoring, providing anonymous individual-level data can maximize benefits to individuals participating in drug checking programs. The dashboard (streetsafe.supply) developed and maintained by the Injury Prevention Research Center at the University of North Carolina–Chapel Hill, which offers mail-based drug checking services, is one exemplar [##UREF##8##48##]. Each sample is assigned an anonymous ID, which individuals make note of prior to submission. Individual results are posted to the dashboard with the associated sample ID, allowing individuals to access the results from their sample. Publishing individual-level results on a dashboard underscores the need to protect participants’ anonymity to avoid both (a) criminalization [##REF##34732582##49##] and (b) retaliation from those who sell drugs for perceived “snitching”, [##REF##28104571##50##, ##REF##29040841##51##] potentially disrupting supply chains or social networks [##REF##28609723##52##].</p>", "<title>Attention to the legal context for implementation and dissemination</title>", "<p id=\"Par31\">Recognition of the legal complexities associated with each approach was also central to the discussion. Asking individuals to provide a sample on-site requires significant trust [##REF##30551090##25##], and in most states, drug possession on SSP premises is prohibited [##REF##35344879##19##], meaning individuals would have to complete the test off-site and bring completed materials at their next visit. Alternatively, the SSP could provide individuals with prepaid mailing supplies, allowing individuals to complete and submit the test off-site simultaneously. Whether SSP participants or staff are responsible for mailing completed testing materials is of consequence to the research partner because staff can ship materials according to a planned schedule, whereas samples ready for analysis will be received sporadically when submitted by individual participants.</p>", "<p id=\"Par32\">The level of data collected and reported should be scrutinized, carefully considering the utility of such information to PWUD as well as how such information could be used by police. At minimum, prospective participants should be fully informed on how data will be used for supply-level monitoring. Scholars have raised concerns about police using supply-level monitoring—and geospatial data, in particular—to target enforcement resources [##REF##34732582##49##]. Protecting participants’ anonymity is paramount to ensure public health monitoring does not facilitate increased—and counterproductive—criminalization among individuals participating in harm reduction programming [##REF##34732582##49##].</p>", "<p id=\"Par33\">Drug paraphernalia laws can prevent PWUD from participating in harm reduction programming [##REF##31536408##53##], and thus, may present a barrier to participation in drug checking services. Paraphernalia laws broadly prohibit the possession of equipment that is associated with illicit drugs, even equipment used for testing, although considerable heterogeneity exists across states [##REF##35344879##19##, ##REF##31536408##53##], and the legal status of FTS has often been ambiguous [##REF##31536408##53##]. In 2021, the Centers for Disease Control and Prevention (CDC) and the Substance Abuse and Mental Health Services Administration (SAMHSA) announced new regulations that now allows federal funding to be used to purchase FTS. Historically, in as many as 30 states, it was illegal to <italic>possess</italic> drug checking equipment, which included FTS, and 33 states prohibited the <italic>distribution</italic> of drug checking equipment [##REF##35344879##19##]. Penalties for violation of drug paraphernalia laws varied widely, ranging from civil fines to multi-year sentences [##REF##35344879##19##]. Even though regulations have changed, and loopholes exist [##REF##31610451##54##], limited awareness may discourage participation and implementation of drug checking programming due to concerns about potential criminalization [##REF##31536408##53##], underscoring the need to promote awareness among PWUD. Furthermore, there are complexities associated with new regulations that still limit participation in the full range of harm reduction services. For example, in Ohio, the recent passage of SB 288 excludes <italic>only</italic> FTS from drug paraphernalia laws [##UREF##9##55##]; rapid immunoassay test strips for other scheduled substances would still be subject to drug paraphernalia laws.</p>", "<p id=\"Par34\">Drug paraphernalia laws are particularly relevant for partners collecting and submitting used paraphernalia for analysis. This approach requires strong working relationships between harm reduction organizations and local law enforcement, which can be facilitated by providing officers with training and resources that detail the well-established benefits of harm reduction services to PWUD—and the community at-large [##REF##19138414##56##]. These relationships, or even partnerships, between harm reduction organizations and law enforcement are critical because officers have discretion in how they respond to, and enforce, substance use-related incidents [##REF##26180948##57##–##UREF##10##60##].</p>", "<title>Processes to accelerate implementation</title>", "<p id=\"Par35\">In addition to considerations for implementation at SSPs and with PWUD, special considerations exist for the implementation of these protocols at academic research institutions conducting downstream analyses of illicit compounds. While analytical reference solutions of controlled substances can be purchased and handled by academic researchers without additional approvals, the purchasing, handling, and disposals of solid illicit drug standards and samples are regulated by government entities at the federal (Drug Enforcement Administration [DEA]), state (State Pharmacy Boards), and local levels. Specifically, academic laboratories wishing to work with solid illicit drugs are required to acquire the license(s) for the schedules of drugs of interest. It is unclear, however, that these regulations apply to <italic>used</italic> FTS, as they are garbage and do not require special protocols for waste disposal. In any case, approvals and documents of support or acknowledgment from government organizations, especially the DEA, may facilitate increased stakeholder support, alleviating concerns about legality and enforcement. Additionally, forming working relationships between harm reduction organizations and local law enforcement can help safeguard PWUD, mitigating concerns about policing and criminalization of those participating in drug checking and other harm reduction services [##REF##19138414##56##, ##REF##26180948##57##, ##REF##34978645##59##, ##UREF##10##60##]. If applications or standard operating procedures are required, these should be initiated as early as possible to enable timely incorporation of samples collected through SSP collaborations.</p>", "<p id=\"Par36\">Collaborations with academic laboratories and SSPs provide an opportunity to develop and validate methods for targeted and non-targeted analysis, which depend on real-world samples because adulterants in the supply can cause chemical interference that would not be observed when tested with pure, analytical-grade compounds. SSPs can provide academic institutions with diverse, real-world samples that enhance the utility of novel tests and technologies, while academic institutions provide access to analytical instrumentation (e.g., LC–MS/MS) that facilitate robust, detailed analyses for drug checking, overcoming the limitations of existing rapid tests and advancing supply-level monitoring efforts [##REF##30551090##25##].</p>", "<title>Implications for public health policy and practice</title>", "<p id=\"Par37\">The USA faces a worsening overdose crisis, exacerbated by supply shifts and the emergence of xylazine, altering the risk environment for PWUD [##REF##28735776##3##, ##REF##35247724##7##, ##REF##35830662##8##]. In the absence of safe supply, drug checking services are an urgent need [##UREF##1##12##, ##REF##34528056##13##], as these services provide PWUD with agency to navigate an unpredictable drug market [##REF##32769054##18##]. Many SSPs and harm reduction programs distribute rapid immunoassay test strips, and community-academic partnerships provide a promising avenue to enhance existing drug checking services for supply-level monitoring, by developing and validating methods for analysis (e.g., xylazine test strips).</p>", "<p id=\"Par38\">A wide variety of technologies exist that can be applied for drug checking services [##REF##34996466##17##, ##REF##31951925##26##, ##REF##36966319##27##], each of which has its own strengths and limitations. Faced with budgetary constraints, harm reduction organizations will have to balance tradeoffs, and although there is likely not a one-size-fits-all approach, the implementation of drug checking services should be guided and informed by key principles. For one, tests should prioritize immediate utility to participants. Additionally, the dissemination of results should carefully balance individual- and supply-level information needs, while ensuring anonymity to mitigate the potential for targeted policing and criminalization among participating individuals and communities [##REF##34732582##49##]. The processes for dissemination should also be considered, looking to existing, trusted data infrastructure used by PWUD (e.g., bad batch alert systems) to maximize the utility of data.</p>", "<p id=\"Par39\">Existing supply monitoring efforts are limited and typically stem from law enforcement seizures and postmortem toxicology results, both of which are subject to selection bias [##REF##34528056##13##]. In the collaborative described herein, SSPs will continue to distribute FTS as normal, but participants can submit the used test strip for analysis rather than discarding it. This approach ensures participants receive immediate results that can inform how they use, while also contributing to supply-level data. The costs associated with testing present a barrier to the scale and sustainability of community–academic partnerships—and to drug checking services more broadly. Opioid settlement funds may provide one mechanism to fund drug checking and other essential harm reduction services that have long been the financial responsibility of community-based organizations [##UREF##11##61##].</p>" ]

[ "<title>Acknowledgements</title>", "<p>The authors extend their thanks and appreciation to James Decker, Gary Bright, Sharona Bishop, and Brittney Nye from Hancock Public Health (Findlay, OH) for their thoughtful review and comments on this project.</p>", "<title>Author contributions</title>", "<p>KJM contributed to conceptualization; HDW, KLH, and ML contributed to methodology; KJM performed writing—original draft; HDW, KAH, KLH, DS, BC, RB, and AT performed writing—review and editing; AT, ML, and SN performed supervision. All authors reviewed and approved the manuscript in its final form.</p>", "<title>Funding</title>", "<p>Funding was received from the following sources to support the development of analytical methods: Berthiaume Institute for Precision Health at the University of Notre Dame (Substance Abuse Fund); Indiana Clinical and Translational Sciences Institute, funded in part by Grant No. UL1TR002529 from the National Institutes of Health National Center for Advancing Translational Sciences; and the National Science Foundation Partnership for Innovation (Grant No. Grant IIP-2016516). The content of this manuscript is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health or any other funding agency.</p>", "<title>Availability of data and materials</title>", "<p>Not applicable.</p>", "<title>Declarations</title>", "<title>Ethics approval and consent to participate</title>", "<p id=\"Par41\">Not applicable.</p>", "<title>Consent for publication</title>", "<p id=\"Par42\">Not applicable.</p>", "<title>Competing interests</title>", "<p id=\"Par43\">No competing interests to disclose.</p>" ]

[ "<fig id=\"Fig1\"><label>Fig. 1</label><caption><p>Summary of four low-barrier methods for drug checking services discussed for implementation in SSPs</p></caption></fig>", "<fig id=\"Fig2\"><label>Fig. 2</label><caption><p>Three FTS (two lots manufactured by BTNX, one lot manufactured by DanceSafe) were run following manufacturer guidelines with aqueous solutions of 5000 ng/mL of each of the drugs or drug metabolites. Each strip was dried, stored for a week, then extracted with 5 mL of water/methanol 9:1 with sonication. Analysis of the amount of drug or drug metabolite that was extracted from the strip into the water/methanol solution was performed as previously described [##REF##36910306##23##]</p></caption></fig>", "<fig id=\"Fig3\"><label>Fig. 3</label><caption><p>Mock dashboard of overdose trends overlaid with supply-level monitoring. Data were constructed to provide an example of how drug checking data can be superimposed on overdose dashboards to assess geospatial and temporal trends to better understand associations between supply shifts and overdose risk [##UREF##6##46##, ##UREF##7##47##]</p></caption></fig>" ]

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{ "acronym": [ "FTS", "FTIR", "idPAD", "LC–MS/MS", "PHVM", "PWUD", "SSP" ], "definition": [ "Fentanyl test strips", "Fourier-transformed infrared spectroscopy", "Illicit drug paper analytical device", "Liquid chromatography with tandem mass spectrometry", "Public health vending machines", "People who use drugs", "Syringe service program" ] }

[ "<title>Introduction</title>", "<p id=\"Par5\">Acute coronary syndrome (ACS) encompasses various types of myocardial ischaemia, including ST-segment elevation myocardial infarction (STEMI), non-STEMI and unstable angina pectoris (UAP). The condition is primarily caused by the destabilisation of coronary atherosclerotic plaques [##REF##37622654##1##, ##REF##27810051##2##]. Emerging evidence suggests that an upregulated inflammatory response and abnormal metabolism of specific lipid molecules play significant roles in the formation, rupture and subsequent development of ACS. In addition to established traditional lipid biomarkers, such as low-density lipoprotein (LDL-C), high-density lipoprotein (HDL-C) and triglycerides (TGs), several metabolomic and lipidemic indicators, including ceramides and apolipoproteins, have been implicated in the occurrence and progression of ACS [##REF##33005245##3##–##REF##26523994##5##].</p>", "<p id=\"Par6\">Ceramide, a sphingolipid derivative of sphingomyelinase, assumes a critical function in preserving the structural integrity of cells and acts as a bioactive lipid participating in diverse cellular signalling pathways related to cell proliferation, differentiation and apoptosis. Substantial research has established a link between ceramides and multiple atherosclerotic processes, encompassing lipoprotein aggregation, cholesterol accumulation in macrophages, the modulation of nitric oxide synthesis, the generation of superoxide anions and the regulation of cytokine expression [##REF##30846529##6##–##REF##25124322##9##]. Notably, ceramides have been implicated in acute coronary events by fostering the infiltration of oxidised LDL-C into vascular walls, monocyte adhesion, atherosclerotic plaque formation and the expansion of the lipid-rich core, rendering it more susceptible to rupture [##REF##22982021##10##]. Intracoronary imaging studies have predominantly identified ceramides within the thin fibrous cap of atheromatous plaques with necrotic cores [##REF##32212040##11##, ##REF##28674269##12##]. Plasma ceramides have exhibited a positive and independent correlation with plaque rupture and erosion in patients suffering from acute myocardial infarction (AMI), as evaluated by optical coherence tomography [##REF##32387714##13##]. Moreover, specific molecular lipid species, particularly ceramide (d18:1/16:0), have been linked to the fraction of necrotic core tissue and lipid core burden in coronary atherosclerosis, serving as predictive markers for the 1-year clinical outcome following coronary angiography (CAG) [##REF##26523994##5##]. Bolstered by both theoretical evidence and empirical findings, ceramide (d18:1/16:0), ceramide (d18:1/18:0) and ceramide (d18:1/24:1) and their ratios to ceramide (d18:1/24:0) have emerged as novel risk indicators in patients diagnosed with confirmed coronary heart disease [##REF##27765765##14##].</p>", "<p id=\"Par7\">In addition, the progression of atherosclerotic cardiovascular disease is influenced by systemic vascular inflammation, which contributes to multiple maladaptive processes [##REF##32176778##15##]. Interleukin-6 (IL-6), a pro-inflammatory cytokine, has been proposed as a potential predictor of coronary artery disease (CAD) severity and has been associated with plaque burden, as assessed by intracoronary imaging [##REF##15602020##16##, ##REF##34670726##17##]. Furthermore, evidence suggests that the activation of the inflammatory pathway is crucial for ceramide biosynthesis [##REF##21490391##18##]. In-vitro experiments have also demonstrated the interaction between ceramides and cytokines in various cellular pathways, highlighting their involvement in inflammation [##UREF##1##19##–##REF##18599066##21##]. Given the interplay between pro-inflammatory cytokines and ceramides, along with their significant roles in the occurrence and progression of ACS, it is conceivable that there is a substantial overlap in the biological processes triggered by these factors in patients diagnosed with ACS.</p>", "<p id=\"Par8\">In summary, we speculated that the concurrent measurement of traditional risk factors, pro-inflammatory cytokines such as tumour necrosis factor-alpha (TNF-α) and IL-6, along with ceramides, could improve the diagnostic accuracy of ACS. However, limited research has explored the relationship between inflammatory factors and ceramides in patients with ACS, as well as the potential diagnostic benefits of concurrently measuring pro-inflammatory cytokines, such as TNF-α and IL-6, alongside ceramides. Therefore, the objective of our study was to investigate the correlation between ceramides and pro-inflammatory cytokines in patients with ACS and to evaluate the potential added value of combining ceramide and pro-inflammatory cytokine testing for early ACS diagnosis.</p>" ]

[ "<title>Method and materials</title>", "<title>Study design and participants</title>", "<p id=\"Par9\">This observational study involved the enrolment of 216 patients who were admitted and underwent CAG for suspected CAD between July 2021 and May 2022 at the Second Hospital of Hebei Medical University, China. The exclusion criteria encompassed moderate to severe chronic kidney disease (estimated glomerular filtration rate ≤ 60 mL/min/1.73 m²), chronic heart failure, acute cerebrovascular disease, a history of malignant tumours and active infectious diseases. Patients who had been prescribed anti-hyperlipidaemic medication or met the diagnostic criteria for AMI without evidence of vascular stenosis were also excluded from the study. For a comprehensive overview of the detailed inclusion and exclusion processes, please refer to Fig. ##FIG##0##1##.</p>", "<p id=\"Par10\">\n\n</p>", "<p id=\"Par11\">The inclusion criterion was patients suspected of having CAD by clinical doctors based on their clinical symptoms (e.g. chest pain, dyspnoea).</p>", "<p id=\"Par12\">Two highly skilled cardiologists independently reviewed all coronary angiograms, and a diagnosis of ACS was determined based on a combination of clinical symptoms, electrocardiographic changes, cardiac biomarkers and findings from CAG that indicated the presence of significant stenosis (≥ 50%) in one or more coronary arteries, following the recommended criteria outlined in the European Society of Cardiology guidelines [##REF##26320110##22##, ##REF##28886621##23##]. Prior to participation in the study, written informed consent was obtained from all of the enrolled participants. The study protocol strictly adhered to the principles outlined in the Declaration of Helsinki and received approval from the Ethics Committee of the Second Hospital of Hebei Medical University (ethics approval number: W2021041).</p>", "<title>Preparation of blood samples</title>", "<p id=\"Par13\">After a minimum fasting period of 12 h following admission, venous blood samples were collected from the patients. The blood was drawn from peripheral veins using ethylenediaminetetraacetic acid-containing tubes to preserve the integrity of the samples for the measurement of ceramides and cytokines. To ensure optimal preservation, aliquots of plasma were promptly prepared and stored at a temperature of − 80℃ until the time of analysis.</p>", "<title>Demographic, clinical and laboratory assessments</title>", "<p id=\"Par14\">The study assessed various demographic and traditional risk factors. Demographic variables included age and gender. The traditional risk factors that were evaluated were body mass index (kg/m2), smoking history, medical history of alcohol consumption, diabetes mellitus (DM) and hypertension. In addition, laboratory variables were measured, including baseline serum lipid markers, such as total cholesterol, LDL-C, HDL-C and TGs. Other variables measured were lipoprotein(a) (LPa), hypersensitive C-reactive protein (hs-CRP), fasting blood glucose and B-natriuretic peptide.</p>", "<title>Ceramide measurements</title>", "<p id=\"Par15\">The quantification of ceramides was conducted by a senior laboratory examiner who was blinded to the clinical details of the participants. To extract plasma ceramides, a liquid–liquid extraction method using methanol was employed. The levels of plasma ceramide (d18:1/14:0), ceramide (d18:1/16:0), ceramide (d18:1/18:0), ceramide (d18:1/20:0), ceramide (d18:1/22:0), ceramide (d18:1/24:0) and ceramide (d18:1/24:1) were measured using the Shimadzu LC-20 A system. The Phenomenex Kinetex C18 analytical column (2.6 μm, 3.0 × 50 mm id.) was used coupled with an AB SCIEX triple Quad 4500 tandem mass spectrometer (Applied Biosystems Inc., USA) equipped with an electrospray ion source. The gradient reverse phase chromatography involved mobile phases including liquid chromatography–mass spectrometry (LC–MS) grade water (A) with 0.1% formic acid-mM ammonium acetate water and (B) 0.1% formic acid-2mM ammonium acetate-acetonitrile-isopropyl alcohol (7:3/v:v). Ceramide (d18:1/17:0) was used as the internal standard. The calibration linearity of ceramides was established by plotting the ratio of the peak response of ceramides to the peak response of their respective stable isotope internal standard in working standard solutions against the quantity of ceramides. To ensure the stability of analysis and calibration verification, three levels of standard solutions (QCL, QCM and QCH) were employed as quality controls and were checked every 10 injections. All ceramides exhibited excellent linearity within the calibration range, with correlation coefficients (R²) of &gt; 0.99. No matrix interference or carryover was observed during the analysis.</p>", "<title>Cytokine measurements</title>", "<p id=\"Par16\">The plasma levels of TNF-α, IL-6 and IL-8 were quantified using enzyme-linked immunosorbent assay (ELISA) kits obtained from Biotech Pack Analytical in Beijing, China. The ELISA kits had a minimum detectable concentration of &lt; 1.0 pg/mL. After the samples were thawed, the ELISA measurements were conducted by a skilled laboratory examiner. The method employed yielded both intra-assay and inter-assay coefficients of variation of &lt; 15% each, ensuring reliable and consistent results.</p>", "<title>Statistical analyses</title>", "<p id=\"Par17\">Statistical analyses were conducted using the IBM SPSS Statistics version 25.0 software (IBM Corp., Armonk, NY, USA). Continuous variables were presented as mean ± standard deviation, while categorical variables were expressed as percentages. The normality of data was assessed using the Kolmogorov–Smirnov test. For normally distributed continuous variables, the student’s t-test was used for inter-group comparisons, whereas the Mann–Whitney U test was employed for non-normally distributed data. Categorical variables were compared using either the chi-square test or Fisher’s exact test, depending on the sample size of patients in the analysis group. The association between ceramides and pro-inflammatory cytokines was evaluated using Pearson’s correlation coefficient. Receiver operating characteristic (ROC) curves and multivariate logistic regression were used to analyse the clinical accuracy of ceramides combined with cytokines in predicting ACS. The performance and discrimination ability of the four diagnostic models were assessed using the R statistical software version 3.4.3. Statistical significance was considered when the two-sided <italic>P</italic>-value was &lt; 0.05.</p>" ]

[ "<title>Results</title>", "<title>Demographic, clinical and laboratory findings</title>", "<p id=\"Par18\">Table ##TAB##0##1## presents the demographic, clinical and laboratory characteristics of all participants included in the study. Out of the total of 216 participants, 138 were diagnosed with ACS, while the remaining participants were classified as non-ACS. Within the ACS group, 11 individuals (7.97%) had STEMI, 25 (18.12%) had NSTEMI and 102 (73.91%) were diagnosed with UAP. All patients with UAP exhibited major vessel stenosis of &gt; 50% as observed via CAG. In comparison with the non-ACS group, a higher percentage of patients with ACS were men (69.6%), current smokers (44.2%) and had a history of DM (37.0%). Additionally, the ACS group demonstrated significantly higher TG and LPa levels, along with lower levels of HDL-C, in comparison with those without ACS. Furthermore, BNP levels were found to be significantly elevated in patients with ACS (<italic>P</italic> &lt; 0.05).</p>", "<p id=\"Par19\">\n\n</p>", "<title>Pro-inflammatory cytokine plasma levels and ceramides of participants</title>", "<p id=\"Par20\">Table ##TAB##1##2## presents the plasma levels of 3 pro-inflammatory cytokines and 7 ceramide species in all participants included in the study. Patients with ACS exhibited significantly higher levels of TNF-α and IL-6 compared with those without ACS (<italic>P</italic> &lt; 0.01). Among the analysed ceramides, ceramide (d18:1/16:0) displayed the most substantial elevation in plasma levels in the patients with ACS, with a <italic>P</italic>-value close to 0. The <italic>P</italic>-values for ceramide (d18:1/24:0) and ceramide (d18:1/22:0) were also close to 0, indicating significant elevation. For ceramide (d18:1/18:0) and ceramide (d18:1/20:0), the <italic>P</italic>-values were &lt; 0.05, indicating a significant increase. In contrast, the <italic>P</italic>-values for ceramide (d18:1/14:0) and ceramide (d18:1/24:1) were 0.097 and 0.361, respectively, suggesting no significant elevation. These findings suggest that the plasma levels of ceramide (d18:1/24:0), ceramide (d18:1/22:0), ceramide (d18:1/18:0) and ceramide (d18:1/20:0) were significantly elevated in the patients with ACS, while no significant elevation was observed for ceramide (d18:1/14:0) or ceramide (d18:1/24:1).</p>", "<p id=\"Par21\">\n\n</p>", "<title>The association between ceramide and inflammatory factors</title>", "<p id=\"Par22\">Table ##TAB##2##3## presents the associations between plasma ceramides and inflammatory factors. The results indicate no significant associations between ceramides and pro-inflammatory cytokines TNF-α, IL-6 or IL-8. However, a mild association was observed between hs-CRP and ceramides d18:1/18:0 and d18:1/20:0, with <italic>P</italic>-values of &lt; 0.5.</p>", "<p id=\"Par23\">\n\n</p>", "<title>Univariable and multivariable logistic regression results: the clinical acute coronary syndrome predictors</title>", "<p id=\"Par24\">Table ##TAB##3##4## presents the results of the univariate logistic and multivariate regression analyses for clinical predictors of ACS. Among the traditional risk factors, age (OR = 0.981, 95% CI: 1.580–4.990, <italic>P</italic> &lt; 0.001), male (OR = 2.808, 95% CI: 1.272–4.767, <italic>P</italic> &lt; 0.001), DM history (OR = 2.462, 95% CI: 1.272–4.767, <italic>P</italic> &lt; 0.01) and being a current smoker (OR = 3.961, 95% CI: 1.999–7.848, <italic>P</italic> &lt; 0.001) were significant predictors of ACS (<italic>P</italic> &lt; 0.05). Additionally, most of the lipid profiles, including TGs (OR = 1.405, 95% CI: 1.008–1.960, <italic>P</italic> &lt; 0.05) and HDL-C (OR = 0.227, 95% CI: 0.081–0.636, <italic>P</italic> &lt; 0.01), were significantly associated with ACS (<italic>P</italic> &lt; 0.05). The pro-inflammatory cytokines, TNF-α (OR = 1.063, 95% CI: 1.018–1.109, <italic>P</italic> &lt; 0.01) and IL-6 (OR = 1.157, 95% CI: 1.077–1.243, <italic>P</italic> &lt; 0.001), were also significant predictors of ACS (<italic>P</italic> &lt; 0.01). Moreover, several ceramides, including ceramide (d18:1/16:0) (OR = 1.016, 95% CI: 1.008–1.024, <italic>P</italic> &lt; 0.001), ceramide (d18:1/18:0) (OR = 1.017, 95% CI: 1.000–1.034, <italic>P</italic> &lt; 0.05), ceramide (d18:1/24:0) (OR = 1.001, 95% CI: 1.000–1.001, <italic>P</italic> &lt; 0.01), ceramide (d18:1/20:0) (OR = 1.016, 95% CI: 1.001–1.032, <italic>P</italic> &lt; 0.05) and ceramide (d18:1/22:0) (OR = 1.003, 95% CI: 1.001–1.005, <italic>P</italic> &lt; 0.05), were significantly associated with ACS.</p>", "<p id=\"Par25\">\n\n</p>", "<p id=\"Par26\">The results of the multivariate regression analysis demonstrated that being male (OR = 2.702, 95% CI: 1.290–5.658, <italic>P</italic> &lt; 0.01), having a DM history (OR = 2.329, 95% CI: 1.077–5.035, <italic>P</italic> &lt; 0.05), being a current smoker (OR = 2.702, 95% CI: 1.204–6.066, <italic>P</italic> &lt; 0.05), LPa (OR = 1.006, 95% CI: 1.000–1.012, <italic>P</italic> &lt; 0.05), IL-6 (OR = 1.173, 95% CI: 1.082–1.271, <italic>P</italic> &lt; 0.001) and ceramide (d18:1/16:0) (OR = 1.018, 95% CI: 1.008–1.028, <italic>P</italic> &lt; 0.001) were all significant predictors of ACS.</p>", "<title>Predictive value of the models for acute coronary syndrome</title>", "<p id=\"Par27\">Table ##TAB##4##5## presents the predictive values of four diagnostic models, which included variables with <italic>P</italic>-values of &lt; 0.05 in the multivariate logistic regression. Model 1 achieved an area under the curve (AUC) of 0.722 for diagnosing ACS. When IL-6 was added to the model, the AUC increased to 0.785. Incorporating traditional risk factors along with ceramide (d18:1/16:0) resulted in an AUC of 0.782. Finally, model 4, which combined traditional risk factors (male gender, history of DM, current smoking status and elevated LPa, IL-6 and ceramide [d18:1/16:0]), demonstrated an AUC of 0.827. The results indicated that the combination of traditional risk factors, IL-6 and ceramide (d18:1/16:0) significantly improved the AUC of model 4 compared with model 1 (0.827 [0.770–0.884] vs. 0.722 [0.653–0.791], <italic>P</italic> &lt; 0.001), model 2 (0.827 [0.770–0.884] vs. 0.785 [0.723–0.846], <italic>P</italic> &lt; 0.05) and model 3 (0.827 [0.770–0.884] vs. 0.782 [0.720–0.845], <italic>P</italic> &lt; 0.05). The ROC curves for the prediction of ACS for the four models are depicted in Fig. ##FIG##1##2##.</p>", "<p id=\"Par28\">\n\n</p>", "<p id=\"Par29\">\n\n</p>" ]

[ "<title>Discussion</title>", "<p id=\"Par30\">Due to the multifaceted nature of the underlying aetiology and mechanisms of ACS, relying on a single biomarker for accurate prediction is unlikely. Therefore, the development of a multi-marker model is crucial to enhance the prediction of ACS occurrence. In this study, a proportional increase in the plasma levels of ceramide (d18:1/16:0), ceramide (d18:1/18:0), ceramide (d18:1/20:0), ceramide (d18:1/22:0), ceramide (d18:1/24:0), TNF-α and IL-6 in patients with ACS was observed. Certain ceramide species, specifically, ceramide (d18:1/16:0), ceramide (d18:1/18:0), ceramide (d18:1/20:0), ceramide (d18:1/22:0) and ceramide (d18:1/24:0), along with TNF-α and IL-6, were identified as independent predictors of ACS, even after adjusting for traditional risk factors.</p>", "<p id=\"Par31\">The proposed model, incorporating the traditional risk factors, ceramide (d18:1/16:0) and IL-6, demonstrated an AUC of 0.827. In comparison, the AUCs of the models considering only the traditional risk factors ceramide (d18:1/16:0) or IL-6 individually were 0.782, 0.785 and 0.722, respectively. These findings suggest that combining the assessment of traditional risk factors, including male gender, history of DM, current smoking status and elevated LPa with ceramide (d18:1/16:0) and IL-6, can enhance the predictive accuracy for ACS.</p>", "<p id=\"Par32\">Although our study did not find any significant associations between the ceramide subspecies and proinflammatory cytokines, in-vitro research suggests that ceramide, as a second messenger of sphingolipids, is linked to various cytokines, including TNF-α and IL-6. Ceramides have been shown to stimulate the secretion of IL-6 and CRP, exerting a direct pro-inflammatory effect [##REF##8106344##24##–##REF##11679405##28##]. Some studies have indicated that cytokines such as TNF-α and IL-6 can impact phospholipid metabolism and subsequently influence ceramide production [##REF##21490391##18##]. However, our study did not find a significant association between any of the ceramide molecules and TNF-α or IL-6, while a mild relationship was observed between hs-CRP and ceramide (d18:1/18:0) and ceramide (d18:1/20:0), which is inconsistent with prior studies. Existing studies refer to research that indicated that TNF-α and IL-6 could impact phospholipid metabolism and, subsequently, influence ceramide production; our study, however, did not find a significant association between any of the ceramide molecules. This discrepancy may be attributed to our relatively small sample size and the lack of serial biomarker measurements. Further research is warranted to explore the relationship between proinflammatory cytokines and ceramide molecules in more detail.</p>", "<p id=\"Par33\">Numerous studies have provided compelling evidence of the strong association between inflammation and ACS [##REF##29066436##29##]. Interleukin-6, IL-18 and TNF-α are found in human plaques and may play roles in plaque progression and rupture since they have been associated with ACS. Additionally, they are associated with a higher incidence of acute cardiovascular events in patients with extreme cardiovascular risk [##REF##38030852##30##]. Among the proinflammatory cytokines, IL-6 (primarily produced by T-cells and macrophages) plays a crucial role in destabilising plaques, promoting atheroprogression and stimulating the production of hs-CRP [##REF##28288972##31##, ##REF##24697653##32##]. Moreover, a comprehensive analysis of multiple studies consistently demonstrated that elevated blood levels of IL-6 independently increase the risk of major adverse cardiovascular events, as well as cardiovascular and all-cause mortality in patients with ACS [##REF##18298837##33##]. Consequently, measuring IL-6 levels in the blood holds promise for improving the risk stratification of patients with ACS [##REF##32811241##34##]. In our study, we observed significantly higher plasma levels of IL-6 in patients with ACS; furthermore, we identified an independent association between IL-6 levels and the occurrence of ACS.</p>", "<p id=\"Par34\">Ceramides, which are bioactive lipids with crucial regulatory functions in pro-inflammatory cytokines, have been implicated in various cardiovascular conditions. Elevated serum ceramide concentrations serve as predictors for cardiovascular atherosclerotic disease, stroke, heart failure and atrial fibrillation [##REF##33133018##35##, ##REF##37456812##36##]. Moreover, specific plasma ceramide levels are correlated with heightened cardiovascular mortality in ambulatory patients with chronic heart failure [##REF##32627354##37##]. A previous study developed a model that combined the measurement of ceramides with high-sensitive troponin T for the detection of ACS in patients presenting with chest pain, achieving an impressive AUC of 0.865 [##REF##31350245##38##]. Another study demonstrated that elevated plasma levels of ceramide (d18:1/16:0), ceramide (d18:1/18:0) and ceramide (d18:1/24:1) were independent predictors of a high atherosclerotic burden in patients with STEMI [##REF##32800902##39##]. Furthermore, ceramide molecules, including ceramide (d18:1/16:0), ceramide (d18:1/18:0) and ceramide (d18:1/24:1), as well as their ratios to ceramide (d18:1/24:0), have emerged as promising risk stratifiers in patients with established CAD [##REF##27765765##14##]. Additionally, ceramides have shown potential as plasma biomarkers for the early prediction of restenosis after percutaneous coronary intervention [##REF##28624380##40##]. However, this study indicated that only ceramide (d18:1/16:0) independently predicted the occurrence of ACS.</p>", "<title>Limitations</title>", "<p id=\"Par35\">Several limitations of the current study should be acknowledged. First, the study was conducted at a single centre, limiting the generalisability of its findings to other populations. Second, the sample size was relatively small, warranting the need for larger prospective studies to validate the conclusions. Third, the absence of serial measurements hindered the ability to establish a temporal relationship between the biomarkers and the onset of ACS. Fourth, uric acid has been identified as a significant determinant of many different outcomes, such as all-cause and cardiovascular mortality, as well as cardiovascular events in patients with chronic coronary syndromes and ACS [##REF##34682873##41##]. However, the biomarker of serum uric acid was not included in this study. Additionally, internal and external validation of the diagnostic models was not performed, which is crucial for assessing their robustness. Therefore, further research is warranted to confirm the diagnostic value of these biomarkers and to establish a validated diagnostic model for ACS.</p>" ]

[ "<title>Conclusion</title>", "<p id=\"Par36\">In conclusion, ceramide (d18:1/16:0) plays an essential role in predicting ACS. In addition, this study’s results support the idea that the simultaneous measurement of traditional risk factors, IL-6 and ceramide (d18:1/16:0) can improve the diagnostic accuracy of ACS. While these findings may not offer novel perspectives for developing new therapeutic approaches, an ACS risk assessment combining IL-6 and ceramide (d18:1/16:0) presents a unique tool for aiding clinical implementation and decision-making in patients suspected of having atherosclerosis. Additionally, ACS risk assessment has the potential to enhance patients’ adherence to medical therapy and lifestyle changes.</p>" ]

[ "<title>Background</title>", "<p id=\"Par1\">There is a growing body of evidence supporting the significant involvement of both ceramides and pro-inflammatory cytokines in the occurrence and progression of acute coronary syndrome (ACS).</p>", "<title>Methods</title>", "<p id=\"Par2\">This study encompassed 216 participants whose laboratory variables were analysed using standardised procedures. Parameters included baseline serum lipid markers, comprising total cholesterol, low-density lipoprotein-cholesterol, high-density lipoprotein-cholesterol, triglycerides (TGs), lipoprotein(a) (LPa), fasting blood glucose, B-natriuretic peptide and hypersensitive C-reactive protein. Liquid chromatography-tandem mass spectrometry measured the concentrations of plasma ceramides. Enzyme-linked immunosorbent assay quantified tumour necrosis factor-α (TNF-α), interleukin 6 (IL6) and IL8. The correlation between ceramides and inflammatory factors was determined through Pearson’s correlation coefficient. Receiver operating characteristic (ROC) curve analysis and multivariate logistic regression evaluated the diagnostic potential of models incorporating traditional risk factors, ceramides and pro-inflammatory cytokines in ACS detection.</p>", "<title>Results</title>", "<p id=\"Par3\">Among the 216 participants, 138 (63.89%) were diagnosed with ACS. Univariate logistic regression analysis identified significant independent predictors of ACS, including age, gender, history of diabetes, smoking history, TGs, TNF-α, IL-6, ceramide (d18:1/16:0), ceramide (d18:1/18:0), ceramide (d18:1/24:0), ceramide (d18:1/20:0) and ceramide (d18:1/22:0). Multivariate logistic regression analysis revealed significant associations between gender, diabetes mellitus history, smoking history, LPa, IL-6, ceramide (d18:1/16:0) and ACS. Receiver operating characteristic analysis indicated that model 4, which integrated traditional risk factors, IL-6 and ceramide (d18:1/16:0), achieved the highest area under the curve (AUC) of 0.827 (95% CI 0.770–0.884), compared with model 3 (traditional risk factors and ceramide [d18:1/16:0]) with an AUC of 0.782 (95% CI 0.720–0.845) and model 2 (traditional risk factors and IL-6), with an AUC of 0.785 (95% CI 0.723–0.846) in ACS detection.</p>", "<title>Conclusions</title>", "<p id=\"Par4\">In summary, incorporating the simultaneous measurement of traditional risk factors, pro-inflammatory cytokine IL-6 and ceramide (d18:1/16:0) can improve the diagnostic accuracy of ACS.</p>", "<title>Keywords</title>" ]

[]

[ "<title>Acknowledgements</title>", "<p>Not applicable.</p>", "<title>Author contributions</title>", "<p>LHQ and GBY conceived of the study, and LFJ, ZL and LL participated in its design and data analysis and statistics. All authors helped to draft the manuscript, read and approved the final manuscript.</p>", "<title>Funding</title>", "<p>Not applicable.</p>", "<title>Data availability</title>", "<p>All data generated or analyzed during this study are included in this published article.</p>", "<title>Declarations</title>", "<title>Ethics approval and consent to participate</title>", "<p id=\"Par39\">The study protocol strictly adhered to the principles set forth in the Declaration of Helsinki and received approval from the Ethics Committee of the Second Hospital of Hebei Medical University (Ethics approval number: W2021041). We obtained signed informed consent from the participants.</p>", "<title>Consent for publication</title>", "<p id=\"Par38\">Not applicable.</p>", "<title>Competing interests</title>", "<p id=\"Par37\">The authors declare no competing interests.</p>" ]

[ "<fig id=\"Fig1\"><label>Fig. 1</label><caption><p>Flow chart. ACS, acute coronary syndrome; AMI,acute myocardial infarction</p></caption></fig>", "<fig id=\"Fig2\"><label>Fig. 2</label><caption><p>The ROC of the four diagnostic models</p></caption></fig>" ]

[ "<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Demographic, clinical and laboratory findings of all the included patients</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Clinical characteristics</th><th align=\"left\">total(n = 216)</th><th align=\"left\">ACS (n = 138)</th><th align=\"left\">non ACS (n = 78)</th><th align=\"left\">\n<italic>P</italic>\n</th></tr></thead><tbody><tr><td align=\"left\">DM(n,%)</td><td align=\"left\">66 (30.6)</td><td align=\"left\">51 (37.0)</td><td align=\"left\">15 (19.2)</td><td align=\"left\">0.007</td></tr><tr><td align=\"left\">Age</td><td align=\"left\">60.61</td><td align=\"left\">59.88</td><td align=\"left\">61.90</td><td align=\"left\">0.170</td></tr><tr><td align=\"left\">Gender</td><td align=\"left\">131 (60.6)</td><td align=\"left\">96 (69.6)</td><td align=\"left\">35 (44.9)</td><td align=\"left\">0.000</td></tr><tr><td align=\"left\">Hypertention(n,%)</td><td align=\"left\">130 (60.2)</td><td align=\"left\">85 (61.2)</td><td align=\"left\">45 (57.7)</td><td align=\"left\">0.574</td></tr><tr><td align=\"left\">Current smoker(n,%)</td><td align=\"left\">74 (34.3)</td><td align=\"left\">61 (44.2)</td><td align=\"left\">13 (16.7)</td><td align=\"left\">0.000</td></tr><tr><td align=\"left\">Alcohol status(n,%)</td><td align=\"left\">40 (18.5)</td><td align=\"left\">31 (22.5)</td><td align=\"left\">9 (11.5)</td><td align=\"left\">0.047</td></tr><tr><td align=\"left\">Presentation of ACS</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\"> STEMI(n,%)</td><td align=\"left\">11 (5.09)</td><td align=\"left\">11 (7.97)</td><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\"> NSTEMI(n,%)</td><td align=\"left\">25 (11.57)</td><td align=\"left\">25 (18.12)</td><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\"> UAP(n,%)</td><td align=\"left\">102 (47.22)</td><td align=\"left\">102 (73.91)</td><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\">BMI(kg/cm²)</td><td align=\"left\">26.59 ± 4.27</td><td align=\"left\">26.08 ± 3.80</td><td align=\"left\">27.51 ± 4.89</td><td align=\"left\">0.018</td></tr><tr><td align=\"left\">TG(mmol/L)</td><td align=\"left\">1.74 ± 0.98</td><td align=\"left\">1.84 ± 1.11</td><td align=\"left\">1.56 ± 0.69</td><td align=\"left\">0.020</td></tr><tr><td align=\"left\">TC(mmol/L)</td><td align=\"left\">4.21 ± 1.27</td><td align=\"left\">4.33 ± 1.15</td><td align=\"left\">3.99 ± 1.43</td><td align=\"left\">0.063</td></tr><tr><td align=\"left\">HDL-C(mmol/L)</td><td align=\"left\">1.16 ± 0.28</td><td align=\"left\">1.12 ± 0.26</td><td align=\"left\">1.23 ± 0.29</td><td align=\"left\">0.004</td></tr><tr><td align=\"left\">LDL-C(mmol/L)</td><td align=\"left\">2.41 ± 0.94</td><td align=\"left\">2.37 ± 0.98</td><td align=\"left\">2.47 ± 0.87</td><td align=\"left\">0.475</td></tr><tr><td align=\"left\">LPa(mmol/L)</td><td align=\"left\">62.48 ± 84.89</td><td align=\"left\">70.69 ± 98.11</td><td align=\"left\">47.97 ± 51.49</td><td align=\"left\">0.027</td></tr><tr><td align=\"left\">FBG(mmol/L)</td><td align=\"left\">6.66 ± 2.73</td><td align=\"left\">6.91 ± 3.01</td><td align=\"left\">6.23 ± 2.10</td><td align=\"left\">0.053</td></tr><tr><td align=\"left\">BNP(pg/mL)</td><td align=\"left\">84.62 ± 144.59</td><td align=\"left\">98.19 ± 163.72</td><td align=\"left\">60.63 ± 98.84</td><td align=\"left\">0.037</td></tr><tr><td align=\"left\">hs-CRP(mg/L)</td><td align=\"left\">3.00 ± 4.34</td><td align=\"left\">2.92 ± 3.77</td><td align=\"left\">3.13 ± 5.22</td><td align=\"left\">0.730</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab2\"><label>Table 2</label><caption><p>Plasma levels of proinflammatory cytokines and ceramide molecules</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Variables</th><th align=\"left\">total(n = 216)</th><th align=\"left\">ACS(n = 138)</th><th align=\"left\">non ACS(n = 78)</th><th align=\"left\">\n<italic>P</italic>\n</th></tr></thead><tbody><tr><td align=\"left\">TNF-α(pg/mL)</td><td char=\"?\" align=\"char\">11.75 ± 8.78</td><td char=\"?\" align=\"char\">13.04 ± 9.21</td><td char=\"?\" align=\"char\">9.47 ± 7.47</td><td align=\"left\">0.004</td></tr><tr><td align=\"left\">IL-6(pg/mL)</td><td char=\"?\" align=\"char\">7.59 ± 5.36</td><td char=\"?\" align=\"char\">9.09 ± 5.70</td><td char=\"?\" align=\"char\">5.93 ± 3.99</td><td align=\"left\">0.000</td></tr><tr><td align=\"left\">IL-8(pg/mL)</td><td char=\"?\" align=\"char\">24.08 ± 14.14</td><td char=\"?\" align=\"char\">24.45 ± 13.65</td><td char=\"?\" align=\"char\">23.41 ± 15.04</td><td align=\"left\">0.604</td></tr><tr><td align=\"left\">Cer (d18:1/16:0)(µmol/L)</td><td char=\"?\" align=\"char\">155.17 ± 48.11</td><td char=\"?\" align=\"char\">165.00 ± 49.09</td><td char=\"?\" align=\"char\">137.79 ± 41.20</td><td align=\"left\">0.000</td></tr><tr><td align=\"left\">Cer (d18:1/18:0)(µmol/L)</td><td char=\"?\" align=\"char\">44.75 ± 18.85</td><td char=\"?\" align=\"char\">46.71 ± 18.81</td><td char=\"?\" align=\"char\">41.28 ± 18.54</td><td align=\"left\">0.042</td></tr><tr><td align=\"left\">Cer (d18:1/24:0)(µmol/L)</td><td char=\"?\" align=\"char\">1835.10 ± 6761.38</td><td char=\"?\" align=\"char\">1933.30 ± 710.43</td><td char=\"?\" align=\"char\">1661.36 ± 559.21</td><td align=\"left\">0.004</td></tr><tr><td align=\"left\">Cer (d18:1/24:1)(µmol/L)</td><td char=\"?\" align=\"char\">486.19 ± 193.99</td><td char=\"?\" align=\"char\">495.28 ± 180.50</td><td char=\"?\" align=\"char\">470.10 ± 216.09</td><td align=\"left\">0.361</td></tr><tr><td align=\"left\">Cer (d18:1/14:0)(µmol/L)</td><td char=\"?\" align=\"char\">2.68 ± 1.36</td><td char=\"?\" align=\"char\">2.80 ± 1.43</td><td char=\"?\" align=\"char\">2.48 ± 1.21</td><td align=\"left\">0.097</td></tr><tr><td align=\"left\">Cer (d18:1/20:0)(µmol/L)</td><td char=\"?\" align=\"char\">54.10 ± 20.53</td><td char=\"?\" align=\"char\">56.27 ± 21.37</td><td char=\"?\" align=\"char\">50.25 ± 18.45</td><td align=\"left\">0.038</td></tr><tr><td align=\"left\">Cer (d18:1/22:0)(µmol/L)</td><td char=\"?\" align=\"char\">421.32 ± 159.87</td><td char=\"?\" align=\"char\">442.57 ± 174.76</td><td char=\"?\" align=\"char\">383.72 ± 121.56</td><td align=\"left\">0.009</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab3\"><label>Table 3</label><caption><p>The pearson correlation coefficients between ceramides and inflammatory factors for the whole study population (n = 216)</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" rowspan=\"2\"/><th align=\"left\" colspan=\"2\">TNF-α</th><th align=\"left\" colspan=\"2\">IL6</th><th align=\"left\" colspan=\"2\">IL8</th><th align=\"left\" colspan=\"2\">hs-CRP</th></tr><tr><th align=\"left\">r</th><th align=\"left\">\n<italic>P</italic>\n</th><th align=\"left\">r</th><th align=\"left\">\n<italic>P</italic>\n</th><th align=\"left\">r</th><th align=\"left\">\n<italic>P</italic>\n</th><th align=\"left\">r</th><th align=\"left\">\n<italic>P</italic>\n</th></tr></thead><tbody><tr><td align=\"left\">Cer (d18:1/16:0)</td><td align=\"left\">-0.081</td><td align=\"left\">0.236</td><td align=\"left\">0.027</td><td align=\"left\">0.696</td><td align=\"left\">-0.030</td><td align=\"left\">0.664</td><td align=\"left\">0.127</td><td align=\"left\">0.063</td></tr><tr><td align=\"left\">Cer (d18:1/18:0)</td><td align=\"left\">-0.035</td><td align=\"left\">0.606</td><td align=\"left\">-0.043</td><td align=\"left\">0.533</td><td align=\"left\">-0.098</td><td align=\"left\">0.152</td><td align=\"left\">0.138</td><td align=\"left\">0.043</td></tr><tr><td align=\"left\">Cer (d18:1/24:0)</td><td align=\"left\">-0.124</td><td align=\"left\">0.069</td><td align=\"left\">-0.041</td><td align=\"left\">0.544</td><td align=\"left\">-0.046</td><td align=\"left\">0.497</td><td align=\"left\">0.099</td><td align=\"left\">0.148</td></tr><tr><td align=\"left\">Cer (d18:1/24:1)</td><td align=\"left\">-0.070</td><td align=\"left\">0.308</td><td align=\"left\">-0.084</td><td align=\"left\">0.220</td><td align=\"left\">-0.066</td><td align=\"left\">0.332</td><td align=\"left\">0.040</td><td align=\"left\">0.186</td></tr><tr><td align=\"left\">Cer (d18:1/14:0)</td><td align=\"left\">-0.131</td><td align=\"left\">0.055</td><td align=\"left\">-0.077</td><td align=\"left\">0.258</td><td align=\"left\">-0.019</td><td align=\"left\">0.786</td><td align=\"left\">0.087</td><td align=\"left\">0.205</td></tr><tr><td align=\"left\">Cer (d18:1/20:0)</td><td align=\"left\">-0.094</td><td align=\"left\">0.171</td><td align=\"left\">-0.130</td><td align=\"left\">0.057</td><td align=\"left\">-0.085</td><td align=\"left\">0.216</td><td align=\"left\">0.139</td><td align=\"left\">0.042</td></tr><tr><td align=\"left\">Cer (d18:1/22:0)</td><td align=\"left\">-0.136</td><td align=\"left\">0.045</td><td align=\"left\">-0.099</td><td align=\"left\">0.149</td><td align=\"left\">-0.050</td><td align=\"left\">0.465</td><td align=\"left\">0.127</td><td align=\"left\">0.063</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab4\"><label>Table 4</label><caption><p>The significant predictors for ACS patients in univariate and multivariate logistic regression</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" rowspan=\"2\">Variables</th><th align=\"left\">Univariate</th><th align=\"left\" rowspan=\"2\"><italic>P</italic> value</th><th align=\"left\">Multivariate</th><th align=\"left\" rowspan=\"2\"><italic>P</italic> value</th></tr><tr><th align=\"left\">OR(95%CI)</th><th align=\"left\">OR(95%CI)</th></tr></thead><tbody><tr><td align=\"left\">Age</td><td align=\"left\">0.981 (1.580–4.990)</td><td align=\"left\">0.000</td><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\">Male</td><td align=\"left\">2.808 (1.272–4.767)</td><td align=\"left\">0.000</td><td align=\"left\">2.702 (1.290–5.658)</td><td align=\"left\">0.008</td></tr><tr><td align=\"left\">DM</td><td align=\"left\">2.462 (1.272–4.767)</td><td align=\"left\">0.008</td><td align=\"left\">2.329 (1.077–5.035)</td><td align=\"left\">0.032</td></tr><tr><td align=\"left\">Hypertention</td><td align=\"left\">1.176 (0.668–2.070)</td><td align=\"left\">0.574</td><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\">Current smoker</td><td align=\"left\">3.961 (1.999–7.848)</td><td align=\"left\">0.000</td><td align=\"left\">2.702 (1.204–6.066)</td><td align=\"left\">0.016</td></tr><tr><td align=\"left\">Alcohol</td><td align=\"left\">2.221 (0.997–4.951)</td><td align=\"left\">0.051</td><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\">BMI</td><td align=\"left\">0.922 (0.862–0.987)</td><td align=\"left\">0.02</td><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\">TG</td><td align=\"left\">1.405 (1.008–1.960)</td><td align=\"left\">0.045</td><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\">TC</td><td align=\"left\">1.240 (0.987–1.557)</td><td align=\"left\">0.065</td><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\">HDL-c</td><td align=\"left\">0.227 (0.081–0.636)</td><td align=\"left\">0.005</td><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\">LDL-c</td><td align=\"left\">0.898 (0.669–1.205)</td><td align=\"left\">0.474</td><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\">LPa</td><td align=\"left\">1.004 (1.000-1.008)</td><td align=\"left\">0.065</td><td align=\"left\">1.006 (1.000-1.012)</td><td align=\"left\">0.04</td></tr><tr><td align=\"left\">FBG</td><td align=\"left\">1.108 (0.986–1.245)</td><td align=\"left\">0.084</td><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\">BNP</td><td align=\"left\">1.002 (1.000-1.005)</td><td align=\"left\">0.083</td><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\">hs-CRP</td><td align=\"left\">0.989 (0.928–1.053)</td><td align=\"left\">0.729</td><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\">TNF-α</td><td align=\"left\">1.063 (1.018–1.109)</td><td align=\"left\">0.005</td><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\">IL-6</td><td align=\"left\">1.157 (1.077–1.243)</td><td align=\"left\">0.000</td><td align=\"left\">1.173 (1.082–1.271)</td><td align=\"left\">0.000</td></tr><tr><td align=\"left\">IL-8</td><td align=\"left\">1.005 (0.985–1.026)</td><td align=\"left\">0.603</td><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\">Cer(d18:1/16:0)</td><td align=\"left\">1.016 (1.008–1.024)</td><td align=\"left\">0.000</td><td align=\"left\">1.018 (1.008–1.028)</td><td align=\"left\">0.000</td></tr><tr><td align=\"left\">Cer(d18:1/18:0)</td><td align=\"left\">1.017 (1.000-1.034)</td><td align=\"left\">0.045</td><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\">Cer(d18:1/24:0)</td><td align=\"left\">1.001 (1.000-1.001)</td><td align=\"left\">0.005</td><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\">Cer(d18:1/24:1)</td><td align=\"left\">1.001 (0.999–1.002)</td><td align=\"left\">0.362</td><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\">Cer(d18:1/14:0)</td><td align=\"left\">1.205 (0.965–1.505)</td><td align=\"left\">0.100</td><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\">Cer (d18:1/20:0)</td><td align=\"left\">1.016 (1.001–1.032)</td><td align=\"left\">0.041</td><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\">Cer (d18:1/22:0)</td><td align=\"left\">1.003 (1.001–1.005)</td><td align=\"left\">0.011</td><td align=\"left\"/><td align=\"left\"/></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab5\"><label>Table 5</label><caption><p>The predictive value of the four models for ACS patients</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Old model</th><th align=\"left\">New model</th><th align=\"left\">AUC(95%CI)</th><th align=\"left\"><italic>P</italic> value -AUC</th></tr></thead><tbody><tr><td align=\"left\">Model 1</td><td align=\"left\">-</td><td align=\"left\">0.722 (0.653-0.791)</td><td align=\"left\"/></tr><tr><td align=\"left\">Model 1</td><td align=\"left\">model 2</td><td align=\"left\">0.785 (0.723-0.846)</td><td align=\"left\">0.01</td></tr><tr><td align=\"left\">Model 1</td><td align=\"left\">model 3</td><td align=\"left\">0.782 (0.720-0.845)</td><td align=\"left\">0.015</td></tr><tr><td align=\"left\">Model 1</td><td align=\"left\">model 4</td><td align=\"left\">0.827 (0.770-0.884)</td><td align=\"left\">0.001</td></tr></tbody></table></table-wrap>" ]

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[ "<table-wrap-foot><p>Continuous variables are presented as mean value ± 1 SD, whereas categorical variables are presented as absolute and relative frequencies</p><p>ACS, acute coronary syndrome; STEMI, ST-elevated myocardial infarction; NSTEMI, non ST-elevated myocardial infarction; UAP, unstable angina pectoris; DM, diabetes mellitus; BMI, body mass index; TG, Triglyceride; TC, total cholesterol; LDL-C, low-density lipoprotein-cholesterol; HDL-C, high-density lipoprotein-cholesterol; LPa, lipoprotein a; FBG, fasting blood glucose; BNP, B-natriuretic peptide; hs-CRP, hypersensitive C-reactive protein</p></table-wrap-foot>", "<table-wrap-foot><p>Continuous variables are presented as mean value ± 1 SD.</p><p>TNF-α, tumor necrosis α; IL-6,interleukin-6;IL-8,interleukin-8;Cer, ceramide</p></table-wrap-foot>", "<table-wrap-foot><p>TNF-α, tumor necrosis α; IL-6, interleukin-6; IL-8, interleukin-8; hs-CRP, hypersensitive C-reactive protein; Cer, ceramide</p></table-wrap-foot>", "<table-wrap-foot><p>OR,odds ratio; CI, confidence interval; DM, diabetes mellitus; BMI, body mass index; TG, Triglyceride; TC, total cholesterol; LDL-C, low-density lipoprotein-cholesterol; HDL-C, high-density lipoprotein-cholesterol; LPa, lipoprotein a; FBG, fasting blood glucose; BNP, B-natriuretic peptide; hs-CRP, hypersensitive C-reactive protein; TNF-α, tumor necrosis α; IL-6, interleukin-6; IL-8, interleukin-8; Cer, ceramide</p></table-wrap-foot>", "<table-wrap-foot><p><italic>P</italic> values AUC for the difference between old model and new model .AUC,area under the curve</p><p>Model 1: included male,DM history,current smoker,LPa;</p><p>Model 2:model 1 + IL 6;</p><p>Model 3:model 1 + ceramide(d18:1/16:0);</p><p>Model 4:model 1 + IL 6 + ceramide (d18:1/16:0)</p></table-wrap-foot>", "<fn-group><fn><p><bold>Publisher’s Note</bold></p><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn></fn-group>" ]

[ "<graphic xlink:href=\"12872_2023_3690_Fig1_HTML\" id=\"d32e315\"/>", "<graphic xlink:href=\"12872_2023_3690_Fig2_HTML\" id=\"d32e1435\"/>" ]

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{ "acronym": [], "definition": [] }

[ "<title>Introduction</title>", "<p id=\"Par8\">Thyroid cancer accounts for about 1% of all malignant tumors in humans and around 33% of head and neck malignant tumors [##UREF##0##1##]. Furthermore, Papillary Thyroid Carcinoma (PTC) accounts for approximately 80–90% of thyroid malignant tumors. The global incidence of thyroid cancer has increased rapidly in recent years, at a rate of 4.5–6.6% annually [##REF##26917552##2##]. Although most PTC patients have a good prognosis, it is noteworthy that 20–90% of PTC patients can develop Lymph Node Metastasis (LNM) [##REF##30777203##3##]. In addition to increasing the risk of local recurrence, LNM can lower the Disease-Free Survival (DFS) rate of PTC patients. Furthermore, LNM may lead to secondary surgery or radiation iodine therapy, affecting patients’ Quality of Life (QoL) [##REF##27457917##4##]. The PTC LNM often occurs in the central region at first, followed by the lateral cervical region, and finally the mediastinal lymph nodes [##REF##30535975##5##]. However, it is noteworthy that metastatic progression does not strictly adhere to these steps, as some stages could be skipped. Therefore, a comprehensive, rational, and appropriate initial surgery can reduce the risk of postoperative recurrence and the possibility of secondary surgery. Herein, we retrospectively analyzed the clinicopathological data of 2384 PTC patients, focusing on the risk factors of Central Lymph Node Metastasis (CLNM) and Lateral Lymph Node Metastasis (LLNM) in PTC patients. We also explored the surgical methods for treating PTC.</p>" ]

[ "<title>Materials and methods</title>", "<title>General information</title>", "<p id=\"Par10\">The Medical Ethics Committee of Inner Mongolia Medical University Affiliated Hospital approved our research plan. Our hospital admitted 2709 thyroid malignant tumor patients from January 2016 to December 2020. Among them, 2384 [88.0%, 353 males (14.8%) and 2031 females (85.2%), age range = 15–83 years, average age = 46.41 ± 10.23 years] were PTC patients. Preoperative ultrasound examination of the thyroid and neck lymph nodes, chest X-ray, and chest CT were performed on all patients, with some undergoing neck CT examination. Preoperative fine-needle biopsy was performed on 210 patients (10%), of which 179 were diagnosed with PTC and LNM. The diagnosis was confirmed through intraoperative freezing and postoperative pathological examination, which yielded a positive rate of 85%. All patients underwent an intraoperative frozen section pathological examination during surgery and a routine postoperative pathological examination. The exclusion criteria were as follows: ① Patients with a previous history of malignant tumors; ② Patients undergoing the present operation as a secondary surgery; ③ Patients that were pathologically diagnosed with non-papillary carcinoma; ④ Patients with upper mediastinal LNM or distant metastasis; ⑤ Patients who underwent non-radical surgery; and ⑥ Patients who did not undergo follow-up examination six months post-surgery.</p>", "<title>Surgical methods</title>", "<p id=\"Par12\">All PTC patients included herein underwent radical thyroidectomy. Among them, 1829 (76.7%) and 555 (23.3%) patients underwent unilateral Central Lymph Node Dissection (CLND) and bilateral CLND, respectively. Preoperative ultrasound examination revealed enlarged lymph nodes in the lateral neck, while Fine-Needle Aspiration (FNA) biopsy showed LNM in 38 cases (1.6%), prompting the need for Lateral Cervical Lymph Node Dissection (LLND).</p>", "<title>Related data analysis</title>", "<p id=\"Par14\">Among the 1829 patients that underwent unilateral CLND, 887 had Central Lymph Node Metastasis (CLNM), with a metastasis rate of 48.5%. On the other hand, among the 555 patients who underwent bilateral CLND, 287 had CLNM, with a metastasis rate of 51.5%. Furthermore, 38 patients underwent lateral neck lymph node dissection, of which 32 cases were confirmed through postoperative pathological examination to have LLNM, with a metastasis rate of 1.3%. Gender, age, tumor size on histology, and multifocal tumor nature (single or multiple lesions) were subjected to univariate analysis to determine if they are linked to a higher risk of central and lateral neck lymph node metastases in 2384 PTC patients (Tables ##TAB##0##1## and ##TAB##1##2##), and BRAF gene testing was performed on 85 patients (Table ##TAB##2##3##).</p>", "<title>Follow up</title>", "<p id=\"Par16\">A follow-up rate of 91.0% was achieved, with 2169 of the 2384 PTC patients completing the full follow-up regimen, while 25 patients were lost. The follow-up period was set for 12–72 months up until December 31, 2021. Among the patients who did not undergo LLND, 47 (2.0%) experienced lateral neck metastasis post-surgery. There were 4 (0.2%) and 2 (0.1%) cases of lung metastasis and bone metastasis, respectively, and no deaths were reported.</p>", "<p id=\"Par17\">\n\n</p>", "<p id=\"Par18\">\n\n</p>", "<p id=\"Par19\">\n\n</p>", "<title>Statistical methods</title>", "<p id=\"Par21\">All statistical analyses were performed using SPSS 26.0 software. Counting data were expressed as percentages. The <italic>χ</italic>^{<italic>2−</italic>}test or Fisher’s exact probability method was used for component comparisons. Binary logistic regression analysis was performed to analyze the relevant risk factors using PTC neck lymph node metastasis as a variable factor (0-no, 1-yes). We used ROC curves to determine the critical value for predicting CLNM based on the size of tumor lesions. Inspection level <italic>α</italic> = 0.05.</p>" ]

[ "<title>Results</title>", "<title>Univariate analysis of factors related to CLNM</title>", "<p id=\"Par23\">Univariate analysis revealed a significant correlation between CLNM and gender, age, lesion size, and multifocal characteristics in PTC patients (<italic>P</italic> &lt; 0.05).</p>", "<title>Multivariate analysis of factors related to CLNM</title>", "<p id=\"Par25\">Herein, we constructed a multivariate logistic regression equation by incorporating gender, age, lesion size, and the multifocal tumor nature. According to the results, the risk of CLNM was significantly higher in males than females (<italic>OR</italic>:5.294,95% <italic>CI</italic>: 3.768–7.438, <italic>P</italic> &lt; 0.05). On the other hand, the risk of CLNM increased with decreasing age and increased with increasing lesion size and number of multifocal lesions (<italic>OR</italic>:3.188, 95% <italic>CI</italic>: 1.963–5.176, <italic>P</italic> &lt; 0.05) (Table ##TAB##3##4##). We created ROC curves for 2384 patients undergoing CLND to further investigate the relationship between CLNM and tumor lesion size. We determined that the critical value for predicting tumor lesion size was 0.855. The AUC was 0.269, with sensitivity and specificity values of 57.9% and 69%, respectively (<italic>P</italic> &lt; 0.05) (Fig. ##FIG##0##1##). The CLNM rates of patients with BRAF gene mutations and those without BRAF gene mutations were 54.4% and 45.6%, respectively. No statistically significant difference was found in the transfer rate between the two groups (<italic>P</italic> = 0.741) (Table ##TAB##2##3##).</p>", "<p id=\"Par26\">\n\n</p>", "<p id=\"Par27\">\n\n</p>", "<title>Analysis of factors related factors to LLNM</title>", "<p id=\"Par29\">Analysis of 38 PTC patients who underwent LLND revealed that the size and number of lesions, as well as the number of CLNMs, were correlated with LLNM (Table ##TAB##1##2##). We also compared LNM in different lateral neck regions (Table ##TAB##4##5##). The LNM rate was higher in zones II, III, and IV than in zones I and V. However, the groups studied had relatively fewer cases, necessitating additional in-depth analysis and research with more cases for each category.</p>", "<p id=\"Par30\">\n\n</p>" ]

[ "<title>Discussion</title>", "<p id=\"Par31\">According to research, PTC, the most common type of thyroid cancer, has an excellent 10-year Survival Rate (SR) of over 90% [##REF##25214837##6##]. Nonetheless, LNM occurs in 20–90% of PTC cases and is generally considered the primary cause of PTC local recurrence. It has been reported that secondary surgery post-recurrence increases the difficulty of postoperative care, reduces patients’ QoL, and affects patients’ SR [##REF##26511531##7##]. Lymph node dissection during PTC surgery increases the risk of iatrogenic complications. Currently, a great controversy remains over the extent of preventive CLND and therapeutic LLND. Balancing treatment methods, avoiding overtreatment of low-risk patients, and identifying patients with more severe conditions or at higher risk of injury (for whom more active treatment methods are needed) are some of the challenges currently faced by Doctors taking care of these patients. Therefore, understanding the nature and risk factors of Cervical Lymph Node Metastases (CNMs) in PTC patients is critical in guiding CLND.</p>", "<p id=\"Par32\">Consistent with other research findings [##REF##25692116##8##, ##REF##27601922##9##], we found an increased risk of CLNM in male patients in this study. This outcome indicates that special emphasis should be placed on evaluating LNM in male PTC patients during preoperative clinical examinations. Herein, PTC patients aged ≤ 30 years were more likely to develop CLNM. Although this finding aligns with some previous research [##REF##34867817##10##, ##REF##31238891##11##], it is noteworthy that some scholars [##REF##26099728##12##] found that CLNM is not related to age. Furthermore, Yang [##REF##34867817##10##] deduced that &gt; 44.5 years old is the threshold for cervical lymph node skip metastasis, with patients in this age group being more prone to cervical lymph node skip metastasis. This study revealed that tumor size (&gt; 0.5 cm) is a risk factor for CLNM in PTC patients, with a tumor size of 0.855 cm as the critical value for predicting CLNM according to the ROC curve. In PTC research, scholars have consistently reported that tumor size is an essential factor in predicting CNM, but with different thresholds. However, it is generally believed that the larger the tumor lesion, the higher the CNM risk [##REF##27127507##13##, ##REF##27601922##14##]. Consistent with other research results [##UREF##1##15##], multifocal tumor foci are also a risk factor for CNM in PTC patients. Furthermore, Liu [##REF##31238891##11##] found that the extension and growth of tumors outside the thyroid gland is a risk factor for CLNM, potentially because the tumor cells invading perithyroidal soft tissue are more likely to metastasize along the rich lymphatic tissue to the surrounding lymph nodes, resulting in LNM. No statistically significant relationship was found between BRAF gene mutations and CLNM. In this regard, BRAF gene mutations are not found to be a risk factor for CLNM.</p>", "<p id=\"Par33\">Although many studies have been conducted on the characteristics and risk factors of LLNM, the findings are highly controversial. Zhang et al. [##REF##22319042##16##] reported that tumors extending outward from the thyroid gland, bilateral lobe tumors, and CLNM are risk factors for LLNM. On the other hand, Niel et al. [##REF##27737333##17##] reported that tumors located at the upper pole, CLNM, and tumors &gt; 1.5 cm in size are risk factors for LLNM. Furthermore, Liu agrees that CLNM is a risk factor for LLNM and that LLND should be conducted more actively when the number of CLNMs is more than three. Contrastingly, a previous study [##REF##27601922##14##] reported that CLNM is not a risk factor for LLNM. This study found that the rate of lateral CNM increased with the increase in tumor size, but the difference was not statistically significant. There is currently no consensus on the scope of lateral neck lymph node dissection, and a controversy remains over whether to routinely clean lymph nodes in Zone V [##REF##12431169##18##, ##REF##16555024##19##]. Here, we found that LLNM mainly occurs in zones II, III, and IV, with less occurrence in regions I and V. It may be appropriate to not clean the lymph nodes in Zones I and V for PTC patients with low-risk factors.</p>", "<p id=\"Par34\">Dr.Ozgur’s team review of the thyroid gland confirms that the thyroid gland has no defined anatomical fibrous capsule,but rather perithyroidal soft tissue [##REF##19949881##20##]. Furthermore, some scholars highlighted the importance of tumor location in the perithyroidal soft tissue, discovering that tumor invasion of the soft tissue could increase the risk of tumor recurrence and death [##REF##20020290##21##]. Wang et al. [##REF##24568507##22##] discovered that PTC invasion and breakthrough of the perithyroidal soft tissue or posterior dorsal soft tissue increases the likelihood of tumor invasion into lymphatic vessels and the risk of CNM.</p>", "<p id=\"Par35\">According to recent research, the V600E mutation of BRAF (v raf murine sarcoma viral oncogene homolog B1) is the most common and critical genetic event in PTC occurrence. The BRAF V600E mutation is solely found in PTC and PTC-derived undifferentiated cancers, and is absent in normal thyroid tissue, thyroid follicles, and other types of thyroid tumors [##REF##31198475##23##]. Numerous studies reported that this mutation is associated with commonly known clinicopathological features of PTC that predict tumor progression and recurrence, such as advanced age, extrathyroidal invasion, LNM, and advanced tumor stages. Additionally, the direct association between the BRAFV600E mutation and clinical PTC progression, recurrence, and treatment failure has been confirmed. Herein, 79 of the 85 PTC patients who underwent BRAF gene testing were found to have BRAF gene mutations, with a mutation rate of 93%. One study indentified several molecular and histopathologic features that correlate with more behavior of Thyroid papillary microcarcinoma(TPMC),such as BARF mutation status, subcapsular location, peri-and intratumoral fibrosis, and multifocality, and provided a practical and simple scoring system to evaluate the clinical behavior of this common type of thyroid cancer. The scoring system relies on BRAF mutation status and three histopathological features to assign tumors into three risk categories. The absence of either of these factors cannot be accurately classified [##REF##21882177##24##]. However, our BRAF gene testing sample size was relatively small and incomplete histopathological features information, necessitating additional research with larger samples to obtain more accurate conclusions. Furthermore, in a previous study, 13 of the PTC patients who underwent BRAF testing and showed no mutations were complicated with Hashimoto’s Thyroiditis (HT). Some studies discovered that the inflammatory process of HT exerts a protective effect on PTC [##REF##34867817##10##]. Many patients with PTC + HT were clinically diagnosed with enlarged cervical lymph nodes [##REF##25851341##25##], which posed more difficulties for preoperative color Doppler ultrasound for determining LNM, leading to more lymph node clearance and complications. As a result, accurately identifying the risk factors for CLNM and determining the need for neck lymph node dissection is even more critical for PTC patients with HT.</p>" ]

[]

[ "<title>Objective</title>", "<p id=\"Par1\">This study aims to identify and analyze the risk factors associated with Cervical Lymph Node Metastasis (CNM) in Papillary Thyroid Carcinoma (PTC) patients.</p>", "<title>Methods</title>", "<p id=\"Par100\">We conducted a retrospective study involving the clinicopathological data of 2384 PTC patients admitted to our hospital between January 2016 and December 2020. All relevant data were statistically processed and analyzed.</p>", "<title>Results</title>", "<p id=\"Par101\">The related risk factors for Central Lymph Node Metastasis (CLNM) were gender (male), age (≤ 30 years old), tumor lesion size (&gt; 0.855 cm), and multifocal tumor foci. The ROC curve revealed that the critical value for predicting CLNM based on tumor lesion size was 0.855 (sensitivity = 57.9%, specificity = 69%, AUC = 0.269, and <italic>P</italic> &lt; 0.05). Lateral Lymph Node Metastasis (LLNM) was positively correlated with tumor diameter. Specifically, the LLNM rate increased with the tumor diameter. LLNM occurrence was significantly higher in zones II, III, and IV than in zones I and V. Although the BRAF gene mutation detection assay has certain clinical benefits in diagnosing PTC and LLNM, no statistically significant difference was found in its relationship with central and lateral neck lymph node metastases (<italic>P</italic> = 0.741).</p>", "<title>Conclusion</title>", "<p id=\"Par102\">Our findings revealed that CLNM is associated with gender (male), age (≤ 30 years old), tumor lesion size (&gt; 0.855 cm), and multiple tumor lesions in PTC patients. Central Lymph Node Dissection (CLND) is recommended for patients with these risk factors. On the other hand, preoperative ultrasound examination, fine-needle pathological examination, and genetic testing should be used to determine whether Lateral Cervical Lymph Node Dissection (LLND) is needed.</p>", "<title>Keywords</title>" ]

[ "<title>Summary</title>", "<p id=\"Par36\">Our study findings can be summarized in five key points. First, male patients, patients aged ≤ 30 years, and those with a tumor lesion size &gt; 0.855 cm should undergo preventive CLND. Second, LLNM presence should be confirmed through color ultrasound examination and fine-needle biopsy before LLND. Third, the lymph node cleaning range should include Zones II, III, and IV, whereas lymph nodes in Zones I and V can be cleaned as appropriate. Fourth, the appropriate surgical method and whether lateral neck lymph node dissection is necessary could be determined through preoperative puncturing of tumor lesions and assessment of enlarged lateral neck lymph nodes. Despite the above-mentioned insightful findings, this study has some shortcomings. Particularly, clinical examination results, imaging features, and tumor location were not examined. Consequently, additional research is required to further explore the relevant risk factors for CNM in PTC patients.</p>" ]

[ "<title>Acknowledgements</title>", "<p>Not applicable.</p>", "<title>Author contributions</title>", "<p>ML designed and directed the study. XW and XZ collected all the clinicopathological data. HS was responsible for the statistical analysis and wrote the manuscript. ML and JM onfirmed the authenticity of all the raw data. All authors have read and approved the final version of the manuscript. All authors have read and approved the final version of the manuscript.</p>", "<title>Funding</title>", "<p>Not applicable.</p>", "<title>Data availability</title>", "<p>The datasets generated and analysed during the current study are not publicly available because internal statistical data of the research unit and has not been uploaded to the database, but are available from the corresponding author on reasonable request.</p>", "<title>Declarations</title>", "<title>Ethics approval and consent to participate</title>", "<p id=\"Par49\">This study was approved by the Ethics Committee of the Affiliated Hospital of Inner Mongolia Medical University. Considering the retrospective nature of the data, the Ethics Committee of the Affiliated Hospital of Inner Mongolia Medical University approved the requirement for informed consent.</p>", "<title>Consent for publication</title>", "<p id=\"Par50\">Not applicable.</p>", "<title>Competing interests</title>", "<p id=\"Par48\">The authors declare no competing interests.</p>" ]

[ "<fig id=\"Fig1\"><label>Fig. 1</label><caption><p>ROC curve analysis predicts CLNM based on tumor lesion size. The ROC results show that 0.855 is the critical tumor lesion size value and the best point for predicting CLNM. The sensitivity, specificity, AUC, and 95%CI at this value are 57.9%, 69%, 0.269, and 0.639–0.707, respectively</p></caption></fig>" ]

[ "<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Univariate analysis of CLNM in 2384 PTC patients who underwent CLND [n (%)]</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" rowspan=\"2\" colspan=\"2\">Influencing factor</th><th align=\"left\" rowspan=\"2\">Number</th><th align=\"left\" colspan=\"2\">Is the central lymph node metastatic</th><th align=\"left\" rowspan=\"2\">OR</th><th align=\"left\" rowspan=\"2\">95%CI</th><th align=\"left\" rowspan=\"2\">P</th></tr><tr><th align=\"left\">Yes</th><th align=\"left\">No</th></tr></thead><tbody><tr><td align=\"left\" colspan=\"2\">Gender</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\" colspan=\"2\"> Male</td><td align=\"left\">353</td><td align=\"left\">236(66.9)</td><td align=\"left\">117(33.1)</td><td align=\"left\">0.181</td><td char=\".\" align=\"char\">1.141–2.232</td><td char=\".\" align=\"char\">&lt; 0.001</td></tr><tr><td align=\"left\" colspan=\"2\"> Female</td><td align=\"left\">2031</td><td align=\"left\">938(46.2)</td><td align=\"left\">1093(53.8)</td><td align=\"left\">1.000</td><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\">Age (years)</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td char=\".\" align=\"char\">&lt; 0.001</td></tr><tr><td align=\"left\"/><td align=\"left\">≤ 30</td><td align=\"left\">151</td><td align=\"left\">127(84.1)</td><td align=\"left\">24(15.9)</td><td char=\".\" align=\"char\">5.926</td><td char=\".\" align=\"char\">3.894–9.021</td><td char=\".\" align=\"char\">&lt; 0.001</td></tr><tr><td align=\"left\"/><td align=\"left\">30–55</td><td align=\"left\">1694</td><td align=\"left\">781(46.1)</td><td align=\"left\">913(53.9)</td><td char=\".\" align=\"char\">1.216</td><td char=\".\" align=\"char\">0.995–1.485</td><td char=\".\" align=\"char\">0.06</td></tr><tr><td align=\"left\"/><td align=\"left\">&gt;55</td><td align=\"left\">539</td><td align=\"left\">266(49.4)</td><td align=\"left\">273(50.6)</td><td char=\".\" align=\"char\">1.000</td><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\">Lesion (cm)</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td char=\".\" align=\"char\">&lt; 0.001</td></tr><tr><td align=\"left\"/><td align=\"left\">≤ 0.5</td><td align=\"left\">1015</td><td align=\"left\">356(35.1)</td><td align=\"left\">659(64.9)</td><td char=\".\" align=\"char\">1.000</td><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\"/><td align=\"left\">0.5-1</td><td align=\"left\">905</td><td align=\"left\">454(50.2)</td><td align=\"left\">451(49.8)</td><td char=\".\" align=\"char\">1.863</td><td char=\".\" align=\"char\">1.551–2.238</td><td char=\".\" align=\"char\">&lt; 0.001</td></tr><tr><td align=\"left\"/><td align=\"left\"> 1–2</td><td align=\"left\">347</td><td align=\"left\">258(74.4)</td><td align=\"left\">89(25.6)</td><td char=\".\" align=\"char\">5.366</td><td char=\".\" align=\"char\">4.083–7.052</td><td char=\".\" align=\"char\">&lt; 0.001</td></tr><tr><td align=\"left\"/><td align=\"left\"> 2–4</td><td align=\"left\">107</td><td align=\"left\">97(90.7)</td><td align=\"left\">10(9.3)</td><td char=\".\" align=\"char\">7.956</td><td char=\".\" align=\"char\">4.609–11.757</td><td char=\".\" align=\"char\">&lt; 0.001</td></tr><tr><td align=\"left\"/><td align=\"left\">&gt;4</td><td align=\"left\">10</td><td align=\"left\">9(90.0)</td><td align=\"left\">1(10.0)</td><td char=\".\" align=\"char\">6.660</td><td char=\".\" align=\"char\">1.510-12.599</td><td char=\".\" align=\"char\">0.008</td></tr><tr><td align=\"left\" rowspan=\"2\">Multifocal</td><td align=\"left\">Single stove</td><td align=\"left\">1573</td><td align=\"left\">536(34.1)</td><td align=\"left\">1137(72.3)</td><td char=\".\" align=\"char\">1.000</td><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\">Multifocal</td><td align=\"left\">811</td><td align=\"left\">638(78.7)</td><td align=\"left\">173(21.3)</td><td char=\".\" align=\"char\">7.823</td><td char=\".\" align=\"char\">6.425–9.525</td><td char=\".\" align=\"char\">&lt; 0.001</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab2\"><label>Table 2</label><caption><p>Univariate analysis of LLNM in 38 PTC patients who underwent LLND [n (%)]</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" rowspan=\"2\" colspan=\"2\">Influence<break/>factor</th><th align=\"left\" rowspan=\"2\">Number of cases</th><th align=\"left\" colspan=\"2\">Is the lateral lymph node metastatic</th><th align=\"left\" rowspan=\"2\">OR</th><th align=\"left\" rowspan=\"2\">95%CI</th><th align=\"left\" rowspan=\"2\">P</th></tr><tr><th align=\"left\">Yes</th><th align=\"left\">No</th></tr></thead><tbody><tr><td align=\"left\" colspan=\"2\">Gender</td><td align=\"left\"/><td align=\"left\" colspan=\"2\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\" colspan=\"2\"> Male</td><td align=\"left\">12</td><td align=\"left\">10(83.3)</td><td align=\"left\">2(16.7)</td><td align=\"left\">0.909</td><td align=\"left\">0.142–5.809</td><td align=\"left\">0.920</td></tr><tr><td align=\"left\" colspan=\"2\"> Female</td><td align=\"left\">26</td><td align=\"left\">22(84.6)</td><td align=\"left\">4(15.4)</td><td align=\"left\">1.000</td><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\">Age (years)</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\" colspan=\"2\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\">0.739</td></tr><tr><td align=\"left\"/><td align=\"left\">≤ 30</td><td align=\"left\">8</td><td align=\"left\">7(87.5)</td><td align=\"left\">1(12.5)</td><td align=\"left\">1.750</td><td align=\"left\">0.084–36.28</td><td align=\"left\">0.718</td></tr><tr><td align=\"left\"/><td align=\"left\">30–55</td><td align=\"left\">25</td><td align=\"left\">21(84.0)</td><td align=\"left\">4(16.0)</td><td align=\"left\">1.000</td><td align=\"left\">0.091–11.028</td><td align=\"left\">0.978</td></tr><tr><td align=\"left\"/><td align=\"left\">&gt;55</td><td align=\"left\">5</td><td align=\"left\">4(80.0)</td><td align=\"left\">1(20.0)</td><td align=\"left\">1.000</td><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\">Lesion (cm)</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\" colspan=\"2\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\">0.955</td></tr><tr><td align=\"left\"/><td align=\"left\">≤ 0.5</td><td align=\"left\">4</td><td align=\"left\">3(75.0)</td><td align=\"left\">1(25.0)</td><td align=\"left\">1.000</td><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\"/><td align=\"left\">0.5-1</td><td align=\"left\">9</td><td align=\"left\">7(77.8)</td><td align=\"left\">2(22.2)</td><td align=\"left\">1.167</td><td align=\"left\">0.109–25.433</td><td align=\"left\">0.913</td></tr><tr><td align=\"left\"/><td align=\"left\"> 1–2</td><td align=\"left\">12</td><td align=\"left\">10(83.3)</td><td align=\"left\">2(16.7)</td><td align=\"left\">1.667</td><td align=\"left\">0.109–25.433</td><td align=\"left\">0.713</td></tr><tr><td align=\"left\"/><td align=\"left\"> 2–4</td><td align=\"left\">10</td><td align=\"left\">9(90.0)</td><td align=\"left\">1(10.0)</td><td align=\"left\">3.000</td><td align=\"left\">0.140-64.262</td><td align=\"left\">0.482</td></tr><tr><td align=\"left\"/><td align=\"left\">&gt;4</td><td align=\"left\">3</td><td align=\"left\">3(100.0)</td><td align=\"left\">0(0)</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\">Multifocal</td><td align=\"left\">Single stove</td><td align=\"left\">15</td><td align=\"left\">12(80.0)</td><td align=\"left\">3(20.0)</td><td align=\"left\">1.000</td><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\"/><td align=\"left\">Multifocal</td><td align=\"left\">23</td><td align=\"left\">20(87.0)</td><td align=\"left\">3(13.0)</td><td align=\"left\">0.600</td><td align=\"left\">0.104–3.463</td><td align=\"left\">0.568</td></tr><tr><td align=\"left\">Number of lymph node metastases in the central region (number)</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\" colspan=\"2\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\">0.374</td></tr><tr><td align=\"left\"/><td align=\"left\">0</td><td align=\"left\">13</td><td align=\"left\">10(76.9)</td><td align=\"left\">3(23.1)</td><td align=\"left\">0.208</td><td align=\"left\">0.019–2.290</td><td align=\"left\">0.200</td></tr><tr><td align=\"left\"/><td align=\"left\">≤ 3</td><td align=\"left\">8</td><td align=\"left\">6(75.0)</td><td align=\"left\">2(25.0)</td><td align=\"left\">0.188</td><td align=\"left\">0.014–2.468</td><td align=\"left\">0.203</td></tr><tr><td align=\"left\"/><td align=\"left\">&gt;3</td><td align=\"left\">17</td><td align=\"left\">16(94.1)</td><td align=\"left\">1(5.9)</td><td align=\"left\">1.000</td><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\">Distant metastasis</td><td align=\"left\"/><td align=\"left\">1</td><td align=\"left\">1(100)</td><td align=\"left\">0(0)</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab3\"><label>Table 3</label><caption><p>Analysis of the correlation between gene mutations and CLNM in patients undergoing BRAF gene testing [n (%)]</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Group</th><th align=\"left\">Total</th><th align=\"left\">Lymph node metastasis</th><th align=\"left\">No lymph node metastasis</th><th align=\"left\">χ²</th><th align=\"left\">P</th></tr></thead><tbody><tr><td align=\"left\">BRAF mutation</td><td align=\"left\">79</td><td align=\"left\">43 (54.4)</td><td align=\"left\">36 (45.6)</td><td align=\"left\">0.109</td><td align=\"left\">0.741</td></tr><tr><td align=\"left\">BRAF not mutated</td><td align=\"left\">17</td><td align=\"left\">10 (58.8)</td><td align=\"left\">7 (41.2)</td><td align=\"left\"/><td align=\"left\"/></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab4\"><label>Table 4</label><caption><p>Multivariate analysis of factors related to CLNM in 2384 PTC patients [n (%)]</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" rowspan=\"2\">Influencing factors</th><th align=\"left\" rowspan=\"2\">Group</th><th align=\"left\" rowspan=\"2\">Number of cases</th><th align=\"left\" colspan=\"2\">Is the central lymph node metastatic</th><th align=\"left\" rowspan=\"2\">OR</th><th align=\"left\" rowspan=\"2\">OR95% CI</th><th align=\"left\" rowspan=\"2\">P-value</th></tr><tr><th align=\"left\">Yes</th><th align=\"left\">No</th></tr></thead><tbody><tr><td align=\"left\" colspan=\"2\">Gender</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\"/><td align=\"left\">Male</td><td align=\"left\">353</td><td align=\"left\">236 (66.9)</td><td align=\"left\">117 (33.1)</td><td align=\"left\">5.294</td><td align=\"left\">3.768–7.438</td><td align=\"left\">&lt; 0.001</td></tr><tr><td align=\"left\"/><td align=\"left\">Female</td><td align=\"left\">2031</td><td align=\"left\">938 (46.2)</td><td align=\"left\">1093 (53.8)</td><td align=\"left\">1.000</td><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\">Age (years)</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\">&lt; 0.001</td></tr><tr><td align=\"left\"/><td align=\"left\">≤ 30</td><td align=\"left\">151</td><td align=\"left\">127 (84.1)</td><td align=\"left\">24 (15.9)</td><td align=\"left\">3.188</td><td align=\"left\">1.963–5.176</td><td align=\"left\">&lt; 0.001</td></tr><tr><td align=\"left\"/><td align=\"left\">30–55</td><td align=\"left\">1694</td><td align=\"left\">781 (46.1)</td><td align=\"left\">913 (53.9)</td><td align=\"left\">1.398</td><td align=\"left\">0.905–1.945</td><td align=\"left\">0.078</td></tr><tr><td align=\"left\"/><td align=\"left\">&gt;55</td><td align=\"left\">539</td><td align=\"left\">266 (49.4)</td><td align=\"left\">273 (50.6)</td><td align=\"left\">1.000</td><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\">Lesion size (cm)</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\">&lt; 0.001</td></tr><tr><td align=\"left\"/><td align=\"left\">≤ 0.5</td><td align=\"left\">1015</td><td align=\"left\">356 (35.1)</td><td align=\"left\">659 (64.9)</td><td align=\"left\">1</td><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\"/><td align=\"left\">0.5- 1</td><td align=\"left\">905</td><td align=\"left\">454 (50.2)</td><td align=\"left\">451 (49.8)</td><td align=\"left\">2.924</td><td align=\"left\">2.227–3.841</td><td align=\"left\">&lt; 0.001</td></tr><tr><td align=\"left\"/><td align=\"left\"> 1–2</td><td align=\"left\">347</td><td align=\"left\">258 (74.4)</td><td align=\"left\">89 (25.6)</td><td align=\"left\">4.000</td><td align=\"left\">2.801–5.711</td><td align=\"left\">&lt; 0.001</td></tr><tr><td align=\"left\"/><td align=\"left\"> 2–4</td><td align=\"left\">107</td><td align=\"left\">97 (90.7)</td><td align=\"left\">10 (9.3)</td><td align=\"left\">6.740</td><td align=\"left\">2.801–10.862</td><td align=\"left\">&lt; 0.001</td></tr><tr><td align=\"left\"/><td align=\"left\">&gt;4</td><td align=\"left\">10</td><td align=\"left\">9 (90.0)</td><td align=\"left\">1 (10.0)</td><td align=\"left\">5.168</td><td align=\"left\">1.218–21.930</td><td align=\"left\">0.026</td></tr><tr><td align=\"left\">Multifocal</td><td align=\"left\">Single</td><td align=\"left\">1573</td><td align=\"left\">536 (34.1)</td><td align=\"left\">1137 (72.3)</td><td align=\"left\">1.000</td><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\"/><td align=\"left\">Many</td><td align=\"left\">811</td><td align=\"left\">638 (78.7)</td><td align=\"left\">173 (21.3)</td><td align=\"left\">0.667</td><td align=\"left\">0.515–0.863</td><td align=\"left\">0.002</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab5\"><label>Table 5</label><caption><p>Analysis of lateral CNMin different regions for the 38 patients who underwent routine LLND [n (%)]</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" rowspan=\"2\">Tumor regions</th><th align=\"left\" rowspan=\"2\">Number of cases (person)</th><th align=\"left\" colspan=\"2\">Is the lateral lymph node metastatic</th><th align=\"left\" rowspan=\"2\">χ²</th><th align=\"left\" rowspan=\"2\">\n<italic>P</italic>\n</th></tr><tr><th align=\"left\">Yes</th><th align=\"left\">No</th></tr></thead><tbody><tr><td align=\"left\">Zone I</td><td align=\"left\">3</td><td align=\"left\">0 (0.0)</td><td align=\"left\">3 (100.0)</td><td align=\"left\" rowspan=\"5\">1.357</td><td align=\"left\" rowspan=\"5\">0.852</td></tr><tr><td align=\"left\">Zone II</td><td align=\"left\">22</td><td align=\"left\">13 (59.1)</td><td align=\"left\">9 (40.9)</td></tr><tr><td align=\"left\">Zone III</td><td align=\"left\">29</td><td align=\"left\">19 (65.5)</td><td align=\"left\">10 (34.5)</td></tr><tr><td align=\"left\">Zone IV</td><td align=\"left\">27</td><td align=\"left\">17 (63.0)</td><td align=\"left\">10 (37.0)</td></tr><tr><td align=\"left\">Zone V</td><td align=\"left\">10</td><td align=\"left\">3 (30.0)</td><td align=\"left\">7 (70.0)</td></tr></tbody></table></table-wrap>" ]

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[ "<title>Background</title>", "<p id=\"Par5\">Monitoring athletes has become an important and present part of sport preparation. The scientific study of quantifying athletes' training began in the early 1990s with the four methods that were most used at the time: retrospective questionnaires, diaries, physiological monitoring and direct observation [##REF##1784872##1##]. Nowadays, there is a plethora of athletic monitoring methods and technologies, varying from the simplest and cheapest, such as diaries [##REF##1784872##1##], to the most complicated and expensive ones, such as the global positioning system (GPS) [##UREF##0##2##].</p>", "<p id=\"Par6\">Frequently monitoring the variables related to performance can help coaches to assess the effectiveness of their training programs and update those to better meet the athletes’ needs. Besides, another reason to frequently monitor athletes is to reduce the time lost to illness [##REF##25669126##3##] and injury [##REF##20840567##4##, ##REF##38098071##5##]. By monitoring the weekly training loads, coaches can make better decisions about the changes in the program to ensure that athletes are not exceeding thresholds that put them in higher risk of injury [##REF##20847703##6##] and illness [##REF##25818900##7##]. Furthermore, monitoring the recovery response after a training session or a competitive match can aid practitioners to balance the adaptation process and recovery. This is particularly important to understand the beginning of the period characterized by a decrease in performance in reaction to high loads (i.e., functional overreaching) [##REF##25134000##8##]. Failing to monitor this response can lead to unplanned fatigue followed by a period of inadequate recovery, phenomenon designed by nonfunctional overreaching [##REF##18050061##9##]. This continuum of unplanned fatigue can result in a syndrome defined by overtraining, in which large decrements in performance occur that are associated to psychological disturbances that can last for months [##REF##23247672##10##].</p>", "<p id=\"Par7\">The particularities of the variables mentioned before alongside with the complexity of the majority of team-sports calendar (e.g., short preparation periods and weeks with high volumes of matches and training sessions) can make the training process hard to monitor and prescribe [##REF##20199119##11##]. The management of the balance between training loads and recovery significantly influences a team’s overall fitness, which, in turn, plays a crucial role in their competitive success [##REF##20840567##4##]. One of the team-sports that has a voluminous competitive calendar is professional volleyball. Volleyball is a sport characterized by a diverse range of physical demands, necessitating well-developed energy systems [##UREF##1##12##, ##REF##18545195##13##]. These include the phosphagen system, which provides immediate energy for high-intensity, short-duration activities like quick sprints or jumps; glycolysis, which predominates in moderate to high-intensity activities lasting from a few seconds up to a minute, contributing to sustained efforts during longer rallies; and the oxidative system, which supports prolonged, lower-intensity activities, crucial for endurance over the course of a match. The effective interplay of these energy systems is essential for optimal performance in volleyball, as players frequently transition between activities of varying intensity and duration [##REF##1588683##14##, ##UREF##2##15##].</p>", "<p id=\"Par8\">Prior research in the field of volleyball has explored various aspects of athletic performance [##UREF##1##12##] and recovery [##UREF##3##16##, ##REF##30113918##17##]. Studies have examined internal and external training loads, investigating how these variables influence players' physiological responses and performance outcomes [##UREF##4##18##, ##REF##37037981##19##]. Key findings have indicated the importance of monitoring training intensity and volume to optimize player readiness and prevent overtraining [##UREF##4##18##]. Additionally, research has highlighted the role of neuromuscular fatigue assessments and well-being measures in understanding athletes' responses to training and competition demands [##UREF##4##18##, ##UREF##5##20##]. In the realm of these neuromuscular assessments, the vertical jump emerges as a particularly crucial measure in volleyball. This is because the act of jumping is central to key actions such as serving, blocking, and attacking [##UREF##1##12##]. The vertical jump, therefore, is not just a frequent movement in volleyball but also a critical skill that significantly influences a team's performance and success. It underscores the importance of precisely monitoring and optimizing training loads, as these directly impact an athlete's ability to perform these jumps effectively and consistently. Despite these advancements, there remains a gap in the systematic synthesis of this literature, particularly in integrating these diverse findings to inform monitoring strategies in volleyball. This gap underscores the need for the current systematic review, aiming to consolidate existing knowledge and identify directions for future research.</p>", "<p id=\"Par9\">Moreover, previous research has shown the importance of conducting systematic reviews about training/match monitoring with increasing attention given to the consensus as to which variables related to training load, fatigue, and well-being are most useful [##UREF##6##21##]. Therefore, the aim of this systematic review was to examine the extent, range, and nature of the evidence on the associations between training load measures, fatigue and well-being assessments used in volleyball training/match monitoring literature to aid the planning of future research.</p>" ]

[ "<title>Methods</title>", "<title>Registration and protocol</title>", "<p id=\"Par10\">This systematic review was conducted in accordance with the recommendations of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 [##REF##33782057##22##]. The study protocol was registered with INPLASY (INPLASY202270059). A PRISMA checklist is provided as a supplementary file (Table S##SUPPL##0##1##).</p>", "<title>Eligibility criteria</title>", "<p id=\"Par11\">Inclusion criteria for this systematic review were as follows: (1) original research papers published in peer-reviewed journals in English, French, Spanish, or Portuguese; (2) subjects were volleyball athletes, with no restrictions on age, thereby including youth, collegiate, and adult players; (3) the study involved at least two evaluation points, encompassing a baseline and a post-intervention measurement. The exclusion criteria were: (a) studies not involving human subjects; (b) research not specifically focused on volleyball training or competition; (c) studies lacking empirical data or not presenting clear methodological descriptions. These criteria were designed to ensure an analysis across various age groups and both male and female athletes, providing a holistic understanding of volleyball training and performance.</p>", "<title>Information sources</title>", "<p id=\"Par12\">The literature search was performed from database inception to March 2023 (date when the search was last conducted) in five electronic databases: PsycINFO, MEDLINE/PubMed, SPORTDiscus, Web of Science, and Scopus. The search was developed to consider research articles published online.</p>", "<title>Search strategy</title>", "<p id=\"Par13\">Scientific peer-reviewed published papers written in English, Portuguese, French, and Spanish were eligible for the present systematic review. The search strategy was developed around keywords for Population (volleyball athletes), Exposure (volleyball training or matches), Country (all), and study type (longitudinal). Included terms for the searches were: ‘training load volleyball’, ‘workload volleyball’, ‘rating of perceived exertion volleyball’, ‘RPE volleyball’, ‘well-being volleyball’, ‘wellness volleyball’, ‘fatigue volleyball’, ‘sleep volleyball’, ‘training response volleyball’, ‘neuromuscular fatigue volleyball’, and ‘neuromuscular status volleyball’. The complete search strategy is available in the supplementary file (Table ##TAB##0##1##).\n</p>", "<title>Selection and data collection process</title>", "<p id=\"Par14\">All retrieved papers were exported to CADIMA software, a tool designed to increase the efficiency of the evidence synthesis process and facilitate reporting of all activities to maximize methodological rigor [##UREF##7##23##]. Duplicates were automatically removed. Titles and abstracts of potentially relevant papers were screened by two reviewers (A.R. and J.R.P.). Disagreements between authors were solved through discussion and, when necessary, the remaining authors (P.C., M.J.C-S. and J.V-S.) were involved. Full‐text copies were acquired for all papers that met title and abstract screening criteria. Full‐text screening was performed by two reviewers (A.R. and J.R.P.). Again, any discrepancies were discussed until the authors reached an agreement and consulted the four other authors when required. In the process of article selection, inter-rater reliability was quantitatively assessed using the Cohen kappa coefficient. For the initial title and abstract screening, the kappa coefficient was 0.810. Similarly, for the full-text review phase, the kappa coefficient was 0.979.</p>", "<title>Data extraction</title>", "<p id=\"Par15\">Data were extracted from each article by the lead author (A.R.). Data not provided or presented non-numerically were identified as “not reported”. The following data, when possible, were extracted from each article: (1) participants’ characteristics (sample size, sex and age); (2) participants’ level (young, collegiate or professional); (3) monitoring period (i.e., seasonal phase(s) and duration); (4) training load measures (e.g., RPE, heart rate, time motion analysis); (5) neuromuscular fatigue tests (e.g., heart rate, biochemical markers); (6) well-being assessment methods (e.g., scale, questionnaire).</p>", "<title>Risk of bias assessment</title>", "<p id=\"Par16\">Methodological quality was assessed using a modified version of the Downs and Black [##REF##9764259##24##] checklist for assessing the methodological quality of randomized and nonrandomized healthcare interventions. This checklist has been validated for use with observational study designs [##REF##9764259##24##] and has been previously used to assess methodological quality in systematic reviews assessing cross-sectional and longitudinal studies [##REF##24682949##25##, ##REF##30225537##26##]. The number of items from the original checklist can be tailored to the scope and needs of the systematic review, with 10–15 items used in previous systematic reviews [##REF##24682949##25##, ##REF##30225537##26##]. For this review, 11 items in the checklist were deemed relevant (Table S##SUPPL##0##3##). Each item is scored as “1” (yes) or “0” (no/unable to determine), and the scores for each of the 11 items are summed to provide the total quality score. The quality of each included article was rated against the checklist independently by two authors (A.R. and J.R.P.). Any disparity in the outcome of the quality appraisal was discussed, and a third author (J.V-S.) was consulted if a decision could not be reached. In the assessment of methodological quality and risk of bias, inter-rater reliability was quantitatively evaluated using the Cohen kappa coefficient. The kappa value obtained was 0.903.</p>", "<title>Data synthesis</title>", "<p id=\"Par17\">Results were not pooled as the studies were heterogeneous in their methods, data, and context. Instead, we presented a narrative synthesis of the findings from included studies. We identified three categories of monitoring interventions through the process of reviewing the included studies. The definitions of these interventions are provided in the supplementary file (Table S##SUPPL##0##2##). Summary tables were provided as means and standard deviations were reported for age of participants, body mass, and body height. The period of each study (i.e., pre-season, competitive period, or both) and the duration of the study, in weeks, were also reported.</p>" ]

[ "<title>Results</title>", "<title>Study selection</title>", "<p id=\"Par18\">The electronic search yielded 2535 articles (PsycINFO = 121, PubMed = 411, SPORTDiscus = 661, Scopus = 731, Web of Science = 611). A total of 868 duplicate records were removed, and a further 1570 irrelevant articles were excluded based on title and abstract; 97 fulltext articles were screened and 66 were removed, leaving 31 articles for inclusion in the review. Reasons for exclusion were study designs did not meet the inclusion criteria (<italic>n</italic> = 33), no volleyball players in the sample (<italic>n</italic> = 20), failure to perform any monitoring strategy (<italic>n</italic> = 7), and duplicate dataset (<italic>n</italic> = 6). The full results of the search are presented in Fig. ##FIG##0##1##.</p>", "<title>Risk of bias in studies</title>", "<p id=\"Par19\">The ratings from the quality appraisal for each article are presented in the supplementary file (Table S##SUPPL##0##4##). Methodological quality scores ranged from 7 to 9 out of 11. The predominant concerns identified in the evaluation of these studies centre around issues of external validity, particularly the representativeness of the study participants. This limitation significantly hampers the generalizability of the findings. The studies fall short in ensuring that the subjects included are reflective of the broader population from which they are drawn, raising questions about the applicability of their conclusions beyond the specific sample studied. In line with previous literature using the Downs and Black checklist [##REF##24682949##25##, ##REF##30225537##26##], no articles were excluded based on methodological quality.</p>", "<title>Study characteristics</title>", "<p id=\"Par20\">Study characteristics for all 31 included studies are presented [##UREF##3##16##–##UREF##4##18##, ##UREF##8##27##–##UREF##20##54##] (Table ##TAB##1##2##). From these 31 articles, 22 included professional athletes [##UREF##3##16##–##UREF##4##18##, ##UREF##9##28##, ##REF##33871236##30##–##REF##33508777##32##, ##REF##32588158##34##–##REF##32303475##36##, ##REF##32015214##38##–##UREF##14##43##, ##REF##29956376##46##, ##REF##29969288##48##, ##REF##29584530##50##–##UREF##19##53##], seven were collegiate-level volleyball athletes [##UREF##8##27##, ##REF##34527756##29##, ##UREF##10##33##, ##UREF##12##37##, ##UREF##15##44##, ##REF##30899353##45##, ##UREF##16##47##], and two included young athletes [##REF##29072031##49##, ##UREF##20##54##]. Nine articles used female volleyball players [##UREF##8##27##–##REF##34527756##29##, ##REF##33508777##32##, ##UREF##10##33##, ##UREF##12##37##, ##UREF##15##44##, ##REF##30899353##45##, ##UREF##16##47##], while the remaining 22 were male volleyball athletes [##UREF##3##16##–##UREF##4##18##, ##REF##33871236##30##, ##REF##30325790##31##, ##REF##32588158##34##–##REF##32303475##36##, ##REF##32015214##38##–##UREF##14##43##, ##REF##29956376##46##, ##REF##29969288##48##–##UREF##20##54##].\n</p>", "<title>Quantifying training stress in volleyball athletes</title>", "<p id=\"Par21\">Quantifying training stress can be done in different ways. The most common one can be achieved by multiplying the training session intensity by the training session duration. Training load can be either internal or external [##REF##16195007##55##]. Internal training load refers to the physiological stress that a training session induces in the athlete [##REF##16195007##55##]. Measures such as heart rate (HR) and rating of perceived exertion (RPE) are the most common methods to monitor internal load [##UREF##0##2##]. On other hand, external training load is defined as the physical work prescribed in the training plan [##REF##16195007##55##]. The most common method of monitoring external load is with time-motion analysis devices, such as GPS, accelerometers, or inertial motion units (IMUs) [##UREF##0##2##].</p>", "<p id=\"Par22\">The effects of different training loads measurements have been investigated in volleyball with durations ranging from one week [##UREF##3##16##, ##REF##29072031##49##] to two seasons [##UREF##8##27##] (Table ##TAB##2##3##). Moreover, the effects of single training load measurement (i.e., internal, or external) [##UREF##3##16##, ##REF##30113918##17##, ##UREF##8##27##, ##UREF##9##28##, ##REF##33871236##30##–##REF##33508777##32##, ##UREF##11##35##–##REF##32015214##38##, ##UREF##13##40##–##REF##29239985##42##, ##UREF##15##44##, ##REF##29956376##46##–##UREF##17##51##, ##UREF##19##53##, ##UREF##20##54##] or a combination of both training load measurements [##UREF##4##18##, ##UREF##10##33##, ##REF##32059245##39##, ##UREF##14##43##] have been investigated. The session rating of perceived exertion (sRPE) (77%) [##UREF##3##16##–##UREF##4##18##, ##UREF##9##28##, ##REF##33871236##30##–##UREF##10##33##, ##UREF##11##35##, ##UREF##12##37##–##UREF##15##44##, ##UREF##16##47##–##UREF##17##51##, ##UREF##19##53##, ##UREF##20##54##] and the IMUs (16%) [##UREF##4##18##, ##UREF##8##27##, ##REF##32059245##39##, ##UREF##14##43##, ##REF##29956376##46##] are the most commonly used training load measurement strategies in volleyball. Other training load measures investigated in the volleyball literature include HR [##REF##33508777##32##], accelerometers [##UREF##10##33##], and video-cameras [##REF##32303475##36##].\n</p>", "<title>Quantifying fitness and fatigue in volleyball athletes</title>", "<p id=\"Par23\">The reduction in maximal voluntary contractile force is designated by neuromuscular fatigue and tests to detect this type of fatigue are broadly used in sport [##UREF##0##2##]. Low-frequency fatigue (i.e., resulted from high-force, high-intensity, or repeated stretch–shortening cycles muscle actions) is frequently a topic of interest while monitoring athletes [##REF##19114749##56##]. Consequently, many research studies have established the reliability and validity of vertical jumps as an indicator of neuromuscular fatigue in athletes [##REF##23170747##57##]. One of the most valid measures of fatigue is the ratio of flight time to contraction time (FT:CT), which can be explained by the fact that time-related variables are more sensitive to fatigue [##REF##24912201##58##]. Nevertheless, other measures such as jump height, peak and mean power, and peak force are also popular among coaches [##UREF##21##59##].</p>", "<p id=\"Par24\">In addition to being used to monitor training stress, submaximal exercise protocols and physiological markers such as HR can be used as objective markers of fatigue. Heart rate variability (HRV) is widely used, in particular the natural logarithm of the square root of the mean sum of squared differences between adjacent normal RR intervals (Ln rMSSD) [##REF##23479420##60##]. Another monitoring tool that can be used is the recovery period after a training session, indicated with the heart rate recovery (HRR) [##REF##20861526##61##]. Finally, examining hormonal and biochemical markers can provide a good indicator of athletes’ adaptation process [##REF##8584849##62##].</p>", "<p id=\"Par25\">Only five studies included fitness and fatigue measurements as tools to monitor volleyball athletes [##UREF##4##18##, ##UREF##9##28##, ##REF##32588158##34##, ##REF##29239985##42##, ##REF##29072031##49##] (Table ##TAB##3##4##). The countermovement jump (CMJ) is the most used fatigue measurement strategy in volleyball [##UREF##4##18##, ##UREF##9##28##, ##REF##29239985##42##, ##REF##29072031##49##]. Other fitness and fatigue monitoring tools are hormonal and biochemical markers [##REF##32588158##34##, ##REF##29239985##42##] and HR variables [##UREF##9##28##].\n</p>", "<title>Quantifying well-being in volleyball athletes</title>", "<p id=\"Par26\">Questionnaires can be useful to monitor athletes’ levels of stress [##REF##1784872##1##] and identify those at greater risk of becoming injured [##REF##26423706##63##]. Research has shown that athletes often have a mood disturbance while developing symptoms of overreaching and overtraining [##UREF##0##2##]. Therefore, assessing athlete’s mood state and level of tension through tools such as the Profile of Mood States (POMS) and the Brunel Mood Scale (BRUMS) can be useful [##REF##15768723##64##]. Wellness inventories, like the Hooper index [##REF##7898325##65##], are also common if the goal is to gather as much information as possible about different metrics, such as fatigue, stress, sleep, or recovery.</p>", "<p id=\"Par27\">The current literature search returned 22 studies that applied some form of well-being questionnaire [##UREF##3##16##–##UREF##4##18##, ##UREF##9##28##, ##REF##34527756##29##, ##REF##30325790##31##–##UREF##11##35##, ##REF##32015214##38##, ##UREF##13##40##–##REF##29239985##42##, ##UREF##15##44##, ##REF##30899353##45##, ##REF##29969288##48##–##UREF##18##52##, ##UREF##20##54##] (Table ##TAB##4##5##). The Hooper index [##UREF##3##16##, ##UREF##9##28##, ##REF##33508777##32##, ##REF##32015214##38##, ##REF##29619798##41##, ##UREF##15##44##, ##REF##29969288##48##], the Total Quality Recovery (TQR) scale [##UREF##3##16##, ##REF##30113918##17##, ##REF##30325790##31##, ##UREF##11##35##, ##UREF##13##40##, ##REF##29584530##50##, ##UREF##17##51##], and general wellness questionnaires [##UREF##4##18##, ##REF##34527756##29##, ##UREF##10##33##, ##UREF##13##40##, ##REF##29072031##49##, ##UREF##17##51##, ##UREF##18##52##] are the most commonly used well-being measurement strategies in volleyball. Other well-being measuring tools investigated in the volleyball literature include the Recovery Stress Questionnaire for Athletes (RESTQ-Sport) [##REF##32588158##34##, ##REF##29239985##42##, ##UREF##20##54##] and the POMS [##UREF##11##35##].\n</p>" ]

[ "<title>Discussions</title>", "<p id=\"Par28\">Literature that has evaluated the effect of all monitoring strategies (i.e., training stress, fitness and fatigue, and well-being) during volleyball training and/or competition is limited. Besides, there is a small number of studies describing the external training load when compared with the internal training load. Furthermore, not only fitness and fatigue monitoring studies are limited, but also have questionable methodologies within volleyball athletes. A sample monitoring system for volleyball is suggested in Fig. ##FIG##1##2##.</p>", "<title>Training stress in volleyball</title>", "<p id=\"Par29\">Seven studies analysed the internal load of volleyball players during the pre-season with the sRPE [##REF##30113918##17##, ##REF##33871236##30##, ##REF##30325790##31##, ##REF##29619798##41##, ##REF##29239985##42##, ##REF##29584530##50##, ##UREF##20##54##]. During the first weeks of pre-season the internal load of the players is defined by a progressive increase characterized by a decrease in performance [##REF##33871236##30##, ##REF##30325790##31##, ##REF##29239985##42##]. This can also be seen with external training load measures, as jump load is higher during the first phase of pre-season [##REF##32303475##36##]. To better prepare athletes for the start of the competition phase, this periodization approach is common in team-sports during the pre-season [##UREF##20##54##, ##REF##26605808##66##]. Coaches are advised to introduce the load progressively and, in the middle of the pre-season period, decrease the training loads to allow recovery and better balance the fitness-fatigue relationship [##UREF##22##67##]. In fact, elevated injury rates have been observed during this period in other sports [##REF##21256078##68##]. This is in line with what is reported in volleyball’s literature, as weekly workloads, acute-chronic workload ratio (ACWR), and incidence of injury values are higher during the pre-season period [##REF##30113918##17##, ##REF##29584530##50##]. Coaches and practitioners should evaluate athletes’ fitness in the beginning of the pre-season period and assess what were the workloads that players were familiarized during the off-season so that weekly internal training load peaks do not occur.</p>", "<p id=\"Par30\">Sixteen studies analysed the internal training load of volleyball athletes during the competitive period with the sRPE method [##REF##30113918##17##, ##UREF##4##18##, ##REF##30325790##31##–##UREF##10##33##, ##UREF##11##35##, ##UREF##12##37##–##REF##29619798##41##, ##UREF##14##43##, ##UREF##15##44##, ##REF##29969288##48##, ##REF##29584530##50##, ##UREF##17##51##]. It can be observed that volleyball periodization is characterized by a wave distribution of the training load during this period [##REF##30325790##31##, ##UREF##11##35##, ##UREF##12##37##, ##UREF##13##40##, ##REF##29969288##48##, ##REF##29584530##50##]. This is distinctive of sports in which the pre-season period is short compared to the competitive period with the objective to adapt the stress applied during training sessions [##REF##20199119##11##]. Due to various travels made and games played against teams of different levels the number of training sessions reduce during the competitive period [##REF##26605808##66##]. Therefore, this wave distribution of the training load can avoid a possible decrement in performance. This can be done by increasing training loads in weeks in which the team has a low possibility of winning or losing the game [##REF##20199119##11##, ##REF##26605808##66##]. In a more in-depth analysis, results of the literature indicate that during the first phase of the competitive period, volleyball athletes experience higher internal loads compared to the second phase of the same period [##REF##30325790##31##, ##UREF##11##35##, ##REF##32015214##38##, ##UREF##13##40##, ##REF##29619798##41##]. The first phase of the competitive period of volleyball professional season is characterized by a focus on the development of fitness components while the second phase comprises the most specific training sessions (technical and tactical skills) [##REF##20199119##11##]. Thus, this can explain these differences in internal load levels observed during the competitive period. Moreover, while looking into a single week, it can be observed that higher sRPE values are recorded during the middle of the week and lower values at the end of the week [##REF##32059245##39##, ##UREF##14##43##, ##REF##29969288##48##]. This is a common strategy to optimize the adaptation process in team sports by augmenting athletes’ recovery status by reducing training loads [##REF##20199119##11##].</p>", "<p id=\"Par31\">There are significant differences in competition and in training jump count, jump height and jump load between positions in female [##UREF##8##27##] and male volleyball athletes [##UREF##10##33##, ##REF##32303475##36##, ##REF##29956376##46##]. Outside hitters had the highest jump height followed by middle blockers and right-side hitters [##UREF##8##27##]. Female [##UREF##8##27##] and male [##REF##32303475##36##, ##REF##29956376##46##] volleyball middle blockers showed a higher jump count and jump rate compared to outside hitters and right-side hitters. This is in line with another study with female volleyball athletes that reported that middle blockers experienced both a higher HR-method internal training load and sRPE than the rest of the players [##REF##33508777##32##]. Middle blockers are often required to be involved in every defensive blocking aspect of the game [##UREF##23##69##], hence their higher values of both external and internal training load. Nevertheless, HR measures of internal training load should be interpreted with caution. While the HR represents a valid means through which to measure exercise intensity in endurance sports, these methods are questionable in team sports, such as volleyball, which are characterized by short but maximal anaerobic efforts [##REF##15179175##70##]. In fact, the results of one study stated no association between well-being and HR-based internal training load [##REF##33508777##32##]. Thus, given the limitations inherent in using the HR for monitoring the intensity of volleyball training sessions, coaches are advised to not use HR-based methods to quantify training stress in this sport.</p>", "<title>Fitness and fatigue in volleyball</title>", "<p id=\"Par32\">One study demonstrated that submaximal exercise heart rate (HRex) values decreased over a period of 4 weeks [##UREF##9##28##]. Reductions in HRex are generally associated with improved aerobic fitness, while elevations in HRex are related to acute fatigue or loss of fitness [##REF##24578692##71##]. One study also showed positive associations between seated Ln rMSSD and training load (i.e., sRPE) in female volleyball athletes [##UREF##9##28##]. These results must be interpreted carefully as these positive associations can vary depending on how loads are being tolerated by athletes. If training loads increase in response to increments in fitness and performance, then seated Ln rMSSD will reduce [##REF##30055959##72##]. On other hand, if converse cardiac-autonomic responses are stimulated through mechanisms of fatigue resulted from high training loads, then seated Ln rMSSD will increase [##REF##22367011##73##]. These inconsistencies in associations between Ln rMSSD and training load show the importance of monitor various markers of fatigue, fitness, load, and well-being. Previous research showed that HRV values return to baseline 24 h after an intense exercise bout in the supine position [##REF##15461995##74##]. Therefore, it can be hypothesized that high training loads induces greater fluctuations in the seated Ln rMSSD compared to supine Ln rMSSD. Thus, coaches and practitioners should have this into consideration when monitoring fatigue of volleyball athletes through HRV.</p>", "<p id=\"Par33\">In response to a high-load exercise, various enzymes and blood markers, such as creatine kinase (CK), increase [##REF##26423706##63##]. This type of exercises induces muscle damage and since CK is released from muscle cells to blood, practitioners have been using CK levels to assess the degree of muscle damage [##REF##17569697##75##]. According to the search conducted, volleyball athletes experience an increase of CK levels during the first weeks of pre-season and a decrease in the final weeks [##REF##32588158##34##, ##REF##29239985##42##]. This is in line with what was already mentioned in this manuscript about the levels of sRPE during the pre-season period. It is expected to observe higher increment in CK levels in individuals with lower physical fitness [##REF##17569697##75##], particularly during initial training periods (i.e., pre-season) characterized as an initial training time followed by a period with no structured training. This also indicates that CK levels increase in response to high training loads, which is in line with what was previously reported [##REF##17569697##75##]. However, CK has a large variability [##REF##26635619##76##] and personnel involved in the collection of this marker must understand the importance of establishing baseline values from many samples over several days. Testosterone and cortisol are other two markers that are associated with cellular catabolism, anabolism, and overreaching [##REF##8584849##62##]. Literature shows that during volleyball pre-season, both testosterone and cortisol levels do not change [##REF##29239985##42##]. This is probably an indicator that volleyball pre-season is not enough to induce disturbances in the balance of the immune system.</p>", "<p id=\"Par34\">Results from a study conducted during the pre-season showed that the CMJ height did not change during a 6-week period, assessed four times during this time-window [##REF##29239985##42##]. Another study revealed that, across a single training week, the CMJ jump height decreased [##REF##29072031##49##]. Both studies’ methodologies indicated that the best of all jumps was retained for analysis. However, when the comparison between highest and average results is possible, the averaged jump results is more sensitive than the highest jump in detecting fatigue or supercompensation effects [##REF##27663764##77##]. Therefore, these results should be interpreted with caution and volleyball coaches should have into consideration that averaged CMJ performance without arm swing should be used to track neuromuscular status.</p>", "<title>Well-being in volleyball</title>", "<p id=\"Par35\">One study reported well-being measures, such as mood, soreness, and sleep duration, as independent predictors of injury in female volleyball athletes [##REF##34527756##29##]. This is aligned with other non-volleyball studies [##REF##33225012##78##]. According to the literature, athletes do not get the sleep duration that is recommended [##REF##22329779##79##] which is a minimum of 7 h to minimize injury risk [##REF##27367265##80##]. Therefore, volleyball staff should seek to include these subjective markers into their daily training monitoring routines to identify athletes with higher injury risk.</p>", "<p id=\"Par36\">Volleyball athletes’ recovery state is lower in the final stage of the pre-season, compared to other points of the competitive period [##REF##30325790##31##]. In the last phase of the pre-season, coaches are advised to employ a taper strategy to avoid the undesirable outcomes of fatigue already mentioned in the beginning of the present manuscript, like nonfunctional overreaching [##REF##25134000##8##]. In fact, the results of a study with professional male volleyball players showed that the odds of injury were inversely proportional to the values of TQR scale (i.e., the less recovered the player, the greater the odds of sustaining an injury) [##REF##30113918##17##]. Likewise, athletes’ readiness to start the competitive period is important since the perception of stress increase whereas their perception of recovery decrease during a volleyball pre-season [##REF##30325790##31##, ##REF##32588158##34##, ##UREF##20##54##]. The results from other studies suggested that the RESTQ-Sport [##REF##29239985##42##] and the Hooper index [##UREF##3##16##, ##UREF##15##44##] are sensitive to an increase in the training load in volleyball athletes, showing promising results as tools to indicate early symptoms of overtraining. Consequently, balancing pre-season training stress and recovery is essential so athletes’ adaptation process is optimized for match-days.</p>", "<p id=\"Par37\">During periods of congested travels and games, volleyball athletes reported poorer well-being responses in questionnaires [##UREF##3##16##, ##UREF##10##33##, ##UREF##11##35##, ##UREF##13##40##, ##REF##29969288##48##, ##UREF##17##51##, ##UREF##18##52##]. Time lost to travel, and the ensuing disruption of routines and training schedules may inhibit the use of recovery and medical interventions. Since travels can decrease the well-being and increase athletes’ risk for illness, coaches and staff should implement some strategies, such as: provide adequate recovery time after travels; avoid flying on the same day as match-day; and encourage athletes to drink water during travels [##REF##29934212##81##]. By tracking well-being values coaches can make informed decisions about the demands that incur from both in and out of sport activities.</p>", "<p id=\"Par38\">During the last stage of the competitive period, higher levels of stress can be observed in professional volleyball athletes [##REF##32015214##38##, ##REF##29619798##41##]. Anxiety of a pre-match situation seems to impact the perception of stress levels by professional athletes [##UREF##24##82##]. This stage is characterized by the decisive matches of the season. On other hand, stress levels in collegiate volleyball athletes may not be as heavily influenced by athletic events during the season and may be more a consequence of the temporal relation to the academic school year [##REF##30899353##45##]. Therefore, challenges that occur in social and academic settings are the offset to higher stress levels in collegiate athletes.</p>", "<title>Limitations, strengths, and recommendations for future research</title>", "<p id=\"Par39\">Many conclusions can be drawn from the available literature on the monitorization strategies in the volleyball context. Studies addressing the responses of the three types of monitorization strategies in volleyball are limited [##UREF##4##18##, ##UREF##9##28##, ##REF##29239985##42##, ##REF##29072031##49##]. Of these four studies, none was conducted during a full season. Thus, future research should examine fitness and fatigue outcomes, internal and external training load data, and well-being questionnaires responses during a longer period (i.e., at least one full season) to better understand the relationship of different monitoring strategies in volleyball athletes. Besides, only five studies analysed fitness and fatigue in this athletic population [##UREF##4##18##, ##UREF##9##28##, ##REF##32588158##34##, ##REF##29239985##42##, ##REF##29072031##49##]. Moreover, none of these studies was performed during a full season and future research should point in that direction. More specifically, fatigue in female volleyball athletes can be even more expanded by analysing the menstrual tracking and biochemical markers to develop a further understanding of how Ln rMSSD responses influence training adaptations.</p>", "<p id=\"Par40\">Although the jump analysis is accepted as a reflection of external load, displacements and changes of direction also seem to affect this dimension (especially for the libero position). Therefore, those movements should be considered in future research as only one study analysed these metrics in a sample of collegiate female volleyball athletes [##UREF##10##33##]. Furthermore, the simple jump count method is not ideal to measure external load. Six studies expressed external load by analysing the jump height of each athlete [##UREF##4##18##, ##UREF##8##27##, ##UREF##10##33##, ##REF##32059245##39##, ##UREF##14##43##, ##REF##29956376##46##]. Still, two volleyball players with different body mass that achieve the same jumping height will not experience the same load. Due to gravity, linear velocity at landing increases with higher jumping height values, which subsequently increases kinetic energy (i.e., energy related to the body mass) levels at landing [##REF##27566896##83##]. So, coaches should consider the vertical displacement of each jump as well as the mass of the athlete to have a better external load metric that is more reflective of what the volleyball athlete is experiencing [##REF##27566896##83##]. Future research should explore the prospective relationship between external load calculated with the parameters mentioned before, the incidence of injury and the landing mechanics of volleyball players. This would potentially inform training and match-play guidelines by designing thresholds for injury prevention purposes.</p>", "<p id=\"Par41\">One notable limitation in the current volleyball literature, and a promising direction for future research, is the exploration of GPS and Local Positioning Systems (LPS) for monitoring external load. While extensively used in outdoor sports, the application of GPS in volleyball, particularly indoor, is less common [##REF##23812857##84##]. However, advancements in LPS technology now allow for its potential application in indoor environments, such as volleyball courts [##UREF##25##85##]. The adoption of these systems could provide detailed insights into player movements, intensity, and workload, which are crucial for training optimization, performance enhancement, and injury prevention [##REF##38098071##5##, ##UREF##25##85##]. This area remains under-researched in volleyball, highlighting a significant gap and an opportunity for future studies. It is recommended that subsequent research investigates the utility and implementation of these technologies in volleyball, offering a comprehensive perspective on managing external load in athletes. Such exploration could substantially contribute to the evolving landscape of volleyball training and competition analysis.</p>", "<p id=\"Par42\">The average CMJ height is more sensitive than highest CMJ height in monitoring the effects of fatigue [##REF##27663764##77##]. However, three of the four studies that used this test to monitor neuromuscular fatigue opted to use the best of all attempts [##UREF##9##28##, ##REF##29239985##42##, ##REF##29072031##49##]. So, average CMJ height should be used in future volleyball studies to track neuromuscular status. Additionally, peak power, mean power, peak velocity, peak force, mean impulse, and calculated power would seem merit worthy in quantifying supercompensation effects [##REF##27663764##77##] and no study evaluated the impact of these variables within volleyball athletes. Nevertheless, the more useful indicators of readiness and neuromuscular fatigue within the plethora of variables that the CMJ give are the FT:CT and reactive strength index modified (RSI_mod) [##UREF##26##86##]. The RSI_mod is obtained by dividing the jump height to the contraction time and, similarly to FT:CT, the emphases of these two variables are jump process and force production [##REF##35455771##87##]. Because time and contraction-specific measures better reflect the strategy employed by the neuromuscular system, compared with jumping height, contraction time is more sensitive to detect adaptations resulted from fatigue [##REF##19211947##88##]. Since the ability of vertical jump height to reflect fatigue in athletes show inconsistencies in the literature [##REF##16895532##89##, ##REF##17219174##90##], future studies in volleyball should consider the use of RSI_mod and FT:CT to monitor neuromuscular fatigue.</p>", "<p id=\"Par43\">While the CMJ test is prevalently used in the current literature, exploring alternative assessments could provide a more comprehensive understanding of neuromuscular responses in volleyball athletes. Tests like the Drop Jump, which involves a short-duration stretch–shortening cycle, can offer insights into reactive strength and plyometric capabilities under fatigued conditions [##REF##35455771##87##]. Additionally, isometric tests, such as isometric mid-thigh pulls or isometric calf raises, could be utilized to assess force in specific joint positions [##UREF##27##91##]. These alternative tests could reveal different dimensions of fatigue that may not be fully captured by the CMJ alone. Incorporating a variety of neuromuscular assessments can help in developing a more nuanced understanding of fatigue patterns in volleyball players, which in turn could inform more effective training and recovery protocols. Therefore, it is recommended that future research in volleyball expand the repertoire of fatigue assessment tools to include dynamic, plyometric, and isometric evaluations, providing a broader spectrum of data to optimize athlete performance.</p>", "<p id=\"Par44\">Finally, to mitigate divergency in fatigue, relative velocity loss thresholds have recently been implemented during the strength training prescription [##REF##31094251##92##]. Thus, velocity based training (VBT) can be a great alternative to the most used percentage-based methods since the latter do not have into consideration training-related fatigue [##UREF##28##93##]. Therefore, strength and conditioning coaches should consider monitoring velocity attained at the start of a training session to help objectively monitor changes in athlete fitness and fatigue. This is a topic that needs more understanding and future research should seek to answer if VBT is a reliable and valid tool to monitor neuromuscular fatigue in volleyball athletes.</p>", "<p id=\"Par45\">Due to the heterogeneity of the measures used, it was not possible to conduct a meta-analysis. Plus, RPE and well-being data can be collected without following specific procedures and across a range of methods (e.g., different RPE scales and/or different operational questions). Therefore, practitioners working in professional volleyball can use this information in various ways with different assessment standards between them and this systematic review did not have that into consideration. Nevertheless, since there is a growing interest in topics related to athletes’ monitoring this study can aid volleyball coaches to select which training load measures, fatigue and well-being assessments can be used with their athletes.</p>" ]

[ "<title>Conclusions</title>", "<p id=\"Par46\">Within the context of team sport athletes, such as volleyball, coaches should use a mixed-methods approach when monitoring these athletes. No single measure can determine how a player is fully coping with the demands of training and matches. Therefore, practitioners not only need a range of methods, but also ensure athletes are familiarized with them to better improve their buy-in and the quality of the data analysis. According to this review, internal training load should be collected daily after training sessions and matches with the sRPE method. External training load should also be measured daily according to the method proposed by Charlton et al. [##REF##27566896##83##] based on jump height, jump count, and kinetic energy. If force platforms are available, neuromuscular fatigue can be assessed weekly using the FT:CT ratio of a CMJ or, in cases where force platforms are not available, the average jump height can also be used. Finally, the Hooper Index has been shown to be a measure of overall wellness, fatigue, stress, muscle soreness, mood, and sleep quality in volleyball when used daily.</p>" ]

[ "<title>Background</title>", "<p id=\"Par1\">Volleyball, with its unique calendar structure, presents distinct challenges in training and competition scheduling. Like many team sports, volleyball features an unconventional schedule with brief off-season and pre-season phases, juxtaposed against an extensive in-season phase characterized by a high density of matches and training. This compact calendar necessitates careful management of training loads and recovery periods. The effectiveness of this management is a critical factor, influencing the overall performance and success of volleyball teams. In this review, we explore the associations between training stress measures, fatigue, and well-being assessments within this context, to better inform future research and practice.</p>", "<title>Methods</title>", "<p id=\"Par2\">A systematic literature search was conducted in databases including PsycINFO, MEDLINE/PubMed, SPORTDiscus, Web of Science, and Scopus. Inclusion criteria were original research papers published in peer-reviewed journals involving volleyball athletes.</p>", "<title>Results</title>", "<p id=\"Par3\">Of the 2535 studies identified, 31 were thoroughly analysed. From these 31 articles, 22 included professional athletes, seven included collegiate-level volleyball athletes, and two included young athletes. Nine studies had female volleyball players, while the remaining 22 had male volleyball athletes.</p>", "<title>Conclusions</title>", "<p id=\"Par4\">Internal training load should be collected daily after training sessions and matches with the session rating of perceived exertion method. External training load should also be measured daily according to the methods based on jump height, jump count, and kinetic energy. If force platforms are available, neuromuscular fatigue can be assessed weekly using the FT:CT ratio of a countermovement jump or, in cases where force platforms are not available, the average jump height can also be used. Finally, the Hooper Index has been shown to be a measure of overall wellness, fatigue, stress, muscle soreness, mood, and sleep quality in volleyball when used daily.</p>", "<title>Supplementary Information</title>", "<p>The online version contains supplementary material available at 10.1186/s13102-024-00807-7.</p>", "<title>Keywords</title>" ]

[ "<title>Supplementary Information</title>", "<p>\n</p>" ]

[ "<title>Acknowledgements</title>", "<p>Not applicable</p>", "<title>Authors’ contributions</title>", "<p>A.R., J.R.P., P.C., and J.V-d-S. conceptualized the systematic review. A.R., J.R.P., P.C., M.J.C-e-S., and J.V-d-S. performed the selection of the eligible studies. A.R. extracted data, synthesized the data, prepared tables and figures, and drafted the manuscript. All authors contributed significantly to the interpretation of results. All authors critically reviewed the manuscript. All authors read and approved the final manuscript.</p>", "<title>Funding</title>", "<p>This research received no external funding.</p>", "<title>Availability of data and materials</title>", "<p>All data are available upon request to the corresponding author.</p>", "<title>Declarations</title>", "<title>Ethics approval and consent to participate</title>", "<p id=\"Par47\">Not applicable.</p>", "<title>Consent to publication</title>", "<p id=\"Par48\">Not applicable.</p>", "<title>Competing interests</title>", "<p id=\"Par49\">The authors declare no competing interests.</p>" ]

[ "<fig id=\"Fig1\"><label>Fig.1</label><caption><p>PRISMA flow diagram</p></caption></fig>", "<fig id=\"Fig2\"><label>Fig.2</label><caption><p>General recommendations for assessing the training load, neuromuscular fatigue, and well-being of volleyball players</p></caption></fig>" ]

[ "<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Search strategy</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Variable</th><th align=\"left\">Search terms</th></tr></thead><tbody><tr><td align=\"left\">Training load</td><td align=\"left\">AB OR SU (“training load” OR “training impulse” OR TRIMP OR “external load” OR “internal load” OR duration OR exposure OR RPE OR “rating of perceived exertion” OR summated-heart-rate-zone OR SHRZ OR PlayerLoad OR BodyLoad OR “global positioning system” OR GPS OR accelerometer)</td></tr><tr><td align=\"left\">Neuromuscular fatigue</td><td align=\"left\">AB OR SU (“neuromuscular fatigue” OR “neuromuscular function” OR “neuromuscular performance” OR “neuromuscular power” OR fatigue OR fatiguing OR fatigability)</td></tr><tr><td align=\"left\">Well-being</td><td align=\"left\">AB OR SU (wellbeing OR well-being OR “well being” OR wellness OR health OR psychological OR “mental state*” OR “state of mind” OR affect OR affective OR affects OR mood* OR emotion* OR anxiety OR confidence OR self-esteem OR self-efficacy OR motivation OR depression OR stress OR tension OR feeling* OR “physical state” OR “physical functioning” OR “perceived recovery” OR “perceived strength” OR soreness OR “quality of life” OR readiness OR vitality OR vigor OR vigour OR sleepiness OR “sleep quality” OR fatigue OR tiredness OR alertness OR distress OR “social function” OR appetite OR overtrain* OR overreach*)</td></tr><tr><td align=\"left\">Volleyball</td><td align=\"left\">AB OR SU (volleyballer* OR “volleyball player*” OR “volleyball athlete*”)</td></tr><tr><td align=\"left\">Final search</td><td align=\"left\">training load OR neuromuscular fatigue OR well-being AND volleyball</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab2\"><label>Table 2</label><caption><p>Characteristics of the included studies</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Author, year</th><th align=\"left\">N</th><th align=\"left\">Level</th><th align=\"left\">M/F</th><th align=\"left\">Age (years)</th><th align=\"left\">Duration</th></tr></thead><tbody><tr><td align=\"left\">Rebelo et al., 2023 [##UREF##4##18##]</td><td align=\"left\">15</td><td align=\"left\">Professional</td><td align=\"left\">M</td><td align=\"left\">28.51 ± 5.39</td><td align=\"left\">5 weeks</td></tr><tr><td align=\"left\">Herring and Fukuda, 2022 [##UREF##8##27##]</td><td align=\"left\">9 (5 across both seasons)</td><td align=\"left\">Collegiate</td><td align=\"left\">F</td><td align=\"left\">NR</td><td align=\"left\">2 seasons</td></tr><tr><td align=\"left\">Berriel et al., 2022 [##REF##33871236##30##]</td><td align=\"left\">16</td><td align=\"left\">Professional</td><td align=\"left\">M</td><td align=\"left\">23.60 ± 4.93</td><td align=\"left\">Pre-season</td></tr><tr><td align=\"left\">Rabbani et al., 2021 [##UREF##9##28##]</td><td align=\"left\">13</td><td align=\"left\">Professional</td><td align=\"left\">F</td><td align=\"left\">25.8 ± 3.0</td><td align=\"left\">4 training camps (≈1 month)</td></tr><tr><td align=\"left\">Haraldsdottir et al., 2021 [##REF##34527756##29##]</td><td align=\"left\">17</td><td align=\"left\">Collegiate</td><td align=\"left\">F</td><td align=\"left\">19.6 ± 1</td><td align=\"left\">1 season</td></tr><tr><td align=\"left\">Andrade et al., 2021 [##REF##30325790##31##]</td><td align=\"left\">15</td><td align=\"left\">Professional</td><td align=\"left\">M</td><td align=\"left\">24 ± 4</td><td align=\"left\">1 season</td></tr><tr><td align=\"left\">Timoteo et al., 2021 [##REF##30113918##17##]</td><td align=\"left\">14</td><td align=\"left\">Professional</td><td align=\"left\">M</td><td align=\"left\">26.7 ± 5.5</td><td align=\"left\">1 season</td></tr><tr><td align=\"left\">Ungureanu et al., 2021 [##REF##33508777##32##]</td><td align=\"left\">12</td><td align=\"left\">Professional</td><td align=\"left\">F</td><td align=\"left\">22 ± 4</td><td align=\"left\">1 season</td></tr><tr><td align=\"left\">Kupperman et al., 2021 [##UREF##10##33##]</td><td align=\"left\">11</td><td align=\"left\">Collegiate</td><td align=\"left\">F</td><td align=\"left\">19.36 ± 1.27</td><td align=\"left\">1 season</td></tr><tr><td align=\"left\">Berriel et al., 2020 [##REF##32588158##34##]</td><td align=\"left\">13</td><td align=\"left\">Professional</td><td align=\"left\">M</td><td align=\"left\">23.80 ± 5.40</td><td align=\"left\">Pre-season</td></tr><tr><td align=\"left\">Horta et al., 2020 [##UREF##11##35##]</td><td align=\"left\">9</td><td align=\"left\">Professional</td><td align=\"left\">M</td><td align=\"left\">26.4 ± 4.0</td><td align=\"left\">1 season</td></tr><tr><td align=\"left\">García-de-Alcaraz et al., 2020 [##REF##32303475##36##]</td><td align=\"left\">11</td><td align=\"left\">Professional</td><td align=\"left\">M</td><td align=\"left\">28.0 ± 6.12</td><td align=\"left\">1 season</td></tr><tr><td align=\"left\">Roy et al., 2020 [##UREF##12##37##]</td><td align=\"left\">15</td><td align=\"left\">Collegiate</td><td align=\"left\">F</td><td align=\"left\">NR</td><td align=\"left\">1/2 season</td></tr><tr><td align=\"left\">Clemente et al., 2020 [##REF##32015214##38##]</td><td align=\"left\">13</td><td align=\"left\">Professional</td><td align=\"left\">M</td><td align=\"left\">31.0 ± 5.0</td><td align=\"left\">1 season</td></tr><tr><td align=\"left\">Lima et al., 2020 [##REF##32059245##39##]</td><td align=\"left\">8</td><td align=\"left\">Professional</td><td align=\"left\">M</td><td align=\"left\">23.0 ± 5.22</td><td align=\"left\">15 weeks</td></tr><tr><td align=\"left\">Duarte et al., 2019 [##UREF##13##40##]</td><td align=\"left\">14</td><td align=\"left\">Professional</td><td align=\"left\">M</td><td align=\"left\">24.0 ± 3.59</td><td align=\"left\">1 season</td></tr><tr><td align=\"left\">Clemente et al., 2019 [##REF##29619798##41##]</td><td align=\"left\">13</td><td align=\"left\">Professional</td><td align=\"left\">M</td><td align=\"left\">31.0 ± 5.0</td><td align=\"left\">1 season</td></tr><tr><td align=\"left\">Horta et al., 2019 [##REF##29239985##42##]</td><td align=\"left\">12</td><td align=\"left\">Professional</td><td align=\"left\">M</td><td align=\"left\">26.9 ± 4.6</td><td align=\"left\">Pre-season</td></tr><tr><td align=\"left\">Silva et al., 2019 [##UREF##14##43##]</td><td align=\"left\">8</td><td align=\"left\">Professional</td><td align=\"left\">M</td><td align=\"left\">23.0 ± 0.2</td><td align=\"left\">1 season</td></tr><tr><td align=\"left\">Roy et al., 2019 [##UREF##15##44##]</td><td align=\"left\">15</td><td align=\"left\">Collegiate</td><td align=\"left\">F</td><td align=\"left\">NR</td><td align=\"left\">1/2 season</td></tr><tr><td align=\"left\">Hyatt and Kavazis, 2019 [##REF##30899353##45##]</td><td align=\"left\">8</td><td align=\"left\">Collegiate</td><td align=\"left\">F</td><td align=\"left\">NR</td><td align=\"left\">1 season</td></tr><tr><td align=\"left\">Skazalski et al., 2018 [##REF##29956376##46##]</td><td align=\"left\">14</td><td align=\"left\">Professional</td><td align=\"left\">M</td><td align=\"left\">NR</td><td align=\"left\">1 season</td></tr><tr><td align=\"left\">Castello et al., 2018 [##UREF##16##47##]</td><td align=\"left\">10</td><td align=\"left\">Collegiate</td><td align=\"left\">F</td><td align=\"left\">19.80 ± 1.23</td><td align=\"left\">8 weeks</td></tr><tr><td align=\"left\">Mendes et al., 2018 [##REF##29969288##48##]</td><td align=\"left\">13</td><td align=\"left\">Professional</td><td align=\"left\">M</td><td align=\"left\">31 ± 5.0</td><td align=\"left\">1 season</td></tr><tr><td align=\"left\">Tavares et al., 2018 [##REF##29072031##49##]</td><td align=\"left\">13</td><td align=\"left\">Young</td><td align=\"left\">M</td><td align=\"left\">18 ± 1</td><td align=\"left\">1 week</td></tr><tr><td align=\"left\">Debien et al., 2018 [##REF##29584530##50##]</td><td align=\"left\">15</td><td align=\"left\">Professional</td><td align=\"left\">M</td><td align=\"left\">24.0 ± 3.6</td><td align=\"left\">1 season</td></tr><tr><td align=\"left\">Brandão et al., 2018 [##UREF##17##51##]</td><td align=\"left\">14</td><td align=\"left\">Professional</td><td align=\"left\">M</td><td align=\"left\">26.7 ± 5.5</td><td align=\"left\">1 season</td></tr><tr><td align=\"left\">Nogueira et al., 2017 [##UREF##18##52##]</td><td align=\"left\">12</td><td align=\"left\">Professional</td><td align=\"left\">M</td><td align=\"left\">23.50 ± 3.39</td><td align=\"left\">1 season</td></tr><tr><td align=\"left\">Horta et al., 2017 [##UREF##19##53##]</td><td align=\"left\">15</td><td align=\"left\">Professional</td><td align=\"left\">M</td><td align=\"left\"><p>G1: 25.9 ± 3.8</p><p>G2: 23.1 ± 3.1</p></td><td align=\"left\">10 weeks (without matches)</td></tr><tr><td align=\"left\">Timoteo et al., 2017 [##UREF##3##16##]</td><td align=\"left\">12</td><td align=\"left\">Professional</td><td align=\"left\">M</td><td align=\"left\">26.7 ± 5.5</td><td align=\"left\">1 week (5 matches)</td></tr><tr><td align=\"left\">de Freitas et al., 2015 [##UREF##20##54##]</td><td align=\"left\">7</td><td align=\"left\">Young</td><td align=\"left\">M</td><td align=\"left\">15.8 ± 0.5</td><td align=\"left\">Pre-season</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab3\"><label>Table 3</label><caption><p>Training stress monitoring strategies and protocols in the volleyball literature</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Author, year</th><th align=\"left\">Training stress measurements</th><th align=\"left\">Methodology</th><th align=\"left\">Results</th></tr></thead><tbody><tr><td align=\"left\">Rebelo et al., 2023 [##UREF##4##18##]</td><td align=\"left\"><p>Internal load—sRPE</p><p>External load—IMU (jump metrics)</p></td><td align=\"left\">Jumping metrics and sRPE of each training session</td><td align=\"left\"><p>wITL (range): 1229.00 ± 247.74 to 2188.13 ± 693.36</p><p>wETL (range): 11,144.92 ± 3648.12 kJ to 18,328.99 ± 8358.20 kJ</p></td></tr><tr><td align=\"left\">Herring and Fukuda, 2022 [##UREF##8##27##]</td><td align=\"left\">External load—IMU (jump metrics)</td><td align=\"left\">53 matches across 2 seasons</td><td align=\"left\"><p>MB—HT: 47.4 ± 5.4; OJC: 89.2 ± 30.7; OJR = 0.95 ± 0.21</p><p>OH—HT: 51.9 ± 2.2; OJC: 72.8 ± 22.8; OJR = 0.77 ± 0.13</p><p>RSH—HT: 45.4 ± 11.4; OJC: 50.3 ± 22.1; OJR = 0.57 ± 0.19</p></td></tr><tr><td align=\"left\">Berriel et al., 2022 [##REF##33871236##30##]</td><td align=\"left\">Internal load—sRPE</td><td align=\"left\">sRPE of each training session</td><td align=\"left\">wITL (range): 1388 ± 111 to 3852 ± 149</td></tr><tr><td align=\"left\">Rabbani et al., 2021 [##UREF##9##28##]</td><td align=\"left\">Internal load—sRPE</td><td align=\"left\">sRPE of each training session</td><td align=\"left\">sRPE (range): 1052 ± 163 to 1105 ± 121</td></tr><tr><td align=\"left\">Andrade et al., 2021 [##REF##30325790##31##]</td><td align=\"left\">Internal load—sRPE</td><td align=\"left\">sRPE of each training session</td><td align=\"left\"><p>PS—TWTL: 3,512.84 ± 876.48</p><p>CPI—TWTL: 2,843.93 ± 1,026.14</p><p>CPII—TWTL: 2,696.40 ± 933.51</p></td></tr><tr><td align=\"left\">Timoteo et al., 2021 [##REF##30113918##17##]</td><td align=\"left\">Internal load—sRPE</td><td align=\"left\">sRPE of each training session</td><td align=\"left\"><p>PS—TWTL: 3,492.75 ± 2,320.68</p><p>CP—TWTL: 3,207.02 ± 2,423.04</p></td></tr><tr><td align=\"left\">Ungureanu et al., 2021 [##REF##33508777##32##]</td><td align=\"left\">Internal load—sRPE, HR</td><td align=\"left\">HR and sRPE of each training session</td><td align=\"left\"><p>MB—sRPE: 534; EHR: 207</p><p>OH—sRPE: 402; EHR: 172</p><p>RSH—sRPE: 463; EHR: 206</p><p>L—sRPE: 313; EHR: 180</p><p>SE—sRPE: 351; EHR: 233</p></td></tr><tr><td align=\"left\">Kupperman et al., 2021 [##UREF##10##33##]</td><td align=\"left\"><p>Internal load—sRPE</p><p>External load—accelerometer (jump metrics, COD, accelerations)</p></td><td align=\"left\">sRPE and accelerometer in each training session and game</td><td align=\"left\"><p>Training sessions:</p><p>OJC: 90.9 ± 51.2; COD: 247.5 ± 121.7; ACC: 93.6 ± 46.9; DEC: 94.8 ± 52.9</p><p>Games:</p><p>OJC: 81.1 ± 49.8; COD: 229.4 ± 124.8; ACC: 85.2 ± 47.2; DEC: 66.0 ± 39.7</p></td></tr><tr><td align=\"left\">Horta et al., 2020 [##UREF##11##35##]</td><td align=\"left\">Internal load—sRPE</td><td align=\"left\">sRPE of each training session</td><td align=\"left\"><p>PS—TWTL: 3,228.44 ± 521.96</p><p>CPI—TWTL: 3,369.44 ± 605.33</p><p>CPII—TWTL: 2,973.22 ± 727.23</p></td></tr><tr><td align=\"left\">García-de-Alcaraz et al., 2020 [##REF##32303475##36##]</td><td align=\"left\">External load—camera (jump count)</td><td align=\"left\">each training session</td><td align=\"left\"><p>MB—OJC: 41,432</p><p>OH—OJC: 40,694</p><p>RSH—OJC: 22,997</p><p>SE—OJC: 13,226</p></td></tr><tr><td align=\"left\">Roy et al., 2020 [##UREF##12##37##]</td><td align=\"left\">Internal load—sRPE</td><td align=\"left\">sRPE of each training session and game</td><td align=\"left\">sRPE: 566 ± 260</td></tr><tr><td align=\"left\">Clemente et al., 2020 [##REF##32015214##38##]</td><td align=\"left\">Internal load—sRPE</td><td align=\"left\">sRPE of each training session and game</td><td align=\"left\"><p>CPI—ACWR: 1.10 ± 0.13; M: 4.28 ± 1.23</p><p>CPII—ACWR: 1.66 ± 0.15; M: 3.39 ± 0.69</p></td></tr><tr><td align=\"left\">Lima et al., 2020 [##REF##32059245##39##]</td><td align=\"left\"><p>Internal load—sRPE</p><p>External load—IMU (jump metrics)</p></td><td align=\"left\">Jumping metrics and sRPE of each training session</td><td align=\"left\">sRPE and OJC was higher in MD-2 and MD-3 than in MD-1</td></tr><tr><td align=\"left\">Duarte et al., 2019 [##UREF##13##40##]</td><td align=\"left\">Internal load—sRPE</td><td align=\"left\">sRPE of each training session and game</td><td align=\"left\"><p>CPI—TWTL: 4,546.0 ± 620.9</p><p>CPII—TWTL: 4,006.6 ± 687.6</p></td></tr><tr><td align=\"left\">Clemente et al., 2019 [##REF##29619798##41##]</td><td align=\"left\">Internal load—sRPE</td><td align=\"left\">sRPE of each training session and game</td><td align=\"left\">CPI &gt; TWTL &gt; CPII</td></tr><tr><td align=\"left\">Horta et al., 2019 [##REF##29239985##42##]</td><td align=\"left\">Internal load—sRPE</td><td align=\"left\">sRPE of each training session</td><td align=\"left\">The TWTL increased progressively from Week 2 to Week 6</td></tr><tr><td align=\"left\">Silva et al., 2019 [##UREF##14##43##]</td><td align=\"left\"><p>Internal load—sRPE</p><p>External load—IMU (jump metrics)</p></td><td align=\"left\">Jumping metrics and sRPE of each training session</td><td align=\"left\"><p>sRPE: MD-1: 462.04 ± 330.05; MD-2: 586.68 ± 365.66; MD-3: 477.44 ± 267.34; MD-4: 466.68 ± 295.71; MD-5: 430.21 ± 215.77</p><p>OJC: MD-1: 106.40 ± 42.77; MD-2: 143.10 ± 60.11; MD-3: 120.31 ± 46.58; MD-4: 118.87 ± 68.61; MD-5: 106.56 ± 35.65</p></td></tr><tr><td align=\"left\">Roy et al., 2019 [##UREF##15##44##]</td><td align=\"left\">Internal load—sRPE</td><td align=\"left\">sRPE of each training session</td><td align=\"left\">sRPE: 566 ± 260</td></tr><tr><td align=\"left\">Skazalski et al., 2018 [##REF##29956376##46##]</td><td align=\"left\">External load—IMU (jump metrics)</td><td align=\"left\">Jumping metrics each training session and game</td><td align=\"left\"><p>MB—OJC: 92</p><p>OH—OJC: 62</p><p>RSH—OJC: 75</p><p>SE—OJC: 121</p></td></tr><tr><td align=\"left\">Castello et al., 2018 [##UREF##16##47##]</td><td align=\"left\">Internal load—sRPE</td><td align=\"left\">sRPE of each training session</td><td align=\"left\">wITL: 2484.32</td></tr><tr><td align=\"left\">Mendes et al., 2018 [##REF##29969288##48##]</td><td align=\"left\">Internal load—sRPE</td><td align=\"left\">sRPE of each training session</td><td align=\"left\">Preparatory weeks: training load had an undulating distribution during the week; regular and congested weeks: training load was higher at the beginning of the week</td></tr><tr><td align=\"left\">Tavares et al., 2018 [##REF##29072031##49##]</td><td align=\"left\">Internal load—sRPE</td><td align=\"left\">sRPE of each training session</td><td align=\"left\">sRPE had an undulating distribution during the week</td></tr><tr><td align=\"left\">Debien et al., 2018 [##REF##29584530##50##]</td><td align=\"left\">Internal load—sRPE</td><td align=\"left\">sRPE of each training session and game</td><td align=\"left\"><p>PS—TWTL: 3,748 ± 472</p><p>CPI—TWTL: 2,858 ± 472</p><p>CPII—TWTL: 3,728 ± 650</p></td></tr><tr><td align=\"left\">Brandão et al., 2018 [##UREF##17##51##]</td><td align=\"left\">Internal load—sRPE</td><td align=\"left\">sRPE of each training session</td><td align=\"left\">Preparatory weeks: training load had an undulating distribution during the week; regular and congested weeks: training load was higher at the beginning of the week</td></tr><tr><td align=\"left\">Horta et al., 2017 [##UREF##19##53##]</td><td align=\"left\">Internal load—sRPE</td><td align=\"left\">sRPE of each training session</td><td align=\"left\">First team players &gt; TWTL &gt; reserve players</td></tr><tr><td align=\"left\">Timoteo et al., 2017 [##UREF##3##16##]</td><td align=\"left\">Internal load—sRPE</td><td align=\"left\">sRPE of each training session and game</td><td align=\"left\">sRPE: Day 2 &gt; Day 1 &gt; Day 6 &gt; Day 3 &gt; Day 5 &gt; Day 4</td></tr><tr><td align=\"left\">de Freitas et al., 2015 [##UREF##20##54##]</td><td align=\"left\">Internal load—sRPE</td><td align=\"left\">sRPE of each training session</td><td align=\"left\"><p>Week 1—TWTL: 1,922 ± 654</p><p>Week 2—TWTL: 1,530 ± 691</p><p>Week 3—TWTL: 1,874 ± 528</p><p>Week 4—TWTL: 1,568 ± 312</p></td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab4\"><label>Table 4</label><caption><p>Fitness and fatigue monitoring strategies and protocols in the volleyball literature</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Author, year</th><th align=\"left\">Fitness and fatigue measurements</th><th align=\"left\">Design</th><th align=\"left\">Results</th></tr></thead><tbody><tr><td align=\"left\">Rebelo et al., 2023 [##UREF##4##18##]</td><td align=\"left\">Fatigue—CMJ</td><td align=\"left\">3 maximal attempts of the CMJ on Matchday -1</td><td align=\"left\">CMJ (range): 44.46 ± 6.09 to 47.24 ± 7.21</td></tr><tr><td align=\"left\">Rabbani et al., 2021 [##UREF##9##28##]</td><td align=\"left\">Fatigue—CMJ, HR</td><td align=\"left\">3 maximal attempts of the CMJ and a submaximal running test at the beginning of the first training session for each camp; Ln rMSSD after waking up (supine and seated)</td><td align=\"left\"><p>CMJ (range): 32.1 ± 3.5 to 35.1 ± 4.1</p><p>HRex (range): 148.0 ± 8.6 to 156.8 ± 7.6</p><p>HRR (range): 37.9 ± 9.8 to 41.7 ± 15.3</p></td></tr><tr><td align=\"left\">Berriel et al., 2020 [##REF##32588158##34##]</td><td align=\"left\">Biochemical markers—CK</td><td align=\"left\">6 times in different weeks of the 16 studied</td><td align=\"left\">CK increased after the first weeks of training and remained stable until the beginning of the pre-competitive period, at which time they dropped significantly</td></tr><tr><td align=\"left\">Horta et al., 2019 [##REF##29239985##42##]</td><td align=\"left\"><p>Fatigue—CMJ</p><p>Biochemical markers—CK, T, Cr</p></td><td align=\"left\">CMJ and blood samples 4 times each 14 days</td><td align=\"left\"><p>CMJ:</p><p>M1: 46.92 ± 5.75; M2: 45.55 ± 6.16; M3: 46.91 ± 5.95; M4: 46.94 ± 5.92</p><p>T:</p><p>M1: 511 ± 100; M2: 559 ± 122; M3: 487 ± 117; M4: 549 ± 61</p><p>Cr:</p><p>M1: 17.3 ± 7.1; M2: 15.5 ± 6.1; M3: 14.2 ± 3.6; M4: 13.8 ± 3.8</p></td></tr><tr><td align=\"left\">Tavares et al., 2018 [##REF##29072031##49##]</td><td align=\"left\">Fatigue—CMJ</td><td align=\"left\">Days 1, 2, 4 and 5</td><td align=\"left\">CMJ height decreased during the week</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab5\"><label>Table 5</label><caption><p>Well-being monitoring strategies and protocols in the volleyball literature</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Author, year</th><th align=\"left\">Well-being measurements</th><th align=\"left\">Design</th><th align=\"left\">Results</th></tr></thead><tbody><tr><td align=\"left\">Rebelo et al., 2023 [##UREF##4##18##]</td><td align=\"left\">Well-being—Questionnaire</td><td align=\"left\">Daily before the first training session</td><td align=\"left\">No differences observed in most wellness items measured during the 5 weeks</td></tr><tr><td align=\"left\">Rabbani et al., 2021 [##UREF##9##28##]</td><td align=\"left\">Well-being—Hooper's index</td><td align=\"left\">Daily before the first training session</td><td align=\"left\">2.19 ± 0.35 to 2.24 ± 0.30</td></tr><tr><td align=\"left\">Haraldsdottir et al., 2021 [##REF##34527756##29##]</td><td align=\"left\">Well-being—Questionnaire</td><td align=\"left\">Daily before the first training session</td><td align=\"left\">7.9 ± 1.2</td></tr><tr><td align=\"left\">Andrade et al., 2021 [##REF##30325790##31##]</td><td align=\"left\">Recovery—TQR Scale</td><td align=\"left\">Daily before the first training session</td><td align=\"left\"><p>PS—TQR: 14.27 ± 1.50</p><p>CPI—TQR: 15.26 ± 1.43</p><p>CPII—TQR: 15.06 ± 1.47</p></td></tr><tr><td align=\"left\">Timoteo et al., 2021 [##REF##30113918##17##]</td><td align=\"left\">Recovery—TQR Scale</td><td align=\"left\">Once per week</td><td align=\"left\"><p>No injured players—16.67 ± 6.09</p><p>Injured players (overuse)—15.26 ± 2.66</p><p>Injured players (trauma)—14.63 ± 2.20</p></td></tr><tr><td align=\"left\">Ungureanu et al., 2021 [##REF##33508777##32##]</td><td align=\"left\">Well-being—Hooper's index</td><td align=\"left\">Daily before the first training session</td><td align=\"left\"><p>MB—15.9</p><p>OH—13.8</p><p>RSH—15.3</p><p>L—15.6</p><p>SE—15.0</p></td></tr><tr><td align=\"left\">Kupperman et al., 2021 [##UREF##10##33##]</td><td align=\"left\">Well-being—Questionnaire</td><td align=\"left\">Daily before the first training session or game</td><td align=\"left\"><p>F: 2.1 ± 0.9</p><p>M: 1.7 ± 0.9</p><p>S: 2.1 ± 1.1</p><p>SO: 1.9 ± 0.9</p></td></tr><tr><td align=\"left\">Berriel et al., 2020 [##REF##32588158##34##]</td><td align=\"left\">Well-being—RESTQ-Sport</td><td align=\"left\">6 times in different weeks of the 16 studied</td><td align=\"left\">M1: 169.01 ± 94.42; M2: 673.92 ± 461.45; M3: 520.77 ± 348.87; M4: 631.76 ± 579.30; M5: 270.78 ± 245.37; M6: 330.23 ± 206.98</td></tr><tr><td align=\"left\">Horta et al., 2020 [##UREF##11##35##]</td><td align=\"left\">Well-being—POMS Recovery—TQR Scale</td><td align=\"left\">Daily before the first training session or game</td><td align=\"left\"><p>PS—V: 20.62 ± 3.96; F: 11.82 ± 2.76; TQR: 14.95 ± 0.79</p><p>CPI—V: 18.31 ± 4.62; F: 12.89 ± 2.73; TQR: 15.33 ± 0.94</p><p>CPII—V: 18.76 ± 3.74; F: 8.65 ± 2.65; TQR: 15.74 ± 1.01</p></td></tr><tr><td align=\"left\">Clemente et al., 2020 [##REF##32015214##38##]</td><td align=\"left\">Well-being—Hooper's index</td><td align=\"left\">Daily before the first training session or game</td><td align=\"left\"><p>CPI—Weekly index: 61.82 ± 11.57</p><p>CPII—Weekly index: 54.46 ± 16.68</p></td></tr><tr><td align=\"left\">Duarte et al., 2019 [##UREF##13##40##]</td><td align=\"left\">Well-being—Questionnaire Recovery—TQR Scale</td><td align=\"left\">First and last training/game of the week</td><td align=\"left\"><p>CPI—TQR: 16.7 ± 1.1</p><p>CPII—TQR: 15.9 ± 1.1</p></td></tr><tr><td align=\"left\">Clemente et al., 2019 [##REF##29619798##41##]</td><td align=\"left\">Well-being—Hooper's index</td><td align=\"left\">Daily before the first training session or game</td><td align=\"left\">CPI &gt; index &gt; CPII</td></tr><tr><td align=\"left\">Horta et al., 2019 [##REF##29239985##42##]</td><td align=\"left\">Well-being—RESTQ-Sport</td><td align=\"left\">Daily before the first training session</td><td align=\"left\">The General Well-being value was lower at Weeks 2 and 6 than at baseline. The Injury at Week 4 was larger than that at baseline</td></tr><tr><td align=\"left\">Roy et al., 2019 [##UREF##15##44##]</td><td align=\"left\">Well-being—Hooper's index</td><td align=\"left\">Daily before the first training session</td><td align=\"left\">10.3 ± 3.5</td></tr><tr><td align=\"left\">Hyatt and Kavazis, 2019 [##REF##30899353##45##]</td><td align=\"left\">Stress scale</td><td align=\"left\">7 times in different parts of the season</td><td align=\"left\">Perceived stress peaked during the mid-season</td></tr><tr><td align=\"left\">Mendes et al., 2018 [##REF##29969288##48##]</td><td align=\"left\">Well-being—Hooper's index</td><td align=\"left\">Daily before the first training session</td><td align=\"left\">Regular weeks: best index score MD; congested weeks: index disrupted several days of the week</td></tr><tr><td align=\"left\">Tavares et al., 2018 [##REF##29072031##49##]</td><td align=\"left\">Well-being—Questionnaire Muscle soreness—Questionnaire</td><td align=\"left\">Daily before the first training session</td><td align=\"left\">Decrease in wellness scores and increase in fatigue and soreness during the week</td></tr><tr><td align=\"left\">Debien et al., 2018 [##REF##29584530##50##]</td><td align=\"left\">Recovery—TQR Scale</td><td align=\"left\">Daily before the first training session</td><td align=\"left\"><p>PS—TQR: 15.63 ± 0.80</p><p>CPI—TQR: 15.02 ± 1.03</p><p>CPII—TQR: 14.75 ± 0.79</p></td></tr><tr><td align=\"left\">Brandão et al., 2018 [##UREF##17##51##]</td><td align=\"left\">Well-being—Questionnaire Recovery—TQR Scale</td><td align=\"left\">Daily before the first training session</td><td align=\"left\"><p>Regular weeks—TQR: 15.61 ± 0.33</p><p>Congested weeks—TQR: 15.58 ± 0.57</p></td></tr><tr><td align=\"left\">Nogueira et al., 2017 [##UREF##18##52##]</td><td align=\"left\">Well-being—Questionnaire</td><td align=\"left\">First and last training of the week</td><td align=\"left\">Decrease in wellness scores and increase in fatigue and soreness during the week</td></tr><tr><td align=\"left\">Timoteo et al., 2017 [##UREF##3##16##]</td><td align=\"left\">Well-being—Hooper's index Recovery—TQR Scale</td><td align=\"left\">Daily before the first training session or game</td><td align=\"left\"><p>Hooper: Day 1 &gt; Day 2 &gt; Day 5 &gt; Day 3 &gt; Day 6 &gt; Day 4</p><p>TQR: Day 1 &gt; Day 2 &gt; Day 6 &gt; Day 5 &gt; Day 4 &gt; Day 3</p></td></tr><tr><td align=\"left\">de Freitas et al., 2015 [##UREF##20##54##]</td><td align=\"left\">Well-being—RESTQ-Sport</td><td align=\"left\">Daily before the first training session</td><td align=\"left\">Higher fatigue and injury scores during the pre-season period</td></tr></tbody></table></table-wrap>" ]

