The exact mechanisms of nucleic acid (NA) delivery with gene electrotransfer (GET) are still unknown, which represents a limitation for its broader use. Further, not knowing the effects that different experimental electrical and biological parameters have on GET additionally hinders GET optimization, resulting in the majority of research being performed using a trial-and-error approach. To explore the current state of knowledge, we conducted a systematic literature review of GET papers in in vitro conditions and performed meta-analyses of the reported GET efficiency. For now, there is no universal GET strategy that would be appropriate for all experimental aims. Apart from the availability of the required electroporation device and electrodes, the choice of an optimal GET approach depends on parameters such as the electroporation medium; type and origin of cells; and the size, concentration, promoter, and type of the NA to be transfected. Equally important are appropriate controls and the measurement or evaluation of the output pulses to allow a fair and unbiased evaluation of the experimental results. Since many experimental electrical and biological parameters can affect GET, it is important that all used parameters are adequately reported to enable the comparison of results, as well as potentially faster and more efficient experiment planning and optimization. Transfection, a method in which foreign nucleic acids (NAs)  are introduced into cells to produce genetically modified cells, is an indispensable method in basic genetic research, bioproduction, gene therapy, cell therapy, and vaccine approaches. Since it was demonstrated in 1982 that naked NAs could be successfully transferred into cells using high-voltage electric pulses  and the GET is achieved when NAs enter the cell and produce a protein or shut down a defective or overexpressed gene. GET is most often used to deliver pDNA, but in recent years, different RNAs have also been delivered by electroporation. Depending on the type and size of NAs introduced into cells, they must cross several barriers before reaching the cytoplasm of the cell (in the case of RNA) or the cell nucleus (in the case of DNA) to exert their therapeutic effect . GET is The electric field leading to cell membrane permeabilization, a crucial step of GET, is governed by the electrode geometry, dielectric properties of the load , and the amplitude of the electric pulses delivered by the electroporator. In vitro, two-plate electrodes with a spacing of at least 1 mm  are most often used, where the cells are exposed to the electric field. They are very commonly provided in the form of commercially available cuvettes. Other types of electrodes include single-cell chambers, microelectrodes (glued to cover glass), and flow-through chambers  ,17. Not The parameters of electric pulses are the pulse shape, amplitude and duration, number of pulses, pulse repetition frequency, and polarity . The expBiological characteristics of the cells and the extracellular environment, such as the composition of the cell membrane, the conductivity of the medium surrounding the cells ,31,32 anIn vitro GET is often performed in a special electroporation media of low conductivity to reduce the power requirements of the electroporation device. In vitro electroporation is also often performed in a cell growth medium, which is an approximation of tissue intracellular fluid ,37. HoweGET efficiency also depends on NA properties such as topology, size, concentration, and mobility. Smaller pDNA molecules have been shown to lead to better GET efficiency, presumably due to their higher mobility . Higher The fact that the exact mechanisms of NA delivery with GET are still unknow represents a limitation for its broader use. Further, not knowing the effects that different experimental electrical and biological parameters have on GET additionally hinders GET optimization. As a result, the majority of research is performed using a trial-and-error approach, which is not always the most successful. In addition, a trial-and-error approach results in a large number of papers. To explore the current state of knowledge on the influence of electrical and biological parameters affecting the efficiency of GET, we conducted a systematic literature review of GET papers in in vitro conditions and performed meta-analyses of the reported GET efficiency. To our knowledge, this is the first systematic review that summarizes all influential parameters considered in reporting GET efficiency. We believe that the results of this study will improve our understanding of GET and provide guidance for future study reports.Only original research, peer-reviewed in vitro papers based on eukaryotic cells were included in the review. Theoretical papers (modeling), in vivo, in ovo and clinical studies, papers reporting GET on prokaryotes, papers in which GET was used only as a tool, papers that did not test multiple electroporation parameters, and papers with only “nucleofection” were excluded, as no parameters were reported that could be used in our analysis. A comprehensive electronic search was carried out in the Scopus, Web of Science, and PubMed databases to identify eligible papers in English language only. The initial search was performed in August 2018 and repeated in May 2021 and July 2022. The following search terms/words were used: Scopus: (ALL (gene AND transfer OR DNA AND vaccination OR plasmid AND DNA) AND ALL (electroporation) AND ALL (optimi* OR compar* OR multiple AND parameter*)) AND ) AND ).