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[ "<supplementary-material content-type=\"local-data\" id=\"MOESM1\"></supplementary-material>" ]

[ "<table-wrap-foot><p><italic>AB</italic> abstract, <italic>SU</italic> subject, * truncation, “” phrase search</p></table-wrap-foot>", "<table-wrap-foot><p><italic>F</italic> female,  <italic>M</italic> male, <italic>NR</italic> not reported, <italic>G1</italic> group 1, <italic>G2</italic> group 2</p></table-wrap-foot>", "<table-wrap-foot><p><italic>COD</italic> change of direction, <italic>CP</italic> competitive period, <italic>CPI</italic> competitive period I, <italic>CPII</italic> competitive period II, <italic>HER</italic> Edwards Heart Rate, <italic>HT</italic> mean jump height from all jumps (cm), <italic>IMU</italic> inertial motion unit; <italic>L</italic> libero, <italic>M</italic> monotony, <italic>MB</italic> middle blocker, <italic>OH</italic> outside hitter, <italic>OJC</italic> overall jump count, <italic>OJR</italic> overall jump rate (jumps/min), <italic>PS</italic> pre-season, <italic>RSH</italic> right-side hitter, <italic>SE</italic> setter, <italic>TWTL</italic>  total weekly training load (arbitrary units), <italic>sRPE</italic> session rating of perceived exertion, <italic>wITL</italic>  weekly internal training load (arbitrary units)</p></table-wrap-foot>", "<table-wrap-foot><p><italic>CK</italic>   creatine kinase, <italic>CMJ</italic> countermovement jump (cm), <italic>Cr</italic> cortisol (ng.dL-1), <italic>HRR</italic> heart rate recovery (b/min), <italic>HRex</italic> submaximal exercise heart rate (b/min), <italic>Ln rMSSD</italic> natural logarithm of the square root of the mean sum of squared differences between adjacent normal RR intervals, <italic>M1</italic> moment one, <italic>M2</italic> moment two, <italic>M3</italic> moment three, <italic>M4</italic> moment four, <italic>T</italic> testosterone (ng.dL-1)</p></table-wrap-foot>", "<table-wrap-foot><p>All data are in arbitrary units</p><p><italic>CPI</italic> competitive period I, <italic>CPII</italic> competitive period II, <italic>F</italic> fatigue, <italic>M</italic> mood, <italic>PS</italic> pre-season, <italic>RESTQ-Sport</italic> Recovery Stress Questionnaire for Athletes, <italic>S</italic> stress, <italic>SD</italic> sleep duration, <italic>SO</italic>   soreness, <italic>SQ</italic> sleep quality, <italic>TQR</italic> total quality recovery scale, <italic>V</italic> vigor</p></table-wrap-foot>", "<fn-group><fn><p><bold>Publisher’s Note</bold></p><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn></fn-group>" ]

[ "<graphic xlink:href=\"13102_2024_807_Fig1_HTML\" id=\"MO1\"/>", "<graphic xlink:href=\"13102_2024_807_Fig2_HTML\" id=\"MO2\"/>" ]

[ "<media xlink:href=\"13102_2024_807_MOESM1_ESM.docx\"><caption><p><bold>Additional file1: Table S1.</bold> PRISMA 2020 Checklist. <bold>Table S2.</bold> Definition of types of monitoring interventions. <bold>Table S3.</bold> Questions from the modified Downs and Black checklist used to evaluate methodological quality of the included articles. <bold>Table S4.</bold> Results of methodological quality assessment for included articles.</p></caption></media>" ]

{ "acronym": [], "definition": [] }

[ "<title>Background</title>", "<p id=\"Par5\">Bacterial polyhydroxyalkanoates (PHAs) are bio-based polymeric materials that show high biodegradability not only in soil but also in the marine environments, and produced from renewable resources; making them promising materials that contribute to achieving Sustainable Development Goals (SDGs).</p>", "<p id=\"Par6\">Poly(3-hydroxybutyrate-<italic>co</italic>-3-hydroxyhexanoate) [P(3HB-<italic>co</italic>-3HHx)] is a practical and by far the most implementable PHA that can be fabricated into various commercial products owing to its resemblance to conventional plastics such as low-density polyethylene and polypropylene [##UREF##0##1##]. To date, the copolymer has been industrially produced from plant oil under the trademark of Green Planet^™ by KANEKA Co. Ltd., at the scale of over 5 thousand tons per year to support the growing demands in single-use plastics: cutlery, straw, container, coffee capsules, films, etc. [##UREF##1##2##]. In contrast to poly(3-hydroxybutyrate) [P(3HB)] homopolymer, P(3HB-<italic>co</italic>-3HHx) is characterized by reduced crystallinity and melting temperature, as well as improved flexibility attributed with the longer side-chain in the 3HHx (C₆) comonomer [##UREF##2##3##].</p>", "<p id=\"Par7\">The copolymer was initially discovered to be synthesized by <italic>Aeromonas caviae</italic> FA440 having the biosynthetic genes clustered as <italic>phaP-C-J</italic>_{<italic>Ac</italic>} encoding phasin, a unique class I PHA synthase accepting C₄-C₇ monomers, and (<italic>R</italic>)-specific enoyl-CoA hydratase, respectively, from plant oils and fatty acids as substrates [##REF##9244271##4##–##UREF##3##6##]. Efficient production of this copolymer from plant oils has been achieved by engineering of high PHA-performing <italic>Ralstonia eutropha</italic> (<italic>Cupriavidus necator</italic>) [##REF##18031343##7##, ##REF##33748082##8##], which were the modification of <italic>β</italic>-oxidation and (<italic>R</italic>)-3HB-CoA-formation pathways as well as introduction of the double mutant (N149S/D171G) of PhaC_{<italic>Ac</italic>} (PhaC_NSDG) [##REF##9457873##5##, ##UREF##4##9##–##REF##23999062##12##]. The catalytic properties of (<italic>R</italic>)-specific enoyl-CoA hydratase PhaJ, linking <italic>β</italic>-oxidation and PHA biosynthesis, is one of key factors regulating 3HHx composition in the resulting PHA polymers.</p>", "<p id=\"Par8\">Metabolic engineering for biosynthesis of P(3HB-<italic>co</italic>-3HHx) from structurally unrelated sugars is another important technology for the cost-effective bioproduction; considering that sugars are relatively inexpensive and can be the alternative to the bioprocess involving plant oils that usually causes severe foaming and complicates the downstream processing [##REF##16215853##13##]. The intracellular formation of (<italic>R</italic>)-3HHx-CoA from sugars has been achieved by artificial reverse <italic>β</italic>-oxidation (rBOX) pathway in which key enzymes are bacterial NADPH-dependent crotonyl-CoA carboxylase/reductase (Ccr) and mammalian ethylmalonyl-CoA decarboxylase (designated as Emd) [##REF##25446974##14##–##REF##35646875##16##]. The Ccr-Emd combination plays a role by connecting crotonyl-CoA formed from acetyl-CoA to butyryl-CoA that is then elongated and converted to (<italic>R</italic>)-3HHx-CoA. Namely, the bifunctional Ccr catalyzes reduction of crotonyl-CoA to butyryl-CoA as well as reductive carboxylation of crotonyl-CoA to ethylmalonyl-CoA, and Emd converts ethylmalonyl-CoA back into butyryl-CoA. The <italic>R. eutropha</italic> strains equipped with the artificial pathway effectively produced P(3HB-<italic>co</italic>-3HHx) from fructose or glucose [##REF##25446974##14##, ##REF##31466527##15##]. The recent study has demonstrated that the artificial pathway driven by Ccr-Emd is also functional chemolitoautotrophically in the engineered <italic>R. eutropha</italic>, enabling the gas fermentation of P(3HB-<italic>co</italic>-3HHx) using CO₂ and H₂ as carbon and energy sources, respectively [##REF##34821745##17##]. The progress of P(3HB-<italic>co</italic>-3HHx) production from structurally unrelated carbon sources and the relevant enzymes are summarized in Additional file ##SUPPL##0##1##: Table S1.</p>", "<p id=\"Par9\">We here discovered that <italic>R. eutropha</italic> possesses a native de novo biosynthesis pathway for (<italic>R</italic>)-3HHx-CoA and NADPH-acetoacetyl-CoA reductases (PhaBs)-independent pathway for provision of (<italic>R</italic>)-3HB-CoA, functional under microaerobic conditions. Despite the numerous studies on <italic>R. eutropha</italic> as a useful host for microbial cell factories, there have been limited information for bioproduction using this bacterium under low-aerobic or microaerobic conditions. These results reflect the metabolic versatility of <italic>R. eutropha</italic> as well as arises the high potential of this bacterium as a valuable platform from the industrial biomanufacturing point of view.</p>" ]

[ "<title>Materials and methods</title>", "<title>Bacterial strains and plasmids</title>", "<p id=\"Par26\">The bacterial strains and plasmids used in this study are listed in Table ##TAB##0##1##. <italic>R. eutropha</italic> strains were cultivated at 30 °C in a nutrient-rich (NR) medium containing 10 g of bonito extract (Kyokuto, Tokyo, Japan), 10 g of polypeptone, and 2 g of yeast extract in 1 L of tap water. <italic>E. coli</italic> strains were grown at 37 °C in a Lysogeny broth (LB) medium for general gene manipulation and transconjugation. Kanamycin (100 mg/L) was added to the medium when necessary.</p>", "<title>Construction of recombinant <italic>R. eutropha</italic> strains</title>", "<p id=\"Par27\">The glucose-utilizable strain NSDG-GG having <italic>phaC</italic>_NSDG was used as a parent strain to construct various deletion mutants in this study. The gene deletion in <italic>R. eutropha</italic> chromosome was carried out through homologous recombination using pk18mobsacB-based suicide vectors, where the targeted genes were <italic>phaB1</italic> (<italic>h16_A2171</italic>), <italic>phaB2-C2</italic> (<italic>h16_A2002-A2003</italic>), <italic>phaB3</italic> (<italic>h16_A2171</italic>), <italic>had</italic> (<italic>h16_A0602</italic>), <italic>paaH1</italic> (<italic>h16_A0282</italic>), <italic>crt2</italic> (<italic>h16_A3307</italic>), <italic>bktB</italic> (<italic>h16_A1445</italic>), <italic>phaJ4a</italic> (<italic>h16_A1070</italic>), <italic>h16_A3330</italic>, and <italic>fadB’</italic> (<italic>h16_A0461</italic>) (Additional file ##SUPPL##0##1##: Table S2). The deletion vectors for <italic>bktB</italic> and <italic>h16_A3330</italic> were constructed by inserting the respective fragments connecting the upstream and downstream regions of the target gene by inverse PCR. The details of the construction are described in the supplementary text and the sequences of oligonucleotide primers used for PCR amplification are shown in Additional file ##SUPPL##0##1##: Table S3. Deletion of other genes were conducted using vectors that had been constructed previously [##REF##25446974##14##, ##REF##30243533##20##, ##REF##22081565##25##, ##REF##22101037##36##]. The previously constructed pBPP-ccr_Me-phaJ4a-emd and pBPP-ccr_Me-phaJ_Ac-emd [##REF##31466527##15##, ##UREF##11##34##] were used to overexpress the genes for P(3HB-<italic>co</italic>-3HHx) synthesis.</p>", "<p id=\"Par28\">Transconjugation of the mobilizable plasmids to <italic>R. eutropha</italic> strains were performed using <italic>E. coli</italic> S17-1 as the donor strain, as previously described [##REF##34507913##37##]. In the cases of chromosomal modification, transconjugants into which the pk18mobsacB-based suicide vector in interest was integrated into the chromosome (pop-in strains) were selected on a Simmons Citrate Agar (BD diagnostics, Franklin Lakes, NJ, USA) plate medium containing 250 mg/L kanamycin. The integrants were plated on an NR agar medium containing 10% (w/v) sucrose for the second recombination event (pop-out strains). Sucrose-resistant isolates were selected based on PCR analysis to confirm the deleted allele. The transconjugants of <italic>R. eutropha</italic> harboring the mobilizable expression vectors were selected on the Simmons Citrate Agar plate medium containing 250 mg/L kanamycin.</p>", "<title>Production and analyses of PHA</title>", "<p id=\"Par29\">PHA production by <italic>R. eutropha</italic> strains was carried out at 30 °C in a 500 mL Sakaguchi flask with 100 mL of a nitrogen-limited mineral salts (MB) medium, which composed of 9 g of Na₂HPO₄·12H₂O, 1.5 g of KH₂PO₄, 0.5 g of NH₄Cl, 0.2 g of MgSO₄·7H₂O, and 1 mL of trace element solution in 1 L of deionized water [##REF##34507913##37##]. A filtered-sterilized solution of glucose was added to the medium to a final concentration of 1% (w/v). In this study, aerobic condition was defined by a reciprocal shaking at 120 strokes/min; meanwhile low-aerated cultivation was conducted under a low-shaking speed of 60 strokes/min. Unless otherwise stated, the medium composition and fermentation condition were fixed throughout the study. After cultivation for 120 h, the cells were harvested, washed with cold deionized water, and then lyophilized. The content and composition of intracellular PHA were determined by gas chromatography after methanolysis of the dried cells in the presence of 15% (v/v) sulfuric acid, as previous described [##UREF##12##38##].</p>" ]

[ "<title>Results</title>", "<title>Unusual P(3HB-<italic>co</italic>-3HHx) biosynthesis profile of <italic>R. eutropha</italic> under low-aerobic condition</title>", "<p id=\"Par10\">Among the three PhaB paralogs in <italic>R. eutropha</italic> H16 [##REF##16964242##18##], it has been reported that the highly-expressed PhaB1 is a major reductase and the weakly expressed PhaB3 fairly compensated P(3HB) synthesis when PhaB1 was absent [##REF##20729355##19##]. During our investigation for PHA copolymer biosynthesis by engineered strains of <italic>R. eutropha</italic>, we noticed that <italic>phaB</italic>-deleted strains showed altered PHA biosynthesis property when shaking rate was reduced to 60 strokes/min from usual 120 strokes/min (Fig. ##FIG##0##1##, Additional file ##SUPPL##0##1##: Table S4). The left panel in Fig. ##FIG##0##1##A shows PHA biosynthesis from glucose by the glucose-assimilating <italic>R. eutropha</italic> strain NSDG-GG harboring <italic>phaC</italic>_NSDG and its respective <italic>phaB</italic>-deleted variants, denoted as parent (NSDG-GG), ΔB1 (∆<italic>phaB1</italic>), ΔB1ΔB3 (∆<italic>phaB1</italic>∆<italic>phaB3</italic>) and ΔB1ΔB3ΔB2-C2 (∆<italic>phaB1</italic>∆<italic>phaB1</italic>∆<italic>phaB2-C2</italic>). The triple mutant ΔB1ΔB3ΔB2-C2 was constructed by deletion of <italic>phaB2</italic> along with <italic>phaC2</italic> encoding the second PHA synthase with unknown physiological function, since they are adjacent to each other. Under the aerobic condition (120 strokes/min), the cellular PHA content was reduced from 85 wt% to 64 wt% by deletion of <italic>phaB1</italic>, and further decreased to 26 wt% by double deletion of <italic>phaB1</italic> and <italic>phaB3</italic>. Additional deletion of <italic>phaB2-phaC2</italic> did not affect the PHA synthesis. These results were consistent with those reported by Budde et al. [##REF##20729355##19##].</p>", "<p id=\"Par11\">Interestingly, relatively higher amount of PHA (63 and 53 wt%) was produced under the slow-shaking condition (60 strokes/min) even by the double and triple <italic>phaB</italic>-deletants, respectively (right panel in Fig. ##FIG##0##1##A, B). The single deletion mutant ΔB1 showed slow PHA formation under this condition but outperformed the aerobic condition after 96 h; meanwhile the double and triple deletants could still produce significant amount of PHA under the low-aerobic cultivation (Fig. ##FIG##0##1##C, Additional file ##SUPPL##0##1##: Table S5), with 4.5-fold production (&gt; 60 wt%) by the double <italic>phaB</italic>-deleted mutant and 3.0-fold (&gt; 50 wt%) by the triple <italic>phaB</italic>-deletant when compared to the aerobic counterpart.</p>", "<p id=\"Par12\">PHA produced by the ΔB1 strain was nearly P(3HB) homopolymer containing negligible fraction of 3HHx (&lt; 0.1 mol%) under the usual aerobic condition as observed so far, and the additional deletion of <italic>phaB3</italic> led to trace but stagnant 3HHx composition (~ 1 mol%). We here found that, under the low aerated condition, the 3HHx fractions in PHA became significant (1.8 mol%) for the ΔB1 strain and they were further increased to 2.9 and 3.9 mol% by the double and triple deletion of <italic>phaB</italic> isologs, respectively (Fig. ##FIG##0##1##D). These results indicated the presence of native pathway for formation of (<italic>R</italic>)-3HHx-CoA from acetyl-CoA precursor in <italic>R. eutropha</italic>, which was independent from PhaB and activated during the low-aerobic cultivation.</p>", "<title>Revisiting PHA induction mode in <italic>R. eutropha</italic>: the effects of nitrogen and oxygen limitation</title>", "<p id=\"Par13\">While PHA biosynthesis is usually induced under unbalanced growth lacking of nitrogen source [##REF##9244271##4##, ##UREF##4##9##, ##UREF##5##10##, ##REF##23999062##12##, ##REF##30243533##20##], the present microaerobic PHA production by <italic>R. eutropha</italic> was done on dual nitrogen and oxygen limitations. We thus investigated the production behavior of the single <italic>phaB1</italic>-deleted ΔB1 strain, on aerobic (O-excess) and low-aerobic (O-limiting) at a varying concentration of nitrogen source (N-excess and N-limiting) (Fig. ##FIG##1##2##, Additional file ##SUPPL##0##1##: Table S6).</p>", "<p id=\"Par14\">Under the aerobic condition, nitrogen limitation was necessary to induce PHA production and increased nitrogen source constituted to the balanced growth that resulted in reduction of PHA accumulation, as observed so far (Fig. ##FIG##1##2##A). In contrast, under the low-aerated condition, the increasing NH₄Cl from 0.5 to 2.0 g/L marked a significant increase in bacterial growth (0.72–2.11 g/L) but only a slight decrease in PHA concentration (1.65–1.53 g/L) (Fig. ##FIG##1##2##B). This implied that PHA synthesis was still induced regardless of nitrogen concentration when oxygen was restricted. It is also worthwhile to mention that total biomass obtained during the low-shaking cultivation tended to be higher than that by the aerobic cultivation, notably under the nitrogen-excess condition (Fig. ##FIG##1##2##A–C). The PHA yield Y_P/S (g-PHA/g-glucose) and cell yield Y_X/S (g-residual cell/g-glucose) in Fig. ##FIG##1##2##D, E indicated higher magnitude for PHA production than aerobic one. The 3HHx compositions were remained to be significant (~ 1.6 mol%) throughout the variation of nitrogen amount under the low-aerobic condition (Fig. ##FIG##1##2##F).</p>", "<p id=\"Par15\">Biosynthesis of PHA based on oxygen limitation has been rare for <italic>R. eutropha</italic> but was investigated under chemolithoautotrophic conditions using H₂ and CO₂ [##UREF##6##21##–##UREF##8##23##]. The present results in Fig. ##FIG##1##2## coincided with those reported by Ishizaki and Tanaka [##UREF##7##22##] that an O-limiting–N-excess condition yielded high cell growth with moderate PHA content and consequent high PHA production, suggesting the shared PHA production mechanism in the different trophic modes.</p>", "<title>Identification of genes responsible for the native rBOX of (<italic>R</italic>)-3HHx-CoA de novo biosynthesis from glucose under low-aerobic conditions</title>", "<p id=\"Par16\">A series of mutants were constructed based on the ∆B1 strain by disrupting endogenous genes potentially responsible to (<italic>R</italic>)-3HHx-CoA formation, and subjected to low-shaking cultivation (Fig. ##FIG##2##3##, Additional file ##SUPPL##0##1##: Table S7). The two (<italic>S</italic>)-3HB-CoA dehydrogenases PaaH1 (H16_A0282) and Had (H16_A0602), as well as the (<italic>S</italic>)-specific crotonase Crt2 (H16_A3307) in <italic>R. eutropha</italic> were reported to be broad substrate specific [##REF##30243533##20##], thus possess capability to function in conversion of 3-oxoacyl-CoA to <italic>trans</italic>-2-enoyl-CoA via (<italic>S</italic>)-3-hydroxyacyl-CoA of C₄ and C₆. The gene deletion analyses indicated the crucial roles of the two dehydrogenases in the (<italic>R</italic>)-3HHx-CoA formation, as the C₆ composition was decreased to 0.6 mol% by the single deletion of <italic>paaH1</italic> and to 0.2 mol% by the double deletion of <italic>paaH1</italic> and <italic>had</italic>. Neither change in cell growth nor PHA synthesis was observed by the deletion of Crt2, unexpectedly. Unlike under usual aerobic condition [##REF##31466527##15##], the introduction of a tandem of <italic>had-crt2</italic> by using a broad host range-expression vector or by replacement of <italic>phaB1</italic> in the chromosomal <italic>pha</italic> operon did not affect the 3HHx composition in the resulting PHA (data not shown). Considering the similar catalytic properties of Had and PaaH1 to each other, the dehydrogenation of 3-oxohexanoyl-CoA was thought to be not the rate-limiting step in the (<italic>R</italic>)-3HHx-CoA formation under the low-aerobic condition. FadB’ (H16_A0461) which is bifunctional (<italic>S</italic>)-3-hydroxyacyl-CoA dehydrogenase/(<italic>S</italic>)-crotonase in <italic>β</italic>-oxidation [##REF##25446974##14##] also showed no changes as well upon the gene deletion.</p>", "<p id=\"Par17\">BktB is one homolog of <italic>β</italic>-ketothiolases with broad substrate specificity and has been reported to be important for condensation of acetyl-CoA and propionyl-CoA/<italic>n</italic>-butyryl-CoA to form 3-oxoacyl-CoAs of C₅-C₆ in the biosynthesis of PHA copolymers [##UREF##3##6##, ##REF##9555876##24##]. Under the microaerobic condition, the deletion of <italic>bktB</italic> markedly decreased the 3HHx incorporation (0.6 mol%), implying partial significance of <italic>bktB</italic> in the pathway as well as function of other thiolase paralog(s) for the condensation. Kawashima et al. [##REF##22081565##25##] demonstrated that PhaJ4a (H16_A1070) was the major (<italic>R</italic>)-enoyl-CoA hydratase in <italic>R. eutropha</italic> that supplies (<italic>R</italic>)-3HHx-CoA through aerobic <italic>β</italic>-oxidation on soybean oil. Here, the disruption of <italic>phaJ4a</italic> resulted in complete block of (<italic>R</italic>)-3HHx-CoA formation in the glucose-fed low-aerobic condition.</p>", "<p id=\"Par18\">The ability of <italic>R. eutropha</italic> to supply (<italic>R</italic>)-3HHx-CoA from glucose under the microaerobic condition indicated the presence of unknown enzyme(s) catalyzing conversion of crotonyl-CoA to butyryl-CoA prior to the chain elongation. In the KEGG database, H16_A3330 is annotated as acryloyl-CoA reductase (NADPH), possibly catalyzing the reduction of the double bond in short-chain 2-enoyl-CoAs. Nevertheless, the <italic>h16_A3330</italic>-deleted strain ΔB1ΔA3330 showed a slight decrease in 3HHx fraction to 1.5 mol%, suggesting only partial participation of this reductase in the formation of butyryl-CoA.</p>", "<title>Concerted effect of the exogenous Ccr-PhaJ-Emd with native rBOX on P(3HB-co-3HHx) synthesis under low-aerobic cultivation</title>", "<p id=\"Par19\">The effects of the artificial rBOX, driven by Ccr_{<italic>Me</italic>}, PhaJ and Emd_{<italic>Mm</italic>} on P(3HB-<italic>co</italic>-3HHx) biosynthesis by <italic>R. eutropha</italic> under the low-aerobic condition were then investigated (Fig. ##FIG##3##4##, Additional file ##SUPPL##0##1##: Table S8). The expression plasmids pBPP-ccr_Me-phaJ4a-emd and pBPP-ccr_Me-phaJ_Ac-emd were used for this purpose, in which <italic>phaJ4a</italic>_{<italic>Re</italic>} and <italic>phaJ</italic>_{<italic>Ac</italic>} are the genes of (<italic>R</italic>)-specific enoyl-CoA hydratase specific to medium-chain-length and short-chain-length substrates, respectively. In addition to the significant increase in the 3HHx composition up to 6.4 ~ 9.8 mol% in the parental NSDG-GG by the vectors, the compositional change was more compelling in all the <italic>phaB</italic>-deleted mutants, as shown by the increase in 3HHx composition up to 32 ~ 38 mol% and 18 mol% by introduction of the vectors harboring <italic>phaJ4a</italic> and <italic>phaJ</italic>_{<italic>Ac</italic>}, respectively. The results demonstrated the concerted action of the artificial and the native rBOX for formation of (<italic>R</italic>)-3HHx-CoA under the low-aerobic condition. The high 3HHx composition in the resulting copolyester by co-expression of PhaJ4a was agreed with the preference of PhaJ4a towards medium-chain-length 2-enoyl-CoA substrates.</p>", "<p id=\"Par20\">The introduction of the either expression vector collectively restored the PHA production capability in all the <italic>phaB</italic>-deletants under both aerobic and microaerobic cultivation. This is most probably due to PhaJ-catalyzed conversion of crotonyl-CoA to (<italic>R</italic>)-3HB-CoA in addition to the conversion of 2-hexenoyl-CoA to (<italic>R</italic>)-3HHx-CoA (Fig. ##FIG##4##5##). The improvement was drastic in double (ΔB1ΔB3) and triple <italic>phaB</italic>-deleted (ΔB1ΔB3ΔB2-C2) strains, with the overall PHA concentration comparable to ΔB1 strain (1.9–2.4 g/L).</p>" ]

[ "<title>Discussion</title>", "<p id=\"Par21\">This study demonstrated that a low-aerobic or microaerobic condition with slow-shaking of the media promoted PHA biosynthesis in <italic>R. eutropha</italic> regardless nitrogen limitation, and moreover led to conditional activation of native reverse <italic>β</italic>-oxidation (rBOX) pathway. This condition was still able to support the bacterial growth as shown in the similar residual cell weight between both shaking conditions. It has been known that P(3HB) functions as an electron sink to maintain cellular redox balance under anaerobic conditions in several facultative anaerobes [##REF##4723225##26##]. Usually, when oxygen availability is restricted, the cells need other pathway(s) to regenerate oxidative cofactors from the reductive form, thus NADPH-dependent reduction of acetoacetyl-CoA to (<italic>R</italic>)-3HB-CoA plays the role in the cofactor regeneration in P(3HB)-producing anaerobes. In the present case of <italic>R. eutropha</italic>, the oxygen respiration was not fully retarded because the cell yield (Y_x/s) under the low-shaking conditions was only slightly lower when compared with those under the usual aerobic conditions (Fig. ##FIG##1##2##E). The excess reducing equivalents not regenerated by the respiration under the limited oxygen availability would promote PHA biosynthesis to balance the cellular redox state. In fact, NADH was accumulated in <italic>R. eutropha</italic> when the terminal electron acceptor O₂ was limited under the anoxic condition [##UREF##9##27##]. The improved PHA biosynthesis under the oxygen limitation has also been seen for <italic>Azotobacter beijerinckii</italic> [##REF##13143##28##], <italic>Azotobacter vinelandii</italic> [##REF##16347925##29##], <italic>Allochromatium vinosum</italic> [##REF##27863495##30##] and halophilic bacterium <italic>Halomonas bluephagenesis</italic> [##REF##30219528##31##].</p>", "<p id=\"Par22\">Our study also demonstrated a striking difference in PHA accumulation trend between aerobic and low-aerobic cultivation. PhaB1 is the major acetoacetyl-CoA reductase for (<italic>R</italic>)-3HB-CoA formation in both the conditions. PhaB3 is the minor reductase under usual aerobic conditions as reported previously [##REF##20729355##19##], however, this is not applicable under the low-aerobic condition since the disruption of <italic>phaB3</italic> resulted in only slight reduction in PHA production. Given the fact that the double and triple <italic>phaB</italic>-deletants could still produce significant amount of PHA (Fig. ##FIG##0##1##), it was suggested that <italic>R. eutropha</italic> possesses other enzyme(s) for the formation of (<italic>R</italic>)-3HB-CoA from acetoacetyl-CoA functional under the low oxygen condition. rBOX for the C₄-intermediates potentially participated in the PhaB-independent formation of (<italic>R</italic>)-3HB-CoA via (<italic>S</italic>)-3HB-CoA with the aid of (<italic>R</italic>)-2-enoyl-CoA hydratase(s). Nevertheless, PaaH1, Had, and Crt2 were not the major enzymes contributing to the (<italic>R</italic>)-3HB-CoA formation. PhaJ4a seemed to partially play the role, as the gene deletion of <italic>phaJ4a</italic> slightly reduced the PHA production in the low-shaking cultivation. Alternatively, unidentified (<italic>R</italic>)-specific reductase such as some isologs of FabG [NADPH 3-oxoacyl-acyl carrier protein (ACP) reductase] [##REF##10418145##32##] and PhaG (3-hydroxyacyl-ACP thioesterase) along with CoA-ligase [##REF##10418145##32##], or enigmatic NADH-dependent (<italic>R</italic>)-reductase may function in providing (<italic>R</italic>)-3HB-CoA from acetoacetyl-CoA in <italic>R. eutropha</italic> under such condition. Investigation such as comparative transcriptomics analysis and gene deletion studies might be required to identify the possible pathways contributes to the microaerobic-mediated (<italic>R</italic>)-3HB-CoA formation.</p>", "<p id=\"Par23\">So far, <italic>R. eutropha</italic> has been believed to lack a pathway for formation of (<italic>R</italic>)-3HHx-CoA from sugar-derived acetyl-CoA molecules, because this bacterium produced only P(3HB) homopolymer from sugars even when a heterologous PHA synthase exhibiting broad substrate specificity (such as PhaC_NSDG) was expressed within the cells. The present results indicated the rBOX for formation of (<italic>R</italic>)-3HHx-CoA from C₄-acyl-CoA intermediates was functional specifically under the low-shaking condition. It was supposed that the robust <italic>β</italic>-oxidation in <italic>R. eutropha</italic> with multiple isologs enables the function of rBOX and the resulting native ability to form (<italic>R</italic>)-3HHx-CoA directing to the copolyester biosynthesis, when needed. The gene disruption analysis for several known enzymes demonstrated the actual functions of BktB, PaaH1/Had, and PhaJ4a in the rBOX pathway for the C₆-intermediares under the low-aerated condition, whereas Crt2 and bifunctional FadB’ did not contribute to it. The conditional activation of rBOX would be also related to the reduced availability of oxygen. Namely, the reduction of crotonyl-CoA and 3-oxohexanoyl-CoA in rBOX would play the role in redox homeostasis, in addition to the reduction of acetoacetyl-CoA to (<italic>R</italic>)-3HB-CoA, as described above (Fig. ##FIG##4##5##). As shown in the previous artificial pathway for biosynthesis of P(3HB-<italic>co</italic>-3HHx) from structurally unrelated sugars [##REF##25446974##14##, ##REF##31466527##15##], the key reaction is the reduction of crotonyl-CoA to butyryl-CoA for thiolase-mediated elongation of C₄ to C₆. However, the native enzyme(s) responsible for the reduction of crotonyl-CoA in <italic>R. eutropha</italic> has been unclear. Although a putative acryloyl-CoA reductase (H16_A3330) had been one candidate for the unidentified reductase, the gene disruption denied the participation of H16_A3330 as a major enzyme in the rBOX pathway. Further investigation is required to identify the missing reductase in <italic>R. eutropha</italic>.</p>", "<p id=\"Par24\">With the aid of artificial pathway driven by heterologous Ccr_{<italic>Me</italic>} and Emd_{<italic>Mm</italic>} along with PhaJ via plasmid expression, copolyesters with higher 3HHx monomer composition could be achieved under the low-aerated condition when compared to the corresponding aerobic cultivation (Fig. ##FIG##3##4##). The results suggested that the crotonyl-CoA reduction step mediated by the unknown native reductase was not sufficient, as well as indicated the importance of chain length-specificity of PhaJ in regulating the 3HHx composition in the resulting copolyesters. The similar trend has also been observed in a recent report focusing on autotrophic production of P(3HB-<italic>co</italic>-3HHx) by the engineered <italic>R. eutropha</italic>. Tanaka et al. [##REF##34821745##17##] conducted the autotrophic cultivation of <italic>R. eutropha</italic> harboring pBPP-ccr_Me-phaJ_4a-emd_Mm on the gas mixture of H₂/O₂/CO₂ (8:1:1), where the oxygen concentration was set to low both to induce PHA synthesis and avoid the risk of hydrogen explosion. They achieved efficient production of P(3HB-<italic>co</italic>-3HHx) from CO₂ and H₂ with higher 3HHx monomer compositions of 44–48 mol% than those obtained by the same strain cultivated on fructose. This was probably due to synergistic correlation between the native rBOX pathway activated under low-aerobic environment and the artificial rBOX, demonstrating one of examples for the usefulness of the application of PHA production under micro-aerobic condition.</p>" ]

[ "<title>Conclusions</title>", "<p id=\"Par25\">Usual bioprocesses using aerobic microbes highly demand oxygen with low solubility in the aqueous media to support efficient cell growth and bioconversion, thus high transfer coefficient of oxygen is achieved by vigorous aeration and/or agitation requiring much energy. This work demonstrated that the low-aerobic cultivation could promote the PHA biosynthesis in <italic>R. eutropha</italic> H16-derived strains, particularly in the <italic>phaBs</italic>-lacking mutants. Moreover, it was found that the low-aerobic condition enabled P(3HB-<italic>co</italic>-3HHx) biosynthesis mediated by the native rBOX, and exogenous Ccr-PhaJ-Emd (artificial rBOX) showed synergistic effect on the (<italic>R</italic>)-3HHx-CoA formation. The knowledge obtained by the current study is expected to be useful for compositional regulation of PHA copolyesters produced not only from sugars but CO₂ as well, considering the natural property of <italic>R. eutropha</italic> as the knallgas bacterium.</p>" ]

[ "<title>Background</title>", "<p id=\"Par1\"><italic>Ralstonia eutropha</italic> H16, a facultative chemolitoautotroph, is an important workhorse for bioindustrial production of useful compounds such as polyhydroxyalkanoates (PHAs). Despite the extensive studies to date, some of its physiological properties remain not fully understood.</p>", "<title>Results</title>", "<p id=\"Par2\">This study demonstrated that the knallgas bacterium exhibited altered PHA production behaviors under slow-shaking condition, as compared to its usual aerobic condition. One of them was a notable increase in PHA accumulation, ranging from 3.0 to 4.5-fold in the mutants lacking of at least two NADPH-acetoacetyl-CoA reductases (PhaB1, PhaB3 and/or phaB2) when compared to their respective aerobic counterpart, suggesting the probable existence of (<italic>R</italic>)-3HB-CoA-providing route(s) independent on PhaBs. Interestingly, PHA production was still considerably high even with an excess nitrogen source under this regime. The present study further uncovered the conditional activation of native reverse <italic>β</italic>-oxidation (rBOX) allowing formation of (<italic>R</italic>)-3HHx-CoA, a crucial precursor for poly(3-hydroxybutyrate-<italic>co</italic>-3-hydroxyhexanoate) [P(3HB-<italic>co</italic>-3HHx)], solely from glucose. This native rBOX led to the natural incorporation of 3.9 mol% 3HHx in a triple <italic>phaB</italic>-deleted mutant (∆<italic>phaB1</italic>∆<italic>phaB1</italic>∆<italic>phaB2-C2</italic>)<italic>.</italic> Gene deletion experiments elucidated that the native rBOX was mediated by previously characterized (<italic>S</italic>)-3HB-CoA dehydrogenases (PaaH1/Had), β-ketothiolase (BktB), (<italic>R</italic>)-2-enoyl-CoA hydratase (PhaJ4a), and unknown crotonase(s) and reductase(s) for crotonyl-CoA to butyryl-CoA conversion prior to elongation. The introduction of heterologous enzymes, crotonyl-CoA carboxylase/reductase (Ccr) and ethylmalonyl-CoA decarboxylase (Emd) along with (<italic>R</italic>)-2-enoyl-CoA hydratase (PhaJ) aided the native rBOX, resulting in remarkably high 3HHx composition (up to 37.9 mol%) in the polyester chains under the low-aerated condition.</p>", "<title>Conclusion</title>", "<p id=\"Par3\">These findings shed new light on the robust characteristics of <italic>Ralstonia eutropha</italic> H16 and have the potential for the development of new strategies for practical P(3HB-<italic>co</italic>-3HHx) copolyesters production from sugars under low-aerated conditions.</p>", "<title>Graphical Abstract</title>", "<p id=\"Par4\">\n\n</p>", "<title>Supplementary Information</title>", "<p>The online version contains supplementary material available at 10.1186/s12934-024-02294-4.</p>", "<title>Keywords</title>" ]

[ "<title>Supplementary Information</title>", "<p>\n</p>" ]

[ "<title>Acknowledgements</title>", "<p>KH thanks Japan Society for Promotion of Science (JSPS) for Postdoctoral Fellowships for Research in Japan. The authors thank Biomaterials Analysis Division, Open Facility Center, Tokyo Institute of Technology for DNA sequencing. We are also grateful to our colleagues, Mari Nakagawa and Dr. Allan Devanadera for construction of pk18msΔbktB and pk18msΔA3330 suicide vectors.</p>", "<title>Author contributions</title>", "<p>KH: Conceptualization, Methodology, Investigation, Validation, Writing- original draft, review &amp; editing, Visualization. IO: Methodology, Writing-review &amp; editing. TF: Project administration, Conceptualization, Methodology, Supervision, Writing-review &amp; editing.</p>", "<title>Funding</title>", "<p>This research was supported by JSPS KAKENHI Grant-in-Aid for JSPS Fellows (20F40100) and NEDO Moonshot R&amp;D program (JPNP18016).</p>", "<title>Availability of data and materials</title>", "<p>All data generated and analyzed during this study were included in this manuscript.</p>", "<title>Declarations</title>", "<title>Ethics approval and consent to participate</title>", "<p id=\"Par30\">Not applicable.</p>", "<title>Consent for publication</title>", "<p id=\"Par31\">Not applicable.</p>", "<title>Competing interests</title>", "<p id=\"Par32\">The authors declare that they have no competing interests.</p>" ]

[ "<fig id=\"Fig1\"><label>Fig. 1</label><caption><p><bold>A</bold> Production of P(3HB-<italic>co</italic>-3HHx) from 1% (w/v) glucose by <italic>R. eutropha</italic> strains under aerobic (120 strokes/min) and low-shaking (60 strokes/min) conditions for 120 h. <bold>B</bold> The final cell growth [RCW (residual cell weight)] (g/L), PHA content (wt%) and PHA concentration (g/L) after the 120 h cultivation. <bold>C</bold> Time-course of PHA concentration during the PHA accumulation phase. <bold>D</bold> 3HHx compositions in the accumulated PHA under aerobic and low-aerobic conditions. The strains are NSDG-GG (parent), ΔB1 (∆<italic>phaB1</italic>), ΔB1ΔB3 (∆<italic>phaB1</italic>∆<italic>phaB3</italic>), and ΔB1ΔB3ΔB2-C2 (∆<italic>phaB1</italic>∆<italic>phaB3</italic>∆<italic>phaB2-C2</italic>). Data are represented as mean values ± standard deviation of three replicates</p></caption></fig>", "<fig id=\"Fig2\"><label>Fig. 2</label><caption><p>Effects of concentration of nitrogen source (0.5, 0.1 and 0.2 g/L) on PHA accumulation by <italic>R. eutropha</italic> ΔB1 strain under aerobic condition (oxygen-excess) (<bold>A</bold>) and low-aerobic condition (oxygen-limiting) (<bold>B</bold>). Supplementation of 0.5 g/L NH₄Cl was considered nitrogen-limiting condition; and those of 1.0 and 2.0 g/L NH₄Cl were defined as nitrogen-excess condition. <bold>C</bold> The final cell growth [RCW (residual cell weight)] (g/L), PHA content (wt%), and PHA concentration (g/L) after the 120 h cultivation. <bold>D</bold> PHA yield to glucose [Y_P/S(g-PHA/g-glucose)]. <bold>E</bold> Cell yield to glucose [Y_X/S(g-residual cell weight/g-glucose)]. <bold>F</bold> 3HHx compositions in the accumulated PHA under aerobic and low-shaking conditions. Data are represented as mean values ± standard deviation of two replicates</p></caption></fig>", "<fig id=\"Fig3\"><label>Fig. 3</label><caption><p>Effects of disruption of genes potentially related to (<italic>R</italic>)-3HHx-CoA formation in <italic>R. eutropha</italic> on P(3HB-<italic>co</italic>-3HHx) biosynthesis from glucose under the low-shaking condition<italic>.</italic> The gene names and the translated enzymes are summarized in Additional file ##SUPPL##0##1##: Table S2. Data are represented as mean values ± standard deviation of three replicates</p></caption></fig>", "<fig id=\"Fig4\"><label>Fig. 4</label><caption><p>Effect of introduction of Ccr-Emd combination along with PhaJ on P(3HB-<italic>co</italic>-3HHx) biosynthesis by <italic>R. eutropha</italic> strains from glucose under (<bold>A</bold>) aerobic (120 s/m) and (<bold>B</bold>) low-aerobic (60 s/m) conditions<italic>.</italic> The strains are NSDG-GG (parent), ΔB1 (∆<italic>phaB1</italic>), ΔB1ΔB3 (∆<italic>phaB1</italic>∆<italic>phaB3</italic>), and ΔB1ΔB3ΔB2-C2 (∆<italic>phaB1</italic>∆<italic>phaB3</italic>∆<italic>phaB2-C2</italic>). CJ4aE<italic>,</italic> pBPP-ccr_Me-phaJ4a-emd; CJ_AcE<italic>,</italic> pBPP-ccr_Me-phaJ_Ac-emd<italic>.</italic> Data are represented as mean values of three replicates</p></caption></fig>", "<fig id=\"Fig5\"><label>Fig. 5</label><caption><p>Metabolic pathways for P(3HB-<italic>co</italic>-3HHx) biosynthesis in <italic>R. eutropha</italic> under the low-aerated conditions. Promoted PHA biosynthesis in <italic>R. eutropha</italic> H16 under slow-shaking condition could be due to the presence of PhaB-independent pathway such as by enigmatic NAD(P)H-dependent (<italic>R</italic>)-specific reductase, or (<italic>S</italic>)-specific route mediated by other isologs of (<italic>S</italic>)-3HB-CoA dehydrogenase(s) and crotonase(s). The microaerobic cultivation has conditionally activated the native rBOX pathway as well, leading to the formation of (<italic>R</italic>)-3HHx-CoA. Further introduction of heterologous Ccr-PhaJ-Emd (artificial rBOX) showed concerted effect with native rBOX on P(3HB-<italic>co</italic>-3HHx) production under aforementioned condition. Black arrows indicate the native pathways whereas purple arrows indicate the plasmid borne artificial pathway</p></caption></fig>" ]

[ "<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Strains and plasmids used in this study</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Strains/plasmids</th><th align=\"left\">Description/genotype</th><th align=\"left\">References</th></tr></thead><tbody><tr><td align=\"left\" colspan=\"3\">Strains</td></tr><tr><td align=\"left\" colspan=\"3\"><italic>Escherichia coli</italic></td></tr><tr><td align=\"left\">  S17-1</td><td align=\"left\"><italic>thi pro hsdR recA</italic> chromosomal RP4; Tra⁺; Tmp^r Str/Spc^r</td><td align=\"left\">Simon et al. [##UREF##10##33##]</td></tr><tr><td align=\"left\" colspan=\"3\"><italic>Ralstonia eutropha</italic></td></tr><tr><td align=\"left\"> H16</td><td align=\"left\">Wild type</td><td align=\"left\">DSM 428</td></tr><tr><td align=\"left\"> NSDG-GG</td><td align=\"left\"><p>H16 derivative; ∆<italic>phaC</italic>::<italic>phaC</italic>_NSDG,</p><p><italic>ΔnagR, nagE</italic>(G793C),<italic> P</italic>_{<italic>A2858</italic>}<italic> -glpFK</italic>_{<italic>Ec</italic>}<italic>-h16_A2858</italic></p></td><td align=\"left\">Zhang et al. [##REF##31466527##15##]</td></tr><tr><td align=\"left\"> ΔB1</td><td align=\"left\">NSDG-GG derivative; Δ<italic>phaB1</italic>_{<italic>Re</italic>}</td><td align=\"left\">Mifune et al. [##UREF##11##34##]</td></tr><tr><td align=\"left\"> ΔB1ΔB3</td><td align=\"left\">NSDG-GGΔB1 derivative; Δ<italic>phaB3</italic></td><td align=\"left\">This study</td></tr><tr><td align=\"left\"> ΔB1ΔB3ΔB2-C2</td><td align=\"left\">NSDG-GGΔB1ΔB3 derivative; Δ<italic>phaB2-phaC2</italic></td><td align=\"left\">This study</td></tr><tr><td align=\"left\"> ΔB1Δhad</td><td align=\"left\">NSDG-GGΔB1 derivative; Δ<italic>had</italic></td><td align=\"left\">This study</td></tr><tr><td align=\"left\"> ΔB1ΔpaaH1</td><td align=\"left\">NSDG-GGΔB1 derivative; Δ<italic>paaH1</italic></td><td align=\"left\">This study</td></tr><tr><td align=\"left\"> ΔB1Δcrt2</td><td align=\"left\">NSDG-GGΔB1 derivative; Δ<italic>crt2</italic></td><td align=\"left\">This study</td></tr><tr><td align=\"left\"> ΔB1ΔhadΔpaaH1</td><td align=\"left\">NSDG-GGΔB1Δhad derivative; Δ<italic>paaH1</italic></td><td align=\"left\">This study</td></tr><tr><td align=\"left\"> ΔB1ΔhadΔpaaH1Δcrt2</td><td align=\"left\">NSDG-GGΔB1ΔhadΔpaaH1 derivative; Δ<italic>crt2</italic></td><td align=\"left\">This study</td></tr><tr><td align=\"left\"> ∆B1∆A3330</td><td align=\"left\">NSDG-GGΔB1 derivative; Δ<italic>h16_A3330</italic></td><td align=\"left\">This study</td></tr><tr><td align=\"left\"> ΔB1ΔbktB</td><td align=\"left\">NSDG-GGΔB1 derivative; Δ<italic>bktB</italic></td><td align=\"left\">This study</td></tr><tr><td align=\"left\"> ΔB1ΔphaJ4a</td><td align=\"left\">NSDG-GGΔB1 derivative; Δ<italic>phaJ4a</italic></td><td align=\"left\">This study</td></tr><tr><td align=\"left\"> ∆B1∆fadB’</td><td align=\"left\">NSDG-GGΔB1 derivative; Δ<italic>fadB’</italic></td><td align=\"left\">This study</td></tr><tr><td align=\"left\" colspan=\"3\">Plasmids</td></tr><tr><td align=\"left\"> pK18mobsacB</td><td align=\"left\">pMB1 <italic>ori</italic>, <italic>mob</italic>, Kan^r, <italic>sacB</italic></td><td align=\"left\">Schafer et al. [##REF##8045426##35##]</td></tr><tr><td align=\"left\"> pk18msΔB3</td><td align=\"left\">pK18mobsacB derivative; <italic>phaB3 del</italic></td><td align=\"left\">Insomphun et al. [##REF##25446974##14##]</td></tr><tr><td align=\"left\"> pk18msΔB2-C2</td><td align=\"left\">pK18mobsacB derivative; <italic>phaB2-phaC2 del</italic></td><td align=\"left\">Subagyo et al. [##REF##34507913##37##]</td></tr><tr><td align=\"left\"> pk18msΔhad</td><td align=\"left\">pK18mobsacB derivative; <italic>had del</italic></td><td align=\"left\">Segawa et al. [##REF##30243533##20##]</td></tr><tr><td align=\"left\"> pk18msΔpaaH1</td><td align=\"left\">pK18mobsacB derivative; <italic>paaH1 del</italic></td><td align=\"left\">Segawa et al. [##REF##30243533##20##]</td></tr><tr><td align=\"left\"> pk18msΔcrt2</td><td align=\"left\">pK18mobsacB derivative; <italic>crt2 del</italic></td><td align=\"left\">Segawa et al. [##REF##30243533##20##]</td></tr><tr><td align=\"left\"> pk18msΔA3330</td><td align=\"left\">pK18mobsacB derivative; <italic>h16_A3330 del</italic></td><td align=\"left\">This study</td></tr><tr><td align=\"left\"> pk18msΔbktB</td><td align=\"left\">pK18mobsacB derivative; <italic>bktB del</italic></td><td align=\"left\">This study</td></tr><tr><td align=\"left\"> pk18msΔphaJ4a</td><td align=\"left\">pK18mobsacB derivative; <italic>phaJ4a del</italic></td><td align=\"left\">Kawashima et al. [##REF##22081565##25##]</td></tr><tr><td align=\"left\"> pK18ms∆fadB’</td><td align=\"left\">pK18mobsacB derivative; <italic>fadB’ del</italic></td><td align=\"left\">Insomphun et al. [##REF##23999062##12##]</td></tr><tr><td align=\"left\"> pBPP-ccr_Me-phaJ4a-emd</td><td align=\"left\">pBBR ori, <italic>mob</italic>,<italic> P</italic>_{<italic>phaP1</italic>}, <italic>ccr</italic>_{<italic>Me</italic>}, <italic>phaJ4a</italic>, <italic>emd</italic>_{<italic>Mm</italic>}, <italic>T</italic>_{<italic>rrnB</italic>}</td><td align=\"left\">Insomphun et al. [##REF##25446974##14##]</td></tr><tr><td align=\"left\"> pBPP-ccr_Me-phaJ_Ac-emd</td><td align=\"left\">pBBR ori, <italic>mob</italic>, <italic>P</italic>_{<italic>phaP1</italic>}, <italic>ccr</italic>_{<italic>Me</italic>}, <italic>phaJ</italic>_{<italic>Ac</italic>}, <italic>emd</italic>_{<italic>Mm</italic>}, <italic>T</italic>_{<italic>rrnB</italic>}</td><td align=\"left\">Insomphun et al. [##REF##25446974##14##]</td></tr></tbody></table></table-wrap>" ]

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[ "<table-wrap-foot><p>The postfix <italic>del</italic> indicates constructs for targeted gene deletion. <italic>Ac</italic>, <italic>Aeromonas caviae</italic>; <italic>Me</italic>, <italic>Methylorubrum extorquen</italic>s; <italic>Mm</italic>, <italic>Mus musculus</italic>. <italic>phaC</italic>_NSDG, a gene encoding N149S/D171G mutant of PHA synthase from <italic>A. caviae</italic></p></table-wrap-foot>", "<fn-group><fn><p><bold>Publisher's Note</bold></p><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn></fn-group>" ]

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[ "<media xlink:href=\"12934_2024_2294_MOESM1_ESM.docx\"><caption><p><bold>Additional file 1: Table S1.</bold> Progress on P(3HB-<italic>co</italic>-3HHx) production from structurally unrelated carbon sources by recombinant bacteria. <bold>Table S2.</bold> Genes involved in this study. <bold>Table S3.</bold> Sequences of primers used in this study. <bold>Table S4.</bold> Effect of shaking condition on P(3HB-<italic>co</italic>-3HHx) biosynthesis by parent NSDG-GG and the <italic>phaB</italic>-deleted mutants. <bold>Table S5.</bold> Time profile of PHA accumulation in <italic>R. eutropha</italic> strains under aerobic and microaerobic cultivation. <bold>Table S6.</bold> Effects of nitrogen and oxygen limitation on PHA production by <italic>R. eutropha</italic> ΔB1 under microaerobic condition. <bold>Table S7.</bold> Effects of gene disruption of endogenous genes potentially related to 3HHx incorporation into PHAs in R. eutropha ΔB1-based strains under microaerobic condition. <bold>Table S8.</bold> Effects of introduction of Ccr-Emd along with PhaJ4a/PhaJAc on P(3HB-<italic>co</italic>-3HHx) biosynthesis by <italic>R. eutropha</italic> NSDG-GG and the <italic>phaB</italic>-deleted strains under aerobic and microaerobic conditions.</p></caption></media>" ]

{ "acronym": [], "definition": [] }

[ "<title>Background</title>", "<p id=\"Par5\">Uncertainty is a significant phenomenon in the illness experience of persons with an oncological disease during their illness trajectory [##REF##31617448##1##]. It is not only limited to the phase of diagnosis and treatment, the experience of uncertainty can persist when oncological therapy has already finished, and affected persons have reached the phase of survivorship. Diagnosis, treatment, medical follow-ups as well as personal and social issues such as work, relationships, and identity are associated with the experience of uncertainty [##REF##26802368##2##, ##REF##22815086##3##]. It can negatively affect physical, psychological, and existential outcomes [##REF##28920751##4##]. Greater uncertainty is associated with increased fatigue, insomnia [##REF##24728586##5##], emotional distress [##REF##28920751##4##], anxiety, depression [##REF##24644189##6##] and lower quality of life [##REF##12692664##7##] and can also influence psychosocial adjustment to the diagnosis of cancer [##UREF##0##8##].</p>", "<p id=\"Par6\">A variety of studies have investigated uncertainty in individuals with several types of cancer at various stages of the disease trajectory [##REF##32822750##9##–##REF##32429954##11##]. In a longitudinal study, Raphaelis, Mayer [##REF##30339152##12##] showed that uncertainty was highly prevalent in women with vulvar neoplasia throughout the course of six months after diagnosis. Vulvar neoplasia includes vulvar cancer and vulvar intraepithelial neoplasia (VIN) as precancerous cellular change in the external female genitalia [##UREF##1##13##]. Although vulvar neoplasia is a rare disease, its incidence has increased globally over the past decade, especially in younger women [##UREF##2##14##]. Mostly, surgery is the first choice of treatment, since there is a limited role for primary radio or chemotherapy [##UREF##1##13##]. Across all stages, treatment for vulvar neoplasia is associated with significant morbidity and impact on quality of life. Symptoms commonly reported after treatment include bleeding, pain, odour, pruritis, sexual dysfunction, urinary incontinence, constipation, and lower extremity oedema [##REF##32580887##15##]. Women have also reported diminished emotional and social functioning, as well as compromised body image and sexuality, emotional and interpersonal distress, particularly if it requires extensive resections of the labia or clitoris [##REF##30339152##12##, ##REF##21771133##16##–##REF##32375773##18##]. According to Senn, Eicher [##REF##23290987##17##], uncertainty is one of the most prevalent psychosocial symptoms, occurring in about 83% of women with vulvar neoplasia [##REF##23290987##17##]. Their experience of uncertainty refers to the risk for disease transmission, progression, and recurrence. Affected women reported about uncertainty with regard to their reproductive and sexual capacities after treatment completion. In addition, vulvar neoplasia remains a stigmatised condition associated with poor hygiene or promiscuity [##REF##21771133##16##]. Affected women felt isolated and ashamed to speak about their condition and their experienced uncertainty [##UREF##3##19##, ##REF##29592536##20##]. This tendency to not talk about the disease because of its location and societal associations may reinforce illness-related uncertainty [##UREF##3##19##, ##REF##22068044##21##, ##REF##24433533##22##].</p>", "<p id=\"Par7\">The phenomenon of uncertainty in illness was theoretically framed by the work of Mishel [##UREF##4##23##–##REF##3203947##25##]. She first developed the Uncertainty in Illness Theory (UIT) [##REF##3203947##25##] and defined uncertainty in illness as the inability to structure the meaning of illness-related events cognitively because of insufficient information. The Reconceptualized Uncertainty in Illness Theory (RUIT) was developed two years later with awareness of the limitations of the UIT, where the development of uncertainty was viewed linearly [##UREF##5##26##]. The theory was reconceptualized through discussions with colleagues and qualitative data from patients with chronic conditions. Finally, RUIT addresses the experience of continuous uncertainty, such as in a chronic or potentially recurring illness. It is the central theoretical proposition of RUIT that the appraisal of uncertainty in chronic illness changes over time – from a danger to an opportunity [##REF##2292449##24##]. As result of this process Mishel described growth toward a new value system, whereas the result of the UIT is a return to the previous level of adaptation [##REF##2292449##24##]. Predominantly qualitative studies have provided empirical support for the RUIT. They affirmed a transformative process that is characterized by the transition towards a new orientation where uncertainty is accepted as an inherent aspect of life [##REF##10418661##27##]. This process was described in various ways by researchers, including themes such as “developing a revised life perspective”, “finding new ways to navigate the world”, “experiencing growth through uncertainty”, “achieving new levels of self-organization”, “setting new goals for living”, “devaluing previously important things”, “redefining what is considered normal”, and “creating new dreams” [##UREF##6##28##]. In all studies the gradual embrace of uncertainty and the restructuring of one’s own reality were identified as significant phenomena of the process, aligning with the assumptions of the RUIT. However, the support of the RUIT differs by population and methodology, i.e., more qualitative than quantitative studies confirmed the RUIT. The samples of these studies included breast cancer survivors [##REF##15712339##29##], women regenerating after cardiac disease [##REF##8582823##30##], chronically ill men [##UREF##7##31##], HIV patients [##REF##12775548##32##], long-term diabetic patients [##REF##2229700##33##], persons with schizophrenia [##REF##7759232##34##], women who have not been diagnosed but were genetically predisposed to hereditary breast and ovarian cancer [##REF##22670560##35##], spouses of heart transplant patients [##REF##3313290##36##], and adolescent survivors of childhood cancer [##REF##12643030##37##]. Although several empirical works supported the proposition of the RUIT, the results have not yet been fed back to the theory in a synthesized form. As a result, it is still not clear in an explanatory manner <italic>how</italic> uncertainty develops in the chronic course of a disease from a danger to an opportunity.</p>", "<p id=\"Par8\">Nevertheless, Mishel’s theoretical considerations opened a new perspective on the phenomenon of uncertainty in chronic illness, such as in cancer, and potential opportunities for the discipline of nursing to intervene therapeutically in the illness trajectory. This is especially relevant for women with vulvar neoplasia as a group characterized by a high recurrence rate [##REF##33736857##38##] and by taboo-related communicative difficulties [##REF##29592536##20##]. While it is already known that women with vulvar neoplasia experience uncertainty up to six months after diagnosis [##REF##30339152##12##], it is unclear whether and how their experience of uncertainty changes during the chronic illness trajectory and how the findings can inform the further development of the RUIT.</p>" ]

[ "<title>Methods</title>", "<p id=\"Par9\">We aimed to explore the development of uncertainty experience in women with vulvar neoplasia over time and to discuss the significance of the results for Mishel’s RUIT [##REF##2292449##24##].</p>", "<title>Design</title>", "<p id=\"Par10\">We conducted a longitudinal qualitative study since we intended to inductively explore the unexplored development of uncertainty over time. For the purposive sample, we included women aged 18 years and older with vulvar neoplasia (initial diagnosis or recurrence) who were about to receive surgical treatment. Between May 2019 and January 2021, gynaecologic oncology nurses invited women of four Swiss and one Austrian women’s clinics to participate in this study.</p>", "<title>Data collection</title>", "<p id=\"Par11\">Data collection took place via qualitative interviews, depending on the participants´ preferences, face-to-face in the hospital or at home, via phone or video call. We recorded the interviews digitally. In addition, notes were taken during and after each interview, which were included in the analysis. Participants were invited to bring a trusted person to the interview. None of the women made use of this option. The first author, a female PhD candidate having a nursing background and working as research associate at a University of health sciences, conducted the interviews at the following points of time: (1) at diagnosis or before surgical treatment, (2) one week later, (3) six months later, (4) nine months later and (5) one year later. The interviewer and the participants met for the first time at the time of the 1st interview. The first three points of time were chosen for reasons of explanatory power according to the results of Raphaelis et al. [##REF##30339152##12##]. We chose the other points of time with an exploratory intent.</p>", "<p id=\"Par12\">We developed a semi-structured interview guide consisting of four central subjects including both backward and forward looking questions to explore processes and change over time [##UREF##8##39##]. We adjusted it after the first three interviews with participants regarding the degree of abstraction of the narrative stimuli. The central topics were: (1) Current status related to the vulvar neoplasia, (2) situations of uncertainty, (3) retrospective reflections on developments over time, (4) outlook for further therapy or the recovery phase.</p>", "<title>Data analysis</title>", "<p id=\"Par13\">We first conducted within-case analyses for the trajectory of each participant. Afterwards, we performed a cross-case analysis for reasons of comparison, thereby intending to reach a higher level of abstraction and to develop a theoretical model. For data management and analysis, we used MAXQDA22© software [##UREF##9##40##].</p>", "<p id=\"Par14\">To explore the individual temporal trajectories of the participants, each individual interview was analyzed separately by the first author. Since we were interested in changes of participants’ uncertainty experience also on a theoretical level, the coding strategy of Grounded Theory was followed [##UREF##10##41##]. In a first step, the data were openly coded, followed by axial coding in order to develop initial concepts. To systematically identify changes over time for each participant, we conducted a longitudinal analysis using Saldaña’s [##UREF##11##42##] framework for longitudinal qualitative research. Framing, descriptive, analytic, and interpretative questions guided the identification of changes over time.</p>", "<p id=\"Par15\">To identify similarities and differences, we performed cross-case analyses. By means of a second coding cycle, the single cases were merged into generic sub-categories and broader categories in order to compare them. Thereby axial and selective coding was used [##UREF##10##41##]. Finally, we synthesized these results in a central model in order to explain the common development of uncertainty experience over time.</p>", "<title>Trustworthiness</title>", "<p id=\"Par16\">To enhance the trustworthiness of our findings, we adhered to the criteria of adequacy, empirical saturation, and theoretical pervasiveness for qualitative social research [##UREF##12##43##]. Therefore, we identified the research question from the field of interest and established it against the background of the theoretical work by Mishel [##REF##2292449##24##] (adequacy and theoretical pervasiveness). We empirically collected data and analyzed them inductively (empirical saturation).</p>", "<title>Ethics</title>", "<p id=\"Par17\">Participation was voluntary and could be withdrawn at any time without giving reasons. Informed consent was ongoing processed at each interview. In addition to study information, the researcher’s role as a PhD student in the study was disclosed.</p>" ]

[ "<title>Results</title>", "<p id=\"Par18\">We conducted 30 interviews between November 2019 and November 2021. Each of the seven participants completed three to five interviews. Four of seven participants completed all five interviews. One participant passed away during the study period due to a postsurgical bleeding, one participant could no longer be reached by telephone after the third interview and another after the fourth. The length of the interviews ranged from 13 to 75 min (Mean = 40).</p>", "<title>Characteristics of the participants</title>", "<p id=\"Par19\">Five women from Austria and two from Switzerland participated in our study. Their age ranged between 28 and 85 years. Four participants were diagnosed with vulvar cancer, three with vulvar intraepithelial neoplasia. Four had an initial diagnosis (Table ##TAB##0##1##).</p>", "<p id=\"Par20\">\n\n</p>", "<title>Development of uncertainty experience in women with vulvar neoplasia</title>", "<p id=\"Par21\">The experience of uncertainty developed in three stages within one year: (1) uncertainty as an existential threat, (2) uncertainty as an inherent part of illness, and (3) uncertainty as a certainty.</p>", "<p id=\"Par22\">The analysis revealed that the experience of uncertainty continuously developed back and forth during the study period of one year. Participants developed different coping strategies in dealing with uncertainty: weighing up potential consequences, avoiding or handling uncertainty, and reframing uncertainty. This fluctuating development of the uncertainty experience is visualized as a cyclical model (Fig. ##FIG##0##1##).</p>", "<p id=\"Par23\">\n\n</p>", "<title>Uncertainty as an existential threat: The Sword of Damocles</title>", "<p id=\"Par24\">The unknown meaning of a new symptom, having to wait for the result of an examination, not understanding the meaning of the diagnosis and its consequences, and realizing an increased risk of developing vulvar cancer or a recurrence were stimuli for uncertainty. Participants reacted to the unknown meanings of these uncertainties by creating an explanation based on their existing knowledge or previous experiences, e.g., through a pre-existing chronic condition or a previous vulvar neoplasia. Against this background they implicitly made a judgment of their individual risk for experiencing existential consequences. Participants [##REF##22815086##3##–##REF##12692664##7##] assessing a high risk of potential consequences by the experienced uncertainty felt threatened by the possibility of suffering from serious health deteriorations or dying:</p>", "<p id=\"Par26\">Symptoms triggered existential uncertainty throughout the different health- and recovery phases. They played a significant role in the diagnostic process, when participants first noticed a changed appearance of the vulva. Symptoms continued to re-stimulate existential uncertainty after cancer treatment was completed and their experience of uncertainty had already developed positively. In this phase all the participants again judged their risk for existential consequences and perceived uncertainty a threat due to the possibility of having a recurrence:</p>", "<p id=\"Par28\">This alarm resulted from the awareness of the increased risk of cancer in participants with a precancerous stage and of recurrence in participants with vulvar cancer. By again weighing up their risk for existential consequences, they once again perceived uncertainty as a threat due to possible cancer recurrence.</p>", "<p id=\"Par29\">Having to wait for the (still) unknown result of an examination occurred several times in the course of the illness trajectory as an uncertainty. This concerned the results of the primary clarification of the diagnosis, and after surgery the histological findings regarding the complete removal of the carcinoma.</p>", "<p id=\"Par31\">Uncertain consequences were the necessity of a further surgery, which would involve, e.g., the removal of the lymph nodes. The consequential possibility of needing an ostomy or a full resection of the vulva because of the uncertain necessity of a radical surgical oncological treatment, threatened participant 2 on the one hand by the risk of experiencing a serious physical change or on the other hand by a “disfigured” intimate area and to not being able to have children in the future (participant 3). Furthermore, the fear that the existing cancer might have metastasized underlay the existential uncertainty (participants 2, 3, 7).</p>", "<p id=\"Par32\">In the longer-term course of the disease or recovery, having to wait for the results of the routine gynecological oncological check-ups was again an uncertainty stimulus for all the participants, even if women experienced no symptoms. Though, available findings were no guarantee to full understanding and comprehension of their meanings to participants. Especially older women (participants 5, 6) experienced uncertainty about the meaning of the diagnosis, and the prospects for their treatment and recovery but would not dare to ask the physician to explain:</p>", "<p id=\"Par34\">One of the participants associated uncertainty with the phrase of the “Sword of Damocles” (interview 1, participant 7). Living under a “Sword of Damocles” was a recurring experience. At a later stage of disease or recovery, women again had to wait for the results of their routine gynecological oncological check-ups. This recurring experience was always a stimulus triggering uncertainty, even if the participants did not experience symptoms. This uncertainty shaped their experience on an affective level. It was a significant stressor which manifested itself by fear, insecurity, sadness, anger, and the feeling of powerlessness:</p>", "<title>Uncertainty as an inherent part of the illness: An accepted companion</title>", "<p id=\"Par36\">The enduring experience of threatening uncertainty was a starting point for employing coping strategies, either dealing with uncertainty, such as reducing it and mentally processing the negative experience of it, or avoiding uncertainty. Reducing uncertainty involved the acquisition of information to support informed decision-making. However, participants not just reduced uncertainty that was based on a lack of information, but on the (still) uncertain outcome of an investigation or a treatment. Therefore, they adopted health promoting behaviors to minimize the probability of occurrence of the uncertain adverse event, such as having metastases Participants 1, 2, 3 and 7 coped with the threatening uncertainty experience by thinking positively, by practicing self-care, as well as by reflecting about their emotional responses. By thinking positively, they encouraged themselves to hope for the best, to think in a constructive manner and to calm themselves:</p>", "<p id=\"Par38\">They found strength in taking uncertainties with a sense of humor and focusing on meaningful things. Their practice of self-care consisted of not letting the stress of the threatening uncertainty get them down, e.g., of letting oneself go (participants 1, 2, 3, 7). Therefore, they promoted their health, not only related to the vulvar neoplasia, by paying attention to their needs, exercising regularly, eating a balanced diet, and reducing other stress factors, e.g., splitting the care of the mother in need of care (participant 1). To cope with the psychological stress of uncertainty, they paid more attention to clearing their minds by engaging in meaningful activities, talking about their fears to people they trust and spending most of their time in familiar surroundings:</p>", "<p id=\"Par40\">If the interviewees were able to overcome existential uncertainty, e.g., by completing cancer treatment or if a symptom cleared up as harmless, it triggered a change in their experience of uncertainty:</p>", "<p id=\"Par42\">Participants [##REF##31617448##1##–##REF##22815086##3##, ##REF##12692664##7##] experienced uncertainty no longer as an existential threat. Instead, they accepted uncertainty as an inherent part of illness and opened up to the concept of it. They accepted that certainty in illness will probably never exist – despite all the information and expert knowledge of professionals as well as their own coping strategies:</p>", "<p id=\"Par44\">The analysis revealed that the remaining uncertainty did not refer to a specific external stimulus, such as surgery or pending findings anymore. From now on the women’s uncertainty experience mainly concerned the irreducible unpredictability regarding the disease course and the prognosis:</p>", "<p id=\"Par46\">Other participants [##REF##28920751##4##–##REF##24644189##6##] who we did not find uncertainty acceptance, reported of repressing uncertainty and its existential threat. This was the case if the interviewees were not able to reduce uncertainty or to cope with it. We found the aim of this avoidance strategy was to restore normality – as if nothing had ever happened. These participants did not want at any price facing uncertainty as a part of their life.</p>", "<p id=\"Par48\">They kept a mentally distance by distracting themselves, in order to not having to think about uncertainty. Participant 5 rejected new information to avoid getting bad news. They furthermore constructed a negative certainty, i.e., being convinced that the uncertainty will in fact occur:</p>", "<p id=\"Par50\">Unlike the other participants, they could not accept uncertainty as a result of their management strategies, but rather gave up under the feeling of having no choice and resigned themselves to the uncertainty and the threat that came with it. Participants implementing the avoidance strategy consequently reported feeling depressed and powerless. They had a little sense of control, as they felt they have no choice.</p>", "<title>Uncertainty as a certainty in illness: A mindset to promote recovery</title>", "<p id=\"Par51\">As participants 1, 2, 3 and 7 had accepted uncertainty, they increasingly observed a positive impact on their recovery and health. As a consequence, they developed a new mindset – with uncertainty in the background and their awareness about it in the foreground. They were convinced that an altered cognitive focus made a positive impact on their recovery and would reduce their risk of cancer recurrence. The new mindset regarding uncertainty was characterized by the realization of the universal nature of the phenomenon. They no longer felt alone with uncertainty in their illness as soon as they became aware of the certainty of uncertainty as a natural part of life - that concerns all aspects of human existence:</p>", "<p id=\"Par53\">In their new mindset women gained trust in their psychological coping strategies. They concluded that their own perspective made a difference and improved their sense of control. This mindset allowed them to experience increased self-confidence in being able to beat cancer. They felt relieved, reported more serenity, mental closure, mental health, and resilience:</p>" ]

[ "<title>Discussion</title>", "<p id=\"Par55\">This longitudinal qualitative study explored how the uncertainty experience developed in women with vulvar neoplasia over the course of one year. The findings were not only of phenomenological interest, but also of theoretical as the study was conducted with sensitivity of the Reconceptualized Uncertainty in Illness Theory [##REF##2292449##24##]. They contribute to a deepened understanding of the uncertainty experience of women with vulvar neoplasia in the illness trajectory but also inform the further development of Mishel’s theory itself.</p>", "<p id=\"Par56\">The development of uncertainty was never complete but oscillating in the chronic course of the disease in women with vulvar neoplasia. Change in uncertainty experience was inhibited by existential uncertainty and promoted by the acceptance of uncertainty. According to the RUIT [##REF##2292449##24##], uncertainty experience changes in a positive way when someone is at the peak level of instability due to uncertainty. In this qualitative longitudinal study, we also identified participants experiencing instability due to threatening uncertainty. Over time, however, we did not observe a development in the experience of uncertainty, as long as uncertainty still was perceived existentially threatening. However, we found commonalities regarding the re-appraisal of uncertainty under other circumstances. In a similar vein as Mishel [##REF##2292449##24##], the participants described a new view of life allowing a change of perspectives with regard to evaluation of uncertainty. However, this development did not lead to perceiving uncertainty as an opportunity. In our study, the development of uncertainty occurred in individuals who were able to reduce the threat of uncertainty or where uncertainty dissolved by external circumstances. Subsequently they did not associate the uncertainty with existential consequences but were still experiencing uncertainty that, however, concerned the unpredictability of the further illness trajectory.</p>", "<p id=\"Par57\">In accordance, Han et al. [##REF##22067431##44##] suggest that existential uncertainty may be a bigger threat for patients than more information-related aspects of uncertainty, i.e., uncertainty associated with diagnosis, prognosis, causal explanations, and treatment. Existential uncertainty encompasses an awareness of the fact that one`s own existence is undetermined but finite. Being existentially uncertain means to live with a constant threat to one’s own existence – a threat reaching beyond the physical domain and affecting the social, personal, and spiritual domains. Dwan and Willig [##REF##33474766##45##] outlined key distinctions between existential uncertainty and other aspects of uncertainty in the experience of persons with cancer. Thereby, the focus is on meaning rather than on facts, on the person rather than on the disease, and the fundamental nature of the human being in the world. Another comparable distinction of existential uncertainty was made by Karlsson, Friberg [##REF##24936149##46##], as they characterized it as living with an unpredictable future, being confronted with one’s own impending mortality, and undergoing personal development. Against this background Penrod [##REF##17346325##47##] cautioned that providing even more information to reduce existential uncertainty may be counterproductive to change the negative experience of uncertainty. However, existential uncertainty that could be overcome may result in existential well-being, that similarly depends on a person’s meaning and purpose of life, and feelings regarding death and suffering [##REF##8630968##48##]. A bidirectional relationship of existential well-being and health-promoting behaviours is assumed [##REF##32593729##49##]. This would explain why individuals changed their perspective on uncertainty as a health-promoting behaviour, as soon as uncertainty was no longer experienced as existentially threatening, but could be accepted, perhaps leading to existential well-being. Greater manifestation of existential well-being is associated with a reduced incidence of depression and an improved overall health condition. Existential well-being is furthermore connected with emotional well-being, that manifests as social engagement, health-promoting behaviours, and positive affect and optimism [##REF##33989673##50##]. A positive mindset and proactive living promote the relationship of existential well-being and health-promoting behaviours. In particular, having a positive mindset to foster health was influenced by a positive self-image, and having sense of control [##UREF##13##51##]. Having a positive mindset and sense of purpose in life were directly associated with health-promoting behaviours of proactive living [##UREF##14##52##]. Especially in individuals living with chronic illness, looking for and having meaning are positively correlated. Following, the promotion of finding meaning in life should have high priority during the management of chronic disease [##REF##34003136##53##] to overcome existential uncertainty and achieve existential well-being.</p>", "<title>Limitations and strength of the study</title>", "<p id=\"Par58\">A limitation concerns the short duration of some interviews. Extended interviews would contribute to an in-depth understanding of individual experiences and to draw conclusions with regard to a longer period of time. Furthermore, due to the peculiarities of the group of interest of women with vulvar neoplasia, the transferability of the results to persons with another oncological condition or chronic disease is limited. However, the longitudinal study design contributes to uncover dynamic processes as they occur and to offer insights into changes and continuities within the life course [##UREF##15##54##].</p>", "<title>Implications</title>", "<p id=\"Par59\">The results of this study show that the experience of uncertainty changes over time in women with vulvar neoplasia, since different types of uncertainty occured during the illness trajectory. Uncertainty associated with existential consequences did not develop in a positive way until participants were able to cope with it. It is therefore important to differentiate between several types of uncertainty. The role of existential uncertainty should be considered as potential inhibitor of change in the interaction with women with vulvar neoplasia and with regard to intervention planning. In the context of cancer, there is growing evidence that meaning-oriented uncertainty interventions might be most useful [##REF##29251694##55##–##REF##29757459##57##]. The combination of existential uncertainty and the identified possibility of change in the experience of uncertainty emphasize the need to develop an own language and understanding of professionals in order to anticipate and address different aspects of patients´ uncertainty experience [##REF##33147429##58##].</p>" ]

[ "<title>Conclusions</title>", "<p id=\"Par60\">The findings provide health care practitioners, especially in the field of psycho-oncology with a deeper understanding of the development of uncertainty experience in the disease trajectory of women with vulvar neoplasia. Our results may inform practice, in particular interactions with affected individuals. Furthermore, the findings strengthen the theoretical basis of uncertainty in chronic illness. They can provide orientation for developing theory-based measurements and interventions. Finally, we reflected the results against the background of the RUIT [##REF##2292449##24##]. Thereby, the results can contribute to theory dynamics in nursing, by informing theory further development and adding to the body of existing theories.</p>" ]

[ "<title>Background</title>", "<p id=\"Par1\">Women with vulvar neoplasia continue to experience uncertainty up to six months post-surgery. Uncertainty in illness is considered a significant psychosocial stressor, that negatively influences symptom distress, self-management strategies and quality of life. According to the <italic>Reconceptualized Uncertainty in Illness Theory</italic>, the appraisal of uncertainty changes positively over time in chronic illness. We aimed at exploring whether and how the experience of uncertainty develops in women with vulvar neoplasia.</p>", "<title>Methods</title>", "<p id=\"Par2\">We selected a purposive sample of seven women diagnosed with vulvar neoplasia in four Swiss and one Austrian women’s clinic. By means of a qualitative longitudinal study, we conducted 30 individual interviews at five points of time during one year after diagnosis. We applied Saldaña’s analytical questions for longitudinal qualitative research.</p>", "<title>Results</title>", "<p id=\"Par3\">First, participants experienced uncertainty as an existential threat, then an inherent part of their illness, and finally a certainty. Women initially associated the existential threat with a high risk for suffering from severe health deteriorations. Participants that could reduce their individually assessed risk by adopting health promoting behaviors, accepted the remaining uncertainty. From now on they reframed uncertainty into a certainty. This new mindset was based on a belief of promoting recovery and reducing the risk of recurrence.</p>", "<title>Conclusions</title>", "<p id=\"Par4\">The long-lasting and oscillating nature of uncertainty should receive attention in supportive oncology care. Uncertainty concerning existential issues is of special importance since it can inhibit a positive development of uncertainty experience.</p>", "<title>Keywords</title>" ]

[]

[ "<title>Acknowledgements</title>", "<p>Many thanks to all participants for their openness to share their experiences and the recruiting nurses for their support.</p>", "<title>Author contributions</title>", "<p>All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Jasmin Eppel-Meichlinger, Hanna Mayer, Enikö Steiner, and Andrea Kobleder. The first draft of the manuscript was written by Jasmin Eppel-Meichlinger and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.</p>", "<title>Funding</title>", "<p>We acknowledge support by Open Access Publishing Fund of Karl Landsteiner University of Health Sciences, Krems, Austria. Furthermore, we would like to thank the Nursing Science Foundation Switzerland for funding this study (ID 2143 − 2017).</p>", "<title>Data availability</title>", "<p>The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.</p>", "<title>Declarations</title>", "<title>Ethics approval and consent to participate</title>", "<p id=\"Par62\">The study received ethical approval from the Cantonal Ethics Committee Bern, Ethics Committee Northwest and Central Switzerland, Ethics Committee Eastern Switzerland, Ethics Committee Ticino, and Ethics Committee of the University Hospital Vienna. Informed consent was obtained from all the participants. The study was conducted in accordance to the Declaration of Helsinki.</p>", "<title>Consent to publish</title>", "<p id=\"Par63\">Not applicable.</p>", "<title>Competing interests</title>", "<p id=\"Par61\">The authors have no relevant financial or non-financial interests to disclose.</p>" ]

[ "<fig id=\"Fig1\"><label>Fig. 1</label><caption><p>Development of uncertainty experience in women with vulvar neoplasia</p></caption></fig>" ]

[ "<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Characteristics of the participants</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\"># Participant</th><th align=\"left\">Nationality</th><th align=\"left\">Age in years</th><th align=\"left\">Diagnosis</th><th align=\"left\">Pre-existing chronic condition</th><th align=\"left\">Disease-related events during the study period</th></tr></thead><tbody><tr><td align=\"left\">1</td><td align=\"left\">Switzerland</td><td char=\".\" align=\"char\">56</td><td align=\"left\">VIN (Initial)</td><td align=\"left\">Chronic pain due to nerve injury</td><td align=\"left\">Occurrence and prolonged persistence of an erythema</td></tr><tr><td align=\"left\">2</td><td align=\"left\">Switzerland</td><td char=\".\" align=\"char\">64</td><td align=\"left\">VC (Initial)</td><td align=\"left\">Alcoholism</td><td align=\"left\">Delayed wound healing, recurrence</td></tr><tr><td align=\"left\">3</td><td align=\"left\">Austria</td><td char=\".\" align=\"char\">28</td><td align=\"left\">VC (Initial)</td><td align=\"left\">None</td><td align=\"left\">Hospitalization due to postoperative sepsis</td></tr><tr><td align=\"left\">4</td><td align=\"left\">Austria</td><td char=\".\" align=\"char\">67</td><td align=\"left\">VC (Recurrence)</td><td align=\"left\">None</td><td align=\"left\">Persistent pain</td></tr><tr><td align=\"left\">5</td><td align=\"left\">Austria</td><td char=\".\" align=\"char\">80</td><td align=\"left\">VIN (Reccurence)</td><td align=\"left\">Uterine &amp; cervical cancer</td><td align=\"left\">Bladder symptoms and a recurrence</td></tr><tr><td align=\"left\">6</td><td align=\"left\">Austria</td><td char=\".\" align=\"char\">85</td><td align=\"left\">VC (Recurrence)</td><td align=\"left\">None</td><td align=\"left\">None</td></tr><tr><td align=\"left\">7</td><td align=\"left\">Austria</td><td char=\".\" align=\"char\">56</td><td align=\"left\">VIN (Initial)</td><td align=\"left\">None</td><td align=\"left\">None</td></tr></tbody></table></table-wrap>" ]

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[ "<disp-quote><p id=\"Par25\"><italic>“On this ward where everyone was practically running around from dying of cancer. That was simply a catastrophe for me. I generally don’t want anyone to end up like that, but I certainly don’t want to”</italic> (Interview 1, Participant 3).</p></disp-quote>", "<disp-quote><p id=\"Par27\"><italic>“I was alarmed […] and had a very bad feeling. It could have been that it has come back, but inside which means that it has spread to the lymph nodes”</italic> (Interview 3, Participant 1).</p></disp-quote>", "<disp-quote><p id=\"Par30\"><italic>“The worst part was the first two weeks, when I heard from my gynecologist that it was vulvar cancer, but I hadn’t had any further examinations, and it was possible that the cancer had already spread. And I don’t have much longer to live. It still hurts me”</italic> (Interview 2, Participant 3).</p></disp-quote>", "<disp-quote><p id=\"Par33\"><italic>“I think they will try to help me… but to what extent it is possible, that is written in the stars for me”</italic> (Interview 2, Participant 6).</p></disp-quote>", "<disp-quote><p id=\"Par35\"><italic>“Well, that’s it – a trembling, an anxiety, like a threat. What will come? But whatever will come, whether good or bad, I have no other choice”</italic> (Interview 1, Participant 6).</p></disp-quote>", "<disp-quote><p id=\"Par37\"><italic>“It depends on how I deal with it. It’s always been like this… I say: What am I suffering from? Nope, I don’t think so. I’m healthy, I’m the greatest, I’m the best, I’m the winner</italic>” (Interview 1, Participant 2).</p></disp-quote>", "<disp-quote><p id=\"Par39\"><italic>“Getting out into nature… That’s my first priority, that I can somehow manage that and through that I can really switch off”</italic> (Interview 2, Participant 5).</p></disp-quote>", "<disp-quote><p id=\"Par41\"><italic>“When I woke up after surgery and they told me that the lymph nodes were fine, it was such a relief…All was well! All was well! I managed that, I got off lightly”</italic> (Interview 3, Participant 7).</p></disp-quote>", "<disp-quote><p id=\"Par43\"><italic>“I can’t do it myself … determine my own fate, that’s actually presumptuous. You don’t have full control over your own life”</italic> (Interview, Participant 1).</p></disp-quote>", "<disp-quote><p id=\"Par45\"><italic>“It is difficult to estimate the course of an illness. You never know how it will end. It’s just part of the game”</italic> (Interview 1, Participant 2).</p></disp-quote>", "<disp-quote><p id=\"Par47\"><italic>“I don’t know, maybe I’m a strange person, but I try to repress everything, the senselessness of it all”</italic> (Interview 3, Participant 5).</p></disp-quote>", "<disp-quote><p id=\"Par49\">“At the moment I feel better because now I know that the cancer is there and that it won’t go away” (Interview 5, Participant 5).</p></disp-quote>", "<disp-quote><p id=\"Par52\"><italic>“Uncertainty for me means…. It’s not just me, everyone is affected by it, I can only take each day as it comes and then solve the problems”</italic> (Interview 4, Participant 1).</p></disp-quote>", "<disp-quote><p id=\"Par54\"><italic>“I have processed it mentally; I am in a positive mood. I am convinced that this makes a difference. Whether something comes back or not […], because the physical and the psychological are so close together, you can’t separate them”</italic> (Interview 2, Participant 2).</p></disp-quote>" ]

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[ "<table-wrap-foot><p>Abbreviations: VIN = Vulvar intraepithelial Neoplasia, VC = Vulvar Cancer</p></table-wrap-foot>", "<fn-group><fn><p><bold>Publisher’s Note</bold></p><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn></fn-group>" ]

[ "<graphic xlink:href=\"12905_2024_2889_Fig1_HTML\" id=\"d32e603\"/>" ]

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{ "acronym": [], "definition": [] }

[ "<title>Introduction</title>", "<p id=\"Par10\">Osteosarcoma (OS) is the most common primary bone tumor in adolescents [##REF##29979330##1##, ##REF##20943636##2##]. The malignancy of OS is high, and most patients develop lung metastasis within one year, so the prognosis is poor [##REF##29065898##3##, ##REF##32373946##4##]. At present, surgery combined with chemotherapy drugs is still the main way of OS treatment, but the effect is limited [##REF##32483726##5##, ##REF##32326444##6##]. It is important to elucidate the underlying mechanisms affecting OS progression at the molecular level for developing potential therapeutic targets of OS.</p>", "<p id=\"Par11\">Circular RNAs (circRNAs) are RNA molecules characterized by covalently closed loops and widely present in eukaryotes, which are mainly formed by back-splicing of exons or introns of genes [##REF##31395983##7##, ##REF##25746834##8##]. Mechanistically, circRNAs have been confirmed to act as microRNA (miRNA) sponges to mediate gene expression [##REF##31481066##9##, ##REF##30103209##10##]. A large amount of evidence shows that circRNA abnormal expression is often related to the occurrence of human diseases [##REF##30176158##11##, ##REF##30249393##12##]. Importantly, studies has confirmed that circRNA is associated with malignant progression of tumors, including OS [##REF##33251132##13##, ##REF##33103338##14##]. Studies had suggested that circTADA2A had an increasing effect on OS cell proliferation and metastasis, which was achieved via sponging miR-203a-3p to upregulate CREB3 [##REF##30940151##15##]. Circ_0001721 was considered to be a potential target for OS treatment, which enhanced OS glycolysis, proliferation and metastasis through regulation of miR-372-3p/MAPK7 [##REF##32982424##16##].</p>", "<p id=\"Par12\">Circ_0000376 is located at chr12: 11199618-11248400 with 48,782 bp length and is derived from PRH1-PRR4 gene. In this study, we screened differentially expressed circRNA in OS tissues and normal tissues using GEO database, and pointed out that circ_0000376 was overexpressed in OS tissues. Previous studies had shown that decreased circ_0000376 expression could lead to decreased OS cell viability and metastasis ability [##REF##34532813##17##]. Therefore, we have reason to believe that circ_0000376 may be a potential target for OS therapy. To further confirm this, we conducted this study and revealed a novel downstream miRNA/mRNA regulatory axis of circ_0000376.</p>" ]

[ "<title>Materials and methods</title>", "<title>Samples collection</title>", "<p id=\"Par13\">The OS tumor tissues and adjacent normal tissues were collected from 33 OS patients at The Third Hospital of Mianyang and stored at -80 °C. Written informed consent was signed from each patient, and our research was approved by The Third Hospital of Mianyang.</p>", "<title>Cell culture and transfection</title>", "<p id=\"Par14\">OS cells (143B, HOS, MG63 and U2OS) and osteoblast cells (hFOB1.19) were bought from ATCC (Manassas, VA, USA) and cultured in DMEM medium (Solarbio, Beijing, China) containing 10% FBS and 1% penicillin–streptomycin. Circ_0000376 small interfering RNA (si-circ_0000376), pCD5 overexpression vector, lentivirus short hairpin RNA (sh-circ_0000376), miR-577 mimic, miR-577 inhibitor (anti-miR-577), pcDNA hexokinase 2 (HK2) overexpression vector, pcDNA lactate dehydrogenase-A (LDHA) overexpression vector, and negative controls were synthesized by RiboBio (Guangzhou, China). They were transfected into OS cells with Lipofectamine 3000 (Invitrogen, Carlsbad, CA, USA).</p>", "<title>Quantitative real-time PCR (qRT-PCR)</title>", "<p id=\"Par15\">Total RNAs were isolated by TRIzol reagent (Invitrogen) and reverse-transcribed into cDNA using Reverse Transcription Kit (Takara, Dalian, China). PCR reaction was conducted with SYBR Green (Takara) and specific primers (Table ##TAB##0##1##). Relative expression was normalized by β-actin or U6 and expressed using 2^−ΔΔCT method. Also, RNA was treated with RNase R solution and then used for qRT-PCR.</p>", "<title>Cell proliferation detection</title>", "<p id=\"Par16\">In cell counting kit 8 (CCK8) assay, OS cells seeded into 96-well plates were cultured for 48 h. CCK8 reagent (Beyotime, Shanghai, China) was added to each well. The absorbance at 450 nm was detected under microplate reader to measure cell viability.</p>", "<p id=\"Par17\">In colony formation assay, OS cells seeded in 12-well plates were cultured for 2 weeks. After that, the colonies were fixed with paraformaldehyde and stained with crystal violet. The number of colonies was counted under microscope.</p>", "<p id=\"Par18\">In EDU assay, OS cells seeded into 96-well plates were stained with EDU solution and DAPI solution (RiboBio). Fluorescence images were captured under fluorescence microscope, and EDU positive cell rate was calculated by ImageJ software.</p>", "<title>Flow cytometry</title>", "<p id=\"Par19\">Annexin V-FITC Apoptosis Detection Kit (Beyotime) was used. OS cells suspended with binding buffer were stained with Annexin V-FITC and propidium iodide. Cell apoptosis rate was analyzed by flow cytometer and CellQuest software.</p>", "<title>Transwell assay</title>", "<p id=\"Par20\">Transwell chamber pre-covered with Matrigel was used. Serum medium was added to the lower chamber, and OS cells suspended with DMEM medium were seeded into the upper chamber. 24 h later, the cells were fixed and stained. Under microscope, the number of invasive cells from 5 fields was counted.</p>", "<title>Cell glycolysis detection</title>", "<p id=\"Par21\">After transfection, the supernatants of OS cells were collected for measuring the glucose consumption, lactate production and ATP/ADP level by Glucose Assay Kit, Lactate Assay Kit and ApoSENSOR ADP/ATP Ratio Assay (BioVision, Milpitas, CA, USA). The ECAR and OCR of cells were analyzed using XF96 Extracellular Flux analyzer (Seahorse Bioscience, Chicopee, MA, USA).</p>", "<title>Western blot (WB) analysis</title>", "<p id=\"Par22\">RIPA buffer (Abcam, Cambridge, MA, USA) was used to obtain total protein. Protein samples were separated via SDS-PAGE gel and transferred onto PVDF membranes. Primary antibodies, including anti-CyclinD1 (1:200, ab16663), anti-MMP9 (1:1000, ab38898), anti-HK2 (1:10000, ab227198), anti-LDHA (1:5000, ab52488), and anti-β-actin (1:1000, ab8227), were used to incubate the membranes, which were then hatched with secondary antibody (1:50,000, ab205718). Protein bands were visualized using ECL reagent (Beyotime), and Image Lab software was used for gray scale analysis.</p>", "<title>Dual-luciferase reporter assay</title>", "<p id=\"Par23\">The binding sequence and mutant sequence of miR-577 in circ_0000376, HK2 3’UTR or LDHA 3’UTR were designed and inserted into the pmirGLO reporter vector, generating the corresponding wild-type and mutant-type vectors. OS cells were co-transfected with the vectors and miRNA. Cells were then harvested to detect luciferase activity using Dual-luciferase Reporter Gene Assay Kit (Beyotime).</p>", "<title>Xenograft models</title>", "<p id=\"Par24\">U2OS cells transfected with sh-NC or sh-circ_0000376 were subcutaneously injected into BALB/c nude mice (6-week-old, Vital River, Beijing, China) to construct xenograft tumor model (n = 6/group). Tumor volume was recorded every 3 days post-injection 7 days. 22 days later, tumor tissues were excised from euthanized mice. Mice tumor tissues were used for preparing paraffin section. Immunohistochemical (IHC) staining was carried out using SP Kit (Solarbio) with anti-HK2 (1:500, ab227198), anti-LDHA (1:2000, ab52488) and anti-Ki67 (1:1000, ab15580). Animal experiment was approved by The Third Hospital of Mianyang.</p>", "<title>Statistical analysis</title>", "<p id=\"Par25\">Data were shown as means ± SD. GraphPad Prism 7.0 was used to perform statistical analyses. Significant differences were compared using Student’s <italic>t</italic>-test or ANOVA. <italic>P</italic> &lt; 0.05 was considered statistically significant.</p>" ]

[ "<title>Results</title>", "<title>Circ_0000376 expression was increased in OS patients and cells</title>", "<p id=\"Par26\">Figure ##FIG##0##1##A exhibited 10 differentially expressed circRNAs in OS tumor tissues and normal tissues in GEO database (accession: GSE96964), among which circ_0000376 (chip: hsa_circRNA_000554) was significantly overexpressed in OS tumor tissues. Through qRT-PCR, circ_0000376 was confirmed to be upregulated in OS tumor tissues compared to adjacent normal tissues (Fig. ##FIG##0##1##B), as well as in 4 OS cell lines compared to hFOB1.19 cells (Fig. ##FIG##0##1##C). After RNA was treated with RNase R, we confirmed that circ_0000376 expression was not significantly affected, while linear RNA GAPDH mRNA expression was markedly reduced (Fig. ##FIG##0##1##D, E). These data confirmed that circ_0000376 could resist RNA digestion.</p>", "<title>Knockdown of circ_0000376 inhibited OS cell growth, invasion and glycolysis</title>", "<p id=\"Par27\">After si-circ_0000376 was transfected into MG63 and U2OS cells, circ_0000376 expression was remarkably decreased (Fig. ##FIG##1##2##A). Then, we evaluated OS cell proliferation, apoptosis, invasion and glycolysis to explore the effect of circ_0000376 knockdown on OS cell progression. As shown in Fig. ##FIG##1##2##B–F, downregulation of circ_0000376 suppressed cell viability, the number of colonies and EDU positive cell rate, while increased cell apoptosis rate. Additionally, circ_0000376 knockdown inhibited the number of invasive cells, glucose consumption, lactate production and ATP/ADP ratios (Fig. ##FIG##1##2##G–J). Moreover, circ_0000376 knockdown resulted in a decrease in ECAR and an increase in OCR in MG63 cells (Additional file ##SUPPL##0##1##: Fig. S1A-B), confirming that circ_0000376 might promote Warburg effect of OS cells. The results exhibited that. WB analysis results indicated that silencing of circ_0000376 also decreased cell cycle protein CyclinD1 expression and invasion protein MMP9 expression in OS cells (Fig. ##FIG##1##2##K–L). These results indicated that circ_0000376 enhanced OS cell proliferation, invasion, glycolysis and inhibited apoptosis.</p>", "<title>Circ_0000376 interacted with miR-577</title>", "<p id=\"Par28\">The starbase software and circinteractome software were used to jointly predict miRNAs that could complement with circ_0000376, and then we focused on miR-577 (Fig. ##FIG##2##3##A). According to their binding sites, we designed the WT/MUT-circ_0000376 reporter vectors (Fig. ##FIG##2##3##B). Besides, miR-577 mimic was used to overexpress miR-577 in MG63 and U2OS cells (Fig. ##FIG##2##3##C). In dual-luciferase reporter assay, we observed that the luciferase activity of WT-circ_0000376 vector without MUT-circ_0000376 vector was reduced by miR-577 mimic, confirming the interaction between circ_0000376 and miR-577 (Fig. ##FIG##2##3##D, E). In OS tumor tissues, miR-577 had decreased expression and was negatively correlated with circ_0000376 expression (Fig. ##FIG##2##3##F–G). Also, miR-577 was lowly expressed in OS cells (MG63 and U2OS) compared to hFOB1.19 cells (Fig. ##FIG##2##3##H). Above data confirmed that circ_0000376 could sponge miR-577.</p>", "<title>The regulation of si-circ_0000376 on OS cell progression was eliminated by anti-miR-577</title>", "<p id=\"Par29\">To explore whether circ_0000376 regulated OS progression via sponging miR-577, the rescue experiments were performed. After co-transfected with si-circ_0000376 and anti-miR-577 into MG63 and U2OS cells, we detected miR-577 expression and confirmed that miR-577 expression promoted by si-circ_0000376 could be decreased by anti-miR-577 (Fig. ##FIG##3##4##A). Analysis results showed that the negative regulation of si-circ_0000376 on cell viability, the number of colonies and EDU positive cell rate were reversed by miR-577 inhibitor (Fig. ##FIG##3##4##B–D and Additional file ##SUPPL##1##2##: Fig. S2A-B). Circ_0000376 knockdown induced cell apoptosis could also be abolished by miR-577 inhibitor (Fig. ##FIG##3##4##E and Additional file ##SUPPL##1##2##: Fig. S2C). Furthermore, the addition of anti-miR-577 overturned the suppressive effects of si-circ_0000376 on the number of invasive cells, glucose consumption, lactate production, ATP/ADP ratio, and the protein expression of CyclinD1 and MMP9 (Fig. ##FIG##3##4##F–K and Additional file ##SUPPL##1##2##: Fig. S2D). Therefore, we confirmed that circ_0000376 might contribute to OS progression via targeting miR-577.</p>", "<title>MiR-577 interacted with HK2 and LDHA</title>", "<p id=\"Par30\">Targetscan software was used to predict the downstream target of miR-577. The 3’UTRs of HK2 and LDHA were discovered to have binding sites with miR-577 (Fig. ##FIG##4##5##A, B). MiR-577 mimic reduced the luciferase activities of the HK2 3’UTR-WT vector and LDHA 3’UTR-WT vector, confirmed that there had interaction relationship between miR-577 and HK2 or LDHA (Fig. ##FIG##4##5##C, D). HK2 and LDHA mRNA expression levels were upregulated in OS tumor tissues, and their expression levels were negatively correlated with miR-577 expression (Fig. ##FIG##4##5##E–H). In OS tumor tissues and cells, we also observed the high HK2 and LDHA expression at the protein levels (Fig. ##FIG##4##5##I–L).</p>", "<title>MiR-577 hindered OS cell progression by targeting HK2 and LDHA</title>", "<p id=\"Par31\">To further confirm that miR-577 mediated OS progression by regulating HK2 and LDHA, we conducted rescue tests, respectively. In MG63 and U2OS cells co-transfected with miR-577 mimic and pcDNA HK2 overexpression vector, we found that miR-577 reduced HK2 protein expression, and this effect was reversed by pcDNA HK2 overexpression vector (Fig. ##FIG##5##6##A). MiR-577 inhibited cell viability, the number of colonies and EDU positive cell rate, while enhanced apoptosis rate. However, these effects were reversed by HK2 overexpression (Fig. ##FIG##5##6##B–E and Additional file ##SUPPL##2##3##: Fig. S3A-C). Moreover, overexpressed HK2 also eliminated the inhibitory effects of miR-577 on the number of invasive cells, glucose consumption, lactate production, ATP/ADP ratio, and the protein expression of CyclinD1 and MMP9 (Fig. ##FIG##5##6##F–K and Additional file ##SUPPL##2##3##: Fig. S3D). Similarly, pcDNA LDHA overexpression vector were transfected into MG63 and U2OS cells with miR-577 mimic. As shown in Fig. ##FIG##6##7##A, pcDNA LDHA overexpression vector increased LDHA protein expression reduced by miR-577. Function experiments suggested that LDHA overexpression overturned the regulation of miR-577 on OS cell proliferation, apoptosis, invasion, and glycolysis (Fig. ##FIG##6##7##B–I and Additional file ##SUPPL##3##4##: Fig. S4A-D). Also, the decreasing effect of miR-577 on the protein expression of CyclinD1 and MMP9 was abolished by overexpressing LDHA (Fig. ##FIG##6##7##J, K). Above all, these results suggested that miR-577 targeted HK2/LDHA to suppress OS progression.</p>", "<title>Interference of circ_0000376 inhibited OS tumor growth</title>", "<p id=\"Par32\">To determine the role of circ_0000376 in vivo, we constructed U2OS cells with stable knockdown circ_0000376 using sh-circ_0000376 (Fig. ##FIG##7##8##A). After that, U2OS cells transfected with sh-NC/sh-circ_0000376 were injected into nude mice. After 22 days, we found that tumor volume and weight were reduced in the sh-circ_0000376 group (Fig. ##FIG##7##8##B, C). In the mice tumor tissues of sh-circ_0000376 group, circ_0000375 expression was inhibited and miR-577 expression was promoted (Fig. ##FIG##7##8##D). Also, The HK2 and LDHA protein expression levels were repressed in sh-circ_0000376 group (Fig. ##FIG##7##8##E, F). Besides, HK2, LDHA and Ki67 positive cells also were decreased in the tumor tissues of sh-circ_0000376 group (Fig. ##FIG##7##8##G). These results showed that circ_0000376 sponged miR-577 to promote HK2/LDHA-mediated glycolysis, thus accelerating OS tumor growth in vivo.</p>" ]

[ "<title>Discussion</title>", "<p id=\"Par33\">Circ_0000376 acts as an oncogenic gene in many tumors. For example, circ_0000376 was considered to be a tumor promoter in lung cancer, which enhanced lung cancer proliferation, glycolysis and metastasis through miRNA/mRNA network [##REF##32716343##18##–##REF##32922073##20##]. Also, circ_0000376 had been shown to play active role the malignant progression of gastric cancer and breast cancer [##REF##32462972##21##, ##REF##33126850##22##]. Here, we investigated circ_0000376 role in OS. The present results suggested that circ_0000376 was overexpressed in OS, and its interference restrained OS cell proliferation, invasion, glycolysis, and accelerated apoptosis. Animal experiments also further showed that circ_0000376 knockdown reduced OS tumorigenesis in vivo. These results provided new evidence that circ_0000376 was a potential therapeutic target for OS. We believed that circ_0000376 promoted OS malignant progression, which was consistent with the previous reports [##REF##34532813##17##].</p>", "<p id=\"Par34\">MiRNA and siRNAs have been confirmed to play vital function in human diseases [##REF##31066454##23##–##REF##37675799##27##]. According to reported studies, circ_0000376 might be involved in regulating OS development through sponging miR-432-5p [##REF##34532813##17##]. Here, we explored the new molecular mechanism of circ_0000376, and confirmed that circ_0000376 sponged miR-577. In many tumors, miR-577 played a negative role in tumor malignant phenotype, such as breast cancer [##REF##29524309##28##] and glioblastoma [##REF##25764520##29##]. MiR-577 suppressed the proliferation and metastasis of papillary thyroid carcinoma cells [##REF##28975989##30##], and could inhibit cervical cancer cell growth and glycolysis [##REF##34306343##31##]. In the previous research, miR-577 had been discovered to be lowly expressed in OS, which could reduce OS proliferation and migration [##REF##29108989##32##]. Similar to this reports, we also found that miR-577 had the ability to inhibit OS progression in this study. In functional experiments, miR-577 suppressed OS cell growth, invasion and glycolysis. Circ_0000376 negatively regulated miR-577 level, and miR-577 inhibitor also revoked si-circ_0000376-mediated OS cell function. These results provided evidence that circ_0000376 targeted miR-577 to regulate OS progression.</p>", "<p id=\"Par35\">Glycolysis is one of the prominent features of malignant tumors and is the main source of energy during tumor growth [##REF##17302740##33##, ##REF##24298908##34##]. HK2, a member of HK family, is a key rate-limiting enzyme in glycolysis pathway, mainly responsible for catalyzing glucose phosphorylation [##REF##32631382##35##]. LDHA is also a key enzyme in the glycolysis pathway that converts pyruvate to lactic acid [##REF##23817426##36##]. Many studies had confirmed that the increased expression of HK2 and LDHA promoted the glycolysis process of tumor cells, thus accelerating the malignant phenotype of tumors, such as hepatocellular carcinoma [##REF##34113130##37##] and bladder cancer [##REF##25266796##38##]. Research had suggested that HK2 was overexpressed in OS, and its overexpression promoted OS cell proliferation and invasion [##REF##28602700##39##, ##REF##31524259##40##]. Besides, LDHA had been shown to be upregulated in OS, which enhanced cell growth and metastasis to promote OS progression [##REF##33962616##41##, ##REF##30213286##42##]. Here, we pointed out that miR-577 targeted HK2 and LDHA. Overexpressed HK2 and LDHA reversed miR-577-mediated the inhibition on OS cell growth, invasion and glycolysis, confirming that miR-577 indeed suppressed OS development through targeting HK2 and LDHA. Importantly, circ_0000376 had a positively regulation on HK2 and LDHA expression, which perfected the mechanism of circ_0000376/miR-577/HK2/LDHA axis.</p>", "<p id=\"Par36\">In summary, we provided strong evidence that circ_0000376 played a key role in OS development, which promoted OS growth, invasion and glycolysis through miR-577/HK2/LDHA pathway (Fig. ##FIG##8##9##). Inhibition of circ_0000376 might be an effective treatment method for OS, offering new evidence that circ_0000376 served as a potential therapeutic target for OS.</p>" ]

[]

[ "<title>Background</title>", "<p id=\"Par1\">Many studies have confirmed that circular RNAs (circRNAs) mediate the malignant progression of various tumors including osteosarcoma (OS). Our study is to uncover novel molecular mechanisms by which circ_0000376 regulates OS progression.</p>", "<title>Methods</title>", "<p id=\"Par2\">The expression of circ_0000376, microRNA (miR)-577, hexokinase 2 (HK2) and lactate dehydrogenase-A (LDHA) was determined by quantitative real-time PCR. OS cell proliferation, apoptosis and invasion were measured using cell counting kit 8 assay, colony formation assay, EdU assay, flow cytometry and transwell assay. Besides, cell glycolysis was assessed by testing glucose consumption, lactate production, and ATP/ADP ratios. Protein expression was examined by western blot analysis. The interaction between miR-577 and circ_0000376 or HK2/LADA was verified by dual-luciferase reporter assay. The role of circ_0000376 on OS tumor growth was explored by constructing mice xenograft models.</p>", "<title>Results</title>", "<p id=\"Par3\">Circ_0000376 had been found to be upregulated in OS tissues and cells. Functional experiments revealed that circ_0000376 interference hindered OS cell growth, invasion and glycolysis. Circ_0000376 sponged miR-577 to reduce its expression. In rescue experiments, miR-577 inhibitor abolished the regulation of circ_0000376 knockdown on OS cell functions. MiR-577 could target HK2 and LDHA in OS cells. MiR-577 suppressed OS cell growth, invasion and glycolysis, and these effects were reversed by HK2 and LDHA overexpression. Also, HK2 and LDHA expression could be regulated by circ_0000376. In vivo experiments showed that circ_0000376 knockdown inhibited OS tumorigenesis.</p>", "<title>Conclusion</title>", "<p id=\"Par4\">Circ_0000376 contributed to OS growth, invasion and glycolysis depending on the regulation of miR-577/HK2/LDHA axis, providing a potential target for OS treatment.</p>", "<title>Graphical Abstract</title>", "<p id=\"Par5\">\n\n</p>", "<title>Supplementary Information</title>", "<p>The online version contains supplementary material available at 10.1186/s13018-023-04520-y.</p>", "<title>Highlights</title>", "<p id=\"Par6\">\n<list list-type=\"bullet\"><list-item><p id=\"Par7\">Circ_0000376 knockdown inhibits OS development and tumor growth.</p></list-item><list-item><p id=\"Par8\">Circ_0000376 sponges miR-577.</p></list-item><list-item><p id=\"Par9\">MiR-577 targets HK2 and LDHA.</p></list-item></list>\n</p>", "<title>Supplementary Information</title>", "<p>The online version contains supplementary material available at 10.1186/s13018-023-04520-y.</p>", "<title>Keywords</title>" ]

[ "<title>Supplementary Information</title>", "<p>Below is the link to the electronic supplementary material.</p>" ]

[ "<title>Acknowledgements</title>", "<p>Not applicable.</p>", "<title>Authors’ contribution</title>", "<p>All authors made substantial contribution to conception and design, acquisition of the data, or analysis and interpretation of the data; take part in drafting the article or revising it critically for important intellectual content; gave final approval of the revision to be published; and agree to be accountable for all aspect of the work.</p>", "<title>Funding</title>", "<p>No funding was received.</p>", "<title>Availability of data and materials</title>", "<p>The analyzed data sets generated during the present study are available from the corresponding author on reasonable request.</p>", "<title>Declarations</title>", "<title>Ethics approval and consent to participate</title>", "<p id=\"Par37\">The present study was approved by the ethical review committee of The Third Hospital of Mianyang. Written informed consent was obtained from all enrolled patients.</p>", "<title>Consent for publication</title>", "<p id=\"Par38\">Patients agree to participate in this work</p>", "<title>Competing interests</title>", "<p id=\"Par39\">The authors declare that they have no competing interests.</p>" ]

[ "<fig id=\"Fig1\"><label>Fig. 1</label><caption><p>Circ_0000376 expression in OS patients and cells. <bold>A</bold> Heat map showed differentially expressed circRNA in OS tumor tissues and normal tissues in GSE96964. <bold>B</bold> Circ_0000376 expression in 33 paired OS tumor tissues and adjacent normal tissues was examined by qRT-PCR. <bold>C</bold> Circ_0000376 expression in OS cells and hFOB1.19 cells was detected by qRT-PCR. <bold>D</bold>, <bold>E</bold> After treated with RNase R, circ_0000376 and linear RNA GAPDH expression was determined by qRT-PCR. *<italic>P</italic> &lt; 0.05, ***<italic>P</italic> &lt; 0.001, ****<italic>P</italic> &lt; 0.0001</p></caption></fig>", "<fig id=\"Fig2\"><label>Fig. 2</label><caption><p>Effects of si-circ_0000376 on OS cell progression. MG63 and U2OS cells were transfected with si-NC and si-circ_0000376. <bold>A</bold> The circ_0000376 expression was evaluated by qRT-PCR. CCK8 assay (<bold>B</bold>), colony formation assay (<bold>C</bold>), EDU assay (<bold>D</bold>), flow cytometry (<bold>E</bold>, <bold>F</bold>) and transwell assay (<bold>G</bold>) were used to measure cell proliferation, apoptosis and invasion. <bold>H</bold>–<bold>J</bold> Glucose consumption, lactate production and ATP/ADP ratio were determined to measure cell glycolysis. <bold>K</bold>, <bold>L</bold> The protein levels of CyclinD1 and MMP9 were tested by WB analysis. **<italic>P</italic> &lt; 0.01, ***<italic>P</italic> &lt; 0.001, ****<italic>P</italic> &lt; 0.0001</p></caption></fig>", "<fig id=\"Fig3\"><label>Fig. 3</label><caption><p>Circ_0000376 sponged miR-577. <bold>A</bold> Venn diagram showed the miRNA predicted by starbase software and circinteractome software together. <bold>B</bold> The binding sites between circ_0000376 and miR-577 were exhibited. <bold>C</bold> The transfection efficiency of miR-577 mimic was assessed by qRT-PCR. <bold>D</bold>, <bold>E</bold> Dual-luciferase reporter assay was used to confirm the interaction between circ_0000376 and miR-577. <bold>F</bold> MiR-577 expression was examined by qRT-PCR in 33 paired OS tumor tissues and adjacent normal tissues. <bold>G</bold> Pearson correlation analysis was used. <bold>H</bold> MiR-577 expression was detected by qRT-PCR in OS cells and hFOB1.19 cells. **<italic>P</italic> &lt; 0.01, ***<italic>P</italic> &lt; 0.001, ****<italic>P</italic> &lt; 0.0001</p></caption></fig>", "<fig id=\"Fig4\"><label>Fig. 4</label><caption><p>Effects of si-circ_0000376 and anti-miR-577 on OS cell progression. MG63 and U2OS cells were transfected with si-circ_0000376 and anti-miR-577. <bold>A</bold> The miR-577 expression was detected by qRT-PCR. Cell proliferation, apoptosis and invasion were determined using CCK8 assay (<bold>B</bold>), colony formation assay (<bold>C</bold>), EDU assay (<bold>D</bold>), flow cytometry (<bold>E</bold>) and transwell assay (<bold>F</bold>). <bold>G</bold>–<bold>I</bold> Cell glycolysis was assessed by glucose consumption, lactate production and ATP/ADP ratio. <bold>J</bold>, <bold>K</bold> WB analysis was used to measure the protein levels of CyclinD1 and MMP9. *<italic>P</italic> &lt; 0.05, **<italic>P</italic> &lt; 0.01, ***<italic>P</italic> &lt; 0.001, ****<italic>P</italic> &lt; 0.0001</p></caption></fig>", "<fig id=\"Fig5\"><label>Fig. 5</label><caption><p>MiR-577 targeted HK2 and LDHA. <bold>A</bold>, <bold>B</bold> The binding sites between miR-577 and HK2 3’UTR or LDHA 3’UTR were exhibited. <bold>C</bold>, <bold>D</bold> Dual-luciferase reporter assay was used to confirm the interaction between miR-577 and HK2 or LDHA. <bold>E</bold> HK2 mRNA expression in 33 paired OS tumor tissues and adjacent normal tissues was determined by qRT-PCR. <bold>F</bold> Pearson correlation analysis was performed. <bold>G</bold> QRT-PCR was used to detect LDHA mRNA expression in 33 paired OS tumor tissues and adjacent normal tissues. <bold>H</bold> Pearson correlation analysis was used. <bold>I</bold>–<bold>L</bold> HK2 and LDHA protein expression levels in OS tissues and cells were detected by WB analysis. **<italic>P</italic> &lt; 0.01, ***<italic>P</italic> &lt; 0.001, ****<italic>P</italic> &lt; 0.0001</p></caption></fig>", "<fig id=\"Fig6\"><label>Fig. 6</label><caption><p>Effects of miR-577 and HK2 on OS cell progression. MG63 and U2OS cells were transfected with miR-577 mimic and pcDNA HK2 overexpression vector. <bold>A</bold> HK2 protein expression was detected by WB analysis. CCK8 assay (<bold>B</bold>), colony formation assay (<bold>C</bold>), EDU assay (<bold>D</bold>), flow cytometry (<bold>E</bold>) and transwell assay (<bold>F</bold>) were employed to analyze cell proliferation, apoptosis and invasion. <bold>G</bold>–<bold>I</bold> Glucose consumption, lactate production and ATP/ADP ratio were analyzed to measure cell glycolysis. <bold>J</bold>–<bold>K</bold> WB analysis was performed to detect CyclinD1 and MMP9 protein levels. **<italic>P</italic> &lt; 0.01, ***<italic>P</italic> &lt; 0.001, ****<italic>P</italic> &lt; 0.0001</p></caption></fig>", "<fig id=\"Fig7\"><label>Fig. 7</label><caption><p>Effects of miR-577 and LDHA on OS cell progression. MG63 and U2OS cells were transfected with miR-577 mimic and pcDNA LDHA overexpression vector. <bold>A</bold> The LDHA protein expression was tested using WB analysis. Cell proliferation, apoptosis and invasion were analyzed by CCK8 assay (<bold>B</bold>), colony formation assay (<bold>C</bold>), EDU assay (<bold>D</bold>), flow cytometry (<bold>E</bold>) and transwell assay (<bold>F</bold>). <bold>G</bold>–<bold>I</bold> Glucose consumption, lactate production and ATP/ADP ratio were examined to evaluate cell glycolysis. <bold>J</bold>–<bold>K</bold> The protein levels of CyclinD1 and MMP9 were analyzed using WB analysis. *<italic>P</italic> &lt; 0.05, **<italic>P</italic> &lt; 0.01, ***<italic>P</italic> &lt; 0.001, ****<italic>P</italic> &lt; 0.0001</p></caption></fig>", "<fig id=\"Fig8\"><label>Fig. 8</label><caption><p>Effects of sh-circ_0000376 on OS tumor growth. <bold>A</bold> Circ_0000376 expression was detected by qRT-PCR in U2OS cells transfected with sh-NC or sh-circ_0000376.<bold> B</bold>–<bold>G</bold> U2OS cells transfected with sh-NC or sh-circ_0000376 were injected into nude mice. Tumor volume (<bold>B</bold>) and weight (<bold>C</bold>) were measured. <bold>D</bold> QRT-PCR was used to detect circ_0000376 and miR-577 expression. <bold>E</bold>, <bold>F</bold> WB analysis was performed to determine HK2 and LDHA protein levels. <bold>G</bold> IHC staining was used to determine HK2, LDHA and Ki67 positive cells. *<italic>P</italic> &lt; 0.05, **<italic>P</italic> &lt; 0.01, ***<italic>P</italic> &lt; 0.001, ****<italic>P</italic> &lt; 0.0001</p></caption></fig>", "<fig id=\"Fig9\"><label>Fig. 9</label><caption><p>Mechanism diagram of this study. Circ_0000376 promoted OS cell proliferation, invasion, glycolysis and inhibited apoptosis by regulating miR-577/HK2/LDHA axis</p></caption></fig>" ]

[ "<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Primer sequences used for qRT-PCR</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Name</th><th align=\"left\"/><th align=\"left\">Primers (5′-3′)</th></tr></thead><tbody><tr><td align=\"left\">circ_0000376</td><td align=\"left\"><p>Forward</p><p>Reverse</p></td><td align=\"left\"><p>TTTGGATGTGGAGGGGAATA</p><p>GAGCCCAGGAGTTCCAGACT</p></td></tr><tr><td align=\"left\">miR-577</td><td align=\"left\"><p>Forward</p><p>Reverse</p></td><td align=\"left\"><p>TGCGGTAGATAAAATATTGG</p><p>CCAGTGCAGGGTCCGAGGT</p></td></tr><tr><td align=\"left\">LDHA</td><td align=\"left\"><p>Forward</p><p>Reverse</p></td><td align=\"left\"><p>ATGGCAACTCTAAAGGATCAGC</p><p>CCAACCCCAACAACTGTAATCT</p></td></tr><tr><td align=\"left\">HK2</td><td align=\"left\"><p>Forward</p><p>Reverse</p></td><td align=\"left\"><p>GAGCCACCACTCACCCTACT</p><p>CCAGGCATTCGGCAATGTG</p></td></tr><tr><td align=\"left\">GAPDH</td><td align=\"left\"><p>Forward</p><p>Reverse</p></td><td align=\"left\"><p>CTCTGCTCCTCCTGTTCGAC</p><p>CGACCAAATCCGTTGACTCC</p></td></tr><tr><td align=\"left\">β-actin</td><td align=\"left\"><p>Forward</p><p>Reverse</p></td><td align=\"left\"><p>CTCCATCCTGGCCTCGCTGT</p><p>GCTGTCACCTTCACCGTTCC</p></td></tr><tr><td align=\"left\">U6</td><td align=\"left\"><p>Forward</p><p>Reverse</p></td><td align=\"left\"><p>CTCGCTTCGGCAGCACA</p><p>AACGCTTCACGAATTTGCGT</p></td></tr></tbody></table></table-wrap>" ]

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[ "<supplementary-material content-type=\"local-data\" id=\"MOESM1\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM2\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM3\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM4\"></supplementary-material>" ]

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[ "<media xlink:href=\"13018_2023_4520_MOESM1_ESM.tif\"><caption><p><bold>Additional file 1: Fig. S1.</bold> Effect of si-circ_0000376 on ECAR and OCR of OS cells. were transfected with si-NC and si-circ_0000376. An XF96 extracellular flux analyzer was employed to analyze the ECAR (A) and OCR (B) of MG63 cells.</p></caption></media>", "<media xlink:href=\"13018_2023_4520_MOESM2_ESM.tif\"><caption><p><bold>Additional file 2: Fig. S2</bold>. The representative pictures of Fig. 4C (A), 4D (B), 4E (C), and 4F (D).**<italic>P</italic>&lt; 0.01, ***<italic>P</italic> &lt; 0.001, ****<italic>P</italic> &lt; 0.0001.</p></caption></media>", "<media xlink:href=\"13018_2023_4520_MOESM3_ESM.tif\"><caption><p><bold>Additional file 3: Fig. S3</bold>. The representative pictures of Fig. 6C (A), 6D (B), 6E (C), and 6F (D).**<italic>P</italic>&lt; 0.01, ***<italic>P</italic> &lt; 0.001, ****<italic>P</italic> &lt; 0.0001.</p></caption></media>", "<media xlink:href=\"13018_2023_4520_MOESM4_ESM.tif\"><caption><p><bold>Additional file 4: Fig. S4.</bold> The representative pictures of Fig. 7C (A), 7D (B), 7E (C), and 7F (D).**P&lt; 0.01, ***<italic>P</italic> &lt; 0.001, ****<italic>P</italic> &lt; 0.0001.</p></caption></media>" ]

{ "acronym": [], "definition": [] }

[ "<title>Background</title>", "<p id=\"Par5\">Primary liver cancer stands as a pervasive and lethal malignancy worldwide, posing grave threats to human life and health [##REF##30207593##1##, ##REF##16250051##2##]. Hepatocellular carcinoma (HCC) accounts for approximately 75–85% of primary liver cancers [##REF##35480307##3##]. Currently, early surgical resection is still considered the first-line treatment to decrease the rate of mortality in patients with HCC [##REF##29307467##4##, ##REF##30367835##5##]. With continuous advancements at the medical level, new therapeutic options, such as interventional therapy, targeted therapy, and immunotherapy, have been proposed [##REF##30367835##5##, ##REF##35459272##6##]. However, the prognoses for HCC patients remain unfavorable, with a persistently poor 5-year survival rate [##REF##29307467##4##]. The main factors leading to the poor prognosis are the insidious onset and the high heterogeneity of tumors, making it difficult to find a therapeutic target for HCC. Additionally, the infiltrative and disseminated nature of HCC tumors makes it practically impossible to completely remove the tumor by surgery, and the rapid drug resistance along with drug side effects also limit the treatment efficacy of drugs [##REF##16250051##2##, ##REF##32760210##7##]. Therefore, an in-depth exploration and understanding of the biological processes involved in the occurrence and progression of HCC is essential for the improvement of clinical diagnosis and treatment in patients with HCC.</p>", "<p id=\"Par6\">Recent investigations have shed light on a distinctive form of programmed cell death known as disulfidptosis, which is triggered by the accumulation of reactive oxygen species and relentless lipid peroxidation induced by disulfide-dependent mechanisms [##REF##36747082##8##, ##REF##37101248##9##]. This disulfidptosis process leads to disulfide stress and ultimately culminates in cell death. Moreover, accumulating evidence shows that disulfidptosis is associated with the progression and prognosis of cancer [##REF##36918690##10##]. For instance, Liu et al. demonstrated that susceptibility of the actin cytoskeleton to disulfide stress leads to disulfidoptosis, proposing a therapeutic avenue targeting disulfidoptosis for cancer treatment [##REF##36747082##8##, ##REF##36918690##10##]. Chen et al. constructed a disulfidptosis-related lncRNAs signature for predicting the prognosis and immunotherapy of glioma [##REF##38066643##11##]. However, novel biomarkers linked to disulfidoptosis for HCC prognosis and therapy remain elusive. Thus, our dedication lies in pinpointing new biomarkers to advance targeted therapies for HCC patients through this innovative mode of cell death.</p>", "<p id=\"Par7\">Long non-coding RNAs (lncRNAs) are non-coding RNAs with more than 200 nucleotides [##REF##35254147##12##]. Recent studies suggest that lncRNAs are related to multiple biological processes in HCC, including cell proliferation, angiogenesis, and invasion, and thus are emerging as new targets for the diagnosis, treatment, and prognosis of HCC [##REF##33115468##13##–##REF##34603293##15##]. Additionally, the construction of lncRNA signatures has proven valuable in predicting the prognosis of HCC patients, offering novel clinical insights for guiding targeted treatment approaches [##REF##32587825##14##]. For example, Xu et al. demonstrated that a ferroptosis-related nine-lncRNA signature can effectively predict prognosis and immune response in HCC [##REF##34603293##15##]. However, the involvement of lncRNAs in the disulfidoptosis process of HCC remains obscure. The potential of disulfidoptosis-related lncRNA (DRLs) signatures as prognostic biomarkers for HCC patients has yet to be systematically evaluated.</p>", "<p id=\"Par8\">In this study, we established a novel DRLs signature designed to predict the overall survival (OS) of HCC patients. Subsequently, we delved into the immune microenvironment of HCC, examined the participation of tumorigenesis pathways, and identified potential drugs for HCC treatment based on the prognostic signature. Furthermore, our findings underscored the functional relevance of TMCC1-AS1 in HCC progression, revealing that its inhibition resulted in suppressed cell proliferation, migration, and invasion. Collectively, this study enhances our comprehension of HCC prognosis and lays the groundwork for developing individualized therapeutic strategies.</p>" ]

[ "<title>Methods</title>", "<title>Data acquisition and determination of prognostic DRLs</title>", "<p id=\"Par11\">The RNA sequencing transcriptome data and clinical information of patients with HCC were retrieved from The Cancer Genome Atlas (TCGA) dataset (<ext-link ext-link-type=\"uri\" xlink:href=\"https://portal.gdc.cancer.gov/\">https://portal.gdc.cancer.gov/</ext-link>). To obviate statistical bias in our study, individuals lacking complete clinical information were excluded. Ultimately, 374 patients with HCC and 50 healthy individuals were included in subsequent analyses (last accessed: 6 May 2023). Ten disulfidptosis-related genes (GYS1, LRPPRC, NCKAP1, NDUFA11, NDUFS1, NUBPL, OXSM, RPN1, SLC3A2, and SLC7A11) were collected based on previously published studies [##REF##36747082##8##–##REF##38066643##11##, ##REF##37152941##16##]. We performed Pearson correlation analysis with a threshold of Pearson’s <italic>R</italic> &gt; 0.4 and <italic>p</italic> &lt; 0.001 to assess the relationship between disulfidptosis-related genes and lncRNAs. Subsequently, univariate Cox regression analysis was performed to evaluate the prognostic significance of the DRLs (<italic>p</italic> &lt; 0.001).</p>", "<title>Construction and validation of the DRL prognostic signature</title>", "<p id=\"Par14\">The entire TCGA set was randomly divided into training and testing sets. The training set was used to establish the DRL signature, and the testing set along with the entire TCGA set was employed to validate the reliability of the signature. Subsequently, the R package “glmnet” was enlisted to establish the Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression, incorporating a penalty parameter determined through 10-fold cross-validation and a significance threshold of 0.05. The computation formula for the risk score is expressed as follows: Risk score = Σ [Exp (lncRNA) × coef (lncRNA)]. Herein, Exp (lncRNA) signifies the expression levels of the included lncRNAs, while coef (lncRNA) denotes their respective regression coefficients. Based on the risk scores (with the median risk score used as a cutoff), all the HCC samples were separated into the low- and high-risk groups. The prognosis of patients with HCC was assessed by K-M curves and ROC curves.</p>", "<title>Independent prognostic analysis and establishment of a nomogram</title>", "<p id=\"Par17\">Univariate and multivariate (<italic>p</italic> &lt; 0.05) Cox regression analyses were conducted to confirm whether the prognostic signature can be used as a clinical prognostic predictor independent of other clinicopathological characteristics (age, gender, grade, and stage) in the patients with HCC using the R package “survival.” Additionally, a nomogram was established to predict the survival of patients with HCC via the R package “survival” and “regplot.” The accuracy of nomogram was estimated using the consistency index (C-index) and calibration curves.</p>", "<title>PCA and functional enrichment analysis</title>", "<p id=\"Par20\">Principal component analysis (PCA) was performed using the R package “scatterplot3d” to weaken the dimensionality, identify the model, and visualize the high-dimensional data of the entire gene expression profiles, disulfidptosis-related genes (DRGs), DRLs, and risk model. The differentially expressed genes (DEGs) between the high- and low-risk groups were identified (|log2fold-change (FC)| &gt; 1 and adjusted <italic>p</italic> &lt; 0.05). Gene Ontology (GO) functional analyses, including cellular component (CC), molecular function (MF), biological processes (BP), and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses, were performed on DEGs using the R package “clusterProfiler,” “org.Hs.e.g.db,” and “enrichplot.”</p>", "<title>Immune-related functional analysis and tumor mutation burden (TMB) analysis</title>", "<p id=\"Par21\">The immune infiltration statuses were analyzed via the tools XCELL, TIMER, QUANTISEQ, MCPCOUNTER, EPIC, CIBERSORT-ABS, and CIBERSORT according to the profile of infiltration estimation for all TCGA tumors [##REF##37101543##17##]. The differences in immune-related functions, infiltrating immune cells, and immune checkpoints between the low and high-risk groups were analyzed using the R package “ggpubr,” “reshape2,” and “ggplot2.” Additionally, we utilized the “maftools” package to examine and integrate the TCGA data and analyzed the difference in TMB between high- and low-risk groups.</p>", "<title>TIDE analysis and drug efficacy evaluation for HCC treatment</title>", "<p id=\"Par22\">We utilized the tumor immunity dysfunction and exclusion (TIDE) algorithm to assess the differences in immunotherapy response between the low-risk and high-risk groups (<ext-link ext-link-type=\"uri\" xlink:href=\"http://tide.dfci.harvard.edu/\">http://tide.dfci.harvard.edu/</ext-link>) [##REF##30127393##18##]. Furthermore, the half-maximal inhibitory concentration (IC50) was used to predict the sensitivity of patients with HCC to chemotherapeutic and targeted therapeutic agents. Screening of therapeutic drugs and observation of drug sensitivity using the R packages included “pRRophetic,” “limma,” “ggpubr,” and “ggplot2” with pFilter = 0.0001.</p>", "<title>Tumor samples collection</title>", "<p id=\"Par25\">A total of eight HCC tissue specimens and eight corresponding normal liver samples were obtained from individuals undergoing surgical resection during the period spanning November 2022 to April 2023 at the First Affiliated Hospital of Zhengzhou University, situated in Henan, China. Following the surgical excision of tissue, the samples were promptly subjected to freezing in liquid nitrogen. The study garnered approval from the Ethics Committee of the First Affiliated Hospital of Zhengzhou University, aligning with the principles set forth in the Declaration of Helsinki.</p>", "<title>Cell culture and reverse transcription quantitative PCR (RT-qPCR)</title>", "<p id=\"Par28\">The hepatocellular carcinoma cell lines (HEP3B and HEPG2) and normal liver control cell (NC) were procured from the National Collection of Authenticated Cell Cultures (Shang Hai, China). HEP3B and HEPG2 cells underwent cultivation in RPMI-1640 medium supplemented with 2 mM l-glutamine and 10% Fetal Bovine Serum (FBS) within a humidified incubator set at 37 °C with 5% CO2. Total cellular RNA was extracted using TRIzol reagent (Invitrogen, Carlsbad, CA, United States). Data normalization was achieved through glyceraldehyde-3-phosphate dehydrogenase (GAPDH) mRNA expression, and calculations were executed using the 2^(-ΔΔCT) method. The primer sequences for RT-qPCR analysis are provided in Supplementary Table ##SUPPL##7##S1##.</p>", "<title>Cell transfection</title>", "<p id=\"Par31\">Two siRNAs targeting TMCC1-AS1 (si-TMCC1-AS1) and a negative control (si-NC) were synthesized by GenePharma (Shanghai, China). Transfection of HEP3B and HEPG2 cells was carried out using si-TMCC1-AS1#1, si-TMCC1-AS1#2, and si-NC with lipofectamine® 3000 (Invitrogen, USA). After 24 h, the transfection efficiency was evaluated using RT-qPCR. The sequences of the siRNAs can be found in Supplementary Table ##SUPPL##7##S2##.</p>", "<title>Cell counting kit-8 (CCK-8) assay</title>", "<p id=\"Par32\">The HCC cells were seeded into 96-well plates at a density of 3 × 10³ cells per well. Subsequently, 10 μL of CCK-8 solution (Dojindo, Tokyo, Japan) was added to each well at 0, 24, 48, and 72 h, followed by a 2-hour incubation period. The absorbance of the cells at 450 nm was then measured using a SpectraMax i3x instrument (Molecular Devices, USA). After 72 h, the proliferation curve of the cells was constructed based on the absorbance values.</p>", "<title>Transwell migration and invasion assays</title>", "<p id=\"Par33\">The migratory and invasive capacities of HCC cells were assessed using 24-well Transwell chambers with an 8 μm pore size (Corning, NY, USA). For the migration assay, 3 × 10⁴ HCC cells were placed in the top compartment containing 250 μL of serum-free medium, while the bottom compartment received 500 μL of medium with 10% FBS. After 48 h of culture, cotton swabs were employed to eliminate cells in the upper compartment. The cells traversing the filter were fixed with 95% ethanol, stained with a 0.5% crystal violet solution, and subsequently imaged and counted using a microscope (Olympus, Tokyo, Japan). In the invasion assay, prior to cell inoculation, the filter was coated with a layer of Matrigel (BD Biosciences, San Jose, CA, USA). The remaining procedures were analogous to those of the migration assay.</p>", "<title>Wound healing assay</title>", "<p id=\"Par34\">Wound healing assays were executed following previously delineated protocols [##REF##37891494##19##]. Briefly, cells were seeded in 6-well plates and incubated at 37 °C. With the cells were completely attached, we scraped the middle of the plate to form a wound and replaced the medium with serum-free medium. After 48 h, the coverage of the line was measured.</p>", "<title>Statistical analysis</title>", "<p id=\"Par35\">The R software (version 4.1.3) was used for all statistical analyses and graph visualization. The classification variables in the training and testing sets were contrasted using the chi-square test. Student’s t-test or one-way ANOVA test was utilized to determine the differences between the high- and low-risk groups. The links between clinicopathological factors, risk score, immune check inhibitors, and immune infiltration levels were assessed using the Pearson correlation test. <italic>P</italic> &lt; 0.05 was considered statistically significant.</p>" ]

[ "<title>Results</title>", "<title>Identification of DRLs in HCC patients</title>", "<p id=\"Par38\">A comprehensive flow diagram is depicted in Fig. ##FIG##0##1##. Initially, we gathered a total of 16,876 lncRNAs from the TCGA database’s HCC project and acquired 10 DRGs from previously published studies. Next, 945 DRLs were found by performing Pearson correlation analysis (|Pearson R| &gt; 0.4 and <italic>p</italic> &lt; 0.001) between lncRNAs and DRGs. Following the criteria of |log2 fold change (FC)| &gt; 1 and <italic>p</italic> &lt; 0.05, we obtained 750 differentially expressed DRLs. A heatmap was established to visualize the differential expression of DRLs between normal and tumor samples (Fig. ##SUPPL##0##S1##A).</p>", "<p id=\"Par39\">\n\n</p>", "<title>Construction and validation of the DRLs prognostic signature</title>", "<p id=\"Par42\">Upon univariate analysis, we identified 11 DRLs from 750 differentially expressed DRLs that exhibited correlations with OS. The forest plot (Fig. ##FIG##1##2##A), heatmap (Fig. ##FIG##1##2##B), and Sankey diagram (Fig. ##SUPPL##0##S1##B) illustrated that all 11 DRLs were upregulated and considered poor prognostic factors for patients with HCC (<italic>p</italic> &lt; 0.001, hazard ratio, HR &gt; 1). In the subsequent Lasso regression analysis aiming at reducing the risk of overfitting (Fig. ##FIG##1##2##C and D), 9 DRLs were found to be associated with OS. Further multivariate Cox regression narrowed this count to 3 DRLs (POLH-AS1, TMCC1-AS1, AC124798.1), which were used to construct the OS prognostic signature. The risk score for each HCC patient was calculated using the following formula: Risk score = (0.413458729998944 × POLH − AS1 expression) + (0.818274047598138 × TMCC1 − AS1 expression) + (0.248268992114983 × AC124798.1 expression. The correlation heatmap depicted the relationship between DRGs and the three selected DRLs (Fig. ##SUPPL##0##S1##C).</p>", "<p id=\"Par43\">\n\n</p>", "<p id=\"Par44\">Patients were stratified into low- and high-risk groups based on the median value of risk scores. As depicted in Fig. ##SUPPL##1##S2##A-C, the low-risk group exhibited significantly extended survival times compared to the high-risk group across the training set, testing set, and the entire set (<italic>P</italic> &lt; 0.01). Furthermore, the distribution plot of risk score and survival status revealed a positive correlation: higher risk scores corresponded to a higher number of deaths in HCC patients (Fig. ##SUPPL##1##S2##D-I). The heatmap highlighted elevated expression levels of three DRLs in the high-risk group relative to the low-risk group (Fig. ##SUPPL##1##S2##J-L). Overall, these findings indicated that patients in the high-risk group experienced worse prognoses.</p>", "<title>Independent prognostic analysis and establishment of a nomogram</title>", "<p id=\"Par47\">To assess the independent prognostic utility of the DRLs signature, we conducted both univariate and multivariate Cox regression analyses. As shown in Fig. ##FIG##2##3##A, univariate Cox regression analysis demonstrated that the prognostic signature of the three DRLs could predict OS outcomes in HCC patients (HR = 1.324; 95% CI, 1.211–1.448; <italic>p</italic> = 0.001). Multivariate Cox regression analysis further affirmed that the prognostic signature of the three DRLs remained an independent prognostic factor for HCC (HR = 1.277, 95% CI, 1.155–1.412, <italic>p</italic> &lt; 0.001) after adjusting for gender, age, grade, and stage (Fig. ##FIG##2##3##B).</p>", "<p id=\"Par48\">\n\n</p>", "<p id=\"Par49\">Subsequently, a nomogram was established employing these independent prognostic factors (stage and risk score) to predict 1-, 3-, and 5-year survival rates for HCC patients (Fig. ##FIG##2##3##C). Calibration curves were developed to validate the nomogram’s effectiveness in predicting survival rates at 1, 3, and 5 years, demonstrating optimal agreement between nomogram predictions and actual survival outcomes (Fig. ##FIG##2##3##D).</p>", "<title>Correlation analysis between DRLs signature and clinical characteristics</title>", "<p id=\"Par50\">To investigate the correlation between the prognostic signature of DRLs and the clinical characteristics of patients with HCC, we examined the relationship between the survival probability and the risk score in different subgroups based on age, grade, and stage. As shown in Fig. ##FIG##3##4##, the results revealed that patients in the low-risk group had a much higher OS rate than patients in the high-risk group. Furthermore, the concordance index (C-index) of the risk score surpassed that of clinical characteristics, including age, gender, grade, and stage (Fig. ##SUPPL##2##S3##A).</p>", "<p id=\"Par51\">\n\n</p>", "<p id=\"Par52\">Additionally, the high-risk group showed a significantly shorter progression-free survival compared to the low-risk group (Fig. ##SUPPL##2##S3##B). Moreover, the AUC value for the risk grade was 0.754, markedly outperforming the predictive accuracy of individual clinical characteristics, such as age (0.531), gender (0.509), grade (0.499), and stage (0.671) (Fig. ##SUPPL##2##S3##C). The AUC of the novel DRL signature for 1-, 3-, and 5-year survival rates were 0.754, 0.699, and 0.671, respectively (Fig. ##SUPPL##2##S3##D). Overall, these findings affirm the reliability of the prognostic signature based on the three DRLs for patients with HCC.</p>", "<title>PCA and functional enrichment analysis</title>", "<p id=\"Par55\">To discern differences between the low- and high-risk groups, we conducted PCA using four expression profiles (entire gene expression profiles, DRGs, DRLs, and the three DRLs risk signature). The results illustrated that the three DRLs exhibited robust discriminatory ability, effectively distinguishing between the low- and high-risk groups (Fig. ##SUPPL##3##S4##A-D).</p>", "<p id=\"Par56\">Then, we identified 2397 DEGs between the low- and high-risk groups in the TCGA set, comprising 2300 upregulated genes and 97 downregulated genes (|log2 fold change (FC)| &gt; 1 and <italic>p</italic> &lt; 0.05) (Fig. ##SUPPL##3##S4##E). Functional enrichment analysis was performed to unravel the biological functions of these DEGs. GO analysis revealed significant enrichment in processes such as organelle fission, chromosomal region, and tubulin binding (Fig. ##FIG##4##5##A). KEGG analysis unveiled enrichment in pathways associated with carcinogenesis, including the PI3K-Akt signaling pathway, cytokine − cytokine receptor interaction, and the cell cycle (Fig. ##FIG##4##5##B). These results strongly suggest the involvement of DRLs in the development and progression of HCC.</p>", "<p id=\"Par57\">\n\n</p>", "<title>Evaluation of the immune microenvironment using the DRLs signature</title>", "<p id=\"Par60\">Immune infiltration stands as a pivotal determinant in countering HCC progression, wielding significant influence over the survival rates of afflicted patients [##REF##35480307##3##, ##REF##34603293##15##]. The heatmap depicting immune responses unveiled substantial correlations between DRLs-scores and various immune cells, encompassing B cells, T cells CD4+, macrophages, and NK cells (Fig. ##FIG##5##6##A). Employing the ssGSEA method, we delved into the association between DRLs-scores and immune cell subpopulations, unraveling distinct patterns of immune cell infiltrations in the high-risk group characterized by elevated abundance of activated dendritic cells (aDCs), immature dendritic cells (iDCs), and regulatory T cells (Tregs), juxtaposed with diminished levels of B cells, neutrophils, and NK cells (Fig. ##FIG##5##6##B). Functional disparities in immune cell subpopulations, including cytolytic activity, major histocompatibility complex (MHC) class I, type I interferon (IFN) response, and type II IFN response, were pronounced between the high- and low-risk groups (Fig. ##FIG##5##6##C). Moreover, immune checkpoint analysis unveiled heightened activation of numerous checkpoints in the high-risk group (Fig. ##FIG##5##6##D). Collectively, these findings underscored the predictive capability of the DRLs signature regarding the immune microenvironment in HCC patients, holding potential utility in steering individualized immunotherapeutic strategies.</p>", "<p id=\"Par61\">\n\n</p>", "<title>TMB, TIDE and drug susceptibility analysis</title>", "<p id=\"Par64\">Accumulating evidence suggests a linkage between TMB status and the clinical responsiveness to immunotherapy in HCC [##REF##33115468##13##, ##REF##34603786##20##]. Notably, our findings demonstrated a heightened frequency of mutations in the high-risk group compared to the low-risk group, particularly among the top 15 genes exhibiting the highest mutation rates (Fig. ##FIG##6##7##A-B). Subsequent categorization of patients into high and low TMB groups based on TMB scores unveiled a superior survival rate in the low TMB group (Fig. ##FIG##6##7##C). An assessment of the synergistic impact of TMB and DRLs-score groups in prognostic stratification revealed that the high-TMB and high-risk subgroup exhibited the poorest prognosis, while the low-TMB and low-risk subgroup displayed a more favorable prognosis. Importantly, even in instances of high or low TMB, the high-risk subgroup consistently manifested a worse prognosis compared to the low-risk counterpart (Fig. ##FIG##6##7##D).</p>", "<p id=\"Par65\">\n\n</p>", "<p id=\"Par66\">Moreover, TIDE analysis was conducted to scrutinize the sensitivity to immunotherapy among HCC patients. Intriguingly, the low-risk group exhibited a higher TIDE score, indicative of a more favorable response to immunotherapy (Fig. ##FIG##6##7##E). Subsequently, drug susceptibility analysis aimed to discern potential therapeutic agents for HCC treatment based on the IC50 of each drug. The outcomes underscored that patient in the low-score group demonstrated lower IC50 values for anti-cancer drugs such as sorafenib, 5-Fluorouracil, and doxorubicin (Fig. ##FIG##6##7##F-H). This implies that individuals in the low-risk group might harbor a heightened sensitivity to these three drugs. Collectively, these results advocate for the utility of the DRLs signature as a promising predictor for treatment efficacy in the context of HCC.</p>", "<title>Identifying TMCC1-AS1 as a diagnostic and prognostic biomarker for HCC</title>", "<p id=\"Par69\">In our pursuit of a prognostic biomarker pertinent to DRLs for HCC patients, we initially scrutinized the expression levels of three DRLs (POLH-AS1, TMCC1-AS1, and AC124798.1) in HCC tissues sourced from the TCGA dataset. The findings illuminated a pronounced upregulation of these three DRLs in HCC tissues relative to normal tissues (Fig. ##SUPPL##4##S5##A-C). Furthermore, diminished expression levels of POLH-AS1, TMCC1-AS1, and AC124798.1 exhibited a significant association with extended overall survival (Fig. ##SUPPL##4##S5##D-F). Then, our exploration delved into the assessment of the Area Under the Curve (AUC) values for the three DRLs, revealing that TMCC1-AS1 displayed commendable discriminatory prowess for diagnosing patients with HCC (Fig. ##SUPPL##4##S5##G-I). This underscores the potential of TMCC1-AS1 as a valuable prognostic and diagnostic biomarker for individuals afflicted with HCC.</p>", "<title>Knockdown of TMCC1-AS1 prevented cell proliferation, migration, and invasion in HCC</title>", "<p id=\"Par72\">To further substantiate the functional role of TMCC1-AS1 in HCC, we initially examined its expression levels in both HCC tissues and cell lines (Fig. ##FIG##7##8##A-B). Notably, TMCC1-AS1 exhibited heightened expression in both HCC tissues and cell lines, namely HEP3B and HEPG2. Subsequent to confirming the elevated expression, we sought to elucidate the impact of TMCC1-AS1 on HCC cell proliferation. Employing siRNA-mediated knockdown of TMCC1-AS1 in HEP3B and HEPG2 cells, we achieved effective silencing, as evidenced by RT-qPCR results (Fig. ##FIG##7##8##C-D). The growth curves further underscored that the depletion of TMCC1-AS1 significantly impeded the growth of HCC cells, implicating its role in promoting cell proliferation (Fig. ##FIG##7##8##E-F). Simultaneously, we also investigated the biological functions of POLH-AS1 and AC124798.1, and the ultimate results were consistent with the functions of TMCC1-AS1 described earlier (Fig. ##SUPPL##5##S6##-7).</p>", "<p id=\"Par73\">\n\n</p>", "<p id=\"Par74\">Moving beyond proliferation, our investigations extended to migration and invasion capabilities. The Transwell assay unveiled that the inhibition of TMCC1-AS1 markedly curtailed cell migration and invasion in both HEP3B and HEPG2 cells (Fig. ##FIG##8##9##A-D). Furthermore, the wound healing assay demonstrated that the depletion of TMCC1-AS1 hampered the speed of wound closure in both cell lines (Fig. ##FIG##8##9##E-H). In summation, these findings strongly suggest that TMCC1-AS1 plays a pivotal role in fueling hepatocellular carcinoma cell proliferation, migration, and invasion in vitro, establishing TMCC1-AS1 as a promising target for therapeutic intervention in HCC.</p>", "<p id=\"Par75\">\n\n</p>" ]

[ "<title>Discussion</title>", "<p id=\"Par76\">As the predominant form of primary liver cancer, hepatocellular carcinoma (HCC) significantly jeopardizes the well-being and survival of afflicted individuals due to its elevated morbidity and mortality rates [##REF##35459272##6##]. Recent years have witnessed substantial progress in HCC treatment with the advent of targeted agents like sorafenib and immune checkpoint inhibitors (ICIs) [##REF##35293027##21##]. Nevertheless, the inherent heterogeneity of HCC results in variable treatment outcomes, with only a subset of patients deriving benefit from ICIs and other targeted drugs [##REF##34093992##22##]. Therefore, the identification of innovative biomarkers for prognostication and predicting therapeutic responses holds paramount clinical significance for those grappling with HCC.</p>", "<p id=\"Par77\">Disulfidoptosis has recently garnered extensive attention in tumorigenesis and cancer therapies [##UREF##0##23##]. It has been proposed that disulfidoptosis-related biomarkers serve as robust prognostic indicators and predictors of antitumor efficacy in various cancers [##UREF##1##24##]. Additionally, several studies have highlighted the pivotal role of long non-coding RNAs (lncRNAs) in the transport and metabolism of disulfide during tumorigenesis and subsequent tumor progression [##REF##37101543##17##, ##REF##26160837##25##, ##REF##34482117##26##]. Nonetheless, the precise involvement of disulfidoptosis-related lncRNAs (DRLs) in HCC remains elusive, necessitating a comprehensive evaluation of their prognostic significance.</p>", "<p id=\"Par78\">In the current study, we identified 11 prognostically significant DRLs from the TCGA dataset, three of which were selected to construct the prognostic DRLs signature. Regardless of training or testing sets, the DRL signature demonstrated robust efficacy in predicting survival outcomes for HCC patients. Subsequently, we examined the relationship between survival probability and risk score across various clinical characteristics. The results revealed a significantly higher overall survival rate in the low-risk group, irrespective of gender, age, grade, or stage, substantiating the validity of the prognostic DRL signature. Furthermore, we delved into tumorigenesis pathways, the immune microenvironment of HCC, and potential drugs for HCC treatment based on the prognostic signature. Lastly, our investigation unveiled that the inhibition of TMCC1-AS1 suppressed the proliferation, migration, and invasion of hepatocellular carcinoma cells. This study provides valuable insights into the molecular mechanisms underpinning HCC progression and offers potential avenues for personalized therapeutic strategies.</p>", "<p id=\"Par79\">Immunotherapy, an advancing and effective anti-tumor treatment, strengthens the therapeutic effect by regulating the tumor immune microenvironment (TIME) [##REF##35459272##6##]. Presently, TIME is acknowledged for its profound intricacy [##REF##32587825##14##, ##REF##35295858##27##]. Numerous studies have certified that TIME is involved in the process of tumor metastasis, immune escape, and immunotherapy resistance by altering the immune response [##REF##35295858##27##, ##UREF##2##28##]. In our study, DEGs between different risk groups were enriched in some immune-related biological processes and pathways. Our results unveiled that many immune cells (including B cells, neutrophils, and NK cells) and many functions of immune cell subpopulations (such as cytolytic activity, MHC class I, type I IFN response, and type II IFN response) were significantly different between high- and low-risk groups. Additionally, immune checkpoint-related genes exhibited higher expression levels in the high-risk group compared to the low-risk group. This provides a foundation for discerning responsive patients for immunotherapy. In brief, these results indicated that DRLs signature could reflect the TIME of HCC, which may contribute to personalized immunotherapy and targeted therapy for patients with HCC.</p>", "<p id=\"Par80\">TMB is currently recognized as a valuable biomarker across various cancers, believed to be linked with the efficacy of immunotherapy for HCC [##REF##35295858##27##, ##REF##33425905##29##, ##REF##35479956##30##]. We observed that the proportion of gene mutations differed significantly between the two groups and that the high-risk group had higher frequency of mutations than the low-risk group in the top 15 genes with the highest mutation rates. Specifically, it was found that patients in the high-risk group had a significantly higher frequency of TP53 mutation (35% vs. 17%). TP53 is a typical tumor suppressor, and its mutation leads to the development and progression of many types of tumors, including HCC [##REF##33425905##29##, ##REF##24665023##31##]. This is consistent with our results where the low TMB group had a higher survival rate than the high TMB group.</p>", "<p id=\"Par81\">Recent studies have elucidated that epigenetics, transport processes, regulated cell death, and the tumor microenvironment are involved in the development of drug resistance in HCC [##REF##25902734##32##, ##REF##32532960##33##]. To enhance the treatment of patients with HCC, we evaluated the drug sensitivity of different anticancer drugs in the treatment of patients with HCC in different DRL-score groups. Based on IC50 values, the drugs of sorafenib, 5-Fluorouracil, and doxorubicin showed better responses in the low-score group than in the high-score group. These findings indicated that DRLs signature could be used as a potential predictor for the efficacy of medical treatment of HCC. Moreover, the occurrence of drug resistance may be reduced by regulating the DRLs; this brings new breakthroughs for the choice of individual therapeutic strategies.</p>", "<p id=\"Par82\">The study outcomes revealed 11 DRLs influencing the survival of HCC patients, with POLH-AS1, TMCC1-AS1, and AC124798.1 selected to compose the prognostic signature. Among them, the expression of POLH-AS1 was confirmed to be upregulated in HCC tissues based on RT-qPCR [##REF##35295858##27##]. Fang et al. investigated a novel risk model with POLH-AS1 for predicting the prognosis of HCC [##REF##35459272##6##]. In addition, Cui et al. identified TMCC1-AS1 as a valuable resource for novel biomarker and therapeutic target identification in HCC [##REF##29047230##34##]. Furthermore, Zhu et al. constructed a prognostic signature with AC124798.1 to predict the prognosis of pancreatic adenocarcinoma [##REF##35433487##35##]. However, few studies have investigated whether these three DRLs contribute to the progression of HCC.</p>", "<p id=\"Par83\">To substantiate the prognostic potential of the identified DRLs, we conducted further investigations using the TCGA dataset. Our findings indicated elevated expression of these DRLs in HCC tissues, correlating with poorer survival outcomes. Notably, TMCC1-AS1 exhibited a higher AUC compared to POLH-AS1 and AC124798.1, suggesting its potential as a more promising biomarker for HCC diagnosis and prognosis. Subsequently, we elucidated the biological roles of TMCC1-AS1 in HCC, revealing significantly lower expression in NC compared to HEP3B and HEPG2 cells. Inhibition of TMCC1-AS1 effectively impeded HCC cell growth, migration, and invasion. These results align with Zhao et al., who observed TMCC1-AS1 as a prognostic biomarker for HCC patients [##REF##30122881##36##]. In summary, TMCC1-AS1 appears to play a role in promoting HCC cell growth and migration in vitro, suggesting its potential as a therapeutic target.</p>", "<p id=\"Par84\">Nevertheless, the study has inevitable limitations. Firstly, the sample data solely originated from TCGA databases, lacking clinical information from external cohorts. Secondly, the absence of comprehensive clinical follow-up data hinders thorough validation and assessment of the prognostic model’s clinical value. Finally, the precise mechanisms through which TMCC1-AS1 influences HCC growth, invasion, and migration remain incompletely understood, necessitating further comprehensive experimental investigations.</p>" ]

[ "<title>Conclusions</title>", "<p id=\"Par85\">Conclusively, the DRLs signature demonstrated promising prognostic value, offering insights into the immune microenvironment and potential therapeutic avenues for HCC. Particularly, TMCC1-AS1 showed potential as a novel prognostic biomarker and therapeutic target for HCC.</p>" ]

[ "<title>Background</title>", "<p id=\"Par1\">Hepatocellular carcinoma (HCC) stands as a prevalent malignancy globally, characterized by significant morbidity and mortality. Despite continuous advancements in the treatment of HCC, the prognosis of patients with this cancer remains unsatisfactory. This study aims at constructing a disulfidoptosis‑related long noncoding RNA (lncRNA) signature to probe the prognosis and personalized treatment of patients with HCC.</p>", "<title>Methods</title>", "<p id=\"Par2\">The data of patients with HCC were extracted from The Cancer Genome Atlas (TCGA) databases. Univariate, multivariate, and least absolute selection operator Cox regression analyses were performed to build a disulfidptosis-related lncRNAs (DRLs) signature. Kaplan–Meier plots were used to evaluate the prognosis of the patients with HCC. Functional enrichment analysis was used to identify key DRLs-associated signaling pathways. Spearman’s rank correlation was used to elucidate the association between the DRLs signature and immune microenvironment. The function of TMCC1-AS1 in HCC was validated in two HCC cell lines (HEP3B and HEPG2).</p>", "<title>Results</title>", "<p id=\"Par3\">We identified 11 prognostic DRLs from the TCGA dataset, three of which were selected to construct the prognostic signature of DRLs. We found that the survival time of low-risk patients was considerably longer than that of high-risk patients. We further observed that the composition and the function of immune cell subpopulations were significantly different between high- and low-risk groups. Additionally, we identified that sorafenib, 5-Fluorouracil, and doxorubicin displayed better responses in the low-score group than those in the high-score group, based on IC50 values. Finally, we confirmed that inhibition of TMCC1-AS1 impeded the proliferation, migration, and invasion of hepatocellular carcinoma cells.</p>", "<title>Conclusions</title>", "<p id=\"Par4\">The DRL signatures have been shown to be a reliable prognostic and treatment response indicator in HCC patients. TMCC1-AS1 showed potential as a novel prognostic biomarker and therapeutic target for HCC.</p>", "<title>Supplementary Information</title>", "<p>The online version contains supplementary material available at 10.1186/s12935-023-03208-x.</p>", "<title>Keywords</title>" ]

[ "<title>Electronic supplementary material</title>", "<p>Below is the link to the electronic supplementary material.</p>", "<p>\n\n</p>", "<p>\n\n</p>", "<p>\n\n</p>", "<p>\n\n</p>", "<p>\n\n</p>", "<p>\n\n</p>", "<p>\n\n</p>", "<p>\n\n</p>" ]

[ "<title>Acknowledgements</title>", "<p>Not applicable.</p>", "<title>Author contributions</title>", "<p>LXX, SC, QQL and DC designed the project, analyzed the data and drafted manuscript. XYC, YX and YJZ downloaded and collated the data. JL, ZXG and JYX analyzed the data. All authors reviewed the manuscript.</p>", "<title>Funding</title>", "<p>This work was supported by the Henan Medical Science and Technology Joint Building Program (no. LHGJ20190255, LHGJ20190262, LHGJ20230239).</p>", "<title>Data availability</title>", "<p>Data from this study can be found in the TCGA databases (<ext-link ext-link-type=\"uri\" xlink:href=\"http://cancergenome.nih.gov\">http://cancergenome.nih.gov</ext-link>).</p>", "<title>Declarations</title>", "<title>Ethics approval and consent to participate</title>", "<p id=\"Par87\">The First Affiliated Hospital of Zhengzhou University’s Ethics Committee approved this study in accordance with the Declaration of Helsinki.</p>", "<title>Consent for publication</title>", "<p id=\"Par88\">Not applicable.</p>", "<title>Competing interests</title>", "<p id=\"Par86\">The authors declare no competing interests.</p>" ]

[ "<fig id=\"Fig1\"><label>Fig. 1</label><caption><p>The flow diagram of the research process</p></caption></fig>", "<fig id=\"Fig2\"><label>Fig. 2</label><caption><p>Construction and validation of the prognostic signature of DRLs. (<bold>A</bold>) Forest plot of univariate analysis results showing 11 OS-related DRLs. (<bold>B</bold>) Heatmap showing the expression of 11 OS-related DRLs in the normal and tumor samples. (<bold>C</bold>) Cross-validation plot for the penalty term. (<bold>D</bold>) Diagram for LASSO expression coefficients. ^**<italic>p</italic> &lt; 0.01, ^***<italic>p</italic> &lt; 0.001</p></caption></fig>", "<fig id=\"Fig3\"><label>Fig. 3</label><caption><p>Independent prognostic analysis and establishment of a nomogram. (<bold>A</bold>) Univariate Cox regression analysis of the clinical characteristics and riskScore with the OS. (<bold>B</bold>) Multivariate analysis of the clinical characteristics and riskScore with the OS. (<bold>C</bold>) A nomogram predicting the 1-, 3- and 5-years survival rates of HCC using stage and independent prognostic factors (stage and risk score). (<bold>D</bold>) The calibration curves showing the concordance between the prediction by nomogram and actual survival</p></caption></fig>", "<fig id=\"Fig4\"><label>Fig. 4</label><caption><p>Relationship between the prognostic signature of DRLs and clinical characteristics. (<bold>A</bold>)–(<bold>F</bold>) Kaplan–Meier curve for overall survival in different clinical features such as age (<bold>A</bold>, <bold>B</bold>), grade (<bold>C</bold>, <bold>D</bold>), and stage (<bold>E</bold>, <bold>F</bold>)</p></caption></fig>", "<fig id=\"Fig5\"><label>Fig. 5</label><caption><p>Functional enrichment analyses. (<bold>A</bold>) GO functional enrichment analysis with bubble plot (BP, biological process; CC, cellular component; MF, molecular function). (<bold>B</bold>) KEGG pathway enrichment analysis with bubble plot</p></caption></fig>", "<fig id=\"Fig6\"><label>Fig. 6</label><caption><p>Infiltrations and functions of immune cells between high- and low-risk groups. (<bold>A</bold>) Heatmap for immune infiltration based on TIMER, CIBERSORT, quanTIseq, MCP-counter, xCELL and EPIC algorithms among high- and low-risk groups. (<bold>B</bold>) Single sample gene set enrichment analysis (ssGSEA) showing different extent of immune cell infiltrations in the high- and low-risk groups. (<bold>C</bold>) ssGSEA analyses showing different functions of immune cell in the high- and low-risk groups. (<bold>D</bold>) The expression of immune checkpoint genes between high- and low-risk groups. ^*<italic>p</italic> &lt; 0.05, ^**<italic>p</italic> &lt; 0.01, ^***<italic>p</italic> &lt; 0.001</p></caption></fig>", "<fig id=\"Fig7\"><label>Fig. 7</label><caption><p>TMB analyses and drug sensitivity between high- and low-risk groups. (<bold>A</bold>-<bold>B</bold>) Waterfall plot displaying the mutation information of the genes with high mutation frequencies in the high- (<bold>A</bold>) and low- (<bold>B</bold>) risk groups. (<bold>C</bold>) Kaplan–Meier curve for OS of patients with HCC in high and low TMB (<italic>p</italic> = 0.031). (<bold>D</bold>) Kaplan–Meier curve for OS of patients with HCC according to the TMB and the risk signature of DRLs. (<bold>E</bold>) The TIDE scores of high- and low-risk groups. (<bold>F</bold>-<bold>H</bold>) The correlation between the risk score of DRLs signature and sensitivity of drugs such as sorafenib (<bold>F</bold>), 5-Fluorouracil (<bold>G</bold>), and doxorubicin (<bold>H</bold>). ^***<italic>p</italic> &lt; 0.001</p></caption></fig>", "<fig id=\"Fig8\"><label>Fig. 8</label><caption><p>Knockdown of TMCC1-AS1 inhibited cell proliferation in HCC. (<bold>A</bold>) The expression of TMCC1-AS1 was assessed in 8 HCC tissues and 8 normal liver tissues by RT-qPCR assay. (<bold>B</bold>) RT-qPCR analysis showing the expression of TMCC1-AS1 in two HCC cell lines (HEP3B and HEPG2) and a normal liver cell (NC). (<bold>C</bold>-<bold>D</bold>) The efficiency of si-TMCC1-AS1 transfection in HEP3B (<bold>C</bold>) and HEPG2 (<bold>D</bold>) cells was assessed by RT-qPCR. (<bold>E</bold>-<bold>F</bold>) Cell proliferation of HEP3B (<bold>E</bold>) and HEPG2 (<bold>F</bold>) cells transfected with control (si-NC) or si-TMCC1-AS1 was measured via CCK8 assay. Data are presented as the mean ± SDs. ^***<italic>p</italic> &lt; 0.001</p></caption></fig>", "<fig id=\"Fig9\"><label>Fig. 9</label><caption><p>Inhibition of TMCC1-AS1 prevented cell migration and invasion in HCC. (<bold>A</bold>-<bold>D</bold>) Representative data from Transwell migration and invasion assays showing the migratory and invasive capacities of TMCC1-AS1-deficient HEP3B (<bold>A</bold>, <bold>B</bold>) and HEPG2 (<bold>C</bold>, <bold>D</bold>) cells. Scales bar, 100 μM. The data are the means ± SDs and are representative of three independent experiments. (<bold>E</bold>-<bold>H</bold>) Representative data from wound healing migration assays showing HEP3B (<bold>E</bold>, <bold>F</bold>) and HEPG2 (<bold>G</bold>, <bold>H</bold>) cell migration of control cells compared to TMCC1-AS1-depleted cells. Scales bar, 100 μM. Data are presented as the mean ± SDs. The data are the means ± SDs and are representative of three independent experiments. ^***<italic>p</italic> &lt; 0.001</p></caption></fig>" ]

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[ "<supplementary-material content-type=\"local-data\" id=\"MOESM1\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM2\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM3\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM4\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM5\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM6\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM7\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM8\"></supplementary-material>" ]

[ "<fn-group><fn><p><bold>Publisher’s Note</bold></p><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn><fn><p>Lixia Xu, Shu Chen and Qiaoqiao Li contributed equally to this article.</p></fn></fn-group>" ]

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[ "<media xlink:href=\"12935_2023_3208_MOESM1_ESM.png\"><caption><p><bold>Supplementary Material 1:</bold> Supplementary Figure S1</p></caption></media>", "<media xlink:href=\"12935_2023_3208_MOESM2_ESM.png\"><caption><p><bold>Supplementary Material 2:</bold> Supplementary Figure S2</p></caption></media>", "<media xlink:href=\"12935_2023_3208_MOESM3_ESM.png\"><caption><p><bold>Supplementary Material 3:</bold> Supplementary Figure S3</p></caption></media>", "<media xlink:href=\"12935_2023_3208_MOESM4_ESM.png\"><caption><p><bold>Supplementary Material 4:</bold> Supplementary Figure S4</p></caption></media>", "<media xlink:href=\"12935_2023_3208_MOESM5_ESM.png\"><caption><p><bold>Supplementary Material 5:</bold> Supplementary Figure S5</p></caption></media>", "<media xlink:href=\"12935_2023_3208_MOESM6_ESM.png\"><caption><p><bold>Supplementary Material 6:</bold> Supplementary Figure S6</p></caption></media>", "<media xlink:href=\"12935_2023_3208_MOESM7_ESM.png\"><caption><p><bold>Supplementary Material 7:</bold> Supplementary Figure S7</p></caption></media>", "<media xlink:href=\"12935_2023_3208_MOESM8_ESM.docx\"><caption><p><bold>Supplementary Material 8:</bold> Supplementary Figure legends and Supplementary Table S1-S2</p></caption></media>" ]

{ "acronym": [ "AUC", "DEGs", "DRGs", "DRLs", "GO", "GSEA", "KEGG", "LASSO", "OS", "PCA", "ROC", "TCGA", "TIME", "TMB" ], "definition": [ "Area under the receiver operating characteristic", "Differentially expressed genes", "Disulfidptosis-related genes", "Disulfidptosis-related lncRNAs", "Gene Ontology", "Gene set enrichment analysis", "Kyoto Encyclopedia of Genes and Genomes", "Least absolute selection operator", "Overall survival", "Principal component analysis", "Receiver operating characteristic", "The Cancer Genome Atlas", "Tumor immune microenvironment", "Tumor mutation burden" ] }

[ "<title>Introduction</title>", "<p id=\"Par6\">Ovarian cancer (OC) is the seventh most common cancer in women and the eighth leading cause of cancer-related death worldwide [##REF##27743768##1##]. At the time of initial diagnosis, over 70% of patients present with advanced disease due to the presence of atypical early symptoms [##REF##34247630##2##]. Currently, for patients with a new diagnosis, the standard first-liner treatment involves cytoreductive surgery combined with platinum-based systematic chemotherapy, with or without the addition of bevacizumab. However, at first relapse, approximately 25% of patients develop platinum-resistant ovarian cancer (PROC), and nearly all patients will experience relapse and eventually develop platinum-resistant [##REF##24767708##3##]. PROC is associated with a poor prognosis and an overall survival (OS) of less than 12 months, presenting a significant therapeutic challenge [##REF##30285216##4##]. In the platinum-resistant setting, monotherapy with docetaxel, paclitaxel, topotecan or pegylated liposomal doxorubicin (PLD) remains the primary therapeutic option, but it results in a remarkably short survival, highlighting the urgent need for better treatment options. Furthermore, several trials have demonstrated that combining chemotherapy agents leads to increased adverse events without improving clinical benefit for PROC [##UREF##0##5##–##REF##21562072##7##].</p>", "<p id=\"Par7\">Tumor angiogenesis has been established as a hallmark of tumor development, growth, and metastasis. This complex process involves multiple signaling pathways. Vascular endothelial growth factor (VEGF), an important driver of angiogenesis in solid tumors, binds to VEGF receptor-1 or -2 (VEGFR-1/VEGFR-2) on target cells [##REF##16355214##8##], thereby activating intracellular tyrosine kinase signaling. VEGF promotes the recruitment of circulating endothelial progenitor cells from the bone marrow and facilitates endothelial cell survival, differentiation, and proliferation during angiogenesis. Angiogenesis also plays a crucial role in the pathogenesis of OC by promoting tumor proliferation and metastasis [##REF##11141340##9##, ##REF##16211363##10##]. The presence of extensive neovascularization is closely associated with a poor prognosis in OC. Anti-VEGF therapy has emerged as a promising therapeutic target with potential clinical benefits for patients with OC, including those with platinum-resistant disease [##REF##22529265##11##–##REF##28238563##14##]. Recently, various anti-VEGF therapies, such as anti-VEGF monoclonal antibodies (e.g., bevacizumab) and VEGF-R tyrosine kinase inhibitors (e.g., sorafenib, pazopanib, apatinib, cediranib, anlotinib), have been evaluated in OC patients [##REF##27141068##15##].</p>", "<p id=\"Par8\">The AURELIA trial, a randomized phase III trial, demonstrated a significant improvement in progression-free survival (PFS) in PROC patients when treated with a combination of bevacizumab and chemotherapy compared to monochemotherapy (hazard ratio (HR) = 0.48; 95% CI: 0.38–0.60). The median PFS was 6.7 months with the combined regimen versus 3.4 months with monochemotherapy. The objective response rate (ORR) also increased by 15.5% compared to chemotherapy alone. However, there was no statistically significant improvement in OS when bevacizumab was combined with chemotherapy (HR = 0.85; 95% CI: 0.66–1.08, <italic>p</italic> &lt; 0.17) [##REF##24637997##16##]. Bevacizumab has been approved by the Food and Drug Administration (FDA) for PROC. Other anti-VEGF agents, such as apatinib, have also shown preliminary evidence of efficacy when combined with chemotherapy for PROC. Wang et al. reported that treatment with apatinib plus PLD resulted in a clinically meaningful improvement in PFS (HR = 0.44; 95% CI: 0.28–0.71, <italic>p</italic> &lt; 0.001). The median PFS was 5.8 months for apatinib plus PLD versus 3.3 months for PLD alone. The median OS was 23.0 months versus 14.4 months for apatinib plus PLD and PLD alone, respectively (HR = 0.66; 95% CI: 0.40–1.09) [##REF##35771546##17##].</p>", "<p id=\"Par9\">Previous meta-analyses have demonstrated that combination therapy offers improved survival benefits compared to chemotherapy alone in ovarian cancer patients [##UREF##1##18##–##REF##28255243##21##]. However, there is a lack of specific meta-analysis focusing on platinum-resistant patients. Given the clinical uncertainty and inconsistent efficacy related to VEGF/VEGFR inhibitors in PROC, a systematic review and meta-analysis was conducted to overcome the limitations of individual studies and provide a more accurate estimation of the efficacy and safety of VEGF/VEGFR inhibitors in PROC.</p>" ]

[ "<title>Materials and methods</title>", "<p id=\"Par10\">This systematic review and meta-analysis adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The study was registered with the International Prospective Register of Systematic Reviews (PROSPERO CRD42023402050).</p>", "<title>Data source and search strategy</title>", "<p id=\"Par11\">Eligible studies were identified by searching databases including Cochrane Library, PubMed, Embase, and Web of Science. The search covered the period from inception to December 2022. The main search terms associated with therapy included (anti-angiogenic OR targeted therapy OR molecular targeted therapy OR bevacizumab OR nintedanib OR pazopanib OR cediranib OR sorafenib OR apatinib OR anlotinib OR lenvatinib OR ramolumab OR VEGF OR VEGFR OR vascular endothelial growth factor). The terms related to the disease included ovarian cancer OR ovarian neoplasm. Subsequently, the reference lists of all relevant articles were also browsed.</p>", "<title>Study selection</title>", "<p id=\"Par12\">The following criteria were used to screen potential trials: (1) prospective phase II and phase III randomized controlled trials (RCTs); (2) patients with OC, peritoneal cancer (PC), or fallopian tube cancers (FTC) that had progressed during platinum therapy (platinum-refractory) or within 6 months of platinum-containing therapy (platinum-resistant); (3) comparison with therapy combining VEGF/VEGFR inhibitors with other drugs (chemotherapy or Poly (ADP-ribose) polymerase (PARP) inhibitors) and chemotherapy alone; (4) the study’s clinical outcomes included at least one of OS, PFS, ORR, and treatment-related adverse events (TRAEs); (5) Only studies published in English were included. The following criteria were excluded: reviews, fundamental studies, editorials, animal studies, comments, and case reports.</p>", "<title>Data extraction and quality assessment</title>", "<p id=\"Par13\">For each eligible study, we extracted the following information: (1) general study information (study name, publication year, first author, study design, trial phase, sample size); (2) basic patient information (region, age, Eastern Cooperative Oncology Group (ECOG) performance status, primary tumor site); (3) control and intervention group. The main outcomes assessed were OS, PFS, ORR, and TRAEs. The risk of bias and methodological quality assessment was performed using the Cochrane Collaboration’s tool in RevMan5.4.</p>", "<title>Statistical analysis</title>", "<p id=\"Par14\">Statistical analysis was conducted using Stata 14.0 and RevMan5.4. Pooled odds ratios (ORs) and 95% confidence interval CI were calculated for ORR and TRAEs, while pooled HRs and 95% (CI) were calculated for OS and PFS. With <italic>I</italic>^{<italic>2</italic>} &gt; 50% and <italic>p</italic> &lt; 0.05 indicating statistically significant heterogeneity [##REF##12958120##22##], a random-effects model was utilized to calculate the HR and OR; otherwise, the fixed-effects model was employed.</p>", "<p id=\"Par15\">Publication bias assessment, sensitivity analysis, and subgroup analysis were conducted to further explore the source of heterogeneity. Begg’s test was performed to evaluate publication bias, and the results indicated the absence of publication bias with <italic>p</italic> &gt; 0.05 [##REF##7786990##23##]. The symmetry of the funnel plot was also visually observed to assess publication bias. Additionally, a sensitivity analysis was carried out by excluding each study to observe any changes in the pooled HR and OR. Subgroup analysis took into account factors such as region, combination therapeutic agents, trial phase, ECOG performance status, publication year, and primary tumor site.</p>" ]

[ "<title>Results</title>", "<title>Study selection and characteristics</title>", "<p id=\"Par16\">A total of 2408 potentially relevant trials were collected through independent evaluation by two authors. After removing irrelevant and duplicate studies, the initial search yielded 1422 abstracts and articles. Finally, eight studies were included (Fig. ##FIG##0##1##) [##REF##24637997##16##, ##REF##35771546##17##, ##REF##30100379##24##–##REF##34716979##29##].</p>", "<p id=\"Par17\">Table ##TAB##0##1## recorded the general information of the studies, therapeutic regimens, and baseline characteristics of the patients. Seven studies were prospective phase II RCTs, and one was a prospective phase III RCT. The studies were published between 2014 and 2022, and a total of 1097 patients were available for the meta-analysis, with a mean age of approximately 61 years.</p>", "<p id=\"Par18\">\n\n</p>", "<title>Risk of bias</title>", "<p id=\"Par19\">Seven studies were deemed to have a high risk of bias in blinding participants and personnel, while five studies had an unclear risk of bias in blinding outcome assessment, and one study had a high risk. The remaining studies were rated as having a low risk of bias (Figure ##SUPPL##0##S1##).</p>", "<title>Meta-analysis of OS and PFS</title>", "<p id=\"Par20\">The pooled effects of HR for OS and PFS were available for all eight trials. The results demonstrated that combination therapy with VEGF/VEGFR inhibitors had a significantly better OS than chemotherapy (HR = 0.72; 95% CI: 0.62–0.84, <italic>p</italic> &lt; 0.0001) (Fig. ##FIG##1##2##A). Compared to chemotherapy, combination therapy with VEGF/VEGFR inhibitors resulted in a significant improvement in PFS (HR = 0.52; 95% CI: 0.45–0.59, <italic>p</italic> &lt; 0.0001) (Fig. ##FIG##1##2##B). Additionally, there were no significant heterogeneities observed in OS and PFS results among the included studies (<italic>I</italic>^{<italic>2</italic>} = 0% and 22.2%, respectively).</p>", "<title>Meta-analysis of ORR</title>", "<p id=\"Par21\">All eight trials with PROC reported ORR. Interestingly, the group of combination therapy exhibited respectable ORRs compared to chemotherapy (OR = 2.34; 95%CI: 1.27–4.32, <italic>p</italic> &lt; 0.0001). There was a high degree of heterogeneity among different studies for ORR (<italic>I</italic>^{<italic>2</italic>} = 69.3%, <italic>p</italic> = 0.002). Subgroup analyses were conducted to determine the source of heterogeneity. A pooled analysis of ORR in patients with PROC was presented in Fig. ##FIG##2##3##.</p>", "<title>Subgroup analysis for OS</title>", "<p id=\"Par22\">Subgroup analyses were conducted based on stratification factors including region, combination therapeutic agents, trial phase, ECOG performance status, publication year, and primary tumor site. The results were displayed in Table ##TAB##1##2## and Figure ##SUPPL##0##S2##. In the subgroup of combination therapeutic agents, a better OS benefit was revealed in combination treatment with chemotherapy (HR = 0.71; 95% CI: 0.61–0.84, <italic>p</italic> &lt; 0.0001). Patients with an ECOG performance status of 0 to 2 showed greater OS benefit in the combination treatment group compared to monochemotherapy (HR = 0.72; 95% CI: 0.61–0.85, <italic>p</italic> &lt; 0.0001). Furthermore, no significant heterogeneity was observed in any of the subgroups.</p>", "<p id=\"Par23\">\n\n</p>", "<p id=\"Par24\">\n\n</p>", "<p id=\"Par29\">\n\n</p>", "<title>Subgroup analysis for PFS</title>", "<p id=\"Par25\">The subgroups of region, trial phase, ECOG performance status, publication year, and primary tumor site suggested that combination therapy exhibited better PFS than those receiving chemotherapy alone (Table ##TAB##2##3## and Figure ##SUPPL##0##S3##). Compared to the chemotherapy group, only the subgroup of combination treatment with PARP inhibitors exhibited no significant difference (HR = 0.76, 95% CI: 0.50–1.15, <italic>p</italic> = 0.192). The heterogeneity within each subgroup was no significant (<italic>p</italic> &gt; 0.05).</p>", "<p id=\"Par26\">\n\n</p>", "<title>Subgroup analysis for ORR</title>", "<p id=\"Par27\">The results were presented in Table ##TAB##3##4## and Figure ##SUPPL##0##S4##. In the subgroup analysis of combination therapeutic agents, the combination therapy with chemotherapy showed a greater benefit in terms of ORR (OR = 2.97; 95% CI: 1.89–4.67, <italic>p</italic> &lt; 0.0001). In the subgroup analysis of ECOG performance status, significant benefit of ORR was observed in patients with ECOG scores of 0 to 2 (OR = 3.14; 95% CI: 1.87–5.27, <italic>p</italic> &lt; 0.0001). The heterogeneities of the two subgroups were reduced.</p>", "<title>Meta-analysis of TRAEs</title>", "<p id=\"Par28\">Six trials reported the incidences of any grade TRAEs and four trials reported grade 3–4 TRAEs. For both any grade TRAEs (OR = 2.06; 95% CI: 1.47–2.89, <italic>p</italic> &lt; 0.0001) and grade 3–4 TRAEs (OR = 2.53; 95% CI: 1.64–3.90, <italic>p</italic> &lt; 0.0001), the combination therapy with VEGF/VEGFR inhibitors was associated with significantly higher incidences compared to chemotherapy (Fig. ##FIG##3##4##).</p>", "<p id=\"Par30\">The meta-analysis indicated that compared to chemotherapy, combination therapy had a higher incidence of any grade hypertension (OR = 4.38, 95%CI 1.28–14.93, <italic>p</italic> = 0.018), mucositis (OR = 3.20, 95%CI 1.25–8.16, <italic>p</italic> = 0.015), proteinuria (OR = 6.15, 95%CI 1.75–21.59, <italic>p</italic> = 0.005), diarrhea (OR = 3.14, 95%CI 1.36–7.25, <italic>p</italic> = 0.007), and hand-foot syndrome (OR = 6.52, 95%CI 1.02–41.70, <italic>p</italic> = 0.048). There was no statistical difference in the incidence of fatigue (OR = 1.64, 95%CI 0.87–3.10, <italic>p</italic> = 0.124), nausea (OR = 1.36, 95%CI 0.72–2.54, <italic>p</italic> = 0.341), and vomiting (OR = 1.74, 95%CI 0.76–4.02, <italic>p</italic> = 0.192) (Table ##TAB##4##5## and Figure ##SUPPL##0##S5##).</p>", "<p id=\"Par31\">\n\n</p>", "<p id=\"Par32\">\n\n</p>", "<p id=\"Par33\">\n\n</p>", "<p id=\"Par34\">\n\n</p>", "<title>Sensitivity analysis and publication bias</title>", "<p id=\"Par35\">Microvariation was observed in the sensitivity analysis when each trial was removed in turn (Figure ##SUPPL##0##S6##). There was no publication biases according to Begg’s test (OS, <italic>p</italic> = 0.107; PFS, <italic>p</italic> = 0.998; ORR, <italic>p</italic> = 0.617), and the funnel plots were mostly symmetric (Figure ##SUPPL##0##S7##).</p>" ]

[ "<title>Discussion</title>", "<p id=\"Par36\">OC is often asymptomatic until it reaches an advanced stage, resulting in delayed diagnosis and poor prognosis. The current screening programs for OC diagnosis are inadequate [##UREF##2##31##]. PROC remains a significant challenge for clinical diagnosis and treatment due to the extreme cellular heterogeneity and the expression of various resistance and immune evasion mechanisms in this advanced stage of tumor complexity [##REF##35063281##25##]. Combination therapy with VEGF/VEGFR inhibitors has shown a higher likelihood of being the most effective treatment compared to chemotherapy. Recent studies have reported encouraging results, particularly in terms of PFS, for several combination strategies involving VEGF/VEGFR inhibitors in PROC. However, the OS outcomes have been uncertain and inconsistent [##REF##24637997##16##, ##REF##35771546##17##]. To address this, a meta-analysis was conducted, which included eight randomized controlled trials in PROC, and demonstrated better OS, PFS, and ORR outcomes with VEGF/VEGFR inhibitors compared to monochemotherapy. Furthermore, heterogeneity was observed in terms of ORR among the included studies.</p>", "<p id=\"Par37\">Subgroup analyses were performed for OS, PFS, and ORR, considering various stratification factors such as region, combination therapeutic agents, trial phase, ECOG performance status, publication year, and primary tumor site. Regardless of OS, PFS, or ORR, combination therapy with chemotherapy showed greater benefits in the subgroup analysis of combination therapeutic agents. Only one trial included combined PARP inhibitors therapy (cediranib plus olaparib), but it failed to demonstrate any superiority in efficacy compared to the standard treatment for patients with PROC [##REF##35063281##25##]. Some studies have reported that cediranib induces the down-regulation of certain genes in the homologous recombination system, which synergistically enhances the effect of olaparib [##REF##31092693##32##, ##REF##34742344##33##]. Liu et al. demonstrated that the combination of cediranib and olaparib significantly prolonged PFS compared to olaparib alone in platinum-sensitive OC patients (HR = 0.50). Additionally, in the gBRCA/unknown-subset, the combination therapy showed significantly improved OS compared to olaparib alone (37.8 versus 23.0 months, <italic>p</italic> = 0.047) [##REF##30753272##34##]. However, disappointing results were observed for both OS and PFS in the platinum resistance trials included in our analysis [##REF##35063281##25##]. It should be noted that due to the limited number of trials, the accuracy of subgroup analysis may be insufficient. It is necessary to explore randomized controlled trials of new combinations of PARP inhibitors with various drugs, such as anti-angiogenesis agents, immune checkpoint inhibitors, or other inhibitors of DNA damage response pathways [##REF##37314974##35##].</p>", "<p id=\"Par38\">The analysis of TRAEs revealed that the combination therapy had significantly higher incidences of both any grade TRAEs and grade 3–4 TRAEs compared to monochemotherapy. These findings were consistent with the previously published safety profile of VEGF/VEGFR inhibitors in OC and other solid tumors [##REF##33076807##36##–##REF##26034384##41##], and no new safety concerns were identified. Most of the TRAEs reported were of grade 1–2, indicating that the adverse events were manageable. Only four trials reported the incidence of grade 3–4 TRAEs. Among them, paclitaxel plus pazopanib treatment had a higher incidence (OR = 3.33, 95% CI: 1.27–8.76), while bevacizumab plus chemotherapy had a lower incidence (OR = 1.68, 95% CI: 0.76–3.69). Combination therapy was associated with a higher incidence of any grade hypertension, mucositis, proteinuria, diarrhea, and hand-foot syndrome. Hypertension is a common adverse effect of VEGF inhibitors, with an incidence of approximately 30% in various clinical trials, and moderate hypertension occurring in 3–16% of cases. Mucositis is another common adverse effect of anti-VEGF therapy, characterized by symptoms such as pain, difficulty swallowing and pronunciation. Mucositis typically manifests 7–10 days after the initiation of treatment, and in the absence of concurrent bacterial, viral, or fungal infections, it is self-limiting and resolves spontaneously within 2–4 weeks. The mechanism underlying proteinuria production involves the regulation of glomerular vascular permeability by the VEGF signaling pathway. Inhibition of VEGF can result in the destruction of glomerular endothelial cells and epithelial cells (podocytes), leading to proteinuria. The use of VEGF-R tyrosine kinase inhibitors can induce hand-foot syndrome, characterized by red spots, swelling, and pain on the extremities, particularly the palms or soles of the feet. This syndrome typically emerges within the first 6 weeks of treatment.</p>", "<p id=\"Par39\">A meta-analysis has demonstrated that combination therapy with VEGF/VEGFR inhibitors yields superior survival benefits compared to chemotherapy for patients with PROC [##UREF##3##42##]. However, the trials included in the analysis encompassed recurrent OC rather than exclusively focusing on platinum-resistant disease, and they encompassed a subset of patients with platinum-sensitive disease as well. Moreover, the most recent clinical trials were not incorporated. Therefore, our study serves as a supplement to previous meta-analyses, offering more comprehensive content and considering more stratification factors. It also addresses the limitations of previous meta-analyses and provides additional treatment options for patients with PROC. Several limitations were encountered in this meta-analysis. Firstly, the RCTs employed various therapeutic agents and had different baseline characteristics, resulting in a high degree of heterogeneity in the data analysis for ORR. In an attempt to stratify based on baseline characteristics to mitigate heterogeneity, subgroup analyses were conducted. In the future, network meta-analysis can be employed to further investigate the efficacy and safety of combination therapy. Secondly, this study only included eight RCTs comparing VEGF/VEGFR inhibitors in combination therapy with chemotherapy in patients with PROC, and the majority of these trials were phase II trials. Further more reliable data would be provided from phase III clinical trials for analysis, especially when combined with VEGF/VEGFR inhibitors and PARP inhibitors, which are expected to be included in future studies. Additionally, it is important to note that this meta-analysis lacks sufficient subgroup analyses, and the inclusion of more stratification factors would be crucial in demonstrating the efficacy of VEGF/VEGFR inhibitors for PROC.</p>" ]

[ "<title>Conclusions</title>", "<p id=\"Par40\">The combination therapy of VEGF/VEGFR inhibitors for PROC has shown superior OS, PFS, and ORR compared to monochemotherapy, particularly when combined with VEGF/VEGFR inhibitors and chemotherapy. However, it is worth mentioning that combination therapy is associated with a higher incidence of certain adverse events, such as hypertension, mucositis, proteinuria, diarrhea, and hand-foot syndrome. Nevertheless, the safety profile of combination therapy remains manageable. The present study provides more treatment options for PROC patients.</p>" ]

[ "<title>Background</title>", "<p id=\"Par1\">Almost all patients with ovarian cancer will experience relapse and eventually develop platinum-resistant. The poor prognosis and limited treatment options have prompted the search for novel approaches in managing platinum-resistant ovarian cancer (PROC). Therefore, a meta-analysis was conducted to evaluate the efficacy and safety of combination therapy with vascular endothelial growth factor (VEGF) /VEGF receptor (VEGFR) inhibitors for PROC.</p>", "<title>Methods</title>", "<p id=\"Par2\">A comprehensive search of online databases was conducted to identify randomized clinical trials published until December 31, 2022. Pooled hazard ratios (HR) was calculated for overall survival (OS) and progression-free survival (PFS), while pooled odds ratio (OR) was calculated for objective response rate (ORR) and treatment-related adverse events (TRAEs). Subgroup analysis was further performed to investigate the source of heterogeneity.</p>", "<title>Results</title>", "<p id=\"Par3\">In total, 1097 patients from eight randomized clinical trials were included in this meta-analysis. The pooled HRs of OS (HR = 0.72; 95% CI: 0.62–0.84, <italic>p</italic> &lt; 0.0001) and PFS (HR = 0.52; 95% CI: 0.45–0.59, <italic>p</italic> &lt; 0.0001) demonstrated a significant prolongation in the combination group compared to chemotherapy alone for PROC. In addition, combination therapy demonstrated a superior ORR compared to monotherapy (OR = 2.34; 95%CI: 1.27–4.32, <italic>p</italic> &lt; 0.0001). Subgroup analysis indicated that the combination treatment of VEGF/VEGFR inhibitors and chemotherapy was significantly more effective than monochemotherapy in terms of OS (HR = 0.71; 95% CI: 0.61–0.84, <italic>p</italic> &lt; 0.0001), PFS (HR = 0.49; 95% CI: 0.42–0.57, <italic>p</italic> &lt; 0.0001), and ORR (OR = 2.97; 95% CI: 1.89–4.67, <italic>p</italic> &lt; 0.0001). Although the combination therapy was associated with higher incidences of hypertension, mucositis, proteinuria, diarrhea, and hand-foot syndrome compared to monochemotherapy, these toxicities were manageable and well-tolerated.</p>", "<title>Conclusions</title>", "<p id=\"Par4\">The meta-analysis demonstrated that combination therapy with VEGF/VEGFR inhibitors yielded better clinical outcomes for patients with PROC compared to monochemotherapy, especially when combined with chemotherapy. This analysis provides more treatment options for patients with PROC.</p>", "<title>Systematic review registration</title>", "<p id=\"Par5\">[<ext-link ext-link-type=\"uri\" xlink:href=\"https://www.crd.york.ac.uk/PROSPERO\">https://www.crd.york.ac.uk/PROSPERO</ext-link>], Prospective Register of Systematic Reviews (PROSPERO), identifier: CRD42023402050.</p>", "<title>Supplementary Information</title>", "<p>The online version contains supplementary material available at 10.1186/s12905-023-02879-y.</p>", "<title>Keywords</title>" ]

[ "<title>Electronic supplementary material</title>", "<p>Below is the link to the electronic supplementary material.</p>", "<p>\n\n</p>" ]

[ "<title>Acknowledgements</title>", "<p>We would like to thank all authors who provided published data for our meta-analysis.</p>", "<title>Author contributions</title>", "<p>HDX and SFL contributed the study concept and design. HDX and LS contributed to the data acquisition. HDX and KLY were responsible for data analysis and editing the manuscript. LS and CHX contributed to critical revision of the manuscript. All authors approved the final version of the manuscript.</p>", "<title>Funding</title>", "<p>This study was funded by Mass spectrometry project of Liaoning Cancer Hospital &amp; Institute, Project number: ZP202019.</p>", "<title>Data availability</title>", "<p>The original datasets for this study are included in the article/Supplementary Material.</p>", "<title>Declarations</title>", "<title>Ethics approval and consent to participate</title>", "<p id=\"Par56\">Not applicable.</p>", "<title>Consent for publication</title>", "<p id=\"Par57\">Not applicable.</p>", "<title>Competing interests</title>", "<p id=\"Par55\">The authors declare that the research was conducted in the absence of any commercial or financial relationships, and they have no competing interests.</p>", "<title>Disclosure</title>", "<p id=\"Par58\">The authors have nothing to disclose.</p>" ]

[ "<fig id=\"Fig1\"><label>Fig. 1</label><caption><p>Flow diagram of the screening and selection process</p></caption></fig>", "<fig id=\"Fig2\"><label>Fig. 2</label><caption><p>Forest plots of OS <bold>(A)</bold> and PFS <bold>(B)</bold> of combination therapy with VEGF/VEGFR inhibitors</p></caption></fig>", "<fig id=\"Fig3\"><label>Fig. 3</label><caption><p>Forest plot of ORR of combination therapy with VEGF/VEGFR inhibitors</p></caption></fig>", "<fig id=\"Fig4\"><label>Fig. 4</label><caption><p>Forest plots of any grade TRAEs <bold>(A)</bold> and grade 3–4 TRAEs <bold>(B)</bold></p></caption></fig>" ]

[ "<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Main characteristic of the eligible studies in the meta-analysis</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Author</th><th align=\"left\">Year</th><th align=\"left\">Study Phase/design</th><th align=\"left\">Numbers of parents</th><th align=\"left\">Median age</th><th align=\"left\">Region</th><th align=\"left\">Arm</th><th align=\"left\">Median OS</th><th align=\"left\">HR (95%CI)</th><th align=\"left\">Median PFS</th><th align=\"left\">HR (95%CI)</th><th align=\"left\">ORR</th><th align=\"left\">Primary tumor site</th></tr></thead><tbody><tr><td align=\"left\">Chekerov et al.</td><td char=\".\" align=\"char\">2018</td><td align=\"left\">II/RCT</td><td align=\"left\">85/89</td><td align=\"left\">59/58</td><td align=\"left\">Germany</td><td align=\"left\">Sorafenib + topotecan vs. placebo + topotecan</td><td align=\"left\">17.1 vs. 10.1</td><td char=\".\" align=\"char\">0.65 ( 0.45–0.93)</td><td align=\"left\">6.7 vs. 4.4</td><td char=\".\" align=\"char\">0.60 (0.43–0.83)</td><td align=\"left\">30.8% vs. 12%</td><td align=\"left\">OC FTC PC</td></tr><tr><td align=\"left\">Colombo et al.</td><td char=\".\" align=\"char\">2022</td><td align=\"left\">II/RCT</td><td align=\"left\">41/41</td><td align=\"left\">64/63</td><td align=\"left\">Italy</td><td align=\"left\">Cediranib + olaparib vs. paclitaxel</td><td align=\"left\">11.6 vs. 9.3</td><td char=\".\" align=\"char\">0.86 ( 0.5–1.46)</td><td align=\"left\">5.6 vs. 3.1</td><td char=\".\" align=\"char\">0.76 (0.50–1.14)</td><td align=\"left\">15.4% vs. 37.5%</td><td align=\"left\">OC FTC PC</td></tr><tr><td align=\"left\">Pignata et al.</td><td char=\".\" align=\"char\">2014</td><td align=\"left\">II/RCT</td><td align=\"left\">37/37</td><td align=\"left\">56/58</td><td align=\"left\">Italy</td><td align=\"left\">paclitaxel + pazopanib vs. paclitaxel</td><td align=\"left\">19.1 vs. 13.7</td><td char=\".\" align=\"char\"><p>0.60</p><p>(0.32–1.13)</p></td><td align=\"left\">6.4 vs. 3.5</td><td char=\".\" align=\"char\">0.42 (0.25–0.69)</td><td align=\"left\">55.6% vs. 25%</td><td align=\"left\">OC FTC PC</td></tr><tr><td align=\"left\">Pujade-Lauraine et al.</td><td char=\".\" align=\"char\">2014</td><td align=\"left\">III/RCT</td><td align=\"left\">179/182</td><td align=\"left\">62/61</td><td align=\"left\">European</td><td align=\"left\">chemotherapy + bevacizumab vs. chemotherapy</td><td align=\"left\">16.6 vs. 13.3</td><td char=\".\" align=\"char\">0.85 (0.66–1.08)</td><td align=\"left\">6.7 vs. 3.4</td><td char=\".\" align=\"char\">0.48 (0.38–0.60)</td><td align=\"left\">30.9% vs. 12.6%</td><td align=\"left\">OC FTC PC</td></tr><tr><td align=\"left\">Roque et al.</td><td char=\".\" align=\"char\">2021</td><td align=\"left\">II/RCT</td><td align=\"left\">39/37</td><td align=\"left\">67/67</td><td align=\"left\">United States</td><td align=\"left\">ixabepilone + bevacizumab vs. ixabepilone</td><td align=\"left\">10.0 vs. 6.0</td><td char=\".\" align=\"char\">0.52 (0.31–0.87)</td><td align=\"left\">5.5 vs. 2.2</td><td char=\".\" align=\"char\">0.33 (0.19–0.55)</td><td align=\"left\">30.8% vs. 8.1%</td><td align=\"left\">OC FTC PC</td></tr><tr><td align=\"left\">Sharma et al.</td><td char=\".\" align=\"char\">2021</td><td align=\"left\">II/RCT</td><td align=\"left\">37/38</td><td align=\"left\">54/53</td><td align=\"left\">India</td><td align=\"left\">Pazopanib + etoposide + cyclophosphamide vs. etoposide + cyclophosphamide</td><td align=\"left\">- vs. 11.2</td><td char=\".\" align=\"char\">0.64 (0.25–1.65)</td><td align=\"left\">5.1 vs. 3.4</td><td char=\".\" align=\"char\">0.67 (0.34–1.30)</td><td align=\"left\">54.1% vs. 55.3%</td><td align=\"left\">OC</td></tr><tr><td align=\"left\">Shoji et al.</td><td char=\".\" align=\"char\">2021</td><td align=\"left\">II/RCT</td><td align=\"left\">52/51</td><td align=\"left\">60/61</td><td align=\"left\">Japan</td><td align=\"left\">chemotherapy + bevacizumab vs. chemotherapy</td><td align=\"left\">15.3 vs. 11.3</td><td char=\".\" align=\"char\">0.67 (0.38–1.17)</td><td align=\"left\">4.0 vs. 3.1</td><td char=\".\" align=\"char\">0.54 (0.32–0.90)</td><td align=\"left\">25% vs. 13.7%</td><td align=\"left\">OC</td></tr><tr><td align=\"left\">Wang et al.</td><td char=\".\" align=\"char\">2022</td><td align=\"left\">II/RCT</td><td align=\"left\">78/74</td><td align=\"left\">54/56</td><td align=\"left\">China</td><td align=\"left\">Apatinib + PLD vs. PLD</td><td align=\"left\">23.0 vs. 14.4</td><td char=\".\" align=\"char\">0.66 (0.40–1.09)</td><td align=\"left\">5.8 vs. 3.3</td><td char=\".\" align=\"char\">0.44 (0.28–0.71)</td><td align=\"left\">43.1% vs. 10.9%</td><td align=\"left\">OC FTC PC</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab2\"><label>Table 2</label><caption><p>The subgroup analysis for OS in patients with PROC</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" rowspan=\"2\">Subgroup</th><th align=\"left\" rowspan=\"2\"/><th align=\"left\" colspan=\"2\">Pooled OS</th><th align=\"left\" colspan=\"2\">Heterogeneity</th></tr><tr><th align=\"left\">HR[95% CI]</th><th align=\"left\">\n<italic>p</italic>\n</th><th align=\"left\">\n<italic>I</italic>\n^{<italic>2</italic>}\n</th><th align=\"left\">\n<italic>p</italic>\n</th></tr></thead><tbody><tr><td align=\"left\" rowspan=\"2\">Combination therapeutic agents</td><td align=\"left\">Chemotherapy</td><td align=\"left\">0.71[0.61, 0.84]</td><td align=\"left\">0.000</td><td align=\"left\">0%</td><td align=\"left\">0.660</td></tr><tr><td align=\"left\">PARP inhibitors</td><td align=\"left\">0.86[0.50, 1.47]</td><td align=\"left\">0.581</td><td align=\"left\">-</td><td align=\"left\">-</td></tr><tr><td align=\"left\" rowspan=\"2\">Trial phase</td><td align=\"left\">phase II</td><td align=\"left\">0.65[0.54, 0.79]</td><td align=\"left\">0.000</td><td align=\"left\">0%</td><td align=\"left\">0.933</td></tr><tr><td align=\"left\">phase III</td><td align=\"left\">0.85[0.66, 1.09]</td><td align=\"left\">0.196</td><td align=\"left\">-</td><td align=\"left\">-</td></tr><tr><td align=\"left\" rowspan=\"2\">Region</td><td align=\"left\">non-Asia</td><td align=\"left\">0.74[0.62, 0.88]</td><td align=\"left\">0.001</td><td align=\"left\">5.4%</td><td align=\"left\">0.376</td></tr><tr><td align=\"left\">Asia</td><td align=\"left\">0.66[0.47, 0.94]</td><td align=\"left\">0.02</td><td align=\"left\">0%</td><td align=\"left\">0.997</td></tr><tr><td align=\"left\" rowspan=\"2\">ECOG</td><td align=\"left\">0–2</td><td align=\"left\">0.72[0.61, 0.85]</td><td align=\"left\">0.000</td><td align=\"left\">0%</td><td align=\"left\">0.539</td></tr><tr><td align=\"left\">0–4</td><td align=\"left\">0.76[0.52, 1.13]</td><td align=\"left\">0.173</td><td align=\"left\">0%</td><td align=\"left\">0.529</td></tr><tr><td align=\"left\" rowspan=\"2\">Primary tumor site</td><td align=\"left\">OC, FTC, PC</td><td align=\"left\">0.73[0.62, 0.86]</td><td align=\"left\">0.000</td><td align=\"left\">0%</td><td align=\"left\">0.492</td></tr><tr><td align=\"left\">OC</td><td align=\"left\">0.66[0.41,1.07]</td><td align=\"left\">0.094</td><td align=\"left\">0%</td><td align=\"left\">0.935</td></tr><tr><td align=\"left\" rowspan=\"2\">Publication year</td><td align=\"left\">within 5 years</td><td align=\"left\">0.66[0.53, 0.81]</td><td align=\"left\">0.000</td><td align=\"left\">0%</td><td align=\"left\">0.880</td></tr><tr><td align=\"left\">5 years ago</td><td align=\"left\">0.81[0.65, 1.02]</td><td align=\"left\">0.075</td><td align=\"left\">1.6%</td><td align=\"left\">0.313</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab3\"><label>Table 3</label><caption><p>The subgroup analysis for PFS in patients with PROC</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" rowspan=\"2\">Subgroup</th><th align=\"left\" rowspan=\"2\"/><th align=\"left\" colspan=\"2\">Pooled OS</th><th align=\"left\" colspan=\"2\">Heterogeneity</th></tr><tr><th align=\"left\">HR[95% CI]</th><th align=\"left\">\n<italic>p</italic>\n</th><th align=\"left\">\n<italic>I</italic>\n^{<italic>2</italic>}\n</th><th align=\"left\">\n<italic>p</italic>\n</th></tr></thead><tbody><tr><td align=\"left\" rowspan=\"2\">Combination therapeutic agents</td><td align=\"left\">Chemotherapy</td><td align=\"left\">0.49[0.42,0.57]</td><td align=\"left\">0.000</td><td align=\"left\">0%</td><td align=\"left\">0.525</td></tr><tr><td align=\"left\">PARP inhibitors</td><td align=\"left\">0.76[0.50,1.15]</td><td align=\"left\">0.192</td><td align=\"left\">-</td><td align=\"left\">-</td></tr><tr><td align=\"left\" rowspan=\"2\">Trial phase</td><td align=\"left\">phase II</td><td align=\"left\">0.54[0.45,0.64]</td><td align=\"left\">0.000</td><td align=\"left\">28.7%</td><td align=\"left\">0.209</td></tr><tr><td align=\"left\">phase III</td><td align=\"left\">0.48[0.38,0.60]</td><td align=\"left\">0.000</td><td align=\"left\">-</td><td align=\"left\">-</td></tr><tr><td align=\"left\" rowspan=\"2\">Region</td><td align=\"left\">non-Asia</td><td align=\"left\">0.51[0.44,0.60]</td><td align=\"left\">0.000</td><td align=\"left\">49.6%</td><td align=\"left\">0.094</td></tr><tr><td align=\"left\">Asia</td><td align=\"left\">0.52[0.38,0.70]</td><td align=\"left\">0.000</td><td align=\"left\">0%</td><td align=\"left\">0.588</td></tr><tr><td align=\"left\" rowspan=\"2\">ECOG</td><td align=\"left\">0–2</td><td align=\"left\">0.49[0.42,0.57]</td><td align=\"left\">0.000</td><td align=\"left\">0%</td><td align=\"left\">0.416</td></tr><tr><td align=\"left\">0–4</td><td align=\"left\">0.67[0.48,0.92]</td><td align=\"left\">0.013</td><td align=\"left\">2.6%</td><td align=\"left\">0.311</td></tr><tr><td align=\"left\" rowspan=\"2\">Primary tumor site</td><td align=\"left\">OC, FTC, PC</td><td align=\"left\">0.51[0.44,0.59]</td><td align=\"left\">0.000</td><td align=\"left\">40%</td><td align=\"left\">0.139</td></tr><tr><td align=\"left\">OC</td><td align=\"left\">0.59[0.39,0.88]</td><td align=\"left\">0.01</td><td align=\"left\">0%</td><td align=\"left\">0.618</td></tr><tr><td align=\"left\" rowspan=\"2\">Publication year</td><td align=\"left\">within 5 years</td><td align=\"left\">0.55[0.46,0.67]</td><td align=\"left\">0.000</td><td align=\"left\">32.4%</td><td align=\"left\">0.193</td></tr><tr><td align=\"left\">5 years ago</td><td align=\"left\">0.47[0.38,0.58]</td><td align=\"left\">0.000</td><td align=\"left\">0%</td><td align=\"left\">0.638</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab4\"><label>Table 4</label><caption><p>The subgroup analysis for ORR in patients with PROC</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" rowspan=\"2\">Subgroup</th><th align=\"left\" rowspan=\"2\"/><th align=\"left\" colspan=\"2\">Pooled OS</th><th align=\"left\" colspan=\"2\">Heterogeneity</th></tr><tr><th align=\"left\">OR[95% CI]</th><th align=\"left\">\n<italic>p</italic>\n</th><th align=\"left\">\n<italic>I</italic>\n^{<italic>2</italic>}\n</th><th align=\"left\">\n<italic>p</italic>\n</th></tr></thead><tbody><tr><td align=\"left\" rowspan=\"2\">Combination therapeutic agents</td><td align=\"left\">Chemotherapy</td><td align=\"left\">2.97[1.89,4.67]</td><td align=\"left\">0.000</td><td align=\"left\">39.9%</td><td align=\"left\">0.125</td></tr><tr><td align=\"left\">PARP inhibitors</td><td align=\"left\">0.30[0.09,1.01]</td><td align=\"left\">0.051</td><td align=\"left\">-</td><td align=\"left\">-</td></tr><tr><td align=\"left\" rowspan=\"2\">Trial phase</td><td align=\"left\">phase II</td><td align=\"left\">2.22[1.03, 4.75]</td><td align=\"left\">0.041</td><td align=\"left\">72.7%</td><td align=\"left\">0.001</td></tr><tr><td align=\"left\">phase III</td><td align=\"left\">3.10[1.79,5.38]</td><td align=\"left\">0.000</td><td align=\"left\">-</td><td align=\"left\">-</td></tr><tr><td align=\"left\" rowspan=\"2\">Region</td><td align=\"left\">non-Asia</td><td align=\"left\">2.36[1.01,5.49]</td><td align=\"left\">0.047</td><td align=\"left\">72.8%</td><td align=\"left\">0.005</td></tr><tr><td align=\"left\">Asia</td><td align=\"left\">2.31[0.77,6.91]</td><td align=\"left\">0.136</td><td align=\"left\">75%</td><td align=\"left\">0.018</td></tr><tr><td align=\"left\" rowspan=\"2\">ECOG</td><td align=\"left\">0–2</td><td align=\"left\">3.14[1.87,5.27]</td><td align=\"left\">0.000</td><td align=\"left\">47.3%</td><td align=\"left\">0.091</td></tr><tr><td align=\"left\">0–4</td><td align=\"left\">0.82[0.12,5.45]</td><td align=\"left\">0.836</td><td align=\"left\">82.8%</td><td align=\"left\">0.016</td></tr><tr><td align=\"left\" rowspan=\"2\">Primary tumor site</td><td align=\"left\">OC, FTC, PC</td><td align=\"left\">2.80[1.34,5.84]</td><td align=\"left\">0.006</td><td align=\"left\">71.3%</td><td align=\"left\">0.004</td></tr><tr><td align=\"left\">OC</td><td align=\"left\">1.37[0.63,2.95]</td><td align=\"left\">0.427</td><td align=\"left\">22.3%</td><td align=\"left\">0.257</td></tr><tr><td align=\"left\" rowspan=\"2\">Publication year</td><td align=\"left\">within 5 years</td><td align=\"left\">2.02[0.83,4.91]</td><td align=\"left\">0.119</td><td align=\"left\">75.9</td><td align=\"left\">0.001</td></tr><tr><td align=\"left\">5 years ago</td><td align=\"left\">3.24[2.00,5.25]</td><td align=\"left\">0.000</td><td align=\"left\">0%</td><td align=\"left\">0.745</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab5\"><label>Table 5</label><caption><p>The TRAEs of combination therapy with VEGF/VEGFR inhibitors in PROC</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" rowspan=\"2\">TRAEs (any grade)</th><th align=\"left\" colspan=\"2\">Pooled ES</th><th align=\"left\" colspan=\"2\">Heterogeneity</th></tr><tr><th align=\"left\">OR[95% CI]</th><th align=\"left\">\n<bold><italic>p</italic></bold>\n</th><th align=\"left\">I²</th><th align=\"left\">\n<bold><italic>p</italic></bold>\n</th></tr></thead><tbody><tr><td align=\"left\">Hypertension</td><td char=\".\" align=\"char\">4.38[1.28–14.93]</td><td char=\".\" align=\"char\">0.018</td><td char=\".\" align=\"char\">72.1%</td><td char=\".\" align=\"char\">0.003</td></tr><tr><td align=\"left\">Mucositis</td><td char=\".\" align=\"char\">3.20[1.25–8.16]</td><td char=\".\" align=\"char\">0.015</td><td char=\".\" align=\"char\">33.0%</td><td char=\".\" align=\"char\">0.225</td></tr><tr><td align=\"left\">Proteinuria</td><td char=\".\" align=\"char\">6.15[1.75–21.59]</td><td char=\".\" align=\"char\">0.005</td><td char=\".\" align=\"char\">0.0%</td><td char=\".\" align=\"char\">0.824</td></tr><tr><td align=\"left\">Diarrhea</td><td char=\".\" align=\"char\">3.14[1.36–7.25]</td><td char=\".\" align=\"char\">0.007</td><td char=\".\" align=\"char\">42.9%</td><td char=\".\" align=\"char\">0.154</td></tr><tr><td align=\"left\">Hand-foot syndrome</td><td char=\".\" align=\"char\">6.52[1.02–41.70]</td><td char=\".\" align=\"char\">0.048</td><td char=\".\" align=\"char\">76.6%</td><td char=\".\" align=\"char\">0.014</td></tr><tr><td align=\"left\">Fatigue</td><td char=\".\" align=\"char\">1.64[0.87–3.10]</td><td char=\".\" align=\"char\">0.124</td><td char=\".\" align=\"char\">61.6%</td><td char=\".\" align=\"char\">0.034</td></tr><tr><td align=\"left\">Nausea</td><td char=\".\" align=\"char\">1.36[0.72–2.54]</td><td char=\".\" align=\"char\">0.341</td><td char=\".\" align=\"char\">59.1%</td><td char=\".\" align=\"char\">0.044</td></tr><tr><td align=\"left\">Vomiting</td><td char=\".\" align=\"char\">1.74[0.76–4.02]</td><td char=\".\" align=\"char\">0.192</td><td char=\".\" align=\"char\">55.0%</td><td char=\".\" align=\"char\">0.064</td></tr></tbody></table></table-wrap>" ]

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[ "<supplementary-material content-type=\"local-data\" id=\"MOESM1\"></supplementary-material>" ]

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[ "<media xlink:href=\"12905_2023_2879_MOESM1_ESM.docx\"><caption><p>Supplementary Material 1</p></caption></media>" ]

{ "acronym": [ "OC", "PROC", "PARP", "PLD", "HR", "CI", "OS", "PFS", "OR", "ORR", "TRAEs", "VEGF", "ECOG", "ASCO", "ESMO", "SGO", "RCT", "RECIST 1.1", "GCIG CA125" ], "definition": [ "ovarian cancer", "platinum-resistant ovarian cancer", "poly (ADP-ribose) polymerase", "pegylated liposomal doxorubicin", "hazard ratio", "confidence intervals", "overall survival", "progression-free survival", "odds ratio", "objective response rate", "treatment-related adverse events", "vascular endothelial growth factor", "Eastern Cooperative Oncology Group", "American Society of Clinical Oncology", "European society of medical oncology", "Society of Gynecologic Oncology", "randomized controlled trial", "Response Evaluation Criteria in Solid Tumors version 1.1", "Gynecological Cancer Intergroup cancer antigen 125" ] }

[ "<title>Introduction</title>", "<p id=\"Par17\">Type 2 Diabetes Mellitus (T2DM) is a complex condition associated with impaired glucose tolerance, insulin resistance and hyperglycemia; its increasing prevalence has become a serious global health challenge. It is accountable for 11.3% of deaths worldwide and is believed to affect approximately 10.9% of the global population [##UREF##0##1##]. T2DM is accompanied by debilitating chronic complications such as kidney disease, retinopathy, neuropathy, microvascular impairment, and cardiovascular complications [##UREF##0##1##, ##REF##29219149##2##].</p>", "<p id=\"Par18\">Cardiovascular complications are responsible for up to 68% of all diabetes-related mortalities. Several studies have revealed that patients with T2DM are at increased risk of coronary disease [##REF##21150011##3##], myocardial infarction [##REF##32651263##4##], heart failure [##REF##33218978##5##], cardiomyopathy [##REF##32116717##6##], and thrombotic events [##REF##35996159##7##]. It has been shown that diabetic patients have a two- to three-fold increase in cardiovascular disease (CVD) development [##REF##26512646##8##]. Various mechanisms have been proposed to explain the increased CVD rates among diabetic patients. Higher incidence of dyslipidemia [##UREF##1##9##], chronic inflammatory states [##UREF##2##10##–##UREF##4##12##], enhanced oxidative stress and reactive oxygen species [##UREF##5##13##], and hypercoagulability [##REF##26781070##14##] are some of the key findings in patients with T2DM that can potentially increase atherosclerosis, plaque formation, and consequently result in increased rates of CVD [##UREF##2##10##, ##REF##11818473##15##]. Thus, it is of great importance to investigate sufficient early detection methods and effective therapeutic approaches for CVD among diabetic patients.</p>", "<p id=\"Par19\">An electrocardiogram (ECG) is a useful and non-invasive assessment that has been utilized for several biomedical uses such as determination of arrhythmias, fibrillations, heart rates, premature contractions, and ischemia [##UREF##6##16##–##UREF##7##19##]. T-wave in ECG represents ventricular repolarization. T-wave abnormalities (TWA) can be an indicator of a variety of conditions such as cardiomyopathy, pulmonary embolism, peri- and myocarditis, and ischemia [##UREF##8##20##–##REF##5478835##23##].</p>", "<p id=\"Par20\">Given the importance of T2DM and its complications, especially those affecting the cardiovascular system, as well as considering the ease of accessibility and practicality of ECG in medical practice, this cross-section study was designed to investigate the prevalence of T-wave abnormalities and its association with T2DM.</p>" ]

[ "<title>Method</title>", "<title>Study design and participants</title>", "<p id=\"Par21\">The current cross-sectional study was conducted on the population of Mashhad stroke and heart atherosclerotic disorder (MASHAD) cohort study [##REF##25943424##24##]. A total of 9704 individuals aged 35 to 65 years were enrolled into this cohort study. A checklist containing participants’ demographic data including age, sex, educational level, and marital status was recorded. Patients whose systolic blood pressure levels were at or above 140 mmHg and/or diastolic blood pressure were at or beyond 90 mmHg—measured using a mercury sphygmomanometer- were considered hypertensive [##REF##24352797##25##]. A fasting blood glucose (FBG) &gt; 126 mg/dl, or being under anti-hyperglycemic medication was defined as diabetic patients [##REF##33500104##26##]. The FBG was measured in a peripheral blood sample following 14 h of fasting [##REF##32858572##27##]. The study was approved by the Human Research ethics committee of Mashhad University of Medical Sciences, Mashhad, Iran, and all participants provided informed consent prior to data collection.</p>", "<title>ECG analysis</title>", "<p id=\"Par22\">A standard resting 12-lead ECG was taken from each participant of the study. These ECGs were interpreted by instructed medical students in accordance with Minnesota coding system [##UREF##9##28##]. Five percent of all ECGs were also read by certified cardiologists. Among the 9704 participants, the ECGs of 9035 participants were available and readable according to Minnesota coding system [##UREF##9##28##].</p>", "<p id=\"Par23\">Four different t-wave abnormalities were described within the coding system including codes 5–1, 5–2, 5–3 and 5–4. The code 5–1 was defined as T amplitude negative 5.0 mm or more in either of leads I, V6, or in lead aVL when R amplitude is ≥ 5.0 mm and code 5–2 was defined as T amplitude negative or diphasic (positive–negative or negative–positive type) with negative phase at least 1.0 mm but not as deep as 5.0 mm in lead I or V6, or in lead aVL when R amplitude is ≥ 5.0 mm. Code 5–3 was described as flat, negative or diphasic t-wave with less than 1 mm negative phase in any leads of I, II or V3 to V6 or in lead aVL when the R amplitude is ≥ 5.0 mm. Lastly, code 5–4 was defined as a positive T amplitude and a T/R amplitude ratio &lt; 1:20 in any of leads I, II, aVL, or V3 through V6. The R-wave amplitude must be ≥ 10.0 mm [##UREF##9##28##].</p>", "<title>Statistical analysis</title>", "<p id=\"Par24\">Qualitative and quantitative variables were summarized as Mean SD and frequency (%), respectively. An independent t-test was used in order to compare the mean of quantitative variables between the two groups. In addition, evaluating the association between qualitative variables was performed using Chi-square and Fisher's exact test. Further analyses were performed in order to investigate the association between T wave impairments and T2DM after adjusting the effect of potential confounders (variables with <italic>P</italic> &lt; 0.25 in the univariate logistic regression model) using the multiple logistic regression (LR) model. Furthermore, receiver operating characteristic (ROC) curves were used to evaluate the ability of the multiple LR model to predict the occurrence of TWA and T2DM. All statistical analyses were carried out using SPSS version 20 and the statistical significance level was considered at 0.05.</p>" ]

[ "<title>Results</title>", "<title>Study population characteristics</title>", "<p id=\"Par25\">A total of 9035 subjects were included into this study, including 1273 diabetic patients and 7762 non-diabetic individuals (Fig. ##FIG##0##1##). The average age was 47.45 ± 8.17 years and 51.77 ± 7.73 years in non-diabetic and diabetic patients which differed significant (<italic>p</italic> &lt; 0.001). Diabetic patients were found to have higher body mass index (BMI), as well as higher rates of hypertension (50.3 vs 27.9%, <italic>p</italic> &lt; 0.001). Marital status and educational levels also showed a significant different distribution between the two diabetic and non-diabetic groups with married being the most prevalent status among studied groups (<italic>P</italic> &lt; 0.001). Table ##TAB##0##1## presents patients’ demographic data distributions.</p>", "<title>T-wave abnormality frequency</title>", "<p id=\"Par26\">A total of 1246 T wave abnormalities were reported among the study sample population, approximately 13.79% of all participants. The most frequent TWA among both groups were code 5–2 (4.9% in diabetics and 3.6% in the control group) and major T-wave abnormalities (5% in diabetics and 3.7% in the control group). Different TWA yielded varying associations with T2DM. While T-wave abnormalities code 5–1 and 5–4 failed to show a significantly different distribution among diabetic and non-diabetic participants (<italic>P</italic> = 0.24 and 0.92 respectively), code 5–2 and 5–3 were shown to be significantly higher among diabetic patients compared to the non-diabetic individuals (<italic>P</italic> = 0.02 and 0.01, respectively). Overall, both major and minor T-wave abnormalities were significantly more frequent among patients with T2DM compared to the control group, (<italic>p</italic> = 0.02 and 0.008, respectively). Figures ##FIG##1##2## and ##FIG##2##3## compare T wave impairments and T2DM distribution.</p>", "<title>T2DM predictive factors</title>", "<p id=\"Par27\">Results from the multiple logistic regression models indicated a significant association between age (OR = 1.05, 95%CI = 1.04–1.05) and BMI (OR = 1.03, 95%CI = 1.02–1.05) with having T2DM. Gender, marital status, and educational level did not show a significant association with having T2DM (all <italic>P</italic> &gt; 0.05) (Tables ##TAB##1##2## and ##TAB##2##3##). Hypertension has been reported to increase the odds of T2DM by 86 percent (95%CI = 1.63–2.12, <italic>p</italic> &lt; 0.001). According to Tables ##TAB##1##2## and ##TAB##2##3##, only major and minor T wave impairments as well as impairments code 5–2 and 5–3 were reported to be higher among diabetic patients and thus only these items were further analyzed by inclusion in the multiple LR model. A model analyzing T-wave abnormality code 5–2 and 5–3 showed that the odds of having T2DM among patients with T-wave code 5–2 and 5–3 abnormalities were 1.07 and 1.31 times as those without these abnormalities, respectively. This observed difference between patients with and without T-wave abnormalities regarding having T2DM failed to yield statistical significance (<italic>P</italic> = 0.63 and 0.12, respectively). The area under the ROC curve (AUC) of the final multiple LR model was 0.6847, which indicates a good predictive power of the final model, as shown in Fig. ##FIG##3##4##A. Also, the results of our model for major and minor T-wave abnormality revealed that the odds of having T2DM in patients who had T major and T minor abnormalities were 1.06 and 1.30 times than those without ischemia abnormalities, respectively. However, this difference did not show a significant difference within the logistic regression model (<italic>P</italic> = 0.65 and 0.11 respectively). The AUC for this model was 0.6846 which suggests a good predictive power of the final model, as shown in Fig. ##FIG##3##4##B. Tables ##TAB##1##2## and ##TAB##2##3## presents the results of the regression model analyses.\n</p>" ]

[ "<title>Discussion</title>", "<p id=\"Par28\">The current study aimed to investigate the distribution of t-wave impairment among diabetic patients and its association with T2DM according to the Minnesota coding system. The primary results showed significantly higher rates of code 5–2 and 5–3 t-wave impairment among diabetic patients. Both minor and major t-wave abnormalities were also significantly higher among diabetics. However, upon adjusting for several factors such as age, gender, and hypertension within the regression model, none of the mentioned t-wave abnormalities showed a significant association with T2DM.</p>", "<p id=\"Par29\">Myocardial ischemia is a relatively frequent finding among diabetic patients and can potentially lead to coronary artery disease [##UREF##10##29##, ##REF##12767534##30##]. Patients with myocardial ischemia can present both symptomatic and asymptomatic, with or without previous cardiovascular events. The rates of silent asymptomatic myocardial ischemia have been shown to be three to six times higher among diabetic patients [##UREF##10##29##]. Atherosclerosis and endothelial damage of vessels has been shown to be strong risk factors for ischemic heart disease (IHD). On the other hand, the formation of plaque and thrombi can lead to acute forms of myocardial ischemia and coronary syndromes [##REF##3552319##31##, ##REF##31685419##32##]. T2DM can contribute substantially to atherogenesis [##REF##14966066##33##], thrombosis [##REF##22197180##34##], and vascular damage [##REF##10882379##35##], therefore leading to increased risks of IHD [##REF##30409037##36##]. Hyperglycemia, increased levels of free fatty acids, and insulin resistance can lead to several destructive mechanisms such as inflammation, oxidative stress, and the production of advanced glycation products (AGE) [##REF##30409037##36##, ##REF##18182449##37##]. Following the increase in AGE production, inflammatory responses are triggered and pro-inflammatory transcription factors such as NF-kB are upregulated [##REF##10908156##38##, ##REF##15306213##39##]. Vascular motion is also affected via the reduction in nitric oxide synthesis and enhanced endothelin-1 release. Upregulated pro-thrombotic tissue factor and plasminogen activator inhibitor-1 levels, as well as decreased tissue plasminogen activator within T2DM, can lead to thrombi formation [##REF##30409037##36##, ##UREF##11##40##]. The results of these various mechanisms is endothelial dysfunction, vasoconstriction, and enhanced plaque formations, which as mentioned before, are key components in the development and progression of IHD [##REF##30409037##36##, ##UREF##11##40##].</p>", "<p id=\"Par30\">Several studies have shown TWA among diabetic patients and their utilization as risk predictors. A 2021 study by Molud et al. studied the relationship between TWA and cardiovascular events among diabetic patients [##REF##33420509##41##]. Minnesota code 5–1 and 5–2 were considered major TWA and codes 5–3 and 5–4 were considered to be minor TWAs. Their results indicated that patients with TWA had increased risks of both cardiovascular and all-cause mortality and major TWA was attributed to higher risk than minor TWAs [##REF##33420509##41##]. They also highlight the usefulness of TWA in prognostication of diabetic patients in long-term settings. According to a prospective longitudinal study by Harms et al. [##REF##36625405##42##] 45% of diabetic patients had or develop ECG abnormalities and 7.5% developed major adverse cardiac events within a 6.6-year follow-up period. Upon grading ECG abnormalities using the Minnesota coding system, 6 and 5% of the diabetic population had minor and major ST-segment/T-wave abnormalities respectively. They also concluded that ST-segment/T-wave abnormalities were associated with heart failure and coronary heart disease. Thus, T-T-wave modifications can be used as risk predictor for cardiovascular events and mortality among diabetic patients. In addition to ST/T-wave changes which exhibit ischemic disorders, signs of decreased conductivity such as PR and QRS prolongation, and hypertrophy such as tall R-wave was also observed in diabetics and were associated with chronic heart disease [##REF##36625405##42##].</p>", "<p id=\"Par31\">T-wave variation and abnormalities have also been shown within several other diabetes-related pathologies other than IHD. T-wave inversion within some diabetic patients can be explained via hyperkalemia. Diabetic ketoacidosis is a state of hyperkalemia and can result in a variety of ECG modifications affecting T-wave, QT, and ST segments [##REF##22806711##43##]. T-wave inversion is also associated with left ventricle hypertrophy findings of ECG among diabetic patients, which might indicate myocardial injury but not coronary disease [##UREF##12##44##]. This finding is contradicted by another study, in which, ST-T changes are significant predictors of coronary artery disease, defined as elevated, depressed, or inversed T waves [##REF##18319217##45##]. The observed difference can be due to sample size or ECG coding and grading system.</p>", "<p id=\"Par32\">Some of the novel ECG parameters such as the QRS-T angle and T-wave axis of the frontal plane have also been investigated in diabetic patients by other studies [##REF##23744129##46##]. It has been shown that 20.9% of diabetic patients have abnormal T-wave axis while 14% of them have increased QRS-T angle. These two ECG parameters are associated with some atherosclerotic disease markers among type II diabetic patients [##REF##23744129##46##].</p>", "<p id=\"Par33\">Studies on the relationship between T2DM and T-wave abnormalities have reported inconsistent results. A Chinese study investigated ECG abnormalities within several disorders such as hypertension, smoking, obesity, and so forth [##REF##32917144##47##]. T2DM was found to be associated with ST elevation but failed to show a significant correlation with other electrocardiogram findings such as ST depression, T-wave and Q-wave impairment, tall R wave, atrial hypertrophy, and axial deviations. Unlike T2DM, hypertension, and hypercholesterolemia were significantly attributed to ST depression and T-wave abnormalities. These findings are in line with the results of our study, since upon adjustment, none of the T-wave abnormalities were associated with T2DM. However, two studies showed a contrary result. Flatter and asymmetric T-waves were observed in patients with type I diabetes, according to the study by Isaksen et al. [##REF##30007776##48##]. This association was also confirmed by a regression model corrected for age, gender, BMI, blood pressure, potassium, and cholesterol. Interestingly, asymmetrical t-wave was significantly associated with both macro and microalbuminuria among type I diabetic patients. An Italian cross-sectional study [##REF##24011993##49##] also confirmed this finding and suggests higher rates of T-wave axis abnormalities – described as T-wave rotation in the frontal plane – in diabetic patients compared to non-diabetic individuals [##REF##24011993##49##]. These differences could be due to a lack of differentiating diabetes types, as well as the ethnicity of the study population.</p>", "<p id=\"Par34\">Even though our analysis showed no significant association between T2DM and T-wave changes in the ECG, several other factors such as hypertension, age, and BMI were found to be significantly associated with T2DM. A meta-analysis of a total of 452,584 patients also showed similar results about the association between T2DM and hypertension (pooled OR:8.32, 95%CI: 3.05–22.71) [##REF##31520712##50##]. The mechanisms by which, diabetes increases risks of hypertension can be explained through disturbed sodium homeostasis, insulin resistance, enhanced volume expansion and prominent resistance within peripheral vessels [##UREF##13##51##]. Our results indicated no significant relationship between gender and T2DM, whereas some studies show a significant contribution of sex and T2DM [##REF##31754750##52##]. A longitudinal study in Iran showed significantly higher rates of T2DM among females while the global prevalence is higher in men [##REF##32013917##53##, ##UREF##14##54##]. These differences in findings can be due to sampling size as well as not differentiating the type of diabetes among different studies. It has been also shown that gender differences poses varied risks of diabetes development among different races [##REF##35422246##55##].</p>", "<p id=\"Par35\">This study is one of very few studies to differentiate T-wave abnormalities into six categories, while most of the studies only summarize them in two. Second, a large population (<italic>n</italic> = 9035) was examined and observed in this cross-section study which belonged to the MASHAD cohort study. Third, some of the interpretations were also controlled by certified cardiologists which reduce the chances of errors. However, our study faced several limitations which need to be considered for future studies. First, available documentation did not differentiate type I or II diabetes, and thus, exact conclusions cannot be made for each type. Second, the age group of the study was limited to 35–65 years old, and variation might exist in ages above or below the cutoff used in our study. Third, only t-waves were used for ischemic changes of the heart, and future studies can use several other modalities, such as other ECG findings, and other para-clinical values to further confirm ischemic diseases of the heart due to T2DM. We also highly encourage future researchers to perform multi-central cohort studies in order to precisely evaluate the relationship between the two. High-quality meta-analyses are needed for confirming our findings.</p>" ]

[ "<title>Conclusion</title>", "<p id=\"Par36\">The results of this study show a significantly higher prevalence of Minnesota codes 5–2, 5–3, major and minor T-wave abnormalities in diabetic patients compared to non-diabetic individuals. However, the association between these abnormalities was not significant using regression models and adjusting for age, gender, and BMI. Considering the aberrant T2DM complications, especially cardiovascular ones, it is highly important to investigate CVD diagnostic tools among diabetics. Given the contrary results of other studies, large-scale studies on the topic of using t-wave abnormalities as ischemic pathologies resulting from T2DM are needed for further identification of sufficient indicative and predictive tools.</p>" ]

[ "<title>Background</title>", "<p id=\"Par1\">Type 2 Diabetes Mellitus (T2DM) has become a major health concern with an increasing prevalence and is now one of the leading attributable causes of death globally. T2DM and cardiovascular disease are strongly associated and T2DM is an important independent risk factor for ischemic heart disease. T-wave abnormalities (TWA) on electrocardiogram (ECG) can indicate several pathologies including ischemia. In this study, we aimed to investigate the association between T2DM and T-wave changes using the Minnesota coding system.</p>", "<title>Methods</title>", "<p id=\"Par2\">A cross-sectional study was conducted on the MASHAD cohort study population. All participants of the cohort population were enrolled in the study. 12-lead ECG and Minnesota coding system (codes 5–1 to 5–4) were utilized for T-wave observation and interpretation. Regression models were used for the final evaluation with a level of significance being considered at <italic>p</italic> &lt; 0.05.</p>", "<title>Results</title>", "<p id=\"Par3\">A total of 9035 participants aged 35–65 years old were included in the study, of whom 1273 were diabetic. The prevalence of code 5–2, 5–3, major and minor TWA were significantly higher in diabetics (<italic>p</italic> &lt; 0.05). However, following adjustment for age, gender, and hypertension, the presence of TWAs was not significantly associated with T2DM (<italic>p</italic> &gt; 0.05). Hypertension, age, and body mass index were significantly associated with T2DM (<italic>p</italic> &lt; 0.05).</p>", "<title>Conclusions</title>", "<p id=\"Par4\">Although some T-wave abnormalities were more frequent in diabetics, they were not statistically associated with the presence of T2DM in our study.</p>", "<title>Keywords</title>" ]

[]

[ "<title>Acknowledgements</title>", "<p>We would like to thank Mashhad University of Medical Sciences for supporting this study.</p>", "<title>Authors’ contributions</title>", "<p>All authors have read and approved the manuscript. Study concept and design: SSS and AI; data collection: FF, ME, HA, BS, AG, SM, and MT; Analysis and interpretation of data: EN and HE; Drafting of the manuscript: TS and AM; Critical revision of the manuscript for important intellectual content: GF, MG, and MM.</p>", "<title>Funding</title>", "<p>The collection of clinical data was financially supported by Mashhad University of Medical Sciences.</p>", "<title>Availability of data and materials</title>", "<p>The authors confirm that the data supporting the findings of this study are available from the corresponding author on request.</p>", "<title>Declarations</title>", "<title>Ethics approval and consent of participate</title>", "<p id=\"Par37\">The study protocol was given approval by the Ethics Committee of Mashhad University of Medical Sciences and written informed consent was obtained from participants.</p>", "<title>Consent of publication</title>", "<p id=\"Par38\">Not applicable.</p>", "<title>Competing interests</title>", "<p id=\"Par39\">The authors declare no competing interests.</p>" ]

[ "<fig id=\"Fig1\"><label>Fig. 1</label><caption><p>The summary of the methodology and the findings</p></caption></fig>", "<fig id=\"Fig2\"><label>Fig. 2</label><caption><p>Comparison of frequency distribution of the T wave impairments between patients with and without T2DM (<italic>n</italic> = 9035)</p></caption></fig>", "<fig id=\"Fig3\"><label>Fig. 3</label><caption><p>compares major T impairment and major T impairment between patients with and without T2DM (<italic>n</italic> = 9035)</p></caption></fig>", "<fig id=\"Fig4\"><label>Fig. 4</label><caption><p><bold>A</bold> showing the predictive power of final multiple LR model including T-wave abnormality code 5–2 and 5–3 to predict diabetes and <bold>B</bold> showing the predictive power of final multiple LR model including major and minor T wave abnormality to predict diabetes</p></caption></fig>" ]

[ "<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Demographic and clinical characteristics of patients under study according to T2DM^a</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" rowspan=\"2\">Variables^a</th><th align=\"left\" rowspan=\"2\"><bold>Total</bold></th><th align=\"left\" colspan=\"2\">T2DM</th><th align=\"left\" rowspan=\"2\"><italic>P</italic>-value</th></tr><tr><th align=\"left\">Yes (<italic>N</italic> = 1273)</th><th align=\"left\">No (<italic>N</italic> = 7762)</th></tr></thead><tbody><tr><td align=\"left\">Age (y)</td><td align=\"left\">51.77 ± 7.73</td><td align=\"left\">47.45 8.17</td><td align=\"left\">51.77 7.73</td><td align=\"left\">&lt; 0.001^*</td></tr><tr><td align=\"left\">Body mass index (kg/m²)</td><td align=\"left\">28.93 ± 4.62</td><td align=\"left\">27.71 4.73</td><td align=\"left\">28.93 4.62</td><td align=\"left\">&lt; 0.001^*</td></tr><tr><td align=\"left\" colspan=\"5\">Gender</td></tr><tr><td align=\"left\"> Male</td><td align=\"left\">3615 (40.00)</td><td align=\"left\">3129 (40.30)</td><td align=\"left\">486 (38.20)</td><td align=\"left\" rowspan=\"2\">0.15</td></tr><tr><td align=\"left\"> Female</td><td align=\"left\">5420 (60.00)</td><td align=\"left\">4633 (59.70)</td><td align=\"left\">787 (61.80)</td></tr><tr><td align=\"left\" colspan=\"5\">Marital status</td></tr><tr><td align=\"left\"> Single</td><td align=\"left\">55 (0.60)</td><td align=\"left\">51 (0.70)</td><td align=\"left\">4 (0.30)</td><td align=\"left\" rowspan=\"3\">&lt; 0.001^*</td></tr><tr><td align=\"left\"> Married</td><td align=\"left\">8418 (93.20)</td><td align=\"left\">7262 (93.60)</td><td align=\"left\">1156 (90.80)</td></tr><tr><td align=\"left\"> Divorced/widowed</td><td align=\"left\">562 (6.20)</td><td align=\"left\">449 (5.80)</td><td align=\"left\">113 (8.90)</td></tr><tr><td align=\"left\" colspan=\"5\">Education level</td></tr><tr><td align=\"left\"> Illiterate</td><td align=\"left\">1155 (12.80)</td><td align=\"left\">927 (11.90)</td><td align=\"left\">228 (17.90)</td><td align=\"left\" rowspan=\"3\">&lt; 0.001^*</td></tr><tr><td align=\"left\"> Lower than diploma</td><td align=\"left\">6815 (75.40)</td><td align=\"left\">5881 (75.80)</td><td align=\"left\">934 (73.40)</td></tr><tr><td align=\"left\"> Higher than diploma</td><td align=\"left\">1065 (11.80)</td><td align=\"left\">954 (12.30)</td><td align=\"left\">111 (8.70)</td></tr><tr><td align=\"left\" colspan=\"5\">Hypertension</td></tr><tr><td align=\"left\"> No</td><td align=\"left\">6220 (69.00)</td><td align=\"left\">5589 (72.10)</td><td align=\"left\">631 (49.70)</td><td align=\"left\" rowspan=\"2\">&lt; 0.001^*</td></tr><tr><td align=\"left\"> yes</td><td align=\"left\">2798 (31.00)</td><td align=\"left\">2159 (27.90)</td><td align=\"left\">639 (50.30)</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab2\"><label>Table 2</label><caption><p>Investigating the association between T-wave impairment and having diabetes adjusted by age, BMI, gender, education, and marital status using multiple LR model</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Variables </th><th align=\"left\">OR^a</th><th align=\"left\">95% CI</th><th align=\"left\"><italic>P</italic>-value</th></tr></thead><tbody><tr><td align=\"left\">Age</td><td align=\"left\">1.05</td><td align=\"left\">1.04- 1.05</td><td align=\"left\">&lt; 0.001^*</td></tr><tr><td align=\"left\">BMI</td><td align=\"left\">1.03</td><td align=\"left\">1.02—1.05</td><td align=\"left\">&lt; 0.001^*</td></tr><tr><td align=\"left\" colspan=\"4\">Gender</td></tr><tr><td align=\"left\"> Female</td><td align=\"left\">Ref</td><td align=\"left\">Ref</td><td align=\"left\" rowspan=\"2\">0.88</td></tr><tr><td align=\"left\"> Male</td><td align=\"left\">1.01</td><td align=\"left\">0.88—1.16</td></tr><tr><td align=\"left\" colspan=\"4\">Marital status</td></tr><tr><td align=\"left\"> Single</td><td align=\"left\">Ref</td><td align=\"left\">Ref</td><td align=\"left\">-</td></tr><tr><td align=\"left\"> Married</td><td align=\"left\">1.20</td><td align=\"left\">0.42—3.37</td><td align=\"left\">0.72</td></tr><tr><td align=\"left\"> Divorced/Widowed</td><td align=\"left\">1.34</td><td align=\"left\">0.46—3.85</td><td align=\"left\">0.58</td></tr><tr><td align=\"left\" colspan=\"4\">Educational level</td></tr><tr><td align=\"left\"> Illiterate</td><td align=\"left\">Ref</td><td align=\"left\">Ref</td><td align=\"left\">-</td></tr><tr><td align=\"left\"> Lower than diploma</td><td align=\"left\">0.94</td><td align=\"left\">0.79—1.11</td><td align=\"left\">0.48</td></tr><tr><td align=\"left\"> Higher than diploma</td><td align=\"left\">0.73</td><td align=\"left\">0.56—0.95</td><td align=\"left\">0.02^*</td></tr><tr><td align=\"left\" colspan=\"4\">Hypertension</td></tr><tr><td align=\"left\"> No</td><td align=\"left\">Ref</td><td align=\"left\">Ref</td><td align=\"left\" rowspan=\"2\">&lt; 0.001^*</td></tr><tr><td align=\"left\"> Yes</td><td align=\"left\">1.86</td><td align=\"left\">1.63—2.12</td></tr><tr><td align=\"left\" colspan=\"4\">Code 5–2 T wave impairment</td></tr><tr><td align=\"left\"> No</td><td align=\"left\">Ref</td><td align=\"left\">Ref</td><td align=\"left\" rowspan=\"2\">0.63</td></tr><tr><td align=\"left\"> Yes</td><td align=\"left\">1.07</td><td align=\"left\">0.80—1.44</td></tr><tr><td align=\"left\" colspan=\"4\">Code 5–3 T wave impairment</td></tr><tr><td align=\"left\"> No</td><td align=\"left\">Ref</td><td align=\"left\">Ref</td><td align=\"left\" rowspan=\"2\">0.12</td></tr><tr><td align=\"left\"> Yes</td><td align=\"left\">1.31</td><td align=\"left\">0.92—1.87</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab3\"><label>Table 3</label><caption><p>Investigating the association between major T-wave impairment and minor T-wave impairment with having diabetes adjusted by age, BMI, gender, education, and marital status using multiple LR model</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Variables </th><th align=\"left\">OR^a</th><th align=\"left\">95% CI</th><th align=\"left\"><italic>P</italic>-value</th></tr></thead><tbody><tr><td align=\"left\">Age</td><td align=\"left\">1.05</td><td align=\"left\">1.04- 1.06</td><td align=\"left\">&lt; 0.001^*</td></tr><tr><td align=\"left\">BMI</td><td align=\"left\">1.03</td><td align=\"left\">1.02—1.05</td><td align=\"left\">&lt; 0.001^*</td></tr><tr><td align=\"left\" colspan=\"4\">Gender</td></tr><tr><td align=\"left\"> Female</td><td align=\"left\">Ref</td><td align=\"left\">Ref</td><td align=\"left\" rowspan=\"2\">0.88</td></tr><tr><td align=\"left\"> Male</td><td align=\"left\">1.01</td><td align=\"left\">0.88—1.16</td></tr><tr><td align=\"left\" colspan=\"4\">Marital status</td></tr><tr><td align=\"left\"> Single</td><td align=\"left\">Ref</td><td align=\"left\">Ref</td><td align=\"left\">-</td></tr><tr><td align=\"left\"> Married</td><td align=\"left\">1.20</td><td align=\"left\">0.42—3.37</td><td align=\"left\">0.72</td></tr><tr><td align=\"left\"> Divorced/Widowed</td><td align=\"left\">1.34</td><td align=\"left\">0.46—3.84</td><td align=\"left\">0.58</td></tr><tr><td align=\"left\" colspan=\"4\">Educational level</td></tr><tr><td align=\"left\"> Illiterate</td><td align=\"left\">Ref</td><td align=\"left\">Ref</td><td align=\"left\">-</td></tr><tr><td align=\"left\"> Lower than diploma</td><td align=\"left\">0.94</td><td align=\"left\">0.79—1.11</td><td align=\"left\">0.48</td></tr><tr><td align=\"left\"> Higher than diploma</td><td align=\"left\">0.73</td><td align=\"left\">0.56—0.95</td><td align=\"left\">0.02^*</td></tr><tr><td align=\"left\" colspan=\"4\">Hypertension</td></tr><tr><td align=\"left\"> No</td><td align=\"left\">Ref</td><td align=\"left\">Ref</td><td align=\"left\" rowspan=\"2\">&lt; 0.001^*</td></tr><tr><td align=\"left\"> Yes</td><td align=\"left\">1.86</td><td align=\"left\">1.63—2.12</td></tr><tr><td align=\"left\" colspan=\"4\">Major T-wave impairment</td></tr><tr><td align=\"left\"> No</td><td align=\"left\">Ref</td><td align=\"left\">Ref</td><td align=\"left\" rowspan=\"2\">0.65</td></tr><tr><td align=\"left\"> Yes</td><td align=\"left\">1.06</td><td align=\"left\">0.79—1.42</td></tr><tr><td align=\"left\" colspan=\"4\">Minor T-wave impairment</td></tr><tr><td align=\"left\"> No</td><td align=\"left\">Ref</td><td align=\"left\">Ref</td><td align=\"left\" rowspan=\"2\">0.11</td></tr><tr><td align=\"left\"> Yes</td><td align=\"left\">1.30</td><td align=\"left\">0.93—1.82</td></tr></tbody></table></table-wrap>" ]

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[ "<table-wrap-foot><p>^*Significance level of 0.05</p><p>^aValues are reported as Mean SD and frequency (%)</p></table-wrap-foot>", "<table-wrap-foot><p>^*Significance level of 0.05</p><p>^a<italic>OR</italic> Odds Ratio</p></table-wrap-foot>", "<table-wrap-foot><p>^*Significance level of 0.05</p><p>^a<italic>OR</italic> Odds Ratio</p></table-wrap-foot>", "<fn-group><fn><p><bold>Publisher’s Note</bold></p><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn><fn><p>Sara Saffar Soflaei, Eisa Nazar, and Toktam Sahranavard contributed equally as first authors.</p></fn></fn-group>" ]

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{ "acronym": [ "T2DM", "TWA", "ECG", "CVD", "MASHAD", "FBG", "LR", "ROC", "BMI", "AUC", "IHD", "AGE" ], "definition": [ "Type 2 Diabetes Mellitus", "T-wave abnormalities", "Electrocardiogram", "Cardiovascular disease", "Mashhad stroke and heart atherosclerotic disorder", "Fasting blood glucose", "Logistic regression", "Receiver operating characteristic", "Body mass index", "Area under the ROC curve", "Ischemic heart disease", "Advanced glycation products" ] }

[ "<title>Background</title>", "<p id=\"Par9\">After the outbreak of the Russo-Ukrainian war on 24 February 2022, 8.2 million Ukrainians have been displaced or led to flee all over Europe, as of May 2023 [##UREF##0##1##]. Data on the consequences of the current war in Ukraine on the psychological well-being of refugees is still limited. Preliminary data on resettled Ukrainian refugees have only been reported from a study conducted in Germany. It found a prevalence rate of depressive and anxiety symptoms of 44.7% and 51.0%, respectively [##REF##36894946##2##]. Refugee mental health assessment is particularly challenging since it may require cultural mediators and/or interpreters to facilitate communication and dialogue, and to fully understand the health status and the underlying needs. If the refugee feels that he/she is heard and understood, he/she may show an enhanced help-seeking behavior when in need [##UREF##1##3##]. This is even more important for mental health conditions, especially those at risk of self-harm and suicide.</p>", "<p id=\"Par10\">In recent years, there was a growing awareness of the role of mental health in global health outcomes, premature deaths, and economic losses [##UREF##2##4##]. Research has shown that the prevalence of mental disorders, such as post-traumatic stress disorder (PTSD) and depression, is higher in the refugees than in the general population. A prevalence rate of 22.7%, 13.8%, and 15.8% for PTSD, depression, and anxiety disorders, respectively, was found in child and adolescents refugees resettled in Europe [##REF##32956381##5##]. Risk factors for mental health are many and diverse and can change depending on the moment and the migration context. They can be distinguished into risk factors of the pre-migratory context, i.e. when the person is in the country of origin, where he or she may be directly or indirectly exposed to war and suffer trauma; during migration, as the journey itself may expose refugees to further traumatic events; and third, the post-migratory context may be a source of further stress for the refugee due to social isolation, unemployment and difficult cultural integration [##REF##29174456##6##]. The importance of mental health and well-being as factors influencing the overall health status of refugees during migration and in the resettlement country has been widely recognized [##REF##32956381##5##].</p>", "<p id=\"Par11\">In order to assess the health status and needs of this vulnerable population, it is crucial to provide primary health workers with reliable and easy-to-use tools that allow a multicultural approach, such as short and simple questionnaires. These can reach large numbers of people and help health workers identify individuals at risk and provide timely assistance.</p>", "<p id=\"Par12\">The General Health Questionnaire (GHQ) is a widely used assessment instrument of current psychological distress developed by Goldberg in 1970. In the following decades, different shortened versions of the original 60-items tool, such as the GHQ-30, GHQ-28, and the GHQ-12, have been proposed [##REF##9122299##7##]. The questionnaire assesses the presence and severity of some psychological and psychosomatic symptoms over the previous few weeks using a self-reported four-point scale expressing whether a particular symptom or behaviour has recently been experienced by the respondent from less to much more than usual. The GHQ-12 most common scoring methods are bimodal (0–0–1-1) and Likert (0–1–2-3) resulting in a total score of 12 or 36 points, respectively [##REF##11197926##8##]. The GHQ-12, due to its ease of use and brevity, has been extensively used to screen psychological distress in primary health care, outpatient settings, and in different cultures and populations [##REF##32324835##9##, ##REF##27310297##10##]. The GHQ-12 has also proved to be a consistent and reliable instrument when used in the refugee population [##REF##33530971##11##]. Therefore, this study aims to translate the 12-item General Health Questionnaire (GHQ-12) into Ukrainian and to test its psychometric features (i.e. construct validity, internal consistency, and concurrent validity).</p>" ]

[ "<title>Methods</title>", "<title>Ethical approval</title>", "<p id=\"Par13\">The research was performed following the ethical standards of the 1964 Declaration of Helsinki and was approved by the Ethical Committee of the University Hospital of Verona on 24/10/2022 (protocol number 63939).</p>", "<title>Study design, setting, and population</title>", "<p id=\"Par14\">This is a cross-sectional validation study. It was carried out in the province of Verona. The reception system in Italy for Ukrainian refugees is built on two different services provided by the governmental authorities, under the Home Office: the Reception and Integration System (RIS), managed at the local level and the Special Reception Centres (SRC), centrally managed [##UREF##3##12##]. Alongside these systems is the extended network of reception consisting of nonprofit organizations, social service centers, religious organizations, and co-housing measures with families or accommodation provided by other private entities. In Verona, the reception network supporting Ukrainian refugees is coordinated among all 98 municipalities in the province and includes about 117 SRC and four projects related to the RIS [##UREF##4##13##, ##UREF##5##14##]. As of April 2023, the number of Ukrainian refugees in the province of Verona reached 2265, of whom 1623 (71.7%) were females [##UREF##6##15##].</p>", "<p id=\"Par15\">All persons who arrived in Italy from Ukraine after 24 February 2022, following the outbreak of the Russian-Ukrainian conflict, were considered eligible for this study. Refugees older than 14 years old whose native language was Ukrainian were included.</p>", "<title>Sample size</title>", "<p id=\"Par16\">According to Mundfrom et colleagues [##UREF##7##16##] considering a ratio of variables to factors (p/f) of 6 and a two-factor solution, as in the original questionnaire [##REF##11037090##17##], in a level of communality set as low, the minimum sample size to obtain an excellent-level criterion (0.98) was 120. Accounting for a drop-out rate of 15%, the target sample of participants was set at 146 for this study.</p>", "<title>Data collection</title>", "<p id=\"Par17\">Data was collected between November and February 2023, progressively including all persons meeting the inclusion criteria until the computed sample size was reached.</p>", "<p id=\"Par18\">Ukrainian refugees were recruited in the province of Verona through the local refugee reception network (i.e., regional and local authorities, SRC, RIS, and non-profit organizations).</p>", "<p id=\"Par19\">A written disclosure about the study was first given and those who agreed to participate signed an informed consent form. Both documents were written in Ukrainian, the participants’ mother language. For those under the age of 18, informed consent was signed by their parents or legal guardian.</p>", "<p id=\"Par20\">Each participant was asked to complete the Ukrainian translation of the GHQ-12 together with a short sociodemographic questionnaire (i.e., age, sex, education level, and marital status) and the subscale for PTSD of the International Trauma Questionnaire (ITQ) to serve as external validation. At all phases of the study, the research team was supported by a cultural mediator.</p>", "<title>Instruments</title>", "<p id=\"Par21\">The original GHQ-12 consists of 12 items to be answered by the participant according to the variation, compared to his or her habitual standard, in the frequency of scenarios or behaviors described in the specific statement of the items (Table ##TAB##0##1##). The GHQ-12 has 6 positive items (answers options: “Better than usual”, “Same as usual”, “Less than usual”, “Much less than usual”) and 6 negative items (answers options: “Not at all”, “No more than usual”, “Rather more than usual”, “Much more than usual”).\n</p>", "<p id=\"Par22\">In the present study, both scoring methods, bimodal and Likert, were evaluated. In the bimodal scoring method, the response categories have a score of 0, 0, 1, 1 for the positive items, while the negative items are scored the other way round (1,1,0,0). Therefore, the score ranges from 0 to 12 points. In the Likert scoring method, the positive items scored from 0 to 3 and the negative ones from 3 to 0, with a score range between 0 and 36 [##REF##32793301##18##]. The most used cut-offs are between 2 and 4 for the bimodal method and ranged between 10 and 15 for the Likert one [##REF##32793301##18##].</p>", "<p id=\"Par23\">The ITQ is a self-report measure that allows a simple and concise assessment of key aspects of PTSD, according to the ICD-11 diagnostic criteria. The ITQ has two main subscales: the first (9 items), concerns PTSD and assesses three symptom domains, namely re-experiencing, avoidance, and sense of threat; the second (9 items), used to assess the complex PTSD, investigates the symptoms of self-organization disorder and the functional impairment caused by them. Each item is answered on a Likert scale from 0 (not at all) to 4 (very much). The cut-off for PTSD is given by a score <underline>&gt;</underline> 2 in at least one of the two items of each of the three symptom domains (re-experiencing, items 1 and 2; avoidance, items 3 and 4; hyperarousal, items 5 and 6) plus at least one of the three indicators of functional impairment (items 7, 8 and 9). The ITQ is available in the Ukrainian language-validated version [##REF##36625445##19##].</p>", "<p id=\"Par24\">The PTSD subscale was used in the present study. Previous studies have analyzed psychological distress by combining the PTSD symptom score from the ITQ and the mental health problem risk score from the GHQ-12 to test the links between mental health, well-being, and conflict exposure [##UREF##8##20##].</p>", "<title>Translation and pilot testing</title>", "<p id=\"Par25\">The translation process followed the WHO guidelines, which include a forward translation into the target language, i.e. Ukrainian, followed by a backward translation into the original language, i.e., English (Fig. ##FIG##0##1##) [##UREF##9##21##].</p>", "<p id=\"Par26\">After obtaining permission from the Author to translate and the license to use the questionnaire, a professional translator provided the first Ukrainian version of the GHQ-12 from the original English questionnaire. This version was then revised with a third party fluent in both languages. The back-translation was carried out independently by a second professional translator who had not seen the original questionnaire in English. Both the authors and a third person reviewed the translation and revised it consensually. To avoid any conceptual losses during the translation process, the consensual retranslation was then compared with the original GHQ-12.</p>", "<p id=\"Par27\">The translated questionnaire was initially administered to a sample of 28 refugees to test the acceptability and comprehensibility of the Ukrainian version. After completing the questionnaire, a cognitive interview was conducted to assess the clarity of the questions, any problems or difficulties in answering, and possible improvement actions. The pilot-sample was recruited based on sociodemographic criteria in order to be representative of both genders and different age groups (adolescents, adults, and elderly). Refugees who participated in the pre-test were not included in the final study sample.</p>", "<p id=\"Par28\">The original English GHQ-12 and the Ukrainian GHQ-12 are available in the ##SUPPL##0##Supplementary material##.</p>", "<title>Statistical analysis</title>", "<p id=\"Par29\">A descriptive statistic was first conducted on sociodemographic data using frequencies and proportions for categorical variables and means and standard deviations (SD) or medians and interquartile ranges (IQRs) for continuous ones. Sample distribution was tested via χ2 and Fisher exact test or Mann-Whitney-U non-parametric, as appropriate.</p>", "<p id=\"Par30\">GHQ-12 internal consistency was assessed through Cronbach’s alpha and McDonald’s omega coefficient testing the reliability and considering satisfactory a coefficient greater than 0.70. A tetrachoric correlation matrix was generated to assess the correlation between all the items of the GHQ-12 scored with a bimodal method.</p>", "<p id=\"Par31\">A confirmatory factor analysis (CFA) was carried out to examine the factor structure of the Ukrainian version of the GHQ-12. First, a single-factor structure that contained all the GHQ-12 items was assessed. Secondly, a two-factor structure was tested encompassing two correlated latent factors: “Anxiety/Depression” (items: q1, q3, q4, q7, q8, q12) and “Social Dysfunction” (items: q2, q5, q6, q9, q10, q11). The two-factor structure was the one suggested by the author of the original English version of the GHQ-12 [##UREF##7##16##].</p>", "<p id=\"Par32\">The models were tested for both the scoring method; for the bimodal method, the diagonally weighted least squares estimator was used and all variables were considered as ordered (ordinal) variables, for the Likert method, the maximum likelihood estimator was used with the Satorra-Bentler adjustment accounting for non-normality and heteroscedasticity of the data [##UREF##10##22##]. Model fit was evaluated using the χ2 test, the comparative fit index (CFI), the Tucker-Lewis index (TLI), the root-mean square error of approximation (RMSE), and the standardized root-mean-square residual (SRMR). Variance explained by latent variables was assessed through Average Variance Extracted (AVE). Criteria for acceptable model fit indices were based on Hooper et al. [##UREF##11##23##].</p>", "<p id=\"Par33\">Pearson product moment statistic (Pearson’s correlation coefficient = <italic>“ρ”</italic>) was used to assess the concurrent validity of the GHQ-12 as the correlation with the ITQ subscale for PTSD. It was expected that the GHQ-12 would positively correlate with the ITQ subscale. A coefficient <italic>“ρ”</italic> above 0.40 was considered satisfactory. Association between single item score of the GHQ-12 and being screened positive for PTSD at the ITQ was conducted via z-test and t-test for bimodal and Likert scoring methods, respectively.</p>", "<p id=\"Par34\">A <italic>p</italic>-value &lt; 0.05 was considered significant. All analyses were performed using the R software (version 4.3.0).</p>" ]

[ "<title>Results</title>", "<title>Sample characteristics</title>", "<p id=\"Par35\">A total of 150 participants were recruited and 141 (94%) completed the questionnaire. The majority were females (<italic>n</italic> = 111, 78.7%), and the median age was 36 years (IQR 23–43). The level of education of the majority of the sample was university degree or higher (<italic>n</italic> = 77, 54.6%) followed by high school diploma (<italic>n</italic> = 32, 22.7%). Concerning marital status, 76 (53.9%) were married or in a de facto union, 39 (27.7%) were single, and 18 (12.8%) were divorced.</p>", "<p id=\"Par36\">The mean score at GHQ-12 scored with the binomial method was 4.8 points (SD 3.4). Using two of the most used cut-offs in literature for the bimodal scoring method, i.e., <underline>&gt;</underline> 3 and <underline>&gt;</underline> 4, the percentage of people screened positive was 97 (68.8%) and 85 (60.3%), respectively. Those with a score equal to or higher to the mean GHQ-12 score for the whole study sample were 72 (51.1%). Table ##TAB##1##2## shows descriptive statistics for the single items of the GHQ-12 based on both scoring methods (bimodal and Likert). The mean score at the ITQ subscale for PTSD was 14.0 points (SD 8.3). People with an ITQ score suggestive of PTSD were 59 (41.8%).\n</p>", "<title>Concurrent validity</title>", "<p id=\"Par37\">Validity was assessed through Pearson correlation coefficient between the total score at GHQ-12 and the ITQ subscale for PTSD. A positive significant correlation was found with a coefficient <italic>“ρ”</italic> equal to 0.53 (0.95CI 0.40–0.64, <italic>p</italic> &lt; 0.001). When looking at the association between the single items and a suggestive score for PTSD at the ITQ, eight items showed a positive significant association (Table ##TAB##1##2##). The items more frequently associated with PTSD and with the highest difference between positive and negative PTSD proportions were item 7 (74.6%), item 5 (83.1%), and item 1 (55.9%).</p>", "<title>Construct validity</title>", "<p id=\"Par38\">The results of the CFA are shown in Table ##TAB##2##3##. The bimodal scoring method had good indices for both single- (model B1, TLI = 0.98, RMSEA = 0.05[0.90CI 0.00–0.07]) and two-factor models (model B2, TLI = 0.98, RMSEA = 0.04[0.90CI 0.00–0.07]). In model B2 the two subscales had a high correlation index, equal to 0.88. Both B1 and B2 models achieved a satisfactory AVE above 0.50. In the Likert scoring method, the single factor model (model L1) didn’t fit the data well (TLI = 0.77, RMSEA = 0.11[0.90CI 0.09–0.13]). The two-factor model (model L2) showed better and acceptable indices (TLI = 0.58, RMSEA = 0.09[0.90CI 0.06–0.11]). Model L2 had a correlation of 0.75 between the two subscales. Figure ##FIG##1##2## shows the standardized parameter estimates for all the four models.\n</p>", "<title>Internal consistency</title>", "<p id=\"Par39\">The mean score of the GHQ-12 items was 0.40 (SD = 0.29). The items with the highest frequency of positive results (i.e., a score equal to 1) were item 5 (66%), item 2 (55%), and item 7 (53%) (Table ##TAB##1##2##). Reliability was tested with Cronbach’s and McDonald’s omega coefficients that were found to be 0.84 (0.95CI 0.80–0.88) and 0.85 (0.95CI 0.81–0.88) in the whole sample, respectively. The alpha and omega coefficients in the two subscales were 0.78 [0.95CI 0.71–0.83] and 0.78 [0095CI 0.72–0.83] for ‘anxiety/depression’ and 0.72 [0.95CI 0.64–0.79] and 0.73 [0.95CI 0.66–0.79] for ‘social dysfunction’. Stratifying by sex both alpha and omega coefficients remained consistent as in the whole sample (alpha: female = 0.84[0.95CI 0.80–0.88], male = 0.85[0.95CI 0.76–0.92]; omega: female = 0.84[0.95CI 0.75–0.92], male = 0.84[0.95CI 0.79–0.88]).</p>", "<p id=\"Par40\">The items with the highest correlation were q7 and q12 (0.695[0.95CI 0.502;0.835]), while those with the lowest were q 1 and q11 (0.114[0.95CI -0.242;0.468) (Fig. ##FIG##2##3##).</p>" ]

[ "<title>Discussion</title>", "<p id=\"Par41\">The present study showed that the Ukrainian translation of GHQ-12 had good reliability and validity and a two-factor structure consistent with the original English version.</p>", "<p id=\"Par42\">The GHQ-12 is a well-known instrument to assess the general well-being and mental health, used in different populations and settings, including low- and middle-income countries [10]. It was widely used in several study designs (cross-sectional, RCT, and longitudinal) among migrants and refugees to screen for mental health disorders [##REF##36068576##24##–##REF##16890296##26##].</p>", "<p id=\"Par43\">Internal reliability of the Ukrainian translation of the GHQ-12 was overall satisfactory in our study (alpha = 0.84). The Ukrainian GHQ-12 also showed a good level of concurrent validity through the correlation with the ITQ (ρ = 0.53). The GHQ-12 has previously been used with satisfactory results for screening refugees for PTSD [##REF##19592432##27##]. This mental disorder is one of those that most affect refugees and one of the main ones examined in the literature on this population [##REF##32885850##28##]. PTSD seriously endangers both the mental and general health of persons, as it can lead to self-harm and suicidal ideation and attempts. Only one study has previously used the GHQ-12 in Ukrainian refugees, although it only evaluated its internal reliability, finding an alpha of 0.83, as in the present study. It didn’t explore the validity and factorial structure of the Ukrainian translation of the GHQ-12 [##REF##36894946##2##].</p>", "<p id=\"Par44\">In the confirmatory factor analysis, both single- (model B1) and two-factor (model B2) structures with bimodal scoring methods fitted data well. The bimodal scoring system has previously proven its validity as a screening tool, as in the case of the present study, whereas the Likert method may be more useful for the follow-up of patients over time [##REF##28758322##29##]. The GHQ-12 was originally developed as a unitary screening measure and the high correlation found in our sample between the two subscales in model B2 and L2 supports this structure. Several multidimensional factor constructions comprising two to three factors have been proposed and tested [##REF##12873334##30##]. A multicentric study of psychological disorders in general health by WHO found a substantial factor variation between the 15 centres involved. However, after rotation two factors expressing “Anxiety/Depression” and “Social Dysfunction” were found for the GHQ-12 [##REF##11037090##17##]. Another study comparing different factorial structures for the GHQ-12 found that a unidimensional model, with a general factor representing the commonality between all items and two orthogonal specific factors reflecting the common variance due to wording effects (negatively and positively worded items) and representing the two previously identified factors, was the best fit [##REF##32595570##31##]. The present study showed that the Ukrainian translation of GHQ-12 is consistent with the factor structures proposed in the literature and very similar to that of the original English version.</p>", "<p id=\"Par45\">Using a binary scoring method, as the original Goldberg version of the GHQ-12, we found a mean score of 4.8 points. Different cut-offs have been proposed in the literature depending on the population involved, mainly ranging between 2 and 4 [##REF##32793301##18##]. As a rule of thumb, it has been proposed to use the mean score for the overall population of respondents as a rough guide to the best threshold [##REF##9723146##32##]. The cut-off of screening tools is also driven by the prevalence of a specific disorder in a given population [##REF##9723146##32##]. In the present study, the sample consisted of Ukrainian refugees. This is a well-known at-risk population for mental health disorders, and we therefore found a higher threshold than that proposed in the literature. Adopting a 5-point cut-off, 51% of the sample showed a suggestive score for mental distress. The GHQ, even in its short 12-item form, is therefore a robust self-report tool for screening people who may be at risk for mental health disorders, especially adolescent and young people [##REF##30617936##33##]. For this reason, it could be particularly useful in the Ukrainian refugee population, made up mainly of young women and children. Simple tools to investigate the prevalence of people at risk of mental health problems are widely used such as the Refugee Health Screener-15 (RHS-15) as a general measure of emotional distress and the Primary Care PTSD Screen for DSM-5 (PC-PTSD-5). They have the advantage of being rapid and easy to be administered, allowing even non-specialized personnel to use them [##REF##37143592##34##]. These questionnaires were used in a school setting to screen Ukrainian refugee adolescents, finding a prevalence of 57.1% and 45.2% above the critical cut-off of RHS-15 and PC-PTSD-5, respectively [##UREF##12##35##]. The GHQ in its short 12-item form can therefore complement these instruments and be used not only by clinicians but also by schools, nonprofit organizations, or social service personnel as a self-report tool to identify persons at risk for mental health at an early stage and to provide them with timely assistance and support.</p>", "<p id=\"Par46\">This study has some limitations. First of all, it was conducted only in the province of Verona, so it may not be representative of the entire population of Ukrainian refugees. Likewise, it involved a particularly high-risk category, so it may not be generalizable to the entire Ukrainian population. Our sample was also unbalanced between males and females, with the latter being the most represented. This sample however reflects the composition of the study population. It would be useful to repeat this in a larger and more general sample of people in Ukraine to see if the results are confirmed. Moreover, a larger sample would have offered the possibility of conducting an analysis based on the item response theory to assess the invariance of the results concerning the characteristics of the participants. Secondly, the validation was assessed on a specific mental disorder, and this could be restrictive compared to the general health explored by the GHQ-12. Future studies, across different regions, should explore how the different cultural contexts may influence the responses and thus the validation of the questionnaire. Furthermore, the use of emerging techniques, such as clinimetric analysis, would be important to apply to verify the clinical properties of the Ukrainian version of the GHQ-12 [##REF##23347455##36##].</p>" ]

[ "<title>Conclusions</title>", "<p id=\"Par47\">The present study showed that the Ukrainian translation of the GHQ-12 had good internal reliability and concurrent validity and showed a factor structure consistent with the original version. It provides a useful tool for assessing general well-being in an at-risk population such as Ukrainian refugees. To the best of our knowledge, this is the first study to provide a comprehensive validation of the Ukrainian translation of the GHQ-12. Future studies may use it on larger population samples both as a screening tool and to study factors associated with general and mental well-being in the resettlement country to improve reception and integration services for this vulnerable population.</p>" ]

[ "<p id=\"Par1\">Following the Russian-Ukrainian conflict, the well-being of millions of Ukrainians has been jeopardised. This study aims to translate and test the psychometric features of the Ukrainian version of the General Health Questionnaire 12 (GHQ-12). The study included Ukrainian refugees housed in Verona (Italy) between November/2022 and February/2023. The Ukrainian translation was obtained through a ‘forward-backward’ translation. Questionnaire was completed by 141 refugees (females: 78.7%). Median age was 36 years (IQR 23–43). Individuals with a score suggestive of psychological distress were 97 (68.8%). Cronbach’s coefficient was 0.84 (0.95CI 0.80–0.88). According to confirmatory factor analysis, both single- (modelB1) and two-factor (model B2) structures with bimodal scoring method fitted the data satisfactorily. The two factors of model B2 had a 0.88 correlation. Pearson coefficient showed a positive significant correlation between the GHQ-12 and International Trauma Questionnaire scores (ρ = 0.53, 0.95CI 0.40–0.64, <italic>p</italic> &lt; 0.001). The GHQ-12 Ukrainian translation showed good psychometric features being a reliable and valid instrument to assess Ukrainian refugees’ general well-being.</p>", "<title>Supplementary Information</title>", "<p>The online version contains supplementary material available at 10.1186/s12955-024-02226-1.</p>", "<title>Keywords</title>" ]

[ "<title>Supplementary Information</title>", "<p>\n</p>" ]

[ "<title>Authors’ contributions</title>", "<p>RB conceptualized and designed the study and made substantial contributions to original writing. AS contributed to conceptualization, data collection and made substantial contributions to original writing. RB and MM were responsible for data analysis. MS was responsible for the translation process and contributed to data collection. EP and LB contributed to conceptualization and to data collection. GV, ST and MR reviewed the study critically. FM conceptualized and designed the study and reviewed it critically.</p>", "<title>Funding</title>", "<p>None.</p>", "<title>Availability of data and materials</title>", "<p>The datasets generated and/or analysed during the current study are available from the corresponding author upon reasonable request.</p>", "<title>Declarations</title>", "<title>Competing interests</title>", "<p id=\"Par48\">The authors declare no competing interests.</p>" ]

[ "<fig id=\"Fig1\"><label>Fig. 1</label><caption><p>Flowchart of the translation, pilot test and validation process of the Ukrainian translation of the General Health Questionnaire 12 (GHQ-12) adopted in the present study</p></caption></fig>", "<fig id=\"Fig2\"><label>Fig. 2</label><caption><p>Standardized parameter estimates from the models fitted on single- and two-factor structure with bimodal scoring method (panels top left and top right) and with Likert scoring method (panels bottom left and bottom right)</p></caption></fig>", "<fig id=\"Fig3\"><label>Fig. 3</label><caption><p>Tetrachoric correlation matrix of the Ukrainian version of the General Heath Questionnaire-12 with bimodal scoring method</p></caption></fig>" ]

[ "<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Original English and Ukrainian translation of the 12 items of the General Health Questionnaire 12 (GHQ-12). UKR: Ukrainian</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th>Item</th><th>GHQ-12</th><th>UKR GHQ-12</th></tr></thead><tbody><tr><td>q1</td><td>Been able to concentrate on what you’re doing?</td><td>Зосередитися на тому, що ви робите?</td></tr><tr><td>q2</td><td>Lost much sleep over worry?</td><td>Втратити сон через хвилювання?</td></tr><tr><td>q3</td><td>Felt you were playing a useful part in things?</td><td>Відчувати, що ви відіграєте корисну роль у справах?</td></tr><tr><td>q4</td><td>Felt capable of making decisions about things?</td><td>Відчувати себе здатними приймати рішення?</td></tr><tr><td>q5</td><td>Felt constantly under strain?</td><td>Постійно відчувати напругу?</td></tr><tr><td>q6</td><td>Felt you couldn’t overcome your difficulties?</td><td>Відчувати, що не можете подолати свої труднощі?</td></tr><tr><td>q7</td><td>Been able to enjoy your normal day-to-day activities?</td><td>Насолоджуватися своєю звичайною повсякденною діяльністю?</td></tr><tr><td>q8</td><td>Been able to face up to your problems?</td><td>Протистояти своїм проблемам?</td></tr><tr><td>q9</td><td>Been feeling unhappy and depressed?</td><td>Почуватися нещасними та пригніченими?</td></tr><tr><td>q10</td><td>Been losing confidence in yourself?</td><td>Втратити впевненість у собі?</td></tr><tr><td>q11</td><td>Been thinking of yourself as a worthless person?</td><td>Вважати себе нікчемною людиною?</td></tr><tr><td>q12</td><td>Been feeling reasonably happy, all things considered?</td><td>Почуватися досить щасливими, незважаючи на обставині?</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab2\"><label>Table 2</label><caption><p>Descriptive table of the total and single item scores in the General Health Questionnaire 12 (GHQ-12) based on the bimodal and the Likert scoring method and their association with the results at the International Trauma Questionnaire (ITQ) assessing the presence of post-traumatic stress disorder (PTSD)</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th/><th colspan=\"4\">GHQ-12 bimodal scoring</th><th colspan=\"4\">GHQ-12 Likert score</th></tr><tr><th>Item</th><th>% score = 1 (overall)</th><th>% score = 1 (ptsd = 1)</th><th>% score = 1 (ptsd = 0)</th><th><italic>p</italic>-Value*</th><th>mean (sd) (overall)</th><th>mean (sd) (ptsd = 1)</th><th>mean (sd) (ptsd = 0)</th><th><italic>p</italic>-Value*</th></tr></thead><tbody><tr><td>q1</td><td>39.0%</td><td>55.9%</td><td>26.8%</td><td>&lt; 0.001</td><td>1.40 (0.88)</td><td>1.66 (0.96)</td><td>1.21 (0.77)</td><td>&lt; 0.001</td></tr><tr><td>q2</td><td>55.3%</td><td>71.2%</td><td>43.9%</td><td>0.001</td><td>1.55 (1.09)</td><td>1.95 (1.01)</td><td>1.26 (1.05)</td><td>&lt; 0.001</td></tr><tr><td>q3</td><td>39.0%</td><td>45.8%</td><td>34.1%</td><td>0.160</td><td>1.35 (0.86)</td><td>1.47 (1.02)</td><td>1.26 (0.72)</td><td>&lt; 0.001</td></tr><tr><td>q4</td><td>22.7%</td><td>30.5%</td><td>17.1%</td><td>0.060</td><td>0.99 (0.80)</td><td>1.05 (0.95)</td><td>0.95 (0.66)</td><td>&lt; 0.001</td></tr><tr><td>q5</td><td>66.0%</td><td>83.1%</td><td>53.7%</td><td>&lt; 0.001</td><td>1.82 (0.87)</td><td>2.12 (0.77)</td><td>1.60 (0.87)</td><td>&lt; 0.001</td></tr><tr><td>q6</td><td>48.2%</td><td>61.0%</td><td>39.0%</td><td>0.010</td><td>1.46 (0.95)</td><td>1.69 (0.90)</td><td>1.29 (0.95)</td><td>&lt; 0.001</td></tr><tr><td>q7</td><td>52.5%</td><td>74.6%</td><td>36.6%</td><td>&lt; 0.001</td><td>1.65 (0.85)</td><td>2.05 (0.84)</td><td>1.35 (0.74)</td><td>&lt; 0.001</td></tr><tr><td>q8</td><td>28.4%</td><td>35.6%</td><td>23.2%</td><td>0.107</td><td>1.13 (0.84)</td><td>1.22 (1.00)</td><td>1.06 (0.71)</td><td>&lt; 0.001</td></tr><tr><td>q9</td><td>46.8%</td><td>55.9%</td><td>40.2%</td><td>0.065</td><td>1.33 (0.97)</td><td>1.46 (1.07)</td><td>1.23 (0.88)</td><td>&lt; 0.001</td></tr><tr><td>q10</td><td>33.3%</td><td>47.5%</td><td>23.2%</td><td>0.003</td><td>1.02 (0.98)</td><td>1.25 (1.06)</td><td>0.85 (0.89)</td><td>&lt; 0.001</td></tr><tr><td>q11</td><td>10.6%</td><td>16.9%</td><td>6.1%</td><td>0.040</td><td>0.43 (0.76)</td><td>0.56 (0.93)</td><td>0.34 (0.59)</td><td>0.853</td></tr><tr><td>q12</td><td>41.1%</td><td>57.6%</td><td>29.3%</td><td>&lt; 0.001</td><td>1.36 (0.92)</td><td>1.63 (1.02)</td><td>1.17 (0.80)</td><td>&lt; 0.001</td></tr><tr><td><bold>Total</bold></td><td/><td/><td/><td/><td/><td/><td/><td/></tr><tr><td>Mean (sd)</td><td>4.8 (3.4)</td><td>6.4 (3.2)</td><td>3.7 (3.1)</td><td>&lt; 0.001</td><td>15.5 (6.8)</td><td>18.1 (7.2)</td><td>13.6 (5.9)</td><td>&lt; 0.001</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab3\"><label>Table 3</label><caption><p>Confirmatory factor analysis of Ukrainian version of the General Health Questionnaire 12. Model fit statistics for single-factor structure (models B1 and L1) and two-factor structure (models B2 and L2) for the bimodal and Likert scoring methods, respectively</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th/><th>Χ2</th><th>Df</th><th>p-Value</th><th>CFI</th><th>TLI</th><th>RMSEA</th><th>0.90CI</th><th>SRMR</th><th>AVE</th></tr></thead><tbody><tr><td><bold>Bimodal</bold></td><td/><td/><td/><td/><td/><td/><td/><td/><td/></tr><tr><td> <bold>Model B1</bold></td><td>63.21</td><td>54</td><td>0.080</td><td>0.981</td><td>0.976</td><td>0.045</td><td>0.000–0.073</td><td>0.097</td><td>0.521</td></tr><tr><td> <bold>Model B2</bold></td><td>64.41</td><td>53</td><td>0.135</td><td>0.985</td><td>0.982</td><td>0.039</td><td>0.000–0.070</td><td>0.093</td><td><p>f1 = 0.581</p><p>f2 = 0.520</p></td></tr><tr><td><bold>Likert</bold></td><td/><td/><td/><td/><td/><td/><td/><td/><td/></tr><tr><td> <bold>Model L1</bold></td><td>144.69</td><td>54</td><td>&lt; 0.001</td><td>0.803</td><td>0.766</td><td>0.109</td><td>0.089–0.129</td><td>0.085</td><td>0.358</td></tr><tr><td> <bold>Model L2</bold></td><td>108.14</td><td>53</td><td>&lt; 0.001</td><td>0.880</td><td>0.851</td><td>0.086</td><td>0.064–0.107</td><td>0.073</td><td><p>f1 = 0.394</p><p>f2 = 0.429</p></td></tr></tbody></table></table-wrap>" ]

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[ "<supplementary-material content-type=\"local-data\" id=\"MOESM1\"></supplementary-material>" ]

[ "<table-wrap-foot><p><italic>*</italic>Z-test, <italic>T</italic> test, <italic>sd</italic> standard deviation</p></table-wrap-foot>", "<table-wrap-foot><p><italic>Df</italic> degrees of freedom: <italic>CFI</italic> comparative fit index: <italic>TLI</italic> Tucker-Lewis index: <italic>RMSEA</italic> root-mean square error of approximation: <italic>SRMR</italic> standardized root-mean-square residual: <italic>AVE</italic> Average Variance Extracted: <italic>f1</italic> Anxiety and depression: <italic>f2</italic> Social dysfunction: <italic>f3</italic> Loss of confidence</p></table-wrap-foot>", "<fn-group><fn><p><bold>Publisher’s Note</bold></p><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn></fn-group>" ]

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[ "<media xlink:href=\"12955_2024_2226_MOESM1_ESM.pdf\"><caption><p><bold>Additional file 1.</bold>\n</p></caption></media>" ]

{ "acronym": [ "CFA", "GHQ", "IQR", "ITQ", "PTSD", "SD", "WHO" ], "definition": [ "Confirmatory Factor Analysis", "General Health Questionnaire", "Inter Quartile Range", "International Trauma Questionnaire", "Post-Traumatic Stress Disorder", "Standard Deviation", "World Health Organization" ] }

[ "<title>Introduction</title>", "<p id=\"Par2\">The human gastrointestinal tract is home to billions of microorganisms that interact symbiotically with their hosts and play a critical role in both health and illness. H. pylori, a gastrointestinal microorganism, is one of the most studied bacteria. The network of interactions that H. pylori have constituted with its host is closely linked to all systems of the organism [##UREF##0##1##]. Numerous systemic illnesses, including neural, hematological, cardiovascular, dermatological, and allergic diseases are linked to H. pylori [##REF##30090002##2##, ##REF##31486239##3##]. Among them, the relationship between H. pylori infection and the risk of allergic diseases is becoming better known and is of some concern to the general public. The interaction of the human immune system and environmental factors leads to allergic diseases, and given the substantial regional heterogeneity of these diseases, it is likely that environmental factors play a significant role in their etiology [##REF##25516679##4##]. As a result, growing evidence from research demonstrating an association between early H. pylori exposure and allergic diseases suggests that early life exposure to H. pylori may act as a preventative factor in the development of allergic disease [##REF##24528371##5##, ##REF##30203591##6##]. However, only a small number of studies have described the immune response to H. pylori and the relationship between the bacteria and the gut microbiota. This paper explored the relationship between H. pylori infection and asthma in terms of immunity and gut microbiota, as well as the use of H. pylori and its related components in the treatment of asthma. It also introduced the most recent developments in the correlation between H. pylori infection and allergic diseases.</p>" ]

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[ "<title>Conclusions</title>", "<p id=\"Par29\">Many domestic and international scholars have made significant progress in recent years by conducting multi-dimensional and multi-angle discussions and studies on the relationship between extra gastric disorders and H. pylori. In terms of microbiota and immunity, this review summarizes recent developments in H. pylori infection and asthma. Topics covered include the relevance of H. pylori to allergic disease, potential mechanisms by which H. pylori infection exerts a protective effect on asthma, and the use of H. pylori in the treatment of asthma. According to the majority of studies, H. pylori infection has a strong negative correlation with the risk of a number of allergic disorders, including asthma and eosinophilic esophagitis.</p>", "<p id=\"Par30\">The hygiene hypothesis suggests that exposure to certain infectious agents may prevent the development of allergic diseases such as asthma, and therefore it is hypothesized that H. pylori infection would exert a protective effect against asthma by promoting immune tolerance. Through a variety of mechanisms, H. pylori infection alters the composition and abundance of the gut microbiota, which in turn exerts a preventive and protective effect against asthma through the gut-pulmonary axis. Dendritic cells can be reprogrammed by H. pylori to become tolerogenic dendritic cells, and tolerogenic dendritic cells promote the production of Treg with high inhibitory activity. Both Th1/Th2 balance and Th17/Treg balance play a significant role in the onset and persistence of asthma and can prevent and protect against asthma when Th1 and Treg are dominant in the ratio. Many studies have demonstrated the great potential of H. pylori neutrophil-activating protein(NAP)in the prevention and treatment of allergic diseases such as asthma. H. pylori, its components, or extracts have certain preventive and therapeutic effects on asthma. It may represent a new way to treat asthma in the future, but it is not widely known by clinical staff. The eradication of H. pylori in asthmatic patients remains to be discussed.</p>", "<p id=\"Par31\">There are still some unanswered questions despite the fact that the studies mentioned above showed an association between H. pylori infection and the risk of allergic disease. The detailed mechanisms that give rise to these correlations are not clear. The mechanisms may be closely interconnected. The hygiene hypothesis is a significant theory rooted in epidemiology. This hypothesis not only explains the negative correlation between H. pylori and asthma from an epidemiological perspective but may also account for other mechanisms, such as alterations in the gut microbiota. Changes in the gut microbiota can affect the balance of Th1/Th2 and Treg/Th17. Tolerogenic dendritic cells can promote the differentiation of T cells into regulatory T cells. Regulatory T cells can not only directly protect against asthma but also influence the balance of Th1/Th2, which is crucial in the onset and progression of asthma.</p>", "<p id=\"Par32\">It’s also unknown if there are any confounding variables besides H. pylori that affect this correlation. Large-scale cohort studies are needed to determine whether the effect of H. pylori on allergic disease is through mediating variables. Further fundamental experimental investigations will be required in the future to investigate and assess these problems and to develop effective strategies for the prevention and treatment of allergic diseases.</p>" ]

[ "<p id=\"Par1\">H. pylori is a gram-negative bacterium that is usually acquired in childhood and can persistently colonize the gastric mucosa of humans, affecting approximately half of the world’s population. In recent years, the prevalence of H. pylori infection has steadily reduced while the risk of allergic diseases has steadily climbed. As a result, epidemiological research indicates a strong negative association between the two. Moreover, numerous experimental studies have demonstrated that eradicating H. pylori increases the risk of allergic diseases. Hence, it is hypothesized that H. pylori infection may act as a safeguard against allergic diseases. The hygiene hypothesis, alterations in gut microbiota, the development of tolerogenic dendritic cells, and helper T cells could all be involved in H. pylori’s ability to protect against asthma. Furthermore, Studies on mice models have indicated that H. pylori and its extracts are crucial in the management of asthma. We reviewed the in-depth studies on the most recent developments in the relationship between H. pylori infection and allergic diseases, and we discussed potential mechanisms of the infection’s protective effect on asthma in terms of microbiota and immunity. We also investigated the prospect of the application of H. pylori and its related components in asthma, so as to provide a new perspective for the prevention or treatment of allergic diseases.</p>", "<title>Keywords</title>" ]