(ALL (gene AND electrotransfer OR electrogene AND transfer) AND ALL (optimi* OR compar* OR multiple AND parameter*)) AND ) AND ).Web of Science: TOPIC: (gene transfer OR DNA vaccination OR plasmid DNA) AND TOPIC: (electroporation) AND TOPIC: (optimi* OR compar* OR multiple parameter*) Refined by: LANGUAGES: (ENGLISH) AND DOCUMENT TYPES: (ARTICLE) Timespan: All years. Databases: WOS, BCI, CCC, DRCI, DIIDW, KJD, MEDLINE, RSCI, SCIELO, ZOOREC. Search language = Auto.TOPIC: (gene electrotransfer OR electrogene transfer) AND TOPIC: (optimi* OR compar* OR multiple parameter*) Refined by: LANGUAGES: (ENGLISH) AND DOCUMENT TYPES: (ARTICLE) Timespan: All years. Databases: WOS, BCI, CCC, DRCI, DIIDW, KJD, MEDLINE, RSCI, SCIELO, ZOOREC.PubMed: Search (gene transfer OR DNA vaccination OR plasmid DNA) AND electroporation AND (optimi* OR compar* OR multiple parameter*) Filters: Full text; English.Search (gene electrotransfer OR electrogene transfer) AND (optimi* OR compar* OR multiple parameter*) Filters: Full text; English.The first screening of potentially relevant papers was performed based on the title and abstract. At this stage, papers that did not report GET or reported GET on prokaryotes, in vivo, in ovo, clinical, and theoretical papers were excluded. We also excluded papers in which GET was used only as a tool and thus did not test multiple electroporation parameters, and papers in which only “nucleofection” was used. After initial screening, full-text versions from the papers that compared GET efficiency depending on varying at least one electrical or biological parameter were obtained if possible. Since many abbreviations are used throughout the paper, we summarized them all in Bibliographic details of the papers, such as authors, journal, and year of publication, were retrieved.Electrical parameters: The type of electrodes, material of the electrodes, device used to apply electric pulses (type and producer), amplitude and duration of electric pulses, and potential measurement of output pulses were collected. The types of electrodes were further divided into six groups: plate, wire, neon tip, electroporation cuvette, custom-made electrodes, and not reported. All types of electrodes that did not match the first four groups were classified into a custom-made electrodes group. If the study reported that electroporation cuvettes were used and the material of the electrodes was not reported, then we determined the material of the electrodes based on the manufacturer of the electrodes. Electroporation devices were separated into two groups, commercially available and prototype electroporation devices. The devices sold on eBay and similar platforms were also grouped under prototype devices. Pulses were first divided in five groups based on pulse duration: short (1–499 µs), medium (500 µs–9.9 ms), long (10 ms or longer), nanosecond pulses (ns) (less than 999 ns), or combinations of pulses (COM). During the review, the need arose, and additional groups were added: exponential pulse , RF-modulated square wave pulse (mod), sinusoidal (SIN), and bipolar square wave pulse (short-BP and medium-BP). Biological parameters: The type, concentration, size, promoter of NA, encoded transgene, number of cells used for GET, whether cells were in the suspension or attached, cell line, origin and type of cell line, and electroporation medium were collected. Types of NA were further divided into plasmid DNA (pDNA), messenger RNA (mRNA), silencing RNA (siRNA) and others which include guide RNA (gRNA), short hairpin RNA (shRNA), oligonucleotides, chromosomal DNA, polymer-DNA complex, MIDGE vectors, minicircles and complementary DNA (cDNA). NA concentrations were divided into 5 categories , size was divided into 4 categories , and promoters were divided into 2 categories . NA with encoded transgenes were classified as reporter, therapeutic, or empty. 5, 105–108, >108 cells/mL) or attached cells. Cell lines were classified as normal, tumor, or primary cells. The number of cells used for GET was divided into 3 categories based on the cell density  was used to extract results presented in graphical form if not given in tables or text.For statistical analysis, the standard deviation or error of the results and the sample size were collected where possible. If it was not clear whether the paper reported the standard deviation or standard error of the mean, then it was assumed to be a standard error. If a range of sample sizes was given, the lowest value was extracted. The online program GraphReader.com , with a free toolbox for meta-analysis developed in MATLAB R2016b [The meta-analysis was performed using MetaLab  were calculated using a t-distribution. Moreover, 19 subgroup analyses were performed according to the type of cell line , electroporation medium , pulses , promoter , and size of NA . The heterogeneity of each subgroup was statistically significant. Therefore, a random effects model was used to obtain the overall GET efficiency of each subgroup. All papers were evaluated based on the defined selection criteria, and the risk of bias assessment was performed for appropriate studies. Identifying the year 1982 as the year that this field emerged, we found a remarkable increase in publications concerned with GET up to 2022 . The first search in August 2018 retrieved 6133 papers. After the elimination of duplicates and ineligible papers, the full-text versions of the 182 remaining papers were obtained. The second search in May 2021 retrieved an additional 781 papers, of which 32 were eligible for our review. The third search was performed in July 2022. An additional 269 papers were retrieved, of which 14 were eligible to include in our review. Finally, data from 228 eligible papers  were extThe only parameters reported in all papers were whether GET was performed on cells in suspension or attached and the type of cell line, namely tumor, normal, or primary. The method used to measure the results was appropriately reported in all but one paper; similarly, cell origin was appropriately