[ "<title>Association between H. Pylori infection and the risk of allergic diseases</title>", "<title>Association between H. pylori and asthma</title>", "<p id=\"Par4\">Asthma is a heterogeneous disease with chronic airway inflammation, bronchial hyperresponsiveness and airway remodeling, and its pathogenesis is very complex [##REF##32011093##7##]. In recent years there have been many studies on the association of H. pylori infection with the risk of asthma. Epidemiological studies have shown a decline in the prevalence of H. pylori infection in the Western World and in some developing countries in contrast to an increase in the incidence of asthma and allergic diseases [##UREF##1##8##]. Studies have demonstrated that H. pylori infection can prevent asthma [##REF##33197219##9##, ##REF##36289457##10##], and it has been noted that CagA-positive H. pylori infection is significantly negatively associated with the risk of asthma [##UREF##2##11##, ##REF##28634020##12##] and may even be negatively associated with the severity of asthma [##UREF##2##11##]. A meta-analysis of 18 cross-sectional studies found that H. pylori infection, especially CagA-positive H. pylori infection, was inversely associated with the prevalence of asthma [##REF##33630702##13##]. Another meta-analysis of 24 studies (8 case-control studies and 16 cross-sectional studies) reached the same conclusion [##REF##28634020##12##]. However, there are questions about the negative association between H. pylori infection and the risk of asthma. Several studies suggest no correlation between H. pylori infection and asthma risk and do not support the notion that H. pylori infection has a protective effect against asthma [##REF##22515362##14##–##REF##34819160##16##]. The aforementioned study analyzed the correlation between H. pylori IgG antibody positivity and the incidence of asthma. A positive H. pylori IgG antibody indicates a previous H. pylori infection but does not necessarily imply a current infection. Therefore, we believe that further studies and experiments are necessary to support and confirm this discovery. Research by Wang et al. pointed out that H. pylori infection was significantly associated with a 1.38-fold increased risk of asthma. This indicates that the risk of asthma is significantly higher in patients with H. pylori infection than in subjects without H. pylori infection [##REF##28389738##17##]. However, the methods of detecting H. pylori and possible H. pylori treatment during the follow-up were not fully addressed. Socioeconomic factors, as potential confounding factors, had not been taken into account in the study. We noticed that a relevant article raised doubts about the conclusion of the study [##REF##28508347##18##]. Although the findings are slightly controversial to some extent, the negative association of H. pylori infection with asthma risk is supported by most scholars.</p>", "<title>Association between H. pylori and eosinophilic esophagitis</title>", "<p id=\"Par6\">Eosinophilic esophagitis (EoE) is a chronic, immune-mediated inflammatory disease whose pathogenesis is not fully understood. The histology is characterized by eosinophil-dominated inflammation with clinical symptoms associated with esophageal dysfunction [##REF##28774845##19##, ##REF##29729305##20##]. Emerging evidence suggests that modifiable host factors and environmental allergen exposure may play a key role in the pathogenesis of eosinophilic esophagitis [##REF##30032346##21##]. The gradual increase in the incidence of eosinophilic esophagitis and the decrease in the rate of H. pylori infection in recent years have given rise to speculation and discussion about the relationship between the two. A strong negative correlation between the presence of H. pylori and esophageal eosinophilia has been demonstrated [##UREF##3##22##]. The results of case-control studies and meta-analyses suggest that H. pylori infection is associated with a reduced risk of eosinophilic esophagitis [##REF##26898731##23##, ##REF##30659992##24##], but the protective effect of H. pylori infection against eosinophilic esophagitis has also been questioned as an uncritical claim that requires the exclusion of confounding factors associated with it and the demonstration of a causal rather than a coincidental trend relationship [##REF##29545632##25##, ##REF##32770542##26##].</p>", "<title>Association between H. pylori and food allergies or allergic rhinitis</title>", "<p id=\"Par8\">The area of the relationship between H. pylori infection and allergic rhinitis has rarely been learned. A study in Japan indicated a negative correlation between H. pylori infection and the incidence of allergic rhinitis in young people [##REF##20594251##27##]. However, there is no further evidence to support this conclusion. Similarly, there has been limited discussion about the relationship between H. pylori infection and food allergies. A systematic review described the relationship between them but did not come to a conclusive result [##REF##27047479##28##]. However, subsequent studies have shown that H. pylori infection has a protective effect against food allergies, including ovalbumin allergy and peanut allergy [##REF##28802077##29##, ##REF##31468633##30##]. Further researches are needed to fully understand the mechanisms behind this relationship and to determine if H. pylori infection could potentially be used as a treatment or preventative measure for food allergies or allergic rhinitis.</p>", "<title>Mechanism of H. pylori protection against asthma</title>", "<p id=\"Par9\">Genetics and environment are two factors essential for the development of asthma in patients. Genetics determines the patients’ special allergies, and susceptibility to asthma, and whether such patients develop the disease or not is highly related to environmental factors. H. pylori infection showed a significant negative association with asthma risk, but as an environmental factor, the specific pathophysiological mechanism by which it exerts a protective effect on asthma remains unclear. From the analysis of some previously published articles on the subject, it is hypothesized that H. pylori may exert its protective effect against asthma through several pathways(Fig. ##FIG##0##1##).</p>", "<p id=\"Par10\">\n\n</p>", "<title>Application of hygiene hypothesis to the protective effect of H. pylori on asthma</title>", "<p id=\"Par13\">The “hygiene hypothesis,” which has been adopted by the infectious and chronic disease research community since the early 1990s, proposes that exposure to certain infectious agents may prevent the development of allergic diseases [##REF##28634020##12##]. Poor hygiene and lower socioeconomic status increase the risk of exposure to bacteria or other antigens, and therefore to H. pylori infection [##REF##30090002##2##, ##REF##33265933##31##]. In recent years, with the improvement of people’s quality of life, hygiene conditions, and socioeconomic status, the rate of H. pylori infection has gradually decreased and the low prevalence of H. pylori infection could explain the recent high prevalence of allergic diseases [##REF##27047479##28##]. Lack of exposure to infection early in life leads to defective immune tolerance, which in turn leads to increased susceptibility to allergic diseases such as asthma [##REF##30032346##21##, ##REF##31856838##32##], leading to the hypothesis that H. pylori infection exerts a protective effect against allergic diseases such as asthma by promoting immune tolerance. It was pointed out that the hygiene hypothesis can explain the negative correlation between H. pylori infection and allergic diseases. However, it only fits to IgE-mediated allergic diseases and not to non-IgE-mediated allergic diseases [##REF##25257099##33##]. IgE-mediated allergic diseases are caused by immunoglobulin E (IgE)-mediated allergic reactions and are the most common type of allergy. Non-IgE-mediated allergic diseases are mediated by other immune cells, and the pathogenesis is very complex, but the incidence is low. Asthma is an IgE-mediated allergic disease, so it can be concluded that the hygiene hypothesis may explain the negative association between H. pylori infection and asthma.</p>", "<title>Alterations in the gut microbiota</title>", "<p id=\"Par16\">The composition of the gut microbiota may regulate the onset and development of H. pylori-associated diseases. The composition of the gut microbiota influences the immune regulation of the body, and microbial drivers have significant effects on immune development, asthma susceptibility, and asthma pathogenesis [##REF##31812180##34##]. It is known that H. pylori is strictly colonized within the human gastric mucosa and that H. pylori in the stomach may be affecting the intestinal microbiota in the following ways. Theoretically, H. pylori in the stomach can affect the intestinal microbiota by interacting with the body’s immune system and also by altering the local gastric environment. Alterations in the local gastric environment include reduced gastric acid and hypergastrinemia during H. pylori infection, with the low gastric acid environment promoting the entry of acid-sensitive bacteria into the distal intestine as probably the most important pathway of effect, leading to alterations in the composition and abundance of the gut microbiota [##UREF##0##1##]. Even perinatal H. pylori exposure can have a significant impact on the composition and diversity of the neonatal gastrointestinal microbiota [##REF##30240703##35##]. Accordingly, it can be concluded that H. pylori infection affects the composition and abundance of the gut microbiota.</p>", "<p id=\"Par17\">Ecological dysregulation caused by alterations in the composition and abundance of the gut microbiota plays a role in asthma [##REF##36275735##36##, ##REF##32072252##37##], especially in the development and progression of asthma in children [##REF##35196534##38##–##REF##29321519##40##]. The gut microbiota exerts its influence on asthma through several known pathways. The gut-pulmonary axis is an important link between the gut microbiota and the respiratory tract [##REF##36275735##36##], and the metabolites produced by the gut microbiota may have an impact on the development of asthma through the gut-pulmonary axis pathway [##REF##36248891##41##, ##REF##33139948##42##]. The gut microbiota is a key regulator of the intestinal epithelial barrier and the immune response [##REF##36793545##43##], which can act on asthma through the induction of tolerance and allergen penetration through the epithelial barrier [##REF##35718258##44##]. In addition, short-chain fatty acids (SCFA) produced by dietary fiber metabolism by the gut microbiota can prevent asthma by affecting the host G protein-coupled receptor GPR 41, shaping pulmonary immune cell differentiation, and improving allergic airway inflammation [##UREF##5##45##].</p>", "<p id=\"Par18\">Studies on the relationship between gut microbiota and asthma development in mothers and infants have shown that alterations in maternal gut microbiota composition affect the risk of asthma in infants [##REF##34310928##46##]. Based on the conclusion that gut microecological dysbiosis has an impact on the development of asthma, it can be hypothesized that the gut microbiota could be a target for the treatment of asthma by altering its composition and abundance and thus exerting a therapeutic effect on asthma. Clinically used probiotics can have a preventive or therapeutic effect on asthma by regulating the gut microbiota [##REF##34612663##47##, ##REF##30691719##48##]. Some studies have also shown that alterations in the composition and abundance of the gut microbiota are not associated with the development of asthma. In a mouse experiment, the gut microbiota was found to be independent of reflecting airway hyperresponsiveness penh values [##REF##35662725##49##]. In a cohort study of adults, no significant differences were found in the composition of the fecal microbiota between asthmatic and non-asthmatic patients [##UREF##4##39##]. The reasons for these results may be due to the underrepresentation of the fecal microbiota to the gut microbiota, the adult immune system is well developed and alterations in the gut microbiota do not or only slightly affect the adult immune system.</p>", "<p id=\"Par19\">H. pylori infection can affect the composition and abundance of the gut microbiota through interactions with the body’s immune system and changes in the local gastric environment. The gut microbiota uses the gut-pulmonary axis as an important linkage pathway to exert a protective effect against asthma, either through metabolites or by modulating immunity(Fig. ##FIG##1##2##). However, a limitation of this research area is that in most of the relevant studies, the fecal microbiota is used instead of the gut microbiota, ignoring the microorganisms remaining in the gut, which may cause bias in the results. In the H. pylori-gut microbiota-asthma liaison pathway, ignoring the possible bias, the gut microbiota can serve as an emerging target for the prevention and treatment of asthma. Modification of the gut microbiota by certain drugs or treatments, which in turn exerts a protective effect against asthma.</p>", "<p id=\"Par21\">\n\n</p>", "<title>The critical role of tolerogenic dendritic cells in the protection of asthma by H. pylori</title>", "<p id=\"Par25\">H. pylori inhibits lipopolysaccharide-induced dendritic cell (DC) maturation and is able to recode dendritic cells into tolerogenic dendritic cells [##REF##22307326##50##, ##REF##29910462##51##]. Some findings show that tolerant dendritic cells do not induce effector functions of T cells, but rather convert naive T cells into FoxP3 + Treg with high suppressive activity. FoxP3 + Treg can prevent airway inflammation and hyperresponsiveness, thus exerting a protective effect against asthma [##REF##22307326##50##]. H. pylori can produce urease, which activates NLRP3, a component of cytoplasmic inflammatory vesicles, and stimulates the TLR2/ NLRP3/IL-18 axis [##REF##32510247##52##]. IL-18 on this axis is a key cytokine for Treg to perform its function, IL-18 produced by dendritic cells is not only the basis for the conversion of CD4 + T cells into Treg but also for Treg to perform its function [##REF##30090002##2##]. γ-glutamyl transpeptidase (GGT) and vacuolar cytotoxin (VacA) are virulence factors of H. pylori, and it was demonstrated that isogenic H. pylori mutants lacking GGT or VacA cannot prevent LPS-induced dendritic cells maturation or drive dendritic cells tolerance, thus the above two virulence factors play a key role in dendritic cell tolerance [##REF##23382221##53##]. Based on the promoting effect of tolerogenic dendritic cells on Treg formation and the protective effect of Treg in asthma, it can be inferred that transforming sufficient numbers of dendritic cells into tolerogenic dendritic cells and maintaining their tolerance status is key for H. pylori to exert a protective effect against asthma.</p>", "<title>The immune balance of Th1/Th2 and Treg/Th17 cells</title>", "<p id=\"Par26\">A large number of cells such as eosinophils, neutrophils, mast cells, and T lymphocytes are involved in the airway inflammation of asthma [##REF##30763933##54##]. Among them, CD4 + T cells are the main lymphocytes that infiltrate the airways and play a crucial role in controlling asthma-related inflammation. Naive CD4 + T cells can differentiate into Th1, Th2, Th17, and Treg. Th1 cells produce IFN-γ, while Th2 cells produce IL-4, IL-5, and IL- 13 [##REF##32143368##55##]. Th2-biased immune responses in genetically susceptible individuals may cause allergic diseases such as asthma [##REF##25207960##56##]. It has been claimed that H. pylori infection affects the Th1/Th2 balance by influencing gastric hormones. When growth inhibitory hormone levels decrease and gastrin production increases, it suppresses the Th2 response and promotes the Th1 response [##UREF##2##11##]. The mechanism by which H. pylori prevents and protects against asthma may be to drive the Th1 inflammatory response and inhibit the Th2-mediated allergic asthmatic response [##REF##25516679##4##, ##REF##24528371##5##, ##REF##22515362##14##]. In the clinic, upregulation of Th1 response or downregulation of Th2 response seems to be a target for the treatment of asthma, but it still needs to be explored and tested in the clinic. Treg and Th17 cells are functionally antagonistic to each other, and the balance of Treg and Th17 cells plays an important role in the development and progression of H. pylori and its associated diseases [##REF##33841407##57##]. Excess IL- 17 has been found in sputum, bronchoalveolar lavage fluid (BALF), and lung tissue in chronic allergic airway inflammation [##REF##30763933##54##]. It is hypothesized that both Th1/Th2 balance and Th17/Treg balance play a key role in the onset and persistence of asthma, and that asthma can be prevented and protected when Th1 and Treg are dominant in the ratio. One study experimented with the relationship between Th1 and Treg responses to H. pylori and allergen-specific IgE levels. The results showed a significant increase in IL- 10(+) Treg in the peripheral blood of H. pylori-infected individuals and correlated with a decrease in plasma IgE concentrations [##REF##27014260##58##]. Th2 and its its cytokines are the basis of inflammation in asthma pathogenesis, and H. pylori exerts a protective effect against asthma by promoting the Th1 response and inhibiting the Th2 response. Th17 and its cytokines are also important in controlling asthma-associated inflammation, and Treg not only antagonism with Th17 but also directly suppresses airway inflammation and hyperresponsiveness in asthma. The protective effect on asthma that can be exerted by enhancing the Treg response is a currently available target for asthma treatment and is a very promising route for the treatment of asthma.</p>", "<p id=\"Par27\">H. pylori affects the onset and development of asthma by influencing the balance of Th1/Th2 and Treg/Th17. This is one of the potential mechanisms, but it is still in the developmental stage, and the exact mechanism remains to be determined. Several factors influence the balance between Th1/Th2 and Treg/Th17. The Th1 response is mainly associated with autoimmune reactions, while the Th2 response is primarily linked to allergic reactions. Bacterial or viral infections can cause their imbalances, and H. pylori may be no exception. Further experiments are needed to explore the distinctiveness and dependability of this mechanism.</p>", "<title>Helicobacter pylori in the treatment of asthma</title>", "<p id=\"Par28\">It has been shown that the protective effect of H. pylori infection against allergic airway disease does not require live bacteria and that treatment with H. pylori extracts is also effective in suppressing allergic airway disease [##REF##29352004##59##]. Even perinatal exposure to H. pylori extract or its immunomodulator VacA can exert a protective effect against allergic airway disease, and this powerful protective effect occurs not only in the first but even in the second generation of offspring [##REF##30240703##35##]. This shows the great scope for the development of H. pylori and its extracts in the prevention and treatment of allergic airway diseases such as asthma, and we may try to intervene in suspected asthma in newborns through perinatal exposure. Helicobacter pylori neutrophil-activating protein (Hp-NAP), the main virulence factor of H. pylori, is a modulator with anti-Th2 inflammatory activity for the prevention of IgE-mediated allergic reactions [##REF##28608279##60##]. Hp-NAP is a member of an extensive superfamily of ferritin-like proteins, which are homopolymers of 12 tetrahelical bundle subunits containing iron ligands, and whose members mostly have DNA-protective functions under starvation conditions [##REF##28608279##60##]. Hp-NAP plays an important role in the protection of H. pylori infection against allergic diseases and is one of the candidates for a new strategy of prevention and protection against allergic diseases. H. pylori neutrophil-activating protein was shown to prevent allergic asthma in mice. Experimental mice exposed to purified rNAP by intraperitoneal injection or inhalation showed a significant reduction of eosinophils in lung tissue and bronchoalveolar lavage fluid (BALF) after stimulated sensitization with nebulized ovalbumin (OVA), and also a significant reduction of inflammatory infiltration in lung tissue. In addition, the treatment group showed lower levels of IL-4 and IL- 13, higher levels of IL- 10 and IFN-γ, and lower levels of serum IgE compared to the control group [##REF##28087613##61##]. Another similar study showed the same results, where a fusion protein CTB-NAP of cholera toxin B (CTB) and neutrophil-activating protein (NAP) was constructed on the surface of Bacillus subtilis, and oral administration of recombinant CTB-NAP spores was effective in preventing asthma in mice [##REF##28608279##60##]. The prevention and treatment of asthma are systematic, the treatment of asthma focuses not only on the acute onset of symptoms but also on preventing the recurrence in clinical remission stage. Therefore, the above studies show the great potential of NAP in the prevention and treatment of allergic diseases such as asthma, but future experiments are still needed to verify whether NAP can cause side effects and toxic effects, and other adverse reactions in humans. Another substance, human protein S, enables a shift to Th1 through the Th1/Th2 balance and promotes Th1 cytokine secretion to exert a powerful protective effect on the development of allergic asthma [##REF##32145080##62##]. It is clinically recognized that H. pylori eradication reduces the risk of gastric cancer, but based on its preventive and protective effects on allergic diseases such as asthma and other systemic diseases, the issue of H. pylori eradication should be considered with caution. Some studies have shown that eradication of H. pylori can restore the intestinal flora to a state similar to that of uninfected individuals [##REF##31857433##63##–##REF##30311721##65##], and others have shown that eradication treatment leads to short-term disruption of the intestinal flora, but that this disruption is restored within weeks to months [##REF##36426355##66##–##REF##32515529##68##]. The us e of H. pylori in the treatment of asthma opens the breadth of research on the association of H. pylori infection and asthma risk, with a novel perspective on the importance of H. pylori infection in asthma. However, the application of H. pylori and its extracts in the treatment of asthma still requires a large number of clinical trials to verify its safety and effectiveness and to exclude its possible adverse reactions.</p>" ]

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[ "<fig id=\"Fig1\"><label>Fig. 1</label><caption><p>Pathways by which H. pylori exerts a protective effect against asthma</p></caption></fig>", "<fig id=\"Fig2\"><label>Fig. 2</label><caption><p>The pathways by which H. pylori affects the gut microbiota and the mechanisms by which the altered gut microbiota affects asthma</p></caption></fig>" ]

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[ "<fn-group><fn><p><bold>Publisher’s Note</bold></p><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn></fn-group>" ]

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[ "<title>Introduction</title>", "<title>Background and rationale</title>", "<p id=\"Par17\">Submucosal tumors (SMTs) histologically include both epithelial and nonepithelial tumors. Nonepithelial tumors typically present as protruding lesions or masses covered with intact mucosa [##UREF##0##1##]. Large SMTs (≥2 cm) in the stomach may lead to early-stage complications such as bleeding or perforation, resulting in symptoms such as abdominal bloating, pain, hematemesis, or melena, which prompt patients to seek medical attention. In contrast, small gastric SMTs (&lt;2 cm) are typically discovered incidentally during endoscopy without any apparent symptoms [##REF##23623056##2##, ##REF##22089421##3##].</p>", "<p id=\"Par18\">The risk of small gastric SMT-MPs has been underestimated [##UREF##1##4##]. Studies suggest that 60–70% of SMT-MPs are pathologically identified as gastrointestinal stromal tumors (GISTs) and categorized as potential malignancies regardless of their size [##REF##23623056##2##, ##REF##22089421##3##]. However, although surgeons propose resection for large gastric SMT-MPs, clinical controversy persists [##UREF##0##1##, ##UREF##2##5##, ##REF##26181401##6##]. In a retrospective study conducted by Ge QC et al. [##REF##28077094##7##], a cutoff value of 1.48 cm was established to predict the malignant potential of GISTs. Tumors larger than 1.48 cm were associated with greater malignant potential, warranting intensive surveillance or endoscopic surgery. According to the modified National Institute of Health, the risk of small GISTs varies only with the mitotic count. The classifications included very-low risk (mitotic count ≤5), intermediate risk (mitotic count between 5 and 10), and high risk (mitotic count &gt;10). Some advocate for imaging surveillance as the primary approach, suggesting resection only when tumor progression is confirmed. This includes cases where the tumor shows signs of increasing size, irregular borders, or pathological confirmation as a cancer [##UREF##3##8##]. Although endoscopic ultrasound (EUS) is a common method for diagnosing gastrointestinal superficial lesions, its role in diagnosing SMT-MPs has not been determined. Additionally, consistent observation of dynamic changes in tumor size and border length for patients with SMT-MPs &lt; 16 mm is challenging. Although EUS-guided fine needle aspiration (EUS-FNA) is often employed for pathology, it may not fully reveal the pathological features of GISTs due to heterogeneity. In conclusion, en bloc resection is crucial for both diagnosis and prognosis [##UREF##4##9##].</p>", "<p id=\"Par19\">Endoscopic resection, in comparison to open or laparoscopic surgery, yields a shorter operation duration, reduced blood loss, and shorter average hospitalization duration [##UREF##4##9##–##REF##28220962##14##]. Endoscopic submucosal resection (ESD) has been demonstrated to be feasible for treating gastric SMTs. Guidelines from the European Society of Gastrointestinal Endoscopy (ESGE) and the American Society for Gastrointestinal Endoscopy (ASGE) recommend ESD as the preferred treatment for most gastric superficial neoplastic lesions [##REF##26317585##15##, ##REF##37498266##16##]. However, its effectiveness is limited for lesions originating from deeper layers such as the muscularis propria, increasing the complexity of the operation and the risk of complications. A systematic review by Ichiro Oda et al., encompassing more than 300 patients with early gastric cancer treated with ESD, identified several complications associated with the procedure. These complications included perforation (1.2–5.2%), bleeding (7% for immediate bleeding, up to 15.6% for delayed bleeding), stenosis (0.7–1.9%), aspiration pneumonia (0.8–1.6%), and air embolism, among others [##REF##23368986##17##]. Although management strategies exist for these adverse events, they demand a higher level of technical expertise, adding to the financial burden and psychological stress on patients. Furthermore, ESD may not always achieve R0 resection, posing challenges for diagnosis and prognosis [##UREF##7##18##, ##UREF##8##19##]. According to an analysis of 733 patients with upper gastrointestinal SMT-MPs, extensive tumor connection was identified as a risk factor for incomplete resection [##REF##26782819##20##]. In a multicenter prospective study by Ye LP et al. involving 692 patients, the R0 resection rate was 84.2% [##UREF##8##19##]. Hence, a more judicious treatment approach is imperative.</p>", "<p id=\"Par20\">We previously introduced a novel endoscopic treatment termed precutting EBL. In this operation, an electrosurgical snare resection is performed to initially remove the mucosa surrounding the tumor, followed by the use of a transparent ligator to suction the tumor. A long-term, single-center study has substantiated its safety and efficacy. Precutting EBL was associated with a significantly shorter operation duration (16.6 min) and lower cost ($603.3 ± 5.9) than ESD ($2783 ± 601), and it was associated with fewer complications [##REF##33737005##21##]. However, precutting EBL has two notable drawbacks. First, pathological specimens were not collected since the tumor spontaneously drops off after ligation, necessitating long-term follow-up for eradication verification. Second, like other ligate-and-let-go techniques, there is a risk of delayed perforation after the operation, which warrants careful consideration [##REF##20541195##22##]. Given that we did not have sufficient samples to assess the possibility of delayed perforation, we opted to perform en bloc resection of lesions after ligation. Although this approach increases the chances of intraoperative perforation, we can promptly address this possibility if it occurs. Consequently, we propose a modified endoscopic operation for small gastric SMT-MPs, termed precutting EBLR. This involves an additional snare resection immediately after ligation. After thorough communication and detailed informed consent, we experimentally performed precutting EBLR on 16 patients. All patients showed rapid postoperative recovery, with no instances of delayed gastric bleeding or perforation. Importantly, subsequent pathological examination confirmed R0 resection in every patient.</p>", "<p id=\"Par21\">To further enhance the clinical validation of precutting EBLR, we opted to initiate a randomized controlled trial comparing the efficacy and safety of ESD and precutting EBLR for the treatment of small gastric SMT-MPs.</p>", "<title>Trial design and objective</title>", "<p id=\"Par22\">This was a single-center, open-label, parallel-group, randomized controlled trial. The main objective of this trial was to verify the efficacy and safety of precutting EBLR in the management of small gastric SMT-MPs. The trial began on December 1, 2022. The procedures included recruitment, informed consent, allocation of participants, intervention, data collection, data monitoring, and statistical analysis. All procedures were conducted at The First Affiliated Hospital of Chongqing Medical University (CQMU). A detailed flowchart for this trial is available in the ##SUPPL##0##Supplementary Materials##. The drafting of this manuscript adheres to the SPIRIT reporting guidelines [##UREF##9##23##]. The SPIRIT checklist is attached as Additional file ##SUPPL##1##2## in Supplementary Materials.</p>" ]

[ "<title>Methods</title>", "<title>Definition</title>", "<p id=\"Par23\">Several key definitions are outlined below:<list list-type=\"order\"><list-item><p id=\"Par24\">Efficacy: the efficacy was determined based on the operation duration, operation cost, and hospitalization duration</p></list-item><list-item><p id=\"Par25\">Operation cost: the sum of the operational and material expenses, retrievable from the hospital system</p></list-item><list-item><p id=\"Par26\">Operation duration: the time from the administration of preoperative anesthesia to the patient's recovery of consciousness in the postoperative period</p></list-item><list-item><p id=\"Par27\">Safety: the ratio of intraoperative to postoperative complications</p></list-item><list-item><p id=\"Par28\">En bloc resection: complete removal of a lesion without any segmentation or partial lesion remaining</p></list-item><list-item><p id=\"Par29\">R0 resection: the absence of cancerous tissue on the edges of the lesion after resection</p></list-item><list-item><p id=\"Par30\">Postoperative gastric bleeding: a patient experienced hematemesis, melena, or an unexplained decrease in hemoglobin levels after the operation</p></list-item><list-item><p id=\"Par31\">Delayed perforation: the occurrence of sudden abdominal pain after the operation, accompanied by the detection of retroperitoneal pneumatosis or free gas through imaging examination</p></list-item><list-item><p id=\"Par32\">Postoperative recurrence: the discovery of a newly investigated tumor-like lesion that is eventually proven to be the same pathology as the previously resected tumor</p></list-item><list-item><p id=\"Par33\">Hospitalization duration: the number of days from admission to discharge</p></list-item></list></p>", "<title>Patient and public involvement</title>", "<p id=\"Par34\">No patients or members of the public were involved in any way in the design of this trial.</p>", "<title>Recruitment</title>", "<p id=\"Par35\">Patients with SMT-MPs admitted to The First Affiliated Hospital of CQMU were recruited. The inclusion criteria were as follows:<list list-type=\"order\"><list-item><p id=\"Par36\">Age between 18 and 80 years</p></list-item><list-item><p id=\"Par37\">SMT-MPs with a diameter less than 1.6 cm confirmed through EUS</p></list-item><list-item><p id=\"Par38\">Preoperative computed tomography (CT) indicated no evidence of tumor metastasis in the liver or other organs</p></list-item><list-item><p id=\"Par39\">Willingness of the patient to undergo treatment with either ESD or precutting EBLR</p></list-item><list-item><p id=\"Par40\">Informed consent was obtained</p></list-item></list></p>", "<p id=\"Par41\">The exclusion criteria were as follows:<list list-type=\"order\"><list-item><p id=\"Par42\">EUS data were not available from The First Affiliated Hospital of CQMU or any other hospitals</p></list-item><list-item><p id=\"Par43\">Contraindications for gastroscopy or endoscopic surgery, such as cardiopulmonary insufficiency rendering the patient unsuitable for endoscopy, shock, or gastrointestinal perforation; inability to cooperate due to psychiatric disorders; acute severe laryngopharyngeal disorders preventing endoscope insertion; acute stage of corrosive esophageal injury; coagulation disorders; or a hemorrhagic tendency</p></list-item><list-item><p id=\"Par44\">Pregnant or breastfeeding</p></list-item><list-item><p id=\"Par45\">Presence of advanced malignant tumors</p></list-item><list-item><p id=\"Par46\">Allergy to oral lidocaine syrup and dimethicone oil</p></list-item><list-item><p id=\"Par47\">Current participation in other clinical trials</p></list-item><list-item><p id=\"Par48\">Option to withdraw from the trial exercised at any time</p></list-item></list></p>", "<p id=\"Par49\">The entire recruitment process is managed by postgraduates MfL and RY. All patients with SMT-MP who met the inclusion criteria were approached for potential participation. Despite the absence of specific literature and sample data on enrollment and recruitment rates, achieving the desired sample size is deemed feasible based on the current participant flow. As the principal investigator of this trial, Physician LD assumes the responsibility of conducting comprehensive communication and obtaining informed consent from patients. Each participant received a copy of the informed consent form detailing the trial's potential benefits and risks. After thoughtful consideration, participants are empowered to make independent decisions about their involvement. The recruitment and informed consent process is devoid of inducements or pressures, ensuring voluntary participation and preventing unwarranted termination or loss to follow-up. Participants retain the option to withdraw from the trial at any point.</p>", "<title>Allocation</title>", "<p id=\"Par50\">The sample size was determined based on the primary outcome, operation duration, using PASS 2011 software (NCSS, LLC, Kaysville, Utah, USA). Drawing from insights obtained from our previous single-arm retrospective study and a trial investigating ESD [##REF##30478693##24##], with a power of 90% (<italic>β</italic> = 0.1) and a significance level (<italic>α</italic>) of 0.05 [##REF##32658647##25##], the estimated primary sample size was approximately 34 patients. To account for a potential dropout rate of 10–20%, the final sample size was set at 40 patients. Consequently, each group included 20 patients.</p>", "<p id=\"Par51\">Randomization was performed by SL using a random numbers table generated by IBM SPSS Statistics 23. From 1 to 40, each order was randomly assigned either the letter A or B with an equal probability. After the generation was completed, participants with the letter A were assigned to the Precutting EBLR group, while those with the letter B were assigned to the ESD group.</p>", "<title>Interventions</title>", "<p id=\"Par52\">The participating operators were required to meet the following criteria:<list list-type=\"order\"><list-item><p id=\"Par53\">Possess more than 5 years of experience in medicine</p></list-item><list-item><p id=\"Par54\">Demonstrated ability to independently conduct endoscopic operations</p></list-item><list-item><p id=\"Par55\">Operators with a history of performing no fewer than 300 endoscopic operations annually and a total of at least 1000 procedures</p></list-item></list></p>", "<p id=\"Par56\">Patients were required to undergo a comprehensive preoperative evaluation to ensure the absence of absolute surgical contraindications. The operation was immediately stopped in the event of an unexpected intraoperative contingency, and appropriate clinical measures were taken accordingly. A detailed analysis and documentation of the possible reasons for such contingencies will be conducted. Intraoperative and postoperative interventions may be adjusted following established guidelines [##UREF##10##26##]. Implementing ESD or precutting EBLR will not require alteration to usual care pathways (including the use of any medication), and these steps will continue for both trial arms. Regarding postdischarge interventions (regular intake of esomeprazole), we contacted each participant via phone to provide reminders for consistency in medication adherence. This approach was approved by the participants when they signed the informed consent form.</p>", "<title>ESD</title>", "<p id=\"Par57\">Initially, a high-viscosity solution is employed to elevate the submucosal covering of the tumor. Subsequently, electrocautery knives are used for dissecting the tissue beneath and surrounding the lesion, leaving a resection bed. In the event of a perforation, closure can be facilitated using titanium clips or a purse-string suture [##UREF##7##18##]. Following the completion of the operation, patients undergo a 48-h observation period during which they fast and regularly take esomeprazole (40 mg, twice daily). Upon discharge, patients are required to continue taking esomeprazole (40 mg, once daily) for 2 weeks.</p>", "<title>Precutting EBLR</title>", "<p id=\"Par58\">Initially, an electrosurgical snare is positioned on the tumor’s mucosal protuberance, followed by snare resection using an electrosurgical current set at 30 W to precut and remove the covering mucosa. Subsequently, an appropriate ligator is chosen based on the tumor size: a small ligator for tumors within 1 cm, a medium ligator for tumors ranging from 1 to 1.2 cm, and a large ligator for tumors greater than 1.2 cm. After proper ligator installation, the tumor is drawn from the surface and effectively removed using an electrosurgical snare. Closure of the perforation caused by ligation is assisted by employing three-armed clips or titanium clips. Finally, the excised tumor is sent for pathological examination. Postsurgery, fast for 12–24 h is required, followed by a liquid diet for 2–3 days and esomeprazole (40 mg, once daily) for 2 weeks. The steps of the operation and postoperative pathology are shown in Fig. ##FIG##0##1##<italic>.</italic></p>", "<title>Devices</title>", "<p id=\"Par59\">CT, EUS (OLYMPUS EU-M2000, 20 MHz, Japan), and standard endoscopy (AOHUA AQ200L, China) were used for preoperative assessment and follow-up. Standard endoscopes (AOHUA AQ200L, China) and loop snares (MICRO-TECH (NANJING) Co., Ltd., China) were used for mucosal protuberance precutting. Small ligators (TIANJIN TY, Medical Organism Material Research Company Ltd., China) were used for tumors ≤ 10 mm in length; medium ligators (OTSC cap plus ligation band, Ovesco Endoscopy AG, Tubingen, Germany) were used for tumors &gt; 10 mm but ≤12 mm in length; and large ligators (colonoscopy transparent cap plus ligation band, OLYMPUS, Japan) were used for tumors &gt;12 mm in length. All the ligators were disposable. Injectors (OLYMPUS, Japan), an IT knife, a dual knife, and an electronic cutting device (EREB VIO 200S, Germany) were used for ESD.</p>", "<title>Outcomes and follow-up</title>", "<p id=\"Par60\">The primary outcome for the trial was operation duration, and the secondary outcome was operation cost. Both outcomes will be assessed prior to discharge. Additional meaningful indicators, also set as secondary outcomes, include estimated blood loss, intraoperative and postoperative adverse events (such as bleeding, immediate and delayed perforation, infection), tumor recurrence, mortality rates, and hospitalization costs.</p>", "<p id=\"Par61\">The trial’s endpoint will be established as 6 months after the operation of the last included patient. Each patient is given a detailed follow-up evaluation via telephone 6 months after discharge to gather information about their postoperative condition. At the 6-month mark, patients are required to undergo an endoscopic re-examination to assess tumor recurrence.</p>" ]

[]

[ "<title>Discussion</title>", "<p id=\"Par67\">Initially, precutting EBL was designed to address the current challenge of treating small gastric SMT-MPs, and a previous study demonstrated its clinical feasibility. However, before we could extend its application to other areas or institutions, notable shortcomings emerged. In response, we promptly started to further modify the operation. This is why precutting EBLs were not tested on a larger scale. Simultaneously, a case of delayed perforation heightened our concern. Forty-eight hours after receiving precutting EBL, a middle-aged male patient suddenly complained of severe abdominal pain. A CT scan revealed a gastric perforation at the site of the lesion. Fortunately, the patient soon recovered and was discharged after immediate closure of the perforation. This case indicated a way to further modify precutting EBL to a certain extent.</p>", "<p id=\"Par68\">Precutting EBLR was proposed. Its local performance in 16 patients revealed its advantages in terms of a shorter operation duration and lower expenses. Encouraged by these findings, we decided to gradually expand the scale of the study in anticipation of promoting precutting EBLR. The current trial is specifically designed to compare precutting EBLR and ESD. The operations were conducted at The First Affiliated Hospital of CQMU, a large-scale 3A general teaching hospital renowned for its high level of clinical and academic research. Located in southwestern China, the hospital attracts a substantial amount of patient flow, mainly from the surrounding regions and provinces. The Department of Gastroenterology at this hospital handles an extensive patient population, both in terms of quantity and variety, providing the necessary conditions to achieve the planned sample size. Prior to conducting this RCT, we collected primary data from 16 patients who underwent precutting EBLR. The data showed a mean operation duration of 21.3 ± 4.5 min (Table ##TAB##1##2##). Moreover, we performed a retrospective study involving 537 patients in whom the use of endoscopic resection for the treatment of small gastric SMTs was analyzed. The study revealed a mean operation duration of 38.3 ± 21.8 min.²⁴ The shorter operation duration of precutting EBLR was evident. In this RCT, we designated the operation duration as the primary outcome. Based on sample size estimation guidelines for clinical studies, we set α and β to 0.05 and 0.9, respectively. With the above numerical values, 17 patients were included in one group. In other words, 34 participants were necessary in total for a 1:1 group ratio. We considered a 10–20% drop-out rate. The final sample size was determined to be 40 patients in total.\n</p>", "<p id=\"Par69\">The primary outcome was set as the operation duration, with the objective of showcasing the main advantage of precutting EBLR. The other outcomes also help to demonstrate the safety and efficacy of the treatment, such as reduced hospitalization costs when the duration is equal or shorter hospitalization duration when the costs are equal. Precutting EBLR holds the potential to emerge as a creative and promising endoscopic approach for treating SMT-MPs, offering a more practical, simpler, and safer alternative. Moreover, this approach has the potential to alleviate the economic burden on both patients and health insurance companies, leading to substantial societal benefits. These advantages also foster the prospect of transforming the resection of gastric small SMT-MPs from a hospitalized operation to an ambulatory operation.</p>", "<p id=\"Par70\">This trial has several limitations. The relatively small sample size may introduce bias if patients are lost to follow-up, and conducting further multicenter studies could address this issue. Additionally, the 6-month follow-up duration might be insufficient to thoroughly observe tumor recurrence. Currently, there is a lack of a specific method for investigating tumor recurrence in a timely manner. In other words, if tumor recurrence occurs at 1 or 6 months after the operation, it is ultimately identified during the re-examination 6 months after discharge. This may lead to an underestimation of the impact of different operations on tumor recurrence.</p>" ]

[]

[ "<title>Background</title>", "<p id=\"Par1\">The management of small gastric submucosal tumors (SMTs) originating from the muscularis propria layer (SMT-MPs) remains a subject of debate. Endoscopic submucosal dissection (ESD) is currently considered the optimal treatment for resection. However, high expenses, complex procedures, and the risk of complications have limited its application. Our previously proposed novel operation, precutting endoscopic band ligation (precutting EBL), has been demonstrated in a long-term, single-arm study to be an effective and safe technique for removing small gastric SMTs. However, the absence of a pathological examination and the potential for delayed perforation have raised concerns. Thus, we modified the precutting EBL by adding endoscopic resection to the snare after ligation and closure, yielding the precutting endoscopic band ligation-assisted resection (precutting EBLR). Moreover, the initial pilot study confirmed the safety and efficacy of the proposed approach and we planned a randomized controlled trial (RCT) to further validate its clinical feasibility.</p>", "<title>Methods</title>", "<p id=\"Par2\">This was a prospective, single-center, open-label, parallel group, and randomized controlled trial. Approximately 40 patients with SMT-MPs will be included in this trial. The patients included were allocated to two groups: ESD and precutting EBLR. The basic clinical data of the patients were collected in detail. To better quantify the difference between ESD and precutting EBLR, the primary outcome was set as the operation duration. The secondary outcomes included total operation cost and hospitalization, intraoperative adverse events, and postoperative recurrence. The primary outcome was tested for superiority, while the secondary outcomes were tested for noninferiority. SPSS is commonly used for statistical analysis.</p>", "<title>Discussion</title>", "<p id=\"Par3\">This study was designed to validate the feasibility of a novel operation for removing gastric SMT-MPs. To intuitively assess this phenomenon, the operation durations of precutting EBLR and ESD were compared, and other outcomes were also recorded comprehensively.</p>", "<title>Trial registration</title>", "<p id=\"Par4\">Chinese Clinical Trial Registry <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.chictr.org.cn/showproj.html?proj=174531\">ChiCTR2200065473</ext-link>. Registered on November 5, 2022.</p>", "<title>Supplementary Information</title>", "<p>The online version contains supplementary material available at 10.1186/s13063-024-07902-7.</p>", "<title>Keywords</title>" ]

[ "<title>Data management</title>", "<title>Collection</title>", "<p id=\"Par62\">All the data were collected and verified by two statisticians simultaneously using a spreadsheet (Microsoft Excel 2016) in accordance with each patient’s personal information and medical images. Patients are assigned numerical codes instead of their names to ensure the confidentiality of personal information. To minimize statistical errors, any controversial data is reviewed and discussed by a third person. Preoperative, intraoperative, and postoperative data are collected. Missing data will be declared in the appendix, and the corresponding participant will be considered withdrawn.</p>", "<p id=\"Par63\">Preoperative data included demographic information (age, sex, date of admission) and tumor characteristics (size, layer, location, shape, and density of the echo site investigated via EUS). Intraoperative data included the operation date, duration, estimated blood loss, and details related to intraoperative perforation (size, duration, and amount of titanium clips). Postoperative data included the size of the resected tumor (assessed by ruler), tumor pathology (tumor type, mitotic count, achievement of R0 resection or not, and immunohistochemistry), postoperative management (duration of fasting and liquid diet, use of medications), postoperative symptoms and adverse events, hospitalization duration and cost, operation cost, and 6-month follow-up outcome.</p>", "<title>Monitoring</title>", "<p id=\"Par64\">The data were monitored by The First Affiliated Hospital of CQMU. In this trial, the platform is exclusively utilized for hospitalization purposes and remains independent of any competing interests. Monthly trial audits will be conducted without the presence of funders or sponsors to assess the progress of each participant. LD will conduct an interim analysis around June 2024, and the trial may be terminated earlier than planned if the data are sufficiently convincing to draw a final conclusion or if a significant proportion of precutting EBLR patients develop unexpected postoperative complications. Adverse events (AEs) or severe adverse events (SAEs) will be promptly reported to the clinical trial team. Relevant information will also be recorded locally for further analysis.</p>", "<title>Statistical analysis</title>", "<p id=\"Par65\">For statistical analysis, commercial software, specifically IBM SPSS Statistics 23, will be used. Normally distributed data are presented as the means and standard deviations (X±S). Student’s <italic>t</italic> test was used to analyze significant differences between groups. The data that conformed to a skewed distribution are expressed as the median and range. Statistical differences between groups were analyzed using the Mann–Whitney <italic>U</italic> test. Categorical data are presented as numbers and percentages and were analyzed using Fisher’s exact test or the chi-square test. To explore potential risk factors, participants were allocated to two subgroups based on tumor recurrence. Relevant data, including age, operation duration, tumor size, tumor layer, pathology, and mitotic count (if there was a GIST), were collected again. Univariate analysis will be conducted to identify differential expression of the genes. Multiple regression analysis was subsequently conducted on the various indicators. <italic>P</italic> &lt; 0.05 indicated statistical significance.</p>", "<title>Participant timeline</title>", "<p id=\"Par66\">See Table ##TAB##0##1##.\n</p>", "<title>Supplementary Information</title>", "<p>\n</p>" ]

[ "<title>Acknowledgements</title>", "<p>Not applicable.</p>", "<title>Protocol amendments</title>", "<p id=\"Par71\">Any protocol changes that could result in deviations will be thoroughly documented in a separate spreadsheet. The protocol update history will be reported to the clinical trial registry in the future. Trial deviations, violations, AEs, and SAEs will also be sent to the registry.</p>", "<title>Authors’ contributions</title>", "<p>The authors read and approved the final manuscript.</p>", "<title>Availability of data and materials</title>", "<p>No identifying images or other personal or clinical details of the participants are presented here or will be presented in reports of the trial results. The datasets used and/or analyzed during the current study, the participant information materials and the informed consent form are available from the corresponding author upon request. The trial results will be available via publication.</p>", "<title>Declarations</title>", "<title>Ethics approval and consent to participate</title>", "<p id=\"Par72\">This study was approved by the Ethics Committee of the First Affiliated Hospital of Chongqing Medical University (approval number: 2022-161) and successfully registered in the Chinese Clinical Trial Registry (registration number: ChiCTR2200065473). Written informed consent to participate will be obtained from all participants.</p>", "<title>Consent for publication</title>", "<p id=\"Par73\">Not applicable.</p>", "<title>Competing interests</title>", "<p id=\"Par74\">The authors declare that they have no competing interests.</p>" ]

[ "<fig id=\"Fig1\"><label>Fig. 1</label><caption><p>Steps of the precutting EBLR and postoperative pathology. <bold>A</bold> The tumor investigated in the fundus of the stomach by white light gastroscopy. <bold>B</bold> The tumor (white arrow) investigated by EUS. <bold>C</bold> The tumor’s covering mucosa was precut and removed. <bold>D</bold> The tumor was drawn from the surface using a transparent ligator. <bold>E</bold> The tumor was removed using an electrosurgical snare. <bold>F</bold> Active perforation was closed with titanium clips. <bold>G</bold> Microscopic view of the tumor. <bold>H</bold> Gastrointestinal stromal tumor was pathologically demonstrated</p></caption></fig>" ]

[ "<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Participant timeline</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th/><th colspan=\"7\">Study period</th></tr><tr><th/><th>Recruitment</th><th>Allocation</th><th colspan=\"3\">Hospitalization</th><th colspan=\"2\">Follow-up</th></tr><tr><th>Timepoint</th><th><italic>−t</italic>_{<italic>1</italic>}</th><th><italic>0</italic></th><th><italic>Admission</italic></th><th><italic>Operation</italic></th><th><italic>Discharge</italic></th><th><italic>6 months</italic></th><th><italic>12 months</italic></th></tr></thead><tbody><tr><td><bold>Recruitment:</bold></td><td colspan=\"7\"/></tr><tr><td> <bold>Eligibility screen</bold></td><td>X</td><td/><td/><td/><td/><td/><td/></tr><tr><td> <bold>Informed consent</bold></td><td>X</td><td/><td/><td/><td/><td/><td/></tr><tr><td> <bold>Allocation</bold></td><td/><td>X</td><td/><td/><td/><td/><td/></tr><tr><td><bold>Interventions:</bold></td><td colspan=\"7\"/></tr><tr><td> <bold>ESD</bold></td><td/><td/><td/><td>X</td><td/><td/><td/></tr><tr><td> <bold>Precutting EBLR</bold></td><td/><td/><td/><td>X</td><td/><td/><td/></tr><tr><td><bold>Assessments:</bold></td><td colspan=\"7\"/></tr><tr><td> <bold>Baseline characteristics</bold></td><td/><td/><td>X</td><td/><td/><td/><td/></tr><tr><td> <bold>Operation duration</bold></td><td/><td/><td/><td>X</td><td/><td/><td/></tr><tr><td> <bold>Estimated blood loss</bold></td><td/><td/><td/><td>X</td><td/><td/><td/></tr><tr><td> <bold>Intraoperative adverse events</bold></td><td/><td/><td/><td>X</td><td/><td/><td/></tr><tr><td> <bold>Postoperative adverse events</bold></td><td/><td/><td/><td/><td>X</td><td>X</td><td>X</td></tr><tr><td> <bold>Operation cost</bold></td><td/><td/><td/><td>X</td><td/><td/><td/></tr><tr><td> <bold>Hospitalization cost</bold></td><td/><td/><td/><td/><td>X</td><td/><td/></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab2\"><label>Table 2</label><caption><p>Perioperative outcomes and follow-up results of precutting EBLR on 16 patients</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th colspan=\"2\">Category</th></tr><tr><th>Tumor specimen characteristics</th><th/></tr></thead><tbody><tr><td> Tumor specimen size^a, cm</td><td/></tr><tr><td>  Mean ± SD</td><td>1.1 ± 0.2</td></tr><tr><td>  Median (range)</td><td>1.0 (0.8–1.4)</td></tr><tr><td> Pathological diagnosis, No.(%)</td><td/></tr><tr><td>  GIST</td><td>11 (68.8%)</td></tr><tr><td>  Leiomyoma</td><td>5 (31.2%)</td></tr><tr><td>Operative outcomes</td><td/></tr><tr><td> En bloc resection</td><td>16 (100%)</td></tr><tr><td> R0 resection</td><td>16 (100%)</td></tr><tr><td> Operative time, min</td><td/></tr><tr><td>  Mean ± SD</td><td>21.3 ± 4.5</td></tr><tr><td>  Median (range)</td><td>21.0 (14.0–30.0)</td></tr><tr><td> Intraoperative perforation, No.(%)</td><td>10 (68.8%)</td></tr><tr><td> Adverse events, No.(%)</td><td/></tr><tr><td>  Major bleeding</td><td>0 (0%)</td></tr><tr><td>  Delayed perforation</td><td>0 (0%)</td></tr><tr><td>  Fever</td><td>0 (0%)</td></tr><tr><td>  Peritonitis</td><td>0 (0%)</td></tr><tr><td> Operative cost, $</td><td/></tr><tr><td>  Mean ± SD</td><td>588.6 ± 55.4</td></tr><tr><td>  Median (range)</td><td>583.0 (499.0–710.0)</td></tr><tr><td>Follow-up outcomes</td><td/></tr><tr><td> Recurrence, No.(%)</td><td>0 (%)</td></tr></tbody></table></table-wrap>" ]

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[ "<supplementary-material content-type=\"local-data\" id=\"MOESM1\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM2\"></supplementary-material>" ]

[ "<table-wrap-foot><p>^aSpecimens’ sizes were measured by rulers after resections</p></table-wrap-foot>", "<fn-group><fn><p><bold>Publisher’s Note</bold></p><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn></fn-group>" ]

[ "<graphic xlink:href=\"13063_2024_7902_Fig1_HTML\" id=\"MO1\"/>" ]

[ "<media xlink:href=\"13063_2024_7902_MOESM1_ESM.docx\"><caption><p><bold>Additional file 1.</bold>\n</p></caption></media>", "<media xlink:href=\"13063_2024_7902_MOESM2_ESM.docx\"><caption><p><bold>Additional file 2.</bold>\n</p></caption></media>" ]

{ "acronym": [ "SMT", "SMT-MP", "ESD", "Precutting EBL", "Precutting EBLR", "RCT", "GIST", "EUS", "CT", "EUS-FNA", "AE", "SAE" ], "definition": [ "Submucosal tumor", "Submucosal tumor originating from the muscularis propria layer", "Endoscopic submucosal dissection", "Precutting endoscopic ligation", "Precutting endoscopic band ligation-assisted resection", "Randomized controlled trial", "Gastrointestinal stromal tumor", "Endoscopic ultrasound", "Computed tomography", "EUS-guided fine needle aspiration", "Adverse event", "Severe adverse event" ] }

[ "<title>Introduction</title>", "<p id=\"Par4\">Cigarette smoking is the leading cause of preventable death worldwide and thus smoking cessation is a critical step for improving global health [##REF##20431525##1##, ##UREF##0##2##]. Nicotine is a psychoactive chemical that can be found in tobacco, causing neurobehavioral responses such as arousal, pleasure, mood/cognitive changes, appetite suppression, and physical signs [##REF##20554984##3##]. Nicotine can act on neuronal nicotinic acetylcholine receptors (nAChRs) in the brain, which are ligand-gated cation channels that are activated and desensitized in response to nicotine binding. Although our understanding of nicotine physiology is still limited, through acting on various nAChR subtypes in the mesolimbic reward pathway (i.e. ventral tegmental area to nucleus accumbens) and habenulo-interpeduncular pathway, nicotine is thought to exert its reinforcing and aversive effects, respectively, that ultimately contribute to nicotine addiction and continued cigarette smoking [##REF##32341069##4##, ##REF##11796143##5##].</p>", "<p id=\"Par5\">Interestingly, while the chronic intake of nicotine is required to develop addiction, both clinical and preclinical studies have shown that “acute dependence”-like symptoms from nicotine (i.e., signs of nicotine withdrawal and tolerance) emerge even with a low level of nicotine intake [##REF##20047690##6##–##REF##8613954##13##]. In both DSM-V and DSM-V-TR [##UREF##2##14##], it has been acknowledged that nicotine withdrawal can occur in adolescent smokers even prior to daily tobacco use, and that significant symptoms of nicotine withdrawal can occur in nondaily smokers. In the clinic [##REF##25938380##7##, ##UREF##3##15##], people frequently report symptoms of withdrawal after their first cigarette, and most smokers report the experience of withdrawal symptoms even before progressing to daily smoking. These findings collectively indicate that nicotine can induce acute dependence in animals. However, while the existence of acute dependence appears undisputable, the behavioral phenotype and pathophysiological significance of acute dependence are still unclear.</p>", "<p id=\"Par6\">In prior studies, rat models of early nicotine withdrawal have been characterized [##REF##22868410##8##, ##REF##34216863##10##, ##REF##16001123##12##], in which reward function and somatic signs were assessed. In this paper, we strived to model and characterize the physical, affective, and cognitive functions during early withdrawal from nicotine in mice, thereby supplying a novel preclinical model of acute dependence. Mimicking light nicotine intake during the initial experimentation stage of cigarette use in novice smokers [##REF##25938380##7##, ##REF##35328565##16##], low-dose nicotine (0.5 mg/kg (-)-nicotine ditartrate, which is nearly equivalent to 0.175 mg/kg free-base nicotine) was systemically administered to mice once daily for three days. The dosage of nicotine was decided based on previous studies showing that intraperitoneal administration of 0.175 mg/kg nicotine to mice should be sufficient to evoke striatal dopamine release and induce behavioral alterations in wild-type mice [##UREF##4##17##–##REF##12932430##19##]. It has been proven that abrupt pharmacological reversal of a drug’s action through inactivation of the target receptors in drug-dependent animals leads to the rapid and predictable emergence of withdrawal-like behaviors [##REF##4734580##20##, ##REF##7862893##21##], such as in the case of naloxone for opioid withdrawal [##REF##3335995##22##–##REF##13680079##24##]. In the case of nicotine withdrawal, administration of the nicotinic antagonist mecamylamine allows experimental control over the onset timing, symptom severity, and replicable measurements of nicotine withdrawal in rodents [##REF##22868410##8##, ##REF##12970387##25##]. As such, mice were challenged with either saline or mecamylamine to elicit spontaneous or precipitated signs of nicotine withdrawal, respectively [##REF##7862893##21##, ##REF##12970387##25##, ##REF##10629764##26##].</p>", "<p id=\"Par7\">Important validity criteria in modeling precipitated drug withdrawal are that (1) the signs of withdrawal should be precipitated by antagonist administration in drug-exposed animals and not in drug-naïve animals, and that (2) the withdrawal signs should be higher/larger in animals after precipitated drug withdrawal than in animals after spontaneous drug withdrawal [##REF##7862893##21##, ##REF##2338644##23##]. We explored these two criteria in our mouse model of early nicotine withdrawal using a battery of behavioral assays that encompass physical, affective, and cognitive domains.</p>" ]

[ "<title>Methods</title>", "<title>Animals</title>", "<p id=\"Par35\">Seven- to eight-weeks-old male C57BL/6 N mice were purchased (Daehan Bio Link, Daejeon, Republic of Korea) 1 week before experimentation. Mice were housed in plastic cages with metal wire grids and were maintained under a 12-h reversed light/dark cycle (lights off at 7:00 AM). Mice had ad libitum access to food and drinking water. Mice were housed in groups of 2 to 4. All animals were randomly assigned to each group.</p>", "<title>Induction of early nicotine withdrawal</title>", "<p id=\"Par36\">(-)-Nicotine ditartrate (Cat. No. 3546; Tocris Bioscience, Abindgon, UK) was dissolved in physiological saline (0.175 mg/kg free-base), and the pH was adjusted to 7.4. Mecamylamine hydrochloride (M9020; Sigma-Aldrich, St. Louis, MO, USA) was dissolved in physiological saline (3.0 mg/kg). Mice were intraperitoneally injected with the nicotine solution (10 ml/kg) once/day for three days and were intraperitoneally injected with the mecamylamine solution on the following day (24 h after the last nicotine injection) to precipitate the behavioral signs of early nicotine withdrawal. The dosing regimen is illustrated in Fig. ##FIG##0##1##.</p>", "<title>Behavioral tests</title>", "<p id=\"Par37\">Mice were handled for more than 3 days (10 min/day) prior to behavioral tests. All behavioral tests were video-recorded for analysis. Each behavioral test was performed with an independent batch of animals. All behavioral tests commenced at 10 min after the last injection of saline or mecamylamine solution. All experiments were replicated at least once. During analysis, the experimenter was blinded to the groups of mice.</p>", "<title>Open field test</title>", "<p id=\"Par38\">The open field test was conducted to measure general locomotor activity and anxiety-like behavior [##REF##35967275##52##]. A white open field box consisting of (in cm; L x W x H) 40 × 40 × 40 inner dimensions was used for the test. The floor luminosity was maintained at 5 lx. Mice were placed facing one side of the wall within the open field box, and allowed to freely explore the box for 30 min. The distance moved in the open field, the time spent immobile in the open field, and the time spent in the center zone (20 × 20 cm) were analyzed using EthoVision XT 11.5 (Noldus, Wageningen, Netherlands).</p>", "<title>Elevated plus maze test</title>", "<p id=\"Par39\">The elevated plus maze test was conducted to measure anxiety-like behavior [##REF##35676711##53##]. An apparatus consisting of an elevated maze with four arms (two white open arms and two black closed arms), each arm consisting of (in cm; L x W) 60 × 10 inner dimensions was used for the test. The closed arms were surrounded by 18-cm-high walls. The center of the elevated plus maze was maintained at 5 lx. The maze was elevated 50 cm above the ground. Mice were placed facing the wall at the end of the closed arm and allowed to freely explore the maze for 5 min. The time spent in each compartment (open arms, closed arms, and center zone) and the number of entries to each arm type were manually analyzed using a stopwatch. An entry was defined as the mouse having three paws into an arm or the center zone of the maze.</p>", "<title>Somatic signs of nicotine withdrawal</title>", "<p id=\"Par40\">Somatic signs were analyzed to measure physical withdrawal symptoms in mice [##REF##12970387##25##, ##REF##10629764##26##]. A clear plexiglass square column consisting of (in cm; L x W x H) 7 × 7 × 30 inner dimensions with openings at the top and bottom was used for measurement of the somatic signs of nicotine withdrawal in mice. The floor luminosity was maintained at 100 lx. Mice were confined in the plexiglass column for 20 min to allow a close-up video-examination of paw and body movements.</p>", "<p id=\"Par41\">The number of events was counted for each sign: paw tremor (rapidly shaking paw(s) two times while the two paws are supported on the ground or columnar wall or three times while three paws are in support), body shakes (wet-dog shakes; rapidly shaking the body with the anteroposterior axis as the axis of rotation), and freezing (continuous immobility with minimal movement and without paw movement for 60 s). For paw tremors or body shakes, (1) the events that occurred within 10 s of each other were counted as a single event (10-s epoch), and (2) the events that appeared 3 s before or after grooming were excluded from analysis (counted as an innate sequence for grooming).</p>", "<title>Passive avoidance test</title>", "<p id=\"Par42\">The passive avoidance test was conducted to measure fear memory [##REF##35676711##53##]. A two-chambered foot-shock apparatus (Jeungdo Bio &amp; Plant Co., Seoul, Republic of Korea) consisting of light (~ 100 lx) and dark chambers separated by a gate was used for the test. Mice were gently placed in the light chamber, and the gate was opened after 1 min. When mice entered the dark chamber, the gate was closed, and an electrical foot-shock (0.2 mV, 2 s) was delivered through the floor grid. Mice were left in the dark compartment for an additional 1 min and then returned to the home cages. On the following day, mice were placed in the light chamber, the gate was opened after 1 min, and mice were allowed to freely explore the two chambers for 10 min. The latency to enter the dark chamber, the time spent in the dark chamber, and the number of entries into the dark chamber were manually analyzed. An entry was defined as the mouse having all four paws into one chamber.</p>", "<title>Spatial object recognition test</title>", "<p id=\"Par43\">The spatial object recognition test was conducted to measure spatial recognition memory [##REF##21487656##54##]. The open field box, two identical objects (blue glossy cylinder) consisting of 7-cm height and 4-cm radius, and a visual cue consisting of (in cm; L × W) 18 × 24 dimensions with a checkered pattern of (in cm) 2 × 2 dimensions were used for the test. The visual cue was attached to one wall of the open field box.</p>", "<p id=\"Par44\">On the training day, the two objects were placed in the corner, 8 cm away from each wall, near the visual cue-attached wall (Fig. ##FIG##5##6##A, middle). On the recall day (24 h after training), one of the two objects placed during the training day was moved perpendicular from its original position and to the opposite wall of the visual cue-attached wall (Fig. ##FIG##5##6##A, right). For both training and recall, mice were placed facing the opposite side of the visual cue-attached wall within the open field box, and allowed to freely explore the box for 10 min. The time spent sniffing each object was manually analyzed using a stopwatch, and the recognition index was calculated. The recognition index, defined in a previous study [##REF##22160349##55##], is as follows:</p>", "<p id=\"Par45\">Here, <italic>T</italic>_{<italic>d</italic>} is the time spent exploring the displaced object, and <italic>T</italic>_{<italic>f</italic>} is the time spent exploring the familiar object.</p>", "<title>Social interaction test</title>", "<p id=\"Par46\">The social interaction test was conducted to measure social behavior [##REF##21799487##56##]. The open field box and a cylindrical stainless steel cage measuring 15-cm high and 5-cm wide (radius) were used for the test. The cage was placed near one wall of the open field box in the central position. During the first session, the cage remained empty. During the next session, a conspecific weighing ~ 90% of the exploring mouse’s body weight was confined in the cage. The two sessions were carried out consecutively. For both sessions, mice were placed facing the opposite side of the wall with a stainless steel cage within the open field box and allowed to freely explore the box for 15 min. The time spent sniffing the cage was manually analyzed, and the social interaction ratio was calculated. The social interaction ratio, as in a previous study [##REF##21799487##56##], was defined as follows:</p>", "<p id=\"Par47\">Here, <italic>T</italic>_{<italic>c</italic>} is the time spent exploring the conspecific-containing cage, and <italic>T</italic>_{<italic>e</italic>} is the time spent exploring the empty cage.</p>", "<title>Statistics</title>", "<p id=\"Par48\">One-way analysis of variance (ANOVA) followed by Holm-Sidak’s post-hoc test (Figs. ##FIG##1##2##, ##FIG##3##4##C, ##FIG##4##5##C and D, ##FIG##5##6##D and ##FIG##6##7##D) and two-way repeated measures (RM) ANOVA followed by Holm-Sidak’s post-hoc test (Figs. ##FIG##2##3##, ##FIG##3##4##B, ##FIG##4##5##B, ##FIG##5##6##B and C and ##FIG##6##7##B and C) were conducted to identify between-subject differences in behavior. The Wilcoxon signed rank test (Figs. ##FIG##3##4##C, ##FIG##5##6##D and ##FIG##6##7##D) was conducted to identify the differences between one sample and a specified hypothetical value. <italic>p</italic> &lt; 0.05 was considered statistically significant. Exact <italic>p</italic> values, <italic>F</italic> values, degrees of freedom, and the sum of signed ranks (W) are provided in the manuscript. Data are displayed as the mean ± standard error of the mean (SEM). Statistical analyses were performed with Prism v6.0 (GraphPad, CA, USA).</p>", "<title>Study approval</title>", "<p id=\"Par49\">All procedures regarding the handling and use of animals in this study were conducted as approved by the Institutional Animal Care and Use Committee (IACUC) of the Korea Institute of Science and Technology (KIST).</p>" ]

[ "<title>Results</title>", "<p id=\"Par8\"> To mimic nicotine exposure from light cigarette use during the initial experimentation stage, C57BL/6 N wild-type mice were treated with nicotine (0.5 mg/kg (-)-nicotine ditartrate in physiological saline, pH adjusted to 7.4) once daily for three days. On the following day, mice were treated with 0.3 mg/kg mecamylamine (MEC) to induce precipitated withdrawal (PW) from nicotine, while other mice were treated with saline to induce spontaneous withdrawal (SW). Mecamylamine or saline was administered 24 h after the last nicotine administration based on previous findings that the somatic signs of nicotine withdrawal intensify 24–48 h after cessation of nicotine administration [##REF##12970387##25##, ##REF##10629764##26##]. Behavioral tests were conducted 10 min after the last injection of MEC or saline. For all experiments, different mice were used and the experimenter was blinded to the experimental conditions during analysis. The overall injection scheme and experimental schedule are depicted in Fig. ##FIG##0##1##.</p>", "<p id=\"Par9\">\n</p>", "<p id=\"Par10\">The open field test was conducted to examine general locomotor function and anxiety-like behavior (Fig. ##FIG##1##2##A) (<italic>n</italic> = 10–11 mice/group). Precipitated withdrawal from nicotine caused a significant decrease in the distance moved compared to the control and spontaneous withdrawal groups (Fig. ##FIG##1##2##B) (Group effect, <italic>F</italic>(3,37) = 6.542, <italic>p</italic> = 0.0012; post-hoc analysis, **<italic>p</italic> = 0.0092 for Control vs. PW, **<italic>p</italic> = 0.0012 for SW vs. PW). In addition, precipitated nicotine withdrawal led to a significant increase in the time spent immobile compared to the control group (Fig. ##FIG##1##2##C) (Group effect, <italic>F</italic>(3,37) = 4.024, <italic>p</italic> = 0.0142; post-hoc analysis, *<italic>p</italic> = 0.0167 for Control vs. PW). Lastly, precipitated nicotine withdrawal significantly reduced the time spent in the center zone compared to the control and spontaneous withdrawal groups (Fig. ##FIG##1##2##D) (Group effect, <italic>F</italic>(3,37) = 4.600, <italic>p</italic> = 0.0078; post-hoc analysis, *<italic>p</italic> = 0.0265 for Control vs. PW, *<italic>p</italic> = 0.0110 for SW vs. PW). These findings show that early precipitated withdrawal from nicotine reduces locomotor activity and increases anxiety-like behavior in the open field, but not early spontaneous withdrawal.</p>", "<p id=\"Par11\">\n</p>", "<p id=\"Par12\">Next, the elevated plus maze test was conducted to further examine anxiety-like behavior (Fig. ##FIG##2##3##A) (<italic>n</italic> = 7–12 mice/group). Unexpectedly, mecamylamine challenge and precipitated nicotine withdrawal caused a significant increase in the time spent in the closed arm (Fig. ##FIG##2##3##B) (Interaction effect, <italic>F</italic>(6,72) = 3.039, <italic>p</italic> = 0.015; post-hoc analysis, *<italic>p</italic> = 0.0245 for Control vs. MEC, *<italic>p</italic> = 0.0296 for Control vs. PW, *<italic>p</italic> = 0.0106 for MEC vs. SW, *<italic>p</italic> = 0.0120 for SW vs. PW). In addition, mecamylamine challenge and precipitated nicotine withdrawal caused a significant reduction in the number of entries into the closed arm (Fig. ##FIG##2##3##C) (Group effect, <italic>F</italic>(3,36) = 14.04, <italic>p</italic> &lt; 0.0001; post-hoc analysis, **<italic>p</italic> = 0.0034 for Control vs. MEC, ****<italic>p</italic> &lt; 0.0001 for Control vs. PW, **<italic>p</italic> = 0.0033 for MEC vs. SW, ****<italic>p</italic> &lt; 0.0001 for SW vs. PW). On the other hand, only precipitated nicotine withdrawal caused a significant reduction in the number of entries into the open arm (Fig. ##FIG##2##3##C) (post-hoc analysis, **<italic>p</italic> = 0.0014 for Control vs. PW, **<italic>p</italic> = 0.0018 for SW vs. PW). These findings indicate that mecamylamine acutely increases anxiety-like behavior and reduces movement in the elevated plus maze.</p>", "<p id=\"Par13\">\n</p>", "<p id=\"Par14\">Then, the somatic signs of early nicotine withdrawal were assessed to further examine the physical aspects. Previous studies have shown that the somatic signs of nicotine withdrawal in rodents include rearing, head shakes, forelimb shakes (paw tremor), body shakes, jumping, abdominal constrictions, teeth chattering/chewing, facial tremor, scratching, grooming, eye blinks, ptosis, genital licking, yawns, immobility, etc. [##REF##7862893##21##, ##REF##12970387##25##, ##REF##10629764##26##]. Previous clinical studies have demonstrated that the reduction in hand steadiness or increased hand tremor is a prominent motor sign of nicotine withdrawal in humans [##UREF##5##27##], while macroscopic physical gestures such as head/body shakes and immobility can be readily translated into the clinic. However, most other somatic signs defined in rodents cannot be translated into the physical symptoms of nicotine withdrawal in humans, since those somatic signs are (1) not observed in the clinic, (2) largely rodent-specific, or (3) more appropriate when included in the category of natural rodent behavior. Moreover, preclinical data from pioneering studies have suggested that paw tremor is the single most replicable somatic sign of withdrawal in rodents observed after both low- and high-dose nicotine treatment [##REF##7862893##21##, ##REF##12970387##25##, ##REF##10629764##26##]. Lastly, a seminal study has shown that episodes of locomotor immobility can be observed after precipitated nicotine withdrawal [##REF##7862893##21##]. Therefore, three replicable and translatable signs of somatic nicotine withdrawal were selected for analysis: paw tremors, body shakes, and immobility.</p>", "<p id=\"Par15\">In the analysis of the somatic signs of early nicotine withdrawal (Fig. ##FIG##3##4##A) (<italic>n</italic> = 10–11 mice/group), precipitated withdrawal from nicotine caused a significant increase specifically in the number of paw tremors compared to all other groups (Fig. ##FIG##3##4##B) (Group effect, <italic>F</italic>(3,39) = 4.540, <italic>p</italic> = 0.0080; Interaction effect, <italic>F</italic>(6,78) = 3.643, <italic>p</italic> = 0.0031; post-hoc comparison, ****<italic>p</italic> &lt; 0.0001 for Control vs. PW, ****<italic>p</italic> &lt; 0.0001 for MEC vs. PW, **<italic>p</italic> = 0.0042 for SW vs. PW). In addition, precipitated withdrawal from nicotine caused a significant increase in the overall number of somatic signs compared to the control and mecamylamine challenge groups (Fig. ##FIG##3##4##C) (Group effect, <italic>F</italic>(3,39) = 4.540; <italic>p</italic> = 0.0080; post-hoc comparison, *<italic>p</italic> = 0.0134 for Control vs. PW, *<italic>p</italic> = 0.0185 for MEC vs. PW). Additionally, both spontaneous and precipitated withdrawal from nicotine caused a significant increase in the overall number of somatic signs compared to a hypothetical value of 2 (the value was decided as the median of the control group, which was 2) (Fig. ##FIG##3##4##C) (SW, sum of signed ranks (W) = 49, ^††<italic>p</italic> = 0.0098; PW, sum of signed ranks (W) = 55, ^††<italic>p</italic> = 0.0020). Furthermore, precipitated nicotine withdrawal showed a significant distancing from other groups in the cumulative distribution plot of somatic signs (Additional file ##SUPPL##0##1##: Fig. S1A). Lastly, precipitated withdrawal from nicotine caused a largely consistent distribution of somatic events throughout time (Additional file ##SUPPL##0##1##: Fig. S1B). These findings show that early precipitated withdrawal from nicotine increases the number of somatic signs, mainly paw tremor.</p>", "<p id=\"Par16\">\n</p>", "<p id=\"Par17\">Next, the passive avoidance test was conducted to examine fear memory (Fig. ##FIG##4##5##A) (<italic>n</italic> = 9–12 mice/group). Early nicotine withdrawal did not alter the latency to enter the dark chamber (Fig. ##FIG##4##5##B), the time spent in the dark chamber (Fig. ##FIG##4##5##C), or the number of entries into the dark chamber (Fig. ##FIG##4##5##D) compared to the other groups. These findings suggest that early withdrawal from nicotine did not affect fear memory.</p>", "<p id=\"Par18\">\n</p>", "<p id=\"Par19\">Then, the spatial object recognition test was conducted to examine spatial recognition memory (Fig. ##FIG##5##6##A) (<italic>n</italic> = 6–10 mice/group). Early nicotine withdrawal did not affect the time spent sniffing all objects during either training or recall (Fig. ##FIG##5##6##B), the time spent sniffing displaced objects during recall (Fig. ##FIG##5##6##C), or the recognition index (Fig. ##FIG##5##6##D) compared to other groups. On the other hand, mice after early precipitated withdrawal from nicotine did not differ in the recognition index compared to the hypothetical value of 50% (Fig. ##FIG##5##6##D) (Control, Sum of signed ranks (W) = 28, ^†<italic>p</italic> = 0.0156; MEC, Sum of signed ranks (W) = 21, ^†<italic>p</italic> = 0.0313; SW, Sum of signed ranks (W) = 49, ^††<italic>p</italic> = 0.0098). These findings suggest that early nicotine withdrawal did not grossly affect spatial recognition memory.</p>", "<p id=\"Par20\">\n</p>", "<p id=\"Par21\"> Finally, the social interaction test was conducted to examine social behavior (Fig. ##FIG##6##7##A) (<italic>n</italic> = 9–11 mice/group). Early nicotine withdrawal did not affect the time spent sniffing the empty or social object (Fig. ##FIG##6##7##B and C), or the social interaction ratio (Fig. ##FIG##6##7##D) compared to other groups. In addition, early nicotine withdrawal did not affect the social interaction ratio when compared to the hypothetical value of 1 (Fig. ##FIG##6##7##D) (Control, Sum of signed ranks (W) = 45, ^††<italic>p</italic> = 0.0039; MEC, Sum of signed ranks (W) = 64, ^††<italic>p</italic> = 0.0020; SW, Sum of signed ranks (W) = 55, ^††<italic>p</italic> = 0.0020; PW, Sum of signed ranks (W) = 55, ^††<italic>p</italic> = 0.0020). These findings suggest that early nicotine withdrawal did not affect social behavior.</p>", "<p id=\"Par22\">\n</p>" ]

[ "<title>Discussion</title>", "<p id=\"Par23\">This study provides evidence that, in mice, early withdrawal from repeated (3 days), low-dose nicotine (0.175 mg/kg free-base) administration induces physical and affective signs of nicotine withdrawal. Novice smokers do not immediately engage in heavy daily smoking; they usually go through the initial experimentation of smoking through “mooching” or “bumming” [##REF##25938380##7##]. In addition, smokers experience a bolus intake of nicotine, not continuous infusion [##REF##10629764##26##]. This mouse model is significant in that it mimics the initial experimentation stage in human smokers and displays meaningful withdrawal-like signs from short-term nicotine exposure. Although early spontaneous withdrawal from nicotine was not sufficient to induce notable signs of withdrawal (except for somatic signs), a single dose of nicotinic antagonist mecamylamine was able to unmask the latent behavioral signs of early nicotine withdrawal. This suggests that short-term, low-dose nicotine exposure increases dependence vulnerability, or drives animals into an acute dependence-like state.</p>", "<p id=\"Par24\">Mounting evidence suggest that withdrawal signs can be precipitated upon short-term nicotine exposure. A seminal study demonstrated that precipitated withdrawal can ensue even after a single dose of nicotine [##REF##22868410##8##]. In the study, mecamylamine was administered 2 h after a single dose of nicotine in rats. The modeling resulted in a significant elevation of intracranial self-stimulation threshold and somatic signs, which lasted for 5 days after mecamylamine-induced precipitation of nicotine withdrawal. These results showed that acute dependence is a replicable and prominent component of nicotine physiology. Our study further supports the existence of acute dependence to nicotine by showcasing a novel mouse model of early nicotine withdrawal, in which the physical (or somatic) signs were most prominent.</p>" ]

[ "<title>Conclusion</title>", "<p id=\"Par34\">In summary, our study demonstrated that early nicotine withdrawal produces behavioral alterations in mice, supporting the preclinical findings [##REF##20047690##6##–##REF##8613954##13##] and clinical observations [##REF##25938380##7##, ##UREF##3##15##] that short-term low-dose nicotine can induce an acute dependence-like state in animals. Although the phenotype of acute dependence on nicotine is clear and its presence might indicate potential vulnerability to the progression toward daily smoking, the pathophysiological significance of acute dependence on nicotine has been neglected. We believe that the phenomenon of early nicotine withdrawal deserves more attention in the field. In the future, (1) the neurobiological mechanisms underlying early nicotine withdrawal could be investigated, (2) the molecular/behavioral differences as well as the progression from acute to chronic dependence on nicotine could be explored in depth, and (3) the potential impact of early nicotine withdrawal on the progression to addiction could be assessed.</p>" ]

[ "<title>Background</title>", "<p id=\"Par1\">Clinical and preclinical research have demonstrated that short-term exposure to nicotine during the initial experimentation stage can lead to early manifestation of withdrawal-like signs, indicating the state of “acute dependence”. As drug withdrawal is a major factor driving the progression toward regular drug intake, characterizing and understanding the features of early nicotine withdrawal may be important for the prevention and treatment of drug addiction. In this study, we corroborate the previous studies by showing that withdrawal-like signs can be precipitated after short-term nicotine exposure in mice, providing a potential animal model of acute dependence on nicotine.</p>", "<title>Results</title>", "<p id=\"Par2\">To model nicotine exposure from light tobacco use during the initial experimentation stage, mice were treated with 0.5 mg/kg (-)-nicotine ditartrate once daily for 3 days. On the following day, the behavioral tests were conducted after implementing spontaneous or mecamylamine-precipitated withdrawal. In the open field test, precipitated nicotine withdrawal reduced locomotor activity and time spent in the center zone. In the elevated plus maze test, the mecamylamine challenge increased the time spent in the closed arm and reduced the number of entries irrespective of nicotine experience. In the examination of the somatic aspect, precipitated nicotine withdrawal enhanced the number of somatic signs. Finally, nicotine withdrawal did not affect cognitive functioning or social behavior in the passive avoidance, spatial object recognition, or social interaction test.</p>", "<title>Conclusions</title>", "<p id=\"Par3\">Collectively, our data demonstrate that early nicotine withdrawal-like signs could be precipitated by the nicotinic antagonist mecamylamine in mice, and that early withdrawal from nicotine primarily causes physical symptoms.</p>", "<title>Supplementary Information</title>", "<p>The online version contains supplementary material available at 10.1186/s12993-024-00227-0.</p>", "<title>Keywords</title>" ]

[ "<title>Addiction versus dependence: the timely question on “acute dependence”</title>", "<p id=\"Par25\">General theories on the transition to addiction dictate that a pattern of chronic, escalating drug intake is required to develop addiction [##REF##17169534##28##, ##REF##23963530##29##]. From an integrative perspective, the hedonic allostasis theory proposes that a spiraling distress cycle takes place during the progression towards drug addiction, in which drug-dependent subjects experience three distinct stages in repetition; preoccupation/anticipation, binge/intoxication, and withdrawal/negative affect [##REF##9311926##30##]. These theories suggest that the term “addiction” refers to a relapsing disease defined by long-term drug taking and seeking.</p>", "<p id=\"Par26\">In comparison to addiction, the term “dependence” should be held separate [##UREF##6##31##, ##REF##34751058##32##] as recognized in DSM-V-TR (March 2022) [##UREF##2##14##], for consistency and clarity in the terminologies used in the category of substance use disorders. The term “addiction” mainly refers to the pathological condition of compulsive drug-taking that stems from chronic drug use, whereas the term “dependence” traditionally refers to the normal, physical adaptations that result in tolerance and withdrawal symptoms and can stem from any psychoactive drug/medication that affects the CNS. As such, DSM-V-TR described that (1) dependence does not necessarily indicate the presence of addiction, and that (2) withdrawal can ensue without comorbid use disorder in a wide assortment of drugs including tobacco, alcohol, cannabis, sedatives, stimulants, and opioids. Importantly, the hedonic allostasis theory indicates that withdrawal/negative affect is an essential component in the development of drug addiction. Integrating these ideas, it could be inferred that physical dependence precedes, and is an independent driving factor of, drug addiction.</p>", "<p id=\"Par27\">The important question is the onset time of physical dependence. The overarching evidence from the 20th century to this date have demonstrated that both tolerance- and withdrawal-like behaviors can develop with nondaily, repeated or even a single experience of drug/medication [##REF##22868410##8##, ##REF##8613954##13##, ##REF##2338644##23##, ##REF##13680079##24##, ##REF##8090806##33##–##REF##14254334##38##], which has been termed “acute dependence”. The most noteworthy are the cases of “acute dependence” on opioids, in which repeated/single dose of opioid agonist (i.e. morphine) followed by administration of opioid antagonist (i.e. naloxone) can effectively precipitate the symptoms of opioid withdrawal in both humans and animals [##REF##3335995##22##–##REF##13680079##24##, ##REF##16938626##36##, ##REF##562464##39##], and has also been acknowledged as a diagnostic criterion for opioid withdrawal throughout DSM-IV to DSM-V-TR [##UREF##2##14##]. Moreover, pioneering studies have suggested that this early manifestation of tolerance/withdrawal symptoms reflects certain initiating factors that may contribute to the development of the full extent of physical dependence [##REF##8613954##13##, ##REF##3335995##22##, ##REF##2338644##23##], which warrants further attention in the field. However, despite the plethora of evidence, the significance of tolerance/withdrawal signs observed during acute dependence has been far neglected to date.</p>", "<title>Behavioral signs of early nicotine withdrawal</title>", "<p id=\"Par28\">The observed signs of early nicotine withdrawal in this study were mild, which can be expected based on the severity of drug withdrawal being correlated with the dose and duration of drug intake. However, the important findings were that (1) short-term nicotine exposure nevertheless induces acute dependence-like signs and that (2) the magnitude of signs from early nicotine withdrawal are comparable to those reported in previous studies. For example, paw tremors were the most prominent somatic sign after early nicotine withdrawal in mice. The number of paw tremors induced by early precipitated withdrawal from nicotine (mean = 8.545) was comparable to those found in pioneering studies that investigated somatic nicotine withdrawal in rodents (mean = 7–10) [##REF##7862893##21##, ##REF##12970387##25##, ##REF##10629764##26##], in which precipitated withdrawal was induced after chronic nicotine exposure.</p>", "<p id=\"Par29\">In the physical aspect, mice displayed decreased locomotor activity in the open field and an increased number of somatic signs after early precipitated withdrawal from nicotine, at levels that were comparable to those observed in the seminal studies by Isola et al. [##REF##10629764##26##] and Damaj et al. [##REF##12970387##25##]. The effects were attributable to the interaction between nicotine exposure and mecamylamine, suggesting that nicotinic antagonism unmasks (or precipitates) the latent physical symptoms of early nicotine withdrawal. Body shakes and immobility were minor somatic signs in mice, although immobility was prominent during the open field test. This indicates that immobility in the open field may reflect the affective aspect due to the mild anxiogenicity of the open field environment.</p>", "<p id=\"Par30\">Regarding the affective aspect, mice displayed increased anxiety-like behavior in the open field test after early precipitated withdrawal from nicotine, but unexpectedly displayed strong anxiety-like behavior in the elevated plus maze test owing to the mecamylamine challenge. Previous studies have consistently demonstrated that nicotine withdrawal causes anxiety-like behaviors [##REF##31633029##40##–##REF##25898242##42##], but have not reported mecamylamine challenge-induced anxiety-like behavior. The differing phenotypes in the open field and elevated plus maze by mecamylamine challenge might be attributable to the relative anxiogenicity of each environment: The open field is mildly anxiogenic, while the elevated plus maze is more anxiogenic [##REF##12191791##43##]. Systemic mecamylamine at 3.0 mg/kg induced anxiety-like behavior in mice, but only when exposed to a strongly anxiogenic environment (i.e., elevated plus maze). In nicotine-naïve animals, mecamylamine microinjection into the dorsal hippocampus was found to have an anxiogenic effect in the elevated plus maze test [##REF##10837844##44##], but subcutaneous mecamylamine injection at 3.0 mg/kg did not affect the time spent in the open arm in the elevated plus maze [##REF##12970387##25##]. Although the gross lack of literature on mecamylamine’s sole effect on control subjects precludes further insight, these results imply that the route of mecamylamine administration might have differential effects on anxiety-like behaviors. Collectively, caution is necessary in the interpretation of anxiety-like behaviors observed during mecamylamine-precipitated nicotine withdrawal.</p>", "<p id=\"Par31\">Regarding cognitive aspects, mice did not display alterations in passive avoidance or spatial object recognition. Previous studies have shown that withdrawal from chronic nicotine treatment impairs learning and memory [##REF##23639437##45##, ##REF##16177040##46##], a phenotype that is distinct from the absence of cognitive dysfunction during early nicotine withdrawal in this study. In addition, mice did not display altered social behavior in the social interaction test after early nicotine withdrawal. A body of clinical studies has suggested that withdrawal from nicotine seems to impair social functioning [##REF##29952615##47##], but whether it could be replicated in rodents has not been investigated to date. At the least, during early nicotine withdrawal, mice do not display overt deficits in social behavior. The lack of cognitive and social phenotypes in early nicotine withdrawal suggests that acute dependence presents a distinct (or at least a less severe) set of behavioral phenotypes compared to that of chronic dependence.</p>", "<title>Limitations of the study</title>", "<p id=\"Par32\">Three limitations of this study warrant caution in the generalization of the findings. First, although the prevalence of cigarette smoking is nearly four times higher in men [##UREF##7##48##], the importance of nicotine withdrawal in women cannot be overlooked, as the burden of nicotine withdrawal seems to be as crucial in women as in men [##REF##9293047##49##, ##REF##25433149##50##]. In addition, three translatable and replicable somatic signs were analyzed in this study, but examination of all other somatic-like signs (e.g., teeth chattering/chewing, jumping, scratching, etc.) may yield more information about the impact of early nicotine withdrawal on animals. Lastly, the widely used markers of nicotine withdrawal, i.e. blood nicotine and cotinine levels, were not measured. However, this was because blood nicotine and cotinine are not reliable markers of nicotine withdrawal as stated in DSM-V-TR [##UREF##2##14##], and because nicotine pharmacokinetics is abnormally higher in mice than in humans [##REF##16896961##51##].</p>", "<p id=\"Par33\">Other experimental limitations of this study warrant further investigation. For instance, only a single dose (0.175 mg/kg free-base nicotine) and single duration (three days of daily exposure) regimen was implemented on a single rodent strain, thus further studies should investigate the impacts of nicotine dosage, exposure duration, and genetic influence on early nicotine withdrawal. In addition, the predictive validity of this mouse model has not been explored (i.e. reversal of withdrawal signs by varenicline or bupropion). The main purposes of this study were to demonstrate the existence of early nicotine withdrawal, and to characterize the phenotype of early nicotine withdrawal. Regardless, the therapeutic effect (and lack thereof) of clinically approved drugs on early nicotine withdrawal and its potential difference with withdrawal from chronic nicotine exposure should be confirmed. Also, the attenuation of early withdrawal symptoms by nicotinic agonists was not examined. This was due to the finding that spontaneous early withdrawal did not induce significant withdrawal signs in mice except for somatic signs, which was expected from the short-term low-dose nicotine administration regimen.</p>", "<title>Supplementary Information</title>", "<p>\n</p>" ]

[ "<title>Acknowledgements</title>", "<p>We cordially thank Tae Kyoo Kim for proofreading.</p>", "<title>Author contributions</title>", "<p>BK: project administration, conceptualization, methodology, funding acquisition, investigation, validation, visualization, formal analysis, data curation, writing—original draft, and writing—review and editing. HI: supervision, project administration, conceptualization, funding acquisition, resources, writing-original draft, and writing—review and editing. All authors read and approved the final manuscript.</p>", "<title>Funding</title>", "<p>This work was supported by the National Research Foundation of Korea (2020R1A2C2004610, 2022R1A6A3A01087565; Republic of Korea).</p>", "<title>Availability of data and materials</title>", "<p>All datasets supporting the findings of this study are available within the article. Source data can be provided from the corresponding author upon request.</p>", "<title>Declarations</title>", "<title>Competing interests</title>", "<p id=\"Par50\">The authors declare no competing interests.</p>" ]

[ "<fig id=\"Fig1\"><label>Fig. 1</label><caption><p>Drug injection and experimentation schedule. Mice were treated with saline or nicotine solution (0.175 mg/kg free-base) once daily for three days. On the following day, mice were treated with saline or mecamylamine solution (MEC; 0.3 mg/kg). All behavioral tests commenced 10 min after the last injection (saline or mecamylamine). Mice treated only with saline were designated as the control group (black). Mice treated with three days of saline followed by mecamylamine were designated the mecamylamine (MEC) group (gray). Mice treated with three days of nicotine followed by saline were designated the early spontaneous withdrawal (SW) group (blue). Mice treated with three days of nicotine followed by mecamylamine were designated the early precipitated withdrawal (PW) group (red)</p></caption></fig>", "<fig id=\"Fig2\"><label>Fig. 2</label><caption><p>Open field test. <bold>A</bold> Illustration of the open field test. <bold>B</bold> The distance moved was significantly reduced after early precipitated withdrawal (PW) from nicotine (asterisks). <bold>C</bold> The time spent immobile was significantly increased after PW from nicotine (asterisk). <bold>D</bold> The time spent in the center zone was significantly reduced after PW from nicotine (asterisks). Data represent the mean ± S.E.M. from 10–11 mice/group</p></caption></fig>", "<fig id=\"Fig3\"><label>Fig. 3</label><caption><p>Elevated plus maze test. <bold>A</bold> Illustration of the elevated plus maze test. <bold>B</bold> The time spent in the closed arm was significantly reduced after mecamylamine injection (MEC) or early precipitated withdrawal (PW) from nicotine (asterisks). <bold>C</bold> The number of entries into the open arm was significantly reduced after PW, and the number of entries into the closed arm was significantly reduced after MEC and PW (asterisks). Data represent the mean ± S.E.M. from 7–12 mice/group</p></caption></fig>", "<fig id=\"Fig4\"><label>Fig. 4</label><caption><p>Somatic withdrawal signs. <bold>A</bold> Illustration of the somatic withdrawal sign examination. <bold>B</bold> The number of paw tremors was significantly increased after early precipitated withdrawal (PW) from nicotine (asterisks). <bold>C</bold> The total number of somatic signs was significantly increased after PW and early spontaneous withdrawal (SW) from nicotine (asterisks). The total number of somatic signs was significantly different from the hypothetical value 2 after SW and PW (crosses). Data represent the mean ± S.E.M. from 10–11 mice/group</p></caption></fig>", "<fig id=\"Fig5\"><label>Fig. 5</label><caption><p>Passive avoidance test. <bold>A</bold> Illustration of the passive avoidance test. <bold>B</bold> The latency to enter the dark chamber did not significantly differ among groups. <bold>C</bold> The time spent in the dark chamber did not significantly differ among groups. <bold>D</bold> The number of entries into the dark chamber did not significantly differ among groups. Data represent the mean ± S.E.M. from 9–12 mice/group</p></caption></fig>", "<fig id=\"Fig6\"><label>Fig. 6</label><caption><p>Spatial object recognition test. <bold>A</bold> Illustration of the spatial object recognition test. <bold>B</bold> The total time spent sniffing objects did not significantly differ among groups. <bold>C</bold> The time spent sniffing each object during recall did not significantly differ among groups. <bold>D</bold> The recognition index did not significantly differ among groups (NS: not significant). The recognition index was significantly different from the hypothetical value of 50% in the control group, after mecamylamine injection (MEC), and after early spontaneous withdrawal (SW) from nicotine (crosses). Data represent the mean ± S.E.M. from 6–10 mice/group</p></caption></fig>", "<fig id=\"Fig7\"><label>Fig. 7</label><caption><p>Social interaction test. <bold>A</bold> Illustration of the social interaction test. <bold>B</bold> The time spent sniffing objects did not significantly differ among groups. <bold>C</bold> The total time spent sniffing each object did not significantly differ among groups. <bold>D</bold> The social interaction ratio did not significantly differ among groups (NS: not significant). The social interaction ratio was significantly different from the hypothetical value of 1 in all groups (crosses). Data represent the mean ± S.E.M. from 9–11 mice/group</p></caption></fig>" ]

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[ "<fn-group><fn><p><bold>Publisher’s Note</bold></p><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn></fn-group>" ]

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[ "<media xlink:href=\"12993_2024_227_MOESM1_ESM.tif\"><caption><p><bold>Additional file 1: Figure S1. </bold>Further examination of the somatic withdrawal signs. (A) The cumulative distribution plot of somatic signs after early precipitated withdrawal (PW) from nicotine was notably distanced from those of all other groups. (B) Raster plot of somatic signs.</p></caption></media>" ]

{ "acronym": [], "definition": [] }

[ "<title>Introduction</title>", "<p id=\"Par5\">There are many challenges impeding progress in our understanding of the immune response following sport-related concussion (SRC). Animal model research has been helpful in hypothesis generation for human studies across the severity spectrum of brain injury from concussion [##UREF##0##1##] to severe traumatic brain injury (TBI) [##REF##27382003##2##, ##REF##23329160##3##]. However, differences in animal and human immune systems make translation challenging [##REF##31627013##4##–##REF##14978070##6##] and it can be difficult to design human experiments to validate animal findings. Human studies, which have relied almost entirely on the evaluation of cytokines and chemokines measured in the systemic circulation [##REF##32343752##7##–##REF##31270219##11##], have been informative but have not yet gone beyond speculative group differences in individual biomarker concentrations between healthy and injured groups, or the identification of correlations between biomarker levels and clinical outcomes (symptoms, recovery, etc). The complexity of the immune response and its pleiotropic and redundant features make interpretation of these findings difficult; it is not clear how elevated/depressed concentrations of individual blood cytokines relate to immune system function or status. While multiple marker panels evaluated with multivariate statistical models can help identify signatures and infer system-level changes, functional interpretation remains difficult when looking at static measures in the blood at a given time.</p>", "<p id=\"Par6\">It may be advantageous to assess immune function by stressing or challenging the immune system and quantifying the reactivity to a particular stimulus with a known signaling pathway [##REF##24656047##12##–##REF##25562703##14##]. Here, when group differences are estimated, they more closely approximate differences in function of a specific facet of the immune system. For example, prior studies on immune biomarkers in SRC have focused on a broad suite of inflammatory cytokines and chemokines [##REF##30684956##8##–##REF##31270219##11##] that are common products of Nuclear Factor Kappa B (NF-kB) transcription [##REF##29158945##15##, ##REF##26479397##16##]. In the innate immune system, NF-kB-mediated cytokine production is classically linked to toll-like receptor (TLR) signalling [##REF##29158945##15##]. While this is a likely mechanism at play in the acute phase post SRC, there are also several other potential pathways that may be involved, such as the sympathoadrenal-immune response [##REF##16557263##17##] or the inflammasome [##REF##27325789##18##]. Without evaluation of cytokine reactivity to direct stimulation of known pathways, it is difficult to understand the etiology of post-injury immune activity.</p>", "<p id=\"Par7\">In addition to shifting the methodological paradigm of assessing immune function using blood biomarkers, statistical considerations may improve the quality of study results. First, a change in focus to effect estimation as opposed to <italic>p</italic> values and significance testing could improve results interpretation. Arbitrary, historically determined cut points have constrained findings into a false dichotomy of mattering (significant) or not mattering (not significant), which is often incorrect, or at the very least oversimplistic in biological systems [##REF##23232612##19##–##UREF##3##23##]. Second, causal modelling that provides <italic>a priori</italic> transparency of scientific beliefs would also provide clarity and simplify efforts at replication [##UREF##2##22##, ##UREF##4##24##]. Heuristic models like directed acyclic graphs (DAGs) can be useful to advance knowledge and inform future studies because they are explicit in their assumptions and come with a set of simple rules for effect estimation [##UREF##2##22##, ##UREF##4##24##]. Indeed, identifying group differences and correlations in data without explicitly expressed scientific beliefs is potentially misleading; confounding and colliding variables can induce false relationships between an exposure and an outcome, mediating/moderating variables erroneously adjusted for can eliminate real effects, and competing causes can hamper precision [##UREF##2##22##, ##UREF##4##24##–##REF##23371353##27##].</p>", "<p id=\"Par8\">The application of causal modelling to study the immune system following SRC can draw from one of several lines of research. First, early animal models and human studies on TBI have suggested acute inflammation in response to the injury [##UREF##0##1##, ##REF##27382003##2##, ##REF##33679762##28##], with speculation of chronic persistence [##REF##33679762##28##–##REF##24289885##30##]. Human SRC data from our group and others has shown that individual inflammatory cytokines and chemokines such as monocyte chemoattractant protein (MCP)-4 and macrophage inflammatory protein (MIP)-1β may be elevated within the first week following injury [##REF##30684956##8##], inflammatory gene expression may be decreased [##REF##26479397##16##], and elevated cytokines have been observed in healthy individuals with a concussion history [##REF##32717401##10##, ##REF##27458972##31##]. Animal model research has also suggested a phenomenon known as ‘microglial priming’ may occur following an initial TBI/concussion [##REF##20880500##29##, ##REF##24289885##30##, ##REF##28186177##32##, ##REF##25445485##33##], possibly leading to an amplified reaction to subsequent injuries and providing a potential pathway to neurodegeneration [##REF##20880500##29##, ##REF##28186177##32##]. Interestingly, we have previously observed an interaction between IL-6 and concussion history in those with an acute SRC [##REF##32343752##7##]. Furthermore, given the noted differences in recovery trajectories in males and females following SRC [##REF##28927725##34##–##REF##30403884##37##], the general difference in male and female immunity [##REF##26442695##38##], and some preliminary work by our group showing potentially contrasting biomarker signatures following injury [##REF##32164571##9##], it seems reasonable that males and females have a different immunological response to SRC. However, and importantly, all the human work, including our own, was done within a null hypothesis significance testing framework that relied upon an arbitrary decision theoretic cut point of <italic>p</italic> &lt; 0.05, without a causal model.</p>", "<p id=\"Par9\">This preliminary study aimed to implement whole blood stimulation within a causal analytical framework to estimate the effect of SRC on immune function. To achieve this, we derived a DAG based on hypotheses generated from prior literature of how SRC and concussion/TBI may alter immunity. Immune function was measured through the stimulation of whole blood <italic>ex-</italic><italic>vivo</italic> using common inflammatory ligands LPS and R848, and subsequent quantitation of a multi marker panel of cytokines and chemokines.</p>" ]

[ "<title>Methods</title>", "<title>Participants</title>", "<p id=\"Par10\">Fifty-two athletes from a Canadian university’s sport program participated in this study during the 2018/2019 academic year; this sub study was part of a larger project conducted by our group from 2013 to 2019. Of the 52-athlete convenience sample, 22 athletes (n = 11 female, n = 11 male) from seven sports were enrolled within a week (median = 4 days, interquartile range [IQR] = 3–5) of being diagnosed with an SRC; 30 healthy athletes (n = 18 female, n = 12 male) from 11 sports were enrolled at the beginning of their competitive season. Concussion diagnosis and medical clearance decisions were made by a staff physician at the university sport medicine clinic in accordance with the Concussion in Sport Group guidelines [##REF##37316210##39##]. Prior to enrollment, all participants were provided written informed consent. All study procedures were in accordance with the declaration of Helsinki, and approved by the Health Science Research Ethics Board, University of Toronto (protocol reference # 27958).</p>", "<title>Blood collection and stimulation</title>", "<p id=\"Par11\">Blood was sampled via standard venipuncture from athletes at the time of study enrolment. Athletes were excluded if they were currently symptomatic from a known infection, illness, or seasonal allergies, or for taking any medications beyond birth control; in the sample used for this study, no athletes were excluded. Blood was drawn via standard venipuncture into 4 ml vacutainers coated with heparin. At this point, heparinized blood was transferred into the TruCulture® system (Rules Based Medicine, Q² Solutions, Texas, USA) for stimulation in two separate tubes containing either the toll like receptor 4 (TLR4) ligand Lipopolysaccharide (LPS, 100 ng/mL) or the TLR7/8 ligand resiquimod (R848, 1 uM). Briefly, 1 ml of blood was pipetted into each of the TruCulture® tubes and placed on a benchtop heatblock (VWR, USA) where they were kept at 37 °C for 24 h. Following stimulation, a plunger was inserted into the tube to separate the cells from the cell supernatant. The supernatant was collected and then stored at -80 °C until analysis.</p>", "<title>Biomarker analysis</title>", "<p id=\"Par12\">Stimulated supernatant samples were analyzed by immunoassay using the protein biomarker platform Olink® (Olink, Uppsala, Sweden). The commercially available ‘Target 48’ cytokine panel was run according to manufacturer’s instructions at a certified clinical research laboratory. Given that the samples were stimulated, a 1:100 dilution was applied before the assays were run. Each stimulated tube was also accompanied by a ‘Null’ control tube without the stimulant present. However, preliminary analyses by our group found that the stimulants used in the current study induced such a substantial level of cytokine production compared to the Null tube (orders of magnitude in most relevant markers) that subtracting the Null cytokine values from the stimulated cytokine values made no difference in the estimates derived from our statistical models. Thus, for simplicity, we only analyzed and reported the results from the stimulated tubes in our sample. The 45 cytokines evaluated using the Target 48 panel can be found at the Olink website using the following link: <ext-link ext-link-type=\"uri\" xlink:href=\"https://olink.com/content/uploads/2021/09/olink-target-cytokine-48-panel-content-v1.0.pdf\">https://olink.com/content/uploads/2021/09/olink-target-cytokine-48-panel-content-v1.0.pdf</ext-link>.</p>", "<title>Symptoms</title>", "<p id=\"Par13\">Athletes reported their symptoms on the day of the blood draw by completing a 22-item post-concussion symptom scale where questions were answered using a seven-point Likert rating. This symptom questionnaire is part of the Sport Concussion Assessment Tool (SCAT), the most widely used tool to assist in the diagnosis, management, and prognosis of individuals with concussion [##UREF##7##40##, ##REF##28446453##41##]. A total symptom score was obtained by summing the presence or absence of each symptom irrespective of severity, with a maximum value of 22; symptom severity was evaluated by summing the rated symptom score for each symptom.</p>", "<title>Data analysis</title>", "<p id=\"Par14\">Our aim was to estimate the effect of SRC on immune function in the acute/subacute phase (within 7 days post-injury). Immune function was measured using a panel of cytokines and chemokines commonly associated with inflammation in response to stimulation with two well-characterized inflammatory agents (LPS and R848), which are known to cause the production several cytokines and chemokines through TLR-mediated signalling [##REF##37316210##39##, ##UREF##7##40##]. The analysis plan consisted of three steps: (1) create a heuristic scientific model in the form of a DAG to make explicit modelling assumptions regarding the effect of SRC on immune function, (2) create two latent cytokine variables representing LPS and R848 reactivity, respectively, and (3) employ the rules of causal inference to estimate the effect of SRC on immune function through student-t regression modelling, with the latent variables created in step 2 serving as proxies of immune function.</p>", "<title>Heuristic directed acyclic graph of concussion and immune function</title>", "<p id=\"Par15\">To arrive at a generative statistical model, we first used a heuristic DAG (Fig. ##FIG##0##1##) to map out our scientific beliefs based on our own prior work and that of others. We believed that SRC would influence immune function, and that the effect would be moderated by sex [##REF##32164571##9##]. Given the historical precedent of ‘priming’ [##REF##32343752##7##, ##REF##20880500##29##, ##REF##25445485##33##, ##REF##26442695##38##] we believed that prior concussion history would interact with an acute concussion to influence immune function. There were two backdoors into the SRC node in our DAG because sex and concussion history were not equal across groups in our sample. Furthermore, we acknowledge the possibility that due to the initial period of rest commonly observed following SRC, and given the relationship between acute exercise and inflammation [##REF##32249021##42##, ##REF##17446409##43##], a potential change in exercise behaviour in an active population may alter immune function,. However, as we did not capture the type and time from exercise in our study, this is an unmeasured mediating variable; hence, given the rules of causal inference [##UREF##4##24##] and the DAG in Fig. ##FIG##0##1##, we could estimate the total effect of SRC on immune function, but were unable to measure the direct effect.</p>", "<p id=\"Par16\">\n\n</p>", "<title>Latent modelling of cytokines</title>", "<p id=\"Par17\">LPS and R848 cause the synthesis and release of inflammatory cytokines and chemokines from cells into the systemic circulation in a coordinated fashion. To capture the nature of this process, we employed a Bayesian latent factor model to estimate a single variable comprised of the weighted contributions of each individual cytokine and chemokine in response to either LPS and R848 stimulation. We then used these variables as a proxy of immune function for downstream modelling of the DAG in Fig. ##FIG##0##1##. For model explanation, including the statistical notation used, please see the Supplementary Material ##SUPPL##0##1##: Supplementary Methods. For raw circulating cytokine/chemokine concentrations, please see Supplemental Table ##SUPPL##1##1##.</p>", "<title>Missing data</title>", "<p id=\"Par18\">\nCytokines and chemokines are often found in low concentrations in the peripheral blood and are frequently below the quantitation range of commercial assays. While stimulation helps alleviate this concern by elevating the blood concentration of several mediators by orders of magnitude, in a large panel of markers there will often be some that either do not respond to stimulation or respond to a lesser degree. Hence, missingness is not completely at random (MCAR) nor is it random (MAR), and therefore requires special consideration [##UREF##2##22##]. In the present study, for values that were below the quantifiable limits of the assay, we used Bayesian multiple imputation [##UREF##2##22##] within a confined range between zero and the lowest quantifiable value found in the sample data for each cytokine. This imputation strategy was validated on its ability to recover the latent structure of simulated data. In our simulations, data structure was preserved when several markers were missing up to 50% of their lowest values. For more information on the imputation strategy, please see the simulated data and code at the GitHub link associated with this publication. A table quantifying the missing data for each of the markers used in this study under each condition can also be found in Supplementary Table ##SUPPL##2##2##.</p>", "<title>Student-t regression</title>", "<p id=\"Par19\">Student-t regressions [##REF##27382003##2##] were used to estimate the total causal effect of SRC on immune function (y in [##REF##27382003##2##]). According to the rules of causal inference [##UREF##4##24##] applied to our DAG (Fig. ##FIG##0##1##), to estimate the total causal effect of SRC on immune function we had to adjust for sex and concussion history. We also interacted these variables, as we believed that concussion history interacts with an acute concussion to modulate the immune response, and we believed that the effect of SRC on immune function differs in males and females (moderating effect). Because the number of SRC participants in our study was low (n = 22) and subclassification of concussion history and sex were needed, data coverage for all model parameters was a concern. Student-t models were chosen in place of linear models due to the adaptive degrees of freedom parameter (ν) which the model can learn to help put the appropriate amount of weight in the tails of the distribution. This served to stabilize model estimates and protect against leverage points [##UREF##2##22##]. Regularizing priors were used for all parameters, and in the interaction term where data coverage was lowest, adaptive priors were used to allow for information sharing and regularization across all interaction term parameter estimates [##UREF##2##22##]. As a result, posterior group-level parameter estimates were used to create posterior contrasts to estimate group differences in LPS and R848 reactivity, respectively. All data were z-score transformed prior to modelling. For the notation of the statistical model, please see the Supplementary Material ##SUPPL##0##1##: Supplementary Methods.</p>", "<title>Algorithm used to provide estimates</title>", "<p id=\"Par20\">Posterior distributions for all estimates were derived using Hamiltonian Monte Carlo as implemented in Stan through RStan [##UREF##8##44##, ##UREF##9##45##] (version 2.21) via R [##UREF##10##46##] (version 4.3) and the RStudio Integrated Development Environment [##UREF##11##47##] (version 2023.03.1). The R package ‘rethinking’ [##UREF##12##48##] was used to aid in the post processing of posterior samples and for the creation of density plotting. Latent factor plots were created using the ‘ggplot2’ [##UREF##13##49##], and tidybayes [##UREF##14##50##] packages. Tables were made using the gt [##UREF##15##51##] and gtsummary [##UREF##16##52##] packages. Latent models were validated on simulated data, and all models were assessed for convergence by inspection of trace plots, R-hat values, and effective sample sizes. For student-t models, a non-centered parameterization was employed to allow full exploration of the entire parameter space and prevention of divergent transitions. Priors were selected via prior predictive simulation to span a scientifically credible range of outcomes, and to regularize posterior parameter estimates. The prior distributions were included in all results figures for transparency and to show the influence of the sample data on the model.</p>", "<p id=\"Par21\">All models were evaluated for out-of-sample performance and leverage points using Pareto-smoothed importance sampling cross-validation via the ‘loo’ package [##UREF##17##53##]. Data and code used in this study for latent modelling, student-t modelling, latent model simulations under varying levels of data missingness, model checks, Stan model files, figures, and tables, can found in a public GitHub repository (<ext-link ext-link-type=\"uri\" xlink:href=\"https://github.com/dibatti5/Di-Battista-et-al-2023-JNI-Whole-blood-stimulation-\">https://github.com/dibatti5/Di-Battista-et-al-2023-JNI-Whole-blood-stimulation-</ext-link>).</p>" ]

[ "<title>Results</title>", "<title>Participants</title>", "<p id=\"Par22\">Participant characteristics can be seen in Table ##TAB##0##1##. Age was similar in both groups (median = 21 years), although there were slightly more females in the healthy group (60% vs. 50% in the SRC group), and more athletes in the healthy group without a history of concussion (60% vs. 45% in the SRC group). In those with a history of concussion, both groups had a median time of ~ 2 years from the time of their last concussion to the time of study enrolment. Athletes with SRC presented with a median total 15 symptoms (IQR = 8–23) and a median symptom severity of 36 (IQR 12–67). The median days to recovery was 37 (IQR 21–71). SRC athlete characteristics can be seen in Table ##TAB##1##2##.</p>", "<p id=\"Par23\">\n\n</p>", "<p id=\"Par24\">\n\n</p>", "<title>Latent cytokine modelling</title>", "<p id=\"Par25\">\nTwo latent variables were derived from stimulated cytokine values: a latent variable of LPS reactivity, and a latent variable of R848 reactivity. The posterior estimates of the cytokine/chemokine correlations to the latent structure for each model can be seen in Fig. ##FIG##1##2##. As expected, cytokines Interleukin (IL)-6, tumor necrosis factor (TNF)-α, colony stimulating factor (CSF)-3, and chemokine ligands (CCLs)-3,4, and C-X-C motif chemokine ligand (CXCL)-8, loaded highly on the LPS latent variable, as these are known to be released in response to LPS through the TLR4/Nuclear Factor Kappa B (NF-κB) pathway [##REF##18304834##54##]. Also as expected, the R848 latent variable had many similar important cytokine loadings [##REF##12032557##55##], but differed slightly from LPS by inducing a greater chemokine response. Latent modelling for both R848 and LPS stimulated conditions was completed on all 52 samples.</p>", "<p id=\"Par26\">\n\n</p>", "<title>Preliminary evidence of an effect of SRC on immune function</title>", "<p id=\"Par28\">Student-t derived posterior estimates of the differences (contrasts) in LPS reactivity and R848 reactivity between athletes with SRC and healthy athletes under the modelling assumptions of our DAG (Fig. ##FIG##0##1##) can be seen in the density plots shown in Figs. ##FIG##2##3## and ##FIG##3##4##. In males with no history of SRC, those with an acute SRC (n = 3) had lower LPS reactivity compared to healthy athletes (n = 8) with 93% posterior probability (pprob) (estimated mean difference (emd) = -0.82 SD units, 90% compatibility interval [CI] -1.15–0.3 SD units); they also had slightly reduced R848 reactivity with 77% pprob (emd = -0.35 SD units, 90% CI = -0.23–0.91 SD units). Conversely, in males with a history of SRC, those with an acute SRC (n = 8) had higher LPS reactivity compared to healthy athletes (n = 4) with 85% pprob (emd = 0.45 SD units, 90% CI -0.16–1.14 SD units), and higher R848 reactivity with 82% pprob (emd = -0.35 SD units, 90% CI = -1.15–0.3 SD units). In females, irrespective of concussion history, there was no effect of SRC on LPS reactivity. However, in females with no concussion history, those with an acute SRC (n = 7) had higher R848 reactivity compared to healthy athletes (n = 10) with 86% pprob (90% CI = -0.18–0.92 SD units).</p>", "<p id=\"Par29\">\n\n</p>", "<p id=\"Par31\">\n\n</p>" ]

[ "<title>Discussion</title>", "<p id=\"Par37\">In this preliminary study, we utilized <italic>ex</italic>-<italic>vivo</italic> whole blood stimulation with known cytokine-producing inflammatory agents to better approximate immune function in individuals following SRC. To foster transparency and reproducibility, we made all statistical modelling assumptions explicit using a causal framework in the form of a DAG. Our DAG was constructed on both our own prior work in the space, as well as others. Our <italic>a priori</italic> heuristic model suggested that SRC would influence immune function, that the effect would be different in males and females, and may be influenced by a prior concussion history. The results of our initial modelling suggest that the effect of an acute SRC on males depends on their concussion history; those with no history of concussion appear to have lower immune reactivity while those with a concussion history appear to have greater immune reactivity compared to their respective healthy counterparts. This effect was not present in females, although there was evidence that females with no concussion history may have increased reactivity to R848 following SRC.</p>", "<p id=\"Par38\">The immune priming hypothesis discovered in animal models of TBI suggests that microglial cells ‘activated’ from a prior injury may overreact to a subsequent injury [##REF##25445485##33##, ##REF##26442695##38##, ##UREF##18##56##]. This process may then compound with successive insults over time, leading to aberrant inflammatory signaling in the brain that may cause/expedite neurodegeneration [##REF##28186177##32##, ##REF##25445485##33##]. A primed microglia is defined by (1) a higher baseline level of inflammatory mediators, (2) a lower threshold for activation, and (3) an exaggerated response following activation [##REF##25445485##33##]. We found that males with an acute SRC and with a history of concussion had an elevated cytokine response to stimulation with both LPS and R848 compared to their healthy counterparts, suggests a potentially overactive or ‘inflamed’ state. If we were to map the priming definition to our proxy of systemic immune function, we found evidence of (3) an exaggerated response following activation – in males. However, we were unable to test (1) and (2), because we did not measure baseline mediators to assess the former, and the current study was not designed to measure the latter. We are encouraged by these findings, and believe the priming hypothesis warrants further investigation in humans.</p>", "<p id=\"Par39\">We observed that males with an acute SRC and no history of concussion had comparatively lower stimulated cytokine levels to their healthy counterparts in response to both LPS and R848, suggesting possible immunosuppression. Downregulated inflammatory genes have been observed previously in the days following SRC [##REF##26479397##16##], although functional interpretation of static gene expression is difficult. For example, IL-6 can be both pro- and anti-inflammatory given the context [##UREF##19##57##], and even then, that a known proinflammatory marker like TNF-α is elevated in the blood doesn’t necessarily reflect the current state of the immune system – it may reflect current activity, or it may reflect a recently-active system that is now anergic and suppressed. In the current study, we attempted to make interpretation more intuitive by approximating the current function and state of the immune system through stimulation. The results of our study suggest that male athletes with their first SRC may be immunosuppressed, but validation on a larger sample is needed.</p>", "<p id=\"Par40\">We found an elevated cytokine/chemokine response to R848 stimulation following SRC in females with no concussion history – the opposite of what we found in males with no history of concussion. Of importance, the results reiterate our prior work on sex differences in cytokine signatures following SRC [##REF##32164571##9##], and further supports the need to evaluate males and females separately following injury, particularly when looking at their biology. It is unclear why we observed these sex-disparate findings, although they are wholly unsurprising given the differences in male and female immune function generally [##REF##27546235##58##–##UREF##20##60##]; indeed, we found that healthy female athletes had a lower cytokine response to LPS compared to healthy male athletes with 86% pprob. However, given the small sample size, and that we found R848 but not LPS reactivity to be altered following SRC in females despite the significant overlap in transcription factor activation between the two stimulants, we caution that further investigation is warranted before these initial findings are generalized.</p>", "<title>Limitations and future directions</title>", "<p id=\"Par41\">We refer to the findings of this study as preliminary because of the limited sample size, relative simplicity of our DAG, and reliance on linear models. The adjustments for sex and concussion history required to estimate the total causal effect of SRC on immune function yielded small effective sample sizes for estimation of the interaction term parameters. However, regularizing priors and pooling of the interaction term helped strengthened the estimates in these low coverage spaces [##UREF##2##22##], and out-of-sample testing revealed no leverage points. The simplicity of the DAG in Fig. ##FIG##0##1## was intentional, in that we wanted to provide an intuitive example of how causal modelling can be used in the SRC biomarker space to estimate causal effects. Beyond the unmeasured effect of exercise, we acknowledge there are many other possible additions/modifications to our causal model, and we hope that our colleagues build upon this in future studies. For example, the role of sex on immune function in this model may be further nuanced by the implications of the female menstrual cycle. Collision sport participation and exposure to repeated head contact may also interact with an acute concussion similarly to concussion history in our model. Genetic variability, presence of comorbid mental health disorders, time from injury to sample acquisition, and many other factors may be added to the DAG in our study or used for the creation of several other DAGs. Because we were explicit in all our assumptions, this will help in the design of future studies regardless of whether they are building upon, replicating, or refuting the findings of this study. Additionally, while we realize that linear models have been useful and intuitive to interpret across much of scientific research, there is no reason to believe that the effects of SRC on immune function are most closely approximated by a line. We believe that there is utility in the simplicity of linear modelling, and that a student-t regression was useful in this sample because of its flexibility in modeling data points in the tails of the distribution. Nonetheless, we encourage future studies to look for non-linear alternatives, including bespoke models, that may better approximate the data generating process. And, finally, it is important to consider that we did not evaluate reactivity of the entire immune system, but rather two specific pathways commonly associated with innate immunity in response to bacterial challenge: the TLR4/NF- κB pathway via LPS, and the TLR7/TLR8 pathway through R848. These two stimulants provided a proxy of the ability of study participants to mount an inflammatory response via two mechanisms that impact a broad suite of cytokines and chemokines. We encourage future studies to continue to look at immune stimulation experiments using different ligands; for example, it would be interesting to know the effects of SRC on viral immunity.</p>" ]

[ "<title>Conclusion</title>", "<p id=\"Par42\">Whole blood stimulation is a practical and insightful technique that can be used to evaluate immune function post SRC. Moreover, employing an explicit causal framework will facilitate the replication of findings and drive enhancements in subsequent research endeavors. Our preliminary findings indicate that SRC impacts immune function, with a more pronounced effect in male athletes. This effect varies according to concussion history: males without a concussion history tend to exhibit a depressed inflammatory response, while males with a concussion history may have an amplified inflammatory response. Replication of this study in a larger cohort with a more sophisticated causal model is necessary.</p>" ]

[ "<title>Purpose</title>", "<p id=\"Par1\">To implement an approach combining whole blood immune stimulation and causal modelling to estimate the impact of sport-related concussion (SRC) on immune function.</p>", "<title>Methods</title>", "<p id=\"Par2\">A prospective, observational cohort study was conducted on athletes participating across 13 university sports at a single academic institute; blood was drawn from 52 athletes, comprised of 22 athletes (n = 11 male, n = 11 female) within seven days of a physician-diagnosed SRC, and 30 healthy athletes (n = 18 female, n = 12 male) at the beginning of their competitive season. Blood samples were stimulated for 24 h under two conditions: (1) lipopolysaccharide (lps, 100ng/mL) or (2) resiquimod (R848, 1uM) using the TruCulture® system. The concentration of 45 cytokines and chemokines were quantitated in stimulated samples by immunoassay using the highly sensitive targeted Proximity Extension Assays (PEA) on the Olink® biomarker platform. A directed acyclic graph (DAG) was used as a heuristic model to make explicit scientific assumptions regarding the effect of SRC on immune function. A latent factor analysis was used to derive two latent cytokine variables representing immune function in response to LPS and R848 stimulation, respectively. The latent variables were then modelled using student-t regressions to estimate the total causal effect of SRC on immune function.</p>", "<title>Results</title>", "<p id=\"Par3\">There was an effect of SRC on immune function in males following SRC, and it varied according to prior concussion history. In males with no history of concussion, those with an acute SRC had lower LPS reactivity compared to healthy athletes with 93% posterior probability (pprob), and lower R848 reactivity with 77% pprob. Conversely, in males with a history of SRC, those with an acute SRC had higher LPS reactivity compared to healthy athletes with 85% pprob and higher R848 reactivity with 82%. In females, irrespective of concussion history, SRC had no effect on LPS reactivity. However, in females with no concussion history, those with an acute SRC had higher R848 reactivity compared to healthy athletes with 86% pprob.</p>", "<title>Conclusion</title>", "<p id=\"Par4\">Whole blood stimulation can be used within a causal framework to estimate the effect of SRC on immune function. Preliminary evidence suggests that SRC affects LPS and R848 immunoreactivity, that the effect is stronger in male athletes, and differs based on concussion history. Replication of this study in a larger cohort with a more sophisticated causal model is necessary.</p>", "<title>Supplementary Information</title>", "<p>The online version contains supplementary material available at 10.1186/s12865-023-00595-8.</p>", "<title>Keywords</title>" ]

[ "<title>Electronic supplementary material</title>", "<p>Below is the link to the electronic supplementary material.</p>", "<p>\n\n</p>", "<p>\n\n</p>", "<p>\n\n</p>" ]

[ "<title>Acknowledgements</title>", "<p>The authors would like to acknowledge Sarah Watling for her help with data collection during the study period.</p>", "<title>Author contributions</title>", "<p>APD, MGH, SR &amp; MS helped with the study design and implementation. AD &amp; MGH wrote the main text and prepared all figures and tables. All authors reviewed the manuscript and approved submission for publication.</p>", "<title>Funding</title>", "<p>This research was funded by the Canadian Institutes of Military and Veterans Health (CIMVHR) Task 7: Understanding Concussion.</p>", "<title>Data availability</title>", "<p>An altered dataset and code used in the study are available in a GitHub repository located at the following link: <ext-link ext-link-type=\"uri\" xlink:href=\"https://github.com/dibatti5/Di-Battista-et-al-2023-JNI-Whole-blood-stimulation-\">https://github.com/dibatti5/Di-Battista-et-al-2023-JNI-Whole-blood-stimulation-</ext-link>.</p>", "<title>Declarations</title>", "<title>Ethical approval and consent to Participate</title>", "<p id=\"Par44\">Prior to enrollment, all participants provided written informed consent. All study procedures were in accordance with the declaration of Helsinki, and approved by the Health Science Research Ethics Board, University of Toronto (protocol reference # 27958).</p>", "<title>Consent for publication</title>", "<p id=\"Par45\">Not applicable.</p>", "<title>Competing interests</title>", "<p id=\"Par43\">The authors declare no competing interests.</p>" ]

[ "<fig id=\"Fig1\"><label>Fig. 1</label><caption><p>A Directed Acylic Graph of the effect of Sport related concussion on immune function. Conc Hx, Concussion history; Imm, immune function; SRC, sport-related concussion; Ex, current exercise status. A heuristic scientific model in the form of a directed acyclic graph (DAG) used to model the effect of SRC on immune function</p></caption></fig>", "<fig id=\"Fig2\"><label>Fig. 2</label><caption><p>Latent variables of blood cytokine levels in response to lipopolysaccharide and resiquimod stimulation. Posterior densities of latent variable loadings for cytokines in all athletes (N = 52) following 24 h stimulation with lipopolysaccharide (LPS, 100 ng/mL) (left panel, red), or resiquimod (R848, 1uM) (right panel, green). The x axis shows the posterior correlation of each cytokine to the latent variable in each stimulation condition, while the grey density plots represent the prior distributions. Density plots were derived from 6000 posterior draws, with dots representing the mean of the posterior densities, and the thick and thin lines representing the 70% and 90% intervals, respectively</p></caption></fig>", "<fig id=\"Fig3\"><label>Fig. 3</label><caption><p>Posterior densities derived from student-t modelling of LPS reactivity. SRC, sport-related concussion; Conc Hx, concussion history; LPS, lipopolysaccharide. Density plots show the posterior distributions for the latent cytokine variable of LPS reactivity across groups (SRC, red; Healthy, blue) (<bold>A</bold>–<bold>D</bold>). The grey lines represent the prior distributions for each group in each comparison. Panels <bold>E–H</bold> show the contrasts (difference in SRC – Healthy) for each of the 4 comparisons: The amount of posterior mass to the right of zero indicates the posterior probability that LPS reactivity is higher in SRC, while the amount of posterior mass to the left of zero indicates the posterior probability that LPS reactivity is lower in SRC. The red shading indicates which side of zero has more of the probability mass. For example, in panel <bold>E</bold>, most of the posterior mass is below zero, indicating that LPS reactivity is lower in athletes with SRC, while panel <bold>F</bold> has most of its posterior mass above zero, indicating that LPS reactivity is higher in SRC; these plots coincide with the distributions in <bold>A &amp; B</bold>, respectively. Panels <bold>G</bold> &amp; <bold>H</bold> are equivocal, and coincide with the distributions in <bold>C &amp; D</bold>, respectively. Plots were derived from 2000 posterior draws</p></caption></fig>", "<fig id=\"Fig4\"><label>Fig. 4</label><caption><p>Posterior densities derived from student-t modelling of R848 reactivity. SRC, sport-related concussion; Conc Hx, concussion history; R848, resiquimod. Density plots show the posterior distributions for the latent cytokine variable of R848 reactivity across groups (SRC, green; Healthy, blue) (<bold>A</bold>–<bold>D</bold>). The grey lines represent the prior distributions for each group in each comparison. Panels <bold>E</bold>–<bold>H</bold> show the contrasts (difference in SRC – Healthy) for each of the 4 comparisons: The amount of posterior mass to the right of zero indicates the posterior probability that R848 reactivity is higher in SRC, while the amount of posterior mass to the left of zero indicates the posterior probability that R848 reactivity is lower in SRC. The green shading indicates which side of zero has more of the probability mass. For example, in panel <bold>E</bold>, most of the posterior mass is below zero, indicating that R848 reactivity is lower in athletes with SRC, while panel <bold>F</bold> has most of its posterior mass above zero, indicating that R848 reactivity is higher in SRC; these plots coincide with the distributions in <bold>A &amp; B</bold>, respectively. Panel <bold>G</bold> has most of its posterior mass above zero, indicating that R848 reactivity is higher in SRC, while Panel <bold>H</bold> is equivocal; these plots coincide with the distributions in <bold>C &amp; D</bold>, respectively. Plots were derived from 2000 posterior draws</p></caption></fig>" ]

[ "<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Participant characteristics</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Characteristic</th><th align=\"left\">Healthy, N = 30^{<italic>1</italic>}</th><th align=\"left\">SRC, N = 22^{<italic>1</italic>}</th></tr></thead><tbody><tr><td align=\"left\">\n<bold>Age</bold>\n</td><td align=\"left\">21 (19, 23)</td><td align=\"left\">21 (20, 22)</td></tr><tr><td align=\"left\">\n<bold>Sex</bold>\n</td><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\"> Female</td><td align=\"left\">18 (60%)</td><td align=\"left\">11 (50%)</td></tr><tr><td align=\"left\"> Male</td><td align=\"left\">12 (40%)</td><td align=\"left\">11 (50%)</td></tr><tr><td align=\"left\">\n<bold>Concussion History</bold>\n</td><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\"> None</td><td align=\"left\">18 (60%)</td><td align=\"left\">10 (45%)</td></tr><tr><td align=\"left\"> One</td><td align=\"left\">9 (30%)</td><td align=\"left\">7 (32%)</td></tr><tr><td align=\"left\"> More than one</td><td align=\"left\">3 (10%)</td><td align=\"left\">5 (23%)</td></tr><tr><td align=\"left\">\n<bold>Years Since Last Concussion</bold>\n</td><td align=\"left\">2 (1, 5)</td><td align=\"left\">2 (1, 5)</td></tr><tr><td align=\"left\">\n<bold>Sport</bold>\n</td><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\"> Basketball</td><td align=\"left\">4 (13%)</td><td align=\"left\">0 (0%)</td></tr><tr><td align=\"left\"> Field Hockey</td><td align=\"left\">1 (3.3%)</td><td align=\"left\">1 (4.5%)</td></tr><tr><td align=\"left\"> Figure Skating</td><td align=\"left\">0 (0%)</td><td align=\"left\">1 (4.5%)</td></tr><tr><td align=\"left\"> Football</td><td align=\"left\">0 (0%)</td><td align=\"left\">2 (9.1%)</td></tr><tr><td align=\"left\"> Ice Hockey</td><td align=\"left\">12 (40%)</td><td align=\"left\">4 (18%)</td></tr><tr><td align=\"left\"> Lacrosse</td><td align=\"left\">2 (6.7%)</td><td align=\"left\">1 (4.5%)</td></tr><tr><td align=\"left\"> Mountain Biking</td><td align=\"left\">0 (0%)</td><td align=\"left\">1 (4.5%)</td></tr><tr><td align=\"left\"> Softball</td><td align=\"left\">0 (0%)</td><td align=\"left\">1 (4.5%)</td></tr><tr><td align=\"left\"> Rowing</td><td align=\"left\">0 (0%)</td><td align=\"left\">1 (4.5%)</td></tr><tr><td align=\"left\"> Rugby</td><td align=\"left\">1 (3.3%)</td><td align=\"left\">8 (36%)</td></tr><tr><td align=\"left\"> Soccer</td><td align=\"left\">2 (6.7%)</td><td align=\"left\">1 (4.5%)</td></tr><tr><td align=\"left\"> Swimming</td><td align=\"left\">0 (0%)</td><td align=\"left\">1 (4.5%)</td></tr><tr><td align=\"left\"> Volleyball</td><td align=\"left\">8 (27%)</td><td align=\"left\">0 (0%)</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab2\"><label>Table 2</label><caption><p>SRC characteristics</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Characteristic</th><th align=\"left\">N = 22¹</th></tr></thead><tbody><tr><td align=\"left\">Days From Injury To Blood Draw</td><td align=\"left\">4 (3–5)</td></tr><tr><td align=\"left\">Total Symptoms</td><td align=\"left\">15 (8–23)</td></tr><tr><td align=\"left\">Symptom Severity</td><td align=\"left\">36 (12–67)</td></tr><tr><td align=\"left\">Days to Clinical Recovery</td><td align=\"left\">37 (21–71)</td></tr><tr><td align=\"left\">Recovery &gt; 30 Days</td><td align=\"left\">13 (68)</td></tr></tbody></table></table-wrap>" ]

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[ "<supplementary-material content-type=\"local-data\" id=\"MOESM1\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM2\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM3\"></supplementary-material>" ]

[ "<table-wrap-foot><p>SRC, Sport-related concussion</p><p>^{<italic>1</italic>} Median (IQR); n (%)</p></table-wrap-foot>", "<table-wrap-foot><p>¹ Median (IQR); n (%)</p><p>SRC, sport-related concussion.</p></table-wrap-foot>", "<fn-group><fn><p><bold>Publisher’s Note</bold></p><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn></fn-group>" ]

[ "<graphic xlink:href=\"12865_2023_595_Fig1_HTML\" id=\"d32e476\"/>", "<graphic xlink:href=\"12865_2023_595_Fig2_HTML\" id=\"d32e873\"/>", "<graphic xlink:href=\"12865_2023_595_Fig3_HTML\" id=\"d32e925\"/>", "<graphic xlink:href=\"12865_2023_595_Fig4_HTML\" id=\"d32e967\"/>" ]

[ "<media xlink:href=\"12865_2023_595_MOESM1_ESM.docx\"><caption><p>Supplementary Material 1</p></caption></media>", "<media xlink:href=\"12865_2023_595_MOESM2_ESM.docx\"><caption><p>Supplementary Material 2</p></caption></media>", "<media xlink:href=\"12865_2023_595_MOESM3_ESM.docx\"><caption><p>Supplementary Material 3</p></caption></media>" ]

{ "acronym": [], "definition": [] }

[ "<title>Introduction</title>", "<p id=\"Par24\">The cumulative harm caused by pollution and inadequate resource and land management of our remaining natural resources is a major connected element of many global concerns. Microalgae, including cyanobacteria, have the ability to address some of these issues by decreasing aquatic pollutants and offering a sustainable supply of biomass for product development, as evidenced by the emerging uses of microalgal biotechnology assisting the United Nations’ Sustainable Development Goals (SDGs) [##UREF##0##1##, ##UREF##1##2##]. The microalgal biomass possesses a wide variety of primary and secondary metabolites that are increasingly being acknowledged for their importance in the production of novel products and biotechnological applications. These valuable products, which can be produced directly from CO₂ via photosynthesis, include pigments such as carotenoids, and chlorophylls, carbon storages such as glycogen and polyhydroxybutyrate (PHB), as well as macromolecular compounds such as proteins, carbohydrates, and lipids [##REF##37298323##3##–##REF##24521880##9##]. There are several strategies for boosting algal biomass in order to achieve the lower carbon restriction that limits the yield of biofuel and bioproducts, including nutritional adjustment, and gene manipulation by genetic and metabolic engineering. The increased CO₂ fixation capacity, such as <italic>RuBisCO</italic> gene overexpression, and glucose utilization, has driven cyanobacteria to have better growth and photosynthesis [##REF##34768898##6##, ##REF##16741604##10##–##REF##28287087##12##], and be able to produce more PHB and lipids [##REF##37047389##4##]. Under nitrogen or phosphorus deficiency, most cyanobacteria preferentially store carbon sources in the forms of glycogen and PHB, which are a consequence of the 2-oxoglutarate balance for carbon/nitrogen control [##REF##24374346##13##–##UREF##3##15##]. In Fig. ##FIG##0##1##, the arginine catabolism flux associated with the polyamine synthesis, proline-glutamate reaction, and GS/GOGAT pathway [##REF##10648527##16##, ##REF##34154397##17##]. Previous studies found that a transposon-mutated <italic>Synechocystis</italic> sp. PCC 6803 significantly enhanced PHB synthesis while lacking the <italic>proA</italic> gene encoding gamma-glutamyl phosphate reductase. This mutant also had decreased proline reduction and increased glutamate accumulation [##REF##19606467##18##]. Furthermore, the arginine–ornithine to proline–glutamate reaction was primarily driven by the deletion of the <italic>adc1</italic> gene, which encodes arginine decarboxylase involved in polyamine biosynthesis, in <italic>Synechocystis</italic> sp. PCC 6803. This undoubtedly boosted PHB production, although the precise mechanism is still unknown [##UREF##2##5##]. Notably, in <italic>Synechocystis</italic> sp. PCC 6803, during nitrogen shortage, the activation of an OmpR-type response regulator (Rre37) may stimulate the metabolic flux from glycogen to PHB as well as the hybrid TCA cycle and arginine–ornithine cycle [##REF##24521880##9##]. In cyanobacteria, glutamate can be synthesized through two alternative systems including GS/GOGAT pathway, and another catalyzed by glutamate dehydrogenase (GDH) [##UREF##4##19##, ##UREF##5##20##]. The GS/GOGAT pathway is the main ammonium assimilation system in <italic>Synechocystis</italic> 6803, while GDH encoded by the <italic>gdhA</italic> gene is regulated by the late stage of growth [##REF##7787182##21##, ##REF##9922243##22##] and when energy supply is limited in <italic>Escherichia coli</italic> [##REF##7913929##23##]. On the other hand, when acetyl-CoA flow is driven to PHB accumulation, it is mostly directed to the TCA cycle and fatty acid synthesis from pyruvate and acetate, except for nutritional constraints (Fig. ##FIG##0##1##). Enzymes which involved in PHB biosynthesis are β-ketothiolase (phaA), acetoacetyl-CoA reductase (phaB), and heterodimeric PHB synthase (phaE and phaC), respectively [##REF##9683655##24##]. Instead of producing new CO₂ fixation, the cyanobacteria that were starved of nutrients favored producing PHB from internally stored carbon storage, such as glycogen [##REF##31010017##8##, ##REF##29124651##25##]. In this study, to supply more glutamate to TCA cycle, we overexpressed the <italic>proC</italic> gene, encoding Δ¹pyrroline-5-carboxylate reductase (Fig. ##FIG##0##1##), in <italic>Synechocystis</italic> sp. PCC6803 wild type and Δ<italic>adc1</italic> mutant strains. Two engineered strains included a <italic>Synechocystis</italic> sp. PCC6803 overexpressing <italic>proC</italic> gene or OXP, and <italic>Synechocystis</italic> sp. PCC6803 overexpressing <italic>proC</italic> gene with a knockout of <italic>adc1</italic> gene in polyamine synthesis or OXP/Δ<italic>adc1</italic> strains. Both engineered strains certainly accumulated higher PHB content, particularly in a nitrogen and phosphorus-deprived BG₁₁ medium with acetate supplementation (BG₁₁-N-P + A). It is important to highlight that, particularly in the presence of BG₁₁-N-P + A, the acetyl-CoA flow was mainly diverted to the PHB biosynthetic pathway.</p>" ]

[ "<title>Materials and Methods</title>", "<title>Construction of <italic>proC-</italic>overexpressing <italic>Synechocystis</italic> sp. PCC 6803</title>", "<p id=\"Par33\">First, the recombinant plasmid pEERM_<italic>ProC</italic> was constructed (Table ##TAB##0##1##), which was naturally transformed into <italic>Synechocystis</italic> sp. PCC 6803 wild type (WT) and Δ<italic>adc1</italic> mutant (obtained from [##UREF##2##5##]), thereby generating a <italic>proC-</italic>overexpressing <italic>Synechocystis</italic> sp. PCC 6803 (OXP), and an OXP lacking <italic>adc1</italic> gene (OXP/Δ<italic>adc1</italic>), respectively. The pEERM_<italic>proC</italic> plasmid was constructed by ligating the <italic>proC</italic> gene fragment amplified by PCR using a pair of ProC-F and ProC-R primers, as shown in Additional file ##SUPPL##0##1##: Table S1, in between the <italic>Spe</italic>I and <italic>Pst</italic>I restriction sites in the pEERM vector [##REF##26133196##26##]. The correct recombinant plasmid pEERM_<italic>proC</italic> was transformed into WT and Δ<italic>adc1</italic> mutant cells by natural transformation to create OXP and OXP/Δ<italic>adc1</italic> strains, respectively. In addition, we also constructed the <italic>Synechocystis</italic> sp. PCC 6803 wild-type control (WTc) and the Δ<italic>adc1</italic> mutant control (Δ<italic>adc1</italic>c) by transforming the empty pEERM vector into WT and Δ<italic>adc1</italic> cells, represented as <italic>Synechocystis</italic> WT or Δ<italic>adc1</italic> mutant containing the <italic>Cm</italic>^{<italic>R</italic>} cassette gene (Fig. ##FIG##1##2##A). For host cell suspension preparation, the host cells (WT or Δ<italic>adc1</italic>) were cultures in BG₁₁ medium until OD₇₃₀ reaching about 0.3–0.5. Then, 10 mL of cell culture was harvested by centrifugation at 5500 rpm (3505 × <italic>g</italic>), 25 °C for 10 min, and cell pellets were resuspended in 500 μL of new BG₁₁ medium. Next step, the host cell suspension was mixed with 10 μL of recombinant plasmid solution, and incubated that mixture overnight in the culture room with continuous light illumination at 40–50 μE/m²/s, at 28–30 °C. Then, the sample mixture was spread on a BG₁₁ agar plate containing 10 μg/mL of chloramphenicol, and incubated in the culture room for 2–3 weeks until survived colonies occurred on plate. Each single colony was picked and streaked on a new BG₁₁ agar plate containing higher concentrations of chloramphenicol (20 and 30 μg/mL), and incubated under same condition until transformant colonies appeared. The obtained transformants were confirmed for gene size, location, and segregation by PCR method using many specific pairs of primers (Additional file ##SUPPL##0##1##: Table S1).</p>", "<title>Strains and culture conditions</title>", "<p id=\"Par34\"><italic>Synechocystis</italic> sp. PCC 6803 wild type (WT), derived from the Berkeley strain 6803 from fresh water in California, USA [##REF##4998365##43##], <italic>Synechocystis</italic> lacking <italic>adc1</italic> gene (Δ<italic>adc1</italic>), and all engineered strains (WTc, Δ<italic>adc1</italic>c, OXP, and OXP/Δ<italic>adc1</italic>) were grown in normal BG₁₁ medium for 16 days. The culture room, set for normal growth condition, was performed at 28–30 °C, with a continuous white light illumination by 40–50 μE/m²/s intensity. The cell culture flasks with the initial cell density at 730 nm (OD₇₃₀) of about 0.05 were placed on the rotary shaker at 160 rpm speed. Cell growth was measured at OD₇₃₀ by spectrophotometer. For nutrient-deprived conditions, all <italic>Synechocystis</italic> strains were initially grown in normal BG₁₁ medium until late-log phase of cell growth before treating them with nutrient-derived media under the same growth condition for 11 days. There were two modified media including a BG₁₁ medium without nitrogen (N) and phosphorus (P) (or BG₁₁-N-P), and a BG₁₁-N-P medium with 0.4%(w/v) acetate (A) addition (or BG₁₁-N-P + A). For BG₁₁-N-P medium, it was a BG₁₁ medium lacking NaNO₃ with KCl added in place of KH₂PO₄, and FeSO₄ added in place of ferric ammonium citrate in equimolar concentrations [##UREF##2##5##]. In addition, the initial OD₇₃₀ of cell culture under nutrient-modified conditions was adjusted to about 0.2. Acetate concentration in medium was determined according to the method of Ref. [##REF##18110435##44##].</p>", "<title>Determinations of intracellular pigments and oxygen evolution rate</title>", "<p id=\"Par35\">Cell culture (1 mL) was harvested by centrifugation at 12,000 rpm (14,383 × <italic>g</italic>) for 10 min. The intracellular pigments including chlorophyll <italic>a</italic> and carotenoids in cell pellets were extracted by N,N-dimethylformamide (DMF) (1 mL), vortexed and incubated under darkness for 10 min. After centrifugation at the same speed, the absorbance of the yellowish supernatant was spectrophotometrically measured at 461, 625, and 664 nm. The contents of chlorophyll <italic>a</italic> and carotenoids were calculated according to Refs. [##REF##16662407##45##, ##REF##8349618##46##]. For oxygen evolution rate, cell culture (10 mL) was harvested by centrifugation at 5500 rpm (3505 × <italic>g</italic>), 25 °C for 10 min. Cell pellets were resuspended in new BG₁₁ medium (1 mL) and incubated under darkness for 30 min before measuring the oxygen evolution. Saturated light source was employed at 25 °C using Clark-type oxygen electrode (Hansatech instruments Ltd., King’s Lynn, UK). The unit of oxygen evolution rate was addressed as μmol O₂/mg chlorophyll a/h [##UREF##2##5##].</p>", "<title>Total RNAs extraction and reverse transcription-polymerase reaction (RT-PCR)</title>", "<p id=\"Par36\">Total RNAs were extracted from <italic>Synechocystis</italic> cells using the TRIzol® Reagent (Invitrogen, Life Technologies Corporation, Carlsbad, CA, USA). The purified RNAs (1 μg) were converted to cDNA by reverse transcription using ReverTra ACE® qPCR RT Master Mix Kit (TOYOBO Co., Ltd., Osaka, Japan). This obtained cDNA was subsequently used as a template for PCR with different pairs of primers (Additional file ##SUPPL##0##1##: Table S1). The PCR conditions were performed by initial denaturing at 95 °C for 5 min, followed by 30 cycles of 95 °C for 30 s, annealing temperature of each gene (Additional file ##SUPPL##0##1##: Table S1) for 30 s, and 72 °C for 35 s, followed by a final extension at 72 °C for 5 min. For <italic>16s</italic> rRNA reference, the PCR condition was the same, but there was 19 cycles instead. Prior to initiating the experiment, the optimum cycle for all genes was determined. Those bands were not saturated; instead, they were in the appropriate cycle. The PCR products were checked by 1.5% (w/v) agarose gel electrophoresis. Quantification of band intensity was detected by Syngene® Gel Documentation (Syngene, Frederick, MD, USA).</p>", "<title>HPLC analysis of PHB contents and Nile red staining</title>", "<p id=\"Par37\">Cell cultures (50 mL) were harvested by centrifugation at 5500 rpm (3505 × <italic>g</italic>) for 10 min. To prepare sample for HPLC detection, cell pellets were hydrolyzed by boiling for 60 min with 98%(v/v) sulfuric acid (800 μL) and 20 mg/mL adipic acid (100 μL), an internal standard [##UREF##2##5##]. After that, the hydrolyzed sample was filtered using a 0.45 μm polypropylene membrane filter which was further detected by HPLC instrument (Shimadzu HPLC LGE System, Kyoto, Japan) using a carbon-18 column, Inert sustain 3-μm (GL Science, Tokyo, Japan), with a UV detector at 210 nm. The running buffer consisted of 30% (v/v) acetonitrile dissolved in 10 mM KH₂PO₄ (pH 7.4), with a flow rate of 1.0 mL/min. Authentic commercial PHB was used as standard, which was prepared as same as the samples. For dry cell weight (DCW), it was determined by incubating cell pellets in an oven at 80 °C for 16–18 h, until obtaining the constant weight.</p>", "<p id=\"Par38\">For Nile red staining, cell culture (1 mL) was harvested by centrifugation at 5500 rpm (3505 × <italic>g</italic>) for 10 min. Cell pellets were resuspended in Nile red staining solution (3 μL). Then, the addition of normal saline (0.9%, w/v, 100 μL) was conducted and incubated overnight under darkness [##UREF##2##5##, ##REF##27213577##40##]. To visualize the stained cells, fluorescent microscope (Carl Zeiss, Oberkochen, Jena, Germany) was applied using a filter cup with 535 excitation wavelength, at a magnification of 100x.</p>", "<title>Extraction and determination of glycogen content</title>", "<p id=\"Par39\">Harvested cell pellets from liquid culture (15–30 mL) were extracted by alkaline hydrolysis [##REF##31400361##47##, ##UREF##8##48##]. The 30% potassium hydroxide (KOH) solution (400 μL) was mixed with cell pellets, and boiled for 1 h. After centrifugation at 12,000 rpm (14,383 g), 4 °C for 10 min, the supernatant was transferred to a new tube and mixed with 900 μL of cold absolute ethanol before incubating at − 20 °C overnight to precipitate glycogen. Next step, the sample mixture was centrifuged at 12,000 rpm (14,383 <italic>g</italic>), 4 °C for 30 min to obtain glycogen pellets which were subsequently dried at 60 °C overnight. Glycogen pellets were dissolved in 1 mL of 10% H₂SO₄. Then, dissolved sample (0.2 mL) was mixed with 10% H₂SO₄ (0.2 mL) and anthrone reagent (0.8 mL) before boiling for 10 min. After cooling down the samples to room temperature, the absorbance of samples was measured at 625 nm by spectrophotometer (modified from [##REF##37047389##4##, ##REF##36153604##7##]). A commercial oyster glycogen was used as the standard which was prepared as similar as the sample. The unit of glycogen content was %w/DCW.</p>", "<title>Extraction and determination of polyamine content</title>", "<p id=\"Par40\">Total polyamines were extracted from <italic>Synechocystis</italic> cells with 5% cold HClO₄ (modified from [##REF##16662279##49##, ##REF##14612248##50##]. After extraction by 5% cold HClO₄ for 1 h on ice, the extracted samples were centrifuged at 12,000 rpm (14,838 \n<italic>g</italic>) for 10 min. The supernatant and pellet fractions were represented as the fraction containing free and bound forms of polyamines, respectively. Both fractions were used to derivatize and quantify the total polyamines. For the derivatization, it was performed with benzoyl chloride using 1,6-hexanediamine as an internal standard. 1 mL of 2 M NaOH was mixed with 500 µL of HClO₄ extract and 10 µL of benzoyl chloride. After vigorously mixing, the mixture was incubated for 20 min at room temperature. To terminate the reaction, saturated NaCl solution (2 mL) was added. The benzoyl polyamines were subsequently extracted by cold diethyl ether (2 mL). In addition, the ether phase (1 mL) was evaporated to dryness, and redissolved in methanol (1 mL). Authentic polyamine standards were prepared as similar as the samples. The polyamine content was detected by high-performance liquid chromatography (HPLC; Shimadzu HPLC LGE System, Kyoto, Japan) with inertsil®ODS-3 C-18 reverse phase column (5 μm; 4.6 × 150 mm) with UV–Vis detector at 254 nm. The mobile phase was a gradient of 60–100% methanol with a flow rate of 0.5 mL/min.</p>", "<title>Quantification of proline, glutamate, and GABA contents</title>", "<p id=\"Par41\">HPLC detection of amino acids, including proline, glutamate, and GABA, was performed using <italic>o</italic>-phthalaldehyde (OPA) and 9-Fluorenylmethyl chloroformate (FMOC) derivatives (modified from [##UREF##9##51##, ##UREF##10##52##]). Cell pellets obtained from cell culture (50 mL) were washed and resuspended in 10 mM potassium phosphate citrate buffer (pH 7.6). Cell suspensions were homogenized using SONOPLUS Ultrasonic homogenizer (BANDELIN electronic GmbH &amp; Co., Berlin, Germany). The supernatant was collected after centrifugation at 12,000 rpm (14,838 g) for 10 min, and concentrated by a Centrivap concentrator (Labconco Corporation, MO, USA). The concentrated sample was further extracted with 600 μL of a mixture of water:chloroform:methanol (3:5:12,v/v/v), followed by 300 μL of chloroform and 450 μL of distilled water before centrifugation again at 5500 rpm (3505 × g), 4 ºC, for 10 min. The upper fraction of water–methanol phase was collected, and evaporated before redissolving in 200 μL of 0.1 N HCl. The sample solution was filtered through a 0.45 μm membrane filter, and then diluted (1:4,v/v) with internal standard solution of norvaline and sarcosine (62.5 mM in 0.1 M HCl). Then, this mixture was again filtered through a 0.45 μm membrane filter before detecting by HPLC with UV–VIS detector (Shimadzu HPLC LGE System, Kyoto, Japan) using 4.6 × 150 mm, 3.5 μm Agilent Zorbax Eclipse AAA analytical column and 4.6 × 12.5 mm, 5.0 μm guard column (Agilent Technologies, CA, USA). For the mobile phase, eluent A was 40 mM Na₂HPO₄, pH 7.8, and eluent B was acetonitrile:methanol:water (45:45:10, v/v/v), with a flow rate of 2 mL/min. The OPA- and FMOC-derivatized amino acids were monitored at 338 and 262 nm, respectively. The unit of amino acid content was nmol/mg protein.</p>" ]

[ "<title>Results</title>", "<title>Overexpression of native <italic>proC</italic> gene in <italic>Synechocystis</italic> sp. PCC 6803 wild-type and mutant strains</title>", "<p id=\"Par25\">Initially, we constructed four engineered <italic>Synechocystis</italic> sp. PCC 6803 strains, including wild-type control (WTc), Δ<italic>adc1</italic> mutant control (Δ<italic>adc1</italic>c), OXP, and OXP/Δ<italic>adc1</italic> by double homologous recombination (Table ##TAB##0##1##, Fig. ##FIG##1##2##A). For the WTc and Δ<italic>adc1</italic>c strains, they were created by replacing the <italic>psbA2</italic> gene with a <italic>Cm</italic>^{<italic>R</italic>} cassette in the genomes of <italic>Synechocystis</italic> sp. PCC 6803 WT and Δ<italic>adc1</italic> mutant, respectively (Fig. ##FIG##1##2##A). To create a recombinant plasmid pEERM_<italic>proC</italic> (Table ##TAB##0##1##), a native <italic>proC</italic> (or <italic>slr0661</italic>) gene fragment with a size of 1.0 kb was ligated between flanking regions of the <italic>psbA2</italic> gene of the pEERM vector and the upstream region of <italic>Cm</italic>^{<italic>R</italic>} cassette (Fig. ##FIG##1##2##A). Next, all overexpressing strains were verified by PCR using specific pairs of primers (Additional file ##SUPPL##0##1##: Table S1) for their complete segregation and gene location. To confirm the complete segregation, PCR products with Up_psbA2-F and Dw_psbA2-R primers confirmed the correct size of 3.2 kb, in OXP and OXP/Δ<italic>adc1</italic> strains (Fig. ##FIG##1##2##B.1 and B.2, respectively), while there were 2.3 kb in WT and Δ<italic>adc1</italic> strains, and 2.2 kb in WTc and Δ<italic>adc1</italic>c strains. PCR products with ProC-F and Cm^R-R primers confirmed the correct size of 1.9 kb in OXP and OXP/Δ<italic>adc1</italic> strains (Fig. ##FIG##1##2##C1 and C2, respectively), compared with no band in WT, WTc, Δ<italic>adc1</italic> and Δ<italic>adc1</italic>c strains. In addition, <italic>ProC</italic> gene overexpression was verified by RT-PCR data in all engineered strains (Fig. ##FIG##1##2##D).</p>", "<title>Growth, intracellular pigment contents, oxygen evolution rates, and metabolite accumulation under normal growth condition</title>", "<p id=\"Par26\">We found a slight increase in cell growth of the Δ<italic>adc1</italic>c and OXP/Δ<italic>adc1</italic> strains in comparison with the WTc and OXP strains (Fig. ##FIG##2##3##A). All strains had comparable amounts of chlorophyll <italic>a</italic>; however, <italic>proC</italic> overexpression in OXP and OXP/Δ<italic>adc1</italic> had an impact on the decrease of carotenoids (Fig. ##FIG##2##3##B, C). In comparison to WTc, all engineered strains showed lower rates of oxygen evolution (Fig. ##FIG##2##3##D). On the other hand, as anticipated, total polyamines (PAs) in both bound and free forms declined in the Δ<italic>adc1</italic>c and OXP/Δ<italic>adc1</italic> strains, but the OXP strain showed a minor decrease in total PAs as compared to the WTc strain (Fig. ##FIG##2##3##E). It was found that bound PAs were the main decrease when the <italic>adc1</italic> gene was disrupted. On day 7 under normal growth condition, the proline levels of OXP and OXP/Δ<italic>adc1</italic> strains were found to be much higher, but the Δ<italic>adc1</italic>c strain has the lowest proline content (Fig. ##FIG##2##3##F). Moreover, the WTc strain exhibited significant glutamate content that was almost ten times greater than proline under normal growth conditions (Fig. ##FIG##2##3##G). All mutant strains, especially the OXP/Δ<italic>adc1</italic> strain, had a greater increase in glutamate content than the WTc. Similarly, on day 7 of culture, the GABA level in WTc was higher than the proline content but somewhat lower than the glutamate content under normal condition (Fig. ##FIG##2##3##H). The GABA content of all engineered strains, especially OXP/Δ<italic>adc1</italic>, was lower than that of the WTc strain. Glutamate appeared to be the preferred compound that <italic>Synechocystis</italic> cells accumulated, followed by GABA and proline. On the other hand, cells substantially produced a low level of PHB by about 4 – 23% w/DCW under normal growth condition (F##FIG##2##i##g. ##FIG##2##3##I). When compared to the WTc, the PHB quantity in the OXP and OXP/Δ<italic>adc1</italic> strains appeared to be larger. In order to adapt to the nutrient-modified medium, cells growing on day 11, which represents the late-log phase of cell growth with the maximum level of PHB accumulation, were subsequently selected.</p>", "<title>Growth, intracellular pigment contents, and metabolite accumulation under nutrient-modified conditions</title>", "<p id=\"Par27\">All <italic>Synechocystis</italic> strains were grown in normal BG₁₁ medium for 11 days before starting the adaptation phase (Fig. ##FIG##3##4##). Both two nutrient-modified media, including BG₁₁ lacking nitrogen and phosphorus (BG₁₁-N-P) and BG₁₁-N-P medium with acetate addition (BG₁₁-N-P + A), caused a certain reduction in growth (Fig. ##FIG##3##4##A–C) and intracellular contents of chlorophyll <italic>a</italic> and carotenoids (F##FIG##3##i##g. ##FIG##3##4##D–I). It is worth noting that all engineered strains had a slightly higher level of cell growth under the BG₁₁-N-P + A condition, in particular Δ<italic>adc1</italic>c (Fig. ##FIG##3##4##C). In addition, the <italic>proC</italic>-overexpressing strains, including OXP and OXP/Δ<italic>adc1</italic>, contained a higher accumulation of chlorophyll a than WTc under the BG₁₁-N-P + A condition (Fig. ##FIG##3##4##F).</p>", "<p id=\"Par28\">For the main carbon storages of glycogen and polyhydroxybutyrate (PHB) (Fig. ##FIG##4##5##), glycogen was markedly accumulated rather than PHB under normal growth condition, in particular for a longer period at days 9–11 of cultivation (Fig. ##FIG##4##5##A and D). The OXP strain contained the highest level of glycogen, up to 30–49% of dry cell weight, among other strains, during 9–11 days under normal BG₁₁ condition (Fig. ##FIG##4##5##D). Both BG₁₁-N-P and BG₁₁-N-P + A conditions promoted the specific induction of PHB synthesis in all strains, whereas cells comparatively reduced the quantity of glycogen compared to those under normal condition (Fig. ##FIG##4##5##B–F). Remarkably, on day 7 of the adaptation phase of both BG₁₁-N-P and BG₁₁-N-P + A media, the OXP/Δ<italic>adc1</italic> strain accumulated the greatest amount of PHB, with around 39.2 and 48.9%w/DCW, respectively (Fig. ##FIG##4##5##B, C). Moreover, in Fig. ##FIG##5##6##, there was a 2.7-fold increase in PHB in the OXP/Δ<italic>adc1</italic> strain compared to WTc at day 7 under a BG₁₁-N-P + A condition. Interestingly, after adapting to both BG₁₁-N-P and BG₁₁-N-P + A media, the PHB accumulation of the OXP strain was later driven to reach its maximum level on day 9. On the other hand, it was anticipated that these BG₁₁-N-P and BG₁₁-N-P + A conditions would result in a reduction in polyamines in all strains, particularly OXP/Δ<italic>adc1</italic> strain (Table ##TAB##1##2##). It was evident from Fig. ##FIG##5##6## that the OXP/Δ<italic>adc1</italic> strain has decreased by 0.8 fold in comparison to WTc (Fig. ##FIG##5##6##). Regarding the proline-glutamate-GABA pathway, glutamate production predominated under typical BG₁₁ condition, particularly in OXP and OXP/Δ<italic>adc1</italic> strains, followed by GABA and proline (Table ##TAB##1##2##). The proline content was presumably increased by BG₁₁-N-P and BG₁₁-N-P + A conditions, based on the greater fold change compared to WTc in OXP and OXP/Δ<italic>adc1</italic> strains (Fig. ##FIG##5##6##). Glutamate accumulation was reduced (Table ##TAB##1##2##), but in the modified strains, specifically the OXP strain, it increased by more than 5—7 times in both BG₁₁-N-P and BG₁₁-N-P + A conditions, compared to the WTc (Fig. ##FIG##5##6##). Moreover, GABA accumulation was mostly decreased under nutrient-modified conditions.</p>", "<p id=\"Par29\">We also stained cells adapted under the BG₁₁-N-P + A condition for 7 days with Nile red dye and visualized them under fluorescent microscopy (Fig. ##FIG##6##7##A). When compared to other strains, the OXP/Δ<italic>adc1</italic> strain manifestly exhibited a high abundance of PHB granules in entire cells. Furthermore, RT-PCR was conducted to measure the transcript levels of 15 different genes (Fig. ##FIG##6##7##B, C). Both in OXP and OXP/Δ<italic>adc1</italic> under normal and BG₁₁-N-P + A conditions, the <italic>proC</italic> transcript level increased. It is noteworthy that OXP and OXP/Δ<italic>adc1</italic> strains likewise exhibited elevated <italic>putA</italic> transcript levels, encoding proline oxidase. Furthermore, BG₁₁-N-P + A condition increased the transcript levels of the <italic>gdhA</italic> and <italic>gad</italic> genes, encoding glutamate dehydrogenase and glutamate decarboxylase, respectively, with the exception of the Δ<italic>adc1</italic>c strain. The transcript levels of the <italic>acs</italic>, <italic>ach</italic>, and <italic>ackA</italic> genes, encoding acetyl-CoA synthase, acetyl-CoA hydrolase, and acetate kinase, respectively, in acetate metabolism, were increased by the acetate supplementation in BG₁₁-N-P medium. Remarkably, all strains under the BG₁₁-N-P + A condition showed an increase in the transcript level of the <italic>gltA</italic> gene, encoding citrate synthase in a first step of the TCA cycle, when compared to the normal BG₁₁ condition. On the other hand, although the BG₁₁-N-P + A condition raised the quantity of the <italic>accA</italic> transcript, encoding acetyl-CoA carboxylase subunit A in fatty acid synthesis, relative to the normal condition, there was a low alteration in the <italic>plsX</italic> transcript level, encoding fatty acid/phospholipid synthesis protein. Strikingly, transcript levels of all <italic>pha</italic> genes, including <italic>phaA</italic>, <italic>phaB</italic>, p<italic>haC</italic>, and <italic>phaE</italic>, were upregulated by BG₁₁-N-P + A condition. The reduction of <italic>glgX</italic> transcript amount, encoding glycogen debranching enzyme in glycogen degradation, was induced by the BG₁₁-N-P + A condition rather than the normal BG₁₁ control.</p>" ]

[ "<title>Discussion</title>", "<p id=\"Par30\">The disruption of the polyamine synthetic <italic>adc1</italic> gene in <italic>Synechocystis</italic> sp. PCC 6803 was initially discovered in a previous study [##UREF##2##5##], but the metabolic regulation remained unclear. In this work, we highlight the remarkable finding that higher PHB synthesis (up to 48.9% of dry cell weight) was caused by enhanced metabolic flux from arginine to proline and glutamate, which is closely related to nutrient stress. The introduction of the native <italic>proC</italic> gene, encoding pyrroline-5-carboxylate reductase of proline synthesis, in <italic>Synechocystis</italic> sp. PCC 6803 wild type (WT) and <italic>adc1</italic> mutant (Δ<italic>adc1</italic>) was constructed, thereby creating OXP and OXP/Δ<italic>adc1</italic> strains, respectively. The <italic>proC</italic> mutant of <italic>Synechocystis</italic> sp. PCC 6803 was previously shown to produce less proline, but a <italic>putA</italic> mutant that lacks the enzyme proline oxidase, which breaks down proline to glutamate, nonetheless accumulated a high amount of proline metabolites without producing any glutamate [##REF##10648527##16##]. Enhanced proline accumulation is indicated in response to environmental stress [##REF##20036181##27##–##REF##36978914##29##]. In plants, the stress response of proline accumulation was controversial depending on different species and organisms; maize seedlings had an increased proline production in response to nitrogen and phosphorus deficiency [##UREF##6##30##], while French bean (<italic>Phaseolus vulgaris</italic> L cv Strike) plants showed a decline in proline accumulation under nitrogen-deprived condition [##UREF##7##31##], as well as a low proline level in <italic>Arabidopsis thaliana</italic> growing under nitrogen-limiting condition [##REF##18508804##32##]. In our study, regarding BG₁₁-N-P and BG₁₁-N-P + A conditions, we found a minor alteration in proline accumulation in WTc as compared to BG₁₁ control (Table ##TAB##1##2##). It is worthy to note that the <italic>adc1</italic> knockout with a decreased polyamine also contained lower proline content than the WTc, except for the BG₁₁-N-P condition. Our results also indicated that glutamate accumulation in all strains was dramatically decreased in response to nitrogen and phosphorus deprivation in comparison to the WTc. Nevertheless, glutamate content increased more than twofold in all engineered strains as compared to WTc results (Fig. ##FIG##7##8##), particularly in OXP and OXP/Δ<italic>adc1</italic> strains. Amidst the deficiency of nitrogen and phosphorus, glutamate might have taken up a rapid key role in the metabolism of amino acids through pathways including the GS/GOGAT pathway, multispecific aminotransferases, GABA synthesis, and reversible conversions to proline and arginine [##REF##11479309##33##–##REF##32321966##37##]. Interestingly, the transcript level of the <italic>gdhA</italic> gene, encoding glutamate dehydrogenase (GDH), was upregulated only in the OXP strain which contained the highest level of glutamate under the BG₁₁-N-P + A condition (Fig. ##FIG##6##7##B, C), with 1.77-fold increase compared to WTc (Fig. ##FIG##7##8##). Our finding demonstrated that the <italic>proC</italic> overexpression and <italic>adc1</italic> disruption in <italic>Synechocystis</italic> (OXP/Δ<italic>adc1</italic>) had noted results in higher proline and/or glutamate contents in comparison with WTc, which partially alleviated cells under nitrogen and phosphorus deficiency, as evidenced by the increased accumulation of chlorophyll <italic>a</italic>, although the stress effect of nutrient deprivation still existed.</p>", "<p id=\"Par31\">In addition, we demonstrated that, under typical BG₁₁ condition, when cells reached the late phase of growth on day 11, they accumulated more glycogen (Fig. ##FIG##4##5##D), in particular the OXP strain with 49%w/DCW, and had less PHB (Fig. ##FIG##4##5##A). The growth phase of cyanobacterial cells, in which the levels of ATP and ADP are elevated during the lag phase and then dropped during the log phase, is directly attributed to the energy charge. The improved metabolism and storage of glycogen significantly contribute to maintaining energy homeostasis [##REF##29669272##38##]. Moreover, the nitrogen and phosphorus deficiency certainly accelerated the glycogen accumulation within 3 days of the adaptation phase (Fig. ##FIG##4##5##E), as well as PHB production (Fig. ##FIG##4##5##B). The impact of glycogen breakdown on PHB synthesis during nitrogen deprivation has been previously reported [##REF##31010017##8##]. Subsequently, in order to improve the acetyl-CoA substrate for PHB synthesis, we added carbon source, herein acetate, to the BG₁₁-N-P medium. On day 7 of the adaptation phase, there was a noticeable increase in PHB accumulation in OXP/Δ<italic>adc1</italic> of around 48.9% w/DCW (Fig. ##FIG##4##5##C). The increased acetate utilization in the OXP/Δ<italic>adc1</italic> strain in comparison to other strains confirmed this finding (Additional file ##SUPPL##0##1##: Fig. S2). Our results indicated that, with the exception of the OXP strain, <italic>Synechocystis</italic> cells favored using exogenous acetate to acetyl-CoA over glycogen breakdown, as demonstrated by a reduced amount of <italic>glgX</italic> transcript under BG₁₁-N-P + A condition (Fig. ##FIG##6##7##B, C). Regarding acetate metabolism, cells acclimated to a BG₁₁-N-P medium containing acetate exhibited significantly higher levels of the transcripts <italic>ackA</italic> and <italic>acs</italic>, encoding acetyl-CoA synthase and acetate kinase, respectively. This is consistent with their enhanced fold change, as noted in Fig. ##FIG##7##8##. It is worth noting that the OXP strain also had a higher <italic>ackA</italic> transcript level than that under normal BG₁₁ medium (Fig. ##FIG##6##7##B), but it showed a decreased fold change when compared to WTc (Figs. ##FIG##6##7##C and ##FIG##7##8##). According to these data, the OXP strain may have utilized acetate less than other strains, which may have contributed to its reduced PHB contents on day 7 under BG₁₁-N-P + A treatment compared to the Δ<italic>adc1</italic>c and OXP/Δ<italic>adc1</italic> strains. This finding was confirmed by higher amount of acetate remaining in the medium during treatment with the OXP strain than other strains (Additional file ##SUPPL##0##1##: Fig. S2). As demonstrated earlier by the <italic>acs</italic> mutant, which did not use the external acetate in medium, it is crucial to stress that the Acs enzyme functions as the primary metabolic route for acetate absorption in <italic>Synechocystis</italic> sp. PCC 6803 [##REF##29456625##39##]. Remarkably, we suggested that the flow of acetyl-CoA metabolite to citrate in the TCA cycle in all strains was induced by the BG₁₁-N-P + A condition, as supported by the upregulated transcript level of <italic>gltA</italic> gene, encoding citrate synthase (Fig. ##FIG##6##7##B, C). Nonetheless, it is crucial to note that the <italic>gltA</italic> transcript levels in the engineered strains, including OXP, <italic>adc1</italic>c, and OXP/Δ<italic>adc1</italic>, were lower than the WTc (Fig. ##FIG##7##8##). This result indicated that the lowered flow of acetyl-CoA to the TCA cycle in engineered strains in comparison to WTc substantially contributed to driving acetyl-CoA to other flux directions, such as PHB biosynthetic pathway and fatty acid synthesis. Then, we postulated that the increased levels of proline and glutamate in the engineered strains OXP, Δ<italic>adc1</italic>c, and OXP/Δ<italic>adc1</italic> were substantially related to the flow of acetyl-CoA to the TCA cycle and a conversion between 2-oxoglutarate and glutamate. Our results have not provided the TCA cycle’s metabolites; further identification of pertinent metabolites or the application of integrative bioinformatic approaches might increase our knowledge of the actual mechanism. For PHB synthesis, it is important to note that all <italic>pha</italic> genes in the PHB synthetic pathway were increased in their transcript amounts under the BG₁₁-N-P + A condition, in particular the <italic>phaC</italic> and <italic>phaE</italic> genes, in comparison to the normal BG₁₁ condition (Fig. ##FIG##6##7##B, C). Nonetheless, our findings demonstrate a strong correlation between the increased amounts of <italic>phaA</italic> and <italic>phaB</italic> transcripts and the improved synthesis of PHB in the modified strains (OXP, Δ<italic>adc1</italic>c, and OXP/Δ<italic>adc1</italic>) (Fig. ##FIG##7##8##). This was in line with a previous study in <italic>Synechocystis</italic> sp. PCC6803, where increased PHB synthesis was associated with overexpression of the <italic>phaAB</italic> gene rather than <italic>phaEC</italic> overexpression during nitrogen deprivation [##REF##27213577##40##]. On the other hand, the acetyl-CoA direction to fatty acid synthesis was also induced by the BG₁₁-N-P + A condition due to the high upregulation of the <italic>accA</italic> transcript level and a slight induction of the <italic>plsX</italic> transcript (Fig. ##FIG##6##7##B, C). This could imply that acetate addition contributes to lipid production in cyanobacteria [##REF##27213577##40##, ##REF##30201972##41##]. According to Ref. [##REF##31617886##42##], cyanobacterial cells grown in high C/low N conditions functioned by preventing the inhibitory interaction of PII protein with ACCase, while cells grown in high N/low C conditions could enhance the PII-ACCase interaction, leading to an inhibition of the ACCase enzyme.</p>" ]

[ "<title>Conclusions</title>", "<p id=\"Par32\">The nitrogen and phosphorus-deprived condition efficiently induced the accumulation of glycogen and PHB in <italic>Synechocystis</italic> sp. PCC 6803. In this study, higher PHB production was attained in three modified <italic>Synechocystis</italic> sp. PCC6803 strains, including Δ<italic>adc1</italic>c, OXP, and OXP/Δ<italic>adc1</italic>, under the nutrient-deprived treatments, in particular nitrogen and phosphorus-deprived BG₁₁ medium with acetate addition (BG₁₁-N-P + A). The <italic>proC</italic> overexpression and <italic>adc1</italic> knockout in <italic>Synechocystis</italic> apparently induced the changes in proline and glutamate contents inside the cells, which partially alleviated cells under nitrogen and phosphorus deprivation. However, the acetate addition, enhancing acetyl-CoA metabolite, significantly boosted the PHB and glycogen storage. These genetically modified strains of <italic>Synechocystis</italic> (Δ<italic>adc1</italic>c, OXP, and OXP/Δ<italic>adc1</italic>) might serve as practicable cell factories for biotechnological applications including biomaterials and biofuels.</p>" ]

[ "<title>Background</title>", "<p id=\"Par1\">Lack of nutrients, in particular nitrogen and phosphorus, has been known in the field to sense glutamate production via 2-oxoglutarate and subsequently accelerate carbon storage, including glycogen and polyhydroxybutyrate (PHB), in cyanobacteria, but a few studies have focused on arginine catabolism. In this study, we first time demonstrated that gene manipulation on <italic>proC</italic> and <italic>adc1</italic>, related to proline and polyamine syntheses in arginine catabolism, had a significant impact on enhanced PHB production during late growth phase and nutrient-modified conditions. We constructed <italic>Synechocystis</italic> sp. PCC 6803 with an overexpressing <italic>proC</italic> gene, encoding Δ¹pyrroline-5-carboxylate reductase in proline production, and <italic>adc1</italic> disruption resulted in lower polyamine synthesis.</p>", "<title>Results</title>", "<p id=\"Par2\">Three engineered <italic>Synechocystis</italic> sp. PCC 6803 strains, including a <italic>ProC</italic>-overexpressing strain (OXP), <italic>adc1</italic> mutant, and an OXP strain lacking the <italic>adc1</italic> gene (OXP/Δ<italic>adc1</italic>), certainly increased the PHB accumulation under nitrogen and phosphorus deficiency. The possible advantages of single <italic>proC</italic> overexpression include improved PHB and glycogen storage in late phase of growth and long-term stress situations. However, on day 7 of treatment, the synergistic impact created by OXP/Δ<italic>adc1</italic> increased PHB synthesis by approximately 48.9% of dry cell weight, resulting in a shorter response to nutrient stress than the OXP strain. Notably, changes in proline and glutamate contents in engineered strains, in particular OXP and OXP/Δ<italic>adc1</italic>, not only partially balanced the intracellular C/N metabolism but also helped cells acclimate under nitrogen (N) and phosphorus (P) stress with higher chlorophyll <italic>a</italic> content in comparison with wild-type control.</p>", "<title>Conclusions</title>", "<p id=\"Par3\">In <italic>Synechocystis</italic> sp. PCC 6803, overexpression of <italic>proC</italic> resulted in a striking signal to PHB and glycogen accumulation after prolonged nutrient deprivation. When combined with the <italic>adc</italic>1 disruption, there was a notable increase in PHB production, particularly in situations where there was a strong C supply and a lack of N and P.</p>", "<title>Supplementary Information</title>", "<p>The online version contains supplementary material available at 10.1186/s13068-024-02458-9.</p>", "<title>Keywords</title>" ]

[ "<title>Supplementary Information</title>", "<p>\n</p>" ]

[ "<title>Acknowledgements</title>", "<p>We gratefully thank Professor Peter Lindblad, Microbial Chemistry, Department of Chemistry–Ångström, Uppsala University, for providing the expression vector pEERM for our work.</p>", "<title>Author contributions</title>", "<p>SU responsible for study conception, experimenter, data collection and analysis, manuscript preparation. SJ study conception, supervision, and design, critical revision and manuscript writing, and final approval of the manuscript. All the authors read and approved the final manuscript.</p>", "<title>Funding</title>", "<p>This research was supported by the 90th Anniversary of Chulalongkorn University Fund (Ratchadaphiseksomphot Endowment Fund) to S.U. and S.J. This Research is also funded by Thailand Science research and Innovation Fund Chulalongkorn University (CU_FRB65_hea(66)_129_23_59) to SJ.</p>", "<title>Availability of data and materials</title>", "<p>Data generated and analyzed during this study are included in the published article.</p>", "<title>Declarations</title>", "<title>Ethics approval and consent to participate</title>", "<p id=\"Par42\">Not applicable.</p>", "<title>Consent for publication</title>", "<p id=\"Par43\">Not applicable. All the authors agree to the submission and publication of this manuscript.</p>", "<title>Competing interests</title>", "<p id=\"Par44\">The authors declare that they have no competing interests.</p>" ]

[ "<fig id=\"Fig1\"><label>Fig. 1</label><caption><p>Overview of the polyamine-proline-glutamate pathways connected to the tricarboxylic acid (TCA) cycle and related biosynthetic pathways of polyhydroxybutyrate (PHB), and glycogen in cyanobacterium <italic>Synechocystis</italic> sp. PCC 6803. Abbreviations of genes are: <italic>acc</italic>, a multisubunit acetyl-CoA carboxylase; <italic>ach</italic>, acetyl-CoA hydrolase; <italic>ackA</italic>, acetate kinase; <italic>acs</italic>, acetyl-CoA synthase; <italic>adc</italic>, arginine decarboxylase; <italic>argD</italic>, N-acetylornithine aminotransferase; <italic>gad</italic>, glutamate decarboxylase; <italic>gdhA</italic>, glutamate dehydrogenase; <italic>glgC</italic>, ADP-glucose pyrophosphorylase; <italic>glgX</italic>, glycogen debranching enzyme; <italic>gltA</italic>, citrate synthase; <italic>plsX</italic>, fatty acid/phospholipid synthesis protein; <italic>phaA</italic>, β-ketothiolase; <italic>phaB</italic>, acetoacetyl-CoA reductase; <italic>phaC</italic> and <italic>phaE</italic>, the heterodimeric PHB synthase; <italic>proA</italic>, gamma-glutamyl phosphate reductase; <italic>proC</italic>, Δ¹pyrroline-5-carboxylate reductase; <italic>pta</italic>, phosphotransacetylase; <italic>putA</italic>, proline oxidase; <italic>speB1</italic>, arginase; <italic>speB2</italic>, agmatinase. Abbreviations of intermediates are: FASII, fatty acid synthesis type II; GABA, gamma-aminobutyric acid; GOGAT, glutamate synthase; G6P, glucose-6-phosphate; GS, glutamine synthetase; 3-PGA, 3-phosphoglycerate; PHB, polyhydroxybutyrate</p></caption></fig>", "<fig id=\"Fig2\"><label>Fig. 2</label><caption><p>Genomic maps and transcript levels of <italic>Synechocystis</italic> sp. PCC 6803 strains. The four constructed strains are <italic>Synechocystis</italic> sp. PCC 6803 wild-type control (WTc), <italic>Synechocystis</italic> sp. PCC 6803 lacking <italic>adc1</italic> gene (Δ<italic>adc1</italic>c), <italic>Synechocystis</italic> sp. PCC 6803 overexpressing <italic>proC</italic> gene (OXP), and Δ<italic>adc1</italic> mutant overexpressing <italic>proC</italic> gene (OXP/Δ<italic>adc1</italic>). PCR analysis employing two pairs of specific primers (Supplementary information Table S1) was used to confirm the accurate integration and placement of each gene fragment into the <italic>Synechocystis</italic> genome. (<bold>A</bold>) The double homologous recombination of both <italic>Cm</italic>^{<italic>R</italic>} gene occurred between the conserved sequences of <italic>psbA2</italic> gene in WTc and Δ<italic>adc1</italic>c, and a <italic>proC</italic>:<italic>Cm</italic>^{<italic>R</italic>} fragment occurred between the conserved sequences of <italic>psbA2</italic> gene in OXP and OXP/Δ<italic>adc1</italic> strains when compared to WT. (<bold>B</bold>) For PCR products using UP_psbA2-F and Dw_psbA2-R primers, (<bold>B.1</bold>) For OXP strain, Lane M: GeneRuler DNA ladder, Lanes OX1, OX2, and OX3: three clones no. 1–3 containing a 3.2 kb fragment of Up_<italic>psbA2</italic>-<italic>proC-Cm</italic>^{<italic>R</italic>}-Dw_<italic>psbA2</italic>, Lanes WT and WTc: negative controls of a 2.4 kb fragment in WT and a 2.2 kb fragment in WTc, respectively. (<bold>B.2</bold>) For OXP/Δ<italic>adc1</italic> strain, Lane M: GeneRuler DNA ladder, Lanes OX1 and OX2: two clones no. 1 and 2 containing a 3.2 kb fragment of Up_<italic>psbA2</italic>-<italic>proC-Cm</italic>^{<italic>R</italic>}-Dw_<italic>psbA2</italic>, Lanes Δ<italic>adc1</italic> and Δ<italic>adc1</italic>c: negative controls of a 2.4 kb fragment in Δ<italic>adc1</italic> and a 2.2 kb fragment in Δ<italic>adc1</italic>c, respectively. (<bold>C</bold>) For PCR products using ProC-F and Cm^R-R primers, (<bold>C.1</bold>) For OXP strain, Lane M: GeneRuler DNA ladder, Lanes OX1, OX2, and OX3: three clones no. 1–3 containing a 1.9 kb fragment of <italic>proC-Cm</italic>^{<italic>R</italic>}, Lanes WT and WTc: negative controls (no band) using WT and WTc as template, respectively. (<bold>C.2</bold>) For OXP/Δ<italic>adc1</italic> strain, Lane M: GeneRuler DNA ladder, Lanes Δ<italic>adc1</italic> and Δ<italic>adc1</italic>c: negative controls (no band) using Δ<italic>adc1</italic> and Δ<italic>adc1</italic>c as template, respectively, Lanes OX1 and OX2: two clones no. 1 and 2 containing a 1.9 kb fragment of <italic>proC-Cm</italic>^{<italic>R</italic>}. (<bold>D</bold>) Transcript levels of <italic>proC</italic> gene determined by RT-PCR using RT-ProC-F and RT-ProC-R primers (Additional file ##SUPPL##0##1##: Table S1) in WT, WTc, Δ<italic>adc1</italic>, Δ<italic>adc1</italic>c, and two overexpressing strains, including OXP and OXP/Δ<italic>adc1.</italic> The 0.8% agarose gel electrophoresis of PCR products was performed from cells grown for 6 days in normal BG₁₁ medium. The <italic>16s</italic> rRNA was used as reference. The cropped gels (in <bold>D</bold>) were taken from the original images of RT-PCR products on agarose gels as shown in Supplementary information Figure S1</p></caption></fig>", "<fig id=\"Fig3\"><label>Fig. 3</label><caption><p>Growth curve (<bold>A</bold>), chlorophyll <italic>a</italic> content (<bold>B</bold>), carotenoid content (<bold>C</bold>), oxygen evolution rate (<bold>D</bold>), contents of polyamines (<bold>E</bold>), proline (<bold>F</bold>), glutamate (<bold>G</bold>), GABA (<bold>H</bold>), and PHB (<bold>I</bold>) of WTc, Δ<italic>adc1</italic>c, OXP, and OXP/Δ<italic>adc1 Synechocystis</italic> sp. PCC 6803 strains. In (<bold>A</bold>–<bold>C</bold>), and (<bold>I</bold>), cells grown in BG₁₁ medium for 16 days. In (<bold>D</bold>–<bold>H</bold>), cells were grown in normal BG₁₁ medium for 7 days, and harvested for metabolite contents. The error bars represent standard deviations of means (mean ± S.D., <italic>n</italic> = 3). In (<bold>D</bold>–<bold>I</bold>), the statistical difference of the results between those values of WTc and that engineered strain is indicated by an asterisk at *<italic>P</italic> &lt; 0.05</p></caption></fig>", "<fig id=\"Fig4\"><label>Fig. 4</label><caption><p>Growth curve (<bold>A</bold>–<bold>C</bold>), chlorophyll <italic>a</italic> content (<bold>D</bold>–<bold>F</bold>), and carotenoid content (<bold>G</bold>–<bold>I</bold>) of <italic>Synechocystis</italic> WTc, Δ<italic>adc1</italic>c, OXP, and OXP/Δ<italic>adc1</italic> strains adapted in normal BG₁₁ medium, BG₁₁ medium with N and P deprivation (BG₁₁-N-P), and BG₁₁-N-P supplemented with 4%(w/v) acetate (BG₁₁-N-P + A) medium for 11 days. The error bars represent standard deviations of means (mean ± S.D., <italic>n</italic> = 3). The statistical difference of the results between those values of WTc and that engineered strain is indicated by an asterisk at *<italic>P</italic> &lt; 0.05</p></caption></fig>", "<fig id=\"Fig5\"><label>Fig. 5</label><caption><p>Contents of PHB (<bold>A</bold>–<bold>C</bold>) and glycogen (<bold>D</bold>–<bold>F</bold>) of <italic>Synechocystis</italic> WTc, Δ<italic>adc1</italic>c, OXP, and OXP/Δ<italic>adc1</italic> strains adapted in normal BG₁₁ medium, BG₁₁ medium with N and P deprivation (BG₁₁-N-P), and BG₁₁-N-P supplemented with 4%(w/v) acetate (BG₁₁-N-P + A) medium for 11 days. The error bars represent standard deviations of means (mean ± S.D., <italic>n</italic> = 3). An asterisk (*<italic>P</italic> &lt; 0.05) denotes the statistical difference in results between those WTc values and that engineered strain at each day</p></caption></fig>", "<fig id=\"Fig6\"><label>Fig. 6</label><caption><p>Fold changes of obtained results of metabolite products in three engineered strains compared with those in <italic>Synechocystis</italic> WTc after adapting cells in BG₁₁, BG₁₁-N-P, and BG₁₁-N-P + A for 7 days. In each box, the number represents the fold change of that value of each engineered strain under each stress condition divided by that value of WTc. The statistical difference in the data between those values of WT and the engineered strain is represented by an asterisk at *<italic>P</italic> &lt; 0.05</p></caption></fig>", "<fig id=\"Fig7\"><label>Fig. 7</label><caption><p>The Nile red stained PHB granules (<bold>A</bold>), relative transcript levels (<bold>B</bold>), and their band intensity ratios of gene/<italic>16s</italic> (<bold>C</bold>) of genes involved in PHB synthesis, glycogen degradation, proline-glutamate conversion, and neighboring pathways in <italic>Synechocystis</italic> WTc, Δ<italic>adc1</italic>c, OXP, and OXP/Δ<italic>adc1</italic> strains under BG₁₁-N-P + A condition at day 7 of treatment. The <italic>16s</italic> rRNA was used as reference control</p></caption></fig>", "<fig id=\"Fig8\"><label>Fig. 8</label><caption><p>Fold changes of obtained results of gene transcript levels and metabolite contents in three engineered strains compared with those in <italic>Synechocystis</italic> WTc after adapting cells in BG₁₁-N-P + A for 7 days. In each box and graph, the number and bar graph represents the fold change of that value of each engineered strain divided by that value of WTc, respectively</p></caption></fig>" ]

[ "<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Strains and plasmids used in this study</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Name</th><th align=\"left\">Relevant genotype</th><th align=\"left\">References</th></tr></thead><tbody><tr><td align=\"left\" colspan=\"3\">Cyanobacterial strains</td></tr><tr><td align=\"left\"> <italic>Synechocystis</italic> sp. PCC 6803</td><td align=\"left\">Wild type</td><td align=\"left\">Pasteur culture collection</td></tr><tr><td align=\"left\"> Δ<italic>adc1</italic></td><td align=\"left\">Δ<italic>adc1</italic> knockout, <italic>Km</italic>^{<italic>R</italic>} inserted between <italic>adc1</italic> gene in <italic>Synechocystis</italic> genome</td><td align=\"left\">[##UREF##2##5##]</td></tr><tr><td align=\"left\"> WT control (WTc)</td><td align=\"left\">WT, <italic>Cm</italic>^{<italic>R</italic>} integrated at flanking region of <italic>psbA2</italic> gene in <italic>Synechocystis</italic> genome</td><td align=\"left\">This study</td></tr><tr><td align=\"left\"> Δ<italic>adc1</italic> control (Δ<italic>adc1</italic>c)</td><td align=\"left\">Δ<italic>adc1</italic>, <italic>Cm</italic>^{<italic>R</italic>} integrated at flanking region of <italic>psbA2</italic> gene in <italic>Synechocystis</italic> genome</td><td align=\"left\">This study</td></tr><tr><td align=\"left\"> OXP</td><td align=\"left\"><italic>ProC</italic>, <italic>Cm</italic>^{<italic>R</italic>} integrated at flanking region of <italic>psbA2</italic> gene in <italic>Synechocystis</italic> WT genome</td><td align=\"left\">This study</td></tr><tr><td align=\"left\"> OXP/Δ<italic>adc1</italic></td><td align=\"left\"><italic>ProC</italic>, <italic>Cm</italic>^{<italic>R</italic>} integrated at flanking region of <italic>psbA2</italic> gene in <italic>Synechocystis</italic> Δ<italic>adc1</italic> genome</td><td align=\"left\">This study</td></tr><tr><td align=\"left\" colspan=\"3\">Plasmids</td></tr><tr><td align=\"left\"> pEERM</td><td align=\"left\">P_psbA2- <italic>Cm</italic>^{<italic>R</italic>}; plasmid containing flanking region of <italic>psbA2</italic> gene</td><td align=\"left\">[##REF##26133196##26##]</td></tr><tr><td align=\"left\"> pEERM_<italic>ProC</italic></td><td align=\"left\">P_psbA2-<italic>ProC</italic>- <italic>Cm</italic>^{<italic>R</italic>}; integrated between <italic>Spe</italic>I and <italic>Pst</italic>I sites of pEERM</td><td align=\"left\">This study</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab2\"><label>Table 2</label><caption><p>Contents of some metabolites related to polyamine-proline-glutamate pathway. Cells were grown in various media for 7 days (means ± S.D., n = 3). The statistical difference in the data between the values of WT and the engineered strain is represented by an asterisk at * <italic>p</italic> &lt; 0.05</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" rowspan=\"2\">Metabolites/Strains</th><th align=\"left\" colspan=\"3\">Contents</th></tr><tr><th align=\"left\">BG₁₁ control</th><th align=\"left\">BG₁₁-N-P</th><th align=\"left\">BG₁₁-N-P + A</th></tr></thead><tbody><tr><td align=\"left\" colspan=\"4\">Total polyamine contents (nmol/10⁸ cells)</td></tr><tr><td align=\"left\"> WTc</td><td char=\"±\" align=\"char\">12.4 ± 0.8</td><td char=\"±\" align=\"char\">7.2 ± 0.2</td><td char=\"±\" align=\"char\">5.4 ± 0.3</td></tr><tr><td align=\"left\"> Δ<italic>adc1</italic>c</td><td char=\"±\" align=\"char\">8.9 ± 0.6*</td><td char=\"±\" align=\"char\">3.8 ± 0.4*</td><td char=\"±\" align=\"char\">5.2 ± 0.4</td></tr><tr><td align=\"left\"> OXP</td><td char=\"±\" align=\"char\">14.3 ± 0.5*</td><td char=\"±\" align=\"char\">6.6 ± 0.1*</td><td char=\"±\" align=\"char\">8.4 ± 0.5*</td></tr><tr><td align=\"left\"> OXP/Δ<italic>adc1</italic></td><td char=\"±\" align=\"char\">10.9 ± 0.2*</td><td char=\"±\" align=\"char\">6.1 ± 0.2*</td><td char=\"±\" align=\"char\">4.6 ± 0.1*</td></tr><tr><td align=\"left\" colspan=\"4\">Proline contents (nmol/mg protein)</td></tr><tr><td align=\"left\"> WTc</td><td char=\"±\" align=\"char\">37.8 ± 3.0</td><td char=\"±\" align=\"char\">21.2 ± 2.0</td><td char=\"±\" align=\"char\">48.5 ± 4.0</td></tr><tr><td align=\"left\"> Δ<italic>adc1</italic>c</td><td char=\"±\" align=\"char\">13.2 ± 0.9*</td><td char=\"±\" align=\"char\">81.5 ± 5.0*</td><td char=\"±\" align=\"char\">19.0 ± 0.6*</td></tr><tr><td align=\"left\"> OXP</td><td char=\"±\" align=\"char\">61.1 ± 5.4*</td><td char=\"±\" align=\"char\">180.4 ± 11.0*</td><td char=\"±\" align=\"char\">130.0 ± 10.0*</td></tr><tr><td align=\"left\"> OXP/Δ<italic>adc1</italic></td><td char=\"±\" align=\"char\">41.5 ± 3.0</td><td char=\"±\" align=\"char\">147.3 ± 12.0*</td><td char=\"±\" align=\"char\">232.5 ± 15.0*</td></tr><tr><td align=\"left\" colspan=\"4\">Glutamate contents (nmol/mg protein)</td></tr><tr><td align=\"left\"> WTc</td><td char=\"±\" align=\"char\">415.2 ± 30.0</td><td char=\"±\" align=\"char\">38.8 ± 3.0</td><td char=\"±\" align=\"char\">58.3 ± 3.0</td></tr><tr><td align=\"left\"> Δ<italic>adc1</italic>c</td><td char=\"±\" align=\"char\">931.0 ± 20.0*</td><td char=\"±\" align=\"char\">43.8 ± 3.0</td><td char=\"±\" align=\"char\">159.0 ± 8.0*</td></tr><tr><td align=\"left\"> OXP</td><td char=\"±\" align=\"char\">747.0 ± 40.0*</td><td char=\"±\" align=\"char\">287.0 ± 20.0*</td><td char=\"±\" align=\"char\">294.8 ± 15.0*</td></tr><tr><td align=\"left\"> OXP/Δ<italic>adc1</italic></td><td char=\"±\" align=\"char\">1784 ± 70.0*</td><td char=\"±\" align=\"char\">193.5 ± 10.0*</td><td char=\"±\" align=\"char\">253.9 ± 10.0*</td></tr><tr><td align=\"left\" colspan=\"4\">GABA contents (nmol/mg protein)</td></tr><tr><td align=\"left\"> WTc</td><td char=\"±\" align=\"char\">338.0 ± 10.0</td><td char=\"±\" align=\"char\">294.8 ± 9.0</td><td char=\"±\" align=\"char\">174.6 ± 5.0</td></tr><tr><td align=\"left\"> Δ<italic>adc1</italic>c</td><td char=\"±\" align=\"char\">226.7 ± 8.0*</td><td char=\"±\" align=\"char\">253.0 ± 8.0*</td><td char=\"±\" align=\"char\">190.4 ± 10.0</td></tr><tr><td align=\"left\"> OXP</td><td char=\"±\" align=\"char\">144.4 ± 8.0*</td><td char=\"±\" align=\"char\">88.5 ± 6.0*</td><td char=\"±\" align=\"char\">112.1 ± 6.0*</td></tr><tr><td align=\"left\"> OXP/Δ<italic>adc1</italic></td><td char=\"±\" align=\"char\">89.8 ± 5.0*</td><td char=\"±\" align=\"char\">159.5 ± 10.0*</td><td char=\"±\" align=\"char\">115.0 ± 5.0*</td></tr></tbody></table></table-wrap>" ]

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[ "<table-wrap-foot><p>P_psbA2, <italic>psbA2</italic> promoter; <italic>Cm</italic>^{<italic>R</italic>}, chloramphenicol resistance cassette</p></table-wrap-foot>", "<fn-group><fn><p><bold>Publisher's Note</bold></p><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn></fn-group>" ]

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[ "<media xlink:href=\"13068_2024_2458_MOESM1_ESM.pdf\"><caption><p><bold>Additional file 1: Table S1.</bold> Primers used in this study. <bold>Figure S1.</bold> Agarose gel electrophoresis of RT-PCR products of proC transcript in Synechocystis sp. PCC 6803 strains grown under normal BG11 condition for 6 days, shown in Figure 2C. The 16s rRNA transcript was used as the reference (a size of 521 bp). ProC transcript size of 315 bp. <bold>Figure S2.</bold> Acetate concentration in BG11-N-P+A medium during adaptation phase of all strains. Cells were treated in BG11-N-P+A medium for 11 days. Medium was sampled at days 0, 1, 3, 5, 7, 9, and 11 for determining acetate concentration (according to the method of Hutchens and Kass, 1949). The error bars represent standard deviations of means (mean ± S.D., n = 3).</p></caption></media>" ]

{ "acronym": [ "ADC", "Arg", "Car", "Chl a", "DCW", "DMF", "FMOC", "GABA", "h", "μg", "mL", "min", "nm", "OD", "OPA", "PCR", "PHB", "rpm", "s", "WT" ], "definition": [ "Arginine decarboxylase", "Arginine", "Carotenoids", "Chlorophyll a", "Dry cell weight", "N,N-Dimethylformamide", "9-Fluorenylmethyl chloroformate", "Gamma-aminobutyric acid", "Hour", "Microgram", "Milliliter", "Minute", "Nanometer", "Optical density", "O-Phthalaldehyde", "Polymerase chain reaction", "Polyhydroxybutyrate", "Revolutions per minute", "Seconds", "Wild type" ] }

[ "<title>Introduction</title>", "<p id=\"Par5\">Osteoarthritis (OA), among the most prevalent degenerative joint diseases, is caused by a multitude of factors, including aging, obesity, strain, and trauma [##REF##31982797##1##, ##REF##30340925##2##]. This condition affects more than approximately 240 million individuals worldwide, predominantly middle-aged and elderly people [##REF##32780145##3##]. Crucially, chondrocytes serve as vital protectors of matrix integrity [##REF##30551412##4##]. Presently, the pathogenesis of OA is thought to involve intricate interplay among multiple factors, and effective prevention and treatment methods are lacking. Hence, the search for efficacious OA treatments has become paramount. Emerging evidence implicates various cytokines in cartilage degradation, with IL-1β a prominent player [##REF##32378978##5##]. Notably, Yang et al. investigated the mechanism by which downregulation of microRNA-23b-3p alleviates IL-1β-induced injury in chondrogenic CHON-001 cells [##REF##31440033##6##]. Consequently, we postulate that inhibiting IL-1β expression may be the key to mitigating OA.</p>", "<p id=\"Par6\">Long noncoding RNAs (lncRNAs) are a class of RNA molecules exceeding 200 nucleotides in length. Serving as a pivotal layer in biological regulation, lncRNAs significantly influence various biological processes, including regulation of the cell cycle and cell differentiation [##REF##32014475##7##]. LINC00958, in particular, has been referenced in numerous contexts, across diverse cancers. Zhou et al. [##REF##33928604##8##] described how LINC00958 drives tumour progression through the miR-4306/CEMIP axis in osteosarcoma. Li et al. proposed that the LINC00958/miR-3174/PHF6 axis orchestrates the cell proliferation, migration, and invasion observed in endometrial cancer [##REF##34859848##9##]. However, the specific mechanism of LINC00958 in the context of OA remains to be explored further.</p>", "<p id=\"Par7\">MicroRNAs (miRNAs), a subclass of noncoding RNAs, have garnered substantial recognition as pivotal regulators of diverse cellular processes, through their binding to target mRNAs [##REF##31918268##10##–##REF##31066454##12##]. Similarly, Ding et al. [##REF##30439423##13##] suggested that miR-93 inhibits chondrocyte apoptosis and inflammation in OA through the TLR4/NF-kappaB (κB) signalling pathway. Wang et al. [##REF##33971492##14##] suggested that circATRNL1 protects against OA by targeting miR-153-3p and KLF5. Concurrently, Fioravanti et al. [##REF##33774326##15##] pinpointed miR-214-3p as a promising therapeutic target in the context of OA pathogenesis. Conversely, downregulation of miR-214-3p has been implicated in activating the NF-κB pathway, exacerbating the progression of OA [##REF##33714889##16##]. However, for a comprehensive understanding, further analysis is warranted to elucidate the complete set of functions and molecular mechanisms governed by miR-214-3p in the contest of OA.</p>", "<p id=\"Par8\">Hence, our study was structured to elucidate the roles of LINC00958 in the pathogenesis of OA. In this study, we propsed the following hypotheses: (i) stimulation of CHON-001 cells with IL-1β may accelerate damage in human chondrocytes, cnstituting an in vitro model for studying inflammation; (ii) LINC00958 exhibits a protective effect against OA-related behaviors of CHON-001 cells following IL-1β treatment; and (iii) the underlying mechanisms responsible for the protective effects of LINC00958 could be intricately linked to the miR-214-3p/FOXM1 axis. These findings could lead to the development of a promising effective therapeutic approach for OA.</p>" ]

[ "<title>Materials and methods</title>", "<title>Cell culture</title>", "<p id=\"Par9\">CHON-001 cells were purchased from the ATCC and grown in DMEM (Thermo Fisher) supplemented with 10% FBS and 1% penicillin–streptomycin under humidified conditions with 5% CO₂ at 37 °C. Subsequently, CHON-001 cells were stimulated with 10 ng/ml IL-1β for 12 h to establish an in vitro cellular model of inflammatory injury.</p>", "<title>Dual-luciferase reporter assay</title>", "<p id=\"Par10\">To investigate the relationships of miR-214-3p with LINC00958 and FOXM1, StarBase was utilized. We employed the WT-LINC00958 and MUT-LINC00958 3′-UTR luciferase reporter plasmids to clarify the potential interactions between miR-214-3p and LINC00958. Through this approach, LINC00958 was identified as a potential target of miR-214-3p. In the luciferase activity assay, the LINC00958 wild-type or mutant plasmid was co-transfected with the miR-214-3p mimic or mimic control into 293 T cells using Lipofectamine 2000 (Invitrogen) following the provided protocol for a duration of 24 h. Subsequently, the luciferase activity was measured using the dual-luciferase reporter assay system (Promega, USA).</p>", "<title>Cell transfection</title>", "<p id=\"Par11\">To modulate LINC00958 or miR-214-3p expression in CHON-001 cells, we used control-siRNA, LINC00958-siRNA, an inhibitor control, a miR-214-3p inhibitor, a mimic control, or a miR-214-3p mimic. All transfections were performed using Lipofectamine^® 3000 reagent (Thermo), following the manufacturer’s instructions, and the cells were incubated for 48 h. Subsequently, we assessed the cell transfection efficiency by qRT-PCR.</p>", "<title>qRT-PCR analysis</title>", "<p id=\"Par12\">We extracted total RNA from CHON-001 cells using TRIzol reagent (TaKaRa, Shiga, Japan) following the manufacturer’s instructions. Subsequently, total RNA was reverse transcribed into cDNA using the PrimeScript RT Reagent Kit (TaKaRa, China). PCR amplification was conducted on an ABI PRISM 7900 sequence detection system (Applied Biosystems, USA) to measure the levels of LINC00958, miR-214-3p, FOXM1, and GAPDH. The expression of target genes was quantified using the 2^−ΔΔCt method.</p>", "<title>MTT assay</title>", "<p id=\"Par13\">Following treatment, CHON-001 cells were cultured in 96-well plates at 37 °C. Subsequently, the cells were treated with 10 μl of MTT solution (5 mg/ml) and incubated for an additional 4 h. Following this incubation step, the solution was carefully removed, and 100 μl of DMSO was added to each well in the dark to dissolve the formazan crystals. Finally, after 15 min of gentle mixing, the optical density (OD) at 490 nm was measured using a multifunctional plate reader (BioTek, USA) following the manufacturer’s instructions.</p>", "<title>Flow cytometry (FCM) analysis</title>", "<p id=\"Par14\">We assessed apoptosis in CHON-001 cells using an Annexin-V/Propidium Iodide (PI) Apoptosis Detection Kit (BD Biosciences) with incubation at room temperature for 10 min following the provided instructions. Apoptotic cells were subsequently quantified using a flow cytometer (BD Technologies), and the data were analysed with FlowJo software.</p>", "<title>Western blotting analysis</title>", "<p id=\"Par15\">Proteins were extracted from CHON-001 cells using RIPA buffer (Beyotime), and protein concentrations were measured using a BCA Protein Assay Kit (Invitrogen, USA). Subsequently, proteins in the samples were separated on a 10% SDS‒PAGE gel and then transferred onto a PVDF membrane (Millipore, USA). After blocking with 5% skim milk in PBST for 1 h, the membranes were incubated overnight at 4 °C with primary antibodies against β-actin, Bax, Bcl-2, and FOXM1 (1:1000 dilutions). The membranes were subsequently incubated for 1 h with secondary antibodies. Finally, protein signals were visualized using the ECL method (Cytiva) following the manufacturer’s instructions.</p>", "<title>ELISA</title>", "<p id=\"Par16\">We collected supernatant samples from CHON-001 cells and measured the concentrations of secreted IL-6, IL-8, and TNF-α using ELISA kits (BD Biosciences) following the manufacturer’s instructions. Subsequently, the OD at 450 nm was measured using a Multiskan Spectrum microplate spectrophotometer (MD, USA).</p>", "<title>Lactate dehydrogenase (LDH) assay</title>", "<p id=\"Par17\">We assessed the release of LDH from CHON-001 cells using an LDH Cytotoxicity Assay Kit (Sigma). Cells were cultured in 12-well plates for 48 h. Following treatment, we collected both the supernatant and total lysate from the CHON-001 cells, and these samples were incubated with the LDH reaction mixture according to the manufacturer’s instructions for 15 min. The absorbance at 490 nm was then measured, and LDH release was quantified using a microplate reader (BioTek, USA).</p>", "<title>Statistical analysis</title>", "<p id=\"Par18\">Statistical analysis was performed using SPSS 20.0 software. The results are presented as the mean ± SD from three independent experiments. The statistical significance of differences among three or more groups and between two groups was assessed using one-way analysis of variance (ANOVA) or Student’s t test, respectively. *<italic>P</italic> &lt; 0.05 and **<italic>P</italic> &lt; 0.01 were considered to indicate statistically significant differences.</p>" ]

[ "<title>Results</title>", "<title>MiR-214-3p was identified as a direct target of LINC00958</title>", "<p id=\"Par19\">To investigate whether LINC00958 functions as a competing endogenous RNA by targeting miRNAs, we employed the target prediction tool StarBase to identify potential target genes. Our analysis revealed that LINC00958 and miR-214-3p may have binding sites (Fig. ##FIG##0##1##A). To further validate this interaction, we employed a dual luciferase reporter system, which confirmed that The miR-214-3p mimics decreased the activity of LINC00958 reporter gene plasmids while having no effect on mutant plasmids (Fig. ##FIG##0##1##B). This evidence strongly suggested that miR-214-3p directly interacts with LINC00958.</p>", "<title>Expression of LINC00958 and miR-214-3p in articular cartilage tissue samples from OA patients and in IL-1β-stimulated CHON-001 cells</title>", "<p id=\"Par20\">Moreover, we assessed the expression levels of LINC00958 and miR-214-3p in articular cartilage tissue samples obtained from OA patients. qRT‒PCR analysis revealed upregulation of LINC00958 in the articular cartilage tissues of OA patients, as shown in Fig. ##FIG##1##2##A, in contrast to the normal control group. Furthermore, as demonstrated in Fig. ##FIG##1##2##B, the expression level of miR-214-3p was significantly lower in the articular cartilage tissues from OA patients than in those from normal control individuals. Additionally, we examined the expression of LINC00958 and miR-214-3p in an in vitro model of chondrocyte inflammatory injury induced by IL-1β. Our findings revealed upregulation of LINC00958 and downregulation of miR-214-3p in IL-1β-stimulated CHON-001 cells (Fig. ##FIG##1##2##C, D). These observations confirmed the involvement of both LINC00958 and miR-214-3p in the progression of OA.</p>", "<title>LINC00958 negatively regulated the expression of miR-214-3p in CHON-001 cells</title>", "<p id=\"Par21\">We then assessed the functional roles of LINC00958 and miR-214-3p in CHON-001 cells. These cells were stimulated with 10 ng/ml IL-1β and transfected with various constructs, including control-siRNA, the miR-214-3p inhibitor, the inhibitor control, and LINC00958-siRNA. The transfection efficiency was determined by qRT‒PCR. As shown in Fig. ##FIG##2##3##A, the introduction of LINC00958-siRNA led to a substantial reduction in LINC00958 expression in CHON-001 cells. In contrast, the miR-214-3p level was significantly lower in cells transfected with the miR-214-3p inhibitor than in cells in the control, control-siRNA, and inhibitor control groups (Fig. ##FIG##2##3##B).</p>", "<p id=\"Par22\">Additionally, LINC00958-siRNA transfection markedly increased the level of miR-214-3p in CHON-001 cells. However, this increase was effectively countered by transfection of the miR-214-3p inhibitor (Fig. ##FIG##2##3##C). These findings aligned with our earlier results, which indicated that IL-1β induced an increase in LINC00958 expression and a decrease in miR-214-3p expression and that these effects were reversed by LINC00958-siRNA transfection. Furthermore, we detected the opposite results in cells transfected with the miR-214-3p inhibitor, as evidenced by the upregulation of LINC00958 expression and the downregulation of miR-214-3p expression (Fig. ##FIG##2##3##D, E). Taken together, these findings provide strong evidence that LINC00958 exerts a negative regulatory effect on miR-214-3p expression in CHON-001 cells.</p>", "<title>Downregulation of LINC00958 alleviated the decrease in the viability and increase in the apoptosis in IL-1β-stimulated CHON-001 cells by targeting miR-214-3p</title>", "<p id=\"Par23\">To elucidate the roles of LINC00958 and miR-214-3p in regulating the viability and apoptosis of CHON-001 cells, we stimulated these cells with 10 ng/ml IL-1β for 12 h. Additionally, we transfected cells with control-siRNA, LINC00958-siRNA, the inhibitor control, or the miR-214-3p inhibitor. Exposure to IL-1β led to a reduction in cell viability (Fig. ##FIG##3##4##A), an increase in LDH release (Fig. ##FIG##3##4##B), increases in apoptotic cell populations (Fig. ##FIG##3##4##C, D), an increase in BAX expression (Fig. ##FIG##3##4##E, F), and inhibition of BCL2 expression (Fig. ##FIG##3##4##E, G). We observed the opposite effects in cells transfected with LINC00958-siRNA. Importantly, these effects were consistently reversed by transfection of the miR-214-3p inhibitor, highlighting the potential of LINC00958 downregulation to mitigate the IL-1β-induced reduction in cell viability and increase in apoptosis in CHON-001 cells and indicating that LINC00958 achieves this modulatory effect by targeting miR-214-3p.</p>", "<title>Downregulation of LINC00958 alleviated the IL-1β-induced release of inflammatory factors from CHON-001 cells</title>", "<p id=\"Par24\">Furthermore, we elucidated the impacts of LINC00958 and miR-214-3p on the inflammatory response in CHON-001 cells by specifically measuring IL-6 (Fig. ##FIG##4##5##A), IL-8 (Fig. ##FIG##4##5##B), and TNF-α (Fig. ##FIG##4##5##C) concentrations. Our ELISA results indicated significant increases in the concentrations of these inflammatory factors in IL-1β-treated CHON-001 cells. Importantly, introduction of LINC00958-siRNA significantly inhibited this inflammatory response compared to that in the control-siRNA group.</p>", "<p id=\"Par25\">However, these inhibitory effects were subsequently reversed following treatment with the miR-214-3p inhibitor. These findings indicated that downregulation of LINC00958 effectively mitigated the IL-1β-induced inflammatory response in CHON-001 cells.</p>", "<title>MiR-214-3p mimic transfection alleviated the decrease in the viability and increase in the apoptosis of IL-1β-induced CHON-001 cells</title>", "<p id=\"Par26\">To gain further insight into the effects of miR-214-3p in IL-1β-stimulated CHON-001 cells, we stimulated cells with 10 ng/ml IL-1β for 12 h. Subsequently, we transfected either the mimic control or the miR-214-3p mimic into the cells. As shown in Figs. ##FIG##3##4##A and ##FIG##5##6##B, miR-214-3p was markedly upregulated in the miR-214-3p mimic group compared to the control and mimic control groups.</p>", "<p id=\"Par27\">As shown by our MTT and LDH release assays, transfection of the miR-214-3p mimic significantly augmented cell viability (Fig. ##FIG##5##6##C) while decreasing LDH release (Fig. ##FIG##5##6##D). Additionally, upregulation of miR-214-3p resulted in suppression of apoptosis (Fig. ##FIG##5##6##E and F), reduced BAX expression (Fig. ##FIG##5##6##G and H), and an increase in BCL2 expression (Fig. ##FIG##5##6##G and I). These findings indicate that the miR-214-3p mimic effectively alleviated the IL-1β-induced decrease in the viability and increase in the apoptosis of CHON-001 cells.</p>", "<title>Upregulation of miR-214-3p relieved IL-1β-treated inflammatory response in CHON-001 cells</title>", "<p id=\"Par28\">Similarly, we investigated the impact of the miR-214-3p mimic on the release of inflammatory factors from CHON-001 cells. Our ELISA results indicated significant reductions in the secretion of IL-6, IL-8, and TNF-α from CHON-001 cells treated with the miR-214-3p mimic (Fig. ##FIG##5##6##J–L). Collectively, these findings strongly suggest that the miR-214-3p mimic alleviated the inflammatory response induced by IL-1β in CHON-001 cells.</p>", "<title>MiR-214-3p negatively regulated FOXM1 expression in CHON-001 cells by targeting FOXM1</title>", "<p id=\"Par29\">Next, we elucidated the potential mechanisms involving miR-214-3p in CHON-001 cells. Utilizing the online database starBase, we identified a binding site for miR-214-3p in FOXM1 (Fig. ##FIG##6##7##A). Subsequently, a dual-luciferase reporter system was used to validate the interaction between miR-214-3p and FOXM1. Notably, transfection of the FOXM1 mimic significantly reduced the luciferase activity in the miR-214-3p-WT group, while no evident changes were observed in the miR-214-3p-MUT group (Fig. ##FIG##6##7##B). Furthermore, western blot and qRT‒PCR analyses revealed that the FOXM1 level was elevated in cells transfected with the miR-214-3p mimic but markedly reduced upon miR-214-3p inhibition (Fig. ##FIG##6##7##C–F). Collectively, these results substantiate the hypothesis that silencing LINC00958 impedes OA progression through the miR-214-3p/FOXM1 axis.</p>" ]

[ "<title>Discussion</title>", "<p id=\"Par30\">OA is a prevalent joint ailment characterized by articular cartilage degeneration and deterioration, and is a significant global public health concern. Accumulating evidence implicates various factors, including factors such as mechanical stress, structural abnormalities, and obesity, in the aetiology of OA [##REF##30340925##2##, ##REF##31492126##17##]. As the global population ages, the incidence of OA continues to greatly increase annually. Recently, traditional Chinese medicinal compounds have garnered attention for their potential use in OA treatment, owing to their anti-inflammatory properties and limited side effects [##REF##32035570##18##]. However, a definitive OA treatment remains elusive, promoting our research to explore novel and effective therapeutic strategies for OA management.</p>", "<p id=\"Par31\">LncRNAs have emerged as pivotal players in the pathogenesis of numerous diseases, including OA [##UREF##0##19##–##UREF##1##21##]. Ji et al. [##REF##32941900##22##] revealed the regulatory role of the lncRNA BLACAT1 in modulating the differentiation of bone marrow stromal stem cells by targeting miR-142-5p in OA. Moreover, LINC00958 has been implicated in various cancer types, including bladder cancer [##REF##34702201##23##], breast cancer [##REF##33531456##24##] and hepatocellular carcinoma [##REF##31915027##25##]. Despite these findings, the specific function of LINC00958 in the context of OA remains unclear. Thus, our research focused on revealing the mechanistic role of LINC00958 in OA. Furthermore, accumulating investigations have revealed the involvement of lncRNAs in disease pathogenesis through their interactions with miRNAs. Initially, we identified the target miRNA of LINC00958 and confirmed the direct interaction between LINC00958 and miR-214-3p. To shed light on the roles of LINC00958 in OA, we examined the expression levels of LINC00958 and miR-214-3p in articular cartilage tissue samples from OA patients. Our findings revealed upregulation of LINC00958 and downregulation of miR-214-3p in articular cartilage tissues of OA patients compared to those of normal control volunteers. Compelling evidence highlights the pivotal role of excessive IL-1β production in arthritic joints, which is closely linked to the onset and progression of OA, through the regulation of chondrocyte apoptosis and inflammatory responses [##REF##30551412##4##]. In our study, we established an in vitro OA model by stimulating CHON-001 cells with 10 ng/ml IL-1β for 12 h. Our data consistently indicated that LINC00958 was upregulated, while miR-214-3p was downregulated in IL-1β-induced CHON-001 cells, coonsistent with previous reports [##REF##33042421##26##]. These findings collectively suggest that LINC00958 may contribute to OA progression by modulating miR-214-3p expression.</p>", "<p id=\"Par32\">Numerous reports have highlighted the important roles played by lncRNAs in various biological functions, including cell viability, apoptosis, and metastasis [##REF##33843432##27##, ##REF##32744323##28##]. Subsequently, we performed on functional analyses with LINC00958-siRNA or miR-214-3p inhibitor to elucidate the mechanism through which they mediate IL-1β’s effects on CHON-001 cells. In our experiments, CHON-001 cells were stimulated with 10 ng/ml IL-1β and subsequently transfected with control-siRNA, the miR-214-3p inhibitor, the inhibitor control, or LINC00958-siRNA. Our results were reproducibly consistent with previous findings, confirming that LINC00958 exerts a negative regulatory effect on miR-214-3p expression in CHON-001 cells. Furthermore, the impact of IL-1β was observed as it led to diminished cell viability and an increase in LDH release. Bcl-2, which is localized primarily in the cytoplasm, exerts its anti-apoptotic effect via targeting to the nucleus. Conversely, Bax, another crucial mediator, diminishes the anti-apoptotic effect of Bcl-2, ultimately leading to apoptotic cell death [##REF##31392787##29##]. Our examination of Bax and Bcl-2 expression in CHON-001 cells revealed that IL-1β stimulation amplified BAX expression while concurrently inhibiting BCL-2 expression. Intriguingly, when LINC00958-siRNA was introduced, we observed contrasting results, specifically a decrease in BAX expression and an increase in BCL-2 expression. Notably, these changes were entirely reversed following miR-214-3p inhibitor transfection. Collectively, these findings illustrate that silencing LINC00958 may mitigate the IL-1β-induced reduction in CHON-001 cell viability and the induction of apoptosis by modulating miR-214-3p expression.</p>", "<p id=\"Par33\">Inflammatory processes also play a pivotal role in driving the progression of OA, contributing to the degradation of joint tissues. Notably, Fu conducted research indicating that hesperidin protects against inflammation induced by IL-1β in human OA chondrocytes [##REF##30233731##30##]. To clarify this observation, we investigated the secretion of inflammatory cytokines from IL-1β-induced CHON-001 cells, focusing on IL-6, IL-8, and TNF-α. Our ELISA results revealed that downregulation of LINC00958 effectively mitigated the inflammatory response provoked by IL-1β in CHON-001 cells. Consequently, suppressing chondrocyte apoptosis and alleviating the inflammatory response might offer valuable therapeutic benefits in OA.</p>", "<p id=\"Par34\">To further investigate specific roles of miR-214-3p in OA, we initially stimulated CHON-001 cells with 10 ng/ml IL-1β for 12 h, and then transfected them with either the mimic control or miR-214-3p mimic. Our subsequent functional assays yielded insightful results revealed that upregulation of miR-214-3p had alleviated the IL-1β-induced decrease in the viability and increase in the apoptosis of CHON-001 cells. This effect was evidenced by the increased cell viability and reduced LDH release. Furthermore, we observed that transfection of the miR-214-3p mimic led to suppression of apoptosis, a reduction in BAX expression, and an increase in BCL-2 expression. To further investigate these findings, we also assessed the impact of the miR-214-3p mimic on the release of inflammatory cytokines. ELISA demonstrated significant reduction in the secretion of IL-6, IL-8, and TNF-α from miR-214-3p mimic-treated CHON-001 cells. Taken together, these results strongly suggest that the miR-214-3p mimic effectively alleviated the IL-1β-induced inflammatory response in CHON-001 cells. Finally, we investigated the potential mechanisms involving miR-214-3p in CHON-001 cells. Utilizing the online database StarBase and a dual-luciferase reporter system, we successfully confirmed the interaction between miR-214-3p and FOXM1. Further Western blotting and qRT-PCR analyses revealed that miR-214-3p mimic transfection increased the FOXM1 level, whereas inhibition of miR-214-3p had the opposite effect, shedding light on the regulatory role of miR-214-3p in this context.</p>", "<p id=\"Par35\">Building upon the aforementioned research, we found that silencing LINC00958 effectively inhibited the progression of OA. This inhibition was achieved primarily through the mitigation of apoptosis and the suppression of the inflammatory response in IL-1β-stimulated CHON-001 cells, which were mediated primarily through the miR-214-3p/FOXM1 axis. Consequently, LINC00958 emerged as a promising candidate therapeutic biomarker in OA. To gain a more comprehensive understanding of the precise role of LINC00958 in the development of OA, additional in vivo experiments that can elucidate the exact underlying mechanisms are needed.</p>" ]

[]

[ "<title>Objective</title>", "<p id=\"Par1\">We investigated the impact of the long noncoding RNA LINC00958 on cellular activity and oxidative stress in osteoarthritis (OA).</p>", "<title>Methods</title>", "<p id=\"Par2\">We performed bioinformatics analysis via StarBase and luciferase reporter assays to predict and validate the interactions between LINC00958 and miR-214-3p and between miR-214-3p and FOXM1. The expression levels of LINC00958, miR-214-3p, and FOXM1 were measured by qRT-PCR and western blotting. To assess effects on CHON-001 cells, we performed MTT proliferation assays, evaluated cytotoxicity with a lactate dehydrogenase (LDH) assay, and examined apoptosis through flow cytometry. Additionally, we measured the levels of apoptosis-related proteins, including BAX and BCL2, using western blotting. The secretion of inflammatory cytokines (IL-6, IL-8, and TNF-α) was measured using ELISA.</p>", "<title>Results</title>", "<p id=\"Par3\">Our findings confirmed that LINC00958 is a direct target of miR-214-3p. LINC00958 expression was upregulated but miR-214-3p expression was downregulated in both OA cells and IL-1β-stimulated CHON-001 cells compared to the corresponding control cells. Remarkably, miR-214-3p expression was further reduced after miR-214-3p inhibitor treatment but increased following LINC00958-siRNA stimulation. Silencing LINC00958 significantly decreased its expression, and this effect was reversed by miR-214-3p inhibitor treatment. Notably, LINC00958-siRNA transfection alleviated the IL-1β-induced inflammatory response, as evidenced by the increased cell viability, reduced LDH release, suppression of apoptosis, downregulated BAX expression, and elevated BCL2 levels. Moreover, LINC00958 silencing led to reduced secretion of inflammatory factors from IL-1β-stimulated CHON-001 cells. The opposite results were observed in the miR-214-3p inhibitor-transfected groups. Furthermore, in CHON-001 cells, miR-214-3p directly targeted FOXM1 and negatively regulated its expression.</p>", "<title>Conclusion</title>", "<p id=\"Par4\">Our findings suggest that downregulating LINC00958 mitigates IL-1β-induced injury in CHON-001 cells through the miR-214-3p/FOXM1 axis. These results imply that LINC00958 plays a role in OA development and may be a valuable therapeutic target for OA.</p>", "<title>Keywords</title>" ]

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[ "<title>Author contributions</title>", "<p>YY designed the study. YY and QH carried out the experiments and wrote the paper. JH, YF and YX analyzed the data and discussed the results. YY and QH revised the manuscript.</p>", "<title>Funding</title>", "<p>This study was supported by the Applied Medicine of Hefei Municipal Health Commission (No. Hwk2021yb013) and the Key Project of the Third People’s Hospital of Hefei (No. SYKZ2020001).</p>", "<title>Availability of data and materials</title>", "<p>The dataset used and/or analyzed in this study is available from the corresponding author upon reasonable request.</p>", "<title>Declarations</title>", "<title>Ethics approval and consent to participate</title>", "<p id=\"Par36\">This study was conducted in compliance with ethical standards as outlined in the 1964 Declaration of Helsinki and its subsequent revisions or equivalent ethical guidelines. The Ethics Committee of the Third People’s Hospital of Hefei granted approval for all experiments conducted in this study. Informed consent was subsequently obtained from all the participating patients.</p>", "<title>Competing interests</title>", "<p id=\"Par37\">The authors declare no competing interests.</p>" ]

[ "<fig id=\"Fig1\"><label>Fig. 1</label><caption><p>LINC00958 directly targeted miR-214-3p. <bold>A</bold> Schematic representation of the miR-214-3p binding site in the LINC00958 3′-UTR. <bold>B</bold> Relative luciferase activity was measured using a dual-luciferase reporter assay. *<italic>P</italic> &lt; 0.05, **<italic>P</italic> &lt; 0.01 versus NC</p></caption></fig>", "<fig id=\"Fig2\"><label>Fig. 2</label><caption><p>LINC00958 was upregulated and miR-214-3p was downregulated in OA patients and IL-1β-stimulated CHON-001 cells. <bold>A</bold>, <bold>B</bold> qRT-PCR analysis of LINC00958 and miR-214-3p expression in articular cartilage tissue samples from OA patients. <bold>C</bold>, <bold>D</bold> The levels of LINC00958 and miR-214-3p in IL-1β-induced CHON-001 cells were measured by qRT-PCR assay. *<italic>P</italic> &lt; 0.05, **<italic>P</italic> &lt; 0.01 versus Healthy control; ^##<italic>P </italic>&lt; 0.01 versus Control</p></caption></fig>", "<fig id=\"Fig3\"><label>Fig. 3</label><caption><p>Knockdown of LINC00958 reduced LINC00958 expression and increased miR-214-3p expression. CHON-001 cells were stimulated with 10 ng/ml IL-1β, and transfected with control-siRNA, the miR-214-3p inhibitor, the inhibitor control or LINC00958-siRNA. <bold>A</bold>–<bold>E</bold> The levels of LINC00958 and miR-214-3p were measured using qRT-PCR. **<italic>P</italic> &lt; 0.01 versus siRNA-NC group; ^##<italic>P</italic> &lt; 0.01 versus Inhibitor NC group; ^&amp;&amp;<italic>P</italic> &lt; 0.01 versus siRNA-LINC00958 + inhibitor NC group. ^■■<italic>P</italic> &lt; 0.01 versus Control group; ^▲▲<italic>P</italic> &lt; 0.01 versus IL-1β + siRNA-NC group; ^★P &lt; 0.05, ^★★<italic>P</italic> &lt; 0.01 versus IL-1β + siRNA-LINC00958 + inhibitor NC group</p></caption></fig>", "<fig id=\"Fig4\"><label>Fig. 4</label><caption><p>Knockdown of LINC00958 inhibited IL-1β-induced chondrocyte apoptosis by upregulating miR-214-3p. <bold>A</bold> Cell viability was evaluated by an MTT assay. <bold>B</bold> LDH release was measured to evaluate cell injury. <bold>C</bold> Quantification of apoptotic CHON-001 cells. <bold>D</bold> Flow cytometry analysis of CHON-001 cell apoptosis. <bold>E</bold>–<bold>G</bold> The protein levels of BAX and BCL2 were measured by Western blotting. ^■■<italic>P</italic> &lt; 0.01 versus Control group, ^▲▲<italic>P</italic> &lt; 0.01 versus IL-1β + siRNA-NC group, ^★<italic>P</italic> &lt; 0.05, ^★★<italic>P</italic> &lt; 0.01 versus IL-1β + siRNA-LINC00958 + inhibitor NC group</p></caption></fig>", "<fig id=\"Fig5\"><label>Fig. 5</label><caption><p>Knockdown of LINC00958 inhibited IL-1β-induced inflammatory injury in chondrocytes by upregulating miR-214-3p. The secretion of IL-6 (<bold>A</bold>), IL-8 (<bold>B</bold>) and TNF-α (<bold>C</bold>) was evaluated by ELISA. ^■■<italic>P</italic> &lt; 0.01 versus control group, ^▲▲<italic>P</italic> &lt; 0.01 versus IL-1β + siRNA-NC group, ^★<italic>P</italic> &lt; 0.05, ^★★<italic>P</italic> &lt; 0.01 versus IL-1β + siRNA-LINC00958 + inhibitor NC group</p></caption></fig>", "<fig id=\"Fig6\"><label>Fig. 6</label><caption><p>MiR-214-3p inhibited IL-1β-induced inflammatory damage in chondrocytes. CHON-001 cells were stimulated by 10 ng/ml IL-1β for 12 h and transfected with mimic control or miR-214-3p mimic. <bold>A</bold>, <bold>B</bold> measurement of the miR-214-3p level using qRT-PCR. <bold>C</bold> MTT assay of cell viability. <bold>D</bold> LDH release was measured to evaluate cell injury. <bold>E</bold> Cellular apoptosis was evaluated by FCM. <bold>F</bold> Quantification of apoptotic cells. <bold>G</bold> Western blotting analysis of BAX and BCL2 protein expression. qRT-PCR analysis of BAX (<bold>H</bold>) and BCL2 (<bold>I</bold>) mRNA expression in the different groups. The release of IL-6 (<bold>J</bold>), IL-8 (<bold>K</bold>) and TNF-α (<bold>L</bold>) was quantified using ELISA. *<italic>P</italic> &lt; 0.05, **<italic>P</italic> &lt; 0.01 versus control group; ^##<italic>P</italic> &lt; 0.01 versus IL-1β + mimic control group</p></caption></fig>", "<fig id=\"Fig7\"><label>Fig. 7</label><caption><p>MiR-214-3p was found to be involved in OA by regulating FOXM1 expression. <bold>A</bold> The putative miR-214-3p binding sites in FOXM1 are shown. <bold>B</bold> The dual-luciferase reporter assay results confirmed the interaction between miR-214-3p and FOXM1. qRT-PCR analysis of miR-214-3p mRNA expression in miR-214-3p mimic-transfected (<bold>C</bold>) and miR-214-3p inhibitor-transfected (<bold>E</bold>) cells. Measurement of the miR-214-3p level in miR-214-3p mimic-transfected (<bold>D</bold>) or miR-214-3p inhibitor-transfected (<bold>F</bold>) cells by western blotting. **<italic>P</italic> &lt; 0.01, versus mimics NC group; ^&amp;&amp;<italic>P</italic> &lt; 0.01 versus inhibitor NC group</p></caption></fig>" ]

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[ "<fn-group><fn><p><bold>Publisher's Note</bold></p><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn><fn><p>Yingchuan Yin and Qiaojuan He contributed equally to this study.</p></fn></fn-group>" ]

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[ "<title>Introduction</title>", "<p id=\"Par5\">Epicardial adipose tissue (EAT) is a metabolically active tissue that structurally neighbors the myocardium and the coronary arteries [##UREF##0##1##, ##REF##19057089##2##]. EAT volume (EATV), as well as visceral adipose tissue, increases in obese patients and correlates with the presence and incidence of coronary artery disease (CAD) independent of traditional CAD risk factors [##REF##19892354##3##, ##UREF##1##4##]. EATV has been reported to be an independent predictor of left ventricular (LV) remodeling and LV diastolic dysfunction in patients with CAD or metabolic syndrome [##REF##23471228##5##–##REF##25306552##7##]. Excessive accumulation of EAT might have a paracrine or mechanical burden on the coronary microcirculation and myocardium [##REF##23471228##5##].</p>", "<p id=\"Par6\">Previously, we found that the EATV index (EAVTI; EATVI = EATV/body surface area, mL/m²) was strongly associated with the prevalence of paroxysmal atrial fibrillation (PAF) and persistent atrial fibrillation (PeAF) in a model adjusted for known atrial fibrillation (AF) risk factors [##UREF##2##8##]. An association between the EAT and AF prevalence has also been reported [##UREF##2##8##, ##REF##24497517##9##]. Mahabadi et al. reported that EATV was significantly associated with prevalent AF, independent of AF risk factors; however, this effect was considerably reduced when corrected for left atrial (LA) size [##REF##24497517##9##].</p>", "<p id=\"Par7\">Sex disparities in the association between EATV and cardiovascular disease have been reported. We previously reported that EATV was a discriminator in men but not in women in patients with CAD [##REF##22963346##10##] or in those who underwent coronary artery bypass graft surgery [##REF##28594865##11##]. Until now, the sex-dependent impact of EATV on LA size has not been elucidated. In addition, there are no reports regarding sex disparities in the association between EATV and AF.</p>", "<p id=\"Par8\">This study evaluated sex differences in the association between EATVI and LA volume index (LAVI) in patients with sinus rhythm (SR) or AF.</p>" ]

[ "<title>Materials and methods</title>", "<title>Participants and data collection</title>", "<p id=\"Par9\">We retrospectively reviewed 267 consecutive Japanese patients who had undergone multi-detector cardiac computed tomography (MDCT) between May 2010 and April 2016 at Tomishiro Central Hospital, Okinawa, Japan, or at the Tokushima University Hospital, Tokushima, Japan (Fig. ##FIG##0##1##, flowchart of patient recruitment). The subjects had undergone MDCT if they had had symptoms suggestive of symptomatic or asymptomatic coronary artery disease (CAD) in a moderate-to-high CAD risk category [##UREF##3##12##] or dyspnea suggestive of paroxysmal or chronic AF. The major exclusion criteria were as follows: LV ejection fraction (LVEF) &lt; 50%; serum creatinine levels &gt; 1.5 mg/dL; CAD, if ≥ 1 major coronary artery branch stenosis ≥ 50%; class III or IV heart failure; iodine-based allergy; overt liver disease; hypothyroidism; and moderate to severe valvular disease. Of the 267, 20 patients were excluded because of hypertrophic cardiomyopathy (n = 7), unmeasured LA volume (n = 9), and unmeasured EATV (n = 4). The remaining 247 patients (165 men and 82 women) were enrolled in the full analysis set. The patients were divided into the SR and AF groups. Clinical data, including CT and echocardiographic datasets, were collected from the electrical records by MM, KO, and GM, and anonymous datasets were analyzed offline by SY and MSh.</p>", "<p id=\"Par10\">\n\n</p>", "<title>Ethics approval and consent to participate</title>", "<p id=\"Par11\">The ethical committees approved the present study (Fukushima Medical University #2019 − 182, Tomishiro Central Hospital R01R027). The need for informed consent was waived by the Ethics Committee/Institutional Review Board of Fukushima Medical University and Tomishiro Central Hospital because of the retrospective nature of the study and the lack of direct patient contact or intervention. All methods were carried out in accordance with the declaration of Helsinki.</p>", "<title>Multi-detector computed tomography</title>", "<p id=\"Par12\">Cardiac CT was performed using a 320-slice CT scanner (Aquilion One; Toshiba Medical Systems, Tokyo, Japan) with 0.275-ms rotation and 0.5/320/0.25 collimation. CT images were acquired using a retrospective, nonhelical electrocardiogram-triggered acquisition mode protocol (tube voltage, 120 kV; tube current, 450 mA × 5 ms) with a thickness of 5-mm slices [##REF##22963346##10##, ##UREF##4##13##, ##REF##31956209##14##]. All reconstructed CT image data were transferred to an offline workstation (Synapse Vincent, ver. 4.4, Fuji Film, Tokyo, Japan). For measurement of EATV, the pericardium was manually traced in each trans-axial slice, and then automated processing detected the voxels with a density range of -190 to -30 Hounsfield units beneath the pericardium. The cranial and caudal borders of the epicardial adipose tissue were set at the edge of the left pulmonary artery origin and the left ventricular apex.</p>", "<title>Transthoracic echocardiography</title>", "<p id=\"Par13\">Experienced technicians performed comprehensive transthoracic echocardiography according to the American Society of Echocardiography guidelines [##REF##27037982##15##, ##REF##25559473##16##]. Under the guidance of staff cardiologists, the left atrium was traced in the apical 4-chamber and 2-chamber views at the mitral valve level in end-systole, with care taken to exclude the left atrial appendage and pulmonary veins. LA volume (mL) was calculated using the biplane area-length method, and LAVI (mL/m²) was divided by body surface area [##REF##25559473##16##]. LVEF was measured using the modified Simpson’s biplane method. Transmitral flow (TMF) velocity was recorded from the apical long-axis or 4-chamber view, and the peak early diastolic (E) TMF velocities were measured. The mitral annular motion velocity pattern was recorded using the apical 4-chamber view with the sample volume located at the lateral or septal side of the mitral annulus using pulsed tissue Doppler echocardiography. The mean peak early diastolic mitral annular velocity (E’) was measured on the septal and lateral sides, and the E to E’ ratio (E/E’) was calculated as a marker of LV filling pressure, as described previously [##REF##31956209##14##].</p>", "<title>Statistical analysis</title>", "<p id=\"Par14\">Continuous variables were expressed as mean ± standard deviation for normal distribution and median [25%, 75%] for non-normal distribution. Categorical variables were expressed as the number of patients with percentages. The t-test and Mann-Whitney U test were used for continuous variables, and Fisher exact test for categorical variables for two-group comparisons. Our patients were a different population, with an SR group suggesting symptomatic or asymptomatic CAD and an AF group with dyspnea suggesting paroxysmal or chronic atrial fibrillation. For this reason, inter-group comparisons were not made between the SR and AF groups but only intra-group comparisons were made. Univariate and multivariate linear regression models were performed to determine factors associated with left atrial volume index in the overall, SR, and AF groups after being divided into men and women. For multivariate analysis, the selected variables were Model 1 (age, BMI, men gender, and EATVI) and Model 2 (Model 1 + LVEF, antihypertensive drug). Univariate and multivariate linear regression models to estimate LAVI were also performed in the following subgroups: ≤65 and &gt; 65-year-old, BMI ≤ 25 and &gt; 25 kg/m², diabetes mellitus yes or no, and hypertension yes or no. Statistical analysis were done by using Exploratory 6.9.4.1 (Exploratory Inc., Mill Valley, CA, USA), Prism 9.3.1 (GraphPad Software Inc., La Jolla, CA, USA), and R 4.0.2 (R Foundation for Statistical Computing, Vienna, Austria). All statistical tests were two-tailed, and statistical significance was set at P &lt; 0.05.</p>" ]

[ "<title>Results</title>", "<title>General characteristics</title>", "<title>Overall</title>", "<p id=\"Par15\">General characteristics were shown in both men and women (Table ##TAB##0##1##). Overall, 247 patients had a mean age of 65 years; 67% were men, 26% had SR, and 74% had AF.</p>", "<p id=\"Par16\">\n\n</p>", "<title>Men vs. women (all rhythm)</title>", "<p id=\"Par17\">Men who had combined SR and AF (n = 165) were younger than women (Table ##TAB##0##1##). EATV was larger in men than in women (men, 131.8 ± 48.3 vs. women, 112.5 ± 42.3; P = 0.002); however, EATVI was comparable between men and women. The use of antihypertensive drugs, angiotensin-converting enzyme (ACE) inhibitors, or angiotensin II receptor antagonists (ARB) was similar, while the use of calcium blockers was lower, and the use of beta-blockers was higher in men. The LA volume was higher in men than in women, and the LAVI was comparable between men and women. E/E’ was lower in men than in women.</p>", "<title>SR vs. AF</title>", "<p id=\"Par18\">In men, age, BMI, EAT, and EATVI were comparable between the SR and AF groups. The prevalence of type 2 diabetes mellitus and hypertension was similar between the SR and AF groups; however, the use of antihypertensive drugs was higher in the AF group. LVEF, LA volume, and LAVI were comparable between the SR and AF groups; however, E/E’ was lower in patients with SR. In women, age, BMI, EAT, and EATVI were similar between the SR and AF groups. The prevalence of type 2 diabetes mellitus and hypertension was comparable between the SR and AF groups; however, the use of antihypertensive drugs was higher in the AF group. LVEF and LAVI were comparable between the SR and AF groups; however, E/E’ was lower in patients with SR.</p>", "<title>Comparison of EATVI and LAVI between SR and AF groups</title>", "<p id=\"Par19\">Overall, EATVI and LAVI were higher in the AF group than in the SR group (Fig. ##FIG##1##2##). In men, EATVI and LAVI were comparable between the SR and AF groups. In women, EATVI was comparable between the SR and AF groups, whereas LAVI was higher in the AF group than in the SR group.</p>", "<p id=\"Par20\">\n\n</p>", "<title>The relationship between EATVI and LAVI</title>", "<title>All rhythm</title>", "<p id=\"Par22\">Univariate analysis showed that EATVI was positively correlated with LAVI in men, but not in women (Fig. ##FIG##2##3##, left panel).</p>", "<p>\n\n</p>", "<title>SR</title>", "<p id=\"Par25\">In overall patients, univariate analysis showed that only age was positively correlated with LAVI (Fig. ##FIG##2##3##, middle panel, and Table ##TAB##1##2## A). In men, univariate analysis showed that only EATVI was positively correlated with LAVI (Fig. ##FIG##2##3##, middle panel, and Table ##TAB##1##2## A). Multivariate analysis revealed that EATVI was not associated with LAVI after correcting for confounding factors (Models 1 and 2)(Table ##TAB##1##2## A). In women, univariate and multivariate analyses showed that no significant factors were correlated with LAVI (Fig. ##FIG##1##2##; Table ##TAB##1##2##).</p>", "<title>AF</title>", "<p id=\"Par26\">Univariate analysis showed that age and beta-blocker use were positively correlated with LAVI in overall patients (Table ##TAB##1##2##B). Univariate and multivariate analyses showed no significant correlation between EATVI and LAVI (Fig. ##FIG##2##3##, right panel, and Table ##TAB##1##2##B). In men, the univariate analysis showed that the use of antihypertensive drugs was positively correlated with LAVI (Table ##TAB##1##2##B). Univariate and multivariate analyses showed no significant correlation between the EATVI and LAVI (Fig. ##FIG##2##3##, right panel). In women, there was a negative correlation between EATVI and LAVI, not significantly, but a trend (Fig. ##FIG##2##3##, right panel). Univariate analysis showed no significant factors correlated with LAVI (Table ##TAB##1##2##B). However, multivariate analysis showed that age was positively correlated and EATVI was negatively correlated with LAVI (Models 1 and 2)(Table ##TAB##1##2##B).</p>", "<p>\n\n</p>", "<title>Subgroup analysis</title>", "<title>Overall</title>", "<p id=\"Par28\">EATVI was positively correlated with LAVI for BMI ≤ 25 in patients with SR (Fig. ##FIG##3##4##, upper row in the column of all rhythm). However, EATVI was not correlated with LAVI for all subgroups in patients with all rhythms or AF (Fig. ##FIG##3##4##, upper row in the column of SR and AF).</p>", "<p id=\"Par29\">\n\n</p>", "<title>Men</title>", "<p id=\"Par30\">In all rhythms (Fig. ##FIG##3##4##, middle row in the column of all rhythm), EATVI was positively correlated with LAVI in the subgroups of SR, DM (no), and HT (yes). In SR, the EATVI was positively correlated with the LAVI in patients aged ≥ 65 years (Fig. ##FIG##3##4##, middle row in the column of SR). No significant factors were associated with LAVI in AF (Fig. ##FIG##3##4##, middle row in the column of AF).</p>", "<title>Women</title>", "<p id=\"Par31\">In all rhythms (Fig. ##FIG##3##4##, lower row in the column of all rhythm), EATVI was negatively correlated with LAVI in the subgroup of patients aged &gt; 65 years. There were no significant factors associated with LAVI in SR (Fig. ##FIG##3##4##, lower row in the column of SR) In AF, EATVI was negatively correlated with LAVI in the subgroups of age &gt; 65 years, BMI &gt; 25, and HT (yes) (Fig. ##FIG##3##4##, lower row in the column of AF).</p>" ]

[ "<title>Discussion</title>", "<p id=\"Par32\">In this study, we evaluated sex differences in the association between EATVI and LAVI in patients with either SR or AF, and found two major findings. First, in overall, that includes both men and women, EATVI and LAVI were not significantly correlated with SR and AF. Second, when analyzed separately in men and women, the relationship between EATVI and LAVI differed between men and women. In patients with SR, there was a positive relationship between EATVI and LAVI in men, but not in women. In contrast, in patients with AF, a negative relationship was found between EATVI and LAVI in women, whereas no association was found in men. This is the first report to evaluate sex differences in the relationship between EATV and LAVI, suggesting that the effect of EAT on LAVI may differ between men and women.</p>", "<title>Relationship between EATVI and LAVI in overall patients</title>", "<p id=\"Par33\">EATV has been reported to be associated with the incidence and prevalence of AF [##REF##26025867##17##, ##UREF##5##18##]. EATV has also been associated with an increased LA size [##UREF##6##19##]. The prevalence and incidence of AF and LA size are closely and mutually related [##REF##24497517##9##, ##UREF##6##19##]. In other words, the larger the LA size, the more AF will develop; conversely, when AF develops, LA size will increase [##REF##26025867##17##, ##UREF##5##18##]. This study showed that EATV and LAVI were not significantly correlated with SR and AF. These results are not consistent with those of previous reports. However, as discussed below, the relationship between EATVI and LAVI was found to be significant when analyzed separately by sex.</p>", "<title>Sex differences in the association between EATVI and LAVI in patients with SR</title>", "<p id=\"Par34\">The relationship between the EATVI and LAVI differed between men and women in both the SR and AF groups. Sex differences in the degree of EATV and its clinical significance have been reported. The relationship between increased EATV and the presence of CAD [##UREF##0##1##] or a history of coronary artery bypass graft surgery [##REF##28594865##11##] was found in men, but not in women. In our patients with SR, there was a positive relationship between EATVI and LAVI in men, but not in women. Fox et al. showed that in patients with SR, EATV correlated with LA dimension in men but not in women, which is in agreement with our results [##UREF##6##19##]. Fox et al. [##UREF##6##19##] and our results support that EATV is involved in LA size or LAVI in men, but not in women.</p>", "<title>Sex differences in the association between EATVI and LAVI in patients with AF</title>", "<p id=\"Par35\">Few studies have reported sex differences in the relationship between EATV and AF. van Rosendael et al. showed that EATV was a factor in the development of AF only in men, even after correcting for AF risk factors [##UREF##7##20##]. We also reported that EATV was a factor in the development of both PAF and PeAF in men [##UREF##2##8##]. To our knowledge, this is the first report to examine the association between EATV and LA structure, such as LA size or LAVI, in patients with AF. Our results showed a negative relationship between EATVI and LAVI in women with AF, suggesting that a larger EATVI suppresses the increase in LAVI in women with AF. Because this was a cross-sectional study, cause-and-effect relationships could not be determined. However, the sex difference in EATVI and LAVI in patients with AF is an interesting result and prompts further investigation in future studies.</p>", "<title>Potential mechanisms of sex differences in the relationship between EATVI and LAVI</title>", "<p id=\"Par36\">In men, there was a significant correlation between EATVI and LAVI in the SR group (Table ##TAB##1##2##). These correlations were found in the SR and DM (no) subgroups for all rhythms and in those aged ≥ 65 years in the SR group (Fig. ##FIG##3##4##); however, no correlations were found in the AF group. Theoretically, the deleterious effects of EATVI on LA size might be observed more clearly in patients with SR than in those with AF [##UREF##6##19##], since AF strongly affects LA function and size [##UREF##8##21##]. In contrast, in women, EATVI was not correlated with LAVI in the SR group but was negatively correlated with LAVI in the AF group (Table ##TAB##1##2##). Currently, there are no reasonable explanations for this; however, we have attempted to provide hypothetical explanations. In women with AF, EATVI was negatively correlated with LAVI in the subgroups of age &gt; 65 years, BMI &gt; 25, and HT (yes). Therefore, it can be assumed that EATVI inhibits LA enlargement in preobese elderly women. Ovarian estradiol inhibits left ventricular remodeling and protects against LA diastolic dysfunction [##REF##31448389##22##]. However, the present study found a negative link between EATVI and LAVI in menopausal women, indicating effects other than those of ovarian estradiol. There are two possible mechanisms through which EATVI inhibits the increase in LAVI. First, EATVI may be linked to the favorable effects of estradiol and the protective adipocytokine profiles. Estradiol declines rapidly after the loss of ovarian function in menopause; however, it is continuously produced in the subcutaneous (SAT) and visceral adipose tissue (VAT) [##REF##29029113##23##] in EAT [##REF##26176800##24##]. We previously showed that anti-inflammatory adiponectin was largely produced, and proinflammatory IL1B and NLRP3 were less abundant in SAT and VAT of menopausal women than of men [##UREF##9##25##]. The anti-inflammatory and anti-fibrotic patterns of estradiol and adipocytokines in menopausal women with a larger EATVI could be protective against LA function. However, previous reports are against the protective effects of EAT on cardiac function in menopausal women [##UREF##10##26##, ##UREF##11##27##]. Second, sex differences in heart cells, including myocytes, endothelial cells, smooth muscle cells, macrophages, fibroblasts, and valve cells, may be linked to the association between EATVI and LAVI [##REF##34166708##28##]. Quantitative and qualitative differences in the local EAT and whole-body adiposity may differentially affect LA function in men and women. However, the sex differences and underlying mechanisms observed in the current study should be reconfirmed in future studies. If there are sex differences in the effect of EATV on LA size and LA function, it suggests that measures to prevent cardiovascular events related to LA abnormalities need to be considered separately for men and women [##UREF##12##29##].</p>", "<title>Limitations</title>", "<p id=\"Par37\">First, the cross-sectional design of this study limited the interpretation of causality. Second, the predominantly Japanese patient sample in two recruit location could be biased and limits the generalizability of our findings to a broader population. Third, we did not measure waist circumference or waist-to-hip ratio, which may have added incremental information on local versus systemic adiposity effects. Fourth, AF frequently develops in elderly individuals, who are typically lean. Our study subjects were relatively young and obese, which may have biased the results. Finally, the small subgroup sizes limited the number of adjusted variables in the binary logistic regression models to avoid overfitting the models. Furthermore, the small subgroup sizes could make β error and tend to yield extreme data with no reproducibility. This is the first report on sex differences in EATVI and LAVI, with an exploratory analysis of the hypothesis not performed a priori. This study is not a conclusive design and we should be careful in the interpretation of this finding. Therefore, future large, unbiased and prospective studies, including external validation, are required to address these conclusions and detailed mechanisms.</p>" ]

[ "<title>Conclusion</title>", "<p id=\"Par38\">We evaluated the sex differences in the association between EATV and LAVI in patients with either SR or AF. We found a positive relationship among men with SR, and a negative relationship among women with AF. This is the first report to evaluate the relationship between EATV and LAVI, divided by sex, and may suggest clinical implications of sex differences in the etiology of AF.</p>" ]

[ "<title>Background</title>", "<p id=\"Par1\">Sex disparities in the association between epicardial adipose tissue volume (EATV) and cardiovascular disease have been reported. The sex-dependent effects of EATV on left atrial (LA) size have not been elucidated.</p>", "<title>Methods</title>", "<p id=\"Par2\">Consecutive 247 subjects (median 65 [interquartile range 57, 75] years; 67% of men) who underwent multi-detector computed tomography without significant coronary artery disease or moderate to severe valvular disease were divided into two groups: patients with sinus rhythm (SR) or atrial fibrillation (AF). Sex differences in the association between the EATV index (EATVI) (mL/m²) and LA volume index (LAVI) in 63 SR (28 men and 35 women) and 184 AF (137 men and 47 women) patients were evaluated using univariate and multivariate regression analyses.</p>", "<title>Results</title>", "<p id=\"Par3\">In overall that includes both men and women, the relationship between EATVI and LAVI was not significantly correlated for patients with SR and AF. The relationship between EATVI and LAVI differed between men and women in both SR and AF groups. In SR patients, there was a positive relationship between EATVI and LAVI in men, but not in women. In contrast, in patients with AF, a negative relationship was found between EATVI and LAVI in women, whereas no association was found in men.</p>", "<title>Conclusions</title>", "<p id=\"Par4\">We evaluated sex differences in the association between EATVI and LAVI in patients with either SR or AF, and found a positive relationship in men with SR and a negative relationship in women with AF. This is the first report to evaluate sex differences in the relationship between EATVI and LAVI, suggesting that EAT may play a role, at least in part, in sex differences in the etiology of AF.</p>", "<title>Keywords</title>" ]

[]

[ "<title>Acknowledgements</title>", "<p>We are deeply grateful to the staff at the Ultrasound Examination Center, Tokushima University Hospital, and Tomishiro Central Hospital for acquiring echocardiographic parameters.</p>", "<title>Author Contributions</title>", "<p>MSh conceptualized the study; SY and MSh analyzed the data and wrote the manuscript; MM, KO, and GM collected and managed the data; OA, SY, KK, TS, HY, DF, HM, and MSa reviewed and approved the final draft.</p>", "<title>Funding</title>", "<p>This study was supported by the Japan Society for the Promotion of Science (JPSP) (Grant No. JP16K01823 and JP17K00924 to MSh).</p>", "<title>Data Availability</title>", "<p>Derived data supporting the findings of this study are available from the corresponding author on reasonable requests.</p>", "<title>Declarations</title>", "<title>Competing interests</title>", "<p id=\"Par39\">The authors declare no competing interests.</p>", "<title>Ethics approval and consent to participate</title>", "<p id=\"Par40\">The ethical committees approved the present study (Fukushima Medical University #2019 − 182, Tomishiro Central Hospital R01R027). The need for informed consent was waived by the Ethics Committee/Institutional Review Board of Fukushima Medical University and Tomishiro Central Hospital because of the retrospective nature of the study and the lack of direct patient contact or intervention.</p>", "<title>Consent for publication</title>", "<p id=\"Par41\">Not applicable.</p>", "<title>Conflict of interest</title>", "<p id=\"Par42\">All authors declared no conflict of interest.</p>" ]

[ "<fig id=\"Fig1\"><label>Fig. 1</label><caption><p>Flowchart of patient recruitment. MDCT: multi-detector row computed tomography; LAV: left atrial volume; EATV: epicardial adipose tissue volume. See the detail in the text</p></caption></fig>", "<fig id=\"Fig2\"><label>Fig. 2</label><caption><p>Comparison of EATVI and LAVI between SR and AF in overall, men, and women. Values are presented as mean ± SD. P values were obtained by a two-tailed unpaired t-test. EATVI, epicardial adipose tissue volume index; SR, sinus rhythm; AF, atrial fibrillation; EATV, epicardial adipose tissue volume; LAVI, left atrial volume index</p></caption></fig>", "<fig id=\"Fig3\"><label>Fig. 3</label><caption><p>Association between EATVI and LAVI in overall, SR and AF. EATVI, epicardial adipose tissue volume index; LAVI, left atrial volume index; SR, sinus rhythm; AF, atrial fibrillation. Univariate linear regression models showed the relationship between EATVI and LAVI in overall, SR and AF groups. The r and P-values are shown in men (blue circles and lines) and women (red circles and lines)</p></caption></fig>", "<fig id=\"Fig4\"><label>Fig. 4</label><caption><p>Sub-analysis for the association between EATVI and LAVI in all rhythm, SR and AF. The interaction between men and women for the association between EATVI and LAVI was evaluated using linear regression models for all rhythm, SR, and AF. EATVI, epicardial adipose tissue volume index; LAVI, left atrial volume index; SR, sinus rhythm; AF, atrial fibrillation</p></caption></fig>" ]

[ "<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>General characteristics of studied patients</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" rowspan=\"3\"/><th align=\"left\" rowspan=\"2\">Overall</th><th align=\"left\" colspan=\"4\">Men</th><th align=\"left\" colspan=\"4\">Women</th><th align=\"left\" rowspan=\"2\"/></tr><tr><th align=\"left\">All rhythm</th><th align=\"left\">SR</th><th align=\"left\">AF</th><th align=\"left\"/><th align=\"left\">All rhythm</th><th align=\"left\">SR</th><th align=\"left\">AF</th><th align=\"left\"/></tr><tr><th align=\"left\">n = 247</th><th align=\"left\">n = 165</th><th align=\"left\">n = 28</th><th align=\"left\">n = 137</th><th align=\"left\">P1</th><th align=\"left\">n = 82</th><th align=\"left\">n = 35</th><th align=\"left\">n = 47</th><th align=\"left\">P1</th><th align=\"left\">P2</th></tr></thead><tbody><tr><td align=\"left\">Age, yo</td><td align=\"left\">65 [57, 72]</td><td align=\"left\">63 [55, 71]**</td><td align=\"left\">68 [59, 74]</td><td align=\"left\">63 [55, 70]</td><td align=\"left\">0.18</td><td align=\"left\">69 [62, 73]**</td><td align=\"left\">66 [57, 78]</td><td align=\"left\">70 [65, 73]</td><td align=\"left\">0.34</td><td align=\"left\">0.001</td></tr><tr><td align=\"left\">Men, n (%)</td><td align=\"left\">165/247 (67)</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\">&lt; 0.001</td></tr><tr><td align=\"left\">Body mass index, kg/m²</td><td align=\"left\">25.8 ± 4.1</td><td align=\"left\">26.1 ± 3.4</td><td align=\"left\">26.1 ± 3.2</td><td align=\"left\">26.1 ± 3.5</td><td align=\"left\">&gt; 0.99</td><td align=\"left\">25.1 ± 5.3</td><td align=\"left\">25.1 ± 6.0</td><td align=\"left\">25.2 ± 4.7</td><td align=\"left\">0.95</td><td align=\"left\">0.1</td></tr><tr><td align=\"left\">EAT, mL</td><td align=\"left\">125.4 ± 47.2</td><td align=\"left\">131.8 ± 48.3**</td><td align=\"left\">120.9 ± 52.4</td><td align=\"left\">134 ± 47.3</td><td align=\"left\">0.19</td><td align=\"left\">112.5 ± 42.3**</td><td align=\"left\">103 ± 42.1</td><td align=\"left\">119.6 ± 41.6</td><td align=\"left\">0.082</td><td align=\"left\">0.002</td></tr><tr><td align=\"left\">EATVI, mL/m²</td><td align=\"left\">71.8 ± 25.7</td><td align=\"left\">71.6 ± 25.3</td><td align=\"left\">67.4 ± 28.3</td><td align=\"left\">72.4 ± 24.6</td><td align=\"left\">0.34</td><td align=\"left\">72.2 ± 26.6</td><td align=\"left\">66.9 ± 27.2</td><td align=\"left\">76.1 ± 25.8</td><td align=\"left\">0.12</td><td align=\"left\">0.87</td></tr><tr><td align=\"left\">Atrial fibrillation, n (%)</td><td align=\"left\">184/247 (74)</td><td align=\"left\">137/165 (83)***</td><td align=\"left\">0/28 (0)</td><td align=\"left\">137/137 (100)</td><td align=\"left\">&lt; 0.001</td><td align=\"left\">47/82 (57)***</td><td align=\"left\">0/35 (0)</td><td align=\"left\">47/47 (100)</td><td align=\"left\">&lt; 0.001</td><td align=\"left\">&lt; 0.001</td></tr><tr><td align=\"left\">Type 2 diabetes mellitus, n (%)</td><td align=\"left\">69/247 (28)</td><td align=\"left\">52/165 (32)</td><td align=\"left\">11/28 (39)</td><td align=\"left\">41/137 (30)</td><td align=\"left\">0.45</td><td align=\"left\">17/82 (21)</td><td align=\"left\">10/35 (29)</td><td align=\"left\">7/47 (15)</td><td align=\"left\">0.22</td><td align=\"left\">0.1</td></tr><tr><td align=\"left\">Hypertension, n (%)</td><td align=\"left\">176/247 (71)</td><td align=\"left\">113/165 (69)</td><td align=\"left\">19/28 (68)</td><td align=\"left\">94/137 (69)</td><td align=\"left\">&gt; 0.99</td><td align=\"left\">63/82 (77)</td><td align=\"left\">29/35 (83)</td><td align=\"left\">34/47 (72)</td><td align=\"left\">0.39</td><td align=\"left\">0.22</td></tr><tr><td align=\"left\">Antihypertensive drug, n (%)</td><td align=\"left\">193/247 (78)</td><td align=\"left\">127/165 (77)</td><td align=\"left\">15/28 (54)</td><td align=\"left\">112/17 (82)</td><td align=\"left\">0.003</td><td align=\"left\">66/82 (81)</td><td align=\"left\">23/35 (66)</td><td align=\"left\">43/47 (92)</td><td align=\"left\">0.009</td><td align=\"left\">0.64</td></tr><tr><td align=\"left\">ACE or ARB, n (%)</td><td align=\"left\">109/247 (44)</td><td align=\"left\">75/165 (46)</td><td align=\"left\">7/28 (25)</td><td align=\"left\">68/137 (50)</td><td align=\"left\">0.029</td><td align=\"left\">34/82 (42)</td><td align=\"left\">12/35 (34)</td><td align=\"left\">22/47 (47)</td><td align=\"left\">0.36</td><td align=\"left\">0.65</td></tr><tr><td align=\"left\">Calcium blocker, n (%)</td><td align=\"left\">101/247 (41)</td><td align=\"left\">59/165 (36)*</td><td align=\"left\">5/28 (18)</td><td align=\"left\">54/137 (39)</td><td align=\"left\">0.051</td><td align=\"left\">42/82 (51)*</td><td align=\"left\">13/35 (37)</td><td align=\"left\">29/47 (62)</td><td align=\"left\">0.048</td><td align=\"left\">0.029</td></tr><tr><td align=\"left\">Beta blocker, n (%)</td><td align=\"left\">91/247 (37)</td><td align=\"left\">69/165 (42)</td><td align=\"left\">3/28 (11)</td><td align=\"left\">66/137 (48)</td><td align=\"left\">0.001</td><td align=\"left\">22/82 (27)</td><td align=\"left\">3/35 (8.6)</td><td align=\"left\">19/47 (40)</td><td align=\"left\">0.003</td><td align=\"left\">0.031</td></tr><tr><td align=\"left\">LVEF, %</td><td align=\"left\">66 [61, 71]</td><td align=\"left\">65 [60, 71]</td><td align=\"left\">63 [60, 66]</td><td align=\"left\">66 [60, 72]</td><td align=\"left\">0.11</td><td align=\"left\">67 [62, 72.8]</td><td align=\"left\">65 [62, 69]</td><td align=\"left\">69 [63, 74]</td><td align=\"left\">0.017</td><td align=\"left\">0.062</td></tr><tr><td align=\"left\">Left atrial volume, mL</td><td align=\"left\">54.9 [42, 71.3]</td><td align=\"left\">60 [44, 78]**</td><td align=\"left\">53.2 [41.9, 64.2]</td><td align=\"left\">61 [45, 78]</td><td align=\"left\">0.17</td><td align=\"left\">51 [38.4, 60]**</td><td align=\"left\">43.2 [35.3, 53.1]</td><td align=\"left\">54 [43.5, 62.5]</td><td align=\"left\">0.01</td><td align=\"left\">0.001</td></tr><tr><td align=\"left\">Left atrial volume index, mL/m²</td><td align=\"left\">32 [24.1, 41.9]</td><td align=\"left\">32.4 [24.5, 42.2]</td><td align=\"left\">29.5 [22.0, 38]</td><td align=\"left\">33.0 [24.3, 42.4]</td><td align=\"left\">0.31</td><td align=\"left\">31 [26, 38.6]</td><td align=\"left\">28 [22.5, 34.5]</td><td align=\"left\">35.9 [28.1, 44.1]</td><td align=\"left\">0.015</td><td align=\"left\">0.71</td></tr><tr><td align=\"left\">E/E’</td><td align=\"left\">9.0 [7.0, 12.0]</td><td align=\"left\">9.0 [7.0, 12.0]*</td><td align=\"left\">6.7 [5.3, 7.8]</td><td align=\"left\">9.0 [7.0, 12.0]</td><td align=\"left\">&lt; 0.001</td><td align=\"left\">10.0 [8.0, 12.4]*</td><td align=\"left\">8.0 [7.0, 10.4]</td><td align=\"left\">11.5 [9.0, 14.0]</td><td align=\"left\">&lt; 0.001</td><td align=\"left\">0.032</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab2\"><label>Table 2</label><caption><p>Linear regression models to determine the factors associated with left atrial volume index</p></caption><table frame=\"hsides\" rules=\"groups\"><tbody><tr><td align=\"left\">\n<bold>A. Sinus rhythm</bold>\n</td><td align=\"left\"/><td align=\"left\" colspan=\"2\"/><td align=\"left\" colspan=\"2\"/><td align=\"left\" colspan=\"2\"/><td align=\"left\" colspan=\"2\"/><td align=\"left\"/><td align=\"left\" colspan=\"3\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\" colspan=\"2\"/><td align=\"left\" colspan=\"2\"/><td align=\"left\" colspan=\"2\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\" colspan=\"1\"/></tr><tr><td align=\"left\">Overall (n = 63)</td><td align=\"left\" colspan=\"7\">Univariate</td><td align=\"left\" colspan=\"2\"/><td align=\"left\" colspan=\"6\">Model 1</td><td align=\"left\" colspan=\"2\"/><td align=\"left\" colspan=\"7\">Model 2</td></tr><tr><td align=\"left\">Factors</td><td align=\"left\">Coefficient</td><td align=\"left\" colspan=\"4\">95% CI</td><td align=\"left\" colspan=\"2\">P value</td><td align=\"left\" colspan=\"2\"/><td align=\"left\">Coefficient</td><td align=\"left\" colspan=\"4\">95% CI</td><td align=\"left\">P value</td><td align=\"left\" colspan=\"2\"/><td align=\"left\" colspan=\"2\">Coefficient</td><td align=\"left\" colspan=\"3\">95% CI</td><td align=\"left\">P value</td><td align=\"left\" colspan=\"1\"/></tr><tr><td align=\"left\">Age, yo</td><td align=\"left\">0.263</td><td align=\"left\" colspan=\"2\">0.042</td><td align=\"left\" colspan=\"2\">0.485</td><td align=\"left\" colspan=\"2\">0.023</td><td align=\"left\" colspan=\"2\"/><td align=\"left\">0.181</td><td align=\"left\" colspan=\"3\">-0.082</td><td align=\"left\">0.444</td><td align=\"left\">0.18</td><td align=\"left\" colspan=\"2\"/><td align=\"left\" colspan=\"2\">0.212</td><td align=\"left\" colspan=\"2\">-0.054</td><td align=\"left\">0.478</td><td align=\"left\">0.12</td><td align=\"left\" colspan=\"1\"/></tr><tr><td align=\"left\">Men</td><td align=\"left\">2.014</td><td align=\"left\" colspan=\"2\">-4.083</td><td align=\"left\" colspan=\"2\">8.11</td><td align=\"left\" colspan=\"2\">0.52</td><td align=\"left\" colspan=\"2\"/><td align=\"left\">-0.195</td><td align=\"left\" colspan=\"3\">-0.841</td><td align=\"left\">0.452</td><td align=\"left\">0.56</td><td align=\"left\" colspan=\"2\"/><td align=\"left\" colspan=\"2\">-0.127</td><td align=\"left\" colspan=\"2\">-0.778</td><td align=\"left\">0.523</td><td align=\"left\">0.7</td><td align=\"left\" colspan=\"1\"/></tr><tr><td align=\"left\">BMI, kg/m²</td><td align=\"left\">-0.195</td><td align=\"left\" colspan=\"2\">-0.816</td><td align=\"left\" colspan=\"2\">0.426</td><td align=\"left\" colspan=\"2\">0.54</td><td align=\"left\" colspan=\"2\"/><td align=\"left\"/><td align=\"left\" colspan=\"3\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\" colspan=\"2\"/><td align=\"left\" colspan=\"2\"/><td align=\"left\" colspan=\"2\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\" colspan=\"1\"/></tr><tr><td align=\"left\">EAT, mL</td><td align=\"left\">0.05</td><td align=\"left\" colspan=\"2\">-0.014</td><td align=\"left\" colspan=\"2\">0.113</td><td align=\"left\" colspan=\"2\">0.13</td><td align=\"left\" colspan=\"2\"/><td align=\"left\"/><td align=\"left\" colspan=\"3\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\" colspan=\"2\"/><td align=\"left\" colspan=\"2\"/><td align=\"left\" colspan=\"2\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\" colspan=\"1\"/></tr><tr><td align=\"left\">EATVI, mL/m²</td><td align=\"left\">0.106</td><td align=\"left\" colspan=\"2\">-0.002</td><td align=\"left\" colspan=\"2\">0.214</td><td align=\"left\" colspan=\"2\">0.06</td><td align=\"left\" colspan=\"2\"/><td align=\"left\">0.073</td><td align=\"left\" colspan=\"3\">-0.054</td><td align=\"left\">0.201</td><td align=\"left\">0.27</td><td align=\"left\" colspan=\"2\"/><td align=\"left\" colspan=\"2\">0.072</td><td align=\"left\" colspan=\"2\">-0.057</td><td align=\"left\">0.2</td><td align=\"left\">0.28</td><td align=\"left\" colspan=\"1\"/></tr><tr><td align=\"left\">LVEF, %</td><td align=\"left\">-0.385</td><td align=\"left\" colspan=\"2\">-0.981</td><td align=\"left\" colspan=\"2\">0.211</td><td align=\"left\" colspan=\"2\">0.21</td><td align=\"left\" colspan=\"2\"/><td align=\"left\"/><td align=\"left\" colspan=\"3\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\" colspan=\"2\"/><td align=\"left\" colspan=\"2\">-0.457</td><td align=\"left\" colspan=\"2\">-1.052</td><td align=\"left\">0.138</td><td align=\"left\">0.14</td><td align=\"left\" colspan=\"1\"/></tr><tr><td align=\"left\">Hypertension, yes</td><td align=\"left\">0.565</td><td align=\"left\" colspan=\"2\">-6.571</td><td align=\"left\" colspan=\"2\">7.7</td><td align=\"left\" colspan=\"2\">0.88</td><td align=\"left\" colspan=\"2\"/><td align=\"left\"/><td align=\"left\" colspan=\"3\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\" colspan=\"2\"/><td align=\"left\" colspan=\"2\">-0.594</td><td align=\"left\" colspan=\"2\">-7.735</td><td align=\"left\">6.548</td><td align=\"left\">0.87</td><td align=\"left\" colspan=\"1\"/></tr><tr><td align=\"left\">Diabetes mellitus, yes</td><td align=\"left\">1.548</td><td align=\"left\" colspan=\"2\">-4.889</td><td align=\"left\" colspan=\"2\">7.984</td><td align=\"left\" colspan=\"2\">0.64</td><td align=\"left\" colspan=\"2\"/><td align=\"left\"/><td align=\"left\" colspan=\"3\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\" colspan=\"2\"/><td align=\"left\" colspan=\"2\"/><td align=\"left\" colspan=\"2\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\" colspan=\"1\"/></tr><tr><td align=\"left\">Antihypertensive drug, yes</td><td align=\"left\">-2.965</td><td align=\"left\" colspan=\"2\">-9.133</td><td align=\"left\" colspan=\"2\">3.203</td><td align=\"left\" colspan=\"2\">0.35</td><td align=\"left\" colspan=\"2\"/><td align=\"left\"/><td align=\"left\" colspan=\"3\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\" colspan=\"2\"/><td align=\"left\" colspan=\"2\"/><td align=\"left\" colspan=\"2\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\" colspan=\"1\"/></tr><tr><td align=\"left\">ACE or ARB use</td><td align=\"left\">-1.447</td><td align=\"left\" colspan=\"2\">-8.06</td><td align=\"left\" colspan=\"2\">5.166</td><td align=\"left\" colspan=\"2\">0.67</td><td align=\"left\" colspan=\"2\"/><td align=\"left\"/><td align=\"left\" colspan=\"3\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\" colspan=\"2\"/><td align=\"left\" colspan=\"2\"/><td align=\"left\" colspan=\"2\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\" colspan=\"1\"/></tr><tr><td align=\"left\">Beta blocker use</td><td align=\"left\">-3.681</td><td align=\"left\" colspan=\"2\">-13.994</td><td align=\"left\" colspan=\"2\">6.633</td><td align=\"left\" colspan=\"2\">0.49</td><td align=\"left\" colspan=\"2\"/><td align=\"left\"/><td align=\"left\" colspan=\"3\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\" colspan=\"2\"/><td align=\"left\" colspan=\"2\"/><td align=\"left\" colspan=\"2\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\" colspan=\"1\"/></tr><tr><td align=\"left\">Men (n = 28)</td><td align=\"left\" colspan=\"7\">Univariate</td><td align=\"left\" colspan=\"2\"/><td align=\"left\" colspan=\"6\">Model 1</td><td align=\"left\" colspan=\"2\"/><td align=\"left\" colspan=\"7\">Model 2</td></tr><tr><td align=\"left\">Factors</td><td align=\"left\">Coefficient</td><td align=\"left\" colspan=\"4\">95% CI</td><td align=\"left\" colspan=\"2\">P value</td><td align=\"left\" colspan=\"2\"/><td align=\"left\">Coefficient</td><td align=\"left\" colspan=\"4\">95% CI</td><td align=\"left\">P value</td><td align=\"left\" colspan=\"2\"/><td align=\"left\" colspan=\"2\">Coefficient</td><td align=\"left\" colspan=\"3\">95% CI</td><td align=\"left\">P value</td><td align=\"left\" colspan=\"1\"/></tr><tr><td align=\"left\">Age, yo</td><td align=\"left\">0.296</td><td align=\"left\" colspan=\"2\">-0.144</td><td align=\"left\" colspan=\"2\">0.736</td><td align=\"left\" colspan=\"2\">0.2</td><td align=\"left\" colspan=\"2\"/><td align=\"left\">0.276</td><td align=\"left\" colspan=\"3\">-0.188</td><td align=\"left\">0.741</td><td align=\"left\">0.26</td><td align=\"left\" colspan=\"2\"/><td align=\"left\" colspan=\"2\">0.219</td><td align=\"left\" colspan=\"2\">-0.273</td><td align=\"left\">0.711</td><td align=\"left\">0.39</td><td align=\"left\" colspan=\"1\"/></tr><tr><td align=\"left\">BMI, kg/m²</td><td align=\"left\">0.86</td><td align=\"left\" colspan=\"2\">-0.744</td><td align=\"left\" colspan=\"2\">2.464</td><td align=\"left\" colspan=\"2\">0.3</td><td align=\"left\" colspan=\"2\"/><td align=\"left\">0.459</td><td align=\"left\" colspan=\"3\">-1.476</td><td align=\"left\">2.394</td><td align=\"left\">0.65</td><td align=\"left\" colspan=\"2\"/><td align=\"left\" colspan=\"2\">0.75</td><td align=\"left\" colspan=\"2\">-1.265</td><td align=\"left\">2.765</td><td align=\"left\">0.47</td><td align=\"left\" colspan=\"1\"/></tr><tr><td align=\"left\">EAT, mL</td><td align=\"left\">0.093</td><td align=\"left\" colspan=\"2\">0</td><td align=\"left\" colspan=\"2\">0.186</td><td align=\"left\" colspan=\"2\">0.061</td><td align=\"left\" colspan=\"2\"/><td align=\"left\"/><td align=\"left\" colspan=\"3\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\" colspan=\"2\"/><td align=\"left\" colspan=\"2\"/><td align=\"left\" colspan=\"2\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\" colspan=\"1\"/></tr><tr><td align=\"left\">EATVI, mL/m²</td><td align=\"left\">0.185</td><td align=\"left\" colspan=\"2\">0.014</td><td align=\"left\" colspan=\"2\">0.355</td><td align=\"left\" colspan=\"2\">0.043</td><td align=\"left\" colspan=\"2\"/><td align=\"left\">0.141</td><td align=\"left\" colspan=\"3\">-0.072</td><td align=\"left\">0.355</td><td align=\"left\">0.21</td><td align=\"left\" colspan=\"2\"/><td align=\"left\" colspan=\"2\">0.122</td><td align=\"left\" colspan=\"2\">-0.096</td><td align=\"left\">0.341</td><td align=\"left\">0.28</td><td align=\"left\" colspan=\"1\"/></tr><tr><td align=\"left\">LVEF, %</td><td align=\"left\">-0.845</td><td align=\"left\" colspan=\"2\">-1.898</td><td align=\"left\" colspan=\"2\">0.207</td><td align=\"left\" colspan=\"2\">0.13</td><td align=\"left\" colspan=\"2\"/><td align=\"left\"/><td align=\"left\" colspan=\"3\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\" colspan=\"2\"/><td align=\"left\" colspan=\"2\">-0.697</td><td align=\"left\" colspan=\"2\">-1.835</td><td align=\"left\">0.442</td><td align=\"left\">0.24</td><td align=\"left\" colspan=\"1\"/></tr><tr><td align=\"left\">Hypertension, yes</td><td align=\"left\">1.071</td><td align=\"left\" colspan=\"2\">-9.914</td><td align=\"left\" colspan=\"2\">12.056</td><td align=\"left\" colspan=\"2\">0.85</td><td align=\"left\" colspan=\"2\"/><td align=\"left\"/><td align=\"left\" colspan=\"3\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\" colspan=\"2\"/><td align=\"left\" colspan=\"2\">-1.261</td><td align=\"left\" colspan=\"2\">-12.338</td><td align=\"left\">9.816</td><td align=\"left\">0.83</td><td align=\"left\" colspan=\"1\"/></tr><tr><td align=\"left\">Diabetes mellitus, yes</td><td align=\"left\">7.935</td><td align=\"left\" colspan=\"2\">-2.125</td><td align=\"left\" colspan=\"2\">17.995</td><td align=\"left\" colspan=\"2\">0.13</td><td align=\"left\" colspan=\"2\"/><td align=\"left\"/><td align=\"left\" colspan=\"3\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\" colspan=\"2\"/><td align=\"left\" colspan=\"2\"/><td align=\"left\" colspan=\"2\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\" colspan=\"1\"/></tr><tr><td align=\"left\">Antihypertensive drug, yes</td><td align=\"left\">-0.019</td><td align=\"left\" colspan=\"2\">-10.313</td><td align=\"left\" colspan=\"2\">10.275</td><td align=\"left\" colspan=\"2\">1</td><td align=\"left\" colspan=\"2\"/><td align=\"left\"/><td align=\"left\" colspan=\"3\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\" colspan=\"2\"/><td align=\"left\" colspan=\"2\"/><td align=\"left\" colspan=\"2\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\" colspan=\"1\"/></tr><tr><td align=\"left\">ACE or ARB use</td><td align=\"left\">1.529</td><td align=\"left\" colspan=\"2\">-10.313</td><td align=\"left\" colspan=\"2\">13.37</td><td align=\"left\" colspan=\"2\">0.8</td><td align=\"left\" colspan=\"2\"/><td align=\"left\"/><td align=\"left\" colspan=\"3\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\" colspan=\"2\"/><td align=\"left\" colspan=\"2\"/><td align=\"left\" colspan=\"2\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\" colspan=\"1\"/></tr><tr><td align=\"left\">Beta blocker use</td><td align=\"left\">-6.663</td><td align=\"left\" colspan=\"2\">-23.063</td><td align=\"left\" colspan=\"2\">9.737</td><td align=\"left\" colspan=\"2\">0.43</td><td align=\"left\" colspan=\"2\"/><td align=\"left\"/><td align=\"left\" colspan=\"3\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\" colspan=\"2\"/><td align=\"left\" colspan=\"2\"/><td align=\"left\" colspan=\"2\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\" colspan=\"1\"/></tr><tr><td align=\"left\">Women (n = 35)</td><td align=\"left\" colspan=\"7\">Univariate</td><td align=\"left\" colspan=\"2\"/><td align=\"left\" colspan=\"6\">Model 1</td><td align=\"left\" colspan=\"2\"/><td align=\"left\" colspan=\"7\">Model 2</td></tr><tr><td align=\"left\">Factors</td><td align=\"left\">Coefficient</td><td align=\"left\" colspan=\"4\">95% CI</td><td align=\"left\" colspan=\"2\">P value</td><td align=\"left\" colspan=\"2\"/><td align=\"left\">Coefficient</td><td align=\"left\" colspan=\"4\">95% CI</td><td align=\"left\">P value</td><td align=\"left\" colspan=\"2\"/><td align=\"left\" colspan=\"2\">Coefficient</td><td align=\"left\" colspan=\"3\">95% CI</td><td align=\"left\">P value</td><td align=\"left\" colspan=\"1\"/></tr><tr><td align=\"left\">Age, yo</td><td align=\"left\">0.246</td><td align=\"left\" colspan=\"2\">0.002</td><td align=\"left\" colspan=\"2\">0.49</td><td align=\"left\" colspan=\"2\">0.056</td><td align=\"left\" colspan=\"2\"/><td align=\"left\">0.306</td><td align=\"left\" colspan=\"3\">-0.033</td><td align=\"left\">0.646</td><td align=\"left\">0.087</td><td align=\"left\" colspan=\"2\"/><td align=\"left\" colspan=\"2\">0.371</td><td align=\"left\" colspan=\"2\">-0.007</td><td align=\"left\">0.749</td><td align=\"left\">0.064</td><td align=\"left\" colspan=\"1\"/></tr><tr><td align=\"left\">BMI, kg/m²</td><td align=\"left\">-0.463</td><td align=\"left\" colspan=\"2\">-1.077</td><td align=\"left\" colspan=\"2\">0.152</td><td align=\"left\" colspan=\"2\">0.15</td><td align=\"left\" colspan=\"2\"/><td align=\"left\">-0.316</td><td align=\"left\" colspan=\"3\">-0.946</td><td align=\"left\">0.313</td><td align=\"left\">0.33</td><td align=\"left\" colspan=\"2\"/><td align=\"left\" colspan=\"2\">-0.259</td><td align=\"left\" colspan=\"2\">-0.916</td><td align=\"left\">0.397</td><td align=\"left\">0.45</td><td align=\"left\" colspan=\"1\"/></tr><tr><td align=\"left\">EAT, mL</td><td align=\"left\">-0.009</td><td align=\"left\" colspan=\"2\">-0.099</td><td align=\"left\" colspan=\"2\">0.081</td><td align=\"left\" colspan=\"2\">0.85</td><td align=\"left\" colspan=\"2\"/><td align=\"left\"/><td align=\"left\" colspan=\"3\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\" colspan=\"2\"/><td align=\"left\" colspan=\"2\"/><td align=\"left\" colspan=\"2\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\" colspan=\"1\"/></tr><tr><td align=\"left\">EATVI, mL/m²</td><td align=\"left\">0.037</td><td align=\"left\" colspan=\"2\">-0.101</td><td align=\"left\" colspan=\"2\">0.176</td><td align=\"left\" colspan=\"2\">0.6</td><td align=\"left\" colspan=\"2\"/><td align=\"left\">-0.066</td><td align=\"left\" colspan=\"3\">-0.248</td><td align=\"left\">0.116</td><td align=\"left\">0.48</td><td align=\"left\" colspan=\"2\"/><td align=\"left\" colspan=\"2\">-0.076</td><td align=\"left\" colspan=\"2\">-0.264</td><td align=\"left\">0.111</td><td align=\"left\">0.43</td><td align=\"left\" colspan=\"1\"/></tr><tr><td align=\"left\">LVEF, %</td><td align=\"left\">-0.05</td><td align=\"left\" colspan=\"2\">-0.768</td><td align=\"left\" colspan=\"2\">0.668</td><td align=\"left\" colspan=\"2\">0.89</td><td align=\"left\" colspan=\"2\"/><td align=\"left\"/><td align=\"left\" colspan=\"3\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\" colspan=\"2\"/><td align=\"left\" colspan=\"2\">-0.331</td><td align=\"left\" colspan=\"2\">-1.096</td><td align=\"left\">0.435</td><td align=\"left\">0.4</td><td align=\"left\" colspan=\"1\"/></tr><tr><td align=\"left\">Hypertension, yes</td><td align=\"left\">0.928</td><td align=\"left\" colspan=\"2\">-8.966</td><td align=\"left\" colspan=\"2\">10.822</td><td align=\"left\" colspan=\"2\">0.86</td><td align=\"left\" colspan=\"2\"/><td align=\"left\"/><td align=\"left\" colspan=\"3\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\" colspan=\"2\"/><td align=\"left\" colspan=\"2\">-0.642</td><td align=\"left\" colspan=\"2\">-10.572</td><td align=\"left\">9.289</td><td align=\"left\">0.9</td><td align=\"left\" colspan=\"1\"/></tr><tr><td align=\"left\">Diabetes mellitus, yes</td><td align=\"left\">-4.856</td><td align=\"left\" colspan=\"2\">-12.947</td><td align=\"left\" colspan=\"2\">3.235</td><td align=\"left\" colspan=\"2\">0.25</td><td align=\"left\" colspan=\"2\"/><td align=\"left\"/><td align=\"left\" colspan=\"3\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\" colspan=\"2\"/><td align=\"left\" colspan=\"2\"/><td align=\"left\" colspan=\"2\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\" colspan=\"1\"/></tr><tr><td align=\"left\">Antihypertensive drug, yes</td><td align=\"left\">-5.171</td><td align=\"left\" colspan=\"2\">-12.83</td><td align=\"left\" colspan=\"2\">2.488</td><td align=\"left\" colspan=\"2\">0.19</td><td align=\"left\" colspan=\"2\"/><td align=\"left\"/><td align=\"left\" colspan=\"3\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\" colspan=\"2\"/><td align=\"left\" colspan=\"2\"/><td align=\"left\" colspan=\"2\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\" colspan=\"1\"/></tr><tr><td align=\"left\">ACEI or ARB use</td><td align=\"left\">-3.084</td><td align=\"left\" colspan=\"2\">-10.874</td><td align=\"left\" colspan=\"2\">4.705</td><td align=\"left\" colspan=\"2\">0.44</td><td align=\"left\" colspan=\"2\"/><td align=\"left\"/><td align=\"left\" colspan=\"3\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\" colspan=\"2\"/><td align=\"left\" colspan=\"2\"/><td align=\"left\" colspan=\"2\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\" colspan=\"1\"/></tr><tr><td align=\"left\">Beta blocker use</td><td align=\"left\">-1.023</td><td align=\"left\" colspan=\"2\">-14.345</td><td align=\"left\" colspan=\"2\">12.3</td><td align=\"left\" colspan=\"2\">0.88</td><td align=\"left\" colspan=\"2\"/><td align=\"left\"/><td align=\"left\" colspan=\"3\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\" colspan=\"2\"/><td align=\"left\" colspan=\"2\"/><td align=\"left\" colspan=\"2\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\" colspan=\"1\"/></tr><tr><td align=\"left\" colspan=\"23\">\n<bold>B. Atrial fibrillation</bold>\n</td><td align=\"left\"/><td align=\"left\" colspan=\"1\"/></tr><tr><td align=\"left\">Overall (n = 184)</td><td align=\"left\" colspan=\"6\">Univariate</td><td align=\"left\" colspan=\"2\"/><td align=\"left\" colspan=\"8\">Model 1</td><td align=\"left\"/><td align=\"left\" colspan=\"6\">Model 2</td><td align=\"left\" colspan=\"1\"/></tr><tr><td align=\"left\">Factors</td><td align=\"left\" colspan=\"2\">Coefficient</td><td align=\"left\" colspan=\"3\">95% CI</td><td align=\"left\">P value</td><td align=\"left\" colspan=\"2\"/><td align=\"left\" colspan=\"3\">Coefficient</td><td align=\"left\" colspan=\"3\">95% CI</td><td align=\"left\" colspan=\"2\">P value</td><td align=\"left\"/><td align=\"left\">Coefficient</td><td align=\"left\" colspan=\"4\">95% CI</td><td align=\"left\">P value</td><td align=\"left\" colspan=\"1\"/></tr><tr><td align=\"left\">Age, yo</td><td align=\"left\" colspan=\"2\">0.23</td><td align=\"left\" colspan=\"2\">0.054</td><td align=\"left\">0.407</td><td align=\"left\">0.011</td><td align=\"left\" colspan=\"2\"/><td align=\"left\" colspan=\"3\">0.277</td><td align=\"left\">0.091</td><td align=\"left\" colspan=\"2\">0.464</td><td align=\"left\" colspan=\"2\">0.004</td><td align=\"left\"/><td align=\"left\">0.273</td><td align=\"left\" colspan=\"2\">0.084</td><td align=\"left\" colspan=\"2\">0.462</td><td align=\"left\">0.005</td><td align=\"left\" colspan=\"1\"/></tr><tr><td align=\"left\">Men</td><td align=\"left\" colspan=\"2\">-1.353</td><td align=\"left\" colspan=\"2\">-5.417</td><td align=\"left\">2.711</td><td align=\"left\">0.51</td><td align=\"left\" colspan=\"2\"/><td align=\"left\" colspan=\"3\">0.483</td><td align=\"left\">-0.016</td><td align=\"left\" colspan=\"2\">0.981</td><td align=\"left\" colspan=\"2\">0.059</td><td align=\"left\"/><td align=\"left\">0.347</td><td align=\"left\" colspan=\"2\">-0.163</td><td align=\"left\" colspan=\"2\">0.858</td><td align=\"left\">0.18</td><td align=\"left\" colspan=\"1\"/></tr><tr><td align=\"left\">BMI, kg/m²</td><td align=\"left\" colspan=\"2\">0.299</td><td align=\"left\" colspan=\"2\">-0.162</td><td align=\"left\">0.76</td><td align=\"left\">0.21</td><td align=\"left\" colspan=\"2\"/><td align=\"left\" colspan=\"3\"/><td align=\"left\"/><td align=\"left\" colspan=\"2\"/><td align=\"left\" colspan=\"2\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\" colspan=\"2\"/><td align=\"left\" colspan=\"2\"/><td align=\"left\"/><td align=\"left\" colspan=\"1\"/></tr><tr><td align=\"left\">EAT, mL</td><td align=\"left\" colspan=\"2\">0.01</td><td align=\"left\" colspan=\"2\">-0.029</td><td align=\"left\">0.048</td><td align=\"left\">0.63</td><td align=\"left\" colspan=\"2\"/><td align=\"left\" colspan=\"3\"/><td align=\"left\"/><td align=\"left\" colspan=\"2\"/><td align=\"left\" colspan=\"2\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\" colspan=\"2\"/><td align=\"left\" colspan=\"2\"/><td align=\"left\"/><td align=\"left\" colspan=\"1\"/></tr><tr><td align=\"left\">EATVI, mL/m²</td><td align=\"left\" colspan=\"2\">0.025</td><td align=\"left\" colspan=\"2\">-0.046</td><td align=\"left\">0.097</td><td align=\"left\">0.49</td><td align=\"left\" colspan=\"2\"/><td align=\"left\" colspan=\"3\">-0.021</td><td align=\"left\">-0.098</td><td align=\"left\" colspan=\"2\">0.056</td><td align=\"left\" colspan=\"2\">0.6</td><td align=\"left\"/><td align=\"left\">-0.017</td><td align=\"left\" colspan=\"2\">-0.093</td><td align=\"left\" colspan=\"2\">0.059</td><td align=\"left\">0.66</td><td align=\"left\" colspan=\"1\"/></tr><tr><td align=\"left\">LVEF, %</td><td align=\"left\" colspan=\"2\">-0.134</td><td align=\"left\" colspan=\"2\">-0.316</td><td align=\"left\">0.047</td><td align=\"left\">0.15</td><td align=\"left\" colspan=\"2\"/><td align=\"left\" colspan=\"3\"/><td align=\"left\"/><td align=\"left\" colspan=\"2\"/><td align=\"left\" colspan=\"2\"/><td align=\"left\"/><td align=\"left\">-0.167</td><td align=\"left\" colspan=\"2\">-0.349</td><td align=\"left\" colspan=\"2\">0.014</td><td align=\"left\">0.073</td><td align=\"left\" colspan=\"1\"/></tr><tr><td align=\"left\">Hypertension, yes</td><td align=\"left\" colspan=\"2\">3.718</td><td align=\"left\" colspan=\"2\">-0.1</td><td align=\"left\">7.536</td><td align=\"left\">0.058</td><td align=\"left\" colspan=\"2\"/><td align=\"left\" colspan=\"3\"/><td align=\"left\"/><td align=\"left\" colspan=\"2\"/><td align=\"left\" colspan=\"2\"/><td align=\"left\"/><td align=\"left\">2.891</td><td align=\"left\" colspan=\"2\">-0.987</td><td align=\"left\" colspan=\"2\">6.769</td><td align=\"left\">0.15</td><td align=\"left\" colspan=\"1\"/></tr><tr><td align=\"left\">Diabetes mellitus, yes</td><td align=\"left\" colspan=\"2\">1.802</td><td align=\"left\" colspan=\"2\">-2.23</td><td align=\"left\">5.834</td><td align=\"left\">0.38</td><td align=\"left\" colspan=\"2\"/><td align=\"left\" colspan=\"3\"/><td align=\"left\"/><td align=\"left\" colspan=\"2\"/><td align=\"left\" colspan=\"2\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\" colspan=\"2\"/><td align=\"left\" colspan=\"2\"/><td align=\"left\"/><td align=\"left\" colspan=\"1\"/></tr><tr><td align=\"left\">Antihypertensive drug, yes</td><td align=\"left\" colspan=\"2\">5.695</td><td align=\"left\" colspan=\"2\">0.897</td><td align=\"left\">10.494</td><td align=\"left\">0.021</td><td align=\"left\" colspan=\"2\"/><td align=\"left\" colspan=\"3\"/><td align=\"left\"/><td align=\"left\" colspan=\"2\"/><td align=\"left\" colspan=\"2\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\" colspan=\"2\"/><td align=\"left\" colspan=\"2\"/><td align=\"left\"/><td align=\"left\" colspan=\"1\"/></tr><tr><td align=\"left\">ACEI or ARB use</td><td align=\"left\" colspan=\"2\">3.327</td><td align=\"left\" colspan=\"2\">-0.189</td><td align=\"left\">6.843</td><td align=\"left\">0.065</td><td align=\"left\" colspan=\"2\"/><td align=\"left\" colspan=\"3\"/><td align=\"left\"/><td align=\"left\" colspan=\"2\"/><td align=\"left\" colspan=\"2\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\" colspan=\"2\"/><td align=\"left\" colspan=\"2\"/><td align=\"left\"/><td align=\"left\" colspan=\"1\"/></tr><tr><td align=\"left\">Beta blocker use</td><td align=\"left\" colspan=\"2\">3.72</td><td align=\"left\" colspan=\"2\">0.202</td><td align=\"left\">7.237</td><td align=\"left\">0.04</td><td align=\"left\" colspan=\"2\"/><td align=\"left\" colspan=\"3\"/><td align=\"left\"/><td align=\"left\" colspan=\"2\"/><td align=\"left\" colspan=\"2\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\" colspan=\"2\"/><td align=\"left\" colspan=\"2\"/><td align=\"left\"/><td align=\"left\" colspan=\"1\"/></tr><tr><td align=\"left\">Men (n = 137)</td><td align=\"left\" colspan=\"6\">Univariate</td><td align=\"left\" colspan=\"2\"/><td align=\"left\" colspan=\"8\">Model 1</td><td align=\"left\"/><td align=\"left\" colspan=\"6\">Model 2</td><td align=\"left\" colspan=\"1\"/></tr><tr><td align=\"left\">Factors</td><td align=\"left\" colspan=\"2\">Coefficient</td><td align=\"left\" colspan=\"3\">95% CI</td><td align=\"left\">P value</td><td align=\"left\" colspan=\"2\"/><td align=\"left\" colspan=\"3\">Coefficient</td><td align=\"left\" colspan=\"3\">95% CI</td><td align=\"left\" colspan=\"2\">P value</td><td align=\"left\"/><td align=\"left\">Coefficient</td><td align=\"left\" colspan=\"4\">95% CI</td><td align=\"left\">P value</td><td align=\"left\" colspan=\"1\"/></tr><tr><td align=\"left\">Age, yo</td><td align=\"left\" colspan=\"2\">0.205</td><td align=\"left\" colspan=\"2\">-0.003</td><td align=\"left\">0.413</td><td align=\"left\">0.056</td><td align=\"left\" colspan=\"2\"/><td align=\"left\" colspan=\"3\">0.218</td><td align=\"left\">-0.006</td><td align=\"left\" colspan=\"2\">0.443</td><td align=\"left\" colspan=\"2\">0.059</td><td align=\"left\"/><td align=\"left\">0.206</td><td align=\"left\" colspan=\"2\">-0.021</td><td align=\"left\" colspan=\"2\">0.432</td><td align=\"left\">0.077</td><td align=\"left\" colspan=\"1\"/></tr><tr><td align=\"left\">BMI, kg/m²</td><td align=\"left\" colspan=\"2\">0.379</td><td align=\"left\" colspan=\"2\">-0.228</td><td align=\"left\">0.985</td><td align=\"left\">0.22</td><td align=\"left\" colspan=\"2\"/><td align=\"left\" colspan=\"3\">0.418</td><td align=\"left\">-0.271</td><td align=\"left\" colspan=\"2\">1.107</td><td align=\"left\" colspan=\"2\">0.24</td><td align=\"left\"/><td align=\"left\">0.302</td><td align=\"left\" colspan=\"2\">-0.391</td><td align=\"left\" colspan=\"2\">0.996</td><td align=\"left\">0.39</td><td align=\"left\" colspan=\"1\"/></tr><tr><td align=\"left\">EAT, mL</td><td align=\"left\" colspan=\"2\">0.033</td><td align=\"left\" colspan=\"2\">-0.012</td><td align=\"left\">0.077</td><td align=\"left\">0.15</td><td align=\"left\" colspan=\"2\"/><td align=\"left\" colspan=\"3\"/><td align=\"left\"/><td align=\"left\" colspan=\"2\"/><td align=\"left\" colspan=\"2\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\" colspan=\"2\"/><td align=\"left\" colspan=\"2\"/><td align=\"left\"/><td align=\"left\" colspan=\"1\"/></tr><tr><td align=\"left\">EATVI, mL/m²</td><td align=\"left\" colspan=\"2\">0.076</td><td align=\"left\" colspan=\"2\">-0.009</td><td align=\"left\">0.162</td><td align=\"left\">0.081</td><td align=\"left\" colspan=\"2\"/><td align=\"left\" colspan=\"3\">0.035</td><td align=\"left\">-0.063</td><td align=\"left\" colspan=\"2\">0.132</td><td align=\"left\" colspan=\"2\">0.49</td><td align=\"left\"/><td align=\"left\">0.036</td><td align=\"left\" colspan=\"2\">-0.06</td><td align=\"left\" colspan=\"2\">0.133</td><td align=\"left\">0.46</td><td align=\"left\" colspan=\"1\"/></tr><tr><td align=\"left\">LVEF, %</td><td align=\"left\" colspan=\"2\">-0.136</td><td align=\"left\" colspan=\"2\">-0.344</td><td align=\"left\">0.072</td><td align=\"left\">0.2</td><td align=\"left\" colspan=\"2\"/><td align=\"left\" colspan=\"3\"/><td align=\"left\"/><td align=\"left\" colspan=\"2\"/><td align=\"left\" colspan=\"2\"/><td align=\"left\"/><td align=\"left\">-0.171</td><td align=\"left\" colspan=\"2\">-0.379</td><td align=\"left\" colspan=\"2\">0.037</td><td align=\"left\">0.11</td><td align=\"left\" colspan=\"1\"/></tr><tr><td align=\"left\">Hypertension, yes</td><td align=\"left\" colspan=\"2\">4.014</td><td align=\"left\" colspan=\"2\">-0.496</td><td align=\"left\">8.525</td><td align=\"left\">0.083</td><td align=\"left\" colspan=\"2\"/><td align=\"left\" colspan=\"3\"/><td align=\"left\"/><td align=\"left\" colspan=\"2\"/><td align=\"left\" colspan=\"2\"/><td align=\"left\"/><td align=\"left\">3.715</td><td align=\"left\" colspan=\"2\">-0.871</td><td align=\"left\" colspan=\"2\">8.301</td><td align=\"left\">0.11</td><td align=\"left\" colspan=\"1\"/></tr><tr><td align=\"left\">Diabetes mellitus, yes</td><td align=\"left\" colspan=\"2\">2.687</td><td align=\"left\" colspan=\"2\">-1.913</td><td align=\"left\">7.286</td><td align=\"left\">0.25</td><td align=\"left\" colspan=\"2\"/><td align=\"left\" colspan=\"3\"/><td align=\"left\"/><td align=\"left\" colspan=\"2\"/><td align=\"left\" colspan=\"2\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\" colspan=\"2\"/><td align=\"left\" colspan=\"2\"/><td align=\"left\"/><td align=\"left\" colspan=\"1\"/></tr><tr><td align=\"left\">Antihypertensive drug, yes</td><td align=\"left\" colspan=\"2\">6.406</td><td align=\"left\" colspan=\"2\">1.034</td><td align=\"left\">11.779</td><td align=\"left\">0.021</td><td align=\"left\" colspan=\"2\"/><td align=\"left\" colspan=\"3\"/><td align=\"left\"/><td align=\"left\" colspan=\"2\"/><td align=\"left\" colspan=\"2\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\" colspan=\"2\"/><td align=\"left\" colspan=\"2\"/><td align=\"left\"/><td align=\"left\" colspan=\"1\"/></tr><tr><td align=\"left\">ACEI or ARB use</td><td align=\"left\" colspan=\"2\">3.74</td><td align=\"left\" colspan=\"2\">-0.446</td><td align=\"left\">7.926</td><td align=\"left\">0.082</td><td align=\"left\" colspan=\"2\"/><td align=\"left\" colspan=\"3\"/><td align=\"left\"/><td align=\"left\" colspan=\"2\"/><td align=\"left\" colspan=\"2\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\" colspan=\"2\"/><td align=\"left\" colspan=\"2\"/><td align=\"left\"/><td align=\"left\" colspan=\"1\"/></tr><tr><td align=\"left\">Beta blocker use</td><td align=\"left\" colspan=\"2\">4.48</td><td align=\"left\" colspan=\"2\">0.312</td><td align=\"left\">8.648</td><td align=\"left\">0.037</td><td align=\"left\" colspan=\"2\"/><td align=\"left\" colspan=\"3\"/><td align=\"left\"/><td align=\"left\" colspan=\"2\"/><td align=\"left\" colspan=\"2\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\" colspan=\"2\"/><td align=\"left\" colspan=\"2\"/><td align=\"left\"/><td align=\"left\" colspan=\"1\"/></tr><tr><td align=\"left\">Women (n = 47)</td><td align=\"left\" colspan=\"6\">Univariate</td><td align=\"left\" colspan=\"2\"/><td align=\"left\" colspan=\"8\">Model 1</td><td align=\"left\"/><td align=\"left\" colspan=\"6\">Model 2</td><td align=\"left\" colspan=\"1\"/></tr><tr><td align=\"left\">Factors</td><td align=\"left\" colspan=\"2\">Coefficient</td><td align=\"left\" colspan=\"3\">95% CI</td><td align=\"left\">P value</td><td align=\"left\" colspan=\"2\"/><td align=\"left\" colspan=\"3\">Coefficient</td><td align=\"left\" colspan=\"3\">95% CI</td><td align=\"left\" colspan=\"2\">P value</td><td align=\"left\"/><td align=\"left\">Coefficient</td><td align=\"left\" colspan=\"4\">95% CI</td><td align=\"left\">P value</td><td align=\"left\" colspan=\"1\"/></tr><tr><td align=\"left\">Age, yo</td><td align=\"left\" colspan=\"2\">0.374</td><td align=\"left\" colspan=\"2\">-0.04</td><td align=\"left\">0.789</td><td align=\"left\">0.084</td><td align=\"left\" colspan=\"2\"/><td align=\"left\" colspan=\"3\">0.455</td><td align=\"left\">0.056</td><td align=\"left\" colspan=\"2\">0.855</td><td align=\"left\" colspan=\"2\">0.031</td><td align=\"left\"/><td align=\"left\">0.461</td><td align=\"left\" colspan=\"2\">0.048</td><td align=\"left\" colspan=\"2\">0.873</td><td align=\"left\">0.034</td><td align=\"left\" colspan=\"1\"/></tr><tr><td align=\"left\">BMI, kg/m²</td><td align=\"left\" colspan=\"2\">0.22</td><td align=\"left\" colspan=\"2\">-0.472</td><td align=\"left\">0.913</td><td align=\"left\">0.54</td><td align=\"left\" colspan=\"2\"/><td align=\"left\" colspan=\"3\">0.46</td><td align=\"left\">-0.2</td><td align=\"left\" colspan=\"2\">1.119</td><td align=\"left\" colspan=\"2\">0.18</td><td align=\"left\"/><td align=\"left\">0.342</td><td align=\"left\" colspan=\"2\">-0.394</td><td align=\"left\" colspan=\"2\">1.079</td><td align=\"left\">0.37</td><td align=\"left\" colspan=\"1\"/></tr><tr><td align=\"left\">EAT, mL</td><td align=\"left\" colspan=\"2\">-0.07</td><td align=\"left\" colspan=\"2\">-0.146</td><td align=\"left\">0.006</td><td align=\"left\">0.079</td><td align=\"left\" colspan=\"2\"/><td align=\"left\" colspan=\"3\"/><td align=\"left\"/><td align=\"left\" colspan=\"2\"/><td align=\"left\" colspan=\"2\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\" colspan=\"2\"/><td align=\"left\" colspan=\"2\"/><td align=\"left\"/><td align=\"left\" colspan=\"1\"/></tr><tr><td align=\"left\">EATVI, mL/m²</td><td align=\"left\" colspan=\"2\">-0.118</td><td align=\"left\" colspan=\"2\">-0.24</td><td align=\"left\">0.004</td><td align=\"left\">0.065</td><td align=\"left\" colspan=\"2\"/><td align=\"left\" colspan=\"3\">-0.151</td><td align=\"left\">-0.272</td><td align=\"left\" colspan=\"2\">-0.03</td><td align=\"left\" colspan=\"2\">0.018</td><td align=\"left\"/><td align=\"left\">-0.145</td><td align=\"left\" colspan=\"2\">-0.269</td><td align=\"left\" colspan=\"2\">-0.021</td><td align=\"left\">0.028</td><td align=\"left\" colspan=\"1\"/></tr><tr><td align=\"left\">LVEF, %</td><td align=\"left\" colspan=\"2\">-0.201</td><td align=\"left\" colspan=\"2\">-0.608</td><td align=\"left\">0.206</td><td align=\"left\">0.34</td><td align=\"left\" colspan=\"2\"/><td align=\"left\" colspan=\"3\"/><td align=\"left\"/><td align=\"left\" colspan=\"2\"/><td align=\"left\" colspan=\"2\"/><td align=\"left\"/><td align=\"left\">-0.15</td><td align=\"left\" colspan=\"2\">-0.563</td><td align=\"left\" colspan=\"2\">0.263</td><td align=\"left\">0.48</td><td align=\"left\" colspan=\"1\"/></tr><tr><td align=\"left\">Hypertension, yes</td><td align=\"left\" colspan=\"2\">2.62</td><td align=\"left\" colspan=\"2\">-4.601</td><td align=\"left\">9.842</td><td align=\"left\">0.48</td><td align=\"left\" colspan=\"2\"/><td align=\"left\" colspan=\"3\"/><td align=\"left\"/><td align=\"left\" colspan=\"2\"/><td align=\"left\" colspan=\"2\"/><td align=\"left\"/><td align=\"left\">1.123</td><td align=\"left\" colspan=\"2\">-6.141</td><td align=\"left\" colspan=\"2\">8.388</td><td align=\"left\">0.76</td><td align=\"left\" colspan=\"1\"/></tr><tr><td align=\"left\">Diabetes mellitus, yes</td><td align=\"left\" colspan=\"2\">-1.029</td><td align=\"left\" colspan=\"2\">-10.148</td><td align=\"left\">8.091</td><td align=\"left\">0.83</td><td align=\"left\" colspan=\"2\"/><td align=\"left\" colspan=\"3\"/><td align=\"left\"/><td align=\"left\" colspan=\"2\"/><td align=\"left\" colspan=\"2\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\" colspan=\"2\"/><td align=\"left\" colspan=\"2\"/><td align=\"left\"/><td align=\"left\" colspan=\"1\"/></tr><tr><td align=\"left\">Antihypertensive drug, yes</td><td align=\"left\" colspan=\"2\">0.979</td><td align=\"left\" colspan=\"2\">-10.659</td><td align=\"left\">12.617</td><td align=\"left\">0.87</td><td align=\"left\" colspan=\"2\"/><td align=\"left\" colspan=\"3\"/><td align=\"left\"/><td align=\"left\" colspan=\"2\"/><td align=\"left\" colspan=\"2\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\" colspan=\"2\"/><td align=\"left\" colspan=\"2\"/><td align=\"left\"/><td align=\"left\" colspan=\"1\"/></tr><tr><td align=\"left\">ACEI or ARB use</td><td align=\"left\" colspan=\"2\">2.24</td><td align=\"left\" colspan=\"2\">-4.237</td><td align=\"left\">8.717</td><td align=\"left\">0.5</td><td align=\"left\" colspan=\"2\"/><td align=\"left\" colspan=\"3\"/><td align=\"left\"/><td align=\"left\" colspan=\"2\"/><td align=\"left\" colspan=\"2\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\" colspan=\"2\"/><td align=\"left\" colspan=\"2\"/><td align=\"left\"/><td align=\"left\" colspan=\"1\"/></tr><tr><td align=\"left\">Beta blocker use</td><td align=\"left\" colspan=\"2\">1.816</td><td align=\"left\" colspan=\"2\">-4.782</td><td align=\"left\">8.414</td><td align=\"left\">0.59</td><td align=\"left\" colspan=\"2\"/><td align=\"left\" colspan=\"3\"/><td align=\"left\"/><td align=\"left\" colspan=\"2\"/><td align=\"left\" colspan=\"2\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\" colspan=\"2\"/><td align=\"left\" colspan=\"2\"/><td align=\"left\"/><td align=\"left\" colspan=\"1\"/></tr></tbody></table></table-wrap>" ]

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[ "<table-wrap-foot><p>P1 between SR vs. AF in each sex; P2 between all-rhythm men vs. women</p></table-wrap-foot>", "<table-wrap-foot><p>ACE or ARB, angiotensin converting enzyme inhibitor or angiotensin receptor blocker; BMI, body mass index; EATV, epicardial adipose tissue volume; EATVI, epicardial adipose tissue volume index; LVEF, left ventricular ejection fraction</p></table-wrap-foot>", "<fn-group><fn><p><bold>Publisher’s Note</bold></p><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn></fn-group>" ]

[ "<graphic xlink:href=\"12872_2023_3569_Fig1_HTML\" id=\"d32e392\"/>", "<graphic xlink:href=\"12872_2023_3569_Fig2_HTML\" id=\"d32e909\"/>", "<graphic xlink:href=\"12872_2023_3569_Fig3_HTML\" id=\"d32e928\"/>", "<graphic xlink:href=\"12872_2023_3569_Fig4_HTML\" id=\"d32e2943\"/>" ]

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{ "acronym": [], "definition": [] }

[ "<title>Background</title>", "<p id=\"Par26\">The codon represents the fundamental connection between genes and proteins when deciphering genetic information. In the 64 standard genetic codes, there are 61 sense codons encoding 20 types of amino acids, and the remaining three are translation termination signals. Compared to the number of codable amino acids, the excess of possible nucleotide triplets results in a redundancy of the genetic code. Indeed, apart from tryptophan and methionine, which are encoded by a single codon, all other gene products are translated by two to six different triplets, a phenomenon defined as codon degeneracy [##REF##10684347##1##]. Multiple codons that are decrypted into an identical amino acid are referred to synonymous codons, which are not uniformly utilized during protein synthesis in many organisms [##REF##19931533##2##]. This species preference for certain codons, termed codon usage bias (CUB), is a consequence of the optimization of the deciphering strategy and plays an imperative role in the gene expression regulation [##REF##27448410##3##, ##REF##28292534##4##]. Information on CUB can provide important insights into exogenous gene expression [##REF##7579660##5##], gene function prediction [##REF##12034849##6##], genetic divergence assessment [##REF##30517879##7##], and organism evolution exploration [##REF##22984349##8##] and can contribute to revealing the molecular mechanisms underlying the environmental adaptation of various species [##REF##29155926##9##].</p>", "<p id=\"Par27\">The degree of CUB divergence differs widely across species, genes, and even within an individual gene [##UREF##0##10##, ##REF##21856647##11##]. Causes for the existence of CUB in organisms are diverse and complicated. In the process of long-term evolution, CUB deviations are primarily driven by natural selection, directional mutation, and random genetic drift [##REF##1752426##12##]. With the continuous progress of genome sequencing and bioinformatics, additional factors of complexity involved in CUB have been established over the last few decades, including genome size [##REF##15448185##13##], gene expression pattern [##REF##9583944##14##] and degree [##REF##29735666##15##], gene length [##REF##26546225##16##], efficient gene translation initiation [##REF##15611189##17##], tRNA abundance [##REF##21102527##18##] and interactions [##REF##29018283##19##], synonymous substitution frequency [##REF##3328816##20##], and mRNA folding [##REF##23293005##21##], among others. Moreover, the patterns of CUB appear to be related to phylogenetic relationships, i.e., the more closely phylogenetically related species tend to share a more similar CUB pattern [##REF##33180191##22##]. Given all of this, CUB is highly complex, and understanding it is challenging when considering the difficulty in determining the relative effect of the various factors. Much more detailed analyses of this fascinating phenomenon are needed to broaden our understanding of its biological implications and applications.</p>", "<p id=\"Par28\">Mitochondria (mt) are semiautonomous energy-producing eukaryotic organelles that drive oxidative phosphorylation for energy metabolism [##REF##16860735##23##]. Ordinarily, plant mt genomes (mitogenomes) exhibit more complex features compared with both their counterparts in animals and the conserved plastid genomes of plants [##UREF##1##24##]. Ongoing advances in sequencing and assembly technologies have significantly promoted the complete sequencing of mitogenomes in land plants, but nevertheless, there is a requirement for more available data to gain more refined knowledge of plant mitogenomes. The analysis of codon preference in plant mitogenomes is of great significance for studying the genetic patterns, phylogenetic relationships, and evolution of their mtDNA. Although the research of CUB in plant mitogenomes has made continuous progress [##REF##20424833##25##–##REF##26110418##27##], it has not been addressed more extensively and intensively like its equivalent nuclear and plastid genomes.</p>", "<p id=\"Par29\"><italic>Hemerocallis citrina</italic> Baroni belongs to the Asphodelaceae family and is a popular perennial herbaceous plant widely cultivated across Asia for food nutrition [##REF##28242413##28##], medicinal properties [##REF##27623554##29##], and landscape beautification [##REF##34025706##30##]. The immature flower buds are generally processed into dried vegetables with high nutraceutical value. <italic>H. citrina</italic>, also respected as the mother’s flower, has a long cultivation history and unparalleled cultural significance in China [##REF##29943113##31##]. Recent studies have demonstrated that <italic>H. citrina</italic> is rich in flavonoids, polyphenols, alkaloids, and anthraquinones [##REF##27623554##29##, ##REF##34025706##30##], making it a potent medicine for anti-inflammatory, antidepressant, and antioxidant uses. The successive acquisition of sequence information for the chloroplast (cp) [##REF##33366896##32##] and nuclear [##REF##33828071##33##] genomes symbolizes the considerable progress of <italic>H. citrina</italic> genomics research in recent years. Our team adopted a strategy of integrating Oxford Nanopore long-read and Illumina short-read sequencing to complete the sequencing, assembly, and annotation of the <italic>H. citrina</italic> mitogenome [##REF##36466251##34##]. However, systematical analysis on the CUB of the mitogenome has not been performed in <italic>H. citrina.</italic> The knowledge gained from CUB research provides useful clues for improving the expression level of exogenous genes and optimizing molecular-assisted breeding programmes in <italic>H. citrina</italic>. Consequently, it is particularly significant to analyze the CUB patterns and further evaluate the evolution and phylogeny of <italic>H. citrina</italic>, considering its tremendous economic benefits and various utilities. In this research, we conducted comprehensive analysis of the CUB of mt genes in <italic>H. citrina</italic>. We investigated the codon composition characteristics and usage patterns and evaluated the factors that influence CUB. Furthermore, relative synonymous codon usage (RSCU)-based cluster and mt protein coding gene (PCG)-based phylogenetic analyses were performed to advance the understanding of the evolution and phylogeny of <italic>H. citrina</italic>. The results derived from this work may help to facilitate the mt gene utilization, genetic improvement, and molecular breeding of <italic>H. citrina</italic>.</p>" ]