reported in all but two papers. More than 90% of papers appropriately described the promoter of NA and when the results were measured. More than 80% of papers appropriately described the pulse generator, electric pulses, and amount of used NA. Electrodes were appropriately described in 77% of papers, and the number of cells used for GET was appropriately described in 70% of papers. The size of the NA and electroporation medium were appropriately described in 69% of papers. In total, 64% of papers reported the used of adequate control groups. The results were appropriately reported in only 55% of papers, and the material of electrodes was appropriately reported in only 49% of papers. The worst reported parameter was the measurement of output pulses; only 22% of papers appropriately measured delivered pulses . Please note that the sum of numbers at each parameter could be higher than the total number of papers used for analysis (228) since one paper could report many experiments using various electrical and/or biological parameters. Overall, the electrode type was well reported . By far,®   . Many different types of electrical pulses have been used in GET papers so far. The most use electrical pulse was square wave pulses. Fewer than one-third of papers reported the use of exponential discharge pulses . By far, the most used NA was pDNA (>90%). Among RNA molecules, siRNA and mRNA were most often used. Fewer papers reported transfection with oligonucleotides, minicircles, gRNA, and cDNA. Chromosomal DNA, polymer-DNA complex, MIDGE vectors, and shRNA were each used only in one paper .The sizes of the NA transfected ranged from 23 bp to 48 kb. Overall, 2–7 kb was the most used size. The size of the NA was one of the categories that was often not reported. Around 25% of papers did not report the NA size . The promoters were divided into constitutive and inducible promoters. The majority of papers reported the use of constitutive promoters . Among cThe majority of transgenes in the papers included in this study were reporters (>90%). In eight papers, NAs that did not encode the transgene were used. They were classified as empty .The NA concentration was also often not reported. Most papers reported concentrations between 10 and 50 µg/mL. Concentrations lower than 10 µg/mL and higher concentrations of NA were also reported; in particular, 51–100 µg/mL and 101–500 µg/mL were also commonly used. Concentrations higher than 500 µg/mL were rarely reported . Cell lines were divided into three groups, namely tumor, normal or primary cells. All three groups of cells were used in a similar number of papers .The cells originated from 14 different species, with human, mouse, and hamster being the most often used, followed by rat and monkey. Other origins that were less common were goat, rabbit, dog, swine, cat, chicken, buck, bovine, and sheep. In two papers, the origin of the cell line was not reported . Cells were more frequently electroporated in the suspension compared to the electroporation of attached cells .5, 105–108, and higher than 108 cells per milliliter. Most papers reported GET with a cell density between 105 and 108  after GET. GET was most often measured 24 or 48 h after GET, followed by 72 h after GET. Overall, 18 papers did not report when GET was measured . The highest overall GET efficiency (86.49%) using pDNA under the control of the constitutive CMV promoter was reported by Zhou et al. in 2020. Adherent HeLa cells in a sugar- and salt-containing electroporation buffer were exposed to an overall equal electric field intensity (100 V/cm) for 0.5 ms by individually addressable sequentially energizing microelectrodes arranged in a nested and non-contact manner . A high overall GET efficiency (80.75%) was also obtained by Cegovnik and Novaković in 2003. They exposed a suspension of B16F1 cells in a sugar- and salt-containing electroporation buffer with the addition of pDNA under the control of the constitutive SV40 promoter to exponentially decreasing and electronically controlled electric pulses in an electroporation cuvette .4 V/cm in a cell culture medium for 10 ms in the presence of pDNA under the control of the constitutive CMV promoter [A somewhat lower but still high overall GET efficiency (77.26%) was also published by Zu et al. in 2020. They constructed a micropillar array of electrodes integrated as the top and bottom wall in a horizontal flow chamber. Then, 293T cells were exposed to an electric field of 625 × 10promoter .All 10 parameters for the overall paper risk of bias assessment were appropriately described in 35 papers, but only 23 papers also reported the standard deviation or error of the results and the sample size. Therefore, only these 23 papers were included in the analysis, which, together, reported 143 overall GET efficiencies obtained using different experimental electrical and biological parameters and could therefore be treated as independent experiments . Since e2 test, there was a high degree of heterogeneity in the primary meta-analysis .The meta-analysis using the random effect model revealed that the pooled overall GET efficiency was 25%  . AccordiSubgroup analysis was conducted according to the type of cell line , electroporation medium , pulses , promoter , and size of NA . The results of the subgroup analyses are given in 2 = 0.0094, p = 0.37). Using meta-regression analysis, we also checked the dependence of the overall GET efficiency on the concentration of the NA. The results demonstrated that concentration of NA was not correlated with the overall GET efficiency .From the cell line point of view, the overall GET efficiency in the tumor cell lines  is statistically significantly higher than the overall GET efficiency in the primary cell lines  and normal cell lines . In