[ "<title>Materials and methods</title>", "<title>Sequence retrieval</title>", "<p id=\"Par43\">The mitogenome sequences of <italic>H. citrina</italic> (MZ726801.1、MZ726802.1, and MZ726803.1) were retrieved from the National Center for Biotechnology Information (NCBI) database (<ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/nuccore/?term=Hemerocallis%20citrina%20mitochondrion\">https://www.ncbi.nlm.nih.gov/nuccore/?term=Hemerocallis%20citrina%20mitochondrion</ext-link>). We extracted the CDS of the mitogenome that started with ATG and ended with TAG, TGA, or TAA. Each CDS was greater than 300 bp in length and had exact multiples of three in the base number. In addition, the sequences used for the subsequent analysis were processed by eliminating duplicate sequences and sequences containing ambiguous bases, i.e., other than A, C, G, and T.</p>", "<title>Analysis of codon usage characteristic parameters</title>", "<p id=\"Par44\">The codon usage indicators of the selected CDS were analyzed using the CodonW v1.4.2 program (<ext-link ext-link-type=\"uri\" xlink:href=\"http://codonw.sourceforge.net/\">http://codonw.sourceforge.net/</ext-link>), including CAI, CBI, Fop, RSCU, GC3s, A3s, T3s, C3s, and G3s. The other codon composition indices, including ENC, GCall, GC1, GC2, and GC3, were determined using the online Cusp and Chips programs from EMBOSS (<ext-link ext-link-type=\"uri\" xlink:href=\"http://www.bioinformatics.nl/emboss-explorer/\">http://www.bioinformatics.nl/emboss-explorer/</ext-link>). Then, correlation analysis of the main characteristic parameters was performed using the Correlation Plot tool in Origin 2022 software based on the Pearson correlation coefficient method.</p>", "<title>ENC-plot analysis</title>", "<p id=\"Par45\">ENC is a vital indicator to evaluate the degree of preference for the imbalanced use of synonymous codons [##REF##2110097##52##]. Usually, the ENC value ranges from 20 to 61 and is negatively correlated with codon preference. A smaller ENC value indicates a gene with a stronger bias, thus displaying extreme preference of using a unique codon to individually encode each amino acid. Conversely, a gene with an ENC value higher than 35 is considered to have weak usage preference and even no bias in the case of an ENC value up to 61 [##REF##29584741##49##]. GC3s represents the average GC content at the ‘silent’ site of synonymous codons and is an important index to reveal the nucleotide composition bias. The ENC-plot was compiled using the ENC value of each gene as the ordinate and GC3s as the abscissa to explore the decisive factor influencing CUB. The standard curve was drawn according to the following equation:  [##REF##2110097##52##]. Under the condition that mutation pressure is the sole determinant of codon usage, the genes are located on or close to the standard curve, whereas when the points fall below and are far away from the excepted curve, this suggests that natural selection and other factors may greatly affect codon bias [##REF##10773076##53##]. In order to better evaluate the difference between the expected and actual ENC values, the ENC ratio was calculated following the previously described formula:  [##REF##14676425##50##].</p>", "<title>Neutrality plot analysis</title>", "<p id=\"Par46\">Neutrality plot analysis is commonly applied to study the correlation among bases at three codon positions, revealing the role of natural selection and mutation pressure in the CUB patterns [##REF##3357886##54##, ##REF##27808241##55##]. In the current neutral graph, an individual mt gene of <italic>H. citrina</italic> is represented by a discrete point. The mean value of GC1 and GC2 for each gene was denoted by GC12, and GC12 and GC3 serve as the respective ordinate and abscissa of the scatterplot. It was assumed that if a notable correlation exists between GC12 and GC3, namely, that the discrete points are diagonally distributed in the plot with a slope close to one, this indicates that the CUB is dominated by mutation pressure. Contrastingly, a regression curve with a slope of zero and no significant correlation between GC12 and GC3 imply pure natural selection [##REF##10368434##56##].</p>", "<title>PR2-plot analysis</title>", "<p id=\"Par47\">In previous studies, the development of codon usage patterns was confirmed to be associated with the base composition at the ‘silent’ site of the codon [##REF##15222899##57##]. PR2-plot analysis is extensively applied to evaluate the bias relationship between A/T and C/G at the synonymous site of the codon and, further, to determine the effects of mutation, selection, or other factors on CUB. The analysis is particularly meaningful for amino acids of a coding gene with four synonymous codons [##REF##10570983##58##]. Consequently, the plan scatter diagram was constructed with A3s/(A3s + T3s) as the ordinate and G3s/(G3s + C3s) as the abscissa. The four-codon amino acids, i.e., valine, proline, threonine, alanine, and glycine, were selected to calculate the composition frequency of the third base position of each gene. The center point of the plot represents A = T and G = C with both coordinates equal to 0.5, presenting that codon bias is entirely caused by mutation; otherwise, natural selection and other factors may act on codon preference. The degree of distribution deviation from the center allows us to determine the direction and degree of the base deviation [##REF##10570983##58##].</p>", "<title>Analysis of RSCU and putative optimal codons</title>", "<p id=\"Par48\">The RSCU value of a codon refers to the ratio between the observed usage value and the expectation, reflecting the relative usage preference for specific codon compositions encoding the same amino acid [##REF##3146682##59##]. When RSCU is equivalent to 1, codon usage is unbiased, and the codon is therefore selected randomly or equally. Codons with RSCU values greater than 1 are taken as high-frequency codons, which illustrates that codon usage is biased with high preference; the converse indicates the specific codon frequency is low [##UREF##3##60##]. For high-frequency codons, the codon whose ENC difference exceeds a certain critical value is determined to be an optimal codon [##REF##6175758##61##]. The optimal codon is the preferred codon identified by calculating and ordering the ENC values of all genes. In general, highly expressed genes represent a large degree of codon preference and thus a small ENC value. On the basis of the above principles, 10% of the genes at the high and low end of the ordered ENC values were selected to establish low- and high-bias gene groups, respectively. The difference between the RSCU values of the codons from the two groups was calculated as ΔRSCU. The codons with RSCU &gt; 1 and ΔRSCU &gt; 0.08 were defined as the optimal codons of the gene [##REF##32089527##62##].</p>", "<title>Clustering of codon usage preference and phylogenetic analyses</title>", "<p id=\"Par49\">To explore the degree of divergence in the mitogenome codon usage more accurately, a cluster analysis was conducted between <italic>H. citrina</italic> and 14 other monocotyledons using SPSS 25.0 software. In the clustering process, each monocotyledon was taken as an object, and the RSCU values corresponding to 59 codons (excluding the codon AUG encoding methionine, UGG encoding tryptophan, and the three stop codons UAA, UAG, and UGA) were used as variables. The cluster pedigree was then established based on the squared Euclidean distance method [##REF##25515024##63##]. Meanwhile, a contiguous sequence was constructed by lining up the 16 conserved mt PCGs (<italic>atp1</italic>, <italic>atp6</italic>, <italic>atp9</italic>, <italic>ccmB</italic>, <italic>ccmC</italic>, <italic>ccmFc</italic>, <italic>ccmFn</italic>, <italic>cob</italic>, <italic>cox3</italic>, <italic>matR</italic>, <italic>nad3</italic>, <italic>nad4L</italic>, <italic>nad6</italic>, <italic>nad7</italic>, <italic>nad9</italic>, and <italic>rps12</italic>) followed by alignment using MAFFT v.7.4.0 program [##REF##23329690##64##] for the analyzed species. The maximum likelihood (ML) phylogenetic tree was constructed based on a Tamura-Nei model using MEGA 7 software [##REF##27004904##65##] with 1,000 bootstrap replicates.</p>" ]

[ "<title>Results</title>", "<title>Codon composition of the <italic>H. citrina</italic> mitogenome</title>", "<p id=\"Par30\">The final 28 protein coding sequences (CDS) of the mitogenome in <italic>H. citrina</italic> were available for codon usage analysis. The overall GC content of the whole mitogenome (GCall) was estimated at 43.59%, and the frequency of GC at each codon position (GC1, GC2, and GC3) was lower than 50% without exception (Table ##TAB##0##1##). Although the percentage of the GC composition in each gene was slightly different, the content order ranking of GC1 &gt; GC2 &gt; GC3 was highly consistent (Table ##TAB##1##2##). Furthermore, the average GC composition at the third position of synonymous codons (GC3s) of the CDS was lower than 50%, and the percentage of each individual base at the synonymous site (A3s, C3s, G3s, and T3s) conformed to the order ranking of T3s &gt; A3s &gt; G3s &gt; C3s (Table ##TAB##0##1##), indicating that the codons of the <italic>H. citrina</italic> mitogenome tend to end in A/T.</p>", "<p id=\"Par31\">In the analysis of 28 CDS in the mitogenome, a total of 8850 codons were also obtained (Table ##TAB##0##1##), involving all 64 types of codons. The codon number of the mt genes in <italic>H. citrina</italic> varies greatly, ranging from 101 in <italic>rps14</italic> to 673 in <italic>matR</italic> (Table ##TAB##1##2##). The effective number of codon (ENC) values range from 39.34 to 60.01, with an average of 53.89, exceeding 50 in the mitogenome. All of the genes had ENC values greater than 35, and up to 75% of them had high (&gt; 50) ENC values, indicating fairly weak CUB in <italic>H. citrina</italic>. In addition, the codon adaptation index (CAI) values of the mt genes ranged from 0.12 to 0.21, with a mean value of 0.17, far less than 1. The values of codon bias index (CBI) and frequency of optimal codons (Fop) were clustered around − 0.18–0.02 and 0.29–0.42, respectively. In conclusion, the above results suggest that both codon bias and mt gene expression are relatively low in <italic>H. citrina</italic>.</p>", "<title>Correlation analysis between CUB parameters</title>", "<p id=\"Par32\">To reveal the role of the composition properties in CUB, Pearson’s correlation analysis was conducted between the important indices of codon usage. The results displayed a significantly positive correlation between GCall and GC1, GC2, and GC3 (<italic>P</italic> &lt; 0.01, Fig. ##FIG##0##1##), indicating an overall strong correlation of the composition among the three codon bases in the mitogenome. The ENC value had a significantly positive correlation with GC3 (<italic>P</italic> &lt; 0.01), implying that the base composition of the synonymous site has a crucial impact on CUB. Simultaneously, ENC positively correlated with the codon counts (CC) (<italic>P</italic> &lt; 0.05), which elucidates that gene length also contributes greatly to codon bias. Further, it was found that CBI and Fop were significantly correlated with GCall (<italic>P</italic> &lt; 0.01) and with GC3 (<italic>P</italic> &lt; 0.05), indicating that GCall is another major factor that affects CUB.</p>", "<title>Cause analysis of codon usage preference</title>", "<p id=\"Par33\">For purpose of understanding whether the G + C mutation bias influences the CUB of <italic>H. citrina</italic>, the ENC for genes were mapped against the GC3s. The ENC-plot of <italic>H. citrina</italic> is displayed in Fig. ##FIG##1##2##. Only a few genes approached the solid curve, inferring that compositional mutation plays a significant role in CUB. However, most of the genes were scattered on both sides away from the standard curve, implying that natural selection has also shaped the CUB patterns. Besides, to better estimate the difference in ENC values, the ENC frequency distribution of the current genes was analyzed. The ENC ratio varied from − 0.15 to 0.25 (Fig. ##FIG##2##3##). Among the 28 mt genes, 19 (67.86%) had an ENC ratio greater than 0, reflected by these genes being distributed below the standard curve. Additionally, 15 genes (53.57%) were distributed within the range of -0.05–0.05 and had slight differences between the actual and expected ENC values. These results further demonstrate that the CUB patterns of the <italic>H. citrina</italic> mitogenome might be shaped by the joint effects of natural selection and mutation pressure.</p>", "<p id=\"Par34\">To determine the relationship among bases at three codon positions, neutrality plot analysis was performed for each mt gene of <italic>H. citrina</italic> (Fig. ##FIG##3##4##). Narrow ranges of GC3 and GC12 (0.2991–0.5676 and 0.3933–0.5199, respectively) were observed, and only a few genes were diagonally distributed in the plot. Moreover, GC12 displayed no significant correlation with GC3 (<italic>r</italic>=-0.1755, <italic>P</italic> &gt; 0.05), indicating that natural selection might have a considerable influence on the CUB of the <italic>H. citrina</italic> mitogenome. In addition, the slope of the regression line was − 0.1038, suggesting the mutation pressure effect accounted for only 10.38%. Consequently, the above results infer that natural selection is superior to mutation pressure in affecting the development of CUB in the <italic>H. citrina</italic> mitogenome.</p>", "<p id=\"Par35\">To further estimate the bias relationship of the four bases of mt genes, Parity rule 2 (PR2)<bold>-</bold>plot analysis was performed on the fourfold degenerate codon families. As depicted in Fig. ##FIG##4##5##, the distribution of genes is not uniform in the PR2-plane. Most of the points are in the lower half of the area along the vertical direction, revealing that the use frequency of T is higher than that of A at the synonymous position. However, in the horizontal direction, more genes are obviously distributed on the left side of the plane, so the content of C is higher than that of G. Consequently, higher levels of pyrimidines (T and C) are confirmed at the ‘silent’ site of the codon in the <italic>H. citrina</italic> mitogenome. The unbalanced usage of bases again illustrates that not only mutation but also selection and other factors determine the CUB patterns of the <italic>H. citrina</italic> mitogenome.</p>", "<title>Determination of RSCU values and putative optimal codons</title>", "<p id=\"Par36\">In the present study, there were 29 codons with RSCU values greater than 1 defined as high-frequency codons (Fig. ##FIG##5##6##), indicating a high bias in the usage of these codons in the mitogenome of <italic>H. citrina.</italic> Excluding UUG (leucine), UCC (serine), and ACC (threonine), the remaining preferentially used codons end in A (11 of 29) or T (15 of 29). These results are further evidence that the mt gene of <italic>H. citrina</italic> is biased toward codons ending in A/T, illustrating that compositional constraints might have an impact on the synonymous CUB patterns of the <italic>H. citrina</italic> mitogenome.</p>", "<p id=\"Par37\">By comparing the RSCU values from the two bias gene groups constructed by the ENC difference, 22 optimal codons were identified whose RSCU values were greater than 1 with ΔRSCU &gt; 0.08 (Table ##TAB##2##3##). In the preferred codons, 19 codons ended with A (7/19) or T (12/19), while only three codons ended with G (2/3) or C (1/3). These results illustrate that both the high-frequency and optimal codons of the mt genes in <italic>H. citrina</italic> tend to end in A/T.</p>", "<title>Cluster and phylogenetic analyses</title>", "<p id=\"Par38\">In order to gain a more accurate understanding of the divergence in the mitogenome codon usage, RSCU-based cluster analysis was conducted between <italic>H. citrina</italic> and other relatives. Since <italic>H. citrina</italic> is the only member of the Asphodelaceae family to have its complete mitogenome sequenced, 14 other monocotyledonous species with published mitogenome data were selected for subsequent comparison, i.e., <italic>Asparagus officinalis</italic> L. and <italic>Chlorophytum comosum</italic> (Thunb.) Baker of Asparagaceae, <italic>Allium cepa</italic> L. and <italic>Allium fistulosum</italic> L. of Amaryllidaceae, <italic>Apostasia shenzhenica</italic> Z.J.Liu &amp; L.J.Chen, <italic>Paphiopedilum micranthum</italic> T. Tang &amp; F. T. Wang, <italic>Gastrodia elata</italic> Blume, and <italic>Dendrobium amplum</italic> Lindl. of Orchidaceae, <italic>Cocos nucifera</italic> L. and <italic>Phoenix dactylifera</italic> L. of Arecaceae, <italic>Zea mays</italic> L. and <italic>Oryza sativa</italic> L. of Poaceae, <italic>Spirodela polyrrhiza</italic> (L.) Schleid. of Araceae, and <italic>Butomus umbellatus</italic> L. of Butomaceae. The RSCU-based cluster analysis results indicated that the analyzed monocotyledons group into two clusters (Fig. ##FIG##6##7##). The first cluster is a separate branch of <italic>Z. mays</italic>, while the second cluster is composed of the remaining 14 monocots. <italic>H. citrina</italic> along with <italic>Allium cepa</italic>, <italic>Allium fistulosum</italic>, <italic>Asparagus officinalis</italic>, <italic>Chlorophytum comosum</italic>, and <italic>S. polyrrhiza</italic> are classified as one clade, indicating that these species have similar codon usage patterns. In addition, the phylogenetic tree based on the mt PCG was also established for validation. As seen in Fig. ##FIG##7##8##, although the 15 analyzed species are samely divided into two clades, there are several differences between the topologies of the two graphs, at least when distant taxa are compared. The analyzed Arecaceae and Orchidaceae plants were classified into different clades of the phylogeny. While <italic>Cocos nucifera</italic> and <italic>Phoenix dactylifera</italic>, which belong to Arecaceae share a similar RSCU with Orchidaceae taxa (<italic>Paphiopedilum micranthum</italic> and <italic>Apostasia shenzhenica)</italic>. <italic>Z. mays</italic> and <italic>O. sativa</italic>, both members of the Poaceae family, were more distantly related in the RSCU-based clustering lineage. <italic>H. citrina</italic> clusters together with <italic>Asparagus officinalis</italic>, <italic>Chlorophytum comosum</italic>, <italic>Allium cepa</italic>, and <italic>Allium fistulosum</italic>, which intensely indicates their close relationships in evolutionary terms. When more closely related species are considered, such as <italic>H. citrina</italic> and Asparagaceae and Amaryllidaceae, a similar codon usage preference is observed. Consequently, <italic>H. citrina</italic> is close to <italic>Asparagus officinalis</italic>, <italic>Chlorophytum comosum</italic>, <italic>Allium cepa</italic>, and <italic>Allium fistulosum</italic> in evolutionary terms, reflecting a certain correlation between CUB and evolutionary relationships. These findings further support the likelihood that species with a close evolutionary relationship might have more similar codon usage preferences. However, it is worth noting that the position of <italic>S. polyrrhiza</italic> in the cluster analysis is quite different from that of the phylogenetic tree. The mt PCG-based phylogenetic tree is closer to the true evolutionary classification of the 15 monocotyledonous species. The discrepancy of taxonomic characters illustrates that the loci mutation of the genome sequence also plays an important role in the evolution of organisms.</p>" ]

[ "<title>Discussion</title>", "<p id=\"Par39\">Codon usage bias (CUB) in genomes is inevitable and refers to the uneven use of synonymous codons in gene coding to account for both gene regulation and molecular evolution. Previous studies have focused on the CUB patterns in many prokaryotes and eukaryotes, which was found to differ across various species and genes [##UREF##0##10##, ##REF##21856647##11##]. The ancestors of terrestrial plants are believed to be unicellular algae, which have undergone a prolonged period of selection favoring the enrichment of GC in their nuclear genomes [##REF##9928484##35##]. However, the CUB of the cp and mt genomes differ from their host cell counterparts in terms of evolutionary rates and patterns [##REF##34822069##36##]. It has been proposed that organellar genes exhibit AT-richness and bias toward A- or T-ending codons in their genomes [##REF##19005776##37##–##REF##31934501##39##]. Extensive studies on the codon preference of the cp genomes have been published for a wide variety of organisms, for instance, <italic>Oryza</italic> plants [##REF##32989601##40##], <italic>Elaeagnus</italic> plants [##REF##36875724##41##], <italic>Epimedium</italic> plants [##REF##36624369##42##], Euphorbiaceae species [##REF##31934501##39##], Asteraceae species [##UREF##2##43##], and Theaceae species [##REF##35360853##44##], among others. Nevertheless, the status of plant mitogenomes has not been well surveyed. Here, we conducted comprehensive analysis on the CUB of the mt genes in <italic>H. citrina</italic>. Composition analysis of codons revealed that the GCall and GC3 of the mt genes were lower than 50%, presenting a preference for A/T-rich nucleotides and A/T-ending codons in <italic>H. citrina</italic>. Moreover, the high-frequency and optimal codons in the <italic>H. citrina</italic> mitogenome are predominantly A/T-ending codons. Similar findings have also been recorded in previous studies on the mitogenomes of <italic>O. sativa</italic> [##REF##16120394##45##], <italic>Triticum aestivum</italic> L., <italic>Z. mays</italic>, <italic>Arabidopsis thaliana</italic> (L.) Heynh., and <italic>Nicotiana tabacum</italic> L. [##REF##19005776##37##]. Our results lend further support to the evidence that the GC composition is the factor that most directly reflects the CUB patterns.</p>", "<p id=\"Par40\">Investigations of the factors influencing CUB in genomes have been continuous since striding into the era of genomics research. Various hypotheses have been proposed toward unraveling the reasons for deviations in CUB. Two typically accepted hypotheses explaining the origin of CUB are the selection–mutation–drift model [##REF##3104616##46##] and neutral theory [##REF##9847205##47##]. Ultimately, although CUB is determined by various factors, it appears that the evolution of CUB is a primary result of the balance between natural selection and directional mutation pressure. Research on <italic>Helianthus annuus</italic> L. suggests that mutation pressure is the most dominant evolutionary driving force of the cp genome [##REF##35725374##48##]. However, in most cp genomes, natural selection would be more prominent in the formation of codon usage patterns [##REF##31934501##39##–##REF##36624369##42##]. With regard to plant mitogenomes, natural selection is considered to be the crucial factor shaping CUB [##REF##19005776##37##, ##REF##16120394##45##]. In our present study, only a few genes approached the expected curve, whereas most genes were discretely distributed in the ENC-plot, implying mutation pressure is a minor factor of CUB. Combined neutrality plot and PR2-plot analyses augment the inference that the CUB of the <italic>H. citrina</italic> mitogenome are attributed to natural selection and mutation pressure, while natural selection is the decisive factor. Moreover, we found significant correlations of ENC with the GC3 and codon counts, suggesting that not only compositional constraints but also gene length contributes greatly to CUB. Therefore, we conclude that not only mutation but also selection and other factors, in combination, significantly contribute to framing the CUB patterns of the <italic>H. citrina</italic> mitogenome, and natural selection is the main determinant.</p>", "<p id=\"Par41\">The diversity of the CUB among various organisms can provide valuable information for species classification and molecular evolution. Research has indicated that there is a certain correlation between the distance of genetic relationships within species and codon usage preferences [##REF##33180191##22##]. Here, we performed RSCU-based cluster analysis between <italic>H. citrina</italic> and 14 other monocots. <italic>H. citrina</italic> along with <italic>Allium cepa</italic>, <italic>Allium fistulosum</italic>, <italic>Asparagus officinalis</italic>, <italic>Chlorophytum comosum</italic>, and <italic>S. polyrrhiza</italic> were classified as one cluster, indicating that they share similar codon usage patterns. The phylogenetic tree, subsequently established based on the mt PCG, confirmed that <italic>H. citrina</italic> is evolutionarily close to <italic>Asparagus officinalis</italic>, <italic>Chlorophytum comosum</italic>, <italic>Allium cepa</italic>, and <italic>Allium fistulosum.</italic> Our findings are quite consistent with research on the cp genome of <italic>Mesona chinensis</italic> Benth [##REF##33180191##22##], displaying a certain correlation between CUB and the evolutionary relationships. However, the phylogenetic relationship of the nuclear genomes between cotton species cannot be well reflected by taxonomic results based on codon RSCU values [##REF##29584741##49##]. The likely explanation is the fairly weak codon usage preference in the <italic>H. citrina</italic> mitogenome, and thus the mt genes are not susceptible to external factors during evolution. Consequently, RSCU-based cluster analysis can complement taxonomic studies of <italic>H. citrina.</italic> Nevertheless, it is worth noting that the position of <italic>S. polyrrhiza</italic> in the cluster analysis is quite different from that of the phylogenetic tree. These results further indicate that the evolutionary relationship based on codon preference characteristics may miss some useful information, such as the non-preference codon information in CDS, which indirectly demonstrates that the non-preference codons also play an important role in organism evolution and phylogeny.</p>", "<p id=\"Par42\">For mitogenomes, although there are tremendous variations in the size, structure, and sequence among different species, the products encoded by mt genes are quite conservative [##UREF##1##24##]. Codon usage preferences affect gene expression through the preferential use of optimal codons to regulate the translational accuracy and efficiency [##REF##19005776##37##]. Therefore, an investigation of CUB in the mitogenome could provide a basic understanding of mitogenomic evolution and offer deeper insight into improving the expression efficiency of exogenous target genes in host organisms. Typically, the optimal genes in the nuclear genome use predominantly C- or G-ending codons, whereas those in the organelle genome prefer A- or T-ending codons [##REF##19005776##37##, ##REF##14676425##50##, ##REF##35035132##51##]. In this study, we identified a total of 29 high-frequency codons and 22 optimal codons, and most of them exhibit a preference for A or T at the synonymous site. Notably, the mitogenomes of higher plants such as <italic>T. aestivum</italic>, <italic>N. tabacum</italic>, <italic>Arabidopsis thaliana</italic>, <italic>Z. mays</italic>, <italic>Phycomitrella patens</italic>, and <italic>Marchntia polymorpha</italic> also tend to have optimal codons that end in A or T [##REF##19005776##37##]. The optimization of codons will contribute essential information for the genetic transformation and protein expression of mt genes in <italic>H. citrina</italic>.</p>" ]

[ "<title>Conclusions</title>", "<p id=\"Par50\">In this study, mt genes of <italic>H. citrina</italic> were systematically analyzed to study the CUB patterns as well as the related forces influencing their evolutionary processes. The mitogenome exhibited weaker CUB and a preference for A/T-rich nucleotides and A/T-ending codons. Extensive measures were applied to evaluate the causes of CUB, as illustrated by the estimate of the codon usage characteristic indices, correlation, ENC-plot, neutrality plot, and PR2-plot analyses. Based on these, the formation of the CUB patterns of the <italic>H. citrina</italic> mitogenome is attributed to the combined effects of multiple factors, with natural selection being the decisive factor. Meanwhile, the RSCU-based cluster analysis and mt PCG-based phylogenetic tree revealed a certain correlation between CUB and evolutionary relationships. The inferred optimal codons also provide essential information for optimizing gene expression in <italic>H. citrina</italic>. In summary, these findings enrich our knowledge on the codon usage patterns of mitogenomes and serve as a fundamental reference for further studies on genetic modification and phylogenetic evolution in <italic>H. citrina</italic>.</p>" ]

[ "<title>Background</title>", "<p id=\"Par1\"><italic>Hemerocallis citrina</italic> Baroni is a traditional vegetable crop widely cultivated in eastern Asia for its high edible, medicinal, and ornamental value. The phenomenon of codon usage bias (CUB) is prevalent in various genomes and provides excellent clues for gaining insight into organism evolution and phylogeny. Comprehensive analysis of the CUB of mitochondrial (mt) genes can provide rich genetic information for improving the expression efficiency of exogenous genes and optimizing molecular-assisted breeding programmes in <italic>H. citrina</italic>.</p>", "<title>Results</title>", "<p id=\"Par2\">Here, the CUB patterns in the mt genome of <italic>H. citrina</italic> were systematically analyzed, and the possible factors shaping CUB were further evaluated. Composition analysis of codons revealed that the overall GC (GCall) and GC at the third codon position (GC3) contents of mt genes were lower than 50%, presenting a preference for A/T-rich nucleotides and A/T-ending codons in <italic>H. citrina</italic>. The high values of the effective number of codons (ENC) are indicative of fairly weak CUB. Significant correlations of ENC with the GC3 and codon counts were observed, suggesting that not only compositional constraints but also gene length contributed greatly to CUB. Combined ENC-plot, neutrality plot, and Parity rule 2 (PR2)-plot analyses augmented the inference that the CUB patterns of the <italic>H. citrina</italic> mitogenome can be attributed to multiple factors. Natural selection, mutation pressure, and other factors might play a major role in shaping the CUB of mt genes, although natural selection is the decisive factor. Moreover, we identified a total of 29 high-frequency codons and 22 optimal codons, which exhibited a consistent preference for ending in A/T. Subsequent relative synonymous codon usage (RSCU)-based cluster and mt protein coding gene (PCG)-based phylogenetic analyses suggested that <italic>H. citrina</italic> is close to <italic>Asparagus officinalis</italic>, <italic>Chlorophytum comosum</italic>, <italic>Allium cepa</italic>, and <italic>Allium fistulosum</italic> in evolutionary terms, reflecting a certain correlation between CUB and evolutionary relationships.</p>", "<title>Conclusions</title>", "<p id=\"Par3\">There is weak CUB in the <italic>H. citrina</italic> mitogenome that is subject to the combined effects of multiple factors, especially natural selection. <italic>H. citrina</italic> was found to be closely related to <italic>Asparagus officinalis</italic>, <italic>Chlorophytum comosum</italic>, <italic>Allium cepa</italic>, and <italic>Allium fistulosum</italic> in terms of their evolutionary relationships as well as the CUB patterns of their mitogenomes. Our findings provide a fundamental reference for further studies on genetic modification and phylogenetic evolution in <italic>H. citrina</italic>.</p>", "<title>Keywords:</title>" ]

[]

[ "<title>Acknowledgements</title>", "<p>The authors thank MDPI for English language editing.</p>", "<title>Authors’ contributions</title>", "<p>KZ and YW conceived the study. KZ and XS performed data analysis and drafted the manuscript. YW and YZ supervised the research and revised the manuscript. All authors have read and approved the final manuscript.</p>", "<title>Funding</title>", "<p>This work was funded by Youth Science and Technology Innovation Project of Tianjin Academy of Agricultural Sciences (Grant No. 2022014), Scientific Research Project of Shanxi Datong University (Grant No. 2022CXY22).</p>", "<title>Availability of data and materials</title>", "<p>The mitochondrial genome datasets generated and analyzed in this study are available in the NCBI, <italic>Hemerocallis citrine</italic> (MZ726801.1-MZ726803.1, <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/nuccore/?term=Hemerocallis%20citrina%20mitochondrion\">https://www.ncbi.nlm.nih.gov/nuccore/?term=Hemerocallis%20citrina%20mitochondrion</ext-link>), <italic>Allium cepa</italic> (KU318712.1, <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/nuccore/KU318712.1\">https://www.ncbi.nlm.nih.gov/nuccore/KU318712.1</ext-link>), <italic>Allium fistulosum</italic> (OL347690.1, <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/nuccore/OL347690.1\">https://www.ncbi.nlm.nih.gov/nuccore/OL347690.1</ext-link>), <italic>Apostasia shenzhenic</italic>a (NC_077647.1, <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/nuccore/NC_077647.1\">https://www.ncbi.nlm.nih.gov/nuccore/NC_077647.1</ext-link>), <italic>Asparagus officinalis</italic> (NC_053642.1, <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/nuccore/NC_053642.1\">https://www.ncbi.nlm.nih.gov/nuccore/NC_053642.1</ext-link>), <italic>Butomus umbellatus</italic> (KC208619.1, <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/nuccore/KC208619.1\">https://www.ncbi.nlm.nih.gov/nuccore/KC208619.1</ext-link>), <italic>Chlorophytum comosum</italic> (MW411187.1, <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/nuccore/MW411187.1\">https://www.ncbi.nlm.nih.gov/nuccore/MW411187.1</ext-link>), <italic>Cocos nucifera</italic> (KX028885.1, <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/nuccore/KX028885.1\">https://www.ncbi.nlm.nih.gov/nuccore/KX028885.1</ext-link>), <italic>Dendrobium amplum</italic> (MH591879.1-MH591896.1, <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/nuccore/?term=Dendrobium+amplum+mitochondrion%2C+complete+genome\">https://www.ncbi.nlm.nih.gov/nuccore/?term=Dendrobium+amplum+mitochondrion%2C+complete+genome</ext-link>), <italic>Gastrodia elata</italic> (MF070084.1-MF070102.1, <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/nuccore/?term=Gastrodia%20elata%20chromosome%20mitochondrion%2C%20complete%20sequence\">https://www.ncbi.nlm.nih.gov/nuccore/?term=Gastrodia%20elata%20chromosome%20mitochondrion%2C%20complete%20sequence</ext-link>), <italic>Paphiopedilum micranthum</italic> (OP465200.1-OP465225.1, <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/nuccore/?term=Paphiopedilum%20micranthum%20chromosome%20mitochondrion%2C%20complete%20sequence\">https://www.ncbi.nlm.nih.gov/nuccore/?term=Paphiopedilum%20micranthum%20chromosome%20mitochondrion%2C%20complete%20sequence</ext-link>), <italic>Phoenix dactylifera</italic> (MH176159.1, <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/nuccore/MH176159.1\">https://www.ncbi.nlm.nih.gov/nuccore/MH176159.1</ext-link>), <italic>Spirodela polyrrhiza</italic> (JQ804980.1, <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/nuccore/JQ804980.1\">https://www.ncbi.nlm.nih.gov/nuccore/JQ804980.1</ext-link>), <italic>Oryza sativa</italic> (NC_011033.1, <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/nuccore/NC_011033.1\">https://www.ncbi.nlm.nih.gov/nuccore/NC_011033.1</ext-link>), and <italic>Zea mays</italic> (NC_007982.1, <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/nuccore/NC_007982.1\">https://www.ncbi.nlm.nih.gov/nuccore/NC_007982.1</ext-link>).</p>", "<title>Declarations</title>", "<title>Ethics approval and consent to participate</title>", "<p id=\"Par51\">Not applicable.</p>", "<title>Consent for publication</title>", "<p id=\"Par52\">Not applicable.</p>", "<title>Competing interests</title>", "<p id=\"Par53\">The authors declare no competing interests.</p>" ]

[ "<fig id=\"Fig1\"><label>Fig. 1</label><caption><p>Correlation analysis of codon parameters in the <italic>H. citrina</italic> mitogenome. *, ** indicate correlations significant at the 0.05 and 0.01 levels, respectively</p></caption></fig>", "<fig id=\"Fig2\"><label>Fig. 2</label><caption><p>ENC-plot analysis of the <italic>H. citrina</italic> mitogenome</p></caption></fig>", "<fig id=\"Fig3\"><label>Fig. 3</label><caption><p>Distribution of ENC frequency of the <italic>H. citrina</italic> mitogenome</p></caption></fig>", "<fig id=\"Fig4\"><label>Fig. 4</label><caption><p>Neutrality plot analysis of the <italic>H. citrina</italic> mitogenome</p></caption></fig>", "<fig id=\"Fig5\"><label>Fig. 5</label><caption><p>PR2-plot analysis of the <italic>H. citrina</italic> mitogenome</p></caption></fig>", "<fig id=\"Fig6\"><label>Fig. 6</label><caption><p>Heat map of codon usage preference based on RSCU values in the <italic>H. citrina</italic> mitogenome</p></caption></fig>", "<fig id=\"Fig7\"><label>Fig. 7</label><caption><p>Clustering lineage plot based on RSCU values of the 15 monocotyledonous mitogenomes</p></caption></fig>", "<fig id=\"Fig8\"><label>Fig. 8</label><caption><p>Phylogenetic tree based on the mt PCG for 15 monocot species</p></caption></fig>" ]

[ "<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Codon composition parameters of the mitogenome in <italic>H. citrina</italic></p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" rowspan=\"2\">Codon counts</th><th align=\"left\" colspan=\"4\">Base composition at the third position of the synonymous codon/%</th><th align=\"left\" colspan=\"5\">GC content/%</th><th align=\"left\" rowspan=\"2\">ENC</th></tr><tr><th align=\"left\">T3s</th><th align=\"left\">C3s</th><th align=\"left\">A3s</th><th align=\"left\">G3s</th><th align=\"left\">GC1</th><th align=\"left\">GC2</th><th align=\"left\">GC3</th><th align=\"left\">GCall</th><th align=\"left\">GC3s</th></tr></thead><tbody><tr><td align=\"left\">8850</td><td align=\"left\">40.32</td><td align=\"left\">22.59</td><td align=\"left\">36.02</td><td align=\"left\">23.05</td><td align=\"left\">48.67</td><td align=\"left\">43.05</td><td align=\"left\">39.05</td><td align=\"left\">43.59</td><td align=\"left\">36.04</td><td char=\".\" align=\"char\">53.89</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab2\"><label>Table 2</label><caption><p>Codon characteristic parameters of mt coding genes in <italic>H. citrina</italic></p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Gene</th><th align=\"left\">Codon counts</th><th align=\"left\">GC1(%)</th><th align=\"left\">GC2(%)</th><th align=\"left\">GC3(%)</th><th align=\"left\">GCall(%)</th><th align=\"left\">CAI</th><th align=\"left\">CBI</th><th align=\"left\">Fop</th><th align=\"left\">ENC</th></tr></thead><tbody><tr><td align=\"left\"><italic>atp1</italic></td><td align=\"left\">510</td><td align=\"left\">57.45</td><td align=\"left\">42.55</td><td align=\"left\">34.90</td><td align=\"left\">44.97</td><td align=\"left\">0.17</td><td align=\"left\">-0.10</td><td align=\"left\">0.35</td><td align=\"left\">52.75</td></tr><tr><td align=\"left\"><italic>atp4</italic></td><td align=\"left\">195</td><td align=\"left\">44.62</td><td align=\"left\">43.08</td><td align=\"left\">37.95</td><td align=\"left\">41.88</td><td align=\"left\">0.16</td><td align=\"left\">-0.08</td><td align=\"left\">0.36</td><td align=\"left\">59.92</td></tr><tr><td align=\"left\"><italic>atp6</italic></td><td align=\"left\">248</td><td align=\"left\">44.76</td><td align=\"left\">37.90</td><td align=\"left\">31.85</td><td align=\"left\">38.17</td><td align=\"left\">0.15</td><td align=\"left\">-0.18</td><td align=\"left\">0.29</td><td align=\"left\">50.34</td></tr><tr><td align=\"left\"><italic>atp8</italic></td><td align=\"left\">281</td><td align=\"left\">45.20</td><td align=\"left\">33.45</td><td align=\"left\">43.42</td><td align=\"left\">40.69</td><td align=\"left\">0.17</td><td align=\"left\">0.01</td><td align=\"left\">0.41</td><td align=\"left\">58.37</td></tr><tr><td align=\"left\"><italic>ccmB</italic></td><td align=\"left\">207</td><td align=\"left\">45.41</td><td align=\"left\">43.48</td><td align=\"left\">34.30</td><td align=\"left\">41.06</td><td align=\"left\">0.17</td><td align=\"left\">-0.07</td><td align=\"left\">0.35</td><td align=\"left\">44.91</td></tr><tr><td align=\"left\"><italic>ccmC</italic></td><td align=\"left\">273</td><td align=\"left\">47.25</td><td align=\"left\">48.35</td><td align=\"left\">35.90</td><td align=\"left\">43.83</td><td align=\"left\">0.17</td><td align=\"left\">-0.01</td><td align=\"left\">0.39</td><td align=\"left\">49.88</td></tr><tr><td align=\"left\"><italic>ccmFc</italic></td><td align=\"left\">449</td><td align=\"left\">48.78</td><td align=\"left\">44.77</td><td align=\"left\">42.32</td><td align=\"left\">45.29</td><td align=\"left\">0.14</td><td align=\"left\">-0.10</td><td align=\"left\">0.35</td><td align=\"left\">57.34</td></tr><tr><td align=\"left\"><italic>ccmFn</italic></td><td align=\"left\">614</td><td align=\"left\">50.16</td><td align=\"left\">48.86</td><td align=\"left\">42.02</td><td align=\"left\">47.01</td><td align=\"left\">0.16</td><td align=\"left\">-0.05</td><td align=\"left\">0.38</td><td align=\"left\">57.28</td></tr><tr><td align=\"left\"><italic>cob</italic></td><td align=\"left\">390</td><td align=\"left\">50.26</td><td align=\"left\">41.54</td><td align=\"left\">34.62</td><td align=\"left\">42.14</td><td align=\"left\">0.16</td><td align=\"left\">-0.12</td><td align=\"left\">0.32</td><td align=\"left\">56.44</td></tr><tr><td align=\"left\"><italic>cox1</italic></td><td align=\"left\">528</td><td align=\"left\">48.30</td><td align=\"left\">45.45</td><td align=\"left\">36.74</td><td align=\"left\">43.50</td><td align=\"left\">0.19</td><td align=\"left\">-0.03</td><td align=\"left\">0.39</td><td align=\"left\">53.89</td></tr><tr><td align=\"left\"><italic>cox2</italic></td><td align=\"left\">273</td><td align=\"left\">52.01</td><td align=\"left\">39.56</td><td align=\"left\">33.33</td><td align=\"left\">41.64</td><td align=\"left\">0.20</td><td align=\"left\">-0.07</td><td align=\"left\">0.36</td><td align=\"left\">49.39</td></tr><tr><td align=\"left\"><italic>cox3</italic></td><td align=\"left\">266</td><td align=\"left\">52.26</td><td align=\"left\">45.11</td><td align=\"left\">35.34</td><td align=\"left\">44.24</td><td align=\"left\">0.20</td><td align=\"left\">-0.04</td><td align=\"left\">0.38</td><td align=\"left\">56.00</td></tr><tr><td align=\"left\"><italic>matR</italic></td><td align=\"left\">673</td><td align=\"left\">53.94</td><td align=\"left\">43.39</td><td align=\"left\">56.76</td><td align=\"left\">51.36</td><td align=\"left\">0.15</td><td align=\"left\">0.02</td><td align=\"left\">0.42</td><td align=\"left\">57.49</td></tr><tr><td align=\"left\"><italic>nad2</italic></td><td align=\"left\">182</td><td align=\"left\">40.66</td><td align=\"left\">38.46</td><td align=\"left\">36.81</td><td align=\"left\">38.64</td><td align=\"left\">0.16</td><td align=\"left\">-0.16</td><td align=\"left\">0.30</td><td align=\"left\">54.26</td></tr><tr><td align=\"left\"><italic>nad3</italic></td><td align=\"left\">119</td><td align=\"left\">44.54</td><td align=\"left\">46.22</td><td align=\"left\">36.97</td><td align=\"left\">42.58</td><td align=\"left\">0.19</td><td align=\"left\">-0.11</td><td align=\"left\">0.33</td><td align=\"left\">48.69</td></tr><tr><td align=\"left\"><italic>nad4</italic></td><td align=\"left\">496</td><td align=\"left\">46.57</td><td align=\"left\">43.95</td><td align=\"left\">36.69</td><td align=\"left\">42.41</td><td align=\"left\">0.16</td><td align=\"left\">-0.06</td><td align=\"left\">0.36</td><td align=\"left\">52.72</td></tr><tr><td align=\"left\"><italic>nad5</italic></td><td align=\"left\">482</td><td align=\"left\">44.40</td><td align=\"left\">46.06</td><td align=\"left\">38.38</td><td align=\"left\">42.95</td><td align=\"left\">0.17</td><td align=\"left\">-0.11</td><td align=\"left\">0.35</td><td align=\"left\">56.49</td></tr><tr><td align=\"left\"><italic>nad6</italic></td><td align=\"left\">232</td><td align=\"left\">46.55</td><td align=\"left\">41.81</td><td align=\"left\">44.40</td><td align=\"left\">44.25</td><td align=\"left\">0.14</td><td align=\"left\">-0.04</td><td align=\"left\">0.36</td><td align=\"left\">58.91</td></tr><tr><td align=\"left\"><italic>nad7</italic></td><td align=\"left\">395</td><td align=\"left\">56.46</td><td align=\"left\">46.08</td><td align=\"left\">30.89</td><td align=\"left\">44.47</td><td align=\"left\">0.17</td><td align=\"left\">-0.05</td><td align=\"left\">0.36</td><td align=\"left\">49.56</td></tr><tr><td align=\"left\"><italic>nad9</italic></td><td align=\"left\">191</td><td align=\"left\">51.83</td><td align=\"left\">42.41</td><td align=\"left\">32.46</td><td align=\"left\">42.23</td><td align=\"left\">0.21</td><td align=\"left\">-0.06</td><td align=\"left\">0.39</td><td align=\"left\">54.87</td></tr><tr><td align=\"left\"><italic>rpl5</italic></td><td align=\"left\">196</td><td align=\"left\">49.49</td><td align=\"left\">36.22</td><td align=\"left\">41.84</td><td align=\"left\">42.52</td><td align=\"left\">0.17</td><td align=\"left\">-0.09</td><td align=\"left\">0.37</td><td align=\"left\">56.43</td></tr><tr><td align=\"left\"><italic>rps1</italic></td><td align=\"left\">167</td><td align=\"left\">47.90</td><td align=\"left\">38.92</td><td align=\"left\">41.92</td><td align=\"left\">42.91</td><td align=\"left\">0.18</td><td align=\"left\">-0.10</td><td align=\"left\">0.35</td><td align=\"left\">49.21</td></tr><tr><td align=\"left\"><italic>rps2</italic></td><td align=\"left\">232</td><td align=\"left\">42.67</td><td align=\"left\">40.95</td><td align=\"left\">36.21</td><td align=\"left\">39.94</td><td align=\"left\">0.17</td><td align=\"left\">-0.12</td><td align=\"left\">0.35</td><td align=\"left\">55.45</td></tr><tr><td align=\"left\"><italic>rps3</italic></td><td align=\"left\">562</td><td align=\"left\">44.31</td><td align=\"left\">40.21</td><td align=\"left\">43.77</td><td align=\"left\">42.76</td><td align=\"left\">0.15</td><td align=\"left\">-0.07</td><td align=\"left\">0.38</td><td align=\"left\">60.01</td></tr><tr><td align=\"left\"><italic>rps4</italic></td><td align=\"left\">345</td><td align=\"left\">41.74</td><td align=\"left\">40.58</td><td align=\"left\">38.55</td><td align=\"left\">40.29</td><td align=\"left\">0.12</td><td align=\"left\">-0.03</td><td align=\"left\">0.39</td><td align=\"left\">55.18</td></tr><tr><td align=\"left\"><italic>rps12</italic></td><td align=\"left\">126</td><td align=\"left\">56.35</td><td align=\"left\">47.62</td><td align=\"left\">30.16</td><td align=\"left\">44.71</td><td align=\"left\">0.15</td><td align=\"left\">-0.02</td><td align=\"left\">0.40</td><td align=\"left\">55.71</td></tr><tr><td align=\"left\"><italic>rps13</italic></td><td align=\"left\">117</td><td align=\"left\">50.43</td><td align=\"left\">40.17</td><td align=\"left\">29.91</td><td align=\"left\">40.17</td><td align=\"left\">0.16</td><td align=\"left\">-0.12</td><td align=\"left\">0.35</td><td align=\"left\">39.34</td></tr><tr><td align=\"left\"><italic>rps14</italic></td><td align=\"left\">101</td><td align=\"left\">42.57</td><td align=\"left\">46.53</td><td align=\"left\">36.63</td><td align=\"left\">41.91</td><td align=\"left\">0.16</td><td align=\"left\">-0.08</td><td align=\"left\">0.35</td><td align=\"left\">51.37</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab3\"><label>Table 3</label><caption><p>RSCU of genes and the optimal codons of the mitogenome in <italic>H. citrina</italic></p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Amino acid</th><th align=\"left\">Codon</th><th align=\"left\">RSCU _High</th><th align=\"left\">RSCU _Low</th><th align=\"left\">ΔRSCU</th><th align=\"left\">Amino acid</th><th align=\"left\">Codon</th><th align=\"left\">RSCU _High</th><th align=\"left\">RSCU _Low</th><th align=\"left\">ΔRSCU</th></tr></thead><tbody><tr><td align=\"left\" rowspan=\"4\">Ala (A)</td><td align=\"left\">GCA^a</td><td align=\"left\">1.14</td><td align=\"left\">0.84</td><td align=\"left\">0.31</td><td align=\"left\" rowspan=\"4\">Pro (P)</td><td align=\"left\">CCA</td><td align=\"left\">0.84</td><td align=\"left\">1.33</td><td align=\"left\">-0.49</td></tr><tr><td align=\"left\">GCC^a</td><td align=\"left\">1.43</td><td align=\"left\">0.84</td><td align=\"left\">0.59</td><td align=\"left\">CCC</td><td align=\"left\">0.95</td><td align=\"left\">1.00</td><td align=\"left\">-0.05</td></tr><tr><td align=\"left\">GCG</td><td align=\"left\">0</td><td align=\"left\">0.47</td><td align=\"left\">-0.47</td><td align=\"left\">CCG</td><td align=\"left\">0.95</td><td align=\"left\">0.83</td><td align=\"left\">0.11</td></tr><tr><td align=\"left\">GCU</td><td align=\"left\">1.43</td><td align=\"left\">1.86</td><td align=\"left\">-0.43</td><td align=\"left\">CCU^a</td><td align=\"left\">1.26</td><td align=\"left\">0.83</td><td align=\"left\">0.43</td></tr><tr><td align=\"left\" rowspan=\"2\">Cys (C)</td><td align=\"left\">UGC</td><td align=\"left\">0</td><td align=\"left\">0.71</td><td align=\"left\">-0.71</td><td align=\"left\" rowspan=\"2\">Gln (Q)</td><td align=\"left\">CAA^a</td><td align=\"left\">1.63</td><td align=\"left\">1.50</td><td align=\"left\">0.13</td></tr><tr><td align=\"left\">UGU^a</td><td align=\"left\">2.00</td><td align=\"left\">1.29</td><td align=\"left\">0.71</td><td align=\"left\">CAG</td><td align=\"left\">0.38</td><td align=\"left\">0.50</td><td align=\"left\">-0.13</td></tr><tr><td align=\"left\" rowspan=\"2\">Asp (D)</td><td align=\"left\">GAC</td><td align=\"left\">0.44</td><td align=\"left\">0.90</td><td align=\"left\">-0.46</td><td align=\"left\" rowspan=\"6\">Arg (R)</td><td align=\"left\">AGA</td><td align=\"left\">0.60</td><td align=\"left\">1.35</td><td align=\"left\">-0.75</td></tr><tr><td align=\"left\">GAU^a</td><td align=\"left\">1.56</td><td align=\"left\">1.10</td><td align=\"left\">0.46</td><td align=\"left\">AGG</td><td align=\"left\">0.60</td><td align=\"left\">1.01</td><td align=\"left\">-0.41</td></tr><tr><td align=\"left\" rowspan=\"2\">Glu (E)</td><td align=\"left\">GAA^a</td><td align=\"left\">1.68</td><td align=\"left\">1.44</td><td align=\"left\">0.24</td><td align=\"left\">CGA^a</td><td align=\"left\">1.60</td><td align=\"left\">1.08</td><td align=\"left\">0.52</td></tr><tr><td align=\"left\">GAG</td><td align=\"left\">0.32</td><td align=\"left\">0.56</td><td align=\"left\">-0.24</td><td align=\"left\">CGC</td><td align=\"left\">0.40</td><td align=\"left\">0.94</td><td align=\"left\">-0.54</td></tr><tr><td align=\"left\" rowspan=\"2\">Phe (F)</td><td align=\"left\">UUC</td><td align=\"left\">0.48</td><td align=\"left\">0.97</td><td align=\"left\">-0.48</td><td align=\"left\">CGG</td><td align=\"left\">1.00</td><td align=\"left\">0.74</td><td align=\"left\">0.26</td></tr><tr><td align=\"left\">UUU^a</td><td align=\"left\">1.52</td><td align=\"left\">1.03</td><td align=\"left\">0.48</td><td align=\"left\">CGU^a</td><td align=\"left\">1.80</td><td align=\"left\">0.88</td><td align=\"left\">0.92</td></tr><tr><td align=\"left\" rowspan=\"4\">Gly (G)</td><td align=\"left\">GGA^a</td><td align=\"left\">1.48</td><td align=\"left\">1.08</td><td align=\"left\">0.40</td><td align=\"left\" rowspan=\"6\">Ser (S)</td><td align=\"left\">AGC</td><td align=\"left\">0.35</td><td align=\"left\">0.59</td><td align=\"left\">-0.24</td></tr><tr><td align=\"left\">GGC</td><td align=\"left\">0.15</td><td align=\"left\">0.61</td><td align=\"left\">-0.46</td><td align=\"left\">AGU</td><td align=\"left\">1.06</td><td align=\"left\">1.12</td><td align=\"left\">-0.06</td></tr><tr><td align=\"left\">GGG</td><td align=\"left\">0.30</td><td align=\"left\">1.22</td><td align=\"left\">-0.92</td><td align=\"left\">UCA</td><td align=\"left\">0.94</td><td align=\"left\">0.99</td><td align=\"left\">-0.05</td></tr><tr><td align=\"left\">GGU^a</td><td align=\"left\">2.07</td><td align=\"left\">1.08</td><td align=\"left\">0.99</td><td align=\"left\">UCC</td><td align=\"left\">1.18</td><td align=\"left\">1.12</td><td align=\"left\">0.06</td></tr><tr><td align=\"left\" rowspan=\"2\">His (H)</td><td align=\"left\">CAC</td><td align=\"left\">0.36</td><td align=\"left\">0.63</td><td align=\"left\">-0.26</td><td align=\"left\">UCG^a</td><td align=\"left\">1.29</td><td align=\"left\">0.66</td><td align=\"left\">0.63</td></tr><tr><td align=\"left\">CAU^a</td><td align=\"left\">1.64</td><td align=\"left\">1.38</td><td align=\"left\">0.26</td><td align=\"left\">UCU</td><td align=\"left\">1.18</td><td align=\"left\">1.52</td><td align=\"left\">-0.34</td></tr><tr><td align=\"left\" rowspan=\"3\">Ile (I)</td><td align=\"left\">AUA</td><td align=\"left\">0.30</td><td align=\"left\">1.12</td><td align=\"left\">-0.82</td><td align=\"left\" rowspan=\"4\">Thr (T)</td><td align=\"left\">ACA</td><td align=\"left\">1.00</td><td align=\"left\">1.10</td><td align=\"left\">-0.10</td></tr><tr><td align=\"left\">AUC</td><td align=\"left\">0.98</td><td align=\"left\">0.85</td><td align=\"left\">0.13</td><td align=\"left\">ACC</td><td align=\"left\">1.00</td><td align=\"left\">1.10</td><td align=\"left\">-0.10</td></tr><tr><td align=\"left\">AUU^a</td><td align=\"left\">1.73</td><td align=\"left\">1.04</td><td align=\"left\">0.69</td><td align=\"left\">ACG</td><td align=\"left\">0.60</td><td align=\"left\">0.94</td><td align=\"left\">-0.34</td></tr><tr><td align=\"left\" rowspan=\"2\">Lys (K)</td><td align=\"left\">AAA^a</td><td align=\"left\">1.13</td><td align=\"left\">0.95</td><td align=\"left\">0.17</td><td align=\"left\">ACU^a</td><td align=\"left\">1.40</td><td align=\"left\">0.86</td><td align=\"left\">0.54</td></tr><tr><td align=\"left\">AAG</td><td align=\"left\">0.88</td><td align=\"left\">1.05</td><td align=\"left\">-0.17</td><td align=\"left\" rowspan=\"4\">Val (V)</td><td align=\"left\">GUA</td><td align=\"left\">0.95</td><td align=\"left\">1.17</td><td align=\"left\">-0.22</td></tr><tr><td align=\"left\" rowspan=\"6\">Leu (L)</td><td align=\"left\">CUA</td><td align=\"left\">0.68</td><td align=\"left\">0.83</td><td align=\"left\">-0.15</td><td align=\"left\">GUC</td><td align=\"left\">0.76</td><td align=\"left\">1.11</td><td align=\"left\">-0.35</td></tr><tr><td align=\"left\">CUC</td><td align=\"left\">0.41</td><td align=\"left\">0.71</td><td align=\"left\">-0.30</td><td align=\"left\">GUG</td><td align=\"left\">0.19</td><td align=\"left\">0.92</td><td align=\"left\">-0.73</td></tr><tr><td align=\"left\">CUG</td><td align=\"left\">0.14</td><td align=\"left\">0.53</td><td align=\"left\">-0.40</td><td align=\"left\">GUU^a</td><td align=\"left\">2.10</td><td align=\"left\">0.80</td><td align=\"left\">1.30</td></tr><tr><td align=\"left\">CUU^a</td><td align=\"left\">1.23</td><td align=\"left\">1.13</td><td align=\"left\">0.10</td><td align=\"left\">Trp (W)</td><td align=\"left\">UGG</td><td align=\"left\">1.00</td><td align=\"left\">1.00</td><td align=\"left\">0</td></tr><tr><td align=\"left\">UUA^a</td><td align=\"left\">2.05</td><td align=\"left\">1.60</td><td align=\"left\">0.44</td><td align=\"left\" rowspan=\"2\">Tyr (Y)</td><td align=\"left\">UAC</td><td align=\"left\">0.13</td><td align=\"left\">0.64</td><td align=\"left\">-0.51</td></tr><tr><td align=\"left\">UUG^a</td><td align=\"left\">1.50</td><td align=\"left\">1.19</td><td align=\"left\">0.31</td><td align=\"left\">UAU^a</td><td align=\"left\">1.87</td><td align=\"left\">1.36</td><td align=\"left\">0.51</td></tr><tr><td align=\"left\" rowspan=\"2\">Asn (N)</td><td align=\"left\">AAC</td><td align=\"left\">0.89</td><td align=\"left\">0.54</td><td align=\"left\">0.35</td><td align=\"left\">Met (M)</td><td align=\"left\">AUG</td><td align=\"left\">1.00</td><td align=\"left\">1.00</td><td align=\"left\">0</td></tr><tr><td align=\"left\">AAU</td><td align=\"left\">1.11</td><td align=\"left\">1.46</td><td align=\"left\">-0.35</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/></tr></tbody></table></table-wrap>" ]

[ "<inline-formula id=\"IEq1\"><alternatives><tex-math id=\"M1\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\text{ENC}_{\\text{expected}}=2+\\text{GC3s}+\\frac{29}{\\text{GC3s}+\\left(1-\\text{GC3s}\\right)^2}$$\\end{document}</tex-math><mml:math id=\"M2\"><mml:mrow><mml:msub><mml:mtext>ENC</mml:mtext><mml:mtext>expected</mml:mtext></mml:msub><mml:mo>=</mml:mo><mml:mn>2</mml:mn><mml:mo>+</mml:mo><mml:mtext>GC3s</mml:mtext><mml:mo>+</mml:mo><mml:mfrac><mml:mn>29</mml:mn><mml:mrow><mml:mtext>GC3s</mml:mtext><mml:mo>+</mml:mo><mml:msup><mml:mfenced close=\")\" open=\"(\"><mml:mn>1</mml:mn><mml:mo>-</mml:mo><mml:mtext>GC3s</mml:mtext></mml:mfenced><mml:mn>2</mml:mn></mml:msup></mml:mrow></mml:mfrac></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq3\"><alternatives><tex-math id=\"M3\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\text{ENC}_{\\text{ratio}}=\\frac{\\text{ENC}_{\\text{expected}}-\\text{ENC}_{\\text{actual}}}{\\text{ENC}_{\\text{expected}}}$$\\end{document}</tex-math><mml:math id=\"M4\"><mml:mrow><mml:msub><mml:mtext>ENC</mml:mtext><mml:mtext>ratio</mml:mtext></mml:msub><mml:mo>=</mml:mo><mml:mfrac><mml:mrow><mml:msub><mml:mtext>ENC</mml:mtext><mml:mtext>expected</mml:mtext></mml:msub><mml:mo>-</mml:mo><mml:msub><mml:mtext>ENC</mml:mtext><mml:mtext>actual</mml:mtext></mml:msub></mml:mrow><mml:msub><mml:mtext>ENC</mml:mtext><mml:mtext>expected</mml:mtext></mml:msub></mml:mfrac></mml:mrow></mml:math></alternatives></inline-formula>" ]

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[ "<table-wrap-foot><p>Optimal codons are presented with^a</p></table-wrap-foot>", "<fn-group><fn><p><bold>Publisher’s Note</bold></p><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn><fn><p>Kun Zhang and Yiheng Wang contributed equally to this work.</p></fn></fn-group>" ]

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{ "acronym": [ "A", "C", "CAI", "CBI", "CDS", "Cp", "CUB", "ENC", "Fop", "G", "GCall", "GC1, GC2, and GC3", "GC12", "GC3s", "Mt", "NCBI", "PCGs", "PR2", "RSCU", "T", "T3s, A3s, C3s, and G3s", "U" ], "definition": [ "Adenine", "Cytosine", "Codon adaptation index", "Codon bias index", "Coding sequences", "Chloroplast", "Codon usage bias", "Effective number of codons", "Frequency of optimal codons", "Guanine", "The overall GC content of the genome", "The GC content at each codon position", "The average value of GC1 and GC2 for each gene", "The average GC content at the third position of synonymous codons", "Mitochondrial", "National Center for Biotechnology Information", "Protein coding genes", "Parity rule 2", "Relative synonymous codon usage", "Thymine", "The frequency of T, A, C, and G at the third position of synonymous codons", "Uracil" ] }

[ "<title>Background</title>", "<p id=\"Par10\">Prostein (P501S), also termed solute carrier family 45 member 3 (SLC45A3) is a protein composed of 553 amino acids which is coded by the SLC45A3 gene at chromosome 1q32.1 [##REF##11245466##1##]. Its function is not well known but some data suggest a role in transmembrane transport of sugars [##REF##25164149##2##]. Prostein is predominantly expressed in the prostate, where its expression is androgen regulated [##REF##15176054##3##]. Prostein is the second most common 5′ partner gene in ETS Transcription Factor ERG (ERG) rearrangements in prostate cancer after Transmembrane Serine Protease 2 (TMPRSS2) [##REF##29088771##4##, ##REF##20118910##5##], another constitutively expressed androgen regulated gene in prostate epithelium [##REF##10485450##6##]. In the brain, prostein plays a role in regulating the lipid metabolism of oligodendrocytes and myelin [##REF##22521588##7##].</p>", "<p id=\"Par11\">A high level of prostein expression is a common feature in prostate cancer. Amanda et al. [##REF##26778368##8##] described prostein positivity in 97% of 59 analyzed prostate cancers. Queisser et al. [##REF##24925052##9##] found prostein expression in 96% of 79 prostate cancers. Sheridan et al. [##REF##17721190##10##] reported prostein positivity in 99% of 53 metastatic prostatic carcinomas. Based on these data, prostein immunohistochemistry (IHC) has been suggested as a diagnostic tool for the distinction of prostatic adenocarcinoma from other tumors. This notion is also supported by data describing high specificity of prostein expression for prostate cancer. For example, Garudadri et al. [##REF##32108621##11##] described a 100% specificity of prostein IHC in a study on 100 prostatic carcinomas and 60 normal and cancerous extra-prostatic tissues. In an analysis of 600 tumors from 20 sites of origin, Mochizuki et al. [##REF##30061246##12##] found prostein positivity in 30 of 30 prostate adenocarcinomas but in only one tumor each of 30 hepatocellular carcinomas and of 30 invasive breast cancers of no special type (NST). Kalos et al. [##REF##15176054##3##] did not detect prostein staining in 3,454 samples of more than 130 tumor entities and subentities while 94% of 60 analyzed prostate cancers showed prostein positivity. Osunkoya et al. [##REF##17868775##13##] did not find prostein positivity in any of 9 colorectal adenocarcinomas infiltrating the prostate. Srinivasan et al. [##REF##21777423##14##] did not see any prostein positivity in 132 urothelial carcinomas. However, Arnesen et al. [##REF##31498176##15##] found prostein positivity in 11 of 14 Sertoli-Leydig or Leydig cell tumors of the testis and ovary and Chuang et al. [##REF##17667550##16##] reported prostein positivity in 7 of 41 invasive urothelial carcinomas.</p>", "<p id=\"Par12\">To further corroborate the potential diagnostic utility of prostein IHC, a comprehensive survey of prostein immunostaining in an even broader range of tumor types is desirable. We therefore evaluated prostein expression in more than 19,000 tumor tissue samples from 152 different tumor types and subtypes as well as 76 different non-neoplastic tissue types by IHC in a tissue microarray (TMA) format.</p>" ]

[ "<title>Materials and methods</title>", "<title>Tissue microarrays (TMAs)</title>", "<p id=\"Par13\">Our normal tissue TMA was composed of 8 samples from 8 different donors for each of 76 different normal tissue types (608 samples on one slide). The cancer TMAs contained a total of 19,202 primary tumors from 152 tumor types and subtypes. The composition of both normal and cancer TMAs is described in detail in the “<xref rid=\"Sec6\" ref-type=\"sec\">Results</xref>” section. Clinico-pathological data including pathological tumor stage (pT), grade, lymph node status (pN), lymphatic vessel (L) and blood vessel (V) infiltration were available for 327 gastric, 2,139 breast, and 2,351 colorectal carcinomas. All samples were from the archives of the Institutes of Pathology, University Hospital of Hamburg, Germany, the Institute of Pathology, Clinical Center Osnabrueck, Germany, and Department of Pathology, Academic Hospital Fuerth, Germany. Tissues were fixed in 4% buffered formalin and then embedded in paraffin. TMA tissue spot diameter was 0.6 mm. The use of archived remnants of diagnostic tissues for manufacturing of TMAs and their analysis for research purposes as well as patient data analysis has been approved by local laws (HmbKHG, § 12) and by the local ethics committee (Ethics commission Hamburg, WF-049/09). All work has been carried out in compliance with the Helsinki Declaration.</p>", "<title>Immunohistochemistry</title>", "<p id=\"Par14\">Freshly cut TMA sections were immunostained on one day and in one experiment. Slides were deparaffinized with xylol, rehydrated through a graded alcohol series and exposed to heat-induced antigen retrieval for 5 min in an autoclave at 121 °C in pH 9.0 DakoTarget Retrieval Solution™ (Agilent, CA, USA; #S2367). Endogenous peroxidase activity was blocked with Dako Peroxidase Blocking Solution™ (Agilent, CA, USA; #52,023) for 10 min. Primary antibody specific for prostein (rabbit recombinant monoclonal, MSVA-460R, MS Validated Antibodies, Hamburg, Germany; #5241-460R) was applied at 37 °C for 60 min at a dilution of 1:150. For the purpose of antibody validation, the normal tissue TMA was also analyzed by the rabbit recombinant monoclonal prostein antibody EPR4795(2) (Abcam, Cambridge, UK; #ab137065) at a dilution of 1:150 and an otherwise identical protocol. Bound antibody was then visualized using the EnVision Kit™ (Agilent, CA, USA; #K5007) according to the manufacturer’s directions. The sections were counterstained with haemalaun. For normal tissues, the staining intensity of positive cells was semi-quantitively recorded (+, ++, +++). For tumor tissues, the percentage of prostein positive neoplastic cells was estimated, and the staining intensity was semi-quantitatively recorded (0, 1+, 2+, 3+). For statistical analyses, the staining results were categorized into four groups. Tumors without any staining were considered negative. Tumors with 1 + staining intensity in ≤ 70% of tumor cells or 2 + intensity in ≤ 30% of tumor cells were considered weakly positive. Tumors with 1 + staining intensity in &gt; 70% of tumor cells, 2 + intensity in 31-70%, or 3 + intensity in ≤ 30% of tumor cells were considered moderately positive. Tumors with 2 + intensity in &gt; 70% or 3 + intensity in &gt; 30% of tumor cells werde considered strongly positive.</p>", "<title>Statistics</title>", "<p id=\"Par15\">Statistical calculations were performed with JMP 16 software (SAS Institute Inc., NC, USA). Contingency tables and the chi²-test were performed to search for associations between prostein immunostaining and tumor phenotype.</p>" ]

[ "<title>Results</title>", "<title>Technical issues</title>", "<p id=\"Par16\">A total of 17,146 (89.3%) of 19,202 tumor samples were interpretable in our TMA analysis. Non-interpretable samples demonstrated lack of unequivocal tumor cells or loss of the tissue spot during technical procedures. A sufficient number of samples (≥ 4) of each normal tissue type was evaluable.</p>", "<title>Prostein in normal tissues</title>", "<p id=\"Par17\">Prostein staining was always granular, cytoplasmic and predominantly perinuclear (“endoplasmatic reticulum pattern”). The staining was particularly strong in acinar cells of the prostate and occurred at lesser intensity in surface epithelial cells of the stomach, in goblet cells of the respiratory epithelium of the lung and (weaker) in bronchial glands, as well as in a subset of epithelial cells of the adenohypophysis. A weak prostein staining was also seen in few colorectal epithelial cells (not in all samples) and in a subset of pancreatic islet cells. A perinuclear granular cytoplasmic prostein positivity also occurred in a small fraction of (monocytic) cells in the spleen and in few cells of lymph nodes. In the brain, some glia cells showed a perinuclear granular cytoplasmic prostein staining. Representative images are shown in Fig. ##FIG##0##1##. All these findings were seen by both antibodies, MSVA-460R and EPR4795(2). An additional cytoplasmic staining in the placenta and in testicular cells of the spermatogenesis was only seen by EPR4795(2) (Supplementary Fig. ##SUPPL##0##1##) and therefore considered an antibody-specific cross-reactivity of EPR4795(2). Prostein immunostaning was absent in skeletal muscle, heart muscle, smooth muscle, myometrium of the uterus, corpus spongiosum of the penis, ovarian stroma, fat, skin (including hair follicles and sebaceous glands), oral mucosa of the lip, surface epithelium of the oral cavity and the tonsil, transitional mucosa of the anal canal, ectocervix, squamous epithelium of the esophagus, urothelium of the renal pelvis and urinary bladder, decidua, placenta, thymus, tonsil, gall bladder, liver, parotid gland, submandibular gland, sublingual gland, duodenum, small intestine, appendix, colorectum, kidney, seminal vesicle, testis, epididymis, breast, endocervix, endometrium, fallopian tube, adrenal gland, parathyroid gland, and the neurohypophysis.</p>", "<title>Prostein in cancer tissues</title>", "<p id=\"Par18\">Similarly, as in normal tissues, prostein immunostaining was typically cytoplasmic, granular and perinuclear in tumors. Prostein positivity, and especially a strong prostein staining was predominantly seen in prostatic adenocarcinomas. 93% of primary prostate cancers and 63% of recurrent prostate cancers showed a strong prostein immunostaining while 98% of primary prostate cancers and 94% of recurrent prostate cancers showed at least a weak positivity. Prostein staining was absent in all 18 small cell neuroendocrine carcinomas of the prostate. Prostein positivity - mostly at a lower level - was also detectable in 1,204 (7.2%) of the 16,709 analyzable extra-prostatic tumors. Of these, 922 (5.5%) showed a weak, 239 (1.4%) a moderate, and only 43 (0.3%) a strong immunostaining. Overall, 50 (34.0%) of 157 extra-prostatic tumor categories showed detectable prostein expression with 12 (8.2%) tumor categories including at least one strongly positive tumor (Table ##TAB##0##1##). Representative images of prostein positive tumors are shown in Fig. ##FIG##1##2##. Extra-prostatic tumors with highest rate of prostein positivity included different subtypes of salivary gland tumors (7.6-44.4%), neuroendocrine neoplasms (15.8-44.4%), adenocarcinomas of the gastrointestinal tract (7.3-14.8%), and biliopancreatic adenocarcinomas (3.6-38.7%), hepatocellular carcinomas (8.1%), as well as adenocarcinomas of other organs of origin (up to 21%). A graphical representation of a ranking order of prostein positive and strongly positive cancers is given in Fig. ##FIG##2##3##. A comparison between prostein expression and tumor phenotype is shown in Table ##TAB##1##2##. Detectable prostein expression was linked to high grade (<italic>p</italic> = 0.0105), HER2 positivity (<italic>p</italic> = 0.0312), and estrogen receptor negativity (<italic>p</italic> = 0.0330) in invasive breast carcinomas of no special type (NST), V0 status (<italic>p</italic> = 0.0139), right sided tumor location (<italic>p</italic> = 0.0479), and KRAS mutations (<italic>p</italic> = 0.0133) in colorectal cancer, pN0 stage (<italic>p</italic> = 0.0424) in pancreatic ductal adenocarcinoma as well as to microsatellite instability in gastric cancers (<italic>p</italic> = 0.0015).\n</p>" ]

[ "<title>Discussion</title>", "<p id=\"Par19\">Our successful analysis of more than 17,000 tumors provided a comprehensive overview on the patterns of prostein expression in cancer. The predominant expression of prostein in prostate cancer was expected since studies analyzing 9-220 tumor cases had earlier identified prostein positivity in up to 100% of prostate cancers [##REF##29088771##4##, ##REF##32108621##11##, ##REF##18234278##17##, ##REF##33848377##18##]. Our positivity rate of 100% in Gleason 3 + 3 = 6, 98% in Gleason 4 + 4 = 8 and 97% in Gleason 5 + 5 = 10 prostate cancers is comparable with results from most previous studies [##REF##15176054##3##, ##REF##18844933##19##]. The concept that prostein IHC can be used to corroborate a suspected prostatic origin of a cancer tissue is further supported by the retained prostein expression in at least 80% of prostate cancers that recurred after hormonal therapy [##REF##18844933##19##]. Sheridan et al. [##REF##17721190##10##] had previously identified prostein positivity in 99% of 53 analyzed prostatic cancer metastases. Hernandez-Llodra et al. [##REF##29088771##4##] have previously suggested that the few prostate cancers with reduced or absent prostein expression might harbor SLC45A3:ERG fusions and that these tumors may be characterized by poor prognosis.</p>", "<p id=\"Par20\">The extensive analysis of non-prostatic tumors in this study identified a considerable number of tumor entities that can also express prostein. Although prostein expression was less frequent and often at markedly lower level in these tumors than in prostate cancer, the characteristic staining pattern with a distinct granular, perinuclear cytoplasmic prostein staining was always retained. The most commonly prostein positive tumors included salivary gland tumors, neuroendocrine neoplasms, various categories of gastrointestinal or biliopancreatic adenocarcinomas, hepatocellular carcinomas as well as adenocarcinomas of other organs of origin. All these tumor entities represent diagnostic options in case of a prostein positive tumor mass. It is of note that in some tumor entities, a perinuclear prostein expression was also observed in cells of monocytic origin such as for example in epitheloid cells accompanying lymphomas or in giant cells of tendon sheath tumors or in pilomatricoma. These findings fit with our observation of prostein positive monocytic cells in the spleen and the lymph node. Our data in primary and recurrent prostate cancer suggest sensitivity of 94–98% for the identification of a prostatic cancer origin, although these numbers might represent a slight underestimate because of an overrepresentation of Gleason 4 + 4, 5 + 5 and recurrent prostate cancers in our cohort. Accordingly, the sensitivity of PSAP (96.5%) and PSA (99.8%) were slightly higher in previous studies of our group analyzing large consecutive prostate cancer cohorts including much higher proportions of Gleason 3 + 3 and 3 + 4 cancer than in the current set of tumors. The specificity for the distinction of prostate cancer was somewhat lower for prostein (91.7%) as compared to the 100% for PSAP and PSA (99.7%) observed in these earlier studies [##REF##31534629##20##, ##REF##37892063##21##]. However, the characteristic granular perinuclear staining pattern that can hardly result from staining artefacts is a major strongpoint of prostein IHC which may thus justify the use of prostein antibodies as a part of a diagnostic panel for the identification of a prostatic cancer origin.</p>", "<p id=\"Par21\">The location of the prostein protein in subcellular vesicles in the cytoplasm and co-localization to other compartments, i.e., the endoplasmatic reticulum fits well with the estimated function of prostein as a sucrose transport protein [##REF##25164149##2##, ##REF##28495876##22##]. However, many of the extra-prostatic tumor entities that were most commonly prostein positive were adenocarcinomas or neuroendocrine tumors. As these cell types share a secretory or neurosecretory function it might be speculated that prostein may have also a general role in cell secretion. The comparison of detectable prostein expression with histopathological and molecular tumor parameters in breast, colon, gastric and pancreatic adenocarcinoma had revealed only few statistically significant associations which do not provide strong evidence for a relevant biological/clinical role of prostein in non-prostatic cancers. It is possible that these findings represent statistical artifacts attributed to the high number of statistical analyses executed in this study.</p>", "<p id=\"Par22\">Considering the large scale of our study, our assay was extensively validated by comparing our IHC findings in normal tissues with data obtained by another independent anti-prostein antibody and RNA data derived from three different publicly accessible databases [##REF##28495876##22##–##REF##25723102##25##]. To ensure an as broad as possible range of proteins to be tested for a possible cross-reactivity, 76 different normal tissues categories were included in this analysis. The validity of our assay was supported by the finding of the highest levels of prostein immunostaining in the prostate, the organ with the highest documented RNA expression level and the finding of prostein positive cell populations in most other organs with documented low level RNA expression such as the stomach, respiratory epithelium, hypophysis, spleen, and the brain. Only RNA expression in the liver could not be corroborated by our assay. That all prostein positive cell types detected by MSVA-460R (islet cells of the pancreas, respiratory epithelium, epithelial cells of the adenohypophysis, surface epithelial cells of the stomach, glia cells in the brain, monocytic cells in the spleen and lymph nodes) were also identified by the independent second antibody EPR4795(2) (Supplementary Fig. ##SUPPL##0##1##) adds further evidence for the validity of our assay. Additional stainings of the placenta and the testis which were only observed by EPR4795(2) were considered antibody specific cross-reactivities of this antibody and suggest that this antibody is less appropriate for prostein assessment.</p>" ]

[ "<title>Conclusion</title>", "<p id=\"Par23\">Our data provide a comprehensive overview on prostein expression in human cancers. The data show that prostein is a highly sensitive prostate cancer marker with positive results in at least 98% of primary prostate cancers. Because prostein can also be expressed in various other tumor entities, the classification of a tumor mass as a prostate cancer should not be made based on prostein positivity alone.</p>" ]

[ "<title>Background</title>", "<p id=\"Par1\">Prostein (P501S), also termed solute carrier family 45 member 3 (SLC45A3) is an androgen regulated protein which is preferentially expressed in prostate epithelial cells. Because of its frequent expression in prostate cancer, prostein was suggested a diagnostic prostate cancer marker.</p>", "<title>Methods</title>", "<p id=\"Par2\">In order to comprehensively assess the diagnostic utility of prostein immunohistochemistry, a tissue microarray containing 19,202 samples from 152 different tumor types and subtypes as well as 608 samples of 76 different normal tissue types was analyzed by immunohistochemistry.</p>", "<title>Results</title>", "<p id=\"Par3\">Prostein immunostaining was typically cytoplasmic, granular and perinuclear. Prostein positivity was seen in 96.7% of 419 prostate cancers including 78.3% with strong staining. In 16,709 extra-prostatic tumors, prostein positivity was observed in 7.2% of all cases but only 0.3% had a strong staining. Overall, 50 different extra-prostatic tumor categories were prostein positive, 12 of which included at least one strongly positive case. Extra-prostatic tumors with highest rates of prostein positivity included different subtypes of salivary gland tumors (7.6-44.4%), neuroendocrine neoplasms (15.8-44.4%), adenocarcinomas of the gastrointestinal tract (7.3-14.8%), biliopancreatic adenocarcinomas (3.6-38.7%), hepatocellular carcinomas (8.1%), and adenocarcinomas of other organs (up to 21%).</p>", "<title>Conclusions</title>", "<p id=\"Par4\">Our data provide a comprehensive overview on prostein expression in human cancers. Prostein is a highly sensitive prostate cancer marker occurring in &gt; 96% of prostate cancers. Because prostein can also be expressed in various other tumor entities, classifying of a tumor mass as a prostate cancer should not be based on prostein positivity alone.</p>", "<title>Supplementary Information</title>", "<p>The online version contains supplementary material available at 10.1186/s13000-023-01434-5.</p>", "<title>Keywords</title>", "<p>Open Access funding enabled and organized by Projekt DEAL.</p>" ]

[ "<title>Supplementary Information</title>", "<p>\n</p>" ]

[ "<title>Acknowledgements</title>", "<p>We are grateful to Laura Behm, Inge Brandt, Maren Eisenberg, and Sünje Seekamp for excellent technical assistance.</p>", "<title>Authors’ contributions</title>", "<p>FV, SK, CB, RS, MK, GS: contributed to conception, design, data collection, data analysis and manuscript writing.FV, SW, MF, AM, FB, AML, DP, AH, ML, FL, VR, DH, CF, KM, CB, PL, SS, DD, AHM, TK, TSC, FJ, NG, EB, and SM: participated in pathology data analysis, data interpretation, and collection of samplesRS, MK, CHM: data analysisSK, RS, GS: study supervisionAll authors agree to be accountable for the content of the work.</p>", "<title>Funding</title>", "<p>Open Access funding enabled and organized by Projekt DEAL.</p>", "<title>Availability of data and materials</title>", "<p>All data generated or analyzed during this study are included in this published article.</p>", "<title>Declarations</title>", "<title>Ethics approval and consent to participate</title>", "<p id=\"Par24\">The use of archived remnants of diagnostic tissues for manufacturing of TMAs and their analysis for research purposes as well as patient data analysis has been approved by local laws (HmbKHG, § 12) and by the local ethics committee (Ethics commission Hamburg, WF-049/09). All work has been carried out in compliance with the Helsinki Declaration.</p>", "<title>Consent for publication</title>", "<p id=\"Par25\">Not required.</p>", "<title>Competing interests</title>", "<p id=\"Par26\">The rabbit recombinant prostein-antibody, clone MSVA-460R was provided from MS Validated Antibodies GmbH (owned by a family member of GS).</p>" ]

[ "<fig id=\"Fig1\"><label>Fig. 1</label><caption><p>Prostein immunostaining of normal tissues. Prostein staining was always granular, cytoplasmic and predominantly perinuclear (“endoplasmatic reticulum pattern”). The panels show a particularly strong prostein staining of acinar cells of the prostate (<bold>A</bold>) while the staining is less intense in surface epithelium of the stomach (<bold>B</bold>). An even weaker prostein positivity (not always involving all samples and all cells) can also be seen in colorectal epithelium (<bold>C</bold>), pancreatic islet cells (<bold>D</bold>), epithelial cells of the adenohypophysis (<bold>E</bold>), respiratory epithelium of the lung (<bold>F</bold>), and in glia cells of the brain (<bold>G</bold>). An intense perinuclear prostein staining also occurs in a subset of monocytic cells of the spleen (<bold>H</bold>)</p></caption></fig>", "<fig id=\"Fig2\"><label>Fig. 2</label><caption><p>Prostein immunostaining in cancer. Prostein staining is usually granular, cytoplasmic and predominantly perinuclear (“endoplasmatic reticulum pattern”). The panels show a particularly strong prostein positivity in a Gleason 3 + 3 = 6 carcinoma (<bold>A</bold>) and a recurrent Gleason 5 + 5 = 10 carcinoma of the prostate (<bold>B</bold>). Prostein staining of tumor cells is less intense but still significant in samples of mucoepidermoid carcinoma of a salivary gland (<bold>C</bold>), neuroendocrine tumor of the lung (<bold>D</bold>), adenocarcinoma of the colon (<bold>E</bold>), and a muscle-invasive urothelial carcinoma of the urinary bladder (<bold>F</bold>). A distinct staining of giant cells is seen in samples of a giant cell tumor of the tendon sheet (<bold>G</bold>) and a pilomatrixoma of the skin (<bold>H</bold>)</p></caption></fig>", "<fig id=\"Fig3\"><label>Fig. 3</label><caption><p>Ranking order of prostein immunostaining in tumors. Both the percentage of positive cases (blue dots) and the percentage of strongly positive cases (orange dots) are shown</p></caption></fig>" ]

[ "<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Prostein immunostaining in human tumors</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\"/><th align=\"left\"/><th align=\"left\"/><th align=\"left\" colspan=\"5\">Prostein immunostaining result</th></tr><tr><th align=\"left\"/><th align=\"left\">Tumor entity</th><th align=\"left\">on TMA (n)</th><th align=\"left\">analyzable (n)</th><th align=\"left\">negative (%)</th><th align=\"left\">weak (%)</th><th align=\"left\">moderate (%)</th><th align=\"left\">strong (%)</th></tr></thead><tbody><tr><td align=\"left\" rowspan=\"7\">Tumors of the skin</td><td align=\"left\">Pilomatricoma</td><td align=\"left\">35</td><td align=\"left\">35</td><td align=\"left\">94.3</td><td align=\"left\">2.9</td><td align=\"left\">2.9</td><td align=\"left\">0.0</td></tr><tr><td align=\"left\">Basal cell carcinoma</td><td align=\"left\">89</td><td align=\"left\">58</td><td align=\"left\">100.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td></tr><tr><td align=\"left\">Benign nevus</td><td align=\"left\">29</td><td align=\"left\">25</td><td align=\"left\">100.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td></tr><tr><td align=\"left\">Squamous cell carcinoma of the skin</td><td align=\"left\">145</td><td align=\"left\">129</td><td align=\"left\">99.2</td><td align=\"left\">0.8</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td></tr><tr><td align=\"left\">Malignant melanoma</td><td align=\"left\">65</td><td align=\"left\">61</td><td align=\"left\">100.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td></tr><tr><td align=\"left\">Malignant melanoma lymph node metastasis</td><td align=\"left\">86</td><td align=\"left\">73</td><td align=\"left\">100.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td></tr><tr><td align=\"left\">Merkel cell carcinoma</td><td align=\"left\">48</td><td align=\"left\">48</td><td align=\"left\">100.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td></tr><tr><td align=\"left\" rowspan=\"19\">Tumors of the head and neck</td><td align=\"left\">Squamous cell carcinoma of the larynx</td><td align=\"left\">109</td><td align=\"left\">96</td><td align=\"left\">100.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td></tr><tr><td align=\"left\">Squamous cell carcinoma of the pharynx</td><td align=\"left\">60</td><td align=\"left\">51</td><td align=\"left\">96.1</td><td align=\"left\">3.9</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td></tr><tr><td align=\"left\">Oral squamous cell carcinoma (floor of the mouth)</td><td align=\"left\">130</td><td align=\"left\">115</td><td align=\"left\">100.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td></tr><tr><td align=\"left\">Pleomorphic adenoma of the parotid gland</td><td align=\"left\">50</td><td align=\"left\">48</td><td align=\"left\">100.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td></tr><tr><td align=\"left\">Warthin tumor of the parotid gland</td><td align=\"left\">104</td><td align=\"left\">100</td><td align=\"left\">100.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td></tr><tr><td align=\"left\">Adenocarcinoma, NOS (Papillary Cystadenocarcinoma)</td><td align=\"left\">14</td><td align=\"left\">10</td><td align=\"left\">80.0</td><td align=\"left\">10.0</td><td align=\"left\">10.0</td><td align=\"left\">0.0</td></tr><tr><td align=\"left\">Salivary duct carcinoma</td><td align=\"left\">15</td><td align=\"left\">12</td><td align=\"left\">100.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td></tr><tr><td align=\"left\">Acinic cell carcinoma of the salivary gland</td><td align=\"left\">181</td><td align=\"left\">144</td><td align=\"left\">55.6</td><td align=\"left\">22.2</td><td align=\"left\">18.1</td><td align=\"left\">4.2</td></tr><tr><td align=\"left\">Adenocarcinoma NOS of the salivary gland</td><td align=\"left\">109</td><td align=\"left\">85</td><td align=\"left\">90.6</td><td align=\"left\">3.5</td><td align=\"left\">4.7</td><td align=\"left\">1.2</td></tr><tr><td align=\"left\">Adenoid cystic carcinoma of the salivary gland</td><td align=\"left\">180</td><td align=\"left\">113</td><td align=\"left\">100.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td></tr><tr><td align=\"left\">Basal cell adenocarcinoma of the salivary gland</td><td align=\"left\">25</td><td align=\"left\">23</td><td align=\"left\">100.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td></tr><tr><td align=\"left\">Basal cell adenoma of the salivary gland</td><td align=\"left\">101</td><td align=\"left\">85</td><td align=\"left\">100.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td></tr><tr><td align=\"left\">Epithelial-myoepithelial carcinoma of the salivary gland</td><td align=\"left\">53</td><td align=\"left\">51</td><td align=\"left\">100.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td></tr><tr><td align=\"left\">Mucoepidermoid carcinoma of the salivary gland</td><td align=\"left\">343</td><td align=\"left\">291</td><td align=\"left\">92.4</td><td align=\"left\">3.4</td><td align=\"left\">4.1</td><td align=\"left\">0.0</td></tr><tr><td align=\"left\">Myoepithelial carcinoma of the salivary gland</td><td align=\"left\">21</td><td align=\"left\">18</td><td align=\"left\">100.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td></tr><tr><td align=\"left\">Myoepithelioma of the salivary gland</td><td align=\"left\">11</td><td align=\"left\">9</td><td align=\"left\">100.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td></tr><tr><td align=\"left\">Oncocytic carcinoma of the salivary gland</td><td align=\"left\">12</td><td align=\"left\">12</td><td align=\"left\">100.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td></tr><tr><td align=\"left\">Polymorphous adenocarcinoma, low grade, of the salivary gland</td><td align=\"left\">41</td><td align=\"left\">27</td><td align=\"left\">100.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td></tr><tr><td align=\"left\">Pleomorphic adenoma of the salivary gland</td><td align=\"left\">53</td><td align=\"left\">40</td><td align=\"left\">100.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td></tr><tr><td align=\"left\" rowspan=\"7\">Tumors of the lung, pleura and thymus</td><td align=\"left\">Adenocarcinoma of the lung</td><td align=\"left\">196</td><td align=\"left\">187</td><td align=\"left\">95.7</td><td align=\"left\">2.1</td><td align=\"left\">0.5</td><td align=\"left\">1.6</td></tr><tr><td align=\"left\">Squamous cell carcinoma of the lung</td><td align=\"left\">80</td><td align=\"left\">71</td><td align=\"left\">100.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td></tr><tr><td align=\"left\">Small cell carcinoma of the lung</td><td align=\"left\">16</td><td align=\"left\">16</td><td align=\"left\">100.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td></tr><tr><td align=\"left\">Mesothelioma, epithelioid</td><td align=\"left\">40</td><td align=\"left\">29</td><td align=\"left\">96.6</td><td align=\"left\">3.4</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td></tr><tr><td align=\"left\">Mesothelioma, biphasic</td><td align=\"left\">77</td><td align=\"left\">71</td><td align=\"left\">98.6</td><td align=\"left\">1.4</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td></tr><tr><td align=\"left\">Thymoma</td><td align=\"left\">29</td><td align=\"left\">28</td><td align=\"left\">100.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td></tr><tr><td align=\"left\">Lung, neuroendocrine tumor (NET)</td><td align=\"left\">29</td><td align=\"left\">27</td><td align=\"left\">55.6</td><td align=\"left\">14.8</td><td align=\"left\">29.6</td><td align=\"left\">0.0</td></tr><tr><td align=\"left\" rowspan=\"20\">Tumors of the female genital tract</td><td align=\"left\">Squamous cell carcinoma of the vagina</td><td align=\"left\">78</td><td align=\"left\">65</td><td align=\"left\">100.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td></tr><tr><td align=\"left\">Squamous cell carcinoma of the vulva</td><td align=\"left\">157</td><td align=\"left\">141</td><td align=\"left\">100.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td></tr><tr><td align=\"left\">Squamous cell carcinoma of the cervix</td><td align=\"left\">136</td><td align=\"left\">126</td><td align=\"left\">100.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td></tr><tr><td align=\"left\">Adenocarcinoma of the cervix</td><td align=\"left\">23</td><td align=\"left\">20</td><td align=\"left\">90.0</td><td align=\"left\">10.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td></tr><tr><td align=\"left\">Endometrioid endometrial carcinoma</td><td align=\"left\">338</td><td align=\"left\">272</td><td align=\"left\">96.7</td><td align=\"left\">2.6</td><td align=\"left\">0.4</td><td align=\"left\">0.4</td></tr><tr><td align=\"left\">Endometrial serous carcinoma</td><td align=\"left\">86</td><td align=\"left\">62</td><td align=\"left\">95.2</td><td align=\"left\">3.2</td><td align=\"left\">0.0</td><td align=\"left\">1.6</td></tr><tr><td align=\"left\">Carcinosarcoma of the uterus</td><td align=\"left\">57</td><td align=\"left\">47</td><td align=\"left\">97.9</td><td align=\"left\">2.1</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td></tr><tr><td align=\"left\">Endometrial carcinoma, high grade, G3</td><td align=\"left\">13</td><td align=\"left\">10</td><td align=\"left\">100.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td></tr><tr><td align=\"left\">Endometrial clear cell carcinoma</td><td align=\"left\">9</td><td align=\"left\">5</td><td align=\"left\">100.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td></tr><tr><td align=\"left\">Endometrioid carcinoma of the ovary</td><td align=\"left\">130</td><td align=\"left\">111</td><td align=\"left\">96.4</td><td align=\"left\">3.6</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td></tr><tr><td align=\"left\">Serous carcinoma of the ovary</td><td align=\"left\">580</td><td align=\"left\">540</td><td align=\"left\">98.3</td><td align=\"left\">1.5</td><td align=\"left\">0.2</td><td align=\"left\">0.0</td></tr><tr><td align=\"left\">Mucinous carcinoma of the ovary</td><td align=\"left\">101</td><td align=\"left\">86</td><td align=\"left\">73.3</td><td align=\"left\">12.8</td><td align=\"left\">14.0</td><td align=\"left\">0.0</td></tr><tr><td align=\"left\">Clear cell carcinoma of the ovary</td><td align=\"left\">51</td><td align=\"left\">51</td><td align=\"left\">98.0</td><td align=\"left\">2.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td></tr><tr><td align=\"left\">Carcinosarcoma of the ovary</td><td align=\"left\">47</td><td align=\"left\">46</td><td align=\"left\">100.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td></tr><tr><td align=\"left\">Granulosa cell tumor of the ovary</td><td align=\"left\">44</td><td align=\"left\">38</td><td align=\"left\">100.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td></tr><tr><td align=\"left\">Leydig cell tumor of the ovary</td><td align=\"left\">4</td><td align=\"left\">4</td><td align=\"left\">100.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td></tr><tr><td align=\"left\">Sertoli cell tumor of the ovary</td><td align=\"left\">1</td><td align=\"left\">1</td><td align=\"left\">100.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td></tr><tr><td align=\"left\">Sertoli Leydig cell tumor of the ovary</td><td align=\"left\">3</td><td align=\"left\">3</td><td align=\"left\">100.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td></tr><tr><td align=\"left\">Steroid cell tumor of the ovary</td><td align=\"left\">3</td><td align=\"left\">3</td><td align=\"left\">100.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td></tr><tr><td align=\"left\">Brenner tumor</td><td align=\"left\">41</td><td align=\"left\">41</td><td align=\"left\">100.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td></tr><tr><td align=\"left\" rowspan=\"6\">Tumors of the breast</td><td align=\"left\">Invasive breast carcinoma of no special type</td><td align=\"left\">1764</td><td align=\"left\">1656</td><td align=\"left\">95.5</td><td align=\"left\">3.8</td><td align=\"left\">0.7</td><td align=\"left\">0.1</td></tr><tr><td align=\"left\">Lobular carcinoma of the breast</td><td align=\"left\">363</td><td align=\"left\">336</td><td align=\"left\">97.9</td><td align=\"left\">2.1</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td></tr><tr><td align=\"left\">Medullary carcinoma of the breast</td><td align=\"left\">34</td><td align=\"left\">33</td><td align=\"left\">93.9</td><td align=\"left\">3.0</td><td align=\"left\">0.0</td><td align=\"left\">3.0</td></tr><tr><td align=\"left\">Tubular carcinoma of the breast</td><td align=\"left\">29</td><td align=\"left\">25</td><td align=\"left\">100.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td></tr><tr><td align=\"left\">Mucinous carcinoma of the breast</td><td align=\"left\">65</td><td align=\"left\">52</td><td align=\"left\">98.1</td><td align=\"left\">1.9</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td></tr><tr><td align=\"left\">Phyllodes tumor of the breast</td><td align=\"left\">50</td><td align=\"left\">40</td><td align=\"left\">100.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td></tr><tr><td align=\"left\" rowspan=\"25\">Tumors of the digestive system</td><td align=\"left\">Adenomatous polyp, low-grade dysplasia</td><td align=\"left\">50</td><td align=\"left\">50</td><td align=\"left\">100.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td></tr><tr><td align=\"left\">Adenomatous polyp, high-grade dysplasia</td><td align=\"left\">50</td><td align=\"left\">50</td><td align=\"left\">100.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td></tr><tr><td align=\"left\">Adenocarcinoma of the colon</td><td align=\"left\">2483</td><td align=\"left\">2220</td><td align=\"left\">78.8</td><td align=\"left\">17.7</td><td align=\"left\">2.9</td><td align=\"left\">0.5</td></tr><tr><td align=\"left\">Gastric adenocarcinoma, diffuse type</td><td align=\"left\">215</td><td align=\"left\">192</td><td align=\"left\">92.7</td><td align=\"left\">6.8</td><td align=\"left\">0.5</td><td align=\"left\">0.0</td></tr><tr><td align=\"left\">Gastric adenocarcinoma, intestinal type</td><td align=\"left\">215</td><td align=\"left\">203</td><td align=\"left\">85.2</td><td align=\"left\">10.3</td><td align=\"left\">4.4</td><td align=\"left\">0.0</td></tr><tr><td align=\"left\">Gastric adenocarcinoma, mixed type</td><td align=\"left\">62</td><td align=\"left\">62</td><td align=\"left\">85.5</td><td align=\"left\">12.9</td><td align=\"left\">1.6</td><td align=\"left\">0.0</td></tr><tr><td align=\"left\">Adenocarcinoma of the esophagus</td><td align=\"left\">83</td><td align=\"left\">66</td><td align=\"left\">97.0</td><td align=\"left\">3.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td></tr><tr><td align=\"left\">Squamous cell carcinoma of the esophagus</td><td align=\"left\">76</td><td align=\"left\">59</td><td align=\"left\">100.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td></tr><tr><td align=\"left\">Squamous cell carcinoma of the anal canal</td><td align=\"left\">91</td><td align=\"left\">80</td><td align=\"left\">100.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td></tr><tr><td align=\"left\">Cholangiocarcinoma</td><td align=\"left\">58</td><td align=\"left\">56</td><td align=\"left\">96.4</td><td align=\"left\">3.6</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td></tr><tr><td align=\"left\">Gallbladder adenocarcinoma</td><td align=\"left\">51</td><td align=\"left\">48</td><td align=\"left\">79.2</td><td align=\"left\">12.5</td><td align=\"left\">8.3</td><td align=\"left\">0.0</td></tr><tr><td align=\"left\">Gallbladder Klatskin tumor</td><td align=\"left\">42</td><td align=\"left\">31</td><td align=\"left\">93.5</td><td align=\"left\">6.5</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td></tr><tr><td align=\"left\">Hepatocellular carcinoma</td><td align=\"left\">312</td><td align=\"left\">270</td><td align=\"left\">91.9</td><td align=\"left\">6.3</td><td align=\"left\">1.5</td><td align=\"left\">0.4</td></tr><tr><td align=\"left\">Ductal adenocarcinoma of the pancreas</td><td align=\"left\">659</td><td align=\"left\">625</td><td align=\"left\">61.3</td><td align=\"left\">28.8</td><td align=\"left\">8.0</td><td align=\"left\">1.9</td></tr><tr><td align=\"left\">Pancreatic/Ampullary adenocarcinoma</td><td align=\"left\">98</td><td align=\"left\">94</td><td align=\"left\">67.0</td><td align=\"left\">24.5</td><td align=\"left\">5.3</td><td align=\"left\">3.2</td></tr><tr><td align=\"left\">Acinar cell carcinoma of the pancreas</td><td align=\"left\">18</td><td align=\"left\">18</td><td align=\"left\">100.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td></tr><tr><td align=\"left\">Gastrointestinal stromal tumor (GIST)</td><td align=\"left\">62</td><td align=\"left\">61</td><td align=\"left\">100.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td></tr><tr><td align=\"left\">Appendix, neuroendocrine tumor (NET)</td><td align=\"left\">25</td><td align=\"left\">20</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td></tr><tr><td align=\"left\">Colorectal, neuroendocrine tumor (NET)</td><td align=\"left\">12</td><td align=\"left\">11</td><td align=\"left\">100.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td></tr><tr><td align=\"left\">Ileum, neuroendocrine tumor (NET)</td><td align=\"left\">53</td><td align=\"left\">53</td><td align=\"left\">100.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td></tr><tr><td align=\"left\">Pancreas, neuroendocrine tumor (NET)</td><td align=\"left\">101</td><td align=\"left\">95</td><td align=\"left\">84.2</td><td align=\"left\">6.3</td><td align=\"left\">9.5</td><td align=\"left\">0.0</td></tr><tr><td align=\"left\">Colorectal, neuroendocrine carcinoma (NEC)</td><td align=\"left\">14</td><td align=\"left\">12</td><td align=\"left\">100.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td></tr><tr><td align=\"left\">Ileum, neuroendocrine carcinoma (NEC)</td><td align=\"left\">8</td><td align=\"left\">8</td><td align=\"left\">100.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td></tr><tr><td align=\"left\">Gallbladder, neuroendocrine carcinoma (NEC)</td><td align=\"left\">4</td><td align=\"left\">4</td><td align=\"left\">75.0</td><td align=\"left\">0.0</td><td align=\"left\">25.0</td><td align=\"left\">0.0</td></tr><tr><td align=\"left\">Pancreas, neuroendocrine carcinoma (NEC)</td><td align=\"left\">14</td><td align=\"left\">14</td><td align=\"left\">100.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td></tr><tr><td align=\"left\" rowspan=\"13\">Tumors of the urinary system</td><td align=\"left\">Non-invasive papillary urothelial carcinoma, pTa G2 low grade</td><td align=\"left\">177</td><td align=\"left\">172</td><td align=\"left\">97.7</td><td align=\"left\">1.7</td><td align=\"left\">0.6</td><td align=\"left\">0.0</td></tr><tr><td align=\"left\">Non-invasive papillary urothelial carcinoma, pTa G2 high grade</td><td align=\"left\">141</td><td align=\"left\">139</td><td align=\"left\">100.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td></tr><tr><td align=\"left\">Non-invasive papillary urothelial carcinoma, pTa G3</td><td align=\"left\">219</td><td align=\"left\">128</td><td align=\"left\">98.4</td><td align=\"left\">1.6</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td></tr><tr><td align=\"left\">Urothelial carcinoma, pT2-4 G3</td><td align=\"left\">735</td><td align=\"left\">630</td><td align=\"left\">96.3</td><td align=\"left\">3.0</td><td align=\"left\">0.5</td><td align=\"left\">0.2</td></tr><tr><td align=\"left\">Squamous cell carcinoma of the bladder</td><td align=\"left\">22</td><td align=\"left\">18</td><td align=\"left\">100.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td></tr><tr><td align=\"left\">Small cell neuroendocrine carcinoma of the bladder</td><td align=\"left\">23</td><td align=\"left\">23</td><td align=\"left\">100.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td></tr><tr><td align=\"left\">Sarcomatoid urothelial carcinoma</td><td align=\"left\">25</td><td align=\"left\">19</td><td align=\"left\">100.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td></tr><tr><td align=\"left\">Urothelial carcinoma of the kidney pelvis</td><td align=\"left\">62</td><td align=\"left\">54</td><td align=\"left\">100.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td></tr><tr><td align=\"left\">Clear cell renal cell carcinoma</td><td align=\"left\">1287</td><td align=\"left\">1135</td><td align=\"left\">99.7</td><td align=\"left\">0.3</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td></tr><tr><td align=\"left\">Papillary renal cell carcinoma</td><td align=\"left\">368</td><td align=\"left\">325</td><td align=\"left\">96.9</td><td align=\"left\">2.2</td><td align=\"left\">0.9</td><td align=\"left\">0.0</td></tr><tr><td align=\"left\">Clear cell (tubulo) papillary renal cell carcinoma</td><td align=\"left\">26</td><td align=\"left\">24</td><td align=\"left\">100.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td></tr><tr><td align=\"left\">Chromophobe renal cell carcinoma</td><td align=\"left\">170</td><td align=\"left\">149</td><td align=\"left\">100.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td></tr><tr><td align=\"left\">Oncocytoma</td><td align=\"left\">257</td><td align=\"left\">228</td><td align=\"left\">99.6</td><td align=\"left\">0.4</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td></tr><tr><td align=\"left\" rowspan=\"14\">Tumors of the male genital organs</td><td align=\"left\">Adenocarcinoma of the prostate, Gleason 3 + 3</td><td align=\"left\">83</td><td align=\"left\">74</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td><td align=\"left\">100.0</td></tr><tr><td align=\"left\">Adenocarcinoma of the prostate, Gleason 4 + 4</td><td align=\"left\">80</td><td align=\"left\">64</td><td align=\"left\">1.6</td><td align=\"left\">1.6</td><td align=\"left\">0.0</td><td align=\"left\">96.9</td></tr><tr><td align=\"left\">Adenocarcinoma of the prostate, Gleason 5 + 5</td><td align=\"left\">85</td><td align=\"left\">74</td><td align=\"left\">2.7</td><td align=\"left\">2.7</td><td align=\"left\">9.5</td><td align=\"left\">85.1</td></tr><tr><td align=\"left\">Adenocarcinoma of the prostate (recurrence)</td><td align=\"left\">258</td><td align=\"left\">207</td><td align=\"left\">5.3</td><td align=\"left\">17.4</td><td align=\"left\">15.0</td><td align=\"left\">62.3</td></tr><tr><td align=\"left\">Small cell neuroendocrine carcinoma of the prostate</td><td align=\"left\">19</td><td align=\"left\">18</td><td align=\"left\">100.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td></tr><tr><td align=\"left\">Seminoma</td><td align=\"left\">682</td><td align=\"left\">673</td><td align=\"left\">94.5</td><td align=\"left\">5.1</td><td align=\"left\">0.4</td><td align=\"left\">0.0</td></tr><tr><td align=\"left\">Embryonal carcinoma of the testis</td><td align=\"left\">54</td><td align=\"left\">49</td><td align=\"left\">100.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td></tr><tr><td align=\"left\">Leydig cell tumor of the testis</td><td align=\"left\">31</td><td align=\"left\">23</td><td align=\"left\">100.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td></tr><tr><td align=\"left\">Sertoli cell tumor of the testis</td><td align=\"left\">2</td><td align=\"left\">1</td><td align=\"left\">100.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td></tr><tr><td align=\"left\">Sex cord stromal tumor of the testis</td><td align=\"left\">1</td><td align=\"left\">1</td><td align=\"left\">100.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td></tr><tr><td align=\"left\">Spermatocytic tumor of the testis</td><td align=\"left\">1</td><td align=\"left\">1</td><td align=\"left\">100.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td></tr><tr><td align=\"left\">Yolk sac tumor</td><td align=\"left\">53</td><td align=\"left\">45</td><td align=\"left\">100.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td></tr><tr><td align=\"left\">Teratoma</td><td align=\"left\">53</td><td align=\"left\">45</td><td align=\"left\">100.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td></tr><tr><td align=\"left\">Squamous cell carcinoma of the penis</td><td align=\"left\">92</td><td align=\"left\">71</td><td align=\"left\">100.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td></tr><tr><td align=\"left\" rowspan=\"9\">Tumors of endocrine organs</td><td align=\"left\">Adenoma of the thyroid gland</td><td align=\"left\">113</td><td align=\"left\">110</td><td align=\"left\">100.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td></tr><tr><td align=\"left\">Papillary thyroid carcinoma</td><td align=\"left\">391</td><td align=\"left\">354</td><td align=\"left\">99.7</td><td align=\"left\">0.3</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td></tr><tr><td align=\"left\">Follicular thyroid carcinoma</td><td align=\"left\">154</td><td align=\"left\">146</td><td align=\"left\">100.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td></tr><tr><td align=\"left\">Medullary thyroid carcinoma</td><td align=\"left\">111</td><td align=\"left\">105</td><td align=\"left\">100.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td></tr><tr><td align=\"left\">Parathyroid gland adenoma</td><td align=\"left\">43</td><td align=\"left\">32</td><td align=\"left\">100.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td></tr><tr><td align=\"left\">Anaplastic thyroid carcinoma</td><td align=\"left\">45</td><td align=\"left\">42</td><td align=\"left\">97.6</td><td align=\"left\">2.4</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td></tr><tr><td align=\"left\">Adrenal cortical adenoma</td><td align=\"left\">50</td><td align=\"left\">48</td><td align=\"left\">100.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td></tr><tr><td align=\"left\">Adrenal cortical carcinoma</td><td align=\"left\">28</td><td align=\"left\">28</td><td align=\"left\">100.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td></tr><tr><td align=\"left\">Phaeochromocytoma</td><td align=\"left\">50</td><td align=\"left\">50</td><td align=\"left\">100.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td></tr><tr><td align=\"left\" rowspan=\"9\">Tumors of haemotopoetic and lymphoid tissues</td><td align=\"left\">Hodgkin Lymphoma</td><td align=\"left\">103</td><td align=\"left\">94</td><td align=\"left\">100.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td></tr><tr><td align=\"left\">Small lymphocytic lymphoma, B-cell type (B-SLL/B-CLL)</td><td align=\"left\">50</td><td align=\"left\">39</td><td align=\"left\">100.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td></tr><tr><td align=\"left\">Diffuse large B cell lymphoma (DLBCL)</td><td align=\"left\">113</td><td align=\"left\">92</td><td align=\"left\">97.8</td><td align=\"left\">2.2</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td></tr><tr><td align=\"left\">Follicular lymphoma</td><td align=\"left\">88</td><td align=\"left\">65</td><td align=\"left\">100.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td></tr><tr><td align=\"left\">T-cell Non Hodgkin lymphoma</td><td align=\"left\">25</td><td align=\"left\">20</td><td align=\"left\">100.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td></tr><tr><td align=\"left\">Mantle cell lymphoma</td><td align=\"left\">18</td><td align=\"left\">12</td><td align=\"left\">100.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td></tr><tr><td align=\"left\">Marginal zone lymphoma</td><td align=\"left\">16</td><td align=\"left\">12</td><td align=\"left\">100.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td></tr><tr><td align=\"left\">Diffuse large B-cell lymphoma (DLBCL) in the testis</td><td align=\"left\">16</td><td align=\"left\">15</td><td align=\"left\">100.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td></tr><tr><td align=\"left\">Burkitt lymphoma</td><td align=\"left\">5</td><td align=\"left\">1</td><td align=\"left\">100.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td></tr><tr><td align=\"left\" rowspan=\"23\">Tumors of soft tissue and bone</td><td align=\"left\">Tendosynovial giant cell tumor</td><td align=\"left\">45</td><td align=\"left\">45</td><td align=\"left\">91.1</td><td align=\"left\">8.9</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td></tr><tr><td align=\"left\">Granular cell tumor</td><td align=\"left\">53</td><td align=\"left\">47</td><td align=\"left\">97.9</td><td align=\"left\">2.1</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td></tr><tr><td align=\"left\">Leiomyoma</td><td align=\"left\">50</td><td align=\"left\">50</td><td align=\"left\">100.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td></tr><tr><td align=\"left\">Leiomyosarcoma</td><td align=\"left\">94</td><td align=\"left\">90</td><td align=\"left\">100.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td></tr><tr><td align=\"left\">Liposarcoma</td><td align=\"left\">145</td><td align=\"left\">144</td><td align=\"left\">100.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td></tr><tr><td align=\"left\">Malignant peripheral nerve sheath tumor (MPNST)</td><td align=\"left\">15</td><td align=\"left\">14</td><td align=\"left\">100.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td></tr><tr><td align=\"left\">Myofibrosarcoma</td><td align=\"left\">26</td><td align=\"left\">26</td><td align=\"left\">100.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td></tr><tr><td align=\"left\">Angiosarcoma</td><td align=\"left\">74</td><td align=\"left\">67</td><td align=\"left\">95.5</td><td align=\"left\">1.5</td><td align=\"left\">3.0</td><td align=\"left\">0.0</td></tr><tr><td align=\"left\">Angiomyolipoma</td><td align=\"left\">91</td><td align=\"left\">89</td><td align=\"left\">100.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td></tr><tr><td align=\"left\">Dermatofibrosarcoma protuberans</td><td align=\"left\">21</td><td align=\"left\">16</td><td align=\"left\">100.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td></tr><tr><td align=\"left\">Ganglioneuroma</td><td align=\"left\">14</td><td align=\"left\">14</td><td align=\"left\">100.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td></tr><tr><td align=\"left\">Kaposi sarcoma</td><td align=\"left\">8</td><td align=\"left\">4</td><td align=\"left\">75.0</td><td align=\"left\">25.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td></tr><tr><td align=\"left\">Neurofibroma</td><td align=\"left\">117</td><td align=\"left\">117</td><td align=\"left\">100.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td></tr><tr><td align=\"left\">Sarcoma, not otherwise specified (NOS)</td><td align=\"left\">74</td><td align=\"left\">68</td><td align=\"left\">100.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td></tr><tr><td align=\"left\">Paraganglioma</td><td align=\"left\">41</td><td align=\"left\">41</td><td align=\"left\">100.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td></tr><tr><td align=\"left\">Ewing sarcoma</td><td align=\"left\">23</td><td align=\"left\">16</td><td align=\"left\">100.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td></tr><tr><td align=\"left\">Rhabdomyosarcoma</td><td align=\"left\">7</td><td align=\"left\">6</td><td align=\"left\">100.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td></tr><tr><td align=\"left\">Schwannoma</td><td align=\"left\">122</td><td align=\"left\">121</td><td align=\"left\">100.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td></tr><tr><td align=\"left\">Synovial sarcoma</td><td align=\"left\">12</td><td align=\"left\">11</td><td align=\"left\">100.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td></tr><tr><td align=\"left\">Osteosarcoma</td><td align=\"left\">44</td><td align=\"left\">41</td><td align=\"left\">100.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td></tr><tr><td align=\"left\">Chondrosarcoma</td><td align=\"left\">40</td><td align=\"left\">38</td><td align=\"left\">100.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td></tr><tr><td align=\"left\">Rhabdoid tumor</td><td align=\"left\">5</td><td align=\"left\">5</td><td align=\"left\">100.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td></tr><tr><td align=\"left\">Solitary fibrous tumor</td><td align=\"left\">17</td><td align=\"left\">17</td><td align=\"left\">100.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td><td align=\"left\">0.0</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab2\"><label>Table 2</label><caption><p>Prostein and tumor phenotype</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\"/><th align=\"left\"/><th align=\"left\"/><th align=\"left\" colspan=\"4\">Prostein immunostaining result</th><th align=\"left\"/></tr><tr><th align=\"left\"/><th align=\"left\"/><th align=\"left\">n</th><th align=\"left\">negative (%)</th><th align=\"left\">weak (%)</th><th align=\"left\">moderate (%)</th><th align=\"left\">strong (%)</th><th align=\"left\">\n<italic>P</italic>\n</th></tr></thead><tbody><tr><td align=\"left\" rowspan=\"18\">Invasive breast carcinoma of no special type</td><td align=\"left\">pT1</td><td align=\"left\">774</td><td align=\"left\">95.9</td><td align=\"left\">3.5</td><td align=\"left\">0.6</td><td align=\"left\">0</td><td align=\"left\">0.2176</td></tr><tr><td align=\"left\">pT2</td><td align=\"left\">626</td><td align=\"left\">94.9</td><td align=\"left\">4.3</td><td align=\"left\">0.8</td><td align=\"left\">0</td><td align=\"left\"/></tr><tr><td align=\"left\">pT3-4</td><td align=\"left\">125</td><td align=\"left\">93.6</td><td align=\"left\">5.6</td><td align=\"left\">0</td><td align=\"left\">0.8</td><td align=\"left\"/></tr><tr><td align=\"left\">G1</td><td align=\"left\">191</td><td align=\"left\">96.3</td><td align=\"left\">3.1</td><td align=\"left\">0.5</td><td align=\"left\">0</td><td align=\"left\">0.0105</td></tr><tr><td align=\"left\">G2</td><td align=\"left\">817</td><td align=\"left\">96.9</td><td align=\"left\">2.7</td><td align=\"left\">0.4</td><td align=\"left\">0</td><td align=\"left\"/></tr><tr><td align=\"left\">G3</td><td align=\"left\">562</td><td align=\"left\">92.3</td><td align=\"left\">6.2</td><td align=\"left\">1.2</td><td align=\"left\">0.2</td><td align=\"left\"/></tr><tr><td align=\"left\">pN0</td><td align=\"left\">698</td><td align=\"left\">95.3</td><td align=\"left\">4.4</td><td align=\"left\">0.3</td><td align=\"left\">0</td><td align=\"left\">0.1691</td></tr><tr><td align=\"left\">pN+</td><td align=\"left\">527</td><td align=\"left\">94.7</td><td align=\"left\">4.2</td><td align=\"left\">1</td><td align=\"left\">0.2</td><td align=\"left\"/></tr><tr><td align=\"left\">pM0</td><td align=\"left\">198</td><td align=\"left\">96.5</td><td align=\"left\">3</td><td align=\"left\">0.5</td><td align=\"left\">0</td><td align=\"left\">0.5637</td></tr><tr><td align=\"left\">pM1</td><td align=\"left\">116</td><td align=\"left\">94.8</td><td align=\"left\">3.4</td><td align=\"left\">1.7</td><td align=\"left\">0</td><td align=\"left\"/></tr><tr><td align=\"left\">HER2 negative</td><td align=\"left\">889</td><td align=\"left\">96.4</td><td align=\"left\">3</td><td align=\"left\">0.6</td><td align=\"left\">0</td><td align=\"left\">0.0312</td></tr><tr><td align=\"left\">HER2 positive</td><td align=\"left\">124</td><td align=\"left\">91.9</td><td align=\"left\">4.8</td><td align=\"left\">3.2</td><td align=\"left\">0</td><td align=\"left\"/></tr><tr><td align=\"left\">ER negative</td><td align=\"left\">215</td><td align=\"left\">92.1</td><td align=\"left\">6</td><td align=\"left\">1.9</td><td align=\"left\">0</td><td align=\"left\">0.033</td></tr><tr><td align=\"left\">ER positive</td><td align=\"left\">746</td><td align=\"left\">96.5</td><td align=\"left\">2.8</td><td align=\"left\">0.7</td><td align=\"left\">0</td><td align=\"left\"/></tr><tr><td align=\"left\">PR negative</td><td align=\"left\">414</td><td align=\"left\">94.2</td><td align=\"left\">4.6</td><td align=\"left\">1.2</td><td align=\"left\">0</td><td align=\"left\">0.1836</td></tr><tr><td align=\"left\">PR positive</td><td align=\"left\">594</td><td align=\"left\">96.6</td><td align=\"left\">2.7</td><td align=\"left\">0.7</td><td align=\"left\">0</td><td align=\"left\"/></tr><tr><td align=\"left\">non-triple negative</td><td align=\"left\">786</td><td align=\"left\">95.8</td><td align=\"left\">3.2</td><td align=\"left\">1</td><td align=\"left\">0</td><td align=\"left\">0.5848</td></tr><tr><td align=\"left\">triple negative</td><td align=\"left\">144</td><td align=\"left\">94.4</td><td align=\"left\">4.9</td><td align=\"left\">0.7</td><td align=\"left\">0</td><td align=\"left\"/></tr><tr><td align=\"left\" rowspan=\"13\">Adenocarcinoma of the pancreas</td><td align=\"left\">pT1</td><td align=\"left\">16</td><td align=\"left\">75</td><td align=\"left\">18.8</td><td align=\"left\">6.3</td><td align=\"left\">0</td><td align=\"left\">0.7582</td></tr><tr><td align=\"left\">pT2</td><td align=\"left\">71</td><td align=\"left\">60.6</td><td align=\"left\">25.4</td><td align=\"left\">11.3</td><td align=\"left\">2.8</td><td align=\"left\"/></tr><tr><td align=\"left\">pT3</td><td align=\"left\">384</td><td align=\"left\">60.9</td><td align=\"left\">29.7</td><td align=\"left\">7.3</td><td align=\"left\">2.1</td><td align=\"left\"/></tr><tr><td align=\"left\">pT4</td><td align=\"left\">30</td><td align=\"left\">70</td><td align=\"left\">16.7</td><td align=\"left\">10</td><td align=\"left\">3.3</td><td align=\"left\"/></tr><tr><td align=\"left\">G1</td><td align=\"left\">17</td><td align=\"left\">52.9</td><td align=\"left\">35.3</td><td align=\"left\">11.8</td><td align=\"left\">0</td><td align=\"left\">0.7482</td></tr><tr><td align=\"left\">G2</td><td align=\"left\">353</td><td align=\"left\">61.8</td><td align=\"left\">28</td><td align=\"left\">7.4</td><td align=\"left\">2.8</td><td align=\"left\"/></tr><tr><td align=\"left\">G3</td><td align=\"left\">108</td><td align=\"left\">62</td><td align=\"left\">30.6</td><td align=\"left\">6.5</td><td align=\"left\">0.9</td><td align=\"left\"/></tr><tr><td align=\"left\">pN0</td><td align=\"left\">108</td><td align=\"left\">58.3</td><td align=\"left\">24.1</td><td align=\"left\">14.8</td><td align=\"left\">2.8</td><td align=\"left\">0.0424</td></tr><tr><td align=\"left\">pN+</td><td align=\"left\">392</td><td align=\"left\">62.5</td><td align=\"left\">29.3</td><td align=\"left\">6.1</td><td align=\"left\">2</td><td align=\"left\"/></tr><tr><td align=\"left\">R0</td><td align=\"left\">253</td><td align=\"left\">62.1</td><td align=\"left\">26.1</td><td align=\"left\">9.1</td><td align=\"left\">2.8</td><td align=\"left\">0.5101</td></tr><tr><td align=\"left\">R1</td><td align=\"left\">208</td><td align=\"left\">63</td><td align=\"left\">27.9</td><td align=\"left\">8.2</td><td align=\"left\">1</td><td align=\"left\"/></tr><tr><td align=\"left\">MMR proficient</td><td align=\"left\">453</td><td align=\"left\">61.8</td><td align=\"left\">28</td><td align=\"left\">7.7</td><td align=\"left\">2.4</td><td align=\"left\">0.8875</td></tr><tr><td align=\"left\">MMR deficient</td><td align=\"left\">3</td><td align=\"left\">66.7</td><td align=\"left\">33.3</td><td align=\"left\">0</td><td align=\"left\">0</td><td align=\"left\"/></tr><tr><td align=\"left\" rowspan=\"7\">Adenocarcinoma of the stomach</td><td align=\"left\">pT1-2</td><td align=\"left\">63</td><td align=\"left\">84.1</td><td align=\"left\">9.5</td><td align=\"left\">6.3</td><td align=\"left\">0</td><td align=\"left\">0.4894</td></tr><tr><td align=\"left\">pT3</td><td align=\"left\">126</td><td align=\"left\">85.7</td><td align=\"left\">11.1</td><td align=\"left\">3.2</td><td align=\"left\">0</td><td align=\"left\"/></tr><tr><td align=\"left\">pT4</td><td align=\"left\">126</td><td align=\"left\">84.9</td><td align=\"left\">13.5</td><td align=\"left\">1.6</td><td align=\"left\">0</td><td align=\"left\"/></tr><tr><td align=\"left\">pN0</td><td align=\"left\">86</td><td align=\"left\">87.2</td><td align=\"left\">9.3</td><td align=\"left\">3.5</td><td align=\"left\">0</td><td align=\"left\">0.8345</td></tr><tr><td align=\"left\">pN+</td><td align=\"left\">223</td><td align=\"left\">86.1</td><td align=\"left\">10.8</td><td align=\"left\">3.2</td><td align=\"left\">0.0</td><td align=\"left\"/></tr><tr><td align=\"left\">MMR proficient</td><td align=\"left\">40</td><td align=\"left\">70</td><td align=\"left\">15</td><td align=\"left\">15</td><td align=\"left\">0</td><td align=\"left\">0.0015</td></tr><tr><td align=\"left\">MMR deficient</td><td align=\"left\">259</td><td align=\"left\">85.7</td><td align=\"left\">12.7</td><td align=\"left\">1.5</td><td align=\"left\">0</td><td align=\"left\"/></tr><tr><td align=\"left\" rowspan=\"18\">Adenocarcinoma of the colon</td><td align=\"left\">pT1</td><td align=\"left\">80</td><td align=\"left\">78.8</td><td align=\"left\">18.8</td><td align=\"left\">1.3</td><td align=\"left\">1.3</td><td align=\"left\">0.0061</td></tr><tr><td align=\"left\">pT2</td><td align=\"left\">414</td><td align=\"left\">70.5</td><td align=\"left\">25.6</td><td align=\"left\">3.1</td><td align=\"left\">0.7</td><td align=\"left\"/></tr><tr><td align=\"left\">pT3</td><td align=\"left\">1195</td><td align=\"left\">81.1</td><td align=\"left\">15.6</td><td align=\"left\">2.9</td><td align=\"left\">0.4</td><td align=\"left\"/></tr><tr><td align=\"left\">pT4</td><td align=\"left\">416</td><td align=\"left\">79.1</td><td align=\"left\">17.8</td><td align=\"left\">2.4</td><td align=\"left\">0.7</td><td align=\"left\"/></tr><tr><td align=\"left\">pN0</td><td align=\"left\">1101</td><td align=\"left\">77.7</td><td align=\"left\">17.9</td><td align=\"left\">3.7</td><td align=\"left\">0.6</td><td align=\"left\">0.0608</td></tr><tr><td align=\"left\">pN+</td><td align=\"left\">993</td><td align=\"left\">79.5</td><td align=\"left\">18.2</td><td align=\"left\">1.8</td><td align=\"left\">0.5</td><td align=\"left\"/></tr><tr><td align=\"left\">V0</td><td align=\"left\">1514</td><td align=\"left\">77.8</td><td align=\"left\">18.1</td><td align=\"left\">3.4</td><td align=\"left\">0.7</td><td align=\"left\">0.0139</td></tr><tr><td align=\"left\">V1</td><td align=\"left\">546</td><td align=\"left\">81.3</td><td align=\"left\">17.2</td><td align=\"left\">1.3</td><td align=\"left\">0.2</td><td align=\"left\"/></tr><tr><td align=\"left\">L0</td><td align=\"left\">684</td><td align=\"left\">80</td><td align=\"left\">15.8</td><td align=\"left\">3.7</td><td align=\"left\">0.6</td><td align=\"left\">0.1454</td></tr><tr><td align=\"left\">L1</td><td align=\"left\">1387</td><td align=\"left\">78.1</td><td align=\"left\">19</td><td align=\"left\">2.4</td><td align=\"left\">0.6</td><td align=\"left\"/></tr><tr><td align=\"left\">right side</td><td align=\"left\">452</td><td align=\"left\">75.4</td><td align=\"left\">19.5</td><td align=\"left\">3.8</td><td align=\"left\">1.3</td><td align=\"left\">0.0479</td></tr><tr><td align=\"left\">left side</td><td align=\"left\">1187</td><td align=\"left\">80.5</td><td align=\"left\">16.7</td><td align=\"left\">2.4</td><td align=\"left\">0.4</td><td align=\"left\"/></tr><tr><td align=\"left\">MMR proficient</td><td align=\"left\">1104</td><td align=\"left\">79.3</td><td align=\"left\">17.7</td><td align=\"left\">2.4</td><td align=\"left\">0.6</td><td align=\"left\">0.5061</td></tr><tr><td align=\"left\">MMR deficient</td><td align=\"left\">85</td><td align=\"left\">77.6</td><td align=\"left\">17.6</td><td align=\"left\">4.7</td><td align=\"left\">0</td><td align=\"left\"/></tr><tr><td align=\"left\">RAS wildtype</td><td align=\"left\">422</td><td align=\"left\">85.5</td><td align=\"left\">12.8</td><td align=\"left\">1.4</td><td align=\"left\">0.2</td><td align=\"left\">0.0133</td></tr><tr><td align=\"left\">RAS mutation</td><td align=\"left\">328</td><td align=\"left\">77.4</td><td align=\"left\">17.7</td><td align=\"left\">4</td><td align=\"left\">0.9</td><td align=\"left\"/></tr><tr><td align=\"left\">BRAF wildtype</td><td align=\"left\">123</td><td align=\"left\">79.7</td><td align=\"left\">16.3</td><td align=\"left\">1.6</td><td align=\"left\">2.4</td><td align=\"left\">0.6308</td></tr><tr><td align=\"left\">BRAF V600E mutation</td><td align=\"left\">16</td><td align=\"left\">75</td><td align=\"left\">12.5</td><td align=\"left\">6.3</td><td align=\"left\">6.3</td><td align=\"left\"/></tr></tbody></table></table-wrap>" ]

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[ "<supplementary-material content-type=\"local-data\" id=\"MOESM1\"></supplementary-material>" ]

[ "<table-wrap-foot><p>\n<italic>Abbreviation</italic>: <italic>pT</italic> Pathological tumor stage, <italic>G</italic> Grade, <italic>pN</italic> Pathological lymph node status, <italic>pM</italic> Pathological status of distant metastasis, <italic>R</italic> Resection margin status, <italic>V</italic> Venous invasion, <italic>L</italic> Lymphatic invasion, <italic>PR</italic> Progesteron receptor, <italic>MMR</italic> Mismatch repair, <italic>ER</italic> Estrogen receptor\n</p></table-wrap-foot>", "<fn-group><fn><p><bold>Publisher’s Note</bold></p><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn></fn-group>" ]

[ "<graphic xlink:href=\"13000_2023_1434_Fig1_HTML\" id=\"d32e475\"/>", "<graphic xlink:href=\"13000_2023_1434_Fig2_HTML\" id=\"d32e2903\"/>", "<graphic xlink:href=\"13000_2023_1434_Fig3_HTML\" id=\"d32e2910\"/>" ]

[ "<media xlink:href=\"13000_2023_1434_MOESM1_ESM.pdf\"><caption><p><bold>Additional file 1: Supplementary Fig. 1.</bold> IHC validation by comparison of antibodies. The panels demonstrate a confirmation of all prostein stainings obtained by MSVA-460R by the independent antibody EPR4795(2). Using MSVA-460R, a granular, predominantly perinuclear staining was seen in epithelial cells of the prostate (A), stomach surface (B), respiratory epithelium (C), the adenohypophysis (D), and of pancreatic islets (E), as well as in some monocytic cells of the spleen (F) while staining was lacking in the first trimenon placenta (G) and the testis (H). Using clone EPR4795(2), identical cell types stained in the prostate (I), stomach (K), respiratory epithelium (L), adenohypophysis (M), pancreatic islets (N), and in the spleen (O). A cytoplasmic staining in the placenta (P) and in testicular cells of the spermatogenesis (Q) was only seen by EPR4795(2) and therefore considered an antibody-specific cross-reactivity of EPR4795(2). The images A-H and I-Q are from consecutive tissue sections.</p></caption></media>" ]

{ "acronym": [ "ERG", "IHC", "SCL45A3", "TMA", "TMPRSS2" ], "definition": [ "ETS Transcription Factor ERG", "Immunohistochemistry", "Solute carrier family 45 member 3", "Tissue microarray", "Transmembrane Serine Protease 2" ] }

[ "<title>Introduction</title>", "<p id=\"Par6\">Histiocytic necrotizing lymphadenitis (HNL) is a self-limiting disease of unknown cause, also known as Kikuchi-Fujimoto disease (KFD). It was first reported in 1972 by KIKUCHI and FUJIMOTO et al. [##REF##25310212##1##]. HNL is a relatively uncommon benign lymph node enlargement, a self-limiting disease that usually occurs in Asian women in their 20 and 30 s. Some studies have shown a prevalence of nearly 1:2 in men and women. The most common symptoms are enlarged lymph nodes in the neck with tenderness and fever. The etiology of HNL is unclear. The association of HNL and malignancy is also seldom discussed.</p>", "<p id=\"Par7\">The coexistence of HNL and tumor is extremely rare. Herein, we report a case of metastatic papillary thyroid carcinoma coexistent with histiocytic necrotizing lymphadenitis in the same lymph node.</p>" ]

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[ "<title>Discussion</title>", "<p id=\"Par29\">The clinical manifestations of the HNL lack specificity and may resolve spontaneously within 1 to 6 months after diagnosis. The most common manifestation of patients is localized cervical lymph node enlargement with tenderness, often accompanied by fever. Other rare symptoms include vomiting, diarrhea, night sweats, upper respiratory symptoms, etc. The disease is rare in extranodal lymph nodes, and is most common in the skin, where skin involvement usually presents as rashes, nodules, erythematous papules and erythema multiforme on the face and upper trunk [##UREF##0##2##]. It also rarely occurs in the bone marrow, liver, submandibular glands [##REF##15183591##3##] and parotid glands [##REF##16275413##4##]. The histological features are patchy and irregular necrotic areas with the expansion of the paracortical area of lymph nodes. Apoptotic bodies, crescent tissue cells, and proliferating plasmacytoid monocytes are seen in the necrotic area, accompanied by abundant nuclear fragments but a lack of neutrophils and eosinophils. According to the different stages of the disease, it is divided into three types: the proliferative type, the necrotizing type and the xanthomatous type. The proliferative type is characterized by the proliferation of histiocytes and plasmacytoid dendritic cells, mixed with small lymphocytes and nuclear fragmentation, while necrosis is rare or absent; the necrotizing type is the most common and is characterized by a significant increase in necrotic components; and the xanthomatous type refers to the predominance of foamy histiocytes in the lesion [##REF##30407860##5##]. This case is the necrotizing type. The disease is self-limiting, and no specific treatment is recommended. The treatment is aimed at relieving symptoms (rest, analgesics and antipyretics) and corticosteroids are available for recurrent disease or for patients with a more severe clinical course [##REF##16275413##4##]. There are no definitive laboratory tests to diagnose HNL, and lymph node biopsy should be performed in persons suspected of this disease to avoid misdiagnosis.</p>", "<p id=\"Par30\">\n\n</p>", "<p id=\"Par31\">The pathogenesis of HNL is still unclear. It is assumed that HNL represents the T-cell mediated immune response of genetically susceptible populations to various antigens, and patients with HNL more often have specific human leukocyte antigen (HLA) Class II alleles, specifically HLA-DPA1 and HLA-DPB1, compared with the general population. These alleles are more prevalent in Asians and extremely rare or absent in whites, which may account for the disease being more common in Asians. Pathogens associated with triggering this response include Epstein‒Barr virus, human herpes virus, microvirus B19, cytomegalovirus, and human herpesvirus. HNL can be associated with autoimmune diseases such as systemic lupus erythematosus, mixed connective tissue disease, psoriasis and other autoimmune diseases, suggesting that it may be a potential manifestation of autoimmune disease [##REF##26060388##6##].</p>", "<p id=\"Par32\">HNL needs to be differentiated from lymphoma, infectious lymphadenitis, systemic lupus erythematosus, infectious mononucleosis, and other diseases. (1) The proliferation of immunoblasts and plasmacytoid dendritic cells at the margins of HNL necrotic foci can mimic the invasion of T cells or B cells of non-Hodgkin’s lymphoma and be easily confused. However, the tumor cells of lymphoma have obvious cell atypia, increased volume, thickened nuclear membrane, increased and enlarged nucleoli, and pathological mitosis, but generally no necrotic lesions. Immunohistochemical staining shows that T cells or B cells are cloning-positive. Focal necrosis, nuclear fragmentation and the histiocytic cells that have engulfed nuclear debris may be present in a small number of lymphomas, especially in T-cell lymphoma. However, positive TCR gene rearrangement, few histiocytic cells, and a long course of disease all support the diagnosis of lymphoma [##UREF##1##7##]. (2) Necrotizing lymphadenitis can be caused by a variety of infectious factors and is easily confused with HNL. Epithelioid histiocytosis with granulomatous formation and scattered giant cells are seen in necrotizing lymphadenitis of tuberculosis, histoplasmosis, leprosy, and cat-scratch disease. In cases of syphilitic necrotizing lymphadenitis, there is usually a prominent perivascular infiltration of plasma cells, while a large number of neutrophils are often present in bacterial infections [##REF##30407860##5##]. Special stains and immunohistochemical stains are helpful in identifying the infectious agents. In our case, the blood culture for acid-fast bacilli of the patient was negative on admission. And the multinucleated giant cells, caseous necrosis and well-formed granulomas were absent, although abundant histiocytes were present. Moreover, Ziehl–Neelsen stain has been done and negative to rule out tuberculosis. (3) The lymph nodes of systemic lupus erythematosus show varying degrees of cortical necrosis, accompanied by nuclear debris and inflammatory cell reactive proliferation. Hematoxylin bodies assist in identification. They are usually located in or near the necrotic foci, but may also be located in the lymphatic sinuses, paracortex or vascular wall.</p>", "<p id=\"Par33\">Clinically abnormal serum immunology, especially positive antinuclear antibodies, is helpful for the diagnosis of systemic lupus erythematosus. (4) Infectious mononucleosis is characterized by interfollicular enlargement, immunoblast proliferation, single-cell apoptosis and necrotic foci are common, and histiocytic and plasmoid dendritic cells are rare [##UREF##2##8##]. (5) The identification of histiocytic proliferative lesions is also essential. we focus on the most common histiocytosis among adults: Langerhans cell histiocytosis (LCH), Erdheim-Chester disease (ECD) and Rosai-Dorfman disease (RDD). The primary differential diagnosis is Langerhans cell histiocytosis (LCH), LCH lesions often show histiocytes mixed with a significant infiltration of inflammatory cells. And neoplastic LCH cells are mononucleated, typically with a coffee bean-shaped nucleus. Binucleated or multinucleated cells with the typical Langerhans cell cleft can be identified [##UREF##3##9##]. Moreover, abundant eosinophils are often observed. Characteristic immunohistochemistry such as S-100 and CD1a are helpful for identification. In our case, S-100 and CD1a were negative. And ECD mostly occurs in long tubular bones and is distributed symmetrically. Additionally, the histology of ECD shows infiltration of tissue by small CD1a– mononucleated histiocytes, sometimes associated with Touton cells. Furthermore, the histology of RDD is a massive expansion of histiocytes in the lymph node sinuses with lymphocytes and plasma cells [##UREF##4##10##]. Abundant plasma cells in the medullary cords and around the venules are typical. In combination with clinic information, histological patterns and immunohistochemistry, the above lesions were excluded.</p>", "<p id=\"Par34\">HNL and papillary thyroid carcinoma coexisting in the same lymph node is uncommon and seldom ever documented, according to a review of the current literature; so far, only two cases have been retrieved [##UREF##5##11##]. At present, 10 cases of HNL combined with other tumors have been reported (Table ##TAB##0##1##), most of which occurred in women (7/11), predominantly in Asia (6/11), aged 27–66 years. The tumors combined with HNL were PTC [##REF##26060388##6##, ##UREF##5##11##] (2 cases), gastric carcinoma [##REF##9378825##12##] (1 case), breast carcinoma [##REF##10809597##13##] (1 case), squamous cell carcinoma of the tongue [##REF##28693162##14##] (1 case), malignant melanoma [##REF##25928106##15##] (1 case), malignant fibrous histiocytoma [##REF##2836294##16##] (1 case), multiple myeloma [##UREF##6##17##] (1 case), and diffuse large B lymphoma in remission [##REF##11070128##18##] (2 cases).</p>", "<p id=\"Par35\">It can be accompanied by fever or no fever, generally without special treatment, and steroids and other hormones can be used for symptomatic treatment. Recurrence is rare (1/11); treatment of the tumor is the main focus when there is tumor coexistence. HNL coexisted with tumors: Cases 1 to 7, similar to our case, were evaluated preoperatively as metastatic tumors of the lymph node, and the HNL occurred on the same side of the tumor. In cases 8–11, lymph nodes were enlarged months or years after tumor treatment, and the biopsy was KFD.</p>", "<p id=\"Par36\">Review the literature on the coexistence of HNL with other tumors: PTC [##REF##26060388##6##, ##UREF##5##11##], gastric carcinoma [##REF##9378825##12##], breast carcinoma [##REF##10809597##13##], squamous cell carcinoma of the tongue [##REF##28693162##14##], and malignant melanoma [##REF##25928106##15##], all of which occurred on the same side of the tumor as the present case, may indicate that HNL can be induced by tumor-associated local antigens and raise the possibility of specific immune responses to antigenic stimulation in HNL. Dequante et al. reported a possible correlation between increased cytotoxic activity of T cells stimulated by the tumor and disease transformation [##UREF##6##17##]. Apoptosis of target cells is induced by two molecular mechanisms of T-cell-mediated cytotoxicity, one perforin-based and the other Fas-based [##UREF##7##19##]. The reason for the simultaneous occurrence of this case may be closely related to the patient’s infection with Epstein‒Barr virus. We speculate that EBV infection of lymph nodes activates a variety of cells, including T cells and histiocytes, and promotes massive T cell proliferation. Activated histiocytes produce various cytokines that, through Fas and FasL interaction, induce apoptosis of T cells.</p>", "<p id=\"Par37\">We speculate that EBV infection of lymph nodes activates a variety of cells, including T cells and histiocytes, and promotes massive proliferation of T cells. Activated histiocytes produce various cytokines that, through Fas and Fasl interaction, induce apoptosis of T cells.</p>", "<p id=\"Par38\">Moreover, FASL was highly expressed in papillary thyroid carcinoma [##UREF##8##20##], suggesting that papillary thyroid carcinoma may increase the cytotoxic activity of T cells and the specific immune response of its own HNL, but there is not enough evidence to show whether this is a causal relationship, and more experiments and data are needed to prove it.</p>" ]

[ "<title>Conclusion</title>", "<p id=\"Par39\">The coexistence of HNL and papillary thyroid carcinoma is unique, and the coexistence of the two diseases is rare, but the reason why the associated diseases can coexist has not been proven. When patients present with enlarged lymph nodes in the neck, they should be considered as a differential diagnosis. The pathological diagnostician should not only focus on the tumor metastasis in the lymph nodes but also pay attention to the inflammatory lesions in the lymph nodes, such as HNL and Castleman, because these lesions may mislead the clinical staging of the tumor by the clinical doctor and lead to unnecessary treatment.</p>" ]

[ "<p id=\"Par1\">Histiocytic necrotizing lymphadenitis (HNL) is a benign, self-limiting disease that is rare clinically. The coexistence of HNL and tumor is rarer. We report a male patient who was preoperatively diagnosed with papillary thyroid carcinoma with cervical lymph nodes metastasis, and the postoperative pathological examination showed histiocytic necrotizing lymphadenitis combined with metastatic papillary thyroid carcinoma in the same single lymph node. More interestingly, Epstein‒Barr virus was positive in these lymph nodes by in situ hybridization. This may suggest a trigger for the coexistence of the two diseases.</p>", "<title>Keywords</title>" ]

[ "<title>Case report</title>", "<p id=\"Par8\">A 48-year-old man was admitted to the hospital with a diagnosis of papillary thyroid carcinoma confirmed by fine needle aspiration of the thyroid after 20 days of physical examination. Ultrasound examination of the thyroid showed that a hypoechoic nodule was detected in the upper pole of the right lobe of the thyroid gland, approximately 1.3 × 1.0 cm, with regular morphology, aspect ratio &lt; 1, fuzzy border, uneven internal echogenicity, and multiple dotted strong echogenicity with rear echo attenuation. Color Doppler flow imaging (CDFI): no significant signal was observed. Enlarged lymph nodes were seen in the II-IV region of the right neck. The largest lymph node was about 2.1 × 1.4 cm, with full morphology, clear borders, thickened cortex, and disappearance of lymphatic portal structures, and scattered strong echogenicity was detected in some of the nodes. CDFI: Blood flow signal was visible in the lymph nodes. A slightly larger lymph node was detected in the II-IV area of the left neck, about 1.0 × 0.5 cm. The findings were “nodule in the right lobe of the thyroid: Thyroid Imaging Reporting and Data System (TI-RADS) category 4a nodule in the middle of the right lobe, multiple enlarged lymph nodes in the right side of the neck; slightly enlarged lymph nodes in the left side of the neck” (Fig. ##FIG##0##1##). The diagnosis of fine needle aspiration of the thyroid and lymph node were shown: (right thyroid) Bethesda grade VI, papillary thyroid carcinoma; (right cervical lymph node) metastatic carcinoma, consistent with metastatic papillary thyroid carcinoma. (Figs. ##FIG##1##2## and ##FIG##2##3##). For further diagnosis and treatment, he was admitted on August 20, 2022. After completing the laboratory and other related examinations, thyroid surgery was performed.</p>", "<p id=\"Par12\">\n\n</p>", "<p id=\"Par13\">\n\n</p>", "<p id=\"Par14\">\n\n</p>", "<p id=\"Par9\">Intraoperative freezing for inspection: Left thyroid gland, about 4.5 × 3 × 2 cm in size, two gray‒white areas with diameters of 0.2 and 0.3 cm were seen on the section, respectively, soft. Right thyroid gland, approximately 4.5 × 3 × 2 cm in size, a grayish white nodule, with a size of 1.2 × 0.9 × 0.8 cm, immediately adjacent to the capsule, and another grayish yellow nodule, 0.2 cm from the capsule, with a diameter of 0. 2 cm, both hard. The frozen section report was given: (left thyroid) benign lesion. (Right thyroid) Papillary thyroid carcinoma. Then, right neck dissection was performed. Postoperative paraffin pathology was shown: (right thyroid) Papillary thyroid carcinoma (diffuse sclerosing variant), invaded with the capsule (Fig. ##FIG##3##4##). Typical metastatic papillary thyroid carcinoma was seen in some lymph nodes, and some lymph nodes showed focal irregular pale pink stained lesion areas in cortical and paracortical areas with numerous nuclear fragments, the proliferation of mononuclear-like histiocytes and plasmacytoid dendritic cells. Coagulative necrosis was seen focally, scattered cellulose deposition, few plasma cells, and no neutrophils were seen (Fig. ##FIG##4##5##). The results of the immunohistochemical staining showed CD3, MPO and CD68 were expressed in most of the cells in the pale pink stained lesion areas. The expression of CD123 was slightly less than that of the previous antibodies. And CD20 was expressed sporadically (Figs. ##FIG##5##6##–##FIG##9##10##). But CD1a was not expressed (Fig. ##FIG##10##11##). CD21 showed a residual follicular dendritic cell network (Fig. ##FIG##11##12##), Ki67 was highly expressed in pale pink stained lesion areas and germinal centers (Fig. ##FIG##12##13##). Epstein‒Barr virus was detected by Epstein‒Barr encoding region (EBER) in situ hybridization. EBER was scattered and positive in the pale pink stained areas (Fig. ##FIG##13##14##). Interestingly, the coexistence of pale pink stained lesions and metastatic PTC was found in the same lymph node (Figs. ##FIG##14##15##–##FIG##16##17##).</p>", "<p id=\"Par15\">\n\n</p>", "<p id=\"Par16\">\n\n</p>", "<p id=\"Par17\">\n\n</p>", "<p id=\"Par18\">\n\n</p>", "<p id=\"Par19\">\n\n</p>", "<p id=\"Par20\">\n\n</p>", "<p id=\"Par22\">\n\n</p>", "<p id=\"Par21\">\n\n</p>", "<p id=\"Par23\">\n\n</p>", "<p id=\"Par24\">\n\n</p>", "<p id=\"Par25\">\n\n</p>", "<p id=\"Par26\">\n\n</p>", "<p id=\"Par27\">\n\n</p>", "<p id=\"Par28\">\n\n</p>", "<p id=\"Par10\">Pathological diagnosis was given: (left) nodular goiter, (right) papillary thyroid carcinoma (diffuse sclerosing variant) (two foci, maximal diameter approximately 1.2 cm and 0.2 cm), the capsule was invaded; metastatic PTC was found in 16 of 57 lymph nodes on the right side of the neck (the maximal diameter of metastatic lesions was 1.8 cm), and some lymph node biopsies showed histiocytic necrotizing lymphadenitis. Three lymph nodes were seen with histiocytic necrotizing lymphadenitis coexisting with metastatic PTC. However, there was no metastatic PTC or HNL in the left cervical or prelaryngeal lymph nodes.</p>", "<p id=\"Par11\">Follow-up: The patient recovered well after surgery and survived disease-free for more than 5 months, and the long-term prognosis remains to be observed further.</p>" ]

[ "<title>Author contributions</title>", "<p>J.L: Conception or design of the work, data collection, analysis, and interpretation, drafting the article. L.C.: Data collection, analysis, and interpretation, Writing- Reviewing and Editing. L.J.: Conception or design of the work. G.Y. and G.Q.: Data collection, analysis. All authors reviewed the manuscript.</p>", "<title>Funding</title>", "<p>The author(s) received no financial support for the research, authorship, and/or publication of this article.</p>", "<title>Data availability</title>", "<p>All data generated or analyzed in this study are included in this article.</p>", "<title>Declarations</title>", "<title>Ethical approval</title>", "<p id=\"Par41\">Written informed consent for publication of this case report and any accompanying images was obtained from the patient’s.</p>", "<title>Competing interests</title>", "<p id=\"Par40\">The authors declare no competing interests.</p>" ]

[ "<fig id=\"Fig1\"><label>Fig. 1</label><caption><p>Ultrasound showed a 1.3 × 1.0 cm hypoechoic nodule with a faint border in the upper pole of the right lobe of the thyroid gland. (A: Thyroid mass)</p></caption></fig>", "<fig id=\"Fig2\"><label>Fig. 2</label><caption><p>FNA smear for the right lobe of the thyroid gland: Microscopy showed that the follicular epithelium was arranged in a papillary structure, with enlarged nuclei, irregular thickening of nuclear membranes, and nuclear grooves and pseudoinclusion bodies</p></caption></fig>", "<fig id=\"Fig3\"><label>Fig. 3</label><caption><p>FNA smear of the right cervical lymph node shows that follicular epithelioid cells are arranged in lamellar nests, and nuclei are enlarged and crowded. Nuclear sulci and internal inclusion bodies can be seen. (hematoxylin and eosin, original magnification, x 400)</p></caption></fig>", "<fig id=\"Fig4\"><label>Fig. 4</label><caption><p>Postoperative paraffin of right thyroid section: Papillary carcinoma of the right thyroid with extensive psammoma body (hematoxylin and eosin, original magnification, x 100)</p></caption></fig>", "<fig id=\"Fig5\"><label>Fig. 5</label><caption><p>Postoperative paraffin of right cervical lymph node section: Irregular faintly stained lesions in lymph nodes: numerous nuclear fragments, mononuclear-like histiocytes and plasmacytoid dendritic cell proliferation, scattered fibrin deposits, no neutrophils (hematoxylin and eosin, original magnification, x 400)</p></caption></fig>", "<fig id=\"Fig6\"><label>Fig. 6</label><caption><p>Postoperative immunohistochemical section of the right cervical lymph node in paraffin: the faintly stained lesion positive for CD3. Envision method x 50</p></caption></fig>", "<fig id=\"Fig7\"><label>Fig. 7</label><caption><p>Postoperative immunohistochemical section of the right cervical lymph node in paraffin: the faintly stained lesion positive for MPO. Envision method x 50</p></caption></fig>", "<fig id=\"Fig8\"><label>Fig. 8</label><caption><p>Postoperative immunohistochemical section of the right cervical lymph node in paraffin: the faintly stained lesion positive for CD68. Envision method x 50</p></caption></fig>", "<fig id=\"Fig9\"><label>Fig. 9</label><caption><p>Postoperative immunohistochemical section of the right cervical lymph node in paraffin: the faintly stained lesion positive for CD123. Envision method x 50</p></caption></fig>", "<fig id=\"Fig10\"><label>Fig. 10</label><caption><p>Postoperative immunohistochemical section of the right cervical lymph node in paraffin: scattered positivity for the faintly stained lesion CD20. Envision method x 50</p></caption></fig>", "<fig id=\"Fig11\"><label>Fig. 11</label><caption><p>Postoperative immunohistochemical section of the right cervical lymph node in paraffin: the faintly stained lesion negative for CD1a. Envision method x 50</p></caption></fig>", "<fig id=\"Fig12\"><label>Fig. 12</label><caption><p>Postoperative immunohistochemical section of the right cervical lymph node in paraffin: the residual FDC network expressed CD21. Envision method x 50</p></caption></fig>", "<fig id=\"Fig13\"><label>Fig. 13</label><caption><p>Postoperative immunohistochemical section of the right cervical lymph node in paraffin: Ki67 was highly expressed in faintly stained lesions and germinal centers. Envision method x 50</p></caption></fig>", "<fig id=\"Fig14\"><label>Fig. 14</label><caption><p>Postoperative in situ hybridization of EBV-EBER in the right cervical lymph nodes in paraffin: scattered positive faintly stained lesions. In situ hybridization method x 400</p></caption></fig>", "<fig id=\"Fig15\"><label>Fig. 15</label><caption><p>Postoperative paraffin section: The coexistence of histiocytic necrotizing lymphadenitis and metastatic PTC was found in the same lymph node. HE X 50</p></caption></fig>", "<fig id=\"Fig16\"><label>Fig. 16</label><caption><p>Postoperative paraffin section: Papillary thyroid cancer in coexisting lymph nodes. HE x 400</p></caption></fig>", "<fig id=\"Fig17\"><label>Fig. 17</label><caption><p>Postoperative paraffin section: Histiocytic necrotizing lymphadenitis in coexisting lymph nodes. HE x 400</p></caption></fig>" ]

[ "<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Summary of cases of HNL combined with other tumors in the literature</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Number</th><th align=\"left\">Year of publication</th><th align=\"left\">Author Nationality</th><th align=\"left\">Sex</th><th align=\"left\">Age</th><th align=\"left\">Concomitant tumor</th><th align=\"left\">Fever or not</th><th align=\"left\">Lymph node metastasis tumor and number</th><th align=\"left\">Merge HNL and number of</th><th align=\"left\">Treatment</th><th align=\"left\">Recurrence or<break/>not</th><th align=\"left\">prognosis</th></tr></thead><tbody><tr><td align=\"left\">1</td><td align=\"left\">1997</td><td align=\"left\">Canada</td><td align=\"left\">Male</td><td align=\"left\">37</td><td align=\"left\">Signet ring cell carcinoma of esophageal-gastric</td><td align=\"left\">-</td><td align=\"left\">NA</td><td align=\"left\">cervical lymph node</td><td align=\"left\">Esophageal-gastrectomy</td><td align=\"left\">NA</td><td align=\"left\">Death by extensive peritoneal and mediastinal metastases</td></tr><tr><td align=\"left\">2</td><td align=\"left\">2000</td><td align=\"left\">Britain</td><td align=\"left\">Fe</td><td align=\"left\">66</td><td align=\"left\">carcinoma of breast</td><td align=\"left\">-</td><td align=\"left\">0/13</td><td align=\"left\">Axillary lymph nodes 12 / 13</td><td align=\"left\">Breast cancer radical surgery</td><td align=\"left\">-</td><td align=\"left\">Symptoms improved</td></tr><tr><td align=\"left\">3</td><td align=\"left\">2015</td><td align=\"left\">Korea</td><td align=\"left\">Ma</td><td align=\"left\">38</td><td align=\"left\">Right PTC</td><td align=\"left\">+</td><td align=\"left\">Several on the right</td><td align=\"left\">One right cervical lymph node</td><td align=\"left\">Total thyroidectomy and central neck dissection, postoperative I131, methylprednisolone and NSAIDs</td><td align=\"left\">-</td><td align=\"left\">Symptoms improved</td></tr><tr><td align=\"left\">4</td><td align=\"left\">2015</td><td align=\"left\">America</td><td align=\"left\">Fe</td><td align=\"left\">30</td><td align=\"left\">Right PTC</td><td align=\"left\">+</td><td align=\"left\">One on the right</td><td align=\"left\">Other lymph nodes in the right neck</td><td align=\"left\">Total thyroidectomy and bilateral central and right lymph node dissection</td><td align=\"left\">-</td><td align=\"left\">Symptoms improved</td></tr><tr><td align=\"left\">5</td><td align=\"left\">2015</td><td align=\"left\">Canada</td><td align=\"left\">Fe</td><td align=\"left\">37</td><td align=\"left\">Melanoma of the thigh</td><td align=\"left\">-</td><td align=\"left\">1/10</td><td align=\"left\">Five left inguinal lymph nodes</td><td align=\"left\">Surgical excision</td><td align=\"left\">-</td><td align=\"left\">NA</td></tr><tr><td align=\"left\">6</td><td align=\"left\">2017</td><td align=\"left\">Japan</td><td align=\"left\">Ma</td><td align=\"left\">48</td><td align=\"left\">Squamous cell carcinoma of the tongue</td><td align=\"left\">NA</td><td align=\"left\">2</td><td align=\"left\">Right posterior neck lymph node</td><td align=\"left\">Surgical excision</td><td align=\"left\">+</td><td align=\"left\">Symptoms improved</td></tr><tr><td align=\"left\">7</td><td align=\"left\">2023</td><td align=\"left\">China</td><td align=\"left\">Ma</td><td align=\"left\">48</td><td align=\"left\">Right PTC</td><td align=\"left\">-</td><td align=\"left\">16/57</td><td align=\"left\">Three right cervical lymph nodes</td><td align=\"left\">Radical thyroidectomy, right functional neck lymph node dissection and right upper mediastinal lymph node biopsy were performed</td><td align=\"left\">-</td><td align=\"left\">Symptoms improved</td></tr><tr><td align=\"left\">8</td><td align=\"left\">1987</td><td align=\"left\">Hong Kong</td><td align=\"left\">Fe</td><td align=\"left\">57</td><td align=\"left\">Recurrent malignant fibrous histiocytoma of thigh</td><td align=\"left\">NA</td><td align=\"left\">There were no tumor metastases</td><td align=\"left\">Left inguinal lymph node</td><td align=\"left\">Extensive tumor resection</td><td align=\"left\">-</td><td align=\"left\">Symptoms improved</td></tr><tr><td align=\"left\">9</td><td align=\"left\">2000</td><td align=\"left\">Japan</td><td align=\"left\">Fe</td><td align=\"left\">27</td><td align=\"left\">Diffuse Large B-cell lymphoma in remission</td><td align=\"left\">-</td><td align=\"left\">/</td><td align=\"left\">Left side of the cervical lymph node</td><td align=\"left\">No treatments</td><td align=\"left\">-</td><td align=\"left\">Symptoms improved</td></tr><tr><td align=\"left\">10</td><td align=\"left\">2000</td><td align=\"left\">Japan</td><td align=\"left\">Fe</td><td align=\"left\">30</td><td align=\"left\">Diffuse Large B-cell lymphoma in remission</td><td align=\"left\">-</td><td align=\"left\">/</td><td align=\"left\">Left side of the axillary lymph node</td><td align=\"left\">/</td><td align=\"left\">-</td><td align=\"left\">Symptoms improved</td></tr><tr><td align=\"left\">11</td><td align=\"left\">2021</td><td align=\"left\">Britain</td><td align=\"left\">Fe</td><td align=\"left\">47</td><td align=\"left\">Multiple myeloma</td><td align=\"left\">+</td><td align=\"left\">NA</td><td align=\"left\">Left side of the axillary lymph node</td><td align=\"left\">Steroid treatment</td><td align=\"left\">-</td><td align=\"left\">Symptoms improved</td></tr></tbody></table></table-wrap>" ]

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[ "<table-wrap-foot><p>*Abbreviations: Fe: Female. Ma: male. NA: not available</p></table-wrap-foot>", "<fn-group><fn><p><bold>Publisher’s Note</bold></p><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn></fn-group>" ]

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{ "acronym": [ "HNL", "KFD", "CDFI", "TI-RADS" ], "definition": [ "Histiocytic necrotizing lymphadenitis", "Kikuchi-Fujimoto disease", "Color Doppler flow imaging", "Thyroid Imaging Reporting and Data System" ] }

[ "<title>Background</title>", "<p id=\"Par5\">The COVID-19 pandemic has exerted a profound impact on society, affecting employment, schooling, healthcare and many other aspects of everyday life. In terms of mental health, increasing rates of symptoms of depression, anxiety, sleep problems, and other conditions have been reported across different continents, demonstrating the global consequences of the pandemic [##UREF##2##7##] According to an estimate based on various data sources worldwide, between January 2020 and January 2021, cases of major depressive disorder increased by 27.6% and cases of anxiety disorders by 25.6%, and this increase was particularly pronounced among women [##UREF##2##7##]. With respect to different age groups, results of studies conducted early on in the pandemic as well as studies on previous epidemics such as severe acute respiratory syndrome (SARS) or Ebola suggested that adolescents and young adults aged between 15 and 25 years showed the sharpest increase in mental health problems [##REF##32112714##4##, ##REF##32419840##15##]. This finding has been replicated over the further course of the pandemic, with adolescents in the transitional stage between childhood and adulthood showing elevated rates of symptoms of depression and anxiety compared to other age groups [##UREF##2##7##]. A recent meta-analysis encompassing 29 studies including children and adolescents reported pooled prevalence estimates of 25.2% for depressive symptoms and 20.5% for anxiety symptoms [##REF##34369987##33##].</p>", "<p id=\"Par6\">A higher burden of the pandemic on mental health in young people has likewise been reported in Austria. For instance, an online survey conducted between February and March 2021 found higher rates of symptoms of depression and anxiety than in the adult population among adolescent samples in school [##REF##34181016##28##] and in apprenticeships [##REF##34501523##11##], with 55% of adolescents showing moderate depressive symptoms and 47% scoring above the cut-off for anxiety symptoms [##REF##34181016##28##]. Moreover, in a further survey conducted between September and November 2021 using the same methodology, the rate of Austrian adolescents reporting mental health problems remained high, with 58% reporting depressive symptoms and 46% reporting anxiety symptoms [##REF##35900473##10##].</p>", "<p id=\"Par7\">The increasing rates of mental health problems have also led to more contacts with the mental healthcare sector. A Danish cohort study of young people (age 5–24 years) reported an overall relative increase of incident psychiatric diagnoses of 5% during the COVID-19 pandemic compared to the expected rates [##UREF##1##3##]. While suicide rates remained stable during the first and subsequent months of the pandemic until June 2021 [##REF##35935344##29##, ##REF##33862016##30##], emergency department visits due to suicidal ideation and suicide attempts increased among adolescents [##REF##35534988##5##, ##REF##35358165##21##, ##REF##36907199##23##, ##REF##34138833##43##]. Only a small number of studies have explored the impact of the COVID-19 pandemic on rates of prescription of psychotropic drugs. A recent study of the general population conducted using Austrian social insurance data focused on prescriptions in 2020, and found no significant increase in psychopharmacological prescriptions during the first lockdowns in 2020 [##REF##36281638##40##]. By contrast, focusing on a younger age population, a Danish nationwide cohort study of 5–24 year-old patients reported an increase in incident use of psychopharmacological interventions, which was especially pronounced in the age group between 12 and 17 years [##UREF##1##3##].</p>", "<p id=\"Par8\">As the COVID-19 pandemic has been associated with an increase in mental health problems throughout society, with a particularly steep increase in adolescents, we sought to assess changes in psychopharmacological medication prescription rates in different age groups before and after the pandemic-related restriction measures. In view of the literature reporting increased rates of symptoms of depression, anxiety and sleep disorders in adolescents, we undertook a detailed analysis of the group of 10–19 year-olds.</p>", "<p id=\"Par9\">The specific focus was on medication classes that are likely to be used in the treatment of children and adolescents with depression and anxiety, namely antidepressants and antipsychotics. We further aimed to track the prescription rates for these classes of psychotropic drugs in Austrian minors throughout the pandemic and compare them to expected rates based on former, pre-pandemic prescription patterns.</p>" ]

[ "<title>Methods</title>", "<title>Data</title>", "<p id=\"Par10\">The analysis was performed based on routine data from the umbrella organization of Austrian social insurance institutions (the Federation of Austrian Social Insurance Institutions), which records data for accounting purposes. The dataset comprises data on all people insured under the statutory social insurance, i.e. 98.5% of the Austrian population, corresponding to approximately 8.82 million people [##UREF##3##8##]. It includes all public prescriptions in the outpatient sector that were collected in pharmacies or from dispensing doctors nationwide within the given time frame, the first quarter of 2013 (Q1 2013) to the fourth quarter of 2021 (Q4 2021). Medication obtained through private outpatient prescriptions or over-the-counter medication are not included, and data from the hospital sector are also not included as the outpatient and inpatient sectors are run by different entities in the Austrian healthcare system. We used quarterly prescription rates of the following medications and respective Anatomical Therapeutic Chemical Codes (ATC Codes): antidepressants (ATC Code N06A) and antipsychotics (ATC Code N05A) for the analysis. A further examination, which is beyond the scope of the present article, revealed that non-selective monoamine reuptake inhibitors (N06AA), monoamine oxidase A inhibitors (N06AG), and the category other antidepressants (N06AX) had very small prescription rates among adolescents, especially among the younger group of 10–14 year-olds, with below 100 prescriptions per quarter on average in most groups and two medication groups showing 0 prescriptions in all adolescent groups.</p>", "<p id=\"Par11\">The prescription rate was defined, based on [##UREF##14##32##], as the proportion of insurees who had at least one prescription dispensed in a given quarter within a specific ATC group per 1000 insurees in the same age and sex group (therefore representing an age- and sex-adjusted rate). An insuree receiving the respective medication is counted distinctly per quarter, meaning he or she is only counted once per quarter even if the prescribed medication is received several times.</p>", "<title>Statistical analysis</title>", "<p id=\"Par12\">An interrupted time series analysis (ITS) was performed to test for the influence of the pandemic on prescription rates for the relevant medication groups. Measures to restrict the spread of COVID-19, such as lockdowns, clearly divided the observation period into a pre- and post-period, with an intervention point dividing these periods chosen in the second quarter of 2020. For the population group of interest, Austrian adolescents, the longest period of home schooling/distance learning occurred from the beginning of November 2020 to 8 February 2021, with the first (shorter) lockdown period taking place in March 2020. Since the first lockdown was restricted to a few weeks, we hypothesized an increase in the prescription rate of antidepressants, antipsychotics, and benzodiazepines starting in Q3 of 2020. To account for underlying short- and long-term trends in the data, pre-existing trends in prescription rates must be taken into account, such that a steady or a seasonal increase in rates is not attributed to the pandemic. ITS controls for issues such as trends and seasonality by longitudinally tracking outcomes before and after an intervention [##REF##33752604##35##].</p>", "<p id=\"Par13\">The dataset for each medication was split into three age groups (10–14 years, 15–19 years, all ages combined, comprising every person insured in Austria, regardless of age) and two genders (male, female), resulting in 18 individual age- and sex-stratified time series. The time series data for each group was modelled from the start of the dataset in early 2013 to the pandemic-related restrictions in the second quarter of 2020 (Q2 2020). This was done to forecast confidence intervals at the 97.5% level based on these models for the post-restriction period from the third quarter of 2020 (Q3 2020) until the end of 2021. These confidence intervals represent expected development paths, based on time series developments only prior to the pandemic-related restrictions, and provide the best projection of what would have happened in the absence of the restrictions. The forecasts were subsequently compared to post-restriction observations in order to analyze deviations (see Figs. ##FIG##0##1##, ##FIG##1##2##). The forecasts were obtained using seasonal autoregressive integrated moving average (ARIMA) models. The analysis was performed using R [##UREF##13##31##], the packages tidyverse, lubridate and tsibble for transformation of the dataset [##UREF##7##18##, ##UREF##16##38##, ##UREF##17##39##], and the package fable to fit and select ARIMA models [##UREF##12##27##]. Each model was fit by choosing the optimal model according to the Fable package automatic model selection (with transformations applied according to prespecification), which uses a variation of the Hyndman-Khandakar algorithm selecting for the smallest AICc value [##UREF##8##19##, ##UREF##9##20##].</p>", "<p id=\"Par14\">As the dataset provided by the Federation of Austrian Social Insurance Institutions consisted of accumulated data without the possibility of identifying individualized data, a waiver was received from the ethical review committee of the Medical University of Vienna.</p>" ]

[ "<title>Results</title>", "<p id=\"Par15\">The results revealed an increase in the prescription rate of antidepressants and antipsychotics, which was especially pronounced in the age groups 10–14 and 15–19 years, and a steeper increase among female adolescents (see Table ##TAB##0##1##).</p>", "<title>Antidepressants</title>", "<p id=\"Par16\">Within the group of antidepressants, prescription rates among males in the 10–14- and 15–19-year age groups exceeded the 97.5% confidence intervals in three out of six observed quarters, while prescription rates for females in those age groups significantly exceeded predictions in five (10–14 year-olds) and six (15–19 year-olds) out of six observed quarters, respectively (see Fig. ##FIG##0##1##). This difference was even more pronounced when considering the differences in relative changes. The growth in prescription rates from Q3 2020 to Q4 2021 was 103.5% for 10–14-year-old females and 45.5% for 15–19-year-old females, while the growth in prescription rates for their male counterparts lay at only 34.7% and 22%, respectively. When combining all age groups, prescription rates only differed significantly from the model forecasts in one quarter for male patients and two quarters for female patients. Thus, increases more often exceeded model forecasts in female than in male patient groups, and these excesses were much more common in younger age groups than in the general population.</p>", "<title>Antipsychotics</title>", "<p id=\"Par17\">A similar pattern emerged regarding the development of antipsychotic prescription rates during the observation period Q3 2020–Q4 2021. Here, the gender gap among the younger age groups was even more pronounced than for antidepressants. As can be seen in Fig. ##FIG##1##2##, forecasts were exceeded in four out of six quarters in 10–14 year-old females and in five out of six quarters in 15–19-year-old females, while male adolescents of both age groups showed no excesses throughout the observation period. This difference is also visible in the relative growth rates between Q3 2020 and Q4 2021, with prescription rates growing by 74.3% in 10–14 year-old females and by 49.3% in 15–19 year-old females, while their male counterparts showed much smaller growth rates (14.8% and 14.4%, respectively).</p>" ]

[ "<title>Discussion</title>", "<p id=\"Par18\">Our analysis focused on the influence of the COVID-19 pandemic on prescription patterns of common psychotropic drugs with a specific focus on adolescents. Given the reported high burden of mental health problems among adolescents during the pandemic [##REF##34369987##33##, ##UREF##19##42##], we specifically examined the age groups of 10–14-year-olds and 15–19-year-olds regarding prescription rates of antidepressants and antipsychotics.</p>", "<title>Antidepressants</title>", "<p id=\"Par19\">Antidepressant prescription rates showed a steep upward trend among females aged 10–19 years, which were significantly above model predictions in four (10–14 year-olds) and five (15–19 year-olds) out of six quarters, respectively. No comparable trend was found in the general population (all age groups combined), with only individual quarters significantly exceeding model predictions. Pre-pandemic rates showed a high degree of stability when looking at the pattern from 2013 onwards. A sharp decline was observed between 2016 and 2017. This coincided with a debate about the efficacy of SSRIs in adolescents based on a meta-analysis [##REF##27289172##6##], which received broad media coverage in Austria and Germany [##UREF##10##22##, ##UREF##15##36##]. Shortly after the start of the pandemic, prescription rates for antidepressants began to increase steadily. Given that SSRIs are recommended as part of the treatment strategy for depression and anxiety in national guidelines [##UREF##0##1##, ##UREF##1##3##]; (AWMF, 2013), these elevated prescription rates can be interpreted as part of an increased treatment effort to counter the rising mental health problems throughout the COVID-19 pandemic.</p>", "<p id=\"Par20\">The increase in prescription rates was more pronounced in female than in male patients, with the steepest increase in antidepressant prescription rates found in female adolescents. This is consistent with findings of increasing rates of depressive symptoms reported worldwide. For instance, in a meta-analysis of 29 studies encompassing data from 80,879 children and adolescents globally, Racine et al., [##REF##34369987##33##] found a pooled prevalence rate of 25.2% for clinically elevated symptoms of depression, with higher rates of depressive symptoms reported in females. Recently, another meta-analysis based on 53 longitudinal studies from 12 countries also reported an increase in depressive symptoms in children and adolescents, which was stronger in female than in male participants [##REF##37126337##24##]. Besides data from international samples, studies on the mental health of Austrian adolescents during the pandemic point in the same direction, with an increase in depression and anxiety symptoms that was especially pronounced in female participants [##REF##35900473##10##, ##REF##34181016##28##].</p>", "<title>Antipsychotics</title>", "<p id=\"Par21\">From all evaluated age groups, the strongest increase in prescription rates was observed in 15–19-year-old females. As the use of antipsychotics in minors is only licensed in Austria for the treatment of bipolar disorder, schizophrenia, or major impulsive aggressive behavior, these rising rates, particularly among females, are highly interesting. Despite research demonstrating a particularly severe impact of the COVID-19 pandemic on people with schizophrenia [##REF##36762657##16##], few studies have explored a potential increase in first episodes of schizophrenia. One study, conducted in Australia, reported an increase in first-episode admissions for schizophrenia among young people following the introduction of lockdown measures [##REF##34651504##26##]. Additionally, while the pandemic has been linked to first presentations of manic episodes [##REF##35716481##34##], evidence regarding potential increases in bipolar disorders is lacking. Atypical antipsychotics have been mentioned as part of an augmentation regime in the treatment of resistant depression [##REF##32020643##14##, ##REF##32402075##37##], but are only suggested for the treatment of psychotic depression in minors [##UREF##11##25##]. Therefore, the rise in prescriptions of antipsychotics might also be interpreted as indicating an increased off-label use for the treatment of depression in minors. Furthermore, some antipsychotics are used as an off-label medication for severe anorexia nervosa [##UREF##6##17##]. As an increase in eating disorders has been reported throughout the pandemic [##REF##35384016##13##], increasing prescription rates of antipsychotic prescriptions may echo an increasing clinical demand in this field. This might further explain the higher prescription rates among females found in the present study, as the rise in eating disorders is especially pronounced among female adolescents [##REF##35384016##13##], potentially accompanied by increased psychopharmacological treatment. Unfortunately, we are unable to link prescriptions to diagnoses, as diagnostic ICD-10 codes are not currently available from the outpatient sector, from which this dataset is derived. Therefore, it can only be hypothesized that the use of antipsychotics in female adolescents corresponds to increased rates of treatment for depression, eating disorders, or other symptoms or disorders such as sleep problems.</p>", "<p id=\"Par22\">Overall, we were able to demonstrate an increase in prescriptions of antidepressants and antipsychotics in adolescents, which was especially pronounced in females. The observed trends are in line with increasing levels of symptoms of depression and anxiety during the COVID-19 pandemic as reported globally [##REF##36907199##23##, ##REF##34369987##33##] and in Austria [##REF##35900473##10##, ##REF##34181016##28##].</p>", "<p id=\"Par23\">Data regarding prescription rates of psychopharmacological agents in children and adolescents during the COVID-19 pandemic are scarce, although a report issued by the German health insurance company DAK described a substantial increase in antidepressant prescriptions in adolescent females between 2019 and 2021 (+ 30% in 10–14-year-olds and + 65% in 15–17-year-olds in those diagnosed with a depressive disorder) [##UREF##18##41##]</p>", "<p id=\"Par24\">Our findings are consistent with this reported increase in antidepressant prescriptions, but extend further than these datasets in terms of the population covered. For example, while the DAK report includes data from 5.7% of German children and adolescents [##UREF##18##41##], our dataset includes approximately 858,000 Austrian adolescents, representing about 99.4% of the Austrian population in this age group. Furthermore, we were able to analyze two different medication groups (antidepressants and antipsychotics).</p>", "<p id=\"Par25\">A study from the US using data from the IQVIA health insurance company (encompassing roughly 8.9 million minors aged between 2 and 17 years) analyzed monthly prescription rates of ADHD medication, antidepressants, antipsychotics, and mood stabilizers between January 2019 and September 2020. The results revealed a spike in overall psychopharmacological prescriptions in April 2020, which subsequently returned to normal, pre-pandemic levels [##REF##36067121##2##]. This trend is in line with the patterns assessed in our data, although it appears that the upward trend in the US sample is more short-lived than in our study. This might be explained by differences in the availability of mental health practitioners, different COVID-19 restrictions, and also different time frames of the respective analyses. A cohort study of Danish youth also reported an increase in incident prescriptions of psychotropic medication between March 2020 and June 2022, which was most prominent in the 12–24 years age group [##UREF##1##3##]. Interestingly, the rise in prescriptions was seen for all groups of psychotropic drugs, including hypnotics and sedatives, psychostimulants, antidepressants, and antipsychotics, but not in the group of anxiolytics. A recent study from Austria [##REF##36281638##40##], analyzing a shorter time frame of 2020 only, observed no significant changes in defined daily doses of psychopharmacological drugs between 2019 and 2020 in any age group. The differences from our findings are likely due to the different time intervals examined in the two studies: While the present study observed cumulative effects over six quarters, the study by [##REF##36281638##40##] focused on medication prescriptions during the national lockdowns in 2020. Nevertheless, it should be noted that in the study by [##REF##36281638##40##], the age group of 10–20-year-olds likewise showed the largest percentage increase in psychopharmacological prescriptions of all age groups.</p>" ]

[ "<title>Conclusion</title>", "<p id=\"Par28\">Data from the Federation of Austrian Social Insurance Institutions show an increase in prescriptions of antidepressants and antipsychotics throughout the pandemic, which was especially pronounced in female adolescents. The increasing rates of depression and anxiety symptoms that have been reported globally and in Austria appear to be associated with increased use of corresponding psychotropic medication. However, the increasing rates of prescriptions of antipsychotics warrant further attention and analysis, given that the evidence for their use in this age group is limited.</p>" ]

[ "<title>Background</title>", "<p id=\"Par1\">The COVID-19 pandemic has impacted many aspects of everyday life, including the (mental) healthcare system. An increase in depression and anxiety symptoms has been reported worldwide, and is particularly pronounced in females and young people. We aimed to evaluate changes in prescription rates for psychopharmacological medication, which is often used to treat depression and anxiety.</p>", "<title>Method</title>", "<p id=\"Par2\">Based on data from the Austrian public health insurance institutions, we conducted an interrupted time series analysis of antidepressants and antipsychotics, comparing prescription rate developments before and throughout the COVID-19 pandemic (2013 to 2021), with a special focus on adolescents (10–19 years) in comparison to the general population. Data were based on all public prescriptions in the outpatient sector nationwide. Age- and sex-stratified time-series models were fitted to the pre-COVID period (first quarter (Q1) of 2013 to second quarter (Q2) of 2020). These were used to generate forecasts for the period from the third quarter (Q3) of 2020 to the fourth quarter (Q4) of 2021, which were subsequently compared to observed developments in order to assess significant deviations from the forecasted development paths.</p>", "<title>Results</title>", "<p id=\"Par3\">For the majority of the evaluated period, we found a significant excess of antidepressant prescriptions among both male and female adolescents (10–14 and 15–19 years) compared to the forecasted development path, while the general population was mostly within 97.5% confidence intervals of the forecasts. Regarding antipsychotics, the interrupted time series analysis revealed a significant excess in the group of female adolescents in almost all quarters, which was especially pronounced in the 15–19 age group. Prescription rates of antipsychotics in the general population only showed a significant excess in two quarters.</p>", "<title>Conclusion</title>", "<p id=\"Par4\">Increased rates of adolescents receiving psychopharmacological treatment echo the epidemiological trends of an increase in depression and anxiety symptoms reported in the literature. This increase is especially pronounced in female adolescents.</p>", "<title>Keywords</title>" ]

[ "<title>Limitations</title>", "<p id=\"Par26\">Despite several strengths of the present study, such as a dataset of 8.82 million insured persons in Austria, representing about 98.5% of the Austrian population, several limitations need to be addressed. First, the use of anonymous, aggregated data limited the depth of analysis. However, given the size of the dataset, we feel that this study contributes interesting information for healthcare planning. Second, not all of these 8.82 million people are necessarily living and registered in Austria and therefore part of the Austrian census, while some people who are registered in Austria and therefore part of the Austrian population are insured in neighboring countries. This is mostly the case with cross-border commuters. It is difficult to quantify the exact number of people who are insured but not part of the Austrian census, although the impact on the sample should be minimal given the large scale of the dataset; at most, it should account for two percentage points of the whole dataset and likely even lower for the age groups of interest, namely adolescents. Third, data on medicines below the Austrian prescription fee threshold (€6.50 in 2021 and adjusted annually) are not collected in the central dataset, as patients pay for prescriptions below this threshold themselves (with the exception of individuals or households that are exempted from prescription fees due to low income or high total healthcare costs) [##UREF##4##9##]. The medications in our dataset are affected by this effect to different degrees, with antipsychotics and antidepressants being mostly above the threshold (85% and 59%, respectively).</p>", "<p id=\"Par27\">With regard to our statistical approach, two points warrant further attention. First, it is difficult to determine the precise timing of the pandemic-related restrictions, as there were several lockdowns on the national level as well as additional federal measures and restrictions, which varied strongly over time. Additionally, there were other restrictions and lockdowns on a regional level which only affected parts of the Austrian population. Second, we expected the effect to occur with a time lag, given that a certain delay can be expected with regard to a possible impact on mental health and subsequent help-seeking followed by prescriptions (as symptoms take time to develop and healthcare professionals need to be sought for treatment). To test the robustness of our results regarding the timing of the pandemic-related restrictions, we used the same modelling procedure but chose two alternative, but in our opinion less fitting, restriction time specifications: the first quarter of 2020 and the third quarter of 2020. In these different specifications, the overall patterns stayed the same. While some quarters lost significance and others gained significance, this did not affect the overall structure of the results.</p>" ]

[ "<title>Author contributions</title>", "<p>MO and PLP wrote the main manuscript text and developed the study design. LL and SD provided support in data provision. MO, ODK and PLP were involved in statistical analyses. MO, PLP, ODK, LL and SD revised the manuscript. All autors reviewed the manuscript.</p>", "<title>Funding</title>", "<p>No funding has been received.</p>", "<title>Availability of data and materials</title>", "<p>The data is owned by the Federation of Austrian Social Insurance Institutions. Requests for data can be sent to the first author.</p>", "<title>Declarations</title>", "<title>Ethical approval and consent to participate</title>", "<p id=\"Par29\">As the dataset provided by the Federation of Austrian Social Insurance Institutions consisted of accumulated data without the possibility of identifying individualized data, a waiver was received from the ethical review committee of the Medical University of Vienna.</p>", "<title>Competing interests</title>", "<p id=\"Par30\">MO, OK, LL, and SD report no conflict of interest. PLP has received research funding from the German Federal Ministry of Education and Research, the German Federal Institute for Drugs and Medical Devices, the Volkswagen Foundation, the Baden-Wuerttemberg Foundation, Servier, Lundbeck, the Vienna Landeszielsteuerungskommission, the Austrian Science Fund, the Hochschuljubiläumsfonds and the Austrian National fund, the Austrian future fund. He works as an advisor for Boehringer Ingelheim and Delta 4. He has received speaker´s fees from Infectopharm, Janssen, GSK, and Oral B.</p>" ]

[ "<fig id=\"Fig1\"><label>Fig. 1</label><caption><p>Prescription rate developments and forecasts for antidepressants stratified by age and sex group. Age groups (10–14 years, 15–19 years, all age groups combined, including adults) according to gender from Quarter 1 (Q1) of 2013 to Quarter 4 (Q4) of 2021</p></caption></fig>", "<fig id=\"Fig2\"><label>Fig. 2</label><caption><p>Prescription rate developments and forecasts for antipsychotics stratified by age and sex group. Age groups (10–14 years, 15–19 years, all age groups combined, including adults) according to gender from Quarter 1 (Q1) of 2013 to Quarter 4 (Q4) of 2021</p></caption></fig>" ]

[ "<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Absolute and relative change in the prescription rate between Quarter 3 (Q3) of 2020 and Quarter 4 (Q4) of 2021</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Medication</th><th align=\"left\">Age</th><th align=\"left\">Sex</th><th align=\"left\">2020 3rd Quarter</th><th align=\"left\">2021 4th quarter</th><th align=\"left\">Change in Percent %</th></tr></thead><tbody><tr><td align=\"left\" colspan=\"6\">Antidepressants</td></tr><tr><td align=\"left\">Antidepressants</td><td align=\"left\">10–14</td><td align=\"left\">Female</td><td char=\".\" align=\"char\">2.05</td><td char=\".\" align=\"char\">4.18</td><td char=\".\" align=\"char\">103.54</td></tr><tr><td align=\"left\">Antidepressants</td><td align=\"left\">10–14</td><td align=\"left\">Male</td><td char=\".\" align=\"char\">1.48</td><td char=\".\" align=\"char\">1.99</td><td char=\".\" align=\"char\">34.69</td></tr><tr><td align=\"left\">Antidepressants</td><td align=\"left\">15–19</td><td align=\"left\">Female</td><td char=\".\" align=\"char\">15.83</td><td char=\".\" align=\"char\">23.03</td><td char=\".\" align=\"char\">45.47</td></tr><tr><td align=\"left\">Antidepressants</td><td align=\"left\">15–19</td><td align=\"left\">Male</td><td char=\".\" align=\"char\">7.81</td><td char=\".\" align=\"char\">9.53</td><td char=\".\" align=\"char\">22.04</td></tr><tr><td align=\"left\">Antidepressants</td><td align=\"left\">All ages</td><td align=\"left\">Female</td><td char=\".\" align=\"char\">79.77</td><td char=\".\" align=\"char\">80.96</td><td char=\".\" align=\"char\">1.49</td></tr><tr><td align=\"left\">Antidepressants</td><td align=\"left\">All ages</td><td align=\"left\">Male</td><td char=\".\" align=\"char\">41.52</td><td char=\".\" align=\"char\">42.37</td><td char=\".\" align=\"char\">2.04</td></tr><tr><td align=\"left\" colspan=\"6\">Antipsychotics</td></tr><tr><td align=\"left\"> Antipsychotics</td><td align=\"left\">10–14</td><td align=\"left\">Female</td><td char=\".\" align=\"char\">1.22</td><td char=\".\" align=\"char\">2.13</td><td char=\".\" align=\"char\">74.26</td></tr><tr><td align=\"left\"> Antipsychotics</td><td align=\"left\">10–14</td><td align=\"left\">Male</td><td char=\".\" align=\"char\">2.73</td><td char=\".\" align=\"char\">3.14</td><td char=\".\" align=\"char\">14.78</td></tr><tr><td align=\"left\"> Antipsychotics</td><td align=\"left\">15–19</td><td align=\"left\">Female</td><td char=\".\" align=\"char\">5.12</td><td char=\".\" align=\"char\">7.65</td><td char=\".\" align=\"char\">49.32</td></tr><tr><td align=\"left\"> Antipsychotics</td><td align=\"left\">15–19</td><td align=\"left\">Male</td><td char=\".\" align=\"char\">4.68</td><td char=\".\" align=\"char\">5.36</td><td char=\".\" align=\"char\">14.40</td></tr><tr><td align=\"left\"> Antipsychotics</td><td align=\"left\">All ages</td><td align=\"left\">Female</td><td char=\".\" align=\"char\">21.01</td><td char=\".\" align=\"char\">22.14</td><td char=\".\" align=\"char\">5.38</td></tr><tr><td align=\"left\"> Antipsychotics</td><td align=\"left\">All ages</td><td align=\"left\">Male</td><td char=\".\" align=\"char\">16.04</td><td char=\".\" align=\"char\">16.94</td><td char=\".\" align=\"char\">5.61</td></tr></tbody></table></table-wrap>" ]

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[ "<table-wrap-foot><p>This table displays the change in the respective prescription rates between the third quarter of 2020, the beginning of the effect, and the fourth quarter of 2021, the end of the observation period</p></table-wrap-foot>", "<fn-group><fn><p><bold>Publisher's Note</bold></p><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn></fn-group>" ]

[ "<graphic xlink:href=\"13034_2023_684_Fig1_HTML\" id=\"MO1\"/>", "<graphic xlink:href=\"13034_2023_684_Fig2_HTML\" id=\"MO2\"/>" ]

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{ "acronym": [], "definition": [] }

[ "<title>Introduction</title>", "<p id=\"Par5\">Vaginal squamous intraepithelial lesions (SIL) are a type of diseases characterized by the occurrence of atypical hyperplasia of vaginal squamous cells and carcinoma in situ, excluding invasive carcinoma [##UREF##0##1##]. They are rare precancerous lesions of the lower genital tract, accounting for approximately 0.4-1% of epithelial tumors of the lower genital tract, with an incidence 100 times lower than that of cervical SIL [##REF##8784884##2##–##REF##16001196##5##]. Currently, the popularization of cervical cancer screening and improvements in detection technology have increased the detection rate of vaginal SIL [##UREF##0##1##, ##REF##22239755##6##].</p>", "<p id=\"Par6\">In 2014, the World Health Organization (WHO) classified vaginal intraepithelial lesions into vaginal low-grade squamous intraepithelial lesions (LSIL) and vaginal high-grade squamous intraepithelial lesions (HSIL) in the <italic>Classification of Tumors of Female Reproduction Organs</italic> [##UREF##1##7##]. Vaginal LSIL can be treated conservatively due to its high potential for spontaneous regression and low risk for progression to malignancy [##REF##24201676##8##]. Even if vaginal HSIL are benign, active treatment is always recommended, as the risk of malignant transformation in vaginal HSIL can reach 4.6-12% [##UREF##0##1##, ##REF##26461231##9##–##REF##16098901##11##]. However, consensus concerning the best optimal management of vaginal HSIL is currently lacking. The treatment for vaginal HSIL needs individualization, so the treatment modalities are diverse at present, including surgical resection, topical pharmaceuticals, photodynamic therapy, laser vaporization, and brachytherapy [##REF##22460272##3##, ##REF##26334358##12##–##REF##36958755##14##].</p>", "<p id=\"Par7\">In general, surgical resection is the mainstay and preferred method, because it can not only provide a specimen for complete histopathological diagnosis to identify occult invasive cancer, but also has high cure rate [##REF##11240702##10##, ##REF##16098901##11##, ##REF##36958755##14##, ##REF##1733213##15##]. In clinical practice, vaginectomy is favored by gynecologists in patients with extensive and persistent vaginal HSIL, or suspicious invasive vaginal HSIL [##REF##36958755##14##]. Anatomically, the vagina is located in the middle of the deep pelvic cavity next to the bladder and rectum, and the space of vaginal cavity is quite small, leading to limited vision in the transvaginal surgery and significantly increasing the difficulty of the procedure. Thus, the application of transvaginal vaginectomy is limited in complex vaginal surgeries which require greater precision because of the restricted space and intricate anatomy of vagina. In recent decades, minimally invasive laparoscopy, including robotic-assisted laparoscopy, has expanded rapidly and has been widely used in a variety of gynecological operations, such as endometrial carcinoma, cervical cancer, endometriosis, pelvic retroperitoneal tumors and pelvic organ prolapse [##REF##35844092##16##–##REF##36773501##20##]. Minimally invasive laparoscopy is characterized by magnifying the surgical field, which contributes to identifying blood vessels and finely separating tissue spaces, reducing intraoperative injury. In addition, long-arm instruments with small end-effector could simplify surgery and increase the flexibility of surgical operation in narrow spaces. Therefore, owing to these technical advantages, the conventional laparoscopic vaginectomy (CLV) has gained popularity by gynecological surgeons [##REF##23725440##21##].</p>", "<p id=\"Par8\">Unlike conventional laparoscopic surgery, the robotic-assisted laparoscopic process system has better high-definition and magnified three-dimensional view and can more precisely visualize the surgical area; it also improves the mobility and increases the range of motion of the instrument’s end-effector. According to previous studies, the robotic-assisted surgery could be considered safer and a more effective surgical tool than conventional laparoscopic surgery for women who have to undergo complex and challenging gynecology surgery [##REF##35844092##16##]. With the increase of the incidence of vaginal HSIL and the popularization of robotic surgery, the use of robotic-assisted laparoscopic vaginectomy (RALV) has likely increased. However, until now, there is no guideline or consensus regarding the optimal surgical approach for vaginectomy. Studies about evaluating the safety and efficiency between RALV and CLV are absent. Therefore, the purpose of our study was to compare the safety and treatment outcomes between the RALV and CLV for selected patients with vaginal HSIL.</p>" ]

[ "<title>Materials and methods</title>", "<title>Study design</title>", "<p id=\"Par10\">This was a retrospective study of patients with vaginal HSIL who underwent either robotic-assisted laparoscopic vaginectomy or conventional laparoscopic vaginectomy in the Department of gynecology, the First Affiliated Hospital of Zhengzhou University from December 2013 to May 2022. Vaginal HSIL was diagnosed through colposcopically guided biopsy before vaginectomy. All the patients are characterized with extensive lesions (beyond the upper third of vagina or multifocal lesions limited to the upper third of vagina but concurrent with cervical HSIL), and/or persistent multifocal lesions (failure of conservative treatment), and/or recurrent lesions, and/or suspicious invasive lesions. Once vaginal HSIL combined with cervical HSIL, cervical cancer would be excluded by cervical conization before vaginectomy. In addition, patients with vaginitis were cured preoperatively and the patients were excluded if they: (1) were diagnosed with vaginal invasive cancer before vaginectomy; (2) had previous hysterectomy for gynecological cancer; (3) had vesical dysfunction (for example, incontinentia urinae or retention of urine); or (4) had incomplete information of follow-up.</p>", "<title>Surgical procedures</title>", "<p id=\"Par12\">The location and range of preoperative lesions were accurately recorded via careful inspection of the total vagina and/or cervix by colposcope. Especially for post-hysterectomy vaginal HSIL, more attention needs to be given to examining the folds of the vaginal cuff, as some lesions may hide in the vaginal angles, making them difficult to identify.</p>", "<p id=\"Par13\">For each patient, the choice of RALV and CLV was based on the final decision of the patients and their family after being informed by the surgeon about the advantages and disadvantages of the two procedures. RALV was performed using the da Vinci-Si Surgical System (Intuitive Surgical Inc, Sunnyvale CA, USA).</p>", "<p id=\"Par14\">Patients were placed in the lithotomy position. After general anesthesia, the surgical area was routinely disinfected and covered with sterile surgical towels, a urethral catheter was inserted, and then trocars were placed by surgeons. In addition, the RALV group needed to connect mechanical arms. Lesion areas were confirmed by applying Lugol’s iodine solution to the total vagina and/or cervix and marked by a suture or marking pen approximately 0.5 cm (at least 0.3 cm) below the edge of the lesion (Fig. ##FIG##0##1##). The uterine manipulator was placed in the vagina for patients with a uterus, but a gauze roll or the cup of the uterine manipulator was placed in the vagina for patients who had received a hysterectomy (Fig. ##FIG##0##1##). For patients with post-hysterectomy vaginal HSIL, the length of the vaginal wall should be resected from the vaginal stump to 0.5 cm (at least 0.3 cm) below the edge of the lesion. For those who had vaginal HSIL combined with cervical HSIL, hysterectomy ± bilateral salpingo-oophorectomy was performed simultaneously in addition to vaginectomy during the operation (Figs. ##FIG##1##2## and ##FIG##2##3##).</p>", "<p id=\"Par15\">\n\n</p>", "<p id=\"Par16\">\n\n</p>", "<p id=\"Par17\">\n\n</p>", "<title>Data collection</title>", "<p id=\"Par19\">The demographic and clinical data, such as age, menopause, body mass index (BMI), the ASA grade (assessed by the The American Society of Anesthesiologists (ASA) Physical Status Classification System), clinical manifestation, comorbidities, previous hysterectomy, status of human papillomavirus (HPV) infection and antecedent cytology, intravaginal estrogen pretreatment, lesions range and treatments of vaginal HSIL before vaginectomy were extracted via our electronic medical record system. We also collected operative data, including the total operation time (defined as the time from skin incision to the time of last closure suture of the skin), estimated blood loss, complications, length of resected vagina, flatus passing time (calculated in days from the end of the operation to the first time of the ability to pass feces or gas), postoperative catheterization time (calculated in days from the end of the operation to the catheter extracted smoothly without paruria), postoperative hospitalization time, postoperative pathology and hospital cost. All procedures were performed by gynecologists with extensive experience in conventional laparoscopic or robotic-assisted surgery. Therefore, a learning curve was not included in the operations. Intraoperative complications included hemorrhage (estimated blood loss exceeding 500 mL) and bladder, ureter, and bowl injury. Postoperative complications were defined as any newly unfavorable episodes occurring during the hospital stay or within 30 days after surgery.</p>", "<p id=\"Par20\">All patients were followed up to assess postoperative outcomes, including the status of homogeneous HPV infection and the regression, remission, persistence, recurrence or progression of vaginal HSIL. The status of homogeneous HPV infection was determined by HPV screening at six months after vaginectomy. Regression was defined as negative colposcopic examination and vaginal biopsy at six months after vaginectomy. Remission was defined as vaginal LSIL diagnosed by vaginal biopsy at six months after vaginectomy. Persistence was defined as vaginal HSIL diagnosed by vaginal biopsy at six months after vaginectomy. The short-term prognosis was defined as the treatment outcomes at six months after vaginectomy. Recurrence was defined as vaginal HSIL again after remission or regression. Progression was defined as invasive vaginal carcinoma, a higher grade lesion than previous vaginal HSIL. Disease-free survival was defined as the time from vaginectomy to disease progression or recurrence. All patients were followed-up for the first time at the third month after the operation, then visited every 3 months for half a year, every 6 months for 2.5 years, and then once a year after 3 years. The pelvic examination, HPV test and thinprep cytologic test (TCT) were conducted as the essential items. Patients were referred for colposcopy when meeting the requirements for colposcopy referral, and histopathological examination was performed if necessary. All of the patients were followed up until February 2023.</p>", "<title>Statistical analysis</title>", "<p id=\"Par22\">SPSS (version 21.0, Chicago, IL, USA) software was used to analyze the data. Quantitative variables are presented as the mean (standard deviation) or median (interquartile range), and were compared using Student’s <italic>t-</italic>test or the <italic>Mann-Whitney U</italic> test, as appropriate. Categorical variables are reported as absolute numbers (percentages) and were compared using the Pearson χ^{<italic>2</italic>} test or the Fisher exact test, as appropriate. Survival curves were generated by using the Kaplan–Meier method, and Cox proportional-hazards models were used to estimate the hazard ratios (HR) and 95% confidence intervals (CI) for the effect of treatment on disease-free survival. <italic>P</italic> &lt; 0.05 (two-tailed) were considered statistically significant.</p>" ]

[ "<title>Results</title>", "<title>Patient Characteristics</title>", "<p id=\"Par24\">We identified 118 patients with vaginal HSIL who underwent either robotic-assisted laparoscopic vaginectomy or conventional laparoscopic vaginectomy from December 2013 to May 2022. As shown in Fig. ##FIG##3##4##, nine patients were excluded. The remaining 109 patients were analyzed in our study, including 77 patients underwent robotic-assisted laparoscopic vaginectomy (RALV group) and 32 patients underwent conventional laparoscopic vaginectomy (CLV group). Among them, 7 patients (5 in the CLV group and 2 in the RALV group) experienced failure of photodynamic therapy, 2 patients in the CLV group experienced recurrence of photodynamic therapy and 3 patients (2 in the CLV group and 1 in the RALV group) experienced failure of laser ablation. The demographic and clinical characteristics of the patients are summarized in Table ##TAB##0##1##. These baseline characteristics were similar between the two groups except for the range of vaginal HSIL. The mean age of the patients was 55.2 years, and the mean BMI was 24.0 kg/m². Most patients (89.0%) were menopausal. One hundred (91.7%) patients had high-risk HPV infection, among which HPV16 infection (66.0%) was the most common type. Forty-three patients underwent previous hysterectomy (32 patients in the CLV group and 11 patients in the RALV group). Indications included cervical HSIL (27 patients in the CLV group and 8 patients in the RALV group), hysteromyoma (1 patient in the CLV group and 3 patients in the RALV group), adenomyosis (1 patient in the CLV group), abnormal uterine bleeding (2 patients in the CLV group), and benign ovarian tumor (1 patient in the CLV group). There was significant difference between the two groups in the range of vaginal HSIL (<italic>P</italic> &lt; 0.001).</p>", "<p id=\"Par25\">\n\n</p>", "<p id=\"Par26\">\n\n</p>", "<title>Operative data</title>", "<p id=\"Par28\">Of all patients, eight patients (25.0%) in the RALV group underwent total vaginectomy with or without hysterosalpingo-oophorectomy, and seven (9.1%) patients in the CLV group underwent total vaginectomy with or without hysterosalpingo-oophorectomy (<italic>P</italic> = 0.059). As shown in Table ##TAB##1##2##, the length of the resected vagina measured after the operation was longer in the RALV group than that in the CLV group (5.0 (4.3–5.9) vs. 3.5 (3.0-4.5), <italic>P</italic> &lt; 0.001). The total operation time in the CLV group (118.2 ± 41.0 min) was similar to that in the RALV group (129.9 ± 43.8 min) (<italic>P</italic> = 0.186). The estimated blood loss was higher in the CLV group than that in the RALV group (<italic>P</italic> = 0.017). The operative complications details were summarized in Table ##TAB##1##2##. The intraoperative complications, including hemorrhage (7.8% vs. 3.1%), bladder injury (13.0% vs. 3.1%,), ureteral injury (2.6% vs. 0) and rectal injury (1.3% vs. 0), more frequently occurred in the CLV group than that in the RALV group. The postoperative complications rate in the CLV group appeared to be higher than that in the RALV group, but the difference was not significant (<italic>P</italic> = 0.192). With respect to flatus passing time, catheterization time and postoperative hospitalization time, these were all longer in the CLV group (all <italic>P</italic> &lt; 0.05). In this study, only one patient (0.9%) who underwent CLV had positive surgical margin, and four patients (3.7%) were ultimately diagnosed with occult vaginal invasive carcinoma after vaginectomy. In addition, the RALV group was associated with significantly higher hospital costs in comparison with the CLV group (53035.1 ± 9539.0 yuan vs. 32706.8 ± 6659.2 yuan, <italic>P</italic> &lt; 0.001).</p>", "<p id=\"Par29\">\n\n</p>", "<title>Follow-up</title>", "<p id=\"Par31\">Regarding the postoperative follow-up, four patients who were diagnosed with occult vaginal invasive carcinoma after vaginectomy were excluded. The median duration of follow-up of 105 patients after vaginectomy was 33.0 (range 7-109) months. Table ##TAB##2##3## showed that during the long-term follow-up, similar prognosis were found between the two groups. Ninety-six patients (91.4%) got homogeneous HPV infection regression at six months after vaginectomy. A total of 94.3% (99/105) of the patients experienced vaginal HSIL regression to disease-free through vaginectomy. Six patients (5 patients in the CLV group and 1 patient in the RALV group) were observed recurrence or progression, but the difference was not significant between the two groups (HR = 0.507; 95% CI, 0.242–17.499) (Fig. ##FIG##4##5##).</p>", "<p id=\"Par32\">\n\n</p>", "<p id=\"Par33\">\n\n</p>" ]

[ "<title>Discussion</title>", "<p id=\"Par34\">Vaginal squamous intraepithelial lesions are the precancerous lesions of invasive vaginal carcinoma, which lack specific clinical manifestations. The vast majority of patients are asymptomatic, and only a small number of people may experience abnormal vaginal secretions or bleeding after sexual intercourse [##REF##26797205##22##]. Undoubtedly, abnormal vaginal secretions are a characteristic clinical symptom of vaginitis rather than other gynecological diseases. Usyk et al. [##REF##32214382##23##], based on a prospective longitudinal cohort study, reported that the cervicovaginal microbiome is related to high-risk HPV progression in cervical squamous intraepithelial lesions. Thus, whether vaginal inflammation is associated with vaginal squamous intraepithelial lesions is intriguing. In this study, only 26.6% (29/109) of patients visited the doctor because of clinical symptoms; the remaining patients were diagnosed from cervical cancer screening. Thus, the timely detection of vaginal SIL appears to remain difficult.</p>", "<p id=\"Par35\">The mean age of patients in our study was 55.2 years, similar to that in Kim’s report [##REF##29185264##24##]. Previous studies had reported that high-risk HPV infection, previous hysterectomy especially for the indication of cervical HSIL, postmenopause, previous irradiation for gynecological cancer, smoking and immunosuppression, are risk factors for vaginal squamous intraepithelial lesions [##REF##36958755##14##, ##REF##29185264##24##–##REF##31860574##29##]. We noted that 91.7% (100/109) of patients had high-risk HPV infection, among which HPV16 infection was more predominant, and these findings are consistent with those of previous related studies [##REF##36958755##14##, ##REF##34460120##30##, ##REF##25155250##31##]. In this study, 43 patients (39.4%) had previously undergone hysterectomy, 35 (81.4%) of whom underwent hysterectomy due to cervical HSIL. Although we did not specifically analyze the relationship between vaginal HSIL and the history of previous hysterectomy, it could obviously show that previous hysterectomy resulting from cervical HSIL was associated with vaginal HSIL. In our current study, 89.0% of patients were postmenopausal, suggesting that vaginal HSIL is more common in postmenopausal women. Li et al. [##REF##22613591##27##], through a case-control study, observed that postmenopausal women had a 2.09 times higher increased risk of developing into vaginal SIL than premenopausal women (<italic>P</italic> = 0.024; 95% CI = 1.10–3.85), indicating that menopause is a high risk factor for vaginal SIL.</p>", "<p id=\"Par36\">Researches have shown that approximately 4.6-12% of occult vaginal invasive cancers are ultimately discovered in the course of initial management of vaginal HSIL [##UREF##0##1##, ##REF##26461231##9##–##REF##16098901##11##, ##REF##26797205##22##]. In addition, Hodeib et al. [##REF##27032375##32##] observed that about 12% vaginal HSIL progressed to vaginal invasive carcinoma during close follow-up after active treatment. In this study, 3.7% (4/109) of patients were diagnosed with occult vaginal carcinoma based on postoperative pathology, and three patients progressed to vaginal carcinoma during the long-term follow-up.</p>", "<p id=\"Par37\">Unfortunately, the managements of vaginal HSIL remain controversial, which include topical pharmaceuticals (such as 5-fluorouracil cream, imiquimod and interferon), laser vaporization, photodynamic therapy, surgery and brachytherapy [##REF##22460272##3##, ##REF##26334358##12##, ##REF##29185264##24##, ##REF##31680701##33##, ##REF##32602195##34##]. In fact, the treatment of vaginal HSIL is individualized in the clinic according to the patient’s age, disease characteristics, status of HPV infection, previous therapeutic procedures and others [##REF##36958755##14##, ##REF##31680701##33##]. Topical pharmaceuticals are prevalent in adjuvant therapy, especially in HPV-induced patients [##REF##23551637##35##]. Young patients with multifocal and exposure-prone vaginal HSIL can be treated with laser vaporization or photodynamic therapy [##REF##29185264##24##]. Surgical resection treatments, which included local resection, partial vaginectomy and total vaginectomy, were characterized by shortening the time to normalization and higher cure rates, the range of which has be reported about 80% [##REF##16098901##11##, ##REF##36958755##14##, ##REF##1733213##15##]. However, surgical management could shorten the length of the vagina, which negatively affects the quality of sexual life, and may place patients at risk for stenosis of the vagina [##REF##1733213##15##]. Therefore, surgical treatments should only be considered for selected patients. Unifocal lesions are usually treated by local resection; partial vaginectomy is suitable for the selected vaginal HSIL, such as extensive lesions, persistent or recurrent lesions, and suspicious invasive lesions. As recommended in the Chinese expert and European expert consensuses on the management of vaginal SIL, the lesions of postmenopausal vaginal HSIL are extensive and involve the entire vagina, or lesions are extensive and persistent, total vaginectomy can be considered [##REF##36958755##14##]. In our study, 94.3% (99/105) of patients had a regression of vaginal HSIL to disease-free through vaginectomy. Brachytherapy exhibits distinct efficacy on vaginal HSIL, with a cure rate of 77-96% [##REF##17481703##36##–##REF##21262875##38##]. However, patients face with the vaginal mucosal atrophy, stenosis, ulcers and injury to the rectum and bladder after brachytherapy, leading to a long-term influence on later quality of life [##REF##23267125##13##]. Therefore, brachytherapy is usually recommended to the patient who cannot tolerate surgery or whose disease is resistant to conservative managements.</p>", "<p id=\"Par38\">This work is the first retrospective study comparing both operative data and patient-centered prognosis between CLV and RALV. We find that RALV was more frequently performed in the patients who had more extensive lesions of the vagina. Indeed, based on the anatomy around the vagina, the longer length of the abnormal vagina needed for resection, the more difficult it is to perform vaginectomy. However, our study suggested that the total operation time did not significantly differ between the two groups (<italic>P</italic> = 0.186). Compared with the CLV group, the RALV group had less estimated blood loss, which is consistent with the results from most other studies comparing robotic-assisted surgery and conventional laparoscopic surgery [##REF##35844092##16##, ##REF##35856237##39##–##REF##32169189##41##]. In addition, the intraoperative complications rate was significantly lower in the RALV group than that in the CLV group (6.3% vs. 24.7%, <italic>P</italic> = 0.026). Among the reported intraoperative complications, it reveals that 10.1% (11/109) of patients experienced bladder injury, which was the main complication during vaginectomy. Choi et al. [##REF##23725440##21##] reported four patients with vaginal squamous intraepithelial lesions who underwent laparoscopic upper vaginectomy, one of whom developed bladder injury. There are venous plexus, vaginal branch of uterine artery and ureter on both sides of the upper vagina. The upper 2/3 of the anterior vaginal wall is adjacent to the bladder through the vesico-vaginal septum, and the venous plexus is densely distributed between them. The lower 1/3 of the anterior vaginal wall is adjacent to the urethra through the urethra-vaginal septum, and the middle part of the posterior vaginal wall is attached to the ampulla of the rectum by a thin layer. Therefore, during vaginectomy, blood vessels, the ureter, the bladder and the rectum are easily damaged, leading to intraoperative complications. The level of estrogen in the body and vaginal elasticity are especially decreased in postmenopausal patients with post-hysterectomy vaginal HSIL. After hysterectomy, the anatomical structures of the vaginal stump are altered and tissue adhesion is formed; consequently, the risks of injury to the ureter, bladder and rectum become higher when the bladder and rectum are pushed down during vaginectomy, making vaginectomy more difficult. However, these challenges could be overcome by robotic surgery. Well-known that robotic surgery system provides three-dimensional visualization, by which the intraoperative field can be magnified approximately 10–15 times [##REF##33175318##42##]. Thus, surgeons can more distinctly identify the anatomy around the vagina and avoid surgical damage; in addition, robotic instruments have multiple degrees of freedom for movement and mini end-effector, as well as tremor-filtering technology and stable cameras, which provide much flexibility and precision for vaginectomy, leading to fewer intraoperative complications. Feng et al. [##REF##36087608##40##] conducted a multicenter randomized controlled trial of rectal cancer surgery and demonstrated that robotic-assisted surgery is more suitable for operations in the deeply narrow pelvic cavity.</p>", "<p id=\"Par39\">We observed that robotic-assisted surgery was associated with faster postoperative recovery in terms of shorter flatus passing time, catheterization time and postoperative hospitalization time, which is consistent with other reports [##REF##35844092##16##, ##REF##36087608##40##]. Fifteen patients underwent total vaginectomy in the current study and did not undergo vaginoplasty. Because this study was retrospective, the preoperative communication informed document showed that patients had been informed about the available vaginoplasty options and the impact of total vaginectomy on their sexual function, but they all chose to refuse vaginoplasty. Undeniably, total vaginectomy can make postoperative sexual intercourse impossible in patients with vaginal HSIL. Although vaginoplasty which is a challenging procedure has high requirement on the surgeon’s technique, it can significantly improve the satisfactory of sexual life [##REF##14585901##43##, ##REF##32840336##44##]. Consequently, vaginoplasty can be considered for selected patients who will be performed with total vaginectomy.</p>", "<p id=\"Par40\">Although the advantages of robotic-assisted vaginectomy are distinct, the hospital costs of robotic surgery is significantly higher than that of conventional laparoscopic surgery, consistent with the finds of other studies [##REF##28029176##45##–##REF##33844089##47##]. The cost is a continuing limitation to those who choose the surgical approach primarily based on their economic status. However, robotic surgery has the potential to be used in telemedicine, and robot-based telemedicine has become a reality in some hospitals. Through the telemedicine system platform, medical care could be performed without restrictions on time and place, and therefore, more potentialities and advantages of robotic surgery will be found. Jang et al. [##REF##31971885##48##] demonstrated the economic feasibility of the robot-based telemedicine system compared with traditional face-to-face medical services through a cost-benefit analysis. Therefore, the shortcomings of robotic surgery regarding the higher hospital costs can be balanced under the utilizing of robot-based telemedicine systems.</p>", "<p id=\"Par41\">The limitations of this study must be considered when interpreting its results. First, our study is limited by single center, retrospective design, which might have selection bias of patients and affect the generalizability and transferability of the results. Second, although our institution, the First Affiliated Hospital of Zhengzhou University, is the largest comprehensive hospital in the Central Plains of China, with a large number of gynecological operations every year, the sample size of our study is still limited due to the low incidence rate of vaginal HSIL. Therefore, multicenter randomized controlled studies should be actively conducted to provide more robust evidence for comparing the advantages and disadvantages of robotic-assisted vaginectomy and conventional laparoscopic vaginectomy in the treatment of vaginal HSIL. Third, the conventional laparoscopy approach used in this study was equipped with two-dimensional cameras. Currently, the latest generation of conventional laparoscopy techniques has been improved with three-dimensional cameras, which has overcome the lack of depth perception in two-dimensional cameras. As this technology evolves, conventional laparoscopic surgery will improve, providing better assistance in vaginectomy.</p>" ]

[ "<title>Conclusions</title>", "<p id=\"Par42\">Our study is the largest retrospective study of patients with vaginal HSIL who underwent vaginectomy via robotic-assisted surgery or conventional laparoscopic surgery. Both the two groups, patients can achieve similar satisfactory treatment outcomes, but patients seem to more frequently benefit from robotic-assisted surgery. Except for higher hospital costs, patients who underwent RALV had less estimated blood loss, lower intraoperative complications rate and experienced a faster postoperative recovery. When vaginectomy is recommended to be performed for a selected patient with vaginal HSIL, robotic-assisted laparoscopic vaginectomy can be considered as a better choice.</p>" ]

[ "<title>Background</title>", "<p id=\"Par1\">Vaginectomy has been shown to be effective for select patients with vaginal high-grade squamous intraepithelial lesions (HSIL) and is favored by gynecologists, while there are few reports on the robotic-assisted laparoscopic vaginectomy (RALV). The aim of this study was to evaluate the safety and treatment outcomes between RALV and the conventional laparoscopic vaginectomy (CLV) for patients with vaginal HSIL.</p>", "<title>Methods</title>", "<p id=\"Par2\">This retrospective cohort study was conducted in 109 patients with vaginal HSIL who underwent either RALV (RALV group) or CLV (CLV group) from December 2013 to May 2022. The operative data, homogeneous HPV infection regression rate and vaginal HSIL regression rate were compared between the two groups. Student’s <italic>t-</italic>test, the <italic>Mann-Whitney U</italic> test, Pearson χ² test or the Fisher exact test, Kaplan-Meier survival analysis and Cox proportional-hazards models were used for data analysis.</p>", "<title>Results</title>", "<p id=\"Par3\">There were 32 patients in the RALV group and 77 patients in the CLV group. Compared with the CLV group, patients in the RALV group demonstrated less estimated blood loss (41.6 ± 40.3 mL vs. 68.1 ± 56.4 mL, <italic>P</italic> = 0.017), lower intraoperative complications rate (6.3% vs. 24.7%, <italic>P</italic> = 0.026), and shorter flatus passing time (2.0 (1.0–2.0) vs. 2.0 (2.0–2.0), <italic>P</italic> &lt; 0.001), postoperative catheterization time (2.0 (2.0–3.0) vs. 4.0 (2.0–6.0), <italic>P</italic> = 0.001) and postoperative hospitalization time (4.0 (4.0–5.0) vs. 5.0 (4.0–6.0), <italic>P</italic> = 0.020). In addition, the treatment outcomes showed that both RALV group and CLV group had high homogeneous HPV infection regression rate (90.0% vs. 92.0%, <italic>P &gt;</italic> 0.999) and vaginal HSIL regression rate (96.7% vs. 94.7%, <italic>P</italic> = 0.805) after vaginectomy. However, the RALV group had significantly higher hospital costs than that in the CLV group (53035.1 ± 9539.0 yuan vs. 32706.8 ± 6659.2 yuan, <italic>P</italic> &lt; 0.001).</p>", "<title>Conclusions</title>", "<p id=\"Par4\">Both RALV and CLV can achieve satisfactory treatment outcomes, while RALV has the advantages of less intraoperative blood loss, fewer intraoperative complications rate and faster postoperative recovery. Robotic-assisted surgery has the potential to become a better choice for vaginectomy in patients with vaginal HSIL without regard to the burden of hospital costs.</p>", "<title>Keywords</title>" ]

[]

[ "<title>Acknowledgements</title>", "<p>Not applicable.</p>", "<title>Author contributions</title>", "<p>Conceptualization, Y.L., R.G., Q.W., J.B. and M.C.; methodology, Y.L., M.M., M.Z, and H.F.; formal analysis, Y.L., R.G., and M.Z.; resources, R.G. and C.W.; data curation, R.G., J.B., Q.W., and C.W.; writing—original draft preparation, Y.L.; writing—review and editing, R.G., M.M., M.C., L.S. and H.F.; funding acquisition, R.G. All authors have read and agreed to the published version of the manuscript.</p>", "<title>Funding</title>", "<p>This research was funded by the young and middle-aged health science and technology innovation leader training project (YXKC2020012).</p>", "<title>Data availability</title>", "<p>The datasets used for analysis during the current study are available from the corresponding author on request.</p>", "<title>Declarations</title>", "<title>Ethics approval and consent to participate</title>", "<p id=\"Par61\">The study was approved by the Ethics Committee and Institutional Review Board of the First Affiliated Hospital of Zhengzhou University (No. 2022-KY-0205-002). All methods were performed in accordance with the relevant guidelines and regulations. Informed consent from patients has been waived by the Ethics Committee and Institutional Review Board of the First Affiliated Hospital of Zhengzhou University due to the retrospective nature of the study.</p>", "<title>Consent for publication</title>", "<p id=\"Par62\">Not applicable.</p>", "<title>Conflict of interest</title>", "<p id=\"Par63\">The authors declare no conflict of interest.</p>", "<title>Competing interests</title>", "<p id=\"Par53\">The authors declare no competing interests.</p>" ]

[ "<fig id=\"Fig1\"><label>Fig. 1</label><caption><p>(<bold>A</bold>) The range of lesions involved cervix and upper 1/2 of the vagina. (<bold>B</bold>) a: The edge of lesions was marked by suture. b: The cup of uterine manipulator was placed in vagina for patients with post-hysterectomy status.</p></caption></fig>", "<fig id=\"Fig2\"><label>Fig. 2</label><caption><p>(<bold>A</bold>) Robotic-assisted laparoscopic total vaginectomy was performed for a post-hysterectomy patient with vaginal HSIL. (<bold>B</bold>) Robotic-assisted laparoscopic partial vaginectomy, hysterectomy and bilateral salpingectomy was performed for a patient with vaginal HSIL and cervical HSIL.</p></caption></fig>", "<fig id=\"Fig3\"><label>Fig. 3</label><caption><p>(<bold>A</bold>) The resected total vagina of a patient with previous hysterectomy. (<bold>B</bold>) The resected partial vagina of a postmenopausal patient combined with cervical HSIL.</p></caption></fig>", "<fig id=\"Fig4\"><label>Fig. 4</label><caption><p>Flow diagram of the cohort study</p></caption></fig>", "<fig id=\"Fig5\"><label>Fig. 5</label><caption><p>Kaplan-Meier disease-free survival curves for the two groups. The HR, 95% CI, and corresponding <italic>P</italic> value were estimated by using Cox proportional-hazards models. Disease recurrence or progression from vaginal HSIL occurred in 5 of 75 patients in the CLV group and 1 of 30 patients in the RALV group</p></caption></fig>" ]

[ "<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Demographic and clinical characteristics</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\"/><th align=\"left\">CLV group<break/>(<italic>n</italic> = 77)</th><th align=\"left\">RALV group<break/>(<italic>n</italic> = 32)</th><th align=\"left\">χ²/t/z</th><th align=\"left\"><italic>P</italic> value</th></tr></thead><tbody><tr><td align=\"left\">Age, years</td><td align=\"left\">54.9 ± 9.0</td><td align=\"left\">56.0 ± 6.5</td><td char=\".\" align=\"char\">-0.627</td><td char=\".\" align=\"char\">0.532</td></tr><tr><td align=\"left\">BMI, kg/m²</td><td align=\"left\">23.9 ± 1.8</td><td align=\"left\">24.1 ± 2.0</td><td char=\".\" align=\"char\">-0.536</td><td char=\".\" align=\"char\">0.593</td></tr><tr><td align=\"left\">ASA grade</td><td align=\"left\"/><td align=\"left\"/><td char=\".\" align=\"char\">1.448</td><td char=\".\" align=\"char\">0.229</td></tr><tr><td align=\"left\"> I</td><td align=\"left\">57 (74.0)</td><td align=\"left\">20 (62.5)</td><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\"> II</td><td align=\"left\">20 (26.0)</td><td align=\"left\">12 (37.5)</td><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\">Menopause</td><td align=\"left\"/><td align=\"left\"/><td char=\".\" align=\"char\">0.472</td><td char=\".\" align=\"char\">0.492</td></tr><tr><td align=\"left\"> Yes</td><td align=\"left\">67 (87.0)</td><td align=\"left\">30 (93.8)</td><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\"> No</td><td align=\"left\">10 (13.0)</td><td align=\"left\">2 (6.3)</td><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\">Infection with high-risk HPV</td><td align=\"left\"/><td align=\"left\"/><td char=\".\" align=\"char\">0.252</td><td char=\".\" align=\"char\">0.969</td></tr><tr><td align=\"left\"> HPV16</td><td align=\"left\">46 (59.7)</td><td align=\"left\">20 (62.5)</td><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\"> HPV18</td><td align=\"left\">5 (6.5)</td><td align=\"left\">2 (6.3)</td><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\"> Other high-risk HPV</td><td align=\"left\">19 (24.7)</td><td align=\"left\">8 (25.0)</td><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\"> Unknown/none</td><td align=\"left\">7 (9.1)</td><td align=\"left\">2 (6.3)</td><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\">Antecedent cytology at diagnosis</td><td align=\"left\"/><td align=\"left\"/><td char=\".\" align=\"char\">3.389</td><td char=\".\" align=\"char\">0.495</td></tr><tr><td align=\"left\"> ASC-US</td><td align=\"left\">17 (22.1)</td><td align=\"left\">10 (31.3)</td><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\"> ASC-H</td><td align=\"left\">9 (11.7)</td><td align=\"left\">3 (9.4)</td><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\"> LSIL</td><td align=\"left\">15 (19.5)</td><td align=\"left\">3 (9.4)</td><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\"> HSIL</td><td align=\"left\">19 (24.7)</td><td align=\"left\">6 (18.8)</td><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\"> Unknown/none</td><td align=\"left\">17 (22.1)</td><td align=\"left\">10 (31.3)</td><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\">Vaginal estrogen pretreatment</td><td align=\"left\"/><td align=\"left\"/><td char=\".\" align=\"char\">2.427</td><td char=\".\" align=\"char\">0.119</td></tr><tr><td align=\"left\"> Yes</td><td align=\"left\">24 (31.2)</td><td align=\"left\">15 (46.9)</td><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\"> No</td><td align=\"left\">53 (68.8)</td><td align=\"left\">17 (53.1)</td><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\">Clinical symptoms</td><td align=\"left\"/><td align=\"left\"/><td char=\".\" align=\"char\">1.446</td><td char=\".\" align=\"char\">0.485</td></tr><tr><td align=\"left\"> Symptom-free</td><td align=\"left\">54 (70.1)</td><td align=\"left\">26 (81.3)</td><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\"><p> Abnormal vaginal</p><p> secretions</p></td><td align=\"left\">16 (20.8)</td><td align=\"left\">4 (12.5)</td><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\"><p> Bleeding after</p><p> sexual intercourse</p></td><td align=\"left\">7 (9.1)</td><td align=\"left\">2 (6.3)</td><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\">Diabetes</td><td align=\"left\"/><td align=\"left\"/><td char=\".\" align=\"char\">&lt; 0.001</td><td char=\".\" align=\"char\">&gt; 0.999</td></tr><tr><td align=\"left\"> Yes</td><td align=\"left\">9 (11.7)</td><td align=\"left\">4 (12.5)</td><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\"> No</td><td align=\"left\">68 (88.3)</td><td align=\"left\">28 (87.5)</td><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\">Range of vaginal HSIL</td><td align=\"left\"/><td align=\"left\"/><td char=\".\" align=\"char\">&gt; 0.999</td><td char=\".\" align=\"char\">&lt; 0.001</td></tr><tr><td align=\"left\"> ≤upper third of vagina</td><td align=\"left\">43 (55.8)</td><td align=\"left\">6 (18.8)</td><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\"> &gt;upper third of vagina</td><td align=\"left\">34 (44.2)</td><td align=\"left\">26 (81.3)</td><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\">Previous hysterectomy</td><td align=\"left\">32 (41.6)</td><td align=\"left\">11 (34.4)</td><td char=\".\" align=\"char\">0.488</td><td char=\".\" align=\"char\">0.485</td></tr><tr><td align=\"left\"><p> Indication for</p><p> hysterectomy (<italic>n</italic> = 43)</p></td><td align=\"left\"/><td align=\"left\"/><td char=\".\" align=\"char\">0.166</td><td char=\".\" align=\"char\">0.684</td></tr><tr><td align=\"left\">  Cervical HSIL</td><td align=\"left\">27 (84.4)</td><td align=\"left\">8 (72.7)</td><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\">  Benign disease</td><td align=\"left\">5 (15.6)</td><td align=\"left\">3 (27.3)</td><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\">Treatment of vaginal HSIL before vaginectomy</td><td align=\"left\"/><td align=\"left\"/><td char=\".\" align=\"char\">0.257</td><td char=\".\" align=\"char\">0.879</td></tr><tr><td align=\"left\"> Photodynamic therapy</td><td align=\"left\">7 (9.1)</td><td align=\"left\">2 (6.3)</td><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\"> Laser ablation</td><td align=\"left\">2 (2.6)</td><td align=\"left\">1 (3.1)</td><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\"> None detection</td><td align=\"left\">68 (88.3)</td><td align=\"left\">29 (90.6)</td><td align=\"left\"/><td align=\"left\"/></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab2\"><label>Table 2</label><caption><p>Operative data in the two groups</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\"/><th align=\"left\">CLV group<break/>(<italic>n</italic> = 77)</th><th align=\"left\">RALV group<break/>(<italic>n</italic> = 32)</th><th align=\"left\">χ²/t/z</th><th align=\"left\"><italic>P</italic> value</th></tr></thead><tbody><tr><td align=\"left\">Total operation time, min</td><td align=\"left\">118.2 ± 41.0</td><td align=\"left\">129.9 ± 43.8</td><td align=\"left\">-1.331</td><td char=\".\" align=\"char\">0.186</td></tr><tr><td align=\"left\">Estimated blood loss, mL</td><td align=\"left\">68.1 ± 56.4</td><td align=\"left\">41.6 ± 40.3</td><td align=\"left\">2.415</td><td char=\".\" align=\"char\">0.017</td></tr><tr><td align=\"left\">Length of resected vagina, cm</td><td align=\"left\">3.5(3.0-4.5)</td><td align=\"left\">5.0(4.3–5.9)</td><td align=\"left\">-4.375</td><td char=\".\" align=\"char\">&lt; 0.001</td></tr><tr><td align=\"left\">Intraoperative complications</td><td align=\"left\">19 (24.7)</td><td align=\"left\">2 (6.3)</td><td align=\"left\">4.934</td><td char=\".\" align=\"char\">0.026</td></tr><tr><td align=\"left\"> Hemorrhage</td><td align=\"left\">6 (7.8)</td><td align=\"left\">1 (3.1)</td><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\"> Bladder injury</td><td align=\"left\">10 (13.0)</td><td align=\"left\">1 (3.1)</td><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\"> Ureteral injury</td><td align=\"left\">2 (2.6)</td><td align=\"left\">0</td><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\"> Rectal injury</td><td align=\"left\">1 (1.3)</td><td align=\"left\">0</td><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\">Postoperative complications</td><td align=\"left\">14 (18.2)</td><td align=\"left\">2 (6.3)</td><td align=\"left\">1.705</td><td char=\".\" align=\"char\">0.192</td></tr><tr><td align=\"left\"> Urinary retention</td><td align=\"left\">3 (3.9)</td><td align=\"left\">1 (3.1)</td><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\"> Infection</td><td align=\"left\">6 (7.8)</td><td align=\"left\">0</td><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\"> VTE in the lower limbs</td><td align=\"left\">4 (5.2)</td><td align=\"left\">1 (3.1)</td><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\"> Surgical incision dehiscence</td><td align=\"left\">1 (1.3)</td><td align=\"left\">0</td><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\">Flatus passing time, day</td><td align=\"left\">2.0 (2.0–2.0)</td><td align=\"left\">2.0 (1.0–2.0)</td><td align=\"left\">-4.050</td><td char=\".\" align=\"char\">&lt; 0.001</td></tr><tr><td align=\"left\">Postoperative catheterization time, day</td><td align=\"left\">4.0 (2.0–6.0)</td><td align=\"left\">2.0 (2.0–3.0)</td><td align=\"left\">-3.216</td><td char=\".\" align=\"char\">0.001</td></tr><tr><td align=\"left\">Postoperative hospitalization time, day</td><td align=\"left\">5.0 (4.0–6.0)</td><td align=\"left\">4.0(4.0–5.0)</td><td align=\"left\">-2.320</td><td char=\".\" align=\"char\">0.020</td></tr><tr><td align=\"left\">Positive surgical margin</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\">-</td><td char=\".\" align=\"char\">1.000</td></tr><tr><td align=\"left\"> Yes</td><td align=\"left\">1 (1.3)</td><td align=\"left\">0</td><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\"> No</td><td align=\"left\">76 (98.7)</td><td align=\"left\">32 (100)</td><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\">Pathology upgrading</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\">0.133</td><td char=\".\" align=\"char\">0.716</td></tr><tr><td align=\"left\"> Yes</td><td align=\"left\">2 (2.6)</td><td align=\"left\">2 (6.2)</td><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\"> No</td><td align=\"left\">75 (97.4)</td><td align=\"left\">30 (93.8)</td><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\">Hospital cost, yuan</td><td align=\"left\">32706.8 ± 6659.2</td><td align=\"left\">53035.1 ± 9539.0</td><td align=\"left\">-10.993</td><td char=\".\" align=\"char\">&lt; 0.001</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab3\"><label>Table 3</label><caption><p>The prognosis of the groups</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\"/><th align=\"left\">CLV group<break/>(<italic>n</italic> = 75)</th><th align=\"left\">RALV group<break/>(<italic>n</italic> = 30)</th><th align=\"left\">χ²</th><th align=\"left\"><italic>P</italic> value</th></tr></thead><tbody><tr><td align=\"left\">Status of homogeneous HPV infection</td><td align=\"left\"/><td align=\"left\"/><td char=\".\" align=\"char\">&lt; 0.001</td><td char=\".\" align=\"char\">&gt; 0.999</td></tr><tr><td align=\"left\"> Negative</td><td char=\".\" align=\"char\">69 (92.0)</td><td align=\"left\">27 (90.0)</td><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\"> Positive</td><td char=\".\" align=\"char\">6 (8.0)</td><td align=\"left\">3 (10.0)</td><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\">Short-term prognosis</td><td align=\"left\"/><td align=\"left\"/><td char=\".\" align=\"char\">0.434</td><td char=\".\" align=\"char\">0.805</td></tr><tr><td align=\"left\"> Regression</td><td char=\".\" align=\"char\">71 (94.7)</td><td align=\"left\">29 (96.7)</td><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\"> Remission</td><td char=\".\" align=\"char\">3 (4.0)</td><td align=\"left\">1 (3.3)</td><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\"> Persistence</td><td char=\".\" align=\"char\">1 (1.3)</td><td align=\"left\">0</td><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\">Long-term prognosis</td><td align=\"left\"/><td align=\"left\"/><td char=\".\" align=\"char\">1.259</td><td char=\".\" align=\"char\">0.533</td></tr><tr><td align=\"left\"> Disease-free</td><td char=\".\" align=\"char\">70 (93.3)</td><td align=\"left\">29 (96.7)</td><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\"> Recurrence</td><td char=\".\" align=\"char\">2 (2.7)</td><td align=\"left\">1 (3.3)</td><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\"> Progression</td><td char=\".\" align=\"char\">3 (4.0)</td><td align=\"left\">0</td><td align=\"left\"/><td align=\"left\"/></tr></tbody></table></table-wrap>" ]

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[ "<table-wrap-foot><p>BMI, body mass index; ASA, American Society of Anesthesiologists; HPV: human papillomavirus; ASC-US: Atypical squamous cells of undetermined significance; ASC-H: Atypical squamous cells-cannot exclude high-grade squamous intraepithelial lesions; LSIL: Low-grade squamous intraepithelial lesions; HSIL: High-grade squamous intraepithelial lesions; CLV, conventional laparoscopic vaginectomy; RALV, robotic-assisted laparoscopic vaginectomy</p></table-wrap-foot>", "<table-wrap-foot><p>VTE, venous thromboembolism; CLV, conventional laparoscopic vaginectomy; RALV, robotic-assisted laparoscopic vaginectomy</p></table-wrap-foot>", "<fn-group><fn><p><bold>Publisher’s Note</bold></p><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn></fn-group>" ]

[ "<graphic xlink:href=\"12905_2024_2882_Fig1_HTML\" id=\"d32e357\"/>", "<graphic xlink:href=\"12905_2024_2882_Fig2_HTML\" id=\"d32e374\"/>", "<graphic xlink:href=\"12905_2024_2882_Fig3_HTML\" id=\"d32e391\"/>", "<graphic xlink:href=\"12905_2024_2882_Fig4_HTML\" id=\"d32e441\"/>", "<graphic xlink:href=\"12905_2024_2882_Fig5_HTML\" id=\"d32e1330\"/>" ]

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{ "acronym": [ "CI", "CLV", "HR", "HSIL", "LSIL", "RALV", "SIL" ], "definition": [ "Confidence intervals", "Conventional laparoscopic vaginectomy", "Hazard ratios", "High-grade squamous intraepithelial lesions", "Low-grade squamous intraepithelial lesions", "Robotic-assisted laparoscopic vaginectomy", "Squamous intraepithelial lesions" ] }

[ "<title>Background</title>", "<p id=\"Par5\">Suicide, characterized as a fatality resulting from a purposeful act of self-directed harm, exhibits systematic variations influenced by factors such as age, gender, and the chosen method of self-harm [##REF##31339847##1##]. Worldwide, suicidal behaviors significantly impact public health, with an estimated incidence of 11.4 suicides per 100,000 people and 804,000 suicide-related fatalities [##REF##26385066##2##]. Suicidal ideation, playing a pivotal role as a significant predictor of both attempted and completed suicides [##REF##27989254##3##], poses a considerable health burden due to its predictive relevance [##REF##35048835##4##]. Therefore, allocating increased clinical attention to suicidal ideation is imperative.</p>", "<p id=\"Par6\">The secondary messenger system of the brain heavily relies on cholesterol, closely linked to the actions of mood stabilizers and antidepressants [##UREF##0##5##]. This could potentially exert an indirect influence on the emergence of suicidal ideation. The intricate relationship that exists between lipid metabolism and suicidal ideation has been explored by multiple studies. The triglyceride-glucose (TyG) index and suicidal ideas were shown to be significantly associated in cross-sectional research involving 21,350 participants who were over the age of nineteen. However, with male individuals, no significant relationship was observed [##REF##37838259##6##]. In a cross-sectional investigation, Hee-Young et al. observed a correlation between lower triglyceride levels and a decreased probability of experiencing suicidal ideation in a sample of 4557 Korean adults over the age of 65 [##REF##26451502##7##]. Anhedonia was associated with lower LDL levels compared to equivalent control groups, but considerations of suicide were linked to more elevated HDL and cholesterol levels in a Chinese study including 287 untreated depressed patients [##REF##31747876##8##].</p>", "<p id=\"Par7\">Furthermore, research has shown that the ratio of non-high-density lipoprotein cholesterol (non-HDL-C) to HDL-C ratio (NHHR) serves as an independent risk indicator of depression in adults in the United States [##REF##37838268##9##]. Approximately 90% of individuals with suicidal ideation have treatable psychological disorders, predominantly depression [##REF##12701661##10##]. In the ongoing research into the association between psychological well-being and lipid metabolism, NHHR serves as a recently created composite indicator that assesses atherogenic lipid profiles and provides extensive insight into lipid particles that are both anti-atherogenic and atherogenic [##REF##25985729##11##]. To determine the NHHR, non-HDL-C levels are divided by comparable HDL-C levels [##REF##37407911##12##]. Previous research has shown that the NHHR exhibits superior diagnostic efficacy in comparison with standard lipid parameters in predicting the risk of cerebrovascular diseases, liver disease, insulin resistance, and metabolic syndrome [##UREF##1##13##–##REF##23545148##15##]. Therefore, exploring the relationship between the NHHR and suicidal ideation may provide valuable insights into the intersection between lipid metabolism and mental health, prompting further investigation into preventive strategies and interventions.</p>", "<p id=\"Par8\">Despite the growing body of evidence associating lipid metabolism to suicidal ideation, suicidal ideation, and NHHR have not been thoroughly studied in any previous studies. Thus, the primary goal for the research was to investigate if suicidal ideation and the NHHR were associated. It is hypothesized that an increased NHHR might be linked to an increased likelihood of suicidal ideation. By shedding light on the links between lipid metabolism and mental health, this research will contribute to resolving the knowledge deficit concerning the association between NHHR and suicide ideology. In essence, this study explores a newly discovered field concerning the potential predictive use of the NHHR for mental health outcomes and focuses on an innovative perspective of suicidal ideation and its association with lipid metabolism.</p>" ]

[ "<title>Methods</title>", "<title>Study population</title>", "<p id=\"Par9\">The National Health and Nutrition Examination Survey (NHANES), an investigation that collects demographic information on the health and nutrient intake of US citizens, is supervised and executed through the National Center for Health Statistics (NCHS). On account of this study’s design’s utilization of a stratified multistage probability sampling process, the samples included in NHANES exhibit ideal representativeness [##UREF##2##16##]. Participants undergo a health check in a mobile examination facility and a standardized in-home interview to assess their physical and medical conditions. Additional tests are conducted to gather pertinent laboratory data. The NCHS Research Ethics Review Board authorized ethical applications for NHANES involving human subjects, along with every participant has officially granted their informed consent. By visiting <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.cdc.gov/nchs/nhanes/\">https://www.cdc.gov/nchs/nhanes/</ext-link>, the public can access all of the NHANES data.</p>", "<p id=\"Par10\">For this research, six cycles spanning 2005 to 2016 in NHANES were selected for the purpose of investigating the association between NHHR and suicidal ideation. This selection was based on the availability of comprehensive data on both the NHHR and suicidal ideation within four cycles. Initially, 60,936 participants were enrolled, with subsequent exclusions for individuals under 18 years of age (<italic>n</italic> = 24,649), those with missing NHHR data (<italic>n</italic> = 3,511), participants lacking data on suicidal ideation (<italic>n</italic> = 2,977), and pregnant individuals (<italic>n</italic> = 511). As a result, the final analytical cohort comprised 29,288 participants, as illustrated in Fig. ##FIG##0##1##.</p>", "<p id=\"Par31\">\n\n</p>", "<title>Assessment of NHHR</title>", "<p id=\"Par11\">The NHHR serves as an independent variable in exposure assessment. The method outlined in prior studies was adopted to determine NHHR, specifically the Non-HDL-C/HDL-C ratio [##REF##36188405##17##]. To derive non-HDL-C, obtained by subtracting HDL-C from total cholesterol (TC), lipid profiles of fasting individuals were analyzed. An automated biochemistry analyzer conducted an enzymatic test to evaluate TC and HDL-C levels. For TC concentration calculations, the research utilized both the Roche Cobas 6000 and Roche Modular P chemical analyzers in analytical procedures.</p>", "<title>Assessment of suicidal ideation</title>", "<p id=\"Par12\">The application of the ninth item of the Patient Health Questionnaire-9 (PHQ-9) could be appropriate for the evaluation of suicidal ideation. The PHQ-9 comprises nine items and is utilized to ascertain whether an individual has exhibited depressive symptoms in the preceding two weeks [##REF##11556941##18##]. Every single question in the survey has a score that spans from the assigned value “absent” at 0 to “nearly every day” at 3, which sums up to an overall total that falls between 0 and 27 [##REF##33126078##19##]. A threshold of 10 is employed to identify the presence of depressive symptoms [##REF##30967483##20##]. In the ninth item, respondents are asked, “Over the last two weeks, how frequently have you experienced thoughts of self-harm or the belief that your life would be better off ended?” “Not at all,” “several days,” “more than half the days,” and “nearly every day” are the available response options. For analytical purposes, all responses are categorized as either absent (no) or present at any frequency (yes) [##REF##29406247##21##].</p>", "<title>Covariates</title>", "<p id=\"Par13\">Potential variables that might introduce confounding effects in the relationship between NHHR and the occurrence of suicidal ideation were considered through the implementation of multivariate-adjusted models. Several variations in covariates were considered within this study’s analysis, encompassing gender (male or female), age (years), race, education level, waist circumference, body mass index (BMI), income-to-poverty ratio (PIR), marital status (married or living with a partner/widowed, divorced, separated, and never married), physical activity (inactive/active), depressive symptoms (non-depressive/depressive), TC (mg/dl), HDL-C (mg/dl), smoking status (smoker/non-smoker), diabetes, and hypertension. Each person’s BMI was classified as being under 25, between 25 and 30 kg/m2, and above 30 kg/m2, which are divided into the categories of normal weight, overweight, and obese, in that order. The operational definition of physical activity involves engaging in activities of moderate or vigorous intensity for a minimum duration of 10 continuous minutes outside occupational or transportation contexts. In contrast, physical inactivity was defined as involvement in the mentioned activities for less than 10 min [##REF##24913317##22##]. The total quantity of dietary cholesterol ingested was determined from the averages of the two 24-hour dietary recall tests, utilizing comprehensive nutrient consumption data. For detailed information on the quantifiable processes of the study variables, <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.cdc.gov/nchs/nhanes/\">www.cdc.gov/nchs/nhanes/</ext-link> is the official website accessible to the public.</p>", "<title>Statistical analysis</title>", "<p id=\"Par14\">The statistical analyses were performed following the protocols outlined by the Centers for Disease Control and Prevention (CDC). These guidelines prescribed the incorporation of relevant NHANES sample weights and the consideration of the complexities inherent in multistage cluster surveys. Standard deviations and means are shown for continuous data, while percentages are employed to depict categorical variables. A Student’s t-test with weights was employed to assess variations among groups based on the existence or lack of suicidal ideation. For evaluating associations between categories, a weighted chi-square test was utilized. By utilizing multivariate logistic regression, an independent association between NHHR and suicidal ideation in three distinct models was examined. Model 1 lacked covariate adjustments, meanwhile, Model 2 incorporated gender, age, and race adjustments. Gender, age, race, marital status, level of education, BMI, PIR, smoking status, diabetes, hypertension, physical activity, and dietary cholesterol were among the additional variables incorporated into Model 3. By implementing penalized spline smooth curve fitting and weighted generalized additive model (GAM) regression analysis, the non-linear association between NHHR and suicidal ideation was examined. Subgroup analyses were conducted using stratified multivariate regression analysis, with stratification based on sex, age, race, BMI, educational level, marital status, hypertension, diabetes, and smoking status. The log-likelihood ratio test model was employed for subgroup analysis, and statistical significance was determined at <italic>P</italic> &lt; 0.05. The analytical procedures were implemented using R 3.4.3 (available at <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.R-project.org\">http://www.R-project.org</ext-link>) and Empower (available at <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.empowerstats.com\">www.empowerstats.com</ext-link>) as software applications.</p>" ]

[ "<title>Results</title>", "<p id=\"Par15\">29,288 individuals made up the study’s sample; 48.52% of them consisted of men and 51.48% were women. The research individuals’ average ages were 48.00 ± 18.70 years. Among them, 28,168 (96.18%) reported an absence of suicidal ideation, while 1,120 (3.82%) indicated exhibiting ideas of suicide. Significant distinctions were observed among the groups classified by the absence or presence of suicidal ideation regarding the following factors: education level, gender, race, marital status, dietary cholesterol, HDL-C, income-to-poverty ratio, smoking status, diabetes, hypertension, physical activity, depressive symptoms, and dietary cholesterol and waist circumference (<italic>P</italic> &lt; 0.05). The attributes pertaining to those who were more likely to encounter suicidal ideas included being male, non-Hispanic White, widowed, divorced, separated, and never married, as well as having some college education or an AA degree and smoking. They also exhibited higher BMI levels, waist circumference, TC levels, and NHHR levels, and had higher rates of active physical activity and depression. However, they had lower rates of diabetes and hypertension, as well as lower household income, dietary cholesterol, and HDL-C levels within the research (<italic>P</italic> &lt; 0.05). Individuals’ clinical and physiological characteristics are categorized according to whether or not they have suicidal ideation in Table ##TAB##0##1##.</p>", "<p id=\"Par16\">\n\n</p>", "<title>The association between NHHR and suicidal ideation</title>", "<p id=\"Par17\">The research results revealed that a positive association existed between elevated NHHR and an increased likelihood of experiencing suicidal ideation. This association existed in the initial unadjusted model and remained statistically significant in subsequent models, even after minimal and thorough adjustments were applied. With all adjustments implemented on Model 3 (OR = 1.06; 95% CI: 1.02–1.11; <italic>P</italic> = 0.0048), it was observed a 6% rise in the likelihood of suicidal ideation for each unit increase in NHHR. To further explore this relationship, it was categorized the continuous variable NHHR into discrete intervals (tertiles) for a sensitivity analysis. In the partially adjusted model (Model 2), tertile 3 exhibited a 20% higher probability of suicidal ideation compared to the lowest NHHR tertile (tertile 1) (OR = 1.20; 95% CI: 1.03–1.39; <italic>P</italic> = 0.0179). However, after extensive adjustments, the observed association (OR = 1.15; 95% CI: 0.94–1.41; <italic>P</italic> = 0.1751) did not reach statistical significance. Furthermore, none of the three models were able to significantly differentiate between tertile 1 and tertile 2 (Table ##TAB##1##2##).</p>", "<p id=\"Par18\">\n\n</p>", "<title>A nonlinear relationship between NHHR and suicidal ideation</title>", "<p id=\"Par19\">Through the application of smooth curve fits and weighted generalized additive models, an in-depth analysis of the nonlinear relationship between NHHR levels and suicidal ideation was undertaken for this study. A non-linear relationship was revealed by the results, as illustrated in Fig. ##FIG##1##2##. Upon further examination, after stratifying by smoking status, it was observed an inverted U-shaped curve with an inflection point at 7.80 within the non-smoker subgroup (Fig. ##FIG##2##3##; Table ##TAB##2##3##). This pattern persisted even when accounting for the same covariates.</p>", "<p id=\"Par32\">\n\n</p>", "<p id=\"Par33\">\n\n</p>", "<p id=\"Par20\">\n\n</p>", "<title>Subgroup analysis</title>", "<p id=\"Par21\">The robustness of the association between NHHR and suicidal ideation was evaluated using subgroup analysis (Table ##TAB##3##4##). Despite this, the <italic>p</italic>-values for the interaction (all <italic>P</italic> &gt; 0.05) indicated no statistically significant association, implying that variables encompassing age, gender, race, BMI, education level, marital status, hypertension, diabetes, and smoking status did not influence the association. Notably, the findings continuously indicate a significant link between NHHR and suicidal ideation even controlling for major demographic variables comprising age, sex, race, BMI, education level, marital status, hypertension, diabetes, and smoking status. This suggests the potential relevance of this association across diverse population settings.</p>", "<p id=\"Par22\">\n\n</p>" ]

[ "<title>Discussion</title>", "<p id=\"Par23\">In this comprehensive study involving 29,288 adults, the findings indicate that individuals with elevated NHHR scores are more likely to experience suicidal ideation. This association holds true across various subgroups, including age, sex, race, BMI, educational level, marital status, hypertension, diabetes, and smoking status. These results are consistent across diverse population settings, as demonstrated by subgroup analysis and interaction tests. Notably, within the non-smoker group, an inverted U-shaped association was discovered between NHHR and suicidal ideation, characterized by an inflection point at 7.80. In accordance with the results, which can be speculated that NHHR may serve as a predictor of suicidal ideation and that regulating lipid levels as determined by NHHR could reduce suicidal ideation and associated behaviors.</p>", "<p id=\"Par24\">From the utmost of present understanding, this research signifies the relationship’s primary investigation between NHHR and suicidal ideation. Growing evidence supports the notion that NHHR is a superior indicator of lipid-related disorder risk [##REF##32576257##23##–##REF##15983339##25##]. While empirical research examining the relationship between NHHR levels and ideation of suicide is lacking, a wealth of literature exists exploring the links between suicidal ideation and various lipid-related factors. In a cross-sectional investigation involving 13,772 adults in Korea, Hana et al. identified a significant association, indicating that reduced levels of LDL-C were linked to an elevated likelihood of suicidal thoughts for male individuals above the age of 19 [##UREF##0##5##]. A potential association was identified by Bałażej et al. between increased levels of TC and LDL and the occurrence of suicidal thoughts in females who were undergoing their initial episode of schizophrenia [##REF##25618471##26##]. A retrospective cohort investigation involving 73 outpatients diagnosed with major depressive disorder suggested a noteworthy reduction in TG levels within the cohort exhibiting suicidal ideation, contrasting with individuals lacking such notions [##REF##24679391##27##]. Suicidal ideation and completed suicides are associated with decreased blood lipid levels, particularly TC, as stated by Shunquan et al. in a meta-analysis involving 65 epidemiological studies [##REF##26505144##28##]. While these findings do not directly present evidence, they indirectly support that NHHR levels are positively associated with suicidal ideation, contributing to the growing body of literature examining the association between profiles of lipids and suicidal tendencies through the use of novel lipid characteristics.</p>", "<p id=\"Par25\">The NHHR, a recently amalgamated metric reflecting atherogenic lipid composition [##REF##35669362##29##], surpasses conventional lipid parameters in evaluating atherosclerosis extent [##REF##24555711##30##]. Kwok RM states that compared to other lipid indicators, the NHHR has a more robust predictive ability for non-alcoholic fatty liver disease (NAFLD) [##REF##23504808##31##]. According to Lin D’s study, the NHHR is a reliable diagnostic instrument for assessing insulin resistance. Compared to normal lipid inspections, this metric demonstrated superior accuracy in predicting conditions associated with the development of diabetes [##REF##28673690##32##]. In summary, the NHHR has demonstrated exceptional predictive efficacy in a variety of studies. Furthermore, the NHHR is a widely accessible method distinguished by its noninvasive nature, ease of accessibility, and cost-effectiveness, presenting promising prospects for clinical implementation.</p>", "<p id=\"Par26\">Various perspectives offer explanations for the association between lipid metabolism and thoughts of suicide. Some theories propose that reduced cholesterol levels may influence the microviscosity of serotonin receptors, impacting serotonin activity and contributing to impulsive and suicidal behaviors [##REF##14523381##33##]. An elevation in the ratio of n-6/n-3 PUFAs which is linked to the induction of a pro-inflammatory state, according to mechanisms regarding the balance of polyunsaturated fatty acids (PUFAs) and inflammation [##REF##29920417##34##]. Further evidence substantiates this association, as studies have linked suicidal ideation with pro-inflammatory cytokines, including IL-6 [##REF##26546783##35##]. Collectively, these findings suggest a potential key component in the pathophysiological model of suicide behavior. According to a theory proposed by Penttinen et al., increased cytokine production, specifically interleukin-2 (IL-2), leads to higher total blood cholesterol and lower serum HDL cholesterol, affecting melatonin release and increasing impulsivity and suicide risk [##REF##7709913##36##, ##UREF##3##37##]. Considering variations in dietary patterns within the target demographic, one plausible interpretation is that PUFA consumption correlates with reduced TG levels, potentially mitigating the risk of suicidal ideation [##REF##15460168##38##, ##REF##11343534##39##]. Therefore, employing the NHHR as a means to assess the non-HDL-C proportion in patients could serve as a more effective tool for evaluating the impact that lipid metabolism has on the occurrence of suicidal ideation.</p>", "<title>Strengths and limitations</title>", "<p id=\"Par28\">This study exhibits several strengths. Firstly, NHANES data was utilized, representing a comprehensive and nationally representative sample obtained using a consistent procedure with a sufficient sample size [##REF##26773020##40##]. Additionally, the research meticulously controlled for confounding covariates, selecting them primarily based on prior investigations that evaluated the association between suicidal ideation and various exposure variables. This approach was undertaken to enhance the reliability and validity of the results. However, it’s essential to acknowledge the inherent limits of this research. First, the assessment of suicidal ideation relied on personal interviews, introducing an inevitable recall bias. Second, though the PHQ-9’s ninth item has been used in prior research to measure suicide ideation, its extensive definition—which includes non-suicidal self-harm—may affect how the study evaluates the item’s association with suicidal ideation. Third, comprehensive validation of the PHQ-9’s utility in assessing suicidal ideation among the general public is lacking. Nonetheless, when it comes to basic internal medicine primary care, PHQ-9 possesses strengthened specificity and sensitivity. Fourth, the cholesterol data analyzed in this study were derived from fasting individuals, with non-fasting data remaining unexplored. Discrepancies in the laboratory testing protocols may introduce potential biases. Fifth, reverse causality was a possibility since the study employed a design with a cross-sectional approach, which hinders the ability to establish a causal relationship. Hence, there remains a necessity for prospective investigations encompassing larger sample sizes to elucidate the causative relationship. Meanwhile, despite adjusting for certain potential covariates, fully mitigating potential confounding factors beyond those adjusted remains elusive within the scope of the research.</p>" ]

[ "<title>Conclusion</title>", "<p id=\"Par30\">Based on the analysis conducted, a notable association was identified between suicidal ideation and higher NHHR scores, emphasizing the potential clinical relevance of lipid metabolism in mental health. Recognizing NHHR as a predictive indicator suggests a proactive two-step approach in routine lipid profile screenings: identifying potential mental health risks through abnormal NHHR levels and conducting comprehensive mental health assessments. This finding provides a valuable tool for early suicide risk detection, particularly in psychiatric care, allowing healthcare professionals to closely monitor mental health, support personalized interventions, and enhance overall psychiatric care effectiveness.</p>" ]

[ "<title>Background</title>", "<p id=\"Par1\">The ratio of non-high-density lipoprotein cholesterol (non-HDL-C) to high-density lipoprotein cholesterol (HDL-C) (NHHR) serves as a reliable lipid indicator associated with atherogenic characteristics. Studies have indicated a potential connection between suicidality and lipid metabolism. This research aims to investigate any possible association between the NHHR and the emergence of suicidal ideation within the confines of the study.</p>", "<title>Methods</title>", "<p id=\"Par2\">This study examined the association between NHHR levels and suicidal ideation using data from the National Health and Nutrition Examination Survey (NHANES), conducted in the United States spanning 2005 and 2016. Calculation of the NHHR corresponds to the proportion of HDL-C to Non-HDL-C. The Patient Health Questionnaire-9’s ninth question was implemented for assessing suicidal ideation. Using subgroup analysis, smooth curve fitting, and multivariate logistic regression analysis, the research was conducted.</p>", "<title>Results</title>", "<p id=\"Par3\">Encompassing a cohort of 29,288 participants, the analysis identified that 3.82% of individuals reported suicidal ideation. After using multivariable logistic regression and thorough adjustments, elevated NHHR levels were significantly and positively associated with a heightened likelihood of suicidal ideation, according to the findings (odds ratio [OR] = 1.06; 95% confidence interval [CI]: 1.02–1.11; <italic>P</italic> = 0.0048). Despite extensive adjustment for various confounding factors, this relationship remained consistent. An inverted U-shaped curve was utilized to illustrate the link between NHHR and suicidal ideation among nonsmokers; the curve’s inflection point was situated at 7.80. Subgroup analysis and interaction tests (all <italic>P</italic> for interaction &gt; 0.05) demonstrated that there was no significant influence of the following variables on this positive relationship: age, sex, race, body mass index, education level, married status, hypertension, diabetes, and smoking status.</p>", "<title>Conclusion</title>", "<p id=\"Par4\">Significantly higher NHHR levels were associated with an elevated likelihood of suicidal ideation. Based on these results, it is probable that NHHR may serve as a predictive indicator of suicidal ideation, emphasizing its potential utility in risk assessment and preventive strategies.</p>", "<title>Keywords</title>" ]

[]

[ "<title>Acknowledgements</title>", "<p>The authors express their gratitude to the NHANES database for their uploading valuable datasets.</p>", "<title>Author contributions</title>", "<p>G.Q.: conceptualization, methodology, data curation, software, writing – original draft. W.D.: data curation, visualization, software. Y.Z.: data curation, formal analysis, validation. L.Z.: data curation, software. Y.W.: writing – original draft. B.W.: conceptualization, funding acquisition, methodology, writing – review &amp; editing, supervision.</p>", "<title>Funding</title>", "<p>This research received no external funding.</p>", "<title>Data availability</title>", "<p>In this study, publicly accessible datasets were examined. These data can be found here: ((<ext-link ext-link-type=\"uri\" xlink:href=\"https://wwwn.cdc.gov/nchs/nhanes/analyticguidelines.aspx\">https://wwwn.cdc.gov/nchs/nhanes/analyticguidelines.aspx</ext-link>, accessed on 1 November 2022).</p>", "<title>Declarations</title>", "<title>Ethics approval and consent to participate</title>", "<p id=\"Par35\">This study was reviewed and approved by the NCHS Ethics Review Board. The patients/participants provided written informed consent to participate in this study.</p>", "<title>Consent for publication</title>", "<p id=\"Par36\">Before participating in the study, all participants signed up with informed permission.</p>", "<title>Institutional Review Board Statement</title>", "<p id=\"Par37\">There was no requirement for institutional review board permission since the NHANES database was open to the public.</p>", "<title>Competing interests</title>", "<p id=\"Par34\">The authors declare no competing interests.</p>" ]

[ "<fig id=\"Fig1\"><label>Fig. 1</label><caption><p>Flowchart of participant selection. NHANES, National Health and Nutrition Examination Survey; NHHR, non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio</p></caption></fig>", "<fig id=\"Fig2\"><label>Fig. 2</label><caption><p>The association between NHHR and suicidal ideation. (<bold>A</bold>) The solid red line represents the smooth curve fit between variables. (<bold>B</bold>) Blue bands represent the 95% confidence interval from the fit. NHHR, non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio</p></caption></fig>", "<fig id=\"Fig3\"><label>Fig. 3</label><caption><p>The association between NHHR and suicidal ideation is stratified by smoking status. NHHR, non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio</p></caption></fig>" ]

[ "<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Characteristics of the study population</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Characteristic</th><th align=\"left\">Total (<italic>N</italic> = 29,288)</th><th align=\"left\">Without suicidal ideation (<italic>N</italic> = 28,168)</th><th align=\"left\">With suicidal ideation (<italic>N</italic> = 1120)</th><th align=\"left\"><italic>P</italic>-value</th></tr></thead><tbody><tr><td align=\"left\">Age(year)</td><td align=\"left\">48.00 ± 18.70</td><td align=\"left\">47.99 ± 18.72</td><td align=\"left\">48.12 ± 18.16</td><td align=\"left\">0.793</td></tr><tr><td align=\"left\">Gender (%)</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\">&lt; 0.001</td></tr><tr><td align=\"left\"> Male</td><td align=\"left\">17,360 (48.52%)</td><td align=\"left\">14,421 (51.20%)</td><td align=\"left\">668 (59.64%)</td><td align=\"left\"/></tr><tr><td align=\"left\"> Female</td><td align=\"left\">18,416 (51.48%)</td><td align=\"left\">13,747 (48.80%)</td><td align=\"left\">452 (40.36%)</td><td align=\"left\"/></tr><tr><td align=\"left\">Race(%)</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\">&lt; 0.001</td></tr><tr><td align=\"left\"> Mexican American</td><td align=\"left\">4876 (16.65%)</td><td align=\"left\">4655 (16.53%)</td><td align=\"left\">221 (19.73%)</td><td align=\"left\"/></tr><tr><td align=\"left\"> Other Hispanic</td><td align=\"left\">2819 (9.63%)</td><td align=\"left\">2642 (9.38%)</td><td align=\"left\">177 (15.80%)</td><td align=\"left\"/></tr><tr><td align=\"left\"> Non-Hispanic White</td><td align=\"left\">12,770 (43.60%)</td><td align=\"left\">12,337 (43.80%)</td><td align=\"left\">433 (38.66%)</td><td align=\"left\"/></tr><tr><td align=\"left\"> Non-Hispanic Black</td><td align=\"left\">6124 (20.91%)</td><td align=\"left\">5915 (21.00%)</td><td align=\"left\">209 (18.66%)</td><td align=\"left\"/></tr><tr><td align=\"left\"> Other Race</td><td align=\"left\">2699 (9.22%)</td><td align=\"left\">2619 (9.30%)</td><td align=\"left\">80 (7.14%)</td><td align=\"left\"/></tr><tr><td align=\"left\">Marital status(%)</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\">&lt; 0.001</td></tr><tr><td align=\"left\"> Married or Living with Partner</td><td align=\"left\">16,537 (59.02%)</td><td align=\"left\">16,059 (59.59%)</td><td align=\"left\">478 (44.67%)</td><td align=\"left\"/></tr><tr><td align=\"left\"> Widowed, divorced, separated, and never married</td><td align=\"left\">11,483 (40.98%)</td><td align=\"left\">10,891 (40.41%)</td><td align=\"left\">592 (55.33%)</td><td align=\"left\"/></tr><tr><td align=\"left\">Education level(%)</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\">&lt; 0.001</td></tr><tr><td align=\"left\"> Less Than 9th Grade</td><td align=\"left\">2919 (10.59%)</td><td align=\"left\">2729 (10.30%)</td><td align=\"left\">190 (18.08%)</td><td align=\"left\"/></tr><tr><td align=\"left\"> 9-11th Grade</td><td align=\"left\">4005 (14.53%)</td><td align=\"left\">3783 (14.27%)</td><td align=\"left\">222 (21.12%)</td><td align=\"left\"/></tr><tr><td align=\"left\"> High School Grad/GED or Equivalent</td><td align=\"left\">6310 (22.90%)</td><td align=\"left\">6070 (22.90%)</td><td align=\"left\">240 (22.84%)</td><td align=\"left\"/></tr><tr><td align=\"left\"> Some College or AA degree</td><td align=\"left\">8045 (29.20%)</td><td align=\"left\">7771 (29.32%)</td><td align=\"left\">274 (26.07%)</td><td align=\"left\"/></tr><tr><td align=\"left\"> College Graduate or above</td><td align=\"left\">6276 (22.78%)</td><td align=\"left\">6151 (23.21%)</td><td align=\"left\">125 (11.89%)</td><td align=\"left\"/></tr><tr><td align=\"left\">Body mass index(kg/m²), (%)</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\">&lt; 0.001</td></tr><tr><td align=\"left\"> &lt; 25</td><td align=\"left\">8767 (30.23%)</td><td align=\"left\">8445 (30.27%)</td><td align=\"left\">322 (29.30%)</td><td align=\"left\"/></tr><tr><td align=\"left\"> 25 to &lt; 30</td><td align=\"left\">9536 (32.88%)</td><td align=\"left\">9220 (33.05%)</td><td align=\"left\">316 (28.75%)</td><td align=\"left\"/></tr><tr><td align=\"left\"> ≥ 30</td><td align=\"left\">10,697 (36.89%)</td><td align=\"left\">10,236 (36.69%)</td><td align=\"left\">461 (41.95%)</td><td align=\"left\"/></tr><tr><td align=\"left\"> Waist circumference(cm)</td><td align=\"left\">98.60 ± 16.47</td><td align=\"left\">98.52 ± 16.40</td><td align=\"left\">100.70 ± 18.06</td><td align=\"left\">&lt; 0.001</td></tr><tr><td align=\"left\"> Income to poverty ratio</td><td align=\"left\">2.49 ± 1.63</td><td align=\"left\">2.52 ± 1.63</td><td align=\"left\">1.72 ± 1.38</td><td align=\"left\">&lt; 0.001</td></tr><tr><td align=\"left\">Smoking status(%)</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\">&lt; 0.001</td></tr><tr><td align=\"left\"> Yes</td><td align=\"left\">12,687 (45.21%)</td><td align=\"left\">12,097 (44.82%)</td><td align=\"left\">590 (55.09%)</td><td align=\"left\"/></tr><tr><td align=\"left\"> No</td><td align=\"left\">15,375 (54.79%)</td><td align=\"left\">14,894 (55.18%)</td><td align=\"left\">481 (44.91%)</td><td align=\"left\"/></tr><tr><td align=\"left\">Diabetes(%)</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\">&lt; 0.001</td></tr><tr><td align=\"left\"> Yes</td><td align=\"left\">3522 (12.03%)</td><td align=\"left\">3317 (11.78%)</td><td align=\"left\">205 (18.30%)</td><td align=\"left\"/></tr><tr><td align=\"left\"> No</td><td align=\"left\">25,124 (85.85%)</td><td align=\"left\">24,235 (86.10%)</td><td align=\"left\">889 (79.38%)</td><td align=\"left\"/></tr><tr><td align=\"left\"> Borderline</td><td align=\"left\">620 (2.12%)</td><td align=\"left\">594 (2.11%)</td><td align=\"left\">26 (2.32%)</td><td align=\"left\"/></tr><tr><td align=\"left\">Hypertension(%)</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\">&lt; 0.001</td></tr><tr><td align=\"left\"> Yes</td><td align=\"left\">10,095 (34.52%)</td><td align=\"left\">9618 (34.19%)</td><td align=\"left\">477 (42.74%)</td><td align=\"left\"/></tr><tr><td align=\"left\"> No</td><td align=\"left\">19,153 (65.48%)</td><td align=\"left\">18,514 (65.81%)</td><td align=\"left\">639 (57.26%)</td><td align=\"left\"/></tr><tr><td align=\"left\">Physical activity(%)</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\">&lt; 0.001</td></tr><tr><td align=\"left\"> Inactive</td><td align=\"left\">12,151 (48.66%)</td><td align=\"left\">11,832 (49.30%)</td><td align=\"left\">319 (32.92%)</td><td align=\"left\"/></tr><tr><td align=\"left\"> active</td><td align=\"left\">12,820 (51.34%)</td><td align=\"left\">12,170 (50.70%)</td><td align=\"left\">650 (67.08%)</td><td align=\"left\"/></tr><tr><td align=\"left\">Depressive symptom(%)</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\">&lt; 0.001</td></tr><tr><td align=\"left\"> Without depression</td><td align=\"left\">26,282 (89.74%)</td><td align=\"left\">25,947 (92.12%)</td><td align=\"left\">335 (29.91%)</td><td align=\"left\"/></tr><tr><td align=\"left\"> With depression</td><td align=\"left\">3006 (10.26%)</td><td align=\"left\">2221 (7.88%)</td><td align=\"left\">785 (70.09%)</td><td align=\"left\"/></tr><tr><td align=\"left\"> Dietary cholesterol (mg)</td><td align=\"left\">286.16 ± 187.71</td><td align=\"left\">286.74 ± 188.01</td><td align=\"left\">270.82 ± 178.99</td><td align=\"left\">0.003</td></tr><tr><td align=\"left\"> TC (mg/dL)</td><td align=\"left\">191.91 ± 41.88</td><td align=\"left\">191.80 ± 41.73</td><td align=\"left\">194.88 ± 45.38</td><td align=\"left\">0.065</td></tr><tr><td align=\"left\"> HDL-C (mg/dL)</td><td align=\"left\">52.74 ± 15.98</td><td align=\"left\">52.81 ± 15.98</td><td align=\"left\">51.05 ± 15.72</td><td align=\"left\">&lt; 0.001</td></tr><tr><td align=\"left\"> NHHR</td><td align=\"left\">2.93 ± 1.45</td><td align=\"left\">2.93 ± 1.45</td><td align=\"left\">3.14 ± 1.58</td><td align=\"left\">&lt; 0.001</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab2\"><label>Table 2</label><caption><p>The association between NHHR and suicidal ideation</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" rowspan=\"2\"/><th align=\"left\">Crude Model (Model 1)</th><th align=\"left\">Partially Adjusted Model (Model 2)</th><th align=\"left\">Fully Adjusted Model (Model 3)</th></tr><tr><th align=\"left\">OR (95% CI) <italic>p</italic>-value</th><th align=\"left\">OR (95% CI) <italic>p</italic>-value</th><th align=\"left\">OR (95% CI) <italic>p</italic>-value</th></tr></thead><tbody><tr><td align=\"left\">NHHR</td><td align=\"left\">1.09 (1.06, 1.13) &lt; 0.0001</td><td align=\"left\">1.08 (1.04, 1.12) &lt; 0.0001</td><td align=\"left\">1.06 (1.02, 1.11) 0.0048</td></tr><tr><td align=\"left\">NHHR Tertiles</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\"> Tertile 1</td><td align=\"left\">Reference</td><td align=\"left\">Reference</td><td align=\"left\">Reference</td></tr><tr><td align=\"left\"> Tertile 2</td><td align=\"left\">1.04 (0.90, 1.22) 0.5819</td><td align=\"left\">0.98 (0.84, 1.14) 0.7553</td><td align=\"left\">0.93 (0.76, 1.15) 0.5030</td></tr><tr><td align=\"left\"> Tertile 3</td><td align=\"left\">1.30 (1.13, 1.51) 0.0004</td><td align=\"left\">1.20 (1.03, 1.39) 0.0179</td><td align=\"left\">1.15 (0.94, 1.41) 0.1751</td></tr><tr><td align=\"left\"> P for trend</td><td align=\"left\">1.12 (1.05, 1.19) 0.0002</td><td align=\"left\">1.08 (1.02, 1.15) 0.0083</td><td align=\"left\">1.07 (0.99, 1.17) 0.0857</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab3\"><label>Table 3</label><caption><p>The threshold effect of NHHR on suicidal ideation stratified by smoking status was analyzed using a two-part linear regression model</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" rowspan=\"2\"/><th align=\"left\" colspan=\"2\">Smoking status</th></tr><tr><th align=\"left\">Yes</th><th align=\"left\">No</th></tr></thead><tbody><tr><td align=\"left\">Fitting by standard linear model</td><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\"> OR (95% CI)</td><td align=\"left\">1.10 (0.96, 1.26)</td><td align=\"left\">1.08 (1.00, 1.16)</td></tr><tr><td align=\"left\"> <italic>P</italic>-value</td><td align=\"left\">0.1596</td><td align=\"left\">0.0502</td></tr><tr><td align=\"left\">Fitting by two-piecewise linear model</td><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\"> Breakpoint (K)</td><td align=\"left\">2.42</td><td align=\"left\">7.80</td></tr><tr><td align=\"left\"> OR1(&lt;K)</td><td align=\"left\">2.03 (0.83, 4.97) 0.1211</td><td align=\"left\">1.14 (1.04, 1.25) 0.0054</td></tr><tr><td align=\"left\"> OR2(&gt;K)</td><td align=\"left\">1.04 (0.87, 1.24) 0.6749</td><td align=\"left\">0.47 (0.11, 2.09) 0.3236</td></tr><tr><td align=\"left\">Logarithmic likelihood ratio test <italic>P</italic>-value</td><td align=\"left\">0.150</td><td align=\"left\">0.028</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab4\"><label>Table 4</label><caption><p>Association between NHHR and suicidal ideation in subgroups</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Subgroup</th><th align=\"left\"/><th align=\"left\">OR(95%CI)</th><th align=\"left\"><italic>P</italic> for interaction</th></tr></thead><tbody><tr><td align=\"left\">Age(year)</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\">0.4618</td></tr><tr><td align=\"left\"> &lt; 50</td><td align=\"left\"><italic>N</italic> = 9156</td><td align=\"left\">1.05 (0.98, 1.12)</td><td align=\"left\"/></tr><tr><td align=\"left\"> ≥ 50</td><td align=\"left\"><italic>N</italic> = 9346</td><td align=\"left\">1.08 (1.02, 1.15)</td><td align=\"left\"/></tr><tr><td align=\"left\">Gender</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\">0.3642</td></tr><tr><td align=\"left\"> Male</td><td align=\"left\"><italic>N</italic> = 9526</td><td align=\"left\">1.05 (0.99, 1.11)</td><td align=\"left\"/></tr><tr><td align=\"left\"> Female</td><td align=\"left\"><italic>N</italic> = 8992</td><td align=\"left\">1.09 (1.02, 1.17)</td><td align=\"left\"/></tr><tr><td align=\"left\">Race</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\">0.5036</td></tr><tr><td align=\"left\"> Mexican American</td><td align=\"left\"><italic>N</italic> = 2669</td><td align=\"left\">1.08 (0.97, 1.20)</td><td align=\"left\"/></tr><tr><td align=\"left\"> Other Hispanic</td><td align=\"left\"><italic>N</italic> = 1841</td><td align=\"left\">1.16 (1.02, 1.32)</td><td align=\"left\"/></tr><tr><td align=\"left\"> Non-Hispanic White</td><td align=\"left\"><italic>N</italic> = 8613</td><td align=\"left\">1.06 (1.00, 1.13)</td><td align=\"left\"/></tr><tr><td align=\"left\"> Non-Hispanic Black</td><td align=\"left\"><italic>N</italic> = 3676</td><td align=\"left\">1.06 (0.93, 1.22)</td><td align=\"left\"/></tr><tr><td align=\"left\"> Other Race</td><td align=\"left\"><italic>N</italic> = 1719</td><td align=\"left\">0.94 (0.76, 1.16)</td><td align=\"left\"/></tr><tr><td align=\"left\">BMI(kg/m2)</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\">0.5389</td></tr><tr><td align=\"left\"> &lt; 25</td><td align=\"left\"><italic>N</italic> = 5155</td><td align=\"left\">1.09 (0.96, 1.24)</td><td align=\"left\"/></tr><tr><td align=\"left\"> 25–30</td><td align=\"left\"><italic>N</italic> = 6199</td><td align=\"left\">1.10 (1.01, 1.20)</td><td align=\"left\"/></tr><tr><td align=\"left\"> &gt; 30</td><td align=\"left\"><italic>N</italic> = 7315</td><td align=\"left\">1.04 (0.98, 1.11)</td><td align=\"left\"/></tr><tr><td align=\"left\">Education level</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\"> Less Than 9th Grade</td><td align=\"left\"><italic>N</italic> = 1610</td><td align=\"left\">1.07 (0.95, 1.22)</td><td align=\"left\"/></tr><tr><td align=\"left\"> 9-11th Grade</td><td align=\"left\"><italic>N</italic> = 2535</td><td align=\"left\">1.03 (0.93, 1.13)</td><td align=\"left\"/></tr><tr><td align=\"left\"> High School Grad/GED or Equivalent</td><td align=\"left\"><italic>N</italic> = 4228</td><td align=\"left\">1.08 (0.99, 1.18)</td><td align=\"left\"/></tr><tr><td align=\"left\"> Some College or AA degree</td><td align=\"left\"><italic>N</italic> = 5537</td><td align=\"left\">1.02 (0.92, 1.12)</td><td align=\"left\"/></tr><tr><td align=\"left\"> College Graduate or above</td><td align=\"left\"><italic>N</italic> = 4600</td><td align=\"left\">1.21 (1.06, 1.38)</td><td align=\"left\"/></tr><tr><td align=\"left\">Marital status</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\">0.5930</td></tr><tr><td align=\"left\"> Married/living with partner</td><td align=\"left\"><italic>N</italic> = 11,162</td><td align=\"left\">1.05 (0.99, 1.12)</td><td align=\"left\"/></tr><tr><td align=\"left\"> Widowed/divorced/separated/ Never married</td><td align=\"left\"><italic>N</italic> = 7343</td><td align=\"left\">1.08 (1.01, 1.15)</td><td align=\"left\"/></tr><tr><td align=\"left\">Hypertension</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\">0.9299</td></tr><tr><td align=\"left\"> Yes</td><td align=\"left\"><italic>N</italic> = 6831</td><td align=\"left\">1.06 (1.00, 1.13)</td><td align=\"left\"/></tr><tr><td align=\"left\"> No</td><td align=\"left\"><italic>N</italic> = 11,670</td><td align=\"left\">1.06 (0.99, 1.13)</td><td align=\"left\"/></tr><tr><td align=\"left\">Diabetes</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\">0.1128</td></tr><tr><td align=\"left\"> Yes</td><td align=\"left\"><italic>N</italic> = 2392</td><td align=\"left\">1.03 (0.93, 1.13)</td><td align=\"left\"/></tr><tr><td align=\"left\"> No</td><td align=\"left\"><italic>N</italic> = 15,688</td><td align=\"left\">1.09 (1.03, 1.15)</td><td align=\"left\"/></tr><tr><td align=\"left\"> Borderline</td><td align=\"left\"><italic>N</italic> = 430</td><td align=\"left\">0.80 (0.55, 1.15)</td><td align=\"left\"/></tr><tr><td align=\"left\">Smoking status</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\">0.7194</td></tr><tr><td align=\"left\"> Yes</td><td align=\"left\"><italic>N</italic> = 8344</td><td align=\"left\">1.06 (1.00, 1.12)</td><td align=\"left\"/></tr><tr><td align=\"left\"> No</td><td align=\"left\"><italic>N</italic> = 10,158</td><td align=\"left\">1.08 (1.00, 1.16)</td><td align=\"left\"/></tr></tbody></table></table-wrap>" ]

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[ "<table-wrap-foot><p>Model 1, no covariates were adjusted. Model 2, age, gender, and race were adjusted. Model 3, age, gender, race, marital status, education level, BMI, income-to-poverty ratio, smoking status, diabetes, hypertension, physical activity, and dietary cholesterol were adjusted. 95% CI, 95% confidence interval; OR, odds ratio</p></table-wrap-foot>", "<table-wrap-foot><p>Age, gender, race, marital status, education level, BMI, income-to-poverty ratio, smoking status, diabetes, hypertension, physical activity, and dietary cholesterol were adjusted. 95% CI, 95% confidence interval; OR, odds ratio</p></table-wrap-foot>", "<table-wrap-foot><p>The results show that the subgroup analysis was adjusted for all presented covariates except the effect modifier. 95% CI, 95% confidence interval; OR, odds ratio</p></table-wrap-foot>", "<fn-group><fn><p><bold>Publisher’s Note</bold></p><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn></fn-group>" ]

[ "<graphic xlink:href=\"12944_2024_2012_Fig1_HTML\" id=\"d32e320\"/>", "<graphic xlink:href=\"12944_2024_2012_Fig2_HTML\" id=\"d32e1039\"/>", "<graphic xlink:href=\"12944_2024_2012_Fig3_HTML\" id=\"d32e1049\"/>" ]

[]

{ "acronym": [ "non-HDL-C", "HDL-C", "NHHR", "PIR", "BMI", "TC" ], "definition": [ "Non-high-density lipoprotein cholesterol", "High-density lipoprotein cholesterol", "Non-HDL-C and HDL-C ratio", "Poverty-to-income ratio", "Body mass index", "Cholesterol" ] }

[ "<title>Background</title>", "<p id=\"Par38\">Ovarian cancer (OC) is the most lethal cancer of the female reproductive system, which is due to the lack of effective screening at the early stage and resistance to chemotherapy as the tumor progresses [##REF##33538338##1##, ##UREF##0##2##]. The preferred treatment for OC is surgery assisted by the combination of paclitaxel and platinum which prolongates the survival of OC patients [##UREF##0##2##]. Nevertheless, the survival rate of OC patients with advanced stage is still low, posing a serious threat to women’s lives [##REF##33538338##1##]. Therefore, predicting individual prognosis for OC is important for both patients and gynecologic oncologists.</p>", "<p id=\"Par39\">Cells can remove incomplete or damaged mitochondria through the mechanism of autophagy selectively and the process is called mitophagy [##REF##37862166##3##]. The body can maintain the integrity of mitochondrial function through mitophagy, so as to achieve the purpose of delaying aging and treating diseases [##REF##37862166##3##, ##REF##36481655##4##]. In recent years, mitophagy is found to contribute to OC progression [##UREF##1##5##, ##REF##32059847##6##]. The specific regulatory mechanism of mitophagy in OC progression may be involved in tumor-associated macrophages [##UREF##2##7##] and cell stemness [##REF##36989771##8##]. Mitophagy is also involved in anticancer activity of drugs in OC, such as platinum [##REF##36481655##4##, ##REF##28963947##9##–##REF##32471883##14##], EGFR tyrosine kinase inhibitors [##REF##29358623##15##], Janus kinases 1/2 inhibitor [##REF##27379204##16##], pardaxin [##UREF##4##17##], nanomedicine [##UREF##1##5##], and epoxycytochalasin H [##REF##32581557##18##]. Despite studies in investigating the role and mechanism of mitophagy in OC, the precise effect of mitophagy in clinical applications remain challenging due to the lack of targetable biomarkers combination.</p>", "<p id=\"Par40\">Long non-coding RNA (lncRNA) refers to a loose RNA transcript with more than 200 nucleotides, which has no protein coding potential [##REF##36596869##19##], and the number of lncRNAs significantly exceeds that of protein-coding genes [##REF##36596869##19##]. Although the functions of lncRNAs in tumorigenesis have been confirmed [##REF##36596869##19##] and our earlier study demonstrated that lncRNA can regulate autophagy in OC [##REF##35706412##20##], little is known about their regulation in mitochondrial function and the mechanism by which lncRNAs regulate mitophagy even remains blank.</p>", "<p id=\"Par41\">Because of the small size and hidden location in the female pelvic cavity, early diagnosis of OC is extremely challenging [##REF##33538338##1##]. Currently, the most commonly used tumor marker for OC screening in clinical practice is Carbohydrate Antigen 125 (CA125) [##REF##36244828##21##] and Human Epididymis Protein 4 (HE4) [##REF##34212386##22##]. Given that other benign diseases can also cause elevated serum biomarkers, the diagnostic specificity and sensitivity of using serum CA125 or HE4 alone are not high [##REF##35907794##23##]. Existing studies have attempted to establish prognostic models for patients with OC based on clinicopathologic characteristics. For instance, the Risk of Ovarian Malignancy Algorithm (ROMA) model incorporated both serum CA125 and HE4, nevertheless, the model did not fully address the challenge of detecting OC with high risk [##REF##35907794##23##].</p>", "<p id=\"Par42\">More and more studies show that gene expression profiles can be used to identify many important prognostic genes in various types of cancer and to map prognostic related molecular models [##REF##37907576##24##, ##REF##37837931##25##]. Based on high-throughput technologies and data sharing, cancer research has entered the era of big data due to large-scale multi-omics data accumulated in The Cancer Genome Atlas (TCGA) [##REF##27008012##26##] and Gene Expression Omnibus (GEO) databases [##REF##30137226##27##]. Bioinformatics is an emerging interdisciplinary subject used for analyzing biological information [##REF##33735179##28##], which takes computer as a tool (mainly R packages) [##UREF##5##29##]. The application of big data from TCGA and GEO databases based on bioinformatics allows us to evaluate the predictive value of mitophagy-related lncRNA (MRL) combinations for OC patients.</p>", "<p id=\"Par43\">The packages in R language software can be used for data mining and statistical analysis [##REF##35648746##30##]. Herein, we mainly utilized R packages to carry out comprehensive analyses of mitophagy-related genes (MRGs) and MRLs for patients with OC. Using weighted co-expression network analysis (WGCNA) and least absolute shrinkage and selection operator (LASSO) Cox regression analysis, we analyzed the landscape of MRGs and MRLs comprehensively. The reliable MRL-model to predict overall survival (OS) and therapeutic strategies was constructed. Our data showed that the MRL-model was associated with immunity characteristics, tumor mutational burden (TMB), immunotherapy, and chemotherapeutic drug sensitivity.</p>" ]

[ "<title>Methods</title>", "<title>Data collection</title>", "<p id=\"Par44\">The processed data were extracted from UCSC-Xena (<ext-link ext-link-type=\"uri\" xlink:href=\"https://xenabrowser.net/datapages/\">https://xenabrowser.net/datapages/</ext-link>) [##REF##32444850##31##]. The Ensemble Gene was converted into Gene Symbol based on gene annotation information in GENCODE [##REF##33270111##32##]. The low-expression mRNAs and lncRNAs were filtered. Collectively, 417 OC samples with expression profiles and prognostic information from TCGA were included. Besides, 88 normal ovarian tissues from GTEx were obtained for identification of differentially expressed genes. We also retrieved four OC datasets that had lncRNA expression profiles and prognostic information from GEO database (<ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/geo/\">https://www.ncbi.nlm.nih.gov/geo/</ext-link>) [##REF##30137226##27##], including 268 OC cases. We selected the dataset from the GPL570 Affymetrix Human Genome U133 Plus 2.0 Array to annotate as many lncRNAs as possible. MRGs were screened from GeneCards (<ext-link ext-link-type=\"uri\" xlink:href=\"https://www.genecards.org\">https://www.genecards.org</ext-link>) [##REF##33676929##33##] based on their relevance score. Furthermore, the somatic mutations were generated with Mutation Annotation Format (MAF) using the “maftools” package (Version 2.16.0) [##REF##30341162##34##].</p>", "<title>Differentially expressed genes screening</title>", "<p id=\"Par45\">Linear regression and Empirical Bayesian [##REF##30903562##35##] were able to shrink the analyzed variances toward a common estimate and the method was conducted using “limma” package (Version 3.10.3) [##REF##25605792##36##] to screen out the differentially expressed MRGs and lncRNAs. Benjamini-Hochberg was used for multiple test correction to obtain greater power relative based on False Discovery Rate (FDR) [##REF##31731339##37##]. The threshold of screening differentially expressed genes was set as adjusted <italic>P</italic> &lt; 0.05 and |logFC| &gt; 0.5.</p>", "<title>Prognostic genes screening</title>", "<p id=\"Par46\">The “survminer” package (Version 0.4.3) was used to determine the optimal cut-point based on the expression of genes, survival time and survival state. The prognostic genes were screened out based on Kaplan-Meier (K-M) curves and logRank test.</p>", "<title>MRLs screening based on WGCNA</title>", "<p id=\"Par47\">We used the “WGCNA” package [##REF##36308698##38##] (Version 1.61) to analyze the expression matrix of lncRNAs, so as to identify highly synergistic lncRNA modules. Firstly, a series of power was set to calculate the square value of correlation coefficient between connectivity k and p(k) and the average connectivity under each power value. The power value whose square value of correlation coefficient reached above 0.85 for the first time was selected. Secondly, based on dynamic pruning and clustering methods, we aggregated highly correlated lncRNAs into modules (correlation coefficient &gt; 0.8). Finally, the correlation between modules and the prognostic MRGs was calculated, and the lncRNA modules associated with multiple MRGs were identified. We defined the modules with the most obvious positive or negative correlation with multiple MRGs as the key modules, and the lncRNAs in these modules were MRLs.</p>", "<title>Establishment of the MRL-model</title>", "<p id=\"Par48\">After obtaining prognostic MRLs, we applied the high-dimensional index regression method of “glmnet” R package (Version 2.0–18), LASSO Cox regression analysis, to screen the combination of prognostic MRLs by utilizing a penalty proportional to the contraction of the regression coefficient based on 20-fold cross-validation analysis, thus addressing multicollinearity [##REF##31443682##39##]. The regression coefficient and the expression level of each MRL was applied to calculate the risk score and construct the MRL-model as follows:</p>", "<p id=\"Par49\">Herein, β_lncRNA was the LASSO regression coefficient of the MRL, and Exp_lncRNA represented the expression value of MRL. The highly correlated MRLs were excluded to prevent the MRL-model from overfitting.</p>", "<title>Validation of the MRL-model</title>", "<p id=\"Par50\">We included four external datasets that had lncRNA expression profile and prognostic information to validate the model: GSE19829 (28 OC samples), GSE26193 (107 OC samples), GSE30161 (58 OC samples), and GSE63885 (75 OC samples). The batch effects of the four external datasets were removed by “sva” R package (Version 3.48.0) [##REF##36258174##40##]. The β_lncRNA was first generated based on TCGA training dataset and the risk score of the GEO validation datasets was calculated based on the formula described above. TCGA training and GEO validation datasets were divided into high-risk group (risk score higher than threshold value), or low-risk group (risk score lower than threshold value) based on the threshold value (median of risk score). K-M curves were used to evaluate the survival outcomes of risk groups for TCGA training and GEO validation datasets, thus validating the effectiveness of predicting prognosis.</p>", "<title>Establishment of the nomogram based on MRL-model</title>", "<p id=\"Par51\">We conducted Univariate Cox regression analysis to assess the prognostic value of MRL-model and clinicopathological parameters. Multivariate Cox regression analysis was further implemented to evaluate and validate their independent prognostic value in TCGA training and GEO validation datasets. Subsequently, the “rms” package (Version 6.7.0) was applied to establish the Nomogram based on MRL-model and clinicopathological parameters [##REF##28951289##41##]. The Nomogram was validated by discrimination and calibration with B = 1000 resampling optimism added to describe the relationship between the actual and the predicted OS probability of the Nomogram, thus evaluating the consistency of the MRL-model. The closer the predicted curve is to 45°, the better the prediction ability.</p>", "<title>Quantitative real-time PCR</title>", "<p id=\"Par52\">A total of 30 OC and 10 normal tissues were collected after approving by Ethics Committee of Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital. The samples obtained were pathologically confirmed as OC or ovarian tissues. Quantitative Real-time PCR analysis (SuperReal PreMix Plus from Tiangen Biotech, Beijing, China) was carried out after extracting total RNA (TRNzol Universal Reagent from Tiangen Biotech, Beijing, China) and reverse transcription (FastKing gDNA Dispelling RT SuperMix from Tiangen Biotech, Beijing, China). The sequence of lncRNA was obtained from LNCipedia (<ext-link ext-link-type=\"uri\" xlink:href=\"https://lncipedia.org/\">https://lncipedia.org/</ext-link>) [##REF##30371849##42##]. The primers of the lncRNAs were designed and provided by Sangon Biotech (Shanghai, China).</p>", "<title>Analysis of functional pathways</title>", "<p id=\"Par53\">The protein-protein interaction (PPI) network was established using STRING (<ext-link ext-link-type=\"uri\" xlink:href=\"https://string-db.org/\">https://string-db.org/</ext-link>) [##REF##36370105##43##] and Cytoscape (Version 3.4.0) [##REF##36512705##44##]. Gene set enrichment analysis (GSEA) was performed in high-risk group versus low-risk group using “GSEA” (Version 4.3.2) [##REF##33287694##45##]. The background gene set was the pathway set in MsigDB molecular label database [##REF##26771021##46##].</p>", "<title>Analysis of immunity features</title>", "<p id=\"Par54\">The carcinogenesis of OC is strongly correlated with the immune microenvironment [##REF##35675036##47##]. Utilizing single-sample gene set enrichment analysis (ssGSEA), we calculated enrichment fraction of 28 immune cells using gene set variation analysis (GSVA, Version 1.48.3) to indicate the relative abundance of each tumor microenvironment-infiltrated cell [##REF##30560866##48##]. In addition, three algorithms, CIBERSORT (Cell-type Identification By Estimating Relative Subsets Of RNA Transcripts, Version 0.1.0) [##REF##35675036##47##], xCELL (Version 1.1.0) [##REF##29141660##49##], MCPcounter (Microenvironment Cell Populations-counter, Version 1.2.0) [##REF##27765066##50##], were used to characterize the cellular composition of complex tissues according to corresponding literature. Further, we estimated immune and stromal scores using ESTIMATE (Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data) algorithm (Version 1.1.7) to indicate the presence of stromal and immune cells [##REF##24113773##51##].</p>", "<title>Analysis of therapy</title>", "<p id=\"Par55\">We predicted potential responses to immune checkpoint blockade (ICB) using the Tumor Immune Dysfunction and Exclusion (TIDE) tool (<ext-link ext-link-type=\"uri\" xlink:href=\"http://tide.dfci.harvard.edu/\">http://tide.dfci.harvard.edu/</ext-link>) [##REF##32102694##52##]. Through contrasting gene expression profiles of OC and dataset of immunotherapy, we compared the discrepancy between the two risk groups in immunotherapy using submap and the <italic>P</italic> value was Bonferroni corrected [##REF##31055200##53##]. The reactivity of chemotherapy drugs were extracted from the Genomics of Drug Sensitivity in Cancer (GDSC) database (<ext-link ext-link-type=\"uri\" xlink:href=\"https://www.cancerrxgene.org/\">https://www.cancerrxgene.org/</ext-link>) [##REF##29186349##54##] and we used “pRRophetic” package (Version 0.5) [##REF##25229481##55##] to analyze cell line expression profiles and OC gene expression profiles by constructing ridge regression model to assess IC50 levels of drugs.</p>", "<title>Construction of ceRNA network</title>", "<p id=\"Par56\">Pearson correlation coefficient (correlation coefficient &gt; 0.2) between mRNAs and lncRNAs was calculated and FDR value (FDR &lt; 0.05) was obtained from Benjamini-Hochberg correction. The local software miranda (Version 3.3a) [##REF##30692017##56##] was used to screen the lncRNA-mRNA pairs (Score ≥ 140 and Energy≤ − 20). We used miRWalk3.0 (<ext-link ext-link-type=\"uri\" xlink:href=\"http://mirwalk.umm.uni-heidelberg.de/search_genes/\">http://mirwalk.umm.uni-heidelberg.de/search_genes/</ext-link>) [##REF##30335862##57##] to obtain the miRNA-mRNA pairs which had been verified by experiment. Further, lncRNAs and mRNAs regulated by the same miRNA with positive co-expression relationship were screened to establish the ceRNA (competing endogenous RNA) network. We used Cytoscape software (Version 3.4.0) for network graph construction [##REF##36512705##44##]. The Degree Centrality of network node were analysed using CytoNCA plug-in (Version 2.1.6) [##REF##25451770##58##].</p>", "<title>Statistical analysis</title>", "<p id=\"Par57\">The statistical analysis and graph visualization were performed by using R programming language [##REF##24132163##59##, ##REF##30357717##60##] or GraphPad Prism. The software, packages and their versions used for statistical analysis were listed in Supplementary Table S##SUPPL##1##1##. The genes with prognostic value were identified based on the hazard ratio (HR) and 95% confidence interval (CI). K-M curves and log-rank test were applied to contrast the survival outcome between two subgroups. Univariate and Multivariate Cox analyses were conducted to determine the independent prognostic value. Wilcox test was used to compare the immune characteristic or drug sensitivity between two groups. The two-tailed <italic>P</italic> lower than 0.05 was considered statistically significant.</p>" ]