addition, the overall GET efficiency in the normal cell lines is statistically significantly higher than the overall GET efficiency in the primary cell lines. In terms of electroporation medium, the two media with the highest overall GET efficiency were balanced salt solutions  and cell culture media . However, none of them were statistically significantly higher than the overall GET efficiency, which was obtained after GET in sugar and salt-based media and in commercial electroporation media. For electrical pulses, the overall GET efficiency after GET with exponential and short-long pulses  is statistically significantly higher than the overall GET efficiencies after GET with other types of pulses, while the overall GET efficiencies according to GET with other types of pulses are not statistically different. In terms of the promoter, the overall GET efficiency after GET with the promoter SV40  is statistically significantly higher than the overall GET efficiency after GET with the promoter CMV  and the overall GET efficiency after GET with other promoters . In addition, the overall GET efficiency after GET with the promoter CMV is statistically significantly higher than overall GET efficiency after GET with other promoters. From the perspective of NA size, the subgroup analysis shows that the overall GET efficiencies according to GET with different sizes of NA are not statistically different. The set of continuous NA size variables was additionally evaluated in a univariable meta-regression analysis. The results showed that the size of the NA was not correlated with the overall efficiency of GET . Using a systematic review, we collected the papers reporting the efficiency of GET as a function of the change in at least one experimental electrical or biological parameter. In the meta-analysis, the overall GET efficiency was evaluated, and the influence of the electrical and biological parameters that affected the overall GET efficiency most were studied. The experimental electrical parameters included the type and geometry of electrodes, the material of the electrodes, the device used to apply the electrical pulses (type and manufacturer), the amplitude and duration of the electrical pulses, and the method used to measure the output pulses. The experimental biological parameters included the type, dosage, size, promoter of NA, encoded transgene, number of cells used for EP, whether the cells were electrotransfected in the suspension or attached, cell line, the origin and type of cell line, and the electroporation medium. To allow a fair and unbiased evaluation of the GET experimental result, the electrical and biological parameters must be specified, appropriate controls must be included in the experiment, and the efficiency of GET with the method of evaluation must be indicated. Although in 228 papers, the efficiency of GET as a function of the change in at least one electrical or biological parameter was reported, only 23 papers contained all desired information and were therefore reliable enough to include in the meta-analysis. All experiments reported in 23 papers resulted in a pooled overall GET efficiency of 25% . The highest overall GET efficiency was above 85% . The comDue to the broad field of GET in this study, we focused only on the in vitro reports that describe the use of different electrical and/or biological parameters. The papers that reported only the electroporation program used but did not specify details of delivered electric pulses, such as duration, number, and amplitude, could not be included in our study. Therefore, for example, the papers using only “nucleofection” were excluded.The risk of bias assessment was performed on 228 papers and included 17 parameters which were previously reported to influence GET efficiency. After the risk of bias assessment, we realized that some parameters were appropriately reported in only a few papers. Among the electric parameters, the measurement of output pulses and electrode material were the least reported. Biological parameters were reported more frequently than electrical parameters; for example, the type of cell line and whether GET was performed on the cell suspension or on the attached cells were reported in all papers. For the meta-analysis, we reduced the number of parameters and the number of categories describing each parameter to the type of cell line, electroporation medium, electric pulses, promoter, and size of the NA. Although each parameter was initially divided into several categories in order to describe all the different variants used in the experiments, some categories were not represented in the experiments included in the meta-analysis, so we merged similar categories.Control groups are an essential part of any scientific experiment, yet 15% of papers included in our review did not report the use of appropriate controls. GET experiments include exposing cells to NAs and electric pulses, and the effect of each should be monitored with appropriate controls. Therefore, GET experiments should include three controls: cells exposed only to electric pulses, cells exposed only to NA, and cells exposed to neither NA nor electric pulses.The review of the collected papers revealed that, although the type of electrodes used in the experiments was generally specified, more than 20% of the papers lacked geometric parameters of the electrodes  that would allow the determination of the electric field. Although it is known that high-voltage electric pulses cause electrode oxidation and metal corrosion, which change the electrical properties of the electrodes and the chemical composition of the surrounding medium depending on the electrode material , the matIn the case of appropriate device use and development, custom-made, prototype electroporation devices can sometimes be even more reliable than commercially available devices. In 85% of the papers, the authors stated the electroporation device used in the experiments, but only 22% of the papers mentioned the measurement or evaluation of the output pulses. For successful and efficient GET, it is necessary that the output pulses are well defined, and only the measurement of the pulses in each experiment ensures that the pulses are delivered as desired. In most studies, rectangular monopolar electric pulses were used, which replaced exponentially decaying pulses that have been used in the early years. The meta-analysis showed that the overall GET efficiency after GET with exponential and short-long pulses is statistically significantly higher than the overall GET efficiencies after GET with other types of pulses. These results are expected and confirm the importance of both cell membrane permeabilization and electrophoretic component of electric pulses in GET in vitro .Among biological parameters, only the type of cell line and whether GET was performed on the suspension or attached cells were described in all 228 papers. The method used for the evaluation of GET was reported in all but one paper, and the origin of the cell line was reported in all but two papers. It was previously shown that GET efficiency depends on the cell line . TherefoFurther, more than 90% of papers appropriately reported when results were measured and the NA promoter. The plasmid must contain the necessary elements for expression in mammalian cells. A proper promoter should be used to express the transgene. The transgene can be expressed constitutively (it transcribes constantly) or inducible (its transcription is controlled) . Among cIn addition, the time at which GET was measured is important since different NA have different expression profiles. GET was most often measured after 24–72 h, with more than 40% of papers reporting measuring of GET after 24 h. We recently showed that the time dynamics of expression of the same NA can vary between cell lines . Based oThe NA concentration and number of cells used for GET were appropriately reported in 80% and 70% of papers, respectively. NA concentration is one of the parameters which is reported to influence GET. A higher NA concentration usually leads to a higher GET ,37,38. C8 cells/mL), pulses with higher amplitudes are needed to achieve cell membrane permeabilization compared to more dilute cell suspension. In addition, cell swelling, which occurs after cell membrane permeabilization, can limit the accessibility and transport of NA between the cells and to the membrane [Cell suspension density can influence the process of cell membrane permeabilization. In a dense suspension (>10membrane . Furthermembrane . Both ceThe biological parameters which were most poorly reported were size of NA and electroporation medium. Both categories were adequately described only in 69% of papers. Contrary to our meta-analysis, some papers showed that NA size and electroporation medium can affect GET. NA of smaller size was reported to enable higher GET ,264,265.Interestingly, our meta-analysis showed the highest overall GET efficiency when balanced salt solutions and cell culture media were used. Although the difference was not statistically significant, we would still expect higher overall GET efficiency in commercial electroporation media, which were supposedly optimized. Conductivity and composition of electroporation medium can affect GET directly by affecting the cell membrane permeabilization or formation of NA aggregates on the cell membrane, or indirectly by affecting cell survival ,141,266.There are different ways of GET efficiency measurement, most often depending on GET application. Nevertheless, to determine the overall GET and be able to compare the GET efficiency between different papers, data on cell survival are also needed. In total, 75% of papers in our review only reported transfection efficiency without also providing data on cell survival. Only 15% of papers reported both transfection efficiency and cell survival. Based on the abovementioned poor reporting of electrical and biological parameters affecting GET, we prepared a table of recommendations for reporting .For now, there is no single or universal GET strategy that is appropriate for all cell lines and aims. Apart from the availability of the required electroporation device and electrodes, the choice of an optimal GET approach or strategy depends on factors such as the electroporation medium; type and origin of cells; and the size, concentration, promoter, and type of the nucleic acids to be transfected. Equally important are the inclusion of appropriate controls in the GET experiment and the measurement or evaluation of the output pulses to allow a fair and unbiased evaluation of the experimental results. Our systematic literature review of different electrical and biological parameters effect on in vitro GET efficiency showed a pooled overall GET efficiency of 25%. The parameters which significantly affected overall GET efficiency were the type of cell line, nucleic acids promoter, and pulse parameters. Overall, GET was significantly higher in tumor cell lines compared to normal and primary cell lines, and in normal cell lines compared to primary cell lines. Overall, GET was also significantly higher when the SV40 promoter was used compared to CMV or other promoters. In addition, exponential and short-long pulses led to significantly higher overall GET compared to all other pulses. However, it needs to be highlighted that these results were obtained based on the meta-analysis of only 23 papers out of 228. This low number of analyzed papers was the result of an incredible and unacceptable lack of reporting of essential information in GET in vitro papers. Since many experimental electrical and biological parameters can affect GET efficiency, it is important that all used parameters are adequately reported to enable the comparison of results, as well as potentially faster and more efficient experiment planning and optimization in further studies. Only the adequate and detailed reporting of all used parameters can enable successful future meta-analyses that could elucidate which electrical and/or biological parameters significantly contribute to higher GET efficiency.