[ "<title>Results</title>", "<p id=\"Par58\">The research flowchart was plotted to summarize the main design of our study (Fig. ##FIG##0##1##).</p>", "<title>Differentially expressed and prognostic genes screening</title>", "<p id=\"Par59\">Compared with normal, there were 52 MRGs differentially expressed in OC (adjusted <italic>P</italic> &lt; 0.05 and |logFC| &gt; 0.5) (Fig. ##FIG##1##2##A). Through prognostic analysis, we found that 22 of the 52 MRGs were significantly correlated with the prognosis (Fig. ##FIG##1##2##B). Among the 22 prognostic MRGs, there were four MRGs correlated with favorable prognosis (HR &lt; 1), including E2F1, MAPK8, MTX1, and UBE2L3. In contrast, the remaining 18 MRGs were associated with a poor prognosis (HR &gt; 1), including BCL2L1, BECN1, CSNK2A1, CSNK2A2, FOXO3, GABARAPL1, MAP1LC3A, MFN2, NBR1, PINK1, RAB7A, SNCA, TBC1D15, TBK1, TFE3, TIGAR, USP30, and VPS13D. The box diagram visually demonstrated the expression differences of these 22 prognostic MRGs between OC and normal tissues (Fig. ##FIG##1##2##C).</p>", "<p id=\"Par60\">To further observe the relationship between the 22 prognostic MRGs and clinicopathological parameters, box plots for each MRG were drawn between different clinical groups. We found that TBC1D15 (<italic>P</italic> &lt; 0.05), UBE2L3 (<italic>P</italic> &lt; 0.05), VPS13D (<italic>P</italic> &lt; 0.05), TFE3 (<italic>P</italic> &lt; 0.01), NBR1 (<italic>P</italic> &lt; 0.01), MFN2 (<italic>P</italic> &lt; 0.01), PINK1 (<italic>P</italic> &lt; 0.05), USP30 (<italic>P</italic> &lt; 0.05), and CSNK2A1 (<italic>P</italic> &lt; 0.01) was associated with the stage of OC (Supplementary Fig. S##SUPPL##0##1##A). Most of the MRGs were not significantly different among other clinical factors, except SNCA (<italic>P</italic> &lt; 0.05) and E2F1 (<italic>P</italic> &lt; 0.05) in Grade (Supplementary Fig. S##SUPPL##0##1##B), CSNK2A2 (<italic>P</italic> = 0.02) in Age (Supplementary Fig. S##SUPPL##0##1##C), TIGAR (<italic>P</italic> &lt; 0.05) and MTX1 (<italic>P</italic> &lt; 0.05) in Macroscopic disease (Supplementary Fig. S##SUPPL##0##1##D).</p>", "<p id=\"Par61\">In summary, the differentially expressed and prognostic MRGs can be used as diagnostic markers to identify cancer and non-cancer as well as different clinical stages. These MRGs are expected to be involved in OC progression and deserve further study.</p>", "<title>The interactions among MRGs</title>", "<p id=\"Par62\">Gene Ontology (GO) enrichment analysis revealed that the MRGs were enriched in mitophagy, mitochondrion disassembly, organelle disassembly, macroautophagy, cellular component disassembly, regulation of mitochondrion organization, and so on (Fig. ##FIG##2##3##A), suggesting that these MRGs were indeed involved in mitophagy and their biological implications for wet experiments. Interestingly, the correlations among the expression of the 22 prognostic MRGs were mostly positive, and CSNK2A2 and NBR1 (<italic>P</italic> &lt; 0.05, Cor = 0.85) were the most positively correlated gene pair (Fig. ##FIG##2##3##B), which further hinted at their similarity in biological functions. To further explore the interactions of these 22 MRGs, the PPI analysis was performed (Fig. ##FIG##2##3##C). By ranking the degree in the PPI network, we could find that BECN1, GABARAPL1, PINK1, SNCA, MAP1LC3A, MEN2, and NBR1, FOXO3, RAB7A, and BCL2L1 were the top nine hub genes (Supplementary Fig. S##SUPPL##0##2##), indicating that these MRGs play a more prominent role in mitophagy in OC. Genetic mutations of the majority of MRGs were not detected in OC samples, except TP53, HUWE1, and VPS13C (Supplementary Fig. ##SUPPL##0##3##), hence most MRGs are wild-type in OC.</p>", "<title>MRLs screening based on WGCNA</title>", "<p id=\"Par63\">We conducted WGCNA analysis on the lncRNAs obtained. Firstly, the soft threshold was set to 9 (Fig. ##FIG##3##4##A). We set β = 3 since the power when the square value of the correlation coefficient between k and p(k) reaches 0.8 for the first time. Based on dynamic pruning and clustering, high correlation genes were aggregated into modules. Then we clustered these modules and merge modules with a correlation coefficient greater than 0.8. To wit, modules with a coefficient of dissimilarity less than 0.2 (Fig. ##FIG##3##4##B), and finally integrated into five modules (Fig. ##FIG##3##4##C). The correlation between 22 prognostic MRGs and module eigengene were further calculated. The blue module (containing 369 lncRNAs) revealed the strongest positive correlation with most MRGs, while the green module (containing 70 lncRNAs) showed the strongest negative correlation with most MRGs (Fig. ##FIG##3##4##D). Therefore, the subsequent analysis was mainly according to the lncRNAs in the two modules. We defined these lncRNAs as MRLs. After the above complex comprehensive analysis, we reliably obtained MRLs closely related to mitophagy, which laid the foundation for the following studies.</p>", "<title>Screening of prognostic MRLs</title>", "<p id=\"Par64\">According to the MRLs in the blue and green modules mentioned above, we performed Univariate Cox regression analysis first. Our data showed that nine MRLs were significantly associated with survival prognosis. Then, eight optimal lncRNA combinations were screened by LASSO Cox Regression algorithm combining the expression value of MRLs, survival time and survival state (Fig. ##FIG##4##5##A-B). The forest map revealed the results of LASSO regression coefficient and Cox regression analysis of the eight optimal MRLs (Fig. ##FIG##4##5##C), including RP5-1120P11.1 (<italic>P</italic> = 0.002; HR = 0.673, 95% CI:0.527–0.860; Coef = − 0.133), RP11-195F19.9 (<italic>P</italic> = 0.002; HR = 1.475, 95% CI:1.152–1.888; Coef = 0.007), USP30-AS1 (<italic>P</italic> = 0.002; HR = 0.683, 95% CI:0.533–0.873; Coef = − 0.049), AC004540.5 (<italic>P</italic> = 0.003; HR = 0.685, 95% CI:0.536–0.875; Coef = − 0.093), ZFAS1 (<italic>P</italic> = 0.003; HR = 1.455, 95% CI:1.138–1.860; Coef = − 0.085), RP11-10A14.5 (<italic>P</italic> = 0.003; HR = 0.691, 95% CI:0.542–0.882; Coef = − 0.011), AC010761.10 (<italic>P</italic> = 0.003; HR = 0.691, 95% CI:0.540–0.883; Coef = − 0.022), and AC003075.4 (<italic>P</italic> = 0.010; HR = 0.725, 95% CI:0.568–0.926; Coef = − 0.111). K-M curves were drawn to evaluate the association between the expression levels of the eight optimal MRLs and OC survival prognosis, including RP5-1120P11.1 (log-rank test <italic>P</italic> = 0.0014), RP11-195F19.9 (log-rank test <italic>P</italic> = 0.0019), USP30-AS1 (log-rank test <italic>P</italic> = 0.0022), AC004540.5 (log-rank test <italic>P</italic> = 0.0024), ZFAS1 (log-rank test <italic>P</italic> = 0.0026), RP11-10A14.5 (log-rank test <italic>P</italic> = 0.0028), AC010761.10 (log-rank test <italic>P</italic> = 0.003), and AC003075.4 (log-rank test <italic>P</italic> = 0.0096) (Fig. ##FIG##4##5##D-K). In summary, except for RP11-195F19.9 and ZFAS1, which were associated with poor prognosis, the remaining MRLs were associated with better prognosis of OC. Therefore, we have screened out the optimal combination of MRLs that are involved in OC progression and will build a prognostic MRL-model to calculate risk score for OC based on the results.</p>", "<title>Identification and validation of the MRL-model</title>", "<p id=\"Par65\">The expression levels of the eight optimal MRLs varied in different samples with risk score and clinical information as shown in Fig. ##FIG##5##6##A. We could see that high expression of ZFAS1 and RP11-195F19.9 was associated with high risk score, but the opposite was true for the remaining six MRLs. Using the same regression coefficient, the risk score of TCGA training and GEO validation datasets was calculated based on the formula described in Methods section. Patients with risk score higher than the median were included in high-risk group, otherwise, they were included in low-risk group. Figure ##FIG##5##6##B, C illustrated the distribution of risk score in the two risk groups, and the good prognosis of patients in the low-risk group was observed in both the TCGA training (log-rank test <italic>P</italic> &lt; 0.0001) (Fig. ##FIG##5##6##D) and GEO validation (log-rank test <italic>P</italic> = 0.012) (Fig. ##FIG##5##6##E) datasets, thus proving the accuracy of the validation.</p>", "<p id=\"Par66\">Based on Univariate Cox regression analysis, the MRL-model was proved to be a prognostic marker (<italic>P</italic> &lt; 0.001; HR = 1.960, 95% CI:1.520–2.528) in TCGA training dataset (Fig. ##FIG##6##7##A). Besides, even though we performed Multivariate Cox regression analysis combining the MRL-model and clinicopathological parameters, the MRL-model remained a significant independent predictive predictor (<italic>P</italic> &lt; 0.001; HR = 1.795, 95% CI:1.371–2.350) in TCGA training dataset (Fig. ##FIG##6##7##B). We further carried out Univariate (Fig. ##FIG##6##7##C) and Multivariate (Fig. ##FIG##6##7##D) Cox regression analyses for GEO validation datasets to validate the above results. Our data revealed that the MRL-model was also a prognostic marker (<italic>P</italic> = 0.011; HR = 1.439, 95% CI:1.086–1.906) and a significant independent predictive predictor (<italic>P</italic> = 0.038; HR = 1.349, 95% CI:1.017–1.789) in the validation datasets. The Nomogram was plotted to make the prediction results more intuitive and readable for TCGA training (Fig. ##FIG##6##7##E) and GEO validation (Fig. ##FIG##6##7##F) datasets. The Nomogram was further validated by discrimination and calibration to describe the relationship between the actual OS probability and the predicted OS probability. We observed that the predicted curve was adjacent to 45° in TCGA training (Supplementary Fig. S##SUPPL##0##4##A) and GEO validation (Supplementary Fig. S##SUPPL##0##4##B) datasets, indicating the favorable prediction ability.</p>", "<p id=\"Par67\">Ulteriorly, we implemented stratification analyses based on clinicopathological parameters to further validate the effectiveness of the MRL-model in predicting OC prognosis. We observed that OC patients in the high-risk group still had the unfavorable survival in consideration of Age &lt; 60 (log-rank test <italic>P</italic> &lt; 0.001) (Supplementary Fig. S##SUPPL##0##5##A), Age &gt; =60 (log-rank test <italic>P</italic> &lt; 0.001) (Supplementary Fig. S##SUPPL##0##5##B), Stage I-II (log-rank test <italic>P</italic> = 0.581) (Supplementary Fig. S##SUPPL##0##5##C), Stage III-IV (log-rank test <italic>P</italic> &lt; 0.001) (Supplementary Fig. S##SUPPL##0##5##D), Grade I-II (log-rank test <italic>P</italic> = 0.348) (Supplementary Fig. S##SUPPL##0##5##E), Grade III-IV (log-rank test <italic>P</italic> &lt; 0.001) (Supplementary Fig. S##SUPPL##0##5##F), Tumor Residual Disease 1-10 mm (log-rank test <italic>P</italic> = 0.002) (Supplementary Fig. S##SUPPL##0##5##G), Tumor Residual Disease &gt; 10 mm (log-rank test <italic>P</italic> = 0.053) (Supplementary Fig. S##SUPPL##0##5##H), White (log-rank test <italic>P</italic> &lt; 0.001) (Supplementary Fig. S##SUPPL##0##5##I) and Nonwhite (log-rank test <italic>P</italic> = 0.113) (Supplementary Fig. S##SUPPL##0##5##J).</p>", "<p id=\"Par68\">To sum up, the MRL-model is a reliable prognostic risk stratification tool for OC and is worthy of further large sample validation for clinical implications.</p>", "<title>Quantitative real-time PCR</title>", "<p id=\"Par69\">The eight optimal MRLs in the MRL-model were examined to compare the difference between normal and cancer tissues via Quantitative Real-time PCR experiments. In TCGA dataset (Supplementary Fig. S##SUPPL##0##6##A-H), AC003075.4 (<italic>P</italic> &lt; 0.0001), AC004540.5 (<italic>P</italic> &lt; 0.0001), AC010761.10 (<italic>P</italic> &lt; 0.0001), RP5-1120P11.1 (<italic>P</italic> &lt; 0.0001), RP11-10A14.5 (<italic>P</italic> &lt; 0.0001), USP30-AS1 (<italic>P</italic> &lt; 0.0001) were highly expressed in OC, conversely, the expression levels of RP11-195F19.9 (<italic>P</italic> &lt; 0.0001) and ZFAS1 (<italic>P</italic> &lt; 0.0001) were low in OC tissues. This difference in expression was also observed in our cohort (Supplementary Fig. S##SUPPL##0##6##I-P): AC003075.4 (<italic>P</italic> = 0.0834), AC004540.5 (<italic>P</italic> &lt; 0.01), AC010761.10 (<italic>P</italic> &lt; 0.05), RP5-1120P11.1 (<italic>P</italic> &lt; 0.05), RP11-10A14.5 (<italic>P</italic> &lt; 0.05), RP11-195F19.9 (<italic>P</italic> &lt; 0.05), USP30-AS1 (<italic>P</italic> &lt; 0.01) and ZFAS1 (<italic>P</italic> &lt; 0.01). Our results further confirm the potential of these MRLs as diagnostic markers for OC. However, more sample size and prognostic follow-up information are needed in future studies.</p>", "<title>Evaluation of functional pathways for the MRL-model</title>", "<p id=\"Par70\">GSEA enrichment analysis was performed on KEGG pathway in high-risk group versus low-risk group based on GSEA software. There were 18 KEGG pathways which were significantly enriched in low-risk group and14 KEGG pathways were significantly enriched in high-risk group. Due to the large number of results, only <italic>P</italic> value&lt; 0.01 was shown in Fig. ##FIG##7##8##A, B, including 12 enrichment pathways in the high-risk group (<italic>P</italic> &lt; 0.01) (Fig. ##FIG##7##8##A) and six enrichment pathways in the low-risk group (<italic>P</italic> &lt; 0.01) (Fig. ##FIG##7##8##B). We could find that the high-risk group was enriched in some classic tumor-related signaling pathways, for example, Wnt signaling pathway, TGF-beta signaling pathway, and Hedgehog signaling pathway (Fig. ##FIG##7##8##A). The low-risk group was mainly enriched in metabolic pathways, such as nicotinate and nicotinamide metabolism, pyrimidine metabolism (Fig. ##FIG##7##8##B). These enriched pathways could provide new insights into the underlying biological implications of OC patients with different risk stratifications.</p>", "<title>Evaluation of mutation for the MRL-model</title>", "<p id=\"Par71\">Our study revealed that TMB was higher in low-risk group than in high-risk group (<italic>P</italic> &lt; 0.05), implying that patients with lower risk score may benefit from immunotherapy (Supplementary Fig. S##SUPPL##0##7##A). The Spearman correlation coefficient between risk score and TMB was negative (r = − 0.1279, <italic>P</italic> = 0.0294), demonstrating that TMB was negatively associated with risk score (Supplementary Fig. S##SUPPL##0##7##B). The distribution variations of the somatic mutations between the two risk groups were also analyzed. The top 20 mutated genes in the two risk groups were TP53 (78, 88%), TTN (21, 26%), MUC16 (7, 6%), CSMD3 (12, 6%), NF1 (6, 6%), TOP2A (6, 5%), USH2A (5, 7%), HMCN1 (3, 4%), FAT3 (7, 4%), RYR2 (8, 4%), MUC17 (5, 4%), FLG (4, 4%), APOB (3, 4%), MACF1 (6, 4%), LRP1B (4, 4%), BRCA1 (3, 3%), DNAH3 (2, 3%), LRRK2 (5, 4%), LRP2 (3, 6%), and SYNE1 (6, 3%) (Fig. ##FIG##7##8##C-D). OC patients with higher risk score had observably lower frequencies of TP53 and TTN (Fig. ##FIG##7##8##C). However, the mutated levels of CSMD3 and RYR2 were opposite (Fig. ##FIG##7##8##D). Overall, patients in the low-risk group had a greater mutation rate, and the lower risk score may be an indicator that immunotherapy is effective.</p>", "<title>Analysis of immunity features and immunotherapy for the MRL-model</title>", "<p id=\"Par72\">To further explore the relationship between immune features and MRL-model, five algorithms, including CIBERSORT (Fig. ##FIG##8##9##A), ssGSEA (Fig. ##FIG##8##9##B), MCPcounter (Fig. ##FIG##8##9##C), xCELL (Fig. ##FIG##8##9##D), and ESTIMATE (Fig. ##FIG##8##9##E), were used to analyze immune features for the MRL-model. The results suggested that OC patients in the two risk groups differed at the level of immune cells (Fig. ##FIG##8##9##A-D). Higher risk score correlated strongly with higher stromal (<italic>P</italic> &lt; 0.001) and estimated (<italic>P</italic> &lt; 0.05) scores, while lower risk score correlated with higher tumor purity (<italic>P</italic> &lt; 0.05) (Fig. ##FIG##8##9##E). Risk score was shown to be significantly positively correlated with Stromal Score (R = 0.17, <italic>P</italic> = 0.00044) (Fig. ##FIG##8##9##F). However, there was no significant correlation between risk score and ESTIMATE Score (R = 0.089, <italic>P</italic> = 0.071), Immune Score (R = -0.004, <italic>P</italic> = 0.93), and Tumor Purity (R = -0.089, <italic>P</italic> = 0.071) (Supplementary Fig. S##SUPPL##0##8##A-B). Furthermore, we investigated the association between the risk score and seven immune checkpoints. Four immune checkpoints, including CD274 (<italic>P</italic> &lt; 0.05), CD47 (<italic>P</italic> &lt; 0.001), LAG3 (<italic>P</italic> &lt; 0.01), and VTCN1 (<italic>P</italic> &lt; 0.001) were under-expressed in high-risk group (Supplementary Fig. S##SUPPL##0##9##). Nevertheless, the expression values of the remaining immune checkpoints did not differ between the two risk groups. Higher TIDE score was not only associated with worse immune checkpoint inhibition therapy, but also with worse survival with anti-CTLA4 and anti-PD1 therapy. From Fig. ##FIG##8##9##G, we could find that TIDE score of OC patients in the low-risk group was lower than that in high-risk group, suggesting that OC patients with low risk score were more sensitive to immune checkpoint blockade therapy (<italic>P</italic> &lt; 0.05). In addition, through the results of subclass mapping, we found that OC patients in the low-risk group may be more sensitive to PDL1 response (Bonferroni corrected <italic>P</italic> = 0.01) (Fig. ##FIG##8##9##H). Therefore, we could conclude that patients in the low-risk group identified by the MRL-model may be more sensitive to immunotherapy, which may provide a reference for clinical immunotherapy of OC.</p>", "<title>Analysis of drug sensitivity for the MRL-model</title>", "<p id=\"Par73\">Based on data from GDSC database, the Spearman’s correlation coefficients between drug susceptibility and expression levels of the eight MRLs in the risk model was calculated (Supplementary Fig. S##SUPPL##0##10##A). Our data showed that AC010761.10 was highly expressed and resistant to most drugs (such as bleomycin) and the level of USP30-AS1 was negatively correlated with several drugs (such as paclitaxel). The results provided new insights into the molecular resistance mechanisms of these MRLs. We found that the IC50 levels of Paclitaxel (<italic>P</italic> = 0.005) and ABT.888 (Veliparib, <italic>P</italic> = 0.002) in the high-risk groups were observably higher than those in the low-risk group, suggesting a negative correlation between risk score and the drug susceptibility (Fig. ##FIG##9##10##A, B). Nevertheless, the exact opposite was observed regarding AG.014699 (Rucaparib, <italic>P</italic> = 0.005) (Fig. ##FIG##9##10##C), Axitinib (<italic>P</italic> = 3.344e-07) (Fig. ##FIG##9##10##D), OSI.906 (Linsitinib, <italic>P</italic> = 9.015e-07) (Fig. ##FIG##9##10##E), AZD.0530 (Saracatinib, <italic>P</italic> = 4.276e-05) (Fig. ##FIG##9##10##F), AMG.706 (Motesanib, <italic>P</italic> = 0.022) (Fig. ##FIG##9##10##G), AP.24534 (Ponatinib, <italic>P</italic> = 0.002) (Fig. ##FIG##9##10##H), and Imatinib (<italic>P</italic> = 4.047e-04) (Fig. ##FIG##9##10##I). Besides, other drugs commonly used in OC chemotherapy, such as Cisplatin (<italic>P</italic> = 0.248), Bleomycin (<italic>P</italic> = 0.347), Gemcitabine (<italic>P</italic> = 0.32), and Vinorelbine (<italic>P</italic> = 0.848) showed no difference between the two subgroups (Supplementary Fig. S##SUPPL##0##10##B-E). The results suggest that chemotherapy drugs have different clinical implications for OC patients with different risk score and OC patients need personalized treatment.</p>", "<title>Construction of ceRNA network</title>", "<p id=\"Par74\">We predicted 35,007 miRNA-mRNA pairs and 878 lncRNA-miRNA pairs primitively. The lncRNA-miRNA-mRNA relationship pairs regulated by the same miRNA were further screened, and mRNA-lncRNA co-expression should be positively correlated (correlation coefficient &gt; 0.2), thus obtaining 3668 lncRNA-miRNA-mRNA pairs. Definitively, there were 539 miRNAs, 73 mRNAs and 8 lncRNAs. Because of the large number of miRNAs, we further counted the number of miRNAs simultaneously regulating multiple lncRNA-mRNA relationship pairs. If a miRNA could simultaneously regulate multiple lncRNA-mRNA relationships, this miRNA may play an important role. Therefore, we focused on screening TOP50 miRNA and extracting its corresponding lncRNA-miRNA-mRNA relationship pair and carried out the construction of ceRNA network. It can be seen Fig. ##FIG##10##11##A, the network consisted of 7 lncRNAs, 50 miRNA and 71 mRNA. The network contained 122 lncRNA-mRNA co-expression pairs, 798 miRNA-mRNA pairs and 116 lncRNA-miRNA pairs. We analyzed the connectivity of each node of the network to obtain the connectivity of mRNA, miRNA and lncRNA. By ranking the connectivity of each node, RNA molecules that may play important roles were identified (Fig. ##FIG##10##11##B). The constructed ceRNA network initially described that MRLs affect mRNA expression by sharing miRNA, which provides foundations for further exploration of the regulatory mechanism of OC based on mitophagy.</p>" ]

[ "<title>Discussion</title>", "<p id=\"Par75\">OC has hidden early symptoms and a poor 5-year survival rate. The accuracy of OC biomarker screening is still low. OC is also a multifactorial and complex disease, and the goal of treatment is to reduce the tumor burden, prolong survival time and improve the quality of life of patients. Patients with different pathologic types receiving similar treatment may have significantly different progression free survival (PFS) and OS [##REF##37637510##61##]. Traditional prognostic factors based on clinicopathological parameters are not sufficient to predict the prognosis of patients [##REF##37637510##61##]. There is still controversy surrounding the existing predictive models’ ability to assess prognosis, hence, there is no marker that can accurately predict the clinical outcome of OC. Identifying OC patients with high-risk clinical outcomes and actively improving their prognosis is the focus of current research. The guidelines and consensus are gradually integrating genetic testing into the standard treatment [##REF##37276540##62##]. The expression of genes associated with OC is gradually improving the situation and future prognostic models based on gene expression profiles need to be further explored. With the continuous development of technology (such as high-throughput sequencing reserved in TCGA), the methods for predicting the prognosis of OC are maturing and improving, and we can improve the standards for searching prognostic factors closely related to clinical outcomes and treatment decisions.</p>", "<p id=\"Par76\">WGCNA analysis can help us to understand the interactions between MRGs and, ultimately, the gene networks or modules associated with mitophagy. The gene expression profiles of OC we extracted from the TCGA database provided sufficient data support for the application of WGCNA analysis in our study. Ulteriorly, we aggregated highly correlated lncRNAs into modules (correlation coefficient &gt; 0.8). The blue module with strong positive correlation with MRGs and green module with strong negative correlation with MRGs were selected by weighted calculation of gene expression profiles several times according to the correlation coefficient and <italic>P</italic> value to screen the lncRNAs with high correlation with mitophagy, thereby reducing the loss of useful information. Traditional lncRNA-mRNA co-expression was calculated based on the Pearson correlation coefficient between genes and then set a hard thresholding to determine whether the network exists [##REF##36452343##63##–##UREF##6##65##]. However, setting the threshold only based on Pearson can lead to the loss of real information. Different from traditional Pearson method, we used soft thresholding (R² &gt; 0.85) of WGCNA to determine whether MRLs and MRGs were associated and weighed the correlation coefficients between genes to obtain the gene co-expression matrix. The connections between genes should meet the scale-free network distribution. The expression patterns of genes in each constructed module are very similar, and hub-gene in the module helps to understand the pathogenesis of disease at the molecular level. In a word, WGCNA analysis can filter out irrelevant noisy data and find key molecular mechanisms related to mitophagy in our study. In subsequent studies, experimental methods are needed to confirm the molecular biological correlation between the MRLs we identified and mitophagy, such as mitochondrial membrane potential measurement [##REF##28711444##66##], mitochondrial morphology observing, mitophagy markers detecting and so on.</p>", "<p id=\"Par77\">Data mining based on bioinformatics can be used to explore important biological phenotypes associated with high-dimensional datasets. TCGA and GEO are databases with large-scale genomic analysis capabilities to assess molecular biological signatures associated with OC. Recent developments in next-generation sequencing technologies have greatly expanded our understanding of lncRNAs, which are more abundant in both quantity and function than mRNAs. There have been some successful cases of molecular marker screening by bioinformatics. Bioinformatics analysis based on a large sample (such as samples from TCGA or GEO) can avoid accidental factors more and has stronger generalization. However, a single bioinformatics algorithm is often used in previous studies, which may lead to excessive data perturbation and poor reliability of results [##REF##35096881##67##–##REF##33179541##69##]. Therefore, using dividing gene modules based on clustering, the target module needs to be selected for regression analysis to analyze the correlation degree between genes and features, thus improving the accuracy of screening lncRNA for prognosis of OC. We carried out comprehensive analyses based on bioinformatics in this study. LASSO Cox Regression analysis was carried out after WGCNA, which can improve the precision of screening prognostic MRLs and provide a basis for improving the prognosis of OC.</p>", "<p id=\"Par78\">Non-coding RNA regulates various physiological and pathological processes in the human body. It has been confirmed that the reproductive disorders are related to non-coding RNA to some extent.</p>", "<p id=\"Par79\">The fertility-sparing measures, including hormonal treatment, hysteroscopic resection [##UREF##7##70##], gametes cryopreservation [##REF##35253158##71##], should be appropriately reserved in the treatment of early-stage or low-risk endometrial cancer (EC) [##UREF##7##70##, ##UREF##8##72##, ##REF##35297279##73##], cervical cancer (CC) [##REF##35953599##74##] and OC [##UREF##9##75##], and the non-coding RNA-based diagnostics and therapeutics are the valuable options for implications for the fertility-sparing process [##UREF##10##76##]. Some lncRNAs have also been reported to be involved in the anti-EC effects of progesterone, which may provide new insights into fertility-sparing process [##UREF##11##77##]. As for MRLs involved in mitophagy, knockdown lncRNA MALAT1 can reduce mitophagy in hepatocellular carcinoma [##REF##33425485##78##]. The lack of methionine down-regulates LINC00079, thus activating mitophagy to inhibit cell proliferation in gastric cancer [##REF##34678458##79##]. Overexpression of the peptide encoded by LINC-PINT decreases the mitophagy of hepatocellular carcinoma in vitro and in vivo [##REF##33754036##80##]. In general, studies on lncRNA regulation of mitophagy are still very rare in human cancers and even blank in OC.</p>", "<p id=\"Par80\">A single gene is often unable to predict the prognosis and treatment outcome of tumor patients accurately and stably, but the comprehensive score that integrates the contribution of multiple genes model can overcome this shortcoming. Eight optimal MRL combinations with prognostic value (log-rank test <italic>P</italic> &lt; 0.05) were screened by integrating WGCNA and LASSO analyses in our study, including AC003075.4 (HR &lt; 1), AC004540.5 (HR &lt; 1), AC010761.10 (HR &lt; 1), RP5-1120P11.1 (HR &lt; 1), RP11-10A14.5 (HR &lt; 1), USP30-AS1 (HR &lt; 1), RP11-195F19.9 (HR &gt; 1), and ZFAS1 (HR &gt; 1). We also found the difference of the eight MRLs between normal and cancer tissues via Quantitative Real-time PCR experiments and TCGA. Hence, the differentially expressed and prognostic MRLs can be used as diagnostic markers to identify cancer and non-cancer as well as be expected to be involved in OC progression and deserve further study. RP5-1120P11.1 was identified to participate in proliferation, cycle regulation, and invasion of OC cells [##REF##35116553##81##]. ZFAS1 has been reported to be involved in biological functions of OC. To be specific, ZFAS1 could regulate OC cell malignancy through ZFAS1/miR-150-5p/Sp1 axis [##REF##28099946##82##]; ZFAS1 could also regulates metastasis and platinum resistance [##REF##28154416##83##] of OC via let-7a/BCL-XL/S axis [##REF##32353736##84##]. For other tumors, it was reported that the repression of mitophagy mediated by lncRNA USP30-AS1 could lead to glioblastoma tumorigenesis [##REF##34653676##85##]. USP30-AS1 was proven to regulate the mass and protein expression of mitochondria, thus mediating mitophagy in glioblastoma cells [##REF##34653676##85##]. Mengyue Chen et al. determined the molecular mechanisms of USP30-AS1/miR-299-3p/PTP4A1 axis in CC malignancy [##REF##33986866##86##]. In acute myeloid leukemia, USP30-AS1 may be a regulator of cancer cell survival [##REF##34694569##87##]. ZFAS1 has been widely proved to be related with the development and progression of human cancers [##REF##34385106##88##], including colorectal cancer [##REF##35036050##89##, ##REF##34743750##90##], nasopharyngeal carcinoma [##REF##34980191##91##], oral squamous cell carcinoma [##REF##34697152##92##], pancreatic cancer [##REF##34552050##93##], and so on. Therefore, the study of the biological relevance of MRLs to OC is still in its infancy, and biological experiments are needed to prove how these MRLs play the role of mitophagy in OC. Besides, our constructed ceRNA network initially described the regulatory mechanism of mitophagy, which needs experimental validation, such as Dual-Luciferase Reporter Assay.</p>", "<p id=\"Par81\">Recognition of prognostic factors and characterization of the molecular classification has great significance in physiology, pathology, treatments, and clinical trials for gynecologic malignant tumors [##UREF##12##94##, ##REF##36203482##95##]. Accurate prognosis assessment and stratified management of OC patients is the key to improve patient survival. Using multiple influencing factors to establish a prognostic model for OC has been attempted in the past decade with unsatisfactory results. Previous research have established prognostic indexes for patients with OC, including FIGO stage, residual lesion size, histological grade, and ascites [##REF##33563640##96##]. However, the study failed to be generalized due to its small sample size and short follow-up time. Although some clinicopathological parameters affect the prognosis of OC patients have reached some consensus in clinical treatment [##UREF##13##97##], no recognized diagnostic guidelines have clearly pointed out. Therefore, there is still a long way to go to construct and standardize the prognostic model of OC and popularize it in clinic. Herein, we established the prognostic model based on the eight optimal MRL combinations. The prognosis and independent prognostic value of the MRL-model was verified and validated in TCGA and GEO databases using K-M (log-rank test <italic>P</italic> &lt; 0.0001; log-rank test <italic>P</italic> = 0.012), Univariate Cox regression (<italic>P</italic> &lt; 0.001, HR = 1.960, 95% CI:1.520–2.528; <italic>P</italic> = 0.011, HR = 1.439, 95% CI:1.086–1.906), and Multivariate Cox regression analyses (<italic>P</italic> &lt; 0.001, HR = 1.795, 95% CI:1.371–2.350; <italic>P</italic> = 0.038, HR = 1.349, 95% CI:1.017–1.789), indicating the generalisability. Although there have been articles published on prognostic models based on mRNAs [##REF##35096881##67##–##REF##33179541##69##, ##UREF##14##98##] or the prognostic models established using clinicopathological factors [##REF##30453728##99##, ##REF##34034697##100##] for OC, none of these prognostic models has been externally validated. The prognostic models were controversy surrounding the existing predictive ability to assess prognosis and there are no uniform prognostic models in clinical practice. It is worth mentioning that the MRL-model can also stratify OC patients with different prognostic risks in consideration of clinicopathological parameters, including ethnic and demographic factors (White or Nonwhite). Our study proposes a new prognostic MRL-model whose clinical applicability deserves further exploration. We also detected the expression of several MRLs in the prognostic MRL-model in tissues, which can also lay a foundation for subsequent studies on lncRNA regulation of mitophagy to a certain extent.</p>", "<p id=\"Par82\">The Nomogram can integrate various clinicopathological parameters to evaluate the possibility of occurrence of clinical events, assign and sum different influencing factors, and show them graphically [##REF##29370910##101##]. In our study, we found that the prognostic MRL-model we established was superior to other clinicopathological parameters in predicting OC survival. Further, the actual OS probability and the predicted OS probability of the Nomogram was validated by discrimination and calibration, which indicated that the Nomogram can be used to quantify risk and assess prognosis in patients with OC by combining multiple factors to determine prognosis.</p>", "<p id=\"Par83\">In addition, we compared the discrepancies between the two risk groups based on the MRL-model in functional pathways (<italic>P</italic> &lt; 0.01), TMB, somatic mutation features, immunity features (Wilcox test, <italic>P</italic> &lt; 0.05), chemotherapeutic drug sensitivity (Wilcox test, <italic>P</italic> &lt; 0.05). Our results suggested that OC patients in the two risk groups differed at the level of immune cells. The tumor immune microenvironment is heterogeneous between patients and tumor types, and these differences in composition may suggest different barriers to anti-tumor immunity that affect a patient’s response to specific immunotherapies [##REF##31940272##102##]. It is necessary to look for heterogeneity of OC patients, stratify the population benefit most from immunotherapy. We also found that the TMB level was higher in low-risk group than in high-risk group (Wilcox test, <italic>P</italic> &lt; 0.05), implying that patients with lower risk score may benefit from immunotherapy. OC patients with low risk score may be more sensitive to immune checkpoint blockade therapy was further confirmed by TIDE score (Wilcox test, <italic>P</italic> &lt; 0.05) and subclass mapping (Bonferroni corrected <italic>P</italic> = 0.01). Cytoreduction surgeries along with neoadjuvant chemotherapy are modern therapeutic regimens for advanced-stage OC, nevertheless, its safety and efficacy still need to be explored [##REF##36704762##103##]. Poly (ADP-ribose) polymerase inhibitors (PARP inhibitors) showed particular benefit for OC patients [##REF##37314974##104##]. Since several patients develop resistance to chemotherapy and PARP inhibitors, we need to further identify effective patient subgroups. Drug susceptibility analysis was implemented, and the results showed that OC patients in the high-risk group were resistant to Paclitaxel (<italic>P</italic> = 0.005) and Veliparib (<italic>P</italic> = 0.002), while patients with low risk score were resistant to Rucaparib (<italic>P</italic> = 0.005), Axitinib (<italic>P</italic> = 3.344e-07), Linsitinib (<italic>P</italic> = 9.015e-07), Saracatinib (<italic>P</italic> = 4.27e-5), Motesanib (<italic>P</italic> = 0.022), Ponatinib (<italic>P</italic> = 0.002), and Imatinib (<italic>P</italic> = 4.047e-4). For patients who are not sensitive to anti-cancer drugs, the treatment regimen should be changed in time to improve the prognosis of patients to a greater extent. However, there is a lack of indicators that can indicate drug reactivity for clinical quality decision making. Therefore, the prognostic MRL-model we established has a certain suggestive effect on the drug sensitivity of OC patients, but further validation using clinical samples is needed. The mechanism of direct or indirect influence of MRL on drug sensitivity is also worthy of further study using experimental validation.</p>", "<p id=\"Par84\">Our study hopes to provide feasible ideas for prognosis screening and precise treatment of OC patients by constructing prognostic risk model. However, there are still some limitations of this study worth mentioning. This study is a retrospective study, which has inherent bias inevitably, such as selection bias that may occur when incomplete information is excluded. The factors that affect the prognosis of OC are complex and diverse, and more clinical factors are not included in the study due to lack of data (such as ethnic and demographic factors). Hence, we are considering including more than 300 Chinese patients to validate the MRL-model. We established the prognostic MRL-model based on the data sources from public databases, and the prognosis and independence of the MRL-model was identified and validated in TCGA and the external GEO datasets. However, the sample size still needs to be expanded and analysis based on mRNA expression profile (such as drug sensitivity) could not be carried out on GEO dataset due to lack of data. Hence, more clinical tissues are needed to verify the reliability of prediction after follow-up. Although the expression levels of eight optimal MRLs in the prognostic MRL-model were examined in the clinical samples we collected, insufficient sample size and lack of clinical data need to be further addressed to make the evidence more solid. The specific mechanism of lncRNA related to mitophagy identified by us has not been developed yet by wet experiment, which needs to be addressed in future studies.</p>" ]

[ "<title>Conclusion</title>", "<p id=\"Par85\">The comprehensive analysis of MRGs and MRLs revealed their roles in expression, prognosis, chemotherapy, immunotherapy and molecular mechanism of OC. By analyzing prognosis, functional pathways, mutation, immunity features, immunotherapy, and drug susceptibility, our findings demonstrated the molecular and clinical significance of the MRL-model, thus stratifying patients with high risk and improving clinical outcomes for OC patients. The MRL-based model we constructed and validated deserves further study for future clinical application after addressing the limitations, such as insufficient sample size, missing demographic factors, lack of external validation and wet experiments.</p>" ]

[ "<title>Background</title>", "<p id=\"Par1\">Both mitophagy and long non-coding RNAs (lncRNAs) play crucial roles in ovarian cancer (OC). We sought to explore the characteristics of mitophagy-related gene (MRG) and mitophagy-related lncRNAs (MRL) to facilitate treatment and prognosis of OC.</p>", "<title>Methods</title>", "<p id=\"Par2\">The processed data were extracted from public databases (TCGA, GTEx, GEO and GeneCards). The highly synergistic lncRNA modules and MRLs were identified using weighted gene co-expression network analysis. Using LASSO Cox regression analysis, the MRL-model was first established based on TCGA and then validated with four external GEO datasets. The independent prognostic value of the MRL-model was evaluated by Multivariate Cox regression analysis. Characteristics of functional pathways, somatic mutations, immunity features, and anti-tumor therapy related to the MRL-model were evaluated using abundant algorithms, such as GSEA, ssGSEA, GSVA, maftools, CIBERSORT, xCELL, MCPcounter, ESTIMATE, TIDE, pRRophetic and so on.</p>", "<title>Results</title>", "<p id=\"Par3\">We found 52 differentially expressed MRGs and 22 prognostic MRGs in OC. Enrichment analysis revealed that MRGs were involved in mitophagy. Nine prognostic MRLs were identified and eight optimal MRLs combinations were screened to establish the MRL-model. The MRL-model stratified patients into high- and low-risk groups and remained a prognostic factor (<italic>P</italic> &lt; 0.05) with independent value (<italic>P</italic> &lt; 0.05) in TCGA and GEO. We observed that OC patients in the high-risk group also had the unfavorable survival in consideration of clinicopathological parameters. The Nomogram was plotted to make the prediction results more intuitive and readable. The two risk groups were enriched in discrepant functional pathways (such as Wnt signaling pathway) and immunity features. Besides, patients in the low-risk group may be more sensitive to immunotherapy (<italic>P</italic> = 0.01). Several chemotherapeutic drugs (Paclitaxel, Veliparib, Rucaparib, Axitinib, Linsitinib, Saracatinib, Motesanib, Ponatinib, Imatinib and so on) were found with variant sensitivity between the two risk groups. The established ceRNA network indicated the underlying mechanisms of MRLs.</p>", "<title>Conclusions</title>", "<p id=\"Par4\">Our study revealed the roles of MRLs and MRL-model in expression, prognosis, chemotherapy, immunotherapy, and molecular mechanism of OC. Our findings were able to stratify OC patients with high risk, unfavorable prognosis and variant treatment sensitivity, thus improving clinical outcomes for OC patients.</p>", "<title>Supplementary Information</title>", "<p>The online version contains supplementary material available at 10.1186/s12905-023-02864-5.</p>", "<title>Keywords</title>" ]

[ "<title>Supplementary Information</title>", "<p>\n</p>" ]

[ "<title>Acknowledgements</title>", "<p>We greatly thank the data gained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO).</p>", "<title>Authors’ contributions</title>", "<p>Yang Sun conceived, designed, and supervised the study. Jianfeng Zheng performed data analysis and drafted the manuscript. Shan Jiang and Xuefen Lin helped to perform data analysis and revise the manuscript. Huihui Wang, Li Liu, and Xintong Cai collected the data and arranged the figures.</p>", "<title>Funding</title>", "<p>This project was funded by grant from the National Natural Science Foundation of China (82374081), the Natural Science Foundation of Fujian Province of China (2020 J011115), Joint Funds for the Innovation of Science and Technology of Fujian Province (2021Y9209), and the Medicine Innovation Foundation of Fujian Province of China (2020CXB007).</p>", "<title>Availability of data and materials</title>", "<p>The RNA sequencing profiles and clinical information of ovary and ovarian cancer can be gained from UCSC-Xena (<ext-link ext-link-type=\"uri\" xlink:href=\"https://xenabrowser.net/datapages/?dataset=TcgaTargetGtex_rsem_isoform_tpm&amp;host=https%3A%2F%2Ftoil.xenahubs.net&amp;removeHub=http%3A%2F%2F127.0.0.1%3A7222\">https://xenabrowser.net/datapages/?dataset=TcgaTargetGtex_rsem_isoform_tpm&amp;host=https%3A%2F%2Ftoil.xenahubs.net&amp;removeHub=http%3A%2F%2F127.0.0.1%3A7222</ext-link>) platform. The RNA sequencing profiles and clinical information of ovarian cancer from Gene Expression Omnibus (GEO) database are available at the following link: GSE19829 (<ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE19829\">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE19829</ext-link>), GSE26193 (<ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE26193\">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE26193</ext-link>), GSE30161 (<ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE30161\">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE30161</ext-link>), and GSE63885 (<ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE63885\">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE63885</ext-link>). The gene annotation information is available at GENCODE database (<ext-link ext-link-type=\"uri\" xlink:href=\"https://www.gencodegenes.org/\">https://www.gencodegenes.org/</ext-link>). The mitophagy-related genes can be gained from GeneCards (<ext-link ext-link-type=\"uri\" xlink:href=\"https://www.genecards.org\">https://www.genecards.org</ext-link>). The R packages can be acquired or installed from CRAN (<ext-link ext-link-type=\"uri\" xlink:href=\"https://cran.r-project.org/mirrors.html\">https://cran.r-project.org/mirrors.html</ext-link>), Biocductor (<ext-link ext-link-type=\"uri\" xlink:href=\"https://www.bioconductor.org/\">https://www.bioconductor.org/</ext-link>), GitHub (<ext-link ext-link-type=\"uri\" xlink:href=\"https://github.com/GitHub\">https://github.com/GitHub</ext-link>), or native software R Studio (<ext-link ext-link-type=\"uri\" xlink:href=\"https://posit.co/downloads/\">https://posit.co/downloads/</ext-link>). Further inquiries can be directed to the corresponding author.</p>", "<title>Declarations</title>", "<title>Ethics approval and consent to participate</title>", "<p id=\"Par86\">The study has been conducted in accordance with the ethical standards, according to the Declaration of Helsinki, and according to national and international guidelines. TCGA belongs to public databases. The patients involved in the database have obtained ethical approval. Users can download relevant data for free for research and publish relevant articles. The studies involving human participants were reviewed and approved by Ethics Committee of Fujian Cancer Hospital. The patients/participants provided their written informed consent to participate in this study.</p>", "<title>Consent for publication</title>", "<p id=\"Par87\">Not applicable.</p>", "<title>Competing interests</title>", "<p id=\"Par88\">The authors declare no competing interests.</p>" ]

[ "<fig id=\"Fig1\"><label>Fig. 1</label><caption><p>The flowchart outlining the main design of our study</p></caption></fig>", "<fig id=\"Fig2\"><label>Fig. 2</label><caption><p>Differentially expressed and prognostic MRGs screening. <bold>A</bold> Heatmap showing the gene expression of the 52 differentially expressed MRGs between OC and normal tissues. <bold>B</bold> The forest map showing the results of prognosis analysis of 22 differentially expressed MRGs with prognostic value. The values in parentheses represent 95% confidence intervals of hazard ratio. <bold>C</bold> Box diagram showing the 22 prognostic MRGs expression between normal and tumor groups. ∗∗∗∗<italic>P</italic> &lt; 0.0001</p></caption></fig>", "<fig id=\"Fig3\"><label>Fig. 3</label><caption><p>The interactions among MRGs. <bold>A</bold> GO enrichment analysis of MRGs showing their significant function in mitophagy. <bold>B</bold> The correlations among the expression of the 22 prognostic MRGs. The color and number of the circle represent the correlation coefficient. The crossed symbol indicates no statistical significance. <bold>C</bold> PPI network encoded by 22 prognostic MRGs showing their interactions</p></caption></fig>", "<fig id=\"Fig4\"><label>Fig. 4</label><caption><p>MRLs screening based on WGCNA. (<bold>A</bold>) WGCNA power result. Left: Soft Threshold (power) represents the weight, and the ordinate represents the square value of the correlation coefficient between connection degree k and p(k). Right: Soft Threshold (power) represents the weight, and the ordinate represents the average connection. It is generally required that the power when the square value of the correlation coefficient between k and p(k) reaches 0.8 for the first time is taken as the β value, which can be seen as β = 3. (<bold>B</bold>) The module clustering result diagram. The vertical axis represents the difference coefficient, and the blue line represents the difference coefficient of 0.2. (<bold>C</bold>) Systematic cluster tree of genes and gene modules generated by dynamic clipping method. Different colors represent different genetic modules. (<bold>D</bold>) Heatmap of the correlation between module eigengenes and 22 prognostic MRGs of OC. Each cell contains the correlation coefficient and <italic>P</italic> value. The top number represents the correlation coefficient, and the parenthesis number represents the significance <italic>P</italic> value</p></caption></fig>", "<fig id=\"Fig5\"><label>Fig. 5</label><caption><p>Screening of prognostic MRLs. <bold>A</bold> LASSO regression of the eight optimal MRLs. <bold>B</bold> Cross-validation for tuning the parameter selection and showing confidence interval under each lambda the in the LASSO regression. The two dashed lines indicate two special λ values: λ_min on the left and λ_1se on the right. The λ values between these two values were considered to be appropriate. The model constructed by λ_1se was the simplest, that was, it used a small number of genes, while λ_min had a higher accuracy rate and used a larger number of genes. Hence, λ_min was selected to build the model for accuracy in our study. <bold>C</bold> The forest map showing the results of Cox regression analysis and LASSO regression coefficient of the eight optimal MRLs. The values in parentheses represented 95% confidence intervals. <bold>D</bold>-<bold>K</bold> Survival analysis of the eight optimal MRLs for RP5-1120P11.1 (<bold>D</bold>), RP11-195F19.9 (<bold>E</bold>), USP30-AS1 (<bold>F</bold>), AC004540.5 (<bold>G</bold>), ZFAS1 (<bold>H</bold>), RP11-10A14.5 (<bold>I</bold>), AC010761.10 (<bold>J</bold>), and AC003075.4 (<bold>K</bold>). The <italic>P</italic> values are tested by log-rank</p></caption></fig>", "<fig id=\"Fig6\"><label>Fig. 6</label><caption><p>Identification and validation of the MRL-model. <bold>A</bold> Heatmap of the associations among the expression levels of the eight MRLs, risk score and clinicopathological parameters. <bold>B, C</bold> Distribution of risk scores and survival status of OC patients in TCGA training (<bold>B</bold>) and GEO validation (<bold>C</bold>) datasets. <bold>D, E</bold> Kaplan-Meier survival curves show survival probability of high-risk or low-risk in TCGA training (<bold>D</bold>) and GEO validation (<bold>E</bold>) datasets. The <italic>P</italic> values are tested by log-rank</p></caption></fig>", "<fig id=\"Fig7\"><label>Fig. 7</label><caption><p>Establishment of the Nomogram based on MRL-model. <bold>A</bold> The Univariate Cox regression analysis for TCGA training dataset. <bold>B</bold> The Multivariate Cox regression analysis for TCGA training dataset. <bold>C</bold> The Univariate Cox regression analysis for GEO validation dataset. <bold>D</bold> The Multivariate Cox regression analysis for GEO validation dataset. The values in parentheses represent 95% confidence intervals. <bold>E</bold> The Nomogram model based on MRL-model and clinicopathological parameters for TCGA training dataset. <bold>F</bold> The Nomogram model based on MRL-model and clinicopathological parameters for GEO validation dataset</p></caption></fig>", "<fig id=\"Fig8\"><label>Fig. 8</label><caption><p>Evaluation of functional pathways and mutation for the MRL-model. <bold>A</bold>, <bold>B</bold> GSEA enrichment analysis of the status of special biological pathways in high-risk group and low-risk group. <bold>C</bold>, <bold>D</bold> The waterfall plot of somatic mutation features established with high- (<bold>C</bold>) and low- (<bold>D</bold>) risk groups. Each column represented an individual patient. The upper barplot showed TMB, the number on the right indicated the mutation frequency in each gene. The right barplot showed the proportion of each variant type</p></caption></fig>", "<fig id=\"Fig9\"><label>Fig. 9</label><caption><p>Analysis of immunity features and immunotherapy for the MRL-model. <bold>A</bold>-<bold>E</bold> Analysis of immune activity between the two risk groups using five algorithms: CIBERSORT (<bold>A</bold>), ssGSEA (<bold>B</bold>), MCPcounter (<bold>C</bold>), xCELL (<bold>D</bold>), and ESTIMATE (<bold>E</bold>). <bold>F</bold> Correlations between risk scores and stromal scores. <bold>G</bold> TIDE score of OC patients in the two risk groups. <bold>H</bold> Subclass mapping showing the difference in immunotherapy between the two risk groups</p></caption></fig>", "<fig id=\"Fig10\"><label>Fig. 10</label><caption><p>Analysis of drug sensitivity for the MRL-model. <bold>A</bold>-<bold>I</bold> Relationships between risk scores and IC50 level of Paclitaxel (<bold>A</bold>), ABT.888 (<bold>B</bold>), AG.014699 (<bold>C</bold>), Axitinib (<bold>D</bold>), OSI.906 (<bold>E</bold>), AZD.0530 (<bold>F</bold>), AMG.706 (<bold>G</bold>), AP.24534 (<bold>H</bold>), and Imatinib (<bold>I</bold>)</p></caption></fig>", "<fig id=\"Fig11\"><label>Fig. 11</label><caption><p>Construction of ceRNA network. <bold>A</bold> ceRNA network. Circle represents differential MRG, red represents up-regulation, green represents down-regulation. Diamond represents differential MRL, pink represents up-regulation, blue represents down-regulation. Green line represents positive correlation. Orange square represents predicted miRNA, gray line represents competitive binding of lncRNA to miRNA. Pink line indicates that miRNA regulate mRNA. <bold>B</bold> The top 25 genes with high connectivity</p></caption></fig>" ]

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[ "<supplementary-material content-type=\"local-data\" id=\"MOESM1\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM2\"></supplementary-material>" ]

[ "<fn-group><fn><p><bold>Publisher’s Note</bold></p><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn><fn><p>Jianfeng Zheng, Shan Jiang and Xuefen Lin contributed equally to this work.</p></fn></fn-group>" ]

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[ "<media xlink:href=\"12905_2023_2864_MOESM1_ESM.pdf\"><caption><p><bold>Additional file 1.</bold>\n</p></caption></media>", "<media xlink:href=\"12905_2023_2864_MOESM2_ESM.pdf\"><caption><p><bold>Additional file 2.</bold>\n</p></caption></media>" ]

{ "acronym": [ "OC", "lncRNA", "CA125", "HE4", "ROMA", "TCGA", "GEO", "MRL", "MRG", "WGCNA", "LASSO", "OS", "TMB", "MAF", "FDR", "K-M", "PPI", "GSEA", "ssGSEA", "GSVA", "CIBERSORT", "MCPcounter", "ESTIMATE", "ICB", "TIDE", "GDSC", "ceRNA", "HR", "CI", "GO", "PFS", "EC", "CC" ], "definition": [ "Ovarian cancer", "Long non-coding RNA", "Carbohydrate Antigen 125", "Human Epididymis Protein 4", "Risk of Ovarian Malignancy Algorithm", "The Cancer Genome Atlas", "Gene Expression Omnibus", "Mitophagy-relate lncRNA", "Mitophagy-relate gene", "Weighted coexpression network analysis", "Least absolute shrinkage and selection operator", "Overall survival", "Tumor mutational burden", "Mutation Annotation Format", "False discovery rate", "Kaplan–Meier", "Protein-protein interaction", "Gene set enrichment analysis", "Single-sample gene set enrichment analysis", "Gene set variation analysis", "Cell-type Identification By Estimating Relative Subsets Of RNA Transcripts", "Microenvironment Cell Populations-counter", "Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data", "Immune checkpoint blockade", "Tumor immune dysfunction and exclusion", "Genomics of Drug Sensitivity in Cancer", "competing endogenous RNA", "Hazard ratio", "Confidence interval", "Gene Ontology", "Progression free survival", "Endometrial cancer", "Cervical cancer" ] }

[ "<title>Background</title>", "<p id=\"Par20\">Globally, adolescents aged 10 to 19 years make up about 16% of the population [##UREF##0##1##]. This age group makes up a higher proportion of sub-Saharan Africa and Nigeria, accounting for 23% and 22.3% respectively [##UREF##0##1##, ##UREF##1##2##]. Adolescence is a period of rapid human development which includes physical, neurodevelopmental, psychological, and social changes with implications for their peculiar health needs. The majority of the serious health challenges in adulthood have roots in the period of adolescence, and about 70% of premature deaths among adults are mostly related to behaviours initiated at adolescence [##UREF##0##1##].</p>", "<p id=\"Par21\">Sexual and Reproductive Health (SRH) issues, including sexually transmitted infections and unintended pregnancies, account for a significant proportion of disease burden among adolescents [##REF##27174305##3##]. A 12-year review of Nigerian adolescents sexual practices and behaviours found that they engage in risky sexual behaviours consisting of early sexual debut, unsafe sexual practices, and concurrent multiple sexual partners [##UREF##2##4##].</p>", "<p id=\"Par22\">There is growing evidence to show that SRH of adolescents can be improved through Comprehensive sexuality education (CSE) [##UREF##3##5##]. The CSE curriculum may also be known as \"life skills,\" \"family life,\" or \"HIV education” or \"holistic sexuality education\" implying the difference in the emphasis of the curricula [##UREF##4##6##]. The policy of the Nigerian government at the national level identifies the pressing SRH needs of adolescents and has acted on its policy commitments by implementing a near-nationwide CSE. Family Life and HIV Education (FLHE) is the form of CSE being implemented by the government into school curricula at the basic and secondary school levels in Nigeria, in addition to teacher's training institutions [##REF##26506660##7##]; its main aim is to prevent HIV/AIDS through awareness and education.</p>", "<p id=\"Par23\">Given the limitations associated with the delivery of FLHE in Nigeria which is mainly via didactic physical lectures, and consequently, low nation-wide implementation and uptake, there is a need for more innovative and effective strategies to reach these adolescents [##REF##26506660##7##, ##UREF##5##8##]. mHealth is one of such innovation with the potential of wider acceptability by the adolescent population. mhealth is the use of \"emerging mobile communications and network technologies for healthcare\" and it has gained prominence in recent years [##UREF##6##9##]. Globally, mobile phone subscriptions have been exponentially increasing, especially in developing countries where mobile subscriptions increased from 1.2 billion in 2005 to over 5.5 billion in 2015 [##UREF##7##10##]. A study done among 726 females between the ages of 12 and 30 years in six states in Nigeria showed that about 98.6% of them had access to a mobile phone [##REF##22916554##11##]. Another study conducted among 249 in-school teenagers in Enugu State in southeast Nigeria found that about 69% of them had access to the internet via their phones, laptops and tablets [##UREF##8##12##].</p>", "<p id=\"Par24\">Adolescents use the internet for their health-related needs, and the proportion who use this service is projected to increase in the next few years [##REF##26881933##13##, ##REF##16756437##14##]. Many adolescents cannot discuss SRH issues with their parents due to poor communication and cultural norms on sexuality issues, and they would rather rely on information from the internet or their peers who may have incorrect or inadequate information [##UREF##9##15##].</p>", "<p id=\"Par25\">Within the context of the current gaps in the delivery of the FLHE in Nigeria and the revolutionization of information access through mHealth in the country, we developed and implemented a mHealth-based CSE curriculum over 12 weeks and assessed its effect on the SRH, attitude, and sexual behaviour of in-school adolescents in Ilorin, Nigeria.</p>" ]

[ "<title>Methods</title>", "<title>Trial design</title>", "<p id=\"Par26\">A two-arm Cluster Randomized Controlled Trial (cRCT) of 8 schools (clusters) with equal allocation was conducted. This number meets the minimum number of clusters required for cRCTs [##REF##28584874##16##]. Individual students served as participants and outcome measures were at the individual participant level. SRH knowledge, attitude and sexual behaviour were assessed at baseline (T₀), immediately after the 12-week intervention (T₁), and 3 months after the intervention (T₂).</p>", "<title>Study setting</title>", "<p id=\"Par27\">The study was conducted between 10th of February 2020 and 28th of August 2020 in secondary schools located in Ilorin, Kwara State, Nigeria. Ilorin is the capital city of Kwara State and has a youth literacy rate of 76.9% and total gross school enrolment ratio of 50.13% (52.57% for males and 47.64% for females) [##UREF##10##17##]. One of the focus areas of the National School Health Programme by the Federal Ministry of Education is the provision of skill-based education and FLHE is part of the skill-based curriculum [##UREF##11##18##].</p>", "<title>Eligibility criteria for schools</title>", "<p id=\"Par28\">To be eligible to participate, schools had to be registered with the Kwara State Ministry of Education. Secondary school commences after 5–6 years of primary (elementary) school, and the system is divided into the junior secondary school (year 1–3) and senior secondary school (year 3–6).</p>", "<title>Eligibility criteria for students</title>", "<p id=\"Par29\">In-school adolescents (aged 10–19 years) in senior secondary school and who had access to the internet at least once a week throughout the study duration were eligible to participate. The students either owned these devices or had access to these devices through their parents/guardians.</p>", "<p id=\"Par30\">Students who had cognitive or visual impairments were excluded from the trial.</p>", "<title>Group assignment and masking</title>", "<p id=\"Par31\">Eligible schools (n = 161) were stratified into public (n = 80) and private schools (n = 81). Eight schools (4 public schools and 4 private schools) were selected using simple random sampling by computer-generated random numbers (Fig. ##FIG##0##1##). Schools were then assigned to a cluster design to avoid contamination bias following consent from the school principals. Researchers were not blinded to the assignment, but students were not informed of their school’s allocation. To reduce contamination bias in this study, schools formed cluster units for allocation into study groups and we ensured they were at least 40 m apart.</p>", "<title>Sample size and sampling strategy</title>", "<p id=\"Par32\">The target sample size for this study was 1280 participants (640 per group) from 8 schools. The superiority trial formula for continuous variables which is used to verify that a new intervention is more effective than the usual intervention from a statistical/clinical point of view was used to calculate the sample size [##REF##22263004##19##]. The statistical power was set at 0.80, alpha at 0.05 and attrition rate of 10% among participants. Mean scores in the control and intervention group were set at 16.61 and 17.47 using a previous study [##UREF##12##20##]. A design effect of 2 was calculated assuming an intraclass correlation of 0.05 and number of individuals per cluster of 21 to allow for possible clustering effect.</p>", "<title>Recruitment and consent/assent</title>", "<p id=\"Par33\">Based on the student enrolment profile of each school (Additional file ##SUPPL##0##1##), proportional allocation was used to allocate sample size to each of the selected schools based on their population in the first stage. In the second stage, disproportionate stratified sampling for between-strata analysis which is used to maximize sample size of each stratum using equal allocation for comparative analysis was employed [##UREF##15##23##, ##UREF##16##24##]. In the third stage, using the nominal roll which contains the list of students in each class, participants in each class were selected using a systematic sampling technique.</p>", "<p id=\"Par34\">A letter of introduction was obtained from the Department of Epidemiology and Community Health, University of Ilorin Teaching Hospital and the Kwara State Ministry of Education to the principals of the schools selected. Prior to the commencement of the study, multiple advocacy visits were paid to the principals, head teachers and others in authority in the selected schools. The visits involved discussions about the study objectives and the link to the government-approved FLHE curriculum, data collection methodology and timeframe, parental/guardian consent forms with the study information leaflet, study questionnaire, etc.</p>", "<p id=\"Par35\">Following adequate briefing and the approval to conduct the study in their school, the principal/head teacher or designated officers introduced the study and the research team to the students. Class-to-class interactive sessions about the study were conducted. Simplified study information leaflets were also distributed to the students and there were opportunities for them to ask questions during the sessions. The selected respondents were given forms which included the study information leaflet to obtain signed written consent from one of their parents or a guardian at least 2 weeks to the commencement of data collection.</p>", "<p id=\"Par36\">In Nigeria, a minor is defined as one who is below the age of 18 years. In this study, those who were 18 years and above gave written consent to participate in the study by themselves. For those less than 18 years, only those who submitted a consent form signed by a parent or guardian and gave verbal assent to participate in the study (obtained on the first day of data collection and witnessed by the research team) were recruited into the study.</p>", "<title>Intervention</title>", "<p id=\"Par37\">Schools allocated to the intervention group were given access to the mHealth-based CSE, which contained 12 modules via accessible online (link: <ext-link ext-link-type=\"uri\" xlink:href=\"http://flhe.noubug.com\">http://flhe.noubug.com</ext-link>) over a 12-week period (24th February 2020 to 23rd May 2020). The 12-module CSE was an adoption of the approved FHLE curriculum for secondary schools in Nigeria that covered six themes: human development, personal skills, sexual health relationships, sexual behaviour, and society and culture [##UREF##14##22##]. Topics across these six themes were covered over the 12-week period (Additional file ##SUPPL##1##2##). Each participant was given a username (not linked to any personal identifier) and password (which each user could change) to access the CSE curriculum online. Participants could ask questions anonymously via the website, and responses were given within 24 h. During this period, a total number of 51 questions were asked by 47 respondents. Majority of the questions, 38 (80.9%) were related to the course contents while 9 (19.1%) were questions requesting for technical support in navigating the site. Participants in both public and private schools were not provided with free data to browse the internet for this study but used their existing internet data sources prior to the study. This was done to assess the sustainability of in-school adolescents utilising mHealth-based interventions without the availability of incentives such as the provision of free data for browsing.</p>", "<title>Control</title>", "<p id=\"Par38\">The control was a 12-week school-as-usual condition. Participants in the control group were not exposed to the mHealth-based intervention. Instead, they were to continue with the usual classroom-based CSE according to the existing school curriculum during the intervention period. However, due to the coronavirus disease 2019 (COVID-19) pandemic, all schools in study area were shut during this period, and this disrupted the regular educational routine of these students.</p>", "<title>Study instrument</title>", "<p id=\"Par39\">We used a questionnaire adapted from the World Health Organisation’s questionnaire for collecting data on SRH behaviours [##UREF##15##23##]. The questions were modified based on the modules covered in the intervention. Section 1 addressed the respondents’ sociodemographic characteristics; Sections 2, 3, 4 and 5 assessed the respondents’ SRH knowledge, attitude and sexual behaviour respectively. The questionnaire was pre-tested among students of two senior secondary schools (one public, one private) other than the selected schools (n = 128). The schools chosen for the pre-test were at least 10km from the study and control schools.</p>", "<p id=\"Par40\">All study tools were tested for accuracy and content validity through the consultation of relevant literature on SRH education for adolescents. They were also reviewed by academic experts, including eight Consultant Public Health Physicians with expertise in SRH, for its content and structure validity. The coefficient of internal reliability analysis of the tool was 0.757, which is fairly high [##UREF##16##24##]. Pre-testing helped determine its level of difficulty, complexity, logical sequence, spot inconsistencies, and standardise questionnaire administration language and style.</p>", "<title>Data collection</title>", "<p id=\"Par41\">Data was collected by Six Research Assistants (RAs) who were trained on the content and administration of the research instrument. Three RAs were Medical Officers in Ilorin, and the other three were adolescents between the ages of 18 and 19 years. Baseline assessment (T₀) was done using paper-based pre-tested interviewer-led, self-administered questionnaires in classrooms under the guidance of the Lead Researcher (OWA) and the RAs (10th–22nd of February 2020).</p>", "<p id=\"Par42\">Attitudinal and behavioural change among adolescents takes substantial time [##REF##12456893##25##]. Thus, to give ample time to measure the effect of the intervention among the respondents, all students in both groups were followed up to assess SRH knowledge, attitude towards SRH and sexual behaviour immediately after the intervention (T₁) and 3 months after the end of the intervention (T₂). Post assessment data at T₁ (24th May 2020–5th June 2020) and T₂ (17th August 2020–28th August 2020) were collected using the pre-tested interviewer-led, self-administered questionnaire administered at baseline.</p>", "<p id=\"Par43\">Due to the COVID-19 pandemic, schools in the study area were temporarily shut down on 23rd March, 2023, 4 weeks into the study. Following the announcement and before the schools were closed, a visit was made to all the schools to inform the respondents about the use of an online Google form for the collection of the post-intervention data. Thus, T₁ and T₂ data were collected online from both the control and intervention groups using a Google form. To maximize response rate, text messages which included the link to the questionnaire were sent to all respondents. Furthermore, class representatives who were selected in each class of all the schools were urged to remind and encourage their peers to fill the online questionnaire using their existing WhatsApp platforms.</p>", "<p id=\"Par44\">Following the final assessment at T₂, all respondents (including those in the control group) were given access to the CSE via the website for 4 months.</p>", "<title>Outcome measures</title>", "<p id=\"Par45\">The primary outcome was participants mean scores in SRH knowledge, SRH attitude and RSB of participants, measured at baseline, T₁ and T₂.</p>", "<title>Computation of composite scores</title>", "<p id=\"Par46\">Section 2 of the study questionnaire contained 65 multiple choice questions that covered the knowledge assessment of SRH. These questions covered knowledge on puberty and pubertal changes, reproductive health, sexually transmitted infections (STIs) including human immunodeficiency virus (HIV), acquired immunodeficiency syndrome (AIDS) and modern contraceptives. Based on the core questionnaire measurement and reference to similar research that adapted the same instrument, a score of 1 was assigned to every correct answer, while a score of 0 was assigned to every incorrect answer [##UREF##12##20##]. Thus, the maximum score for knowledge was 65 points, and the minimum score was 0 point. The scores were summed up and converted to 100%. Mean scores were calculated for both groups. Also, the individual scores were categorised into 3: good (&gt; 66%), fair (34.0–65.9%) and poor (&lt; 34%). This is as categorised in a previous study that assessed the SRH knowledge of adolescents in Ibadan, Nigeria [##REF##29109856##26##].</p>", "<p id=\"Par47\">Section 3 of the questionnaire focused on the attitudinal assessment of SRH. It consisted of a list of 13 statements describing attitudinal disposition (such as their perception towards premarital sex, contraceptive use, and sex education) which were answered on a 5-point Likert scale (1—agree a lot, 2—agree, 3—indifferent, 4—disagree, 5—disagree a lot). Each item was rated 1 to 5 with total scores ranging from 13 to 65. Questions 44, 45 and 46 were reverse scored. The items were summed up and converted to 100%. Mean scores were obtained in both groups. Individual scores were also categorised into 2: positive (≥ 50%) and negative (&lt; 50%). This is as categorised in a previous study that assessed the attitude of adolescents towards SRH [##UREF##17##27##].</p>", "<p id=\"Par48\">Section 4 of the questionnaire consisted of 15 questions regarding sexual behaviour. The first item asked respondents if they were sexually active. The prevalence of risky sexual behaviour was defined as reporting one or more of the following: multiple sexual partners, exchange of material gift or money for sex, inconsistent/incorrect/non-use use of condoms at least once during sexual intercourse, getting infected by an STI, and sexual debut before the age of 18 years [##UREF##18##28##]. An affirmative answer to any of the questions was scored one. Thus, the total scores for risky sexual behaviour ranged from 1 to 5. Those who did not report any of the listed behaviour were categorised as practising protective sexual behaviour, while those who affirmed to practising any of the listed behaviour were categorised as practising risky sexual behaviour. The prevalence of risky sexual behaviour was calculated, and mean scores were also calculated in both groups.</p>", "<p id=\"Par49\">Among respondents in the intervention group, uptake of CSE was scored using the number of modules completed at P1 and P2. Completion of each module was given a score of 1. Number of completed modules were summed up and converted to 100%. Mean scores were also calculated among respondents in private and public schools.</p>", "<p id=\"Par50\">In addition, we identified factors influencing the primary outcomes (SRH knowledge, attitude and sexual behaviour) using multivariate analysis using binary logistic regression.</p>", "<title>Statistical analysis</title>", "<p id=\"Par51\">Statistical analyses were performed using StataCorp. 2019. Stata Statistical Software: Release 16. College Station, TX: StataCorp LLC. Data visualisations were created using R-Studio Version 1.3.1073.</p>", "<p id=\"Par52\">All continuous data were first tested for normality using the Shapiro–Wilk and Shapiro-Francia tests. All continuous variables including scores for dependent variables were normally distributed and thus mean and standard deviation were used as summary statistics. Respondents’ baseline socio-demographic characteristics measured as categorical variables were summarized using frequencies and percentages and presented in tabular form. The between-group differences in the distribution of continuous data were visually inspected using box plots and statistically compared using the independent samples t-test. Pearson’s Chi Square and Fisher’s exact tests were used to assess whether there are statistically significant relationships between categorical predictor variables and categorical outcome variables.</p>", "<p id=\"Par53\">The predictor variables which yielded a p-value less than 0.25 during bivariate analysis were used for multivariate binary logistic regression analysis for the identification of factors influencing SRH knowledge, attitude, and sexual behaviour. In the multivariate model, factors associated with dependent variables were evaluated using adjusted Odds Ratios (AORs) and 95% Confidence Intervals (CI). For the AOR estimator, the Hosmer–Lemeshow test was used to determine the model’s goodness of fit with the likelihood ratio test as a primary measure of model fit. The relative importance of individual predictors in the model were assessed using the t-statistic for each model parameter. The main analysis was intention-to-treat based on the randomisation of clusters. Repeated measures ANOVA was used in assessing the effectiveness of the study intervention. Throughout the analysis, a p-value &lt; 0.05 was considered statistically significant.</p>", "<title>Patient and public involvement statement</title>", "<p id=\"Par54\">This trial did not involve patients but rather in-school adolescents. The intervention developed upon an already existing programme targeting in-school adolescents. The choice of an mHealth intervention is premised on the interest and high levels of update of mobile technology by adolescents. In developing the intervention, a pilot study was conducted which enabled incorporation of the inputs of adolescents as users of the intervention. Furthermore, adolescents were included among the data collectors.</p>" ]

[ "<title>Results</title>", "<title>Characteristics of the participants</title>", "<p id=\"Par55\">More than half of the respondents in both groups were in the age range 15–17 years (Table ##TAB##0##1##). The proportion of males and females in both groups were almost equal. Public school enrolment accounted for 480 (75.0%) and 459 (71.7%) in the control and intervention groups respectively. More than two thirds of the respondents had been exposed to sexuality education at home, accounting for 475 (74.2%) and 468 (73.1%) in the control and intervention groups respectively. For all the aforementioned variables, there were no statistically significant differences between the two study groups thereby confirming group equivalence due to effective randomization.</p>", "<title>Baseline SRH knowledge, attitude, and RSB</title>", "<p id=\"Par56\">Most respondents (63.9%) had a fair knowledge of SRH, accounting for 401 (62.7%) and 417 (65.2%) in the control and intervention groups respectively (Table ##TAB##1##2##). The mean knowledge score was 62.67 (SD = 9.90) in the control group and 61.97 (SD = 10.35) in the intervention group (p value = 0.218). Furthermore, most of the respondents had a positive attitude towards SRH, accounting for 475 (74.2%) and 483 (75.5%) in the control and intervention groups respectively (p value = 0.607). The mean attitude score was 64.54 (SD = 20.48) in the control group and 75.46 (SD = 18.32) in the intervention group (p value = 0.063). The prevalence of RSB was found to be 9.7% in the control group and 9.2% in the intervention group at baseline. Among those who were sexually active, almost all of them practised risky sexual behaviour, accounting for 86.1% and 93.5% in the control and intervention groups respectively. Regarding the mean score of RSB, the scores at baseline in the control and intervention groups were 4.69 (SD = 15.56) and 4.66 (SD = 14.42) respectively. There was no statistically significant difference in SRH knowledge, attitude and risky sexual behaviour between both groups.</p>", "<title>Intervention effect</title>", "<p id=\"Par57\">In the intervention group, uptake rates (completion of at least 75% of the mHealth-based curriculum and 100% completion of the questionnaire) at T₁ and T₂ were 94.9% and 97.5% respectively. Table ##TAB##2##3## presents the results from the Repeated Measures ANOVA to assess the effect of the intervention on knowledge, attitude and sexual behaviour in the study groups. Figure ##FIG##1##2## provides a graphic illustration of these results.</p>", "<p id=\"Par58\">The analysis shows that in the control group there were no statistically significant changes in the mean SRH knowledge score, the mean SRH attitude score, and the mean RSB score (p = 0.073, 0.142 and 0.572 respectively) from T₀ to T₂. However, in the intervention group, there was a statistically significant main effect of the mHealth-based intervention on the mean knowledge score [F (1.431, 875.761) = 2117.252, ρ =  &lt; 0.001, ηp2 = 0.776). Bonferroni post hoc tests showed that the respondents had significantly higher mean knowledge score at T₀, compared to T₁ (59.45 ± 13.99 versus 83.09 ± 12.98, respectively; ρ =  &lt; 0.001). At T₂, the mean knowledge score increased to 88.19 (SD = 9.45), which was significantly higher than the mean at T₀ (ρ =  &lt; 0.001) and T₁ (ρ =  &lt; 0.001). Similarly, the intervention has a statistically significant effect on the mean attitude score [ F (1.485, 908.885) = 148.493, ρ =  &lt; 0.001, ηp2 = 0.195) and the Bonferroni post hoc tests showed that the respondents had significantly higher mean attitude score at T₀, compared to T₁ (75.46 ± 18.32 versus 82.07 ± 20.46, respectively; ρ =  &lt; 0.001). At T₂, mean attitude score increased to 89.61 ± 10.19, which was significantly higher than the mean at T₀ (ρ =  &lt; 0.001) and T₁ (ρ =  &lt; 0.001). Nevertheless, even though the mean RSB score declined from T₀ to T₁ in the intervention group (4.89 ± 15.87 versus 4.76 ± 15.50, respectively) and again at T₂, (4.73 ± 15.48), these improvements were not statistically significant [F (2, 1224) = 0.558, ρ = 0.572, ηp2 = 0.001)].</p>", "<title>Predictive analysis</title>", "<p id=\"Par59\">As shown in Table ##TAB##3##4##, gender (p = 0.012) and type of school (p = 0.001) were significantly associated with knowledge. Age range was also found to be significantly associated with attitude (p = 0.003). Age, gender, class, and father’s employment type were statistically associated with RSB (p &lt; 0.001; p &lt; 0.001; p = 0.004; and p &lt; 0.001 respectively).</p>", "<p id=\"Par60\">The multivariate analysis showed that females had higher odds of having good SRH knowledge compared with females (AOR = 2.5, 95% CI 1.04, 6.13). Male respondents had less odds of practising protective sexual behaviour (AOR = 0.3, 95% CI 0.15, 0.55). Based on class, respondents in SS2 (AOR = 5.2, 95% CI 1.75, 15.33) and SS3 (AOR = 6.2, 95% CI 1.93, 20.06) had higher odds of practising protective sexual behaviour compared to those in SS1. Respondents whose fathers were self-employed had higher odds (AOR = 3.0, 95% CI 1.12, 8.01) of practising protective sexual behaviour.</p>", "<title>Attrition</title>", "<p id=\"Par61\">At T₁ and T₂ the attrition rate in the control group was 3% and 5% respectively, whereas in the intervention group it was 2.5% and 4.2% respectively. Total number of respondents at T₂ was 1221 (attrition rate of 4.6%).</p>" ]

[ "<title>Discussion</title>", "<p id=\"Par62\">To the best of our knowledge, this is the first cRCT to assess the effect of an mHealth-based CSE on the SRH knowledge, attitude, and practice of RSB among in-school adolescents in Ilorin, Nigeria. The study was conducted as a proof of concept to promote the national uptake of the FLHE curriculum using mHealth. At baseline, the respondents in the two study groups had comparable sociodemographic characteristics and on average, their baseline SRH knowledge, attitude and RSB profiles were not significantly dissimilar. This suggests that the randomization achieved equivalence in both study groups.</p>", "<p id=\"Par63\">More than half of the respondents in both groups were in the middle adolescence stage (15 to 17 years). Similar studies have shown that most in-school adolescents in senior secondary schools in Nigeria were in the middle/late adolescence stage, and they were unmarried [##REF##20690272##29##, ##UREF##19##30##]. This stage of adolescence is typified by advanced development of secondary sexual characteristics [##UREF##20##31##]. During this period, they crave identification to affirm self-image, pre-occupied with fantasies and idealism, and in terms of sexuality, they are testing their ability to attract the opposite sex [##UREF##20##31##].</p>", "<p id=\"Par64\">At baseline, more than three-fifths and one-third of respondents in the study groups had fair and good SRH knowledge respectively. The survey showed that some adolescents had misconceptions regarding the reproductive system and sexual maturity. Similar studies have also shown that despite having an overall good knowledge of reproductive health, misconceptions regarding the need to have sex multiple times before a girl can get pregnant persist [##REF##20690272##29##, ##REF##19024423##32##, ##REF##21706952##33##]. These misconception could put adolescents at risk of unwanted pregnancies and STIs. Generally, knowledge of STIs, including HIV/AIDS was good in the current study. However, less than half of them were aware of hepatitis B, chlamydia, and genital herpes as STIs. Previous studies have also found knowledge of HIV to be consistently higher than other STIs among adolescents in sub Saharan Africa [##UREF##21##34##, ##REF##31691455##35##]. HIV/AIDS receives relatively higher attention which may be due to its perceived risk compared to other STIs in Nigeria. Numerous programmes focus on HIV/AIDS among adolescents, including a National HIV Strategy for Adolescents and Young People [##UREF##22##36##]. This may suggest that adolescents may be less concerned about STIs other than HIV/AIDS which can equally put their reproductive health at risk.</p>", "<p id=\"Par65\">Good knowledge can lead to positive attitude, which can, in turn, lead to less RSB practice. The health belief model hinges on this relationship [##UREF##23##37##]. About three-quarters of the respondents had positive attitude towards SRH and majority of the students expressed conservative attitudes towards premarital sex. However, the notions of those who had negative attitude should be addressed. Almost a third of the respondents in this study had the perception that having multiple sexual partners is a norm. Only about two thirds of respondents in this study thought contraceptives were important in preventing STIs and another one third of them did not see the need for them or their partners to use a condom. Studies in Nigeria, Ghana and Uganda have shown that a significant proportion of adolescents allude to this perception [##UREF##24##38##–##UREF##25##40##]. These findings suggest that a significant number of adolescents have negative SRH attitude which may be detrimental to their reproductive health.</p>", "<p id=\"Par66\">This study found that about one-tenth were sexually active. Of these, the prevalence of risky sexual behaviour (i.e. reported multiple sexual partners, exchange of material gift/money for sex, inconsistent/incorrect/non-use use of condoms at least once, infection by an STI, and sexual debut before the age of 18 years) was found to be more than four-fifths in both study groups. Findings from northern Nigeria and Cape Coast Metropolis Ghana showed that 10% and 13.8% respectively of in-school adolescents were sexually active [##REF##20690272##29##, ##UREF##26##41##]. However, many studies have reported a significantly higher proportion in other parts of the country and Africa, ranging from 24.7% to 73.8% [##UREF##21##34##, ##REF##12511453##42##–##REF##31170791##46##]. Scientific evidence has shown a high and increasing rate of sexual activity among adolescents in Nigeria, and an early sexual debut is becoming a concern, particularly among females [##REF##12511453##42##, ##UREF##28##47##, ##UREF##29##48##]. Early sexual debut among females has been associated with a high rate of STIs including HIV/AIDS and unintended pregnancies—the latter of which could in turn lead to unsafe abortions, high maternal mortality and infant mortality [##UREF##2##4##]. Intra-country and inter countries disparities are not unexpected, as these could be linked to rapid urbanization, sociocultural and socioeconomic factors [##REF##11000707##43##, ##REF##31170791##46##]. The higher rates of sexual activity among adolescents in other settings may be linked to differences in data collection methods, relatively higher rates of rapid urbanisation and the cultural differences in these cities compared to Ilorin.</p>", "<title>Intervention effect</title>", "<p id=\"Par67\">The advancement in information technology can be leveraged to improve SRH knowledge. In the current study, the level of completion of the mHealth-based CSE curriculum was high. Within 12 weeks, more than two-thirds of the respondents had completed the course. Within 24 weeks, more than four-fifths had completed the course. The high level of uptake of the curriculum suggests the feasibility of using mHealth-based interventions for SRH interventions among adolescents.</p>", "<p id=\"Par68\">Post intervention (T₁ and T₂), there was no statistically significant difference in knowledge, attitude, and sexual behaviour of respondents in the control group. In the intervention group, however, there was a statistically significant increase in the proportion of respondents who had good knowledge of SRH and an increase in mean knowledge score from baseline to T₁ and T₂ among respondents in the intervention group. Also, there was a statistically significant increase in the proportion of respondents who had positive attitude at T₁ and T₂ and an increase in mean attitude score in the intervention group. However, there was no statistically significant difference in proportion of respondents who practised RSB among respondents in the intervention group at T₁ and T₂, and in the mean risky sexual behaviour score compared to baseline in the intervention group.</p>", "<p id=\"Par69\">As put forward by many authors, this finding highlights the importance of CSE in improving adolescents' knowledge and attitude towards SRH [##UREF##19##30##, ##REF##19357762##49##–##REF##28974359##51##]. In 2007, an internet-based and mobile helpline sexual health information platform was implemented in Nigeria [##UREF##31##52##]. In 2012, when the programme was evaluated, it was found to be 10–20% more effective as a teaching method than classroom-based teaching of CSE. These findings suggest that mHealth-based interventions are effective in improving the knowledge and attitude of adolescents. Given the current global reality, as seen during the COVID-19 pandemic, online learning plays and will continue to play a significant role in educational institutions. Educational and health institutions in Nigeria should consider implementing mHealth-based strategies in reaching adolescents.</p>", "<p id=\"Par70\">There was no statistically significant decrease in the prevalence of RSB in the control and intervention groups. In contrast, a quasi-experimental study in which in-school adolescents in Ilorin were exposed to a sex education programme found that post-intervention (immediately after the 8-week programme), those in the intervention group reported less at-risk sexual behaviours compared with the control group [##REF##19357762##49##]. The disparity in findings might be due to the difference in study designs and the sample size. The current study was a cRCT with 1280 respondents while the other study was a quasi-experimental study with 24 participants. Furthermore, the findings from this study regarding the effect of the study intervention on RSB is not unexpected due to the interval between implementing the intervention and evaluating behavioural change. Behavioural change among adolescents is not straightforward; it is a spiral process that usually requires ample time and motivation before adopting healthy sexual behaviours [##REF##12456893##25##]. However, using the construct of the health belief model, good knowledge and positive attitude are steps in the right direction towards reducing the practise of risky sexual behaviour [##REF##29619242##53##].</p>", "<p id=\"Par71\">This study showed that being a female was a positive predictor of good SRH knowledge. This is consistent with findings from Iran, where females were found to have better knowledge of SRH compared to their male colleagues [##REF##27077496##54##]. However, this finding is in contrast to the report from Nicaragua, Central America, where adolescent males were more likely to have a better knowledge of SRH because they are more exposed to the media and education [##UREF##32##55##]. A review of the gender differences in academic performance in the global north and global south found that girls predominantly outperform boys across these settings [##UREF##33##56##]. In addition, there has been a significant increase in girls' enrolment into schools in Nigeria [##UREF##34##57##]. These reasons may account for the reported differences.</p>", "<p id=\"Par72\">Being male was found to be a positive predictor of RSB, while being in a more senior class and having a self-employed father were negative predictors. Similar studies have shown that males are more likely to practise RSB compared to females [##REF##29085388##58##, ##REF##27242369##59##]. This may be related to the notion that boys are more adventurous and more likely to take risks than girls [##REF##24416689##60##]. Respondents in more senior classes are more likely to be aware of the consequences of RSB from lessons taught in class which might explain the practice of less RSB among this group compared to those in junior classes. A study conducted in Cameroon corroborates the fact that adolescents whose fathers are unemployed are more likely to practice RSB [##UREF##35##61##]. Transactional sex has been identified as a means of survival for adolescents from low socioeconomic backgrounds, particularly among females [##UREF##25##40##, ##UREF##36##62##]. Adolescents whose fathers are unemployed are likely to have financial constraints and may practice RSB for financial gains.</p>" ]

[ "<title>Conclusion</title>", "<p id=\"Par78\">This study has contributed to the body of knowledge on the effect of mHealth-based CSE among in-school adolescents. A structured mHealth-based intervention delivered over a period of 12 weeks was found to have improved the SRH knowledge and increased positive attitude towards SRH among in-school adolescents who took the course. Such an intervention could help bridge the SRH knowledge and attitude gap among in-school adolescents. Our study findings also suggest that in large scale programmes, males should also be targeted in the implementation of SRH interventions for adolescents. They are less likely to have good SRH knowledge and more likely to practice RSB. Age-appropriate sexuality education curriculum should be implemented as early as possible so that younger adolescents in junior classes can benefit from SRH knowledge which will help them practice protective sexual behaviour. Also, the association between the practice of RSB and unemployment of their fathers, shows the effect of multi-causal factors including socioeconomic factors on the sexual behaviour of adolescents. This study suggests that an improved standard of living in the society especially among parents of adolescents could help reduce risky sexual behaviour among in-school adolescents.</p>" ]

[ "<title>Background</title>", "<p id=\"Par1\">The implementation of the country-wide comprehensive sexuality education (CSE) curriculum among in-school adolescents remains abysmally low and mHealth-based interventions are promising. We assessed the effect of a mHealth-based CSE on the sexual and reproductive health (SRH) knowledge, attitude and behaviour of in-school adolescents in Ilorin, northcentral Nigeria.</p>", "<title>Methods</title>", "<p id=\"Par2\">Using schools as clusters, 1280 in-school adolescents were randomised into intervention and control groups. Data was collected at baseline (T₀), immediately after the intervention (T₁) and 3 months afterwards (T₂) on SRH knowledge, attitude and practice of risky sexual behaviour (RSB). Data analysis included test of associations using Chi-square, independent t-test and repeated measures ANOVA. Predictors were identified using binary logistic regression.</p>", "<title>Results</title>", "<p id=\"Par3\">In the intervention group, there was a statistically significant main effect on mean knowledge score (F = 2117.252, p =  &lt; 0.001) and mean attitude score (F = 148.493, p =  &lt; 0.001) from T₀ to T₂ compared to the control group which showed no statistically significant main effects in knowledge (p = 0.073), attitude (p = 0.142) and RSB (p = 0.142). Though the mean RSB score declined from T₀ to T₂, this effect was not statistically significant (F = 0.558, p = 0.572). Post-intervention, being female was a positive predictor of good SRH knowledge; being male was a positive predictor of RSB while being in a higher-class level was a negative predictor of RSB.</p>", "<title>Conclusion</title>", "<p id=\"Par4\">The mHealth-based CSE was effective in improving SRH knowledge and attitude among in-school adolescents. This strategy should be strengthened to bridge the SRH knowledge and attitude gap among in-school adolescents.</p>", "<p id=\"Par5\"><italic>Trial registration</italic> Retrospectively registered on the Pan African Clinical Trial Registry (pactr.samrc.ac.za) on 19 October 2023. Identification number: PACTR202310485136014</p>", "<title>Supplementary Information</title>", "<p>The online version contains supplementary material available at 10.1186/s12978-023-01735-4.</p>", "<title>Plain Language Summary</title>", "<p id=\"Par6\">In Nigeria, the implementation of a nationwide sex education programme for adolescents going to schools is below expectation but using mobile health (mHealth) interventions could help. In this study, we looked at how a mHealth-based sex education programme affected the sexual and reproductive health (SRH) knowledge, attitude, and behaviour of in-school adolescents in Ilorin, Nigeria. We divided 1280 students into two groups, one received the mHealth-based intervention and the other did not receive it. We collected data before the intervention, right after it, and 3 months later to see any changes in SRH knowledge, attitudes, and risky sexual behaviours. We used various statistical tests to analyze the data and find patterns. The results showed that the group that received the mHealth intervention had significant improvements in their knowledge and attitudes about SRH from the start of the study to 3 months after the intervention. However, the control group, which didn't get the intervention, didn't show these improvements significantly. While the risky sexual behaviour score decreased slightly in the intervention group, this change was not significant. After the intervention, we found that being female was associated with better SRH knowledge, while being male was linked to more risky sexual behaviours. Also, being in a higher class level was associated with low risky behaviour. In conclusion, using mHealth for sex education helped improve the SRH knowledge and attitudes of students. This approach could be scaled to fill the gap in SRH knowledge and attitudes among adolescents in schools.</p>", "<title>Supplementary Information</title>", "<p>The online version contains supplementary material available at 10.1186/s12978-023-01735-4.</p>", "<title>Keywords</title>" ]

[ "<title>Implications for policy and practice</title>", "<p id=\"Par73\">Stakeholders in the Federal and State Ministries of Education are urged to implement an mHealth-based FLHE curriculum in the country. This mode of delivery has the potential to scale-up the country-wide coverage of the curriculum which is currently low due to the associated challenges with the current classroom-based mode of delivery. However, equity considerations should be made in the implementation of this approach. Provision should be made to students without the required technology to ensure equitable access to the curriculum.</p>", "<p id=\"Par74\">Programme managers in governmental and non-governmental organisations are advised to be intentional in targeting adolescent males during the planning and implementation of SRH programmes. Males were found to be more likely to have poor SRH knowledge and practise risky sexual behaviour compared to females. Targeted programmes could help improve the SRH knowledge of males, and also reduce their practice of risky sexual behaviour.</p>", "<p id=\"Par75\">Policymakers and implementers in the educational sector are advised to implement age-appropriate comprehensive sexuality education early in secondary schools. This could address the poor attitude towards SRH found among respondents in lower senior secondary school classes. These stakeholders are also urged to consider the socioeconomic factors of adolescents and their families. The determinants of sexual behaviour are multi-causal, and they include factors beyond the adolescents. This could help address the higher prevalence of risky sexual behaviour among adolescents with unemployed fathers.</p>", "<title>Study limitations</title>", "<p id=\"Par76\">The self-reported nature and sensitivity of the questions asked could have led to respondents under-reporting their sexual behaviours. This was minimised by continuously reassuring the respondents of the confidentiality of their responses and persuading them to be as sincere as possible. Furthermore, during the implementation of the study, students in the control group were expected to continue receiving comprehensive sexuality education as part of the existing curriculum. However, schools were shut down due to the COVID-19 pandemic and this disrupted the regular educational routine of students. This might have had an effect on their performance in the post-intervention evaluation. Post-intervention data from the control and intervention groups were analysed separately to reduce the effect of this limitation. The post intervention effect was measured immediately after the intervention and 3 months after the intervention. Usually, 3 months follow-up period is not long enough to confidently report a sustained behavioural impact of the intervention. Due to the nature of the study, only students who had access to the internet participated in the study. Therefore, findings may not be representative of students without internet access and out-of-school adolescents.</p>", "<p id=\"Par77\">Despite these limitations, however, the study provides useful information for policymakers and stakeholders involved in adolescent SRH in Nigeria. Future studies could consider (1) a study which exposes in-school adolescents to mHealth-based CSE over a longer period of time, to assess the long-term effects of this intervention e.g. 6 months or 12 months (2) a study that involves out-of-school adolescents.</p>", "<title>Supplementary Information</title>", "<p>\n</p>" ]

[ "<title>Acknowledgements</title>", "<p>We would like to acknowledge trainers in the Department of Epidemiology and Community Healthm University of Ilorin Teaching Hospital, Nigeria who made valuable contributions to this work: Prof. G.K. Osagbemi, Prof. A.G. Salaudeen, Prof. I.S. Abdulraheem, Prof. S.A. Aderibigbe, Dr. H.A. Ameen, Prof. M.M.B. Uthman, Prof. M.J. Saka and Dr. S.T. Abdulsalam. We also thank Mrs Yemisi Oyetunde and other staff members of the Kwara State Ministry of Education for their support. We also appreciate the cooperation of the Principals and other staff members of the participating schools. We appreciate Akinwole Akinpelu and Emmanuel Agwasim for the Information and Communication Technology support provided during the implementation of this study. We also thank Mr Adegboye and Mrs Aworinde for the support during data collection and analysis. We are grateful to all the research participants who used their time and resources during the course of this study.</p>", "<title>Author contributions</title>", "<p>OWA contributed to the conceptualization, data curation, formal analysis, investigation, methodology, project administration, resources, visualization, writing (original draft), MM contributed to the data curation, formal analysis, visualization, writing (review and editing), EUI contributed to the methodology, validation, writing (review and editing), KE contributed to writing (review and editing) and visualisation, OAB contributed to the methodology, supervision, validation, writing (review and editing), OM contributed to the methodology, supervision, validation, writing (review and editing), and TMA contributed to the methodology, supervision, validation, writing (review and editing). All authors read and approved the final manuscript.</p>", "<title>Funding</title>", "<p>This study was self-funded. It is drawn from the dissertation submitted to the National Postgraduate Medical College of Nigeria as part of requirements for the award of the Fellowship of the College in the Faculty of Public Health and Community Medicine.</p>", "<title>Availability of data and materials</title>", "<p>The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.</p>", "<title>Declarations</title>", "<title>Ethics approval and consent to participate</title>", "<p id=\"Par79\">Ethical approval (Ref: ERC PAN/2019/07/1928) was obtained from the Ethical Review Committee of the University of Ilorin Teaching Hospital prior to the start of the study. The trial was also registered in the Nigeria Clinical Trial Registry (14911136). A letter of introduction was obtained from the Department of Epidemiology and Community Health, University of Ilorin Teaching Hospital, and the Kwara State Ministry of Education to the Principals of the schools selected. The trial was also retrospectively registered on the Pan African Clinical Trial Registry (PACTR202310485136014).</p>", "<title>Consent for publication</title>", "<p id=\"Par80\">Not applicable.</p>", "<title>Competing interests</title>", "<p id=\"Par81\">The authors declare that they have no competing interests.</p>" ]

[ "<fig id=\"Fig1\"><label>Fig. 1</label><caption><p>Consort flowchart for the trial</p></caption></fig>", "<fig id=\"Fig2\"><label>Fig. 2</label><caption><p>Effect of the mHealth-based Intervention on knowledge, attitude and sexual behaviour in the control and intervention groups: repeated Measures graphical illustration</p></caption></fig>" ]

[ "<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Characteristics of respondents</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Variable</th><th align=\"left\">Control (%)</th><th align=\"left\">Intervention (%)</th><th align=\"left\">Total (%)</th><th align=\"left\">χ²/t</th><th align=\"left\">p value</th></tr><tr><th align=\"left\"/><th align=\"left\">n = 640</th><th align=\"left\">n = 640</th><th align=\"left\">n = 1280</th><th align=\"left\"/><th align=\"left\"/></tr></thead><tbody><tr><td align=\"left\" colspan=\"6\"><italic>Respondents’ characteristics</italic></td></tr><tr><td align=\"left\">Age groups (years)</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td char=\".\" align=\"char\">2.002</td><td char=\".\" align=\"char\">0.368</td></tr><tr><td align=\"left\"> 12–14</td><td align=\"left\">204 (31.9)</td><td align=\"left\">208 (32.5)</td><td align=\"left\">412 (32.2)</td><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/></tr><tr><td align=\"left\"> 15–17</td><td align=\"left\">366 (57.2)</td><td align=\"left\">377 (58.9)</td><td align=\"left\">743 (58.0)</td><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/></tr><tr><td align=\"left\"> &gt; 17</td><td align=\"left\">70 (10.9)</td><td align=\"left\">55 (8.6)</td><td align=\"left\">125 (9.8)</td><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/></tr><tr><td align=\"left\">Mean age ± SD</td><td align=\"left\">15.25 ± 1.69</td><td align=\"left\">15.21 ± 1.65</td><td align=\"left\">15.23 ± 1.67</td><td char=\".\" align=\"char\">0.452</td><td char=\".\" align=\"char\">0.651</td></tr><tr><td align=\"left\">Gender</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td char=\".\" align=\"char\">31.3</td><td char=\".\" align=\"char\">0.576</td></tr><tr><td align=\"left\"> Female</td><td align=\"left\">324 (50.6)</td><td align=\"left\">314 (49.1)</td><td align=\"left\">638 (49.8)</td><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/></tr><tr><td align=\"left\"> Male</td><td align=\"left\">316 (49.4)</td><td align=\"left\">326 (50.9)</td><td align=\"left\">642 (50.2)</td><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/></tr><tr><td align=\"left\">School type</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td char=\".\" align=\"char\">1.763</td><td char=\".\" align=\"char\">0.184</td></tr><tr><td align=\"left\"> Public</td><td align=\"left\">480 (75.0)</td><td align=\"left\">459 (71.7)</td><td align=\"left\">939 (73.4)</td><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/></tr><tr><td align=\"left\"> Private</td><td align=\"left\">160 (25.0)</td><td align=\"left\">181 (28.3)</td><td align=\"left\">341 (26.6)</td><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/></tr><tr><td align=\"left\">Class</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td char=\".\" align=\"char\">0.0005</td><td char=\".\" align=\"char\">0.998</td></tr><tr><td align=\"left\"> SS1</td><td align=\"left\">214 (33.4)</td><td align=\"left\">215 (33.6)</td><td align=\"left\">429 (33.5)</td><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/></tr><tr><td align=\"left\"> SS2</td><td align=\"left\">214 (33.4)</td><td align=\"left\">214 33.4)</td><td align=\"left\">428 (33.4)</td><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/></tr><tr><td align=\"left\"> SS3</td><td align=\"left\">212 (33.1)</td><td align=\"left\">211 (33.0)</td><td align=\"left\">423 (33.1)</td><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/></tr><tr><td align=\"left\">Subject combination</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td char=\".\" align=\"char\">5.763</td><td char=\".\" align=\"char\">0.056</td></tr><tr><td align=\"left\"> Science</td><td align=\"left\">238 (37.2)</td><td align=\"left\">236 (36.9)</td><td align=\"left\">474 (37.1)</td><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/></tr><tr><td align=\"left\"> Art</td><td align=\"left\">201 (31.4)</td><td align=\"left\">236 (36.9)</td><td align=\"left\">437 (34.1)</td><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/></tr><tr><td align=\"left\"> Commercial</td><td align=\"left\">201 (31.4)</td><td align=\"left\">168 (26.2)</td><td align=\"left\">369 (28.8)</td><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/></tr><tr><td align=\"left\">Tribe</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td char=\".\" align=\"char\">7.149</td><td char=\".\" align=\"char\">0.067</td></tr><tr><td align=\"left\"> Yoruba</td><td align=\"left\">507 (79.2)</td><td align=\"left\">531 (83.0)</td><td align=\"left\">1038 (81.0)</td><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/></tr><tr><td align=\"left\"> Hausa</td><td align=\"left\">27 (4.3)</td><td align=\"left\">33 (5.2)</td><td align=\"left\">60 (4.7)</td><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/></tr><tr><td align=\"left\"> Igbo</td><td align=\"left\">31 (4.8)</td><td align=\"left\">17 (2.7)</td><td align=\"left\">48 (3.8)</td><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/></tr><tr><td align=\"left\"> Others</td><td align=\"left\">75 (11.7)</td><td align=\"left\">59 (9.2)</td><td align=\"left\">134 (10.5)</td><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/></tr><tr><td align=\"left\">Marital status</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td char=\".\" align=\"char\">5.321</td><td char=\".\" align=\"char\">0.070</td></tr><tr><td align=\"left\"> Single</td><td align=\"left\">628 (98.1)</td><td align=\"left\">614 (95.6)</td><td align=\"left\">1242 (97.0)</td><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/></tr><tr><td align=\"left\"> Married</td><td align=\"left\">4 (0.6)</td><td align=\"left\">9 (1.4)</td><td align=\"left\">13 (1.0)</td><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/></tr><tr><td align=\"left\"> Others</td><td align=\"left\">8 (1.3)</td><td align=\"left\">17 (2.7)</td><td align=\"left\">25 (2.0)</td><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/></tr><tr><td align=\"left\">Religion</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td char=\".\" align=\"char\">3.516</td><td char=\".\" align=\"char\">0.061</td></tr><tr><td align=\"left\"> Christianity</td><td align=\"left\">240 (37.5)</td><td align=\"left\">208 (32.5)</td><td align=\"left\">448 (35.0)</td><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/></tr><tr><td align=\"left\"> Islam</td><td align=\"left\">400 (62.5)</td><td align=\"left\">432 (67.5)</td><td align=\"left\">832 (65.0)</td><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/></tr><tr><td align=\"left\">Who respondents lived with</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td char=\".\" align=\"char\">6.341</td><td char=\".\" align=\"char\">0.092</td></tr><tr><td align=\"left\"> Parents</td><td align=\"left\">532 (83.1)</td><td align=\"left\">563 (88.0)</td><td align=\"left\">1095 (88.5)</td><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/></tr><tr><td align=\"left\"> Other relatives</td><td align=\"left\">87 (13.6)</td><td align=\"left\">63 (9.8)</td><td align=\"left\">150 (11.7)</td><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/></tr><tr><td align=\"left\"> Alone</td><td align=\"left\">14 (2.2)</td><td align=\"left\">8 (1.3)</td><td align=\"left\">22 (1.7)</td><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/></tr><tr><td align=\"left\"> Others</td><td align=\"left\">7 (1.1)</td><td align=\"left\">6 (0.9)</td><td align=\"left\">13 (1.0)</td><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/></tr><tr><td align=\"left\" colspan=\"6\"><italic>Respondents’ family characteristics</italic></td></tr><tr><td align=\"left\">Type of family</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td char=\".\" align=\"char\">3.529</td><td char=\".\" align=\"char\">0.060</td></tr><tr><td align=\"left\"> Polygamous</td><td align=\"left\">181 (28.3)</td><td align=\"left\">212 (33.1)</td><td align=\"left\">393 (30.7)</td><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/></tr><tr><td align=\"left\"> Monogamous</td><td align=\"left\">459 (71.7)</td><td align=\"left\">428 (66.9)</td><td align=\"left\">887 (69.3)</td><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/></tr><tr><td align=\"left\">Respondents’ marital status</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td char=\".\" align=\"char\">5.321</td><td char=\".\" align=\"char\">0.070</td></tr><tr><td align=\"left\"> Single</td><td align=\"left\">628 (98.1)</td><td align=\"left\">614 (95.6)</td><td align=\"left\">1242 (97.0)</td><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/></tr><tr><td align=\"left\"> Married</td><td align=\"left\">4 (0.6)</td><td align=\"left\">9 (1.4)</td><td align=\"left\">13 (1.0)</td><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/></tr><tr><td align=\"left\"> Others</td><td align=\"left\">8 (1.3)</td><td align=\"left\">17 (2.7)</td><td align=\"left\">25 (2.0)</td><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/></tr><tr><td align=\"left\">Parents marital status</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td char=\".\" align=\"char\">6.679</td><td char=\".\" align=\"char\">0.083</td></tr><tr><td align=\"left\"> Married</td><td align=\"left\">542 (84.7)</td><td align=\"left\">569 (88.9)</td><td align=\"left\">1111 (86.8)</td><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/></tr><tr><td align=\"left\"> Separated</td><td align=\"left\">65 (10.2)</td><td align=\"left\">40 (6.3)</td><td align=\"left\">105 (8.2)</td><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/></tr><tr><td align=\"left\"> Widowed</td><td align=\"left\">22 (3.4)</td><td align=\"left\">21 (3.3)</td><td align=\"left\">43 (3.4)</td><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/></tr><tr><td align=\"left\"> Divorced</td><td align=\"left\">11 (1.7)</td><td align=\"left\">10 (1.6)</td><td align=\"left\">21 (1.6)</td><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/></tr><tr><td align=\"left\">Father’s employment status</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td char=\".\" align=\"char\">4.138</td><td char=\".\" align=\"char\">0.126</td></tr><tr><td align=\"left\"> Unemployed</td><td align=\"left\">28 (4.4)</td><td align=\"left\">44 (6.9)</td><td align=\"left\">72 (5.6)</td><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/></tr><tr><td align=\"left\"> Self employed</td><td align=\"left\">374 (58.4)</td><td align=\"left\">354 (55.3)</td><td align=\"left\">728 (56.9)</td><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/></tr><tr><td align=\"left\"> Civil servant</td><td align=\"left\">238 (37.2)</td><td align=\"left\">242 (37.8)</td><td align=\"left\">480 (37.5)</td><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/></tr><tr><td align=\"left\">Mother’s employment status</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td char=\".\" align=\"char\">4.850</td><td char=\".\" align=\"char\">0.088</td></tr><tr><td align=\"left\"> Unemployed</td><td align=\"left\">44 (6.9)</td><td align=\"left\">46 (7.2)</td><td align=\"left\">90 (7.0)</td><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/></tr><tr><td align=\"left\"> Self employed</td><td align=\"left\">333 (52.0)</td><td align=\"left\">369 (57.7)</td><td align=\"left\">702 (54.8)</td><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/></tr><tr><td align=\"left\"> Civil servant</td><td align=\"left\">263 (41.1)</td><td align=\"left\">225 (35.1)</td><td align=\"left\">488 (38.1)</td><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/></tr><tr><td align=\"left\">Number of siblings</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td char=\".\" align=\"char\">7.804</td><td char=\".\" align=\"char\">0.050</td></tr><tr><td align=\"left\"> 0</td><td align=\"left\">16 (2.5)</td><td align=\"left\">4 (0.6)</td><td align=\"left\">20 (1.6)</td><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/></tr><tr><td align=\"left\"> 1–3</td><td align=\"left\">255 (39.8)</td><td align=\"left\">252 (39.4)</td><td align=\"left\">507 (39.6)</td><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/></tr><tr><td align=\"left\"> 4–6</td><td align=\"left\">310 (48.4)</td><td align=\"left\">317 (49.5)</td><td align=\"left\">627 (49.0)</td><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/></tr><tr><td align=\"left\"> 7–18</td><td align=\"left\">59 (9.2)</td><td align=\"left\">67 (10.5)</td><td align=\"left\">126 (9.8)</td><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/></tr><tr><td align=\"left\">Sexuality education at home</td><td align=\"left\">475 (74.2)</td><td align=\"left\">468 (73.1)</td><td align=\"left\">337 (26.3)</td><td char=\".\" align=\"char\">0.197</td><td char=\".\" align=\"char\">0.657</td></tr><tr><td align=\"left\">Sexuality education in school</td><td align=\"left\">538 (84.1)</td><td align=\"left\">524 (81.1)</td><td align=\"left\">1062 (83.0)</td><td char=\".\" align=\"char\">1.084</td><td char=\".\" align=\"char\">0.298</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab2\"><label>Table 2</label><caption><p>Baseline profiles for SRH knowledge, attitude, and risky sexual behaviour</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Variable</th><th align=\"left\">Control (%)</th><th align=\"left\">Intervention (%)</th><th align=\"left\">Total (%)</th><th align=\"left\">χ²/t</th><th align=\"left\"><italic>p</italic> value</th></tr><tr><th align=\"left\"/><th align=\"left\">n = 640</th><th align=\"left\">n = 640</th><th align=\"left\">n = 1280</th><th align=\"left\"/><th align=\"left\"/></tr></thead><tbody><tr><td align=\"left\" colspan=\"6\">SRH knowledge at baseline</td></tr><tr><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td char=\".\" align=\"char\">1.527</td><td char=\".\" align=\"char\">0.466</td></tr><tr><td align=\"left\"> Poor knowledge</td><td align=\"left\">3 (0.4)</td><td align=\"left\">5 (0.8)</td><td align=\"left\">8 (0.6)</td><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/></tr><tr><td align=\"left\"> Fair knowledge</td><td align=\"left\">401 (62.7)</td><td align=\"left\">417 (65.2)</td><td align=\"left\">818 (63.9)</td><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/></tr><tr><td align=\"left\"> Good knowledge</td><td align=\"left\">236 (36.9)</td><td align=\"left\">218 (34.0)</td><td align=\"left\">454 (35.5)</td><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/></tr><tr><td align=\"left\"> Mean score (SD)</td><td align=\"left\">62.67 (9.90)</td><td align=\"left\">61.97 (10.35)</td><td align=\"left\">62.32 (10.13)</td><td char=\".\" align=\"char\">1.234</td><td char=\".\" align=\"char\">0.218</td></tr><tr><td align=\"left\" colspan=\"6\">SRH attitude at baseline</td></tr><tr><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td char=\".\" align=\"char\">0.266</td><td char=\".\" align=\"char\">0.606</td></tr><tr><td align=\"left\"> Negative attitude</td><td align=\"left\">165 (25.8)</td><td align=\"left\">157 (24.5)</td><td align=\"left\">322 (25.2)</td><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/></tr><tr><td align=\"left\"> Positive attitude</td><td align=\"left\">475 (74.2)</td><td align=\"left\">483 (75.5)</td><td align=\"left\">958 (74.8)</td><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/></tr><tr><td align=\"left\"> Mean score (SD)</td><td align=\"left\">64.54 (20.48)</td><td align=\"left\">75.46 (18.32)</td><td align=\"left\">70.00 (19.40)</td><td char=\".\" align=\"char\">1.859</td><td char=\".\" align=\"char\">0.063</td></tr><tr><td align=\"left\" colspan=\"6\">RSB at baseline</td></tr><tr><td align=\"left\"> Practice of RSB</td><td align=\"left\">62 (9.7)</td><td align=\"left\">59 (9.2)</td><td align=\"left\">121 (9.5)</td><td char=\".\" align=\"char\">0.082</td><td char=\".\" align=\"char\">0.774</td></tr><tr><td align=\"left\"> Mean score (SD)</td><td align=\"left\">4.69 (15.56)</td><td align=\"left\">4.66 (14.42)</td><td align=\"left\">4.68 (14.99)</td><td char=\".\" align=\"char\">− 0.37</td><td char=\".\" align=\"char\">0.788</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab3\"><label>Table 3</label><caption><p>Effect of the mHealth-based Intervention on knowledge, attitude and sexual behaviour in the control and intervention groups: repeated measures ANOVA estimates</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" rowspan=\"2\"/><th align=\"left\" colspan=\"4\">Control Group</th><th align=\"left\" colspan=\"4\">Intervention group</th></tr><tr><th align=\"left\">Mean ± SD</th><th align=\"left\">F Ratio</th><th align=\"left\"><italic>p</italic> value</th><th align=\"left\">ηp²</th><th align=\"left\">Mean ± SD</th><th align=\"left\">F Ratio</th><th align=\"left\"><italic>p</italic> value</th><th align=\"left\">ηp²</th></tr></thead><tbody><tr><td align=\"left\" colspan=\"2\">SRH knowledge</td><td align=\"left\">21.459</td><td align=\"left\">0.073</td><td char=\".\" align=\"char\">0.014</td><td align=\"left\"/><td char=\".\" align=\"char\">2117.252</td><td align=\"left\"><bold>&lt; 0.001</bold></td><td char=\".\" align=\"char\">0.776</td></tr><tr><td align=\"left\"> T₀</td><td align=\"left\">63.74 ± 10.10</td><td char=\".\" align=\"char\"/><td align=\"left\"/><td char=\".\" align=\"char\"/><td align=\"left\">59.46 ± 13.99</td><td char=\".\" align=\"char\"/><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\"> T₁</td><td align=\"left\">72.56 ± 13.76</td><td char=\".\" align=\"char\"/><td align=\"left\"/><td char=\".\" align=\"char\"/><td align=\"left\">83.09 ± 12.98</td><td char=\".\" align=\"char\"/><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\"> T₂</td><td align=\"left\">74.23 ± 14.03</td><td char=\".\" align=\"char\"/><td align=\"left\"/><td char=\".\" align=\"char\"/><td align=\"left\">88.19 ± 9.45</td><td char=\".\" align=\"char\"/><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\" colspan=\"2\">SRH attitude</td><td align=\"left\">12.203</td><td align=\"left\">0.142</td><td char=\".\" align=\"char\">0.012</td><td align=\"left\"/><td char=\".\" align=\"char\">148.493</td><td align=\"left\"><bold>&lt; 0.001</bold></td><td char=\".\" align=\"char\">0.195</td></tr><tr><td align=\"left\"> T₀</td><td align=\"left\">76.72 ± 17.15</td><td char=\".\" align=\"char\"/><td align=\"left\"/><td char=\".\" align=\"char\"/><td align=\"left\">75.46 ± 15.87</td><td char=\".\" align=\"char\"/><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\"> T₁</td><td align=\"left\">80.59 ± 15.23</td><td char=\".\" align=\"char\"/><td align=\"left\"/><td char=\".\" align=\"char\"/><td align=\"left\">82.07 ± 20.46</td><td char=\".\" align=\"char\"/><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\"> T₂</td><td align=\"left\">81.75 ± 14.80</td><td char=\".\" align=\"char\"/><td align=\"left\"/><td char=\".\" align=\"char\"/><td align=\"left\">89.61 ± 10.19</td><td char=\".\" align=\"char\"/><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\" colspan=\"2\">Risky sexual behaviour</td><td align=\"left\">5.769</td><td align=\"left\">0.572</td><td char=\".\" align=\"char\">0.009</td><td align=\"left\"/><td char=\".\" align=\"char\">0.558</td><td align=\"left\">0.572</td><td char=\".\" align=\"char\"><bold>0.001</bold></td></tr><tr><td align=\"left\"> T₀</td><td align=\"left\">4.98 ± 15.50</td><td char=\".\" align=\"char\"/><td align=\"left\"/><td char=\".\" align=\"char\"/><td align=\"left\">4.89 ± 15.87</td><td char=\".\" align=\"char\"/><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\"> T₁</td><td align=\"left\">4.77 ± 14.87</td><td char=\".\" align=\"char\"/><td align=\"left\"/><td char=\".\" align=\"char\"/><td align=\"left\">4.76 ± 15.50</td><td char=\".\" align=\"char\"/><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\"> T₂</td><td align=\"left\">5.01 ± 12.78</td><td char=\".\" align=\"char\"/><td align=\"left\"/><td char=\".\" align=\"char\"/><td align=\"left\">4.73 ± 15.48</td><td char=\".\" align=\"char\"/><td align=\"left\"/><td align=\"left\"/></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab4\"><label>Table 4</label><caption><p>Univariate analysis of sociodemographic factors associated with respondents’ SRH knowledge, attitude and SRB, post Intervention</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Variable</th><th align=\"left\" colspan=\"2\">Knowledge</th><th align=\"left\" colspan=\"2\">Attitude</th><th align=\"left\">Behaviour</th><th align=\"left\"/></tr><tr><th align=\"left\"/><th align=\"left\">Fair (%)</th><th align=\"left\">Good (%)</th><th align=\"left\">Negative (%)</th><th align=\"left\">Positive (%)</th><th align=\"left\">Risky (%)</th><th align=\"left\">Protective (%)</th></tr><tr><th align=\"left\"/><th align=\"left\">n = 26</th><th align=\"left\">n = 587</th><th align=\"left\">n = 1</th><th align=\"left\">n = 612</th><th align=\"left\">n = 55</th><th align=\"left\">n = 558</th></tr></thead><tbody><tr><td align=\"left\" colspan=\"2\">Age group (years)</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\"> 12–14</td><td align=\"left\">7 (3.4)</td><td align=\"left\">197 (96.6)</td><td align=\"left\">0</td><td align=\"left\">204 (100.0)</td><td align=\"left\">14 (6.9)</td><td align=\"left\">190 (93.1)</td></tr><tr><td align=\"left\"> 15–17</td><td align=\"left\">19 (5.3)</td><td align=\"left\">340 (94.7)</td><td align=\"left\">0</td><td align=\"left\">359 (100.0)</td><td align=\"left\">25 (7.0)</td><td align=\"left\">334 (93.0)</td></tr><tr><td align=\"left\"> &gt; 17</td><td align=\"left\">0</td><td align=\"left\">50 (100)</td><td align=\"left\">1 (2.0)</td><td align=\"left\">49 (98.0)</td><td align=\"left\">16 (32.0)</td><td align=\"left\">34 (68.0)</td></tr><tr><td align=\"left\"/><td align=\"left\" colspan=\"2\">F = 3.521 ρ = 0.172</td><td align=\"left\" colspan=\"2\">F = 11.278 ρ = <bold>0.003</bold></td><td align=\"left\" colspan=\"2\">χ² = 35.348 ρ = <bold> &lt; 0.001</bold></td></tr><tr><td align=\"left\" colspan=\"3\">Gender</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\"> Female</td><td align=\"left\">7 (2.2)</td><td align=\"left\">305 (97.8)</td><td align=\"left\">0</td><td align=\"left\">312 (100)</td><td align=\"left\">10 (3.2)</td><td align=\"left\">302 (96.8)</td></tr><tr><td align=\"left\"> Male</td><td align=\"left\">19 (6.3)</td><td align=\"left\">282 (93.7)</td><td align=\"left\">1 (0.3)</td><td align=\"left\">300 (99.7)</td><td align=\"left\">45 (15.0)</td><td align=\"left\">256 (85.0)</td></tr><tr><td align=\"left\"/><td align=\"left\" colspan=\"2\">χ² = 6.244 ρ = <bold>0.012</bold></td><td align=\"left\" colspan=\"2\">F = 1.038 ρ = 0.308</td><td align=\"left\" colspan=\"2\">χ² = 23.102 ρ = <bold> &lt; 0.001</bold></td></tr><tr><td align=\"left\" colspan=\"2\">School type</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\"> Public</td><td align=\"left\">23 (5.3)</td><td align=\"left\">411 (94.7)</td><td align=\"left\">1 (0.2)</td><td align=\"left\">433 (99.8)</td><td align=\"left\">35 (8.1)</td><td align=\"left\">399 (91.1)</td></tr><tr><td align=\"left\"> Private</td><td align=\"left\">0</td><td align=\"left\">179 (100.0)</td><td align=\"left\">0</td><td align=\"left\">179 (100.0)</td><td align=\"left\">20 (11.2)</td><td align=\"left\">159 (88.8)</td></tr><tr><td align=\"left\"/><td align=\"left\" colspan=\"2\">F = 9.693 ρ = <bold>0.001</bold></td><td align=\"left\" colspan=\"2\">F = 0.413 ρ = 0.520</td><td align=\"left\" colspan=\"2\">χ² = 1.500 ρ = 0.221</td></tr><tr><td align=\"left\" colspan=\"2\">Class</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\"> SS 1</td><td align=\"left\">10 (4.8)</td><td align=\"left\">200 (95.2)</td><td align=\"left\">0</td><td align=\"left\">211 (100.0)</td><td align=\"left\">10 (4.7)</td><td align=\"left\">201 (95.3)</td></tr><tr><td align=\"left\"> SS 2</td><td align=\"left\">13 (1.6)</td><td align=\"left\">195 (98.4)</td><td align=\"left\">0</td><td align=\"left\">210 (100.0)</td><td align=\"left\">29 (13.8)</td><td align=\"left\">181 (86.2)</td></tr><tr><td align=\"left\"> SS 3</td><td align=\"left\">3 (4.2)</td><td align=\"left\">187 (95.8)</td><td align=\"left\">1 (0.5)</td><td align=\"left\">191 (99.5)</td><td align=\"left\">16 (8.3)</td><td align=\"left\">176 (91.7)</td></tr><tr><td align=\"left\"/><td align=\"left\" colspan=\"2\">F = 5.477 ρ = 0.065</td><td align=\"left\" colspan=\"2\">F = 2.196 ρ = 0.334</td><td align=\"left\" colspan=\"2\">² = 10.741 <bold>ρ = 0.004</bold></td></tr><tr><td align=\"left\" colspan=\"2\">Subject combination</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\"> Science</td><td align=\"left\">6 (2.6)</td><td align=\"left\">223 (97.4)</td><td align=\"left\">0</td><td align=\"left\">225 (100.0)</td><td align=\"left\">19 (8.4)</td><td align=\"left\">206 (91.6)</td></tr><tr><td align=\"left\"> Art</td><td align=\"left\">13 (5.9)</td><td align=\"left\">209 (94.1)</td><td align=\"left\">1 (0.4)</td><td align=\"left\">229 (99.6)</td><td align=\"left\">22 (9.6)</td><td align=\"left\">208 (90.4)</td></tr><tr><td align=\"left\"> Commercial</td><td align=\"left\">7 (4.3)</td><td align=\"left\">155 (95.7)</td><td align=\"left\">0</td><td align=\"left\">158 (100.0)</td><td align=\"left\">14 (8.9)</td><td align=\"left\">144 (91.1)</td></tr><tr><td align=\"left\"/><td align=\"left\" colspan=\"2\">χ² = 2.515 ρ = 0.284</td><td align=\"left\" colspan=\"2\">F = 2.86 ρ = 0.239</td><td align=\"left\" colspan=\"2\">χ² = 0.178 ρ = 0.915</td></tr><tr><td align=\"left\" colspan=\"2\">Marital status</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\"> Single</td><td align=\"left\">25 (4.1)</td><td align=\"left\">582 (95.9)</td><td align=\"left\">1 (0.2)</td><td align=\"left\">606 (99.8)</td><td align=\"left\">52 (8.6)</td><td align=\"left\">555 (91.4)</td></tr><tr><td align=\"left\"> Married</td><td align=\"left\">0</td><td align=\"left\">2 (100.0)</td><td align=\"left\">0</td><td align=\"left\">2 (100.0)</td><td align=\"left\">1 (50.0)</td><td align=\"left\">1 (50.0)</td></tr><tr><td align=\"left\"> Others</td><td align=\"left\">1 (25.0)</td><td align=\"left\">3 (75.0)</td><td align=\"left\">0</td><td align=\"left\">4 (100.0)</td><td align=\"left\">1 (25.0)</td><td align=\"left\">3 (75.0)</td></tr><tr><td align=\"left\"/><td align=\"left\" colspan=\"2\">F = 4.355 ρ = 0.113</td><td align=\"left\" colspan=\"2\">F = 0.01 ρ = 0.995</td><td align=\"left\" colspan=\"2\">χ² = 5.574 ρ = 0.062</td></tr><tr><td align=\"left\" colspan=\"2\">Tribe</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\"> Yoruba</td><td align=\"left\">22 (4.2)</td><td align=\"left\">500 (95.8)</td><td align=\"left\">1 (0.2)</td><td align=\"left\">521 (99.8)</td><td align=\"left\">41 (7.9)</td><td align=\"left\">481 (92.1)</td></tr><tr><td align=\"left\"> Hausa</td><td align=\"left\">0</td><td align=\"left\">30 (100.0)</td><td align=\"left\">0</td><td align=\"left\">30 (100.0)</td><td align=\"left\">4 (13.3)</td><td align=\"left\">26 (86.7)</td></tr><tr><td align=\"left\"> Igbo</td><td align=\"left\">0</td><td align=\"left\">15 (100.0)</td><td align=\"left\">0</td><td align=\"left\">15 (100.0)</td><td align=\"left\">2 (13.3)</td><td align=\"left\">13 (86.7)</td></tr><tr><td align=\"left\"> Others</td><td align=\"left\">4 (8.7)</td><td align=\"left\">42 (91.3)</td><td align=\"left\">0</td><td align=\"left\">46 (100.0)</td><td align=\"left\">8 (17.4)</td><td align=\"left\">38 (82.6)</td></tr><tr><td align=\"left\"/><td align=\"left\" colspan=\"2\">F = 4.241 ρ = 0.237</td><td align=\"left\" colspan=\"2\">F = 0.175 ρ = 0.982</td><td align=\"left\" colspan=\"2\">χ² = 5.839 ρ = 0.119</td></tr><tr><td align=\"left\" colspan=\"2\">Religion</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\"> Christianity</td><td align=\"left\">10 (5.1)</td><td align=\"left\">186 (94.9)</td><td align=\"left\">0</td><td align=\"left\">196 (100.0)</td><td align=\"left\">16 (8.2)</td><td align=\"left\">180 (91.8)</td></tr><tr><td align=\"left\"> Islam</td><td align=\"left\">16 (3.8)</td><td align=\"left\">401 (96.2)</td><td align=\"left\">1 (0.2)</td><td align=\"left\">416 (99.8)</td><td align=\"left\">39 (9.4)</td><td align=\"left\">378 (90.6)</td></tr><tr><td align=\"left\"/><td align=\"left\" colspan=\"2\">χ² = 0.525 ρ = 0.469</td><td align=\"left\" colspan=\"2\">F = 0.471 ρ = 0.493</td><td align=\"left\" colspan=\"2\">χ² = 0.124 ρ = 0.725</td></tr><tr><td align=\"left\" colspan=\"2\">Type of family</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\"> Polygamous</td><td align=\"left\">6 (2.9)</td><td align=\"left\">201 (97.1)</td><td align=\"left\">1 (0.5)</td><td align=\"left\">206 (99.5)</td><td align=\"left\">15 (7.2)</td><td align=\"left\">192 (92.8)</td></tr><tr><td align=\"left\"> Monogamous</td><td align=\"left\">20 (4.9)</td><td align=\"left\">386 (95.1)</td><td align=\"left\">0</td><td align=\"left\">406 (100.0)</td><td align=\"left\">40 (9.9)</td><td align=\"left\">366 (90.1)</td></tr><tr><td align=\"left\"/><td align=\"left\" colspan=\"2\">χ² = 1.388 ρ = 0.239</td><td align=\"left\" colspan=\"2\">F = 1.965 ρ = 0.1609</td><td align=\"left\" colspan=\"2\">χ² = 1.14 ρ = 0.285</td></tr><tr><td align=\"left\" colspan=\"2\">Parents marital status</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\"> Married</td><td align=\"left\">23 (4.2)</td><td align=\"left\">523 (95.8)</td><td align=\"left\">1 (0.2)</td><td align=\"left\">545 (99.8)</td><td align=\"left\">47 (8.6)</td><td align=\"left\">499 (91.4)</td></tr><tr><td align=\"left\"> Separated</td><td align=\"left\">2 (5.1)</td><td align=\"left\">37 (94.9)</td><td align=\"left\">0</td><td align=\"left\">39 (94.9)</td><td align=\"left\">5 (12.8)</td><td align=\"left\">34 (87.2)</td></tr><tr><td align=\"left\"> Widowed</td><td align=\"left\">1 (5.3)</td><td align=\"left\">18 (94.7)</td><td align=\"left\">0</td><td align=\"left\">19 (94.7)</td><td align=\"left\">2 (10.5)</td><td align=\"left\">17 (89.5)</td></tr><tr><td align=\"left\"> Divorced</td><td align=\"left\">0</td><td align=\"left\">9 (100.0)</td><td align=\"left\">0</td><td align=\"left\">9 (100.0)</td><td align=\"left\">1 (11.1)</td><td align=\"left\">8 (88.9)</td></tr><tr><td align=\"left\"/><td align=\"left\" colspan=\"2\">F = 0.524 ρ = 0.913</td><td align=\"left\" colspan=\"2\">F = 0.123 ρ = 0.989</td><td align=\"left\" colspan=\"2\">F = 0.902 ρ = 0.825</td></tr><tr><td align=\"left\" colspan=\"3\">Father’s employment status</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\"> Unemployed</td><td align=\"left\">0</td><td align=\"left\">28 (100.0)</td><td align=\"left\">0</td><td align=\"left\">28 (100.0)</td><td align=\"left\">9 (32.1)</td><td align=\"left\">19 (67.9)</td></tr><tr><td align=\"left\"> Self-employed</td><td align=\"left\">20 (5.7)</td><td align=\"left\">330 (94.3)</td><td align=\"left\">0</td><td align=\"left\">350 (100.0)</td><td align=\"left\">37 (10.6)</td><td align=\"left\">313 (89.4)</td></tr><tr><td align=\"left\"> Civil servant</td><td align=\"left\">6 (2.6)</td><td align=\"left\">229 (97.4)</td><td align=\"left\">1</td><td align=\"left\">234 (0.4)</td><td align=\"left\">9 (3.8)</td><td align=\"left\">226 (96.2)</td></tr><tr><td align=\"left\"/><td align=\"left\" colspan=\"2\">F = 4.759 ρ = 0.092</td><td align=\"left\" colspan=\"2\">F = 1.611 ρ = 0.446</td><td align=\"left\">χ² = 27.111</td><td align=\"left\">ρ<bold> &lt; 0.001</bold></td></tr><tr><td align=\"left\" colspan=\"3\">Mother’s employment status</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\"> Unemployed</td><td align=\"left\">1 (4.8)</td><td align=\"left\">41 (95.2)</td><td align=\"left\">1 (2.4)</td><td align=\"left\">41 (97.6)</td><td align=\"left\">3 (7.1)</td><td align=\"left\">39 (92.9)</td></tr><tr><td align=\"left\"> Self-employed</td><td align=\"left\">18 (5.0)</td><td align=\"left\">341 (95.0)</td><td align=\"left\">0</td><td align=\"left\">359 (100.0)</td><td align=\"left\">32 (8.9)</td><td align=\"left\">327 (91.1)</td></tr><tr><td align=\"left\"> Civil servant</td><td align=\"left\">6 (2.8)</td><td align=\"left\">206 (97.2)</td><td align=\"left\">0</td><td align=\"left\">212 (100.0)</td><td align=\"left\">20 (9.4)</td><td align=\"left\">192 (90.6)</td></tr><tr><td align=\"left\"/><td align=\"left\" colspan=\"2\">F = 1.988 ρ = 0.370</td><td align=\"left\" colspan=\"2\">F = 1.895 ρ = 0.387</td><td align=\"left\" colspan=\"2\">F = 0.229 ρ = 0.891</td></tr><tr><td align=\"left\" colspan=\"2\">Number of siblings</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\"> 0</td><td align=\"left\">0</td><td align=\"left\">4 (100)</td><td align=\"left\">0</td><td align=\"left\">4</td><td align=\"left\">0</td><td align=\"left\">4 (100.0)</td></tr><tr><td align=\"left\"> 1–3</td><td align=\"left\">10 (4.0)</td><td align=\"left\">237 (96.0)</td><td align=\"left\">1</td><td align=\"left\">246</td><td align=\"left\">19 (7.7)</td><td align=\"left\">228 (91.3)</td></tr><tr><td align=\"left\"> 4–6</td><td align=\"left\">12 (3.8)</td><td align=\"left\">300 (96.2)</td><td align=\"left\">0</td><td align=\"left\">312</td><td align=\"left\">28 (9.0)</td><td align=\"left\">284 (91.0)</td></tr><tr><td align=\"left\"> ≥ 7</td><td align=\"left\">4 (8.0)</td><td align=\"left\">46 (92.0)</td><td align=\"left\">0</td><td align=\"left\">50</td><td align=\"left\">5 (10.0)</td><td align=\"left\">45 (90.0)</td></tr><tr><td align=\"left\"/><td align=\"left\" colspan=\"2\">F = 2.059 ρ = 0.560</td><td align=\"left\" colspan=\"2\">F = 1.484 ρ = 0.685</td><td align=\"left\" colspan=\"2\">F = 0.815 ρ = 0.845</td></tr><tr><td align=\"left\" colspan=\"2\">Who respondent lives with</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\"> Parents</td><td align=\"left\">22 (4.0)</td><td align=\"left\">529 (96.0)</td><td align=\"left\">1 (0.2)</td><td align=\"left\">550 (99.8)</td><td align=\"left\">44 (8.0)</td><td align=\"left\">507 (92.0)</td></tr><tr><td align=\"left\"> Other relatives</td><td align=\"left\">4 (7.7)</td><td align=\"left\">48 (92.3)</td><td align=\"left\">0</td><td align=\"left\">52 (100.0)</td><td align=\"left\">9 (17.3)</td><td align=\"left\">43 (82.7)</td></tr><tr><td align=\"left\"> Alone</td><td align=\"left\">0</td><td align=\"left\">6 (100.0)</td><td align=\"left\">0</td><td align=\"left\">6 (100.0)</td><td align=\"left\">1 (16.7)</td><td align=\"left\">5 (83.3)</td></tr><tr><td align=\"left\"> Others</td><td align=\"left\">0</td><td align=\"left\">4 (100.0)</td><td align=\"left\">0</td><td align=\"left\">4 (100.0)</td><td align=\"left\">1 (25.0)</td><td align=\"left\">3 (75.0)</td></tr><tr><td align=\"left\"/><td align=\"left\" colspan=\"2\">F = 2.051 ρ = 0.562</td><td align=\"left\" colspan=\"2\">F = 0.113 ρ = 0.990</td><td align=\"left\" colspan=\"2\">F = 6.774 ρ = 0.079</td></tr><tr><td align=\"left\" colspan=\"3\">Sex education at home</td><td align=\"left\" colspan=\"2\"/><td align=\"left\" colspan=\"2\"/></tr><tr><td align=\"left\"> Yes</td><td align=\"left\">20 (4.4)</td><td align=\"left\">438 (95.6)</td><td align=\"left\">1 (0.2)</td><td align=\"left\">457 (99.8)</td><td align=\"left\">44 (9.6)</td><td align=\"left\">414 (90.4)</td></tr><tr><td align=\"left\"/><td align=\"left\" colspan=\"2\">χ² = 0.07 ρ = 0.791</td><td align=\"left\" colspan=\"2\">F = 0.339 ρ = 0.560</td><td align=\"left\" colspan=\"2\">χ² = 0.893 ρ = 0.344</td></tr><tr><td align=\"left\" colspan=\"2\">Sex education in school</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/></tr><tr><td align=\"left\"> Yes</td><td align=\"left\">23 (4.4)</td><td align=\"left\">496 (95.6)</td><td align=\"left\">1 (0.2)</td><td align=\"left\">518 (99.8)</td><td align=\"left\">42 (8.1)</td><td align=\"left\">477 (91.9)</td></tr><tr><td align=\"left\"/><td align=\"left\" colspan=\"2\">F = 0.301 ρ = 0.583</td><td align=\"left\" colspan=\"2\">F = 0.181 ρ = 0.671</td><td align=\"left\" colspan=\"2\">χ² = 3.208 ρ = 0.073</td></tr></tbody></table></table-wrap>" ]

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[ "<supplementary-material content-type=\"local-data\" id=\"MOESM1\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM2\"></supplementary-material>" ]

[ "<table-wrap-foot><p>χ²—Pearson’s Chi square test; t—independent t test; level of significance—p value &lt; 0.05</p></table-wrap-foot>", "<table-wrap-foot><p>χ²—Pearson’s Chi square test; t—independent t test; level of statistical significance—p value &lt; 0.05</p></table-wrap-foot>", "<table-wrap-foot><p>SD: Standard deviation; ηp²: Partial eta square; level of statistical significance—p &lt; 0.05 (bold)</p></table-wrap-foot>", "<table-wrap-foot><p>χ²—Pearson’s Chi square test, F—Fisher’s exact test, level of significance—p value &lt; 0.05 (bold)</p></table-wrap-foot>", "<fn-group><fn><p><bold>Publisher's Note</bold></p><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn></fn-group>" ]

[ "<graphic xlink:href=\"12978_2023_1735_Fig1_HTML\" id=\"MO1\"/>", "<graphic xlink:href=\"12978_2023_1735_Fig2_HTML\" id=\"MO2\"/>" ]

[ "<media xlink:href=\"12978_2023_1735_MOESM1_ESM.docx\"><caption><p><bold>Additional file 1.</bold> Distribution of selected schools in the control and intervention groups.</p></caption></media>", "<media xlink:href=\"12978_2023_1735_MOESM2_ESM.docx\"><caption><p><bold>Additional file 2.</bold> Topics covered in the intervention group.</p></caption></media>" ]

{ "acronym": [ "AIDS", "RSB", "CI", "COVID-19", "cRCT", "CSE", "FLHE", "HIV", "SRH", "STI", "T0", "T1", "T2" ], "definition": [ "Acquired immunodeficiency syndrome", "Risky sexual behaviour", "Confidence interval", "Coronavirus disease 2019", "Cluster Randomized Controlled Trial", "Comprehensive sexuality education", "Family Life and HIV Education", "Human immunodeficiency virus", "Sexual and reproductive health", "Sexually transmitted infections", "Assessment at baseline", "Assessment immediately after the 12-week intervention", "Assessment 3 months after the intervention" ] }

[ "<title>Introduction</title>", "<p id=\"Par5\">Renal cell cancer (RCC) accounts approximately 2% of global cancer diagnoses worldwide [##REF##30207593##1##]. RCCs have recently been re-classified pathologically with molecular-driven criteria as well as cytoplasmic feature-based diagnoses [##UREF##0##2##]. RCC has survival from 40 to 91% according to various subtypes when non-metastatic [##REF##35083144##3##]. However, these rates decreases significantly less than 20% in case of distant metastases [##UREF##1##4##].</p>", "<p id=\"Par6\">Upon growing evidence on carcinogenesis, tumor-promoting inflammation as well as genomic instability and mutability have been suggested to be enabling characteristics of cancer [##UREF##2##5##]. Inflammatory cells have been shown to accelerate tumoral genetic evolution towards malignancy via actively mutagenic reactive oxygen species [##UREF##3##6##]. As well, inflammation have been suggested to produce molecules including growth factors, proangiogenic factors, extracellular matrix-modifying enzymes within tumoral microenvironment, thereby facilitate angiogenesis, invasion, and metastasis [##REF##34099557##7##, ##UREF##4##8##].</p>", "<p id=\"Par7\">Systemic Inflammatory Response Index (SIRI) and systemic immune-inflammation index (SII) are markers of such inflammatory tumor-supportive microenvironment. SIRI includes the counts of neutrophils, monocytes and lymphocytes with the formulation of [monocyte count x neutrophil count / lymphocyte count]. SII includes the counts of lymphocyte, neutrophil and platelet with the formulation of [ platelet count x neutrophil count / lymphocyte count] [##UREF##5##9##]. SII has been suggested to be an independent predictor of overall survival and cancer-spesific survival of patients with non-metastatic RCC [##UREF##6##10##]. Else, both SII and SIRI has been associated with advanced stages and larger tumors in localized renal cancers [##UREF##7##11##].</p>", "<p id=\"Par8\">In this study, we aimed to evaluate predictive value of SIRI and SII for metastases in RCC.</p>" ]

[ "<title>Materials and methods</title>", "<p id=\"Par9\">Seventy-two patients who were diagnosed with RCC and underwent surgery in Urology Clinic and Medical Oncology Clinic of Istanbul Training and Research Hospital between July 2022 and January 2023 or were included in the treatment planning in the medical oncolgy unit were included in the study. Male and female patients older than 18 years of age who had preoperative laboratory tests and inflammatory indices could be calculated were included in the study. Information related to patients was obtained from patients’ medical records at the hospital system.</p>", "<p id=\"Par10\">Patients were diagnosed with renal cell carcinoma through surgery or biopsy. The diagnoses of metastases of patients was determined by lymph node dissection or FDG-PET imaging.</p>", "<p id=\"Par11\">51 of the patients were male and 21 were female. Twenty-two of the patients had metastatic RCC. 50 patients had non-metastatic RCC.</p>", "<p id=\"Par12\">Patients older than 18 years of age, who underwent radical or partial nephrectomy due to kidney tumor and were diagnosed with RCC in their pathology, who did not undergo surgery but were diagnosed with RCC in their pathology by biopsy, and whose hematological parameter studies were performed in the last 1 week were included in this study.</p>", "<p id=\"Par13\">Patients with another malignancy, patients who were diagnosed with or suspected infection within 1 week before admission, patients who received steroid therapy or immunosuppressive therapy at the time of admission, patients with known autoimmune disease, and patients who received blood transfusion within the last 1 month were excluded from the study.</p>", "<p id=\"Par14\">Laboratory results and histopathological findings, tumor stages and grades of the patients of the patients included in the study were recorded. The metastatic and non-metastatic groups were compared with each other by confirming the metastasis status by imaging methods of the patients whose histopathological findings were recorded. Using the laboratory results of these two groups, inflammatory indices such as SIRI and SII were calculated and their effectiveness in terms of metastasis were compared. Statistical analyses were performed with SPSS statistics software (IBM Corp. Released 2011. IBM SPSS Statistics for Windows, Version 20.0. Armonk, NY: IBM Corp.). Comparisons of groups were done with Chi-square test and Student’s t test where appropriate. The mean values were presented with their 95% Confidence intervals. Receiver operating characteristic curve (ROC) was used to illustrate related sensitivity and specificity of ADC values. Statistical significance was set at less than 0.05. All methods were carried out in accordance with relevant guidelines and regulations. All experimental protocols were approved by university ethics committee. Informed consent was obtained from all subjects and/or their legal guardian.</p>" ]

[ "<title>Results</title>", "<p id=\"Par15\">A total of 72 patients who met the exclusion and inclusion criteria during the study period were included in the study as stated in the methods. Twenty (28%) of the patients were female patients and the remaining 52 (72%) were male patients. The mean age of the patients was 60.25 ± 11.72 years. The mean age for women was 62.35 ± 13.84 years, and the mean age for men was 59.44 ± 10.84 (<italic>p</italic> = 0.349). The mean age of metastatic patients was 60.60 ± 12.46 years, while the mean age of non-metastatic patients was 60.12 ± 11.55 (<italic>p</italic> &gt; 0.05).</p>", "<p id=\"Par16\">Twenty-two (31%) of the patients had metastatic RCC and 50 of the patients (69%) had non-metastatic RCC. The mean body mass index (BMI) of the patients was 28.10 ± 6.18. The BMI was 30.38 ± 9.27 in women and the mean BMI in men was 27.10 ± 3.93 (<italic>p</italic> = 0.046). While the mean BMI of the metastatic patients was 30.17 ± 10.10, it was 27.49 ± 4.41 in non-metastatic patients (<italic>p</italic> &gt; 0.05).</p>", "<p id=\"Par17\">At least 1 comorbid disease was present in 66% of the patients. According to the frequency of comorbid diseases of the patients, 44% had hypertension, 20% had diabetes mellitus, 17% had cardiovascular disease, 9% had chronic obstructive pulmonary disease and 10% had other diseases. Tumors were located unilaterally in all patients included in the study, and right and left locations (57% right and 43% left) had similar rates (<italic>p</italic> = 0.239).</p>", "<p id=\"Par18\">The diagnosis was made by percutaneous renal biopsy in 8 of the patients (11%), while the diagnosis was made by surgical excision (radical nephrectomy/partial nephrectomy) in 64 cases (89%). As the surgical approach in surgical excision, laparoscopic surgery was used in 89% (57 patients) and open surgery in 11% (7 patients). Due to the fact that there were signs of metastases in the imaging performed at the time of diagnosis, a biopsy was performed on these 8 patients for verification purposes and they were diagnosed with RCC as a result of biopsy. In addition, only two patients were diagnosed with RCC by biopsy of their metastatic mass. Radical total nephrectomy was performed in 38% of patients (<italic>n</italic> = 24) and partial nephrectomy was performed in 62% of the remaining (<italic>n</italic> = 40) patients. Renal ischemia was performed in 75% of the patients who underwent partial nephrectomy, and the remaining 25% did not. Simultaneous lymph node dissection was performed in 9% (6/64) of the patients who underwent surgical excision. Surgical complication developed as pleural injury in 1.5% of the patients.</p>", "<p id=\"Par19\">The histological subtypes of RCC specimens in our study consisted of 72% clear cell, 17% chromophobe cell, 7% papillary type and 4% other subtypes. T stages of the patients in our study consisted of 29% pT1a, 33% pT1b, 6% pT2a and 32% pT3a.</p>", "<p id=\"Par20\">When the metastatic and non-metastatic groups were compared, statistically significant differences were observed between the two groups in terms of lymphocyte and platelet counts (<italic>p</italic> &lt; 0.01) (Table ##TAB##0##1##).</p>", "<p id=\"Par21\">\n\n</p>", "<p id=\"Par22\">When the metastatic and non-metastatic groups were compared, statistically significant differences were observed between the two groups in terms of SIRI and SII values (<italic>p</italic> &lt; 0.05 for SIRI, <italic>p</italic> &lt; 0.001 for SII) (Table ##TAB##1##2##.)</p>", "<p id=\"Par23\">\n\n</p>", "<p id=\"Par24\">Median SIRI values for non-metastatic and metastatic groups were 1.26 and 2.1, respectively (mean ± standard deviation 1.76 ± 1.9 and 3.12 ± 4.22 respectively (<italic>p</italic> &lt; 0.05). Median SII values for non-metastatic and metastatic groups were 566 and 1434, respectively (mean ± standard deviation 870 ± 1019 and 1537 ± 917 respectively (<italic>p</italic> &lt; 0.001).</p>", "<p id=\"Par25\">The area under the curve in metastatic patients was 0.809 for SII and 0.737 for SIRI. The ROC curve is shown in the Fig. ##FIG##0##1##. The various cut-off values, specificity, and sensitivity are shown in the Table ##TAB##2##3##.</p>", "<p id=\"Par26\">\n\n</p>", "<p id=\"Par27\">\n\n</p>" ]

[ "<title>Discussion</title>", "<p id=\"Par28\">About one-third of patients present with metastatic RCC at the time of presentation. RCC is one of the cancers in which the immune system is most activated [##UREF##8##12##]. In order to better evaluate the outcomes of the patients, it is necessary to identify some predictive factors for reliable prognostic and metastasis prediction. In this study, thrombocyte, lymphocyte, SIRI and SII were found to be independent predictive factors in predicting metastasis from the blood parameters of the patients at the time of admission.</p>", "<p id=\"Par29\">Increasing evidence suggests a complex interaction between leukocytes and various types of cancer, including RCC. SIRI, which is an indicator of inflammation and mainly based on peripheral neutrophil, lymphocyte and monocyte counts, was first suggested to be a reliable prognostic factor in a study conducted by Qi et al. in 2016 including 177 patients with pancreatic cancer [##UREF##9##13##].</p>", "<p id=\"Par30\">Nebojsa et al. showed that SIRI is an independent prognostic factor for the presence of lymphovascular invasion (LVI) in a study of 491 patients who underwent cystectomy due to BC [##UREF##10##14##]. This study suggests to us that a high SIRI will contribute metastasis through LVI. Therefore this situation can also be adapted to our study.</p>", "<p id=\"Par31\">Hu et al. conducted a study of patients with non-metastatic RCC involving 646 patients. Multivariate analysis conducted in this study has shown that SII is an independent predictor of overall survival (OS) and cancer-specific survival (CSS). In addition, it was found that SII was associated with lymphovascular invasion, positive lymph node and more aggressive phenoptype [##UREF##6##10##]. We did not compare SII of phenotypes in our study.</p>", "<p id=\"Par32\">Zhang et al. conducted a retrospective study on 209 BC patients who underwent radical cystectomy. In this study, it was found that SII is an independent predictor for overall survival. In addition, SII was an accurate prognostic marker than neutrophil/lymphocyte ratio (NLR), platelet/lymphocyte ratio (PLR) and C-reactive protein/albumin ratio [##UREF##11##15##]. In another study, Jan et al. showed that SII was superior to NLR, PLR and monocyte-to-lymphocyte ratio (MLR) for prognostıc factor in patients with upper urinary tract cancer [##REF##30374917##16##].</p>", "<p id=\"Par33\">In the meta-analysis of patients with urological cancer, which included 14 studies with 3744 patients, it was shown that high SII value is associated with poor prognosis [##UREF##5##9##]. On the other hand, there is no study in the literature investigating the effectiveness of inflammation biomarkers in predicting metastasis in patients with RCC. In this sense, we hope that our study will contribute to the literature.</p>", "<p id=\"Par34\">Aktepe et al., in a retrospective review of the data of 150 people with metastatic RCC who received tyrosine kinase inhibitor, showed that the PLR was superior to the NLR in terms of assessing OS [##UREF##12##17##]. In our study, when compared with the non-metastatic group, especially high platelet and low lymphocyte levels were observed in the metastatic group. In this case, it is seen that the rate of PLR is higher in the metastatic group. Therefore, high platelet count and low lymphocyte count can guide us about the risk of metastasis.</p>", "<p id=\"Par35\">In a study by Takuya et al., in which the records of 268 nephrectomized patients were examined, it was shown that reactive thrombocytosis in renal cell carcinomas developed due to hypercytocinemia. It has also been reported that the presence of IL-6 and high CRP in the liver triggers thrombocytosis and IL-6 induces differentiation from megakaryocytes to platelets and leads to an abnormal inflammatory response. In addition, it has been stated that the tumor itself triggers thrombocytosis. It has been reported that thrombocytosis and tumor progression may also be a marker [##UREF##13##18##]. When the platelet counts were compared in our study, a statistically significant difference was observed between the metastatic and non-metastatic groups, and it is thought that it may contribute to the prediction of metastasis.</p>", "<p id=\"Par36\">Zheng et al. investigated the relationship of SIRI with lymph node metastasis in patients with upper system urothelial carcinoma who underwent radical nephrectomy between 2003 and 2016. SIRI value was found to be associated with lymphovascular invasion and lymph node metastasis [##UREF##14##19##]. Chen et al. also investigated the association of SIRI with 3-year and 5-year survival and prognosis in clear cell RCC. They found that other inflammatory parameters, NLR, were statistically more significant than PLR values in both 3-year and 5-year follow-up [##UREF##15##20##]. In our study, we have not done any research on the comparison of SIRI, NLR and PLR values.</p>", "<p id=\"Par37\">According to the results of the meta-analysis, which included 30 retrospective studies published between 2016 and 2020, although it was found to be associated with SIRI value, TNM stage and lymphovascular invasion, its relationship with metastasis was not evaluated. This meta-analysis study includes cohort studies of different numbers of gastrointestinal cancers, lung cancer, cervical cancer, breast cancer, urological cancer and soft tissue cancers [##REF##34987341##21##]. Our study shows that SIRI can be a parameter that can be used to predict metastasis.</p>", "<p id=\"Par38\">High SII value was found to be associated with advanced TNM stage and poor prognosis [##REF##32172455##22##]. In our study, it was shown that the level of SII is associated with the risk of metastasis.</p>", "<p id=\"Par39\">This study has several limitations. Our study involves small number of patients. More comprehensive results and possible mechanisms can be revealed by evaluating the results of more patients that can be done in this regard. First, carrying out our study with a larger group and in a wider period will contribute more to the results.</p>", "<p id=\"Par40\">Obtaining the data of patients in a single center is one of the factors that restrict the study. A study involving multicenter patients is needed.</p>", "<p id=\"Par41\">Some of the diagnoses of metastases established by imaging methods that is not confirmed by biopsy is one of the factor that restricted this study. The diagnoses of metastases have been performed by biopsy in 32% (7/22) and radiologically 68% (15/22). However, the diagnoses performed radiologically were clear due to properties of cross-sectional imaging including MRI and CT both of which had been proved to have high sensitivity and specificity in cases with aforementioned properties [##UREF##16##23##].</p>", "<p id=\"Par42\">Incorporating SIRI and SII into routine assessments could provide nuanced prognostic insights, aiding clinicians in identifying patients at an elevated risk of metastatic progression. The integration of these markers may guide personalized treatment strategies, allowing for interventions tailored to an individual’s inflammatory profile. SIRI and SII could serve as valuable tools for monitoring treatment responses dynamically, offering insights into the effectiveness of specific therapeutic approaches. Combining these markers with emerging technologies, such as radiomics and genomics, may offer a more comprehensive understanding of RCC. The combination of radiomics features and genomics data has achieved good results [##UREF##17##24##]. Considering the intrinsic heterogeneity of renal lesions, the integration of both radiogenomics and hematological markers could potentially provide a more comprehensive risk stratification for RCC patients. This collaborative approach has the potential to refine predictive models for RCC metastases, improving the accuracy of prognostic assessments and guiding clinical decision-making.</p>", "<p id=\"Par43\">As a result of our study, inflammation parameters obtained from venous blood samples taken from patients can be used to predict metastasis. Low lymphocyte, high platelet count, increased SIRI and SII values indicate a high probability of metastasis. We think that it would be beneficial to conduct more comprehensive studies based on repeated measurement results by evaluating the results of more patients.</p>" ]

[ "<title>Conclusions</title>", "<p id=\"Par44\">According to the results of this study, it is seen that the risk of metastasis may be higher in patients with RCC who have high SIRI and SII values, low lymphocyte count and increased platelet count, which are among the inflammatory parameters obtained from the venous blood sample at the time of diagnosis of patients with RCC. This technique is cheap and accessible.</p>", "<p id=\"Par45\">High SIRI, SII, neutrophils and low lymphocytes at the time of diagnosis alert us in terms of metastasis research. These laboratory tests may show us the way for early recognition of metastasis in the future. We hope that laboratory tests will be able to show whether imaging is necessary for the diagnosis of metastasis of RCC in the future. A predictive model can be developed using these tests in the future. Therefore, early recognition of metastasis may be useful in planning treatment and follow up.</p>" ]

[ "<title>Objectives</title>", "<p id=\"Par1\">In this prospective cross-sectional clinical study, we aimed to determine the efficiency of preoperative hematological markers namely SIRI (systemic inflammatory response index) and SII (systemic inflammatory index) for renal cell cancer to predict the possibility of postoperative metastases.</p>", "<title>Methods</title>", "<p id=\"Par2\">Istanbul Education and Research Hospital, Clinic of Urology and Medical Oncology in the clinic between the dates of June 2022 to 2023 February, a diagnosis of renal cell cancer by surgical or medical oncology units imported into the treatment planning of 72 patients were included in the study. All cases with diagnoses of renal cell carcinoma were searched from hospital records. Patients with secondary malignancy, hematological or rheumatological disorders or ones with recent blood product transfusion or diagnoses of infection within the 1-month-time of diagnoses were excluded for data analyses. The data within complete blood counts (CBC) analyzed just before the time of renal biopsy or surgery were studied for SIRI and SII calculations. Twenty-two metastatic and 50 non-metastatic RCC patients were included. SIRI and SII values were compared among groups to seek change of values in case of metastasis and in non-metastatic patients a cut-off value were sought to indicate malignancy before pathological diagnosis.</p>", "<title>Results</title>", "<p id=\"Par3\">Mean age of non-metastatic RCC patients were 60.12+/-11.55 years and metastatic RCC patients were 60.25+/-11.72. Histological sub-types of the RCC specimens were clear cell (72%), chromophobe cell (17%), papillary cell (7%) and others (4%). Median SIRI values for non-metastatic and metastatic groups were 1.26 and 2.1 (mean+/-S.D. 1.76 +/-1.9 and 3.12+/-4.22 respectively (<italic>p</italic> &lt; 0.05). Median SII values for non-metastatic and metastatic groups were 566 and 1434 (mean+/-S.D. 870 +/-1019 and 1537+/-917) respectively (<italic>p</italic> &lt; 0.001). AUC for detection of metastasis were 0.809 for SII and 0.737 for SIRI.</p>", "<title>Conclusions</title>", "<p id=\"Par4\">SIRI and SII indexes seem to show a moderate efficiency to show metastases in RCC.</p>", "<title>Keywords</title>" ]

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[ "<title>Author contributions</title>", "<p>All authors contributed to the study of conception and design. HK coordinated and managed all parts of the study. TE carried out the literature search. All authors conducted data collection and performed preliminary data preparations. HK conducted data analyses and all the authors contributed to the interpretation of data. EA wrote the draft of the paper and all authors provided substantive feedback on the paper and contributed to the final manuscript. All authors read and approved the final manuscript.</p>", "<title>Funding</title>", "<p>None applicable.</p>", "<title>Data availability</title>", "<p>All data is available from corresponding author on reasonable request.</p>", "<title>Declarations</title>", "<title>Ethics approval and consent to participate</title>", "<p id=\"Par47\">Approved by the Health Sciences University Istanbul Health Practice and Research Center, Clinical Research and Ethics Committee (22.07.2022/Desicion Number: 235). All procedures performed in studies involving human participants were in accordance with the ethical standards the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Informed consent was obtained from all individual participants included in the study.</p>", "<title>Consent for publication</title>", "<p id=\"Par48\">Not applicable.</p>", "<title>Competing interests</title>", "<p id=\"Par46\">The authors declare no competing interests.</p>" ]

[ "<fig id=\"Fig1\"><label>Fig. 1</label><caption><p>The area under the curve in metastatic patients was 0.809 for SII and 0.737 for SIRI. The ROC curve is shown in the graph</p></caption></fig>" ]

[ "<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Comparison of patients’ blood parameter values between metastatic and non-metastatic groups</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\"/><th align=\"left\">Neutrophil count</th><th align=\"left\">Monocyte count</th><th align=\"left\">Lymphocyte count*</th><th align=\"left\">Leukocyte count</th><th align=\"left\">Thrombocyte<break/>Count*</th></tr></thead><tbody><tr><td align=\"left\">Non-metastatic</td><td char=\"?\" align=\"char\">5.73 ± 3.36</td><td char=\"?\" align=\"char\">0.54 ± 0.13</td><td char=\"?\" align=\"char\">2.23 ± 0.83</td><td char=\"?\" align=\"char\">8.75 ± 3.64</td><td char=\"?\" align=\"char\">263.19 ± 83.08</td></tr><tr><td align=\"left\">Metastatic</td><td char=\"?\" align=\"char\">5.80 ± 2.04</td><td char=\"?\" align=\"char\">0.65 ± 0.39</td><td char=\"?\" align=\"char\">1.52 ± 0.57</td><td char=\"?\" align=\"char\">8.29 ± 2.16</td><td char=\"?\" align=\"char\">357.45 ± 124.47</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab2\"><label>Table 2</label><caption><p>Comparison of SIRI and SII metastatic and non-metastatic groups from the blood parameter values of the patients</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\"/><th align=\"left\">SIRI*</th><th align=\"left\">SII**</th></tr></thead><tbody><tr><td align=\"left\">Non-metastatic</td><td char=\"?\" align=\"char\">1.75 ± 1.92</td><td char=\"?\" align=\"char\">869.59 ± 1018.95</td></tr><tr><td align=\"left\">Metastatic</td><td char=\"?\" align=\"char\">3.12 ± 4.22</td><td char=\"?\" align=\"char\">1537.80 ± 916.82</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab3\"><label>Table 3</label><caption><p>Various cut-off values, specificities, and sensitivities of SIRI and SII for indication of metastasis</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\"/><th align=\"left\">Metastasis (+) ≥ if;</th><th align=\"left\">Sensitivity</th><th align=\"left\">1 - Specificity</th></tr></thead><tbody><tr><td align=\"left\" colspan=\"4\">\n<bold>SIRI</bold>\n</td></tr><tr><td align=\"left\"/><td align=\"left\">1.2565</td><td align=\"left\">0.950</td><td align=\"left\">0.500</td></tr><tr><td align=\"left\"/><td align=\"left\">1.2966</td><td align=\"left\">0.950</td><td align=\"left\">0.481</td></tr><tr><td align=\"left\"/><td align=\"left\">1.3039</td><td align=\"left\">0.900</td><td align=\"left\">0.481</td></tr><tr><td align=\"left\"/><td align=\"left\">1.3165</td><td align=\"left\">0.900</td><td align=\"left\">0.462</td></tr><tr><td align=\"left\"/><td align=\"left\">1.3538</td><td align=\"left\">0.850</td><td align=\"left\">0.442</td></tr><tr><td align=\"left\"/><td align=\"left\">1.3669</td><td align=\"left\">0.800</td><td align=\"left\">0.442</td></tr><tr><td align=\"left\"/><td align=\"left\">1.3844</td><td align=\"left\">0.750</td><td align=\"left\">0.442</td></tr><tr><td align=\"left\"/><td align=\"left\">1.4112</td><td align=\"left\">0.750</td><td align=\"left\">0.423</td></tr><tr><td align=\"left\"/><td align=\"left\">1.5084</td><td align=\"left\">0.700</td><td align=\"left\">0.385</td></tr><tr><td align=\"left\"/><td align=\"left\">1.5434</td><td align=\"left\">0.650</td><td align=\"left\">0.385</td></tr><tr><td align=\"left\"/><td align=\"left\">1.5561</td><td align=\"left\">0.650</td><td align=\"left\">0.365</td></tr><tr><td align=\"left\"/><td align=\"left\">1.6560</td><td align=\"left\">0.600</td><td align=\"left\">0.308</td></tr><tr><td align=\"left\"/><td align=\"left\">1.7110</td><td align=\"left\">0.550</td><td align=\"left\">0.308</td></tr><tr><td align=\"left\"/><td align=\"left\">1.7987</td><td align=\"left\">0.550</td><td align=\"left\">0.288</td></tr><tr><td align=\"left\"/><td align=\"left\">2.0584</td><td align=\"left\">0.500</td><td align=\"left\">0.250</td></tr><tr><td align=\"left\" colspan=\"4\">\n<bold>SII</bold>\n</td></tr><tr><td align=\"left\"/><td align=\"left\">504.1073</td><td align=\"left\">0.950</td><td align=\"left\">0.654</td></tr><tr><td align=\"left\"/><td align=\"left\">509.4605</td><td align=\"left\">0.950</td><td align=\"left\">0.635</td></tr><tr><td align=\"left\"/><td align=\"left\">516.0549</td><td align=\"left\">0.950</td><td align=\"left\">0.615</td></tr><tr><td align=\"left\"/><td align=\"left\">540.6602</td><td align=\"left\">0.900</td><td align=\"left\">0.558</td></tr><tr><td align=\"left\"/><td align=\"left\">546.6142</td><td align=\"left\">0.900</td><td align=\"left\">0.538</td></tr><tr><td align=\"left\"/><td align=\"left\">673.8309</td><td align=\"left\">0.850</td><td align=\"left\">0.365</td></tr><tr><td align=\"left\"/><td align=\"left\">701.1920</td><td align=\"left\">0.800</td><td align=\"left\">0.365</td></tr><tr><td align=\"left\"/><td align=\"left\">707.2042</td><td align=\"left\">0.800</td><td align=\"left\">0.346</td></tr><tr><td align=\"left\"/><td align=\"left\">744.0929</td><td align=\"left\">0.750</td><td align=\"left\">0.308</td></tr><tr><td align=\"left\"/><td align=\"left\">760.6262</td><td align=\"left\">0.750</td><td align=\"left\">0.288</td></tr><tr><td align=\"left\"/><td align=\"left\">949.7018</td><td align=\"left\">0.650</td><td align=\"left\">0.231</td></tr><tr><td align=\"left\"/><td align=\"left\">1039.7418</td><td align=\"left\">0.650</td><td align=\"left\">0.212</td></tr><tr><td align=\"left\"/><td align=\"left\">1136.2209</td><td align=\"left\">0.600</td><td align=\"left\">0.173</td></tr><tr><td align=\"left\"/><td align=\"left\">1142.0997</td><td align=\"left\">0.600</td><td align=\"left\">0.154</td></tr><tr><td align=\"left\"/><td align=\"left\">1200.1863</td><td align=\"left\">0.550</td><td align=\"left\">0.135</td></tr></tbody></table></table-wrap>" ]

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