Conclusions: NLR may be a potential marker for predicting the clinical response and persistence of TNF-α blockers in AS patients.Background: This study explores the association of neutrophil-to-lymphocyte (NLR), monocyte-to-lymphocyte (MLR), and platelet-to-lymphocyte (PLR) ratios with the 3-month treatment response and persistence of tumor necrosis factor-alpha (TNF-α) blockers in patients with ankylosing spondylitis (AS). Methods: This retrospective cohort study investigated 279 AS patients who were newly initiated on TNF-α blockers between April 2004 and October 2019 and 171 sex- and age-matched healthy controls. Response to TNF-α blockers was defined as a reduction in the Bath AS Disease Activity Index of ≥50% or 20 mm, and persistence referred to the time interval from the initiation to discontinuation of TNF-α blockers. Results: Patients with AS had significantly increased NLR, MLR, and PLR ratios as compared to controls. The frequency of non-response at 3 months was 3.7%, and TNF-α blockers’ discontinuation occurred in 113 (40.5%) patients during the follow-up period. A high baseline NLR but not high baseline MLR and PLR showed an independently significant association with a higher risk of non-response at 3 months (OR = 12.3,  Ankylosing spondylitis (AS) is a chronic rheumatic disease that mainly affects the spine and sacroiliac joints, leading to progressive ankylosis of the axial skeleton. This condition also manifests as peripheral inflammatory arthritis, enthesitis, and extra-articular diseases such as psoriasis, uveitis, and inflammatory bowel diseases (IBD), which can reduce the quality of life and lead to disability and increased mortality . The priRecently, neutrophil-to-lymphocyte (NLR), monocyte-to-lymphocyte (MLR), and platelet-to-lymphocyte ratios (PLR) have gained attention as biomarkers that reflect the inflammatory status in rheumatic disorders, including AS ,6, rheump < 0.001; MLR: 0.24 vs. 0.16; p < 0.001; PLR: 117.29 vs. 115.11; p = 0.036). The mean age was 34.5 years, and the majority of the patients were males (82.8%). The mean Bath AS Disease Activity Index (BASDAI) was 6.8, and adalimumab was the most frequently prescribed TNF-α blocker (66.3%), followed by etanercept (23.3%) and infliximab (10.4%). p = 0.028 for baseline NLR; and ρ = 0.154, p = 0.01 for baseline PLR) and 3-month BASDAI . p = 0.025). After adjusting for confounders, high baseline MLR and PLR tended to increase the possibility of non-response, but this association did not show statistical significance . Ten out of the 279 enrolled patients with AS discontinued TNF-α inhibitors therapy within the first 3 months of the index date, and the 3-month treatment response data were obtained only in the remaining 269 patients. Of these 269 patients, 10 (3.7%) did not achieve the 3-month treatment response to TNF-α blockers and were classified into the non-response group. The relationships of baseline NLR, MLR, and PLR with the 3-month treatment response, which were analyzed by logistic regression models, are summarized in n = 61, 54%) was the most common cause of TNF-α blockers’ discontinuation, followed by lack of efficacy (LOE)  and adverse events (AE) . As shown in p = 0.01) for TNF-α blockers’ discontinuation. Additionally, female sex and hip involvement were significantly related to non-persistence for TNF-α blockers, after the adjustment of potential confounders.Discontinuation of TNF-α blockers occurred in 113 (40.5%) patients during the follow-up period. The Kaplan–Meier curve for drug persistence in all the study patients is shown in The current study analyzed the role of baseline NLR, MLR, and PLR for the prediction of the clinical response and continuation of anti-TNF-α drugs in patients with AS. These three markers were significantly higher in patients with AS as compared to healthy controls, and were positively correlated with serum CRP and ESR levels. Additionally, NLR and PLR in patients with AS had a weak positive correlation with disease activity, as assessed by the BASDAI. After the adjustment of potential confounders, a high baseline NLR was significantly linked with a higher risk of a poor treatment response and discontinuation of TNF-α blockers in patients with AS, but this relationship was not found in those with high baseline MLR and PLR, respectively. The findings of our study suggest that NLR not only reflects disease activity but can also predict the clinical response and long-term adherence of TNF-α blockers in patients with AS. Similar to our results, two recent meta-analyses demonstrated that NLR levels in patients with AS were significantly higher than those in healthy participants ,6. Our dDrug persistence is interpreted as a composite clinical measure of efficacy and safety in real-world situations. One of the major results of this study was that a high baseline NLR was a significantly predictive factor for the risk of TNF-α blockers’ non-persistence in patients with AS. Previous studies have reported that in patients with AS, elevated levels of CRP and ESR and increased disease activity are significantly associated with the risk of the discontinuation of TNF-α inhibitors ,25,26. TWe also observed that a higher baseline NLR was significantly predictive for a 3-month treatment response of TNF-α blockers in patients with AS. Multiple studies have reported that lower baseline disease activity is a significant predictor for remission in patients with AS after TNF-α inhibitors’ treatment ,30,31, iUnlike NLR, baseline MLR and PLR in patients with AS did not show a significant association with the treatment response and continuation of TNF-α inhibitors in our study, although they significantly correlated with ESR and CRP. Thus, neutrophils may be more closely related to the action of TNF-α in the pathogenesis of AS than monocytes and platelets. However, this was somewhat unexpected because macrophages/monocytes, but not neutrophils, are a major source of TNF-α . InterleThis study has a number of limitations that need to be addressed. First, because this was a retrospective cohort study conducted in a single center, all potential confounding factors could not be fully adjusted. Second, the causal relationship of NLR with clinical response and persistence to TNF-α inhibitors could not be fully established because NLR was measured only once in our study. In addition, we only assessed the 3-months clinical response of TNF-α inhibitors, which can be biased by reverse causation or unknown confounding factors. Thus, longitudinal changes in the NLR in patients with AS need to be investigated to determine the causal relationship between NLR and treatment outcome. Third, the small number of AS patients with a poor treatment response to anti-TNF-α agents may have led to insufficient statistical power. This is a retrospective cohort study using electronic medical records from a tertiary referral center in South Korea. In the present study, we analyzed data of 279 patients with AS and compared them with 171 sex- and age (±2 years)-matched healthy controls. For age matching, the patients with AS and the healthy controls were matched by year of birth. If no control was found, this age-matching criterion was expanded stepwise, in age decrements or increments of 1 year to a maximum of 2 years. If there was no eligible healthy subject that could be matched to an AS patient within 2 years of age, we only evaluated the data of healthy controls. For sex matching, we matched the male-to-female ratio between AS patient and control groups. These patients with AS had been newly prescribed with TNF-α blockers as the first-line biological treatment due to poor response to NSAIDs between April 2004 and October 2019 by experienced rheumatologists. All patients with AS met the modified New York Criteria  and wereAbsolute counts of neutrophils, monocytes, lymphocytes, platelets, and CRP were measured at the index date (±14 days) for the participants in this study. NLR, MLR, and PLR were calculated as follows: absolute count of neutrophil ÷ absoluted count of lymphocyte; absolute count of monocyte ÷ absolute count of lymphoycte; absolute count of platelet ÷ absolute count of lymphocyte, respectively. In patients with AS, data regarding ESR, disease duration, BASDAI, type of TNF-α blockers, HLA-B27, presence of previous and/or current uveitis, psoriasis, peripheral arthritis, hip joint involvement, IBD, and concomitant medications were obtained. Disease duration referred to the time period between the AS diagnosis date and the index date. Uveitis referred to an episode of ophthalmic diagnosis by an ophthalmologist, and psoriasis was defined as being diagnosed by a dermatologist. Peripheral arthritis referred to the presence of one or more swollen joint in the past or present, excluding the hip joints . Hip joiTreatment response was assessed after the use of TNF-α blockers for 3 months. Furthermore, a decrease in BASDAI of ≥50% or 20 mm compared with baseline BASDAI was considered as having a clinical response . BecauseDrug persistence was defined as the time duration between the index date and the date of discontinuation of TNF-α blockers, and was expressed in months. If the index TNF-α blockers were switched to other TNF-α blockers or IL-17 inhibitors, or the index TNF-α blockers were restarted >90 days (permissible gap) after the last prescription, it was considered to be non-persistent to TNF-α blockers. Literature review indicated that 90 days permissible gap has been widely used for studying the long-term adherence of TNF-α blockers for the treatment of rheumatic diseases ,43,44,45p < 0.1 in the univariable models. To assess whether baseline NLR, MLR, and PLR were independently associated with the risk of TNF-α blockers’ discontinuation, we used multivariable Cox proportional hazard regression models that included variables with p < 0.1 in the univariable models. Baseline NLR, MLR, and PLR were dichotomized using a median split (high versus low groups) and included in both logistic regression and Cox proportional hazard regression models. Because the baseline NLR, MLR, and PLR were highly correlated with each other, these variables were separately analyzed in the multivariable logistic and regression models to prevent the possibility of multicollinearity. We regarded p < 0.05 as statistical significance, and used STATA version 15.0 of Windows software  for statistical analyses.Mean ± standard deviation (SD), median (interquartile range), or number (percent) were used to express continuous and categorical variables. Student’s t-test, Mann–Whitney U test, chi-square test, or Fisher’s exact test were applied for group comparisons. Correlations between continuous variables with or without normal distributions were estimated using the Spearman’s correlation analysis. Drug persistence of TNF-α blockers were estimated by Kaplan–Meier curves and compared with log-rank test. The relationship of baseline NLR, MLR, and PLR with 3-month treatment response to TNF-α blockers was assessed using multivariable logistic regression models adjusted for variables with In summary, the present study suggests that NLR serves as a useful marker for detecting a 3-months clinical response and persistence of TNF-α blockers in patients with AS. Because NLR is inexpensive, easily available, and a routinely measured parameter in real-world clinical practice, we believe that it can be a valuable and useful biomarker in the assessment of clinical outcomes in patients with AS. However, the baseline MLR and PLR did not show these associations. However, due to the limitations of this study, further longitudinal studies are warranted to confirm our results.


Comorbidities are expected to impact the pathophysiology of heart failure (HF) with preserved ejection fraction (HFpEF). However, comorbidity profiles are usually reduced to a few comorbid disorders. Systems medicine approaches can model phenome-wide comorbidity profiles to improve our understanding of HFpEF and infer associated genetic profiles.We retrospectively explored 569 comorbidities in 29,047 HF patients, including 8062 HFpEF and 6585 HF with reduced ejection fraction (HFrEF) patients from a German university hospital. We assessed differences in comorbidity profiles between HF subtypes via multiple correspondence analysis. Then, we used machine learning classifiers to identify distinctive comorbidity profiles of HFpEF and HFrEF patients. Moreover, we built a comorbidity network (HFnet) to identify the main disease clusters that summarized the phenome-wide comorbidity. Lastly, we predicted novel gene candidates for HFpEF by linking the HFnet to a multilayer gene network, integrating multiple databases. To corroborate HFpEF candidate genes, we collected transcriptomic data in a murine HFpEF model. We compared predicted genes with the murine disease signature as well as with the literature.COL3A1, LOX, SMAD9, PTHL), hypertrophy , oxidative stress , and endoplasmic reticulum stress (ATF6). Finally, predicted genes were significantly overrepresented in the murine transcriptomic disease signature providing additional plausibility for their relevance.We found a high degree of variance between the comorbidity profiles of HFpEF and HFrEF, while each was more similar to HFmrEF. The comorbidities present in HFpEF patients were more diverse than those in HFrEF and included neoplastic, osteologic and rheumatoid disorders. Disease communities in the HFnet captured important comorbidity concepts of HF patients which could be assigned to HF subtypes, age groups, and sex. Based on the HFpEF comorbidity profile, we predicted and recovered gene candidates, including genes involved in fibrosis (We applied systems medicine concepts to analyze comorbidity profiles in a HF patient cohort. We were able to identify disease clusters that helped to characterize HF patients. We derived a distinct comorbidity profile for HFpEF, which was leveraged to suggest novel candidate genes via network propagation. The identification of distinctive comorbidity profiles and candidate genes from routine clinical data provides insights that may be leveraged to improve diagnosis and identify treatment targets for HFpEF patients.The online version contains supplementary material available at 10.1186/s12916-023-02922-7. Heart failure (HF) with preserved ejection fraction (HFpEF) represents an unmet public health concern with rising prevalence , 2. AccuSystems medicine attempts to model disease in a holistic manner. One facet of this, network medicine, is used to analyze complex systems such as patients, organs, or cells via network representation , 8. ComoCardiovascular diseases are particularly suited for system medicine approaches due to the typical multiorgan involvement  and multIn this study, we applied a network medicine approach to describe comorbidity patterns in HFpEF and investigate a shared genetic background associated with these patterns. We first demonstrated that comorbidity profiles vary between HFpEF and HFrEF patients and derived distinct comorbidity profiles for each cohort. Then, we built a comorbidity network that contained disease clusters relevant for HF patients. The construction of a multilayer heterogeneous network by integration of prior knowledge resources allowed us to translate the comorbidity profiles into a gene signature for HFpEF. We corroborated this signature in the cardiac transcriptome of a murine HFpEF model. This network medicine approach allowed us to identify distinct comorbidity profiles and novel genetic patterns in HFpEF.The study population was derived from a research data warehouse containing data from patients that visited the Department of Cardiology, Angiology, and Pneumology at Heidelberg University Hospital, Heidelberg, Germany . HeidelbFactoMiner ..48].Gene Ontology (GO) gene networks have been constructed before, and we used the GO networks constructed by .transcript per million value > 1 [Each gene network was reduced to remove loops and multiple edges. To filter for genes relevant in cardiac tissue, gene networks were subset to genes expressed in the human heart on RNA or protein level. For protein expression, we used proteomic data , 51, whealue > 1 . We chosalue > 1 , 33.To predict genes from diseases within the HFhetnet, we relied on a network propagation algorithm developed for multilayer networks . This aWe assessed the performance of the link prediction task within the HFhetnet. For a given disease that was present in the HFhetnet and directly linked to two or more genes, we attempted to predict those genes after removing the direct links from the HFhetnet and running RWR-MH with the disease as seed node. The position of the target genes in the resulting probability ranking was then assessed with multiple metrics to estimate success of disease gene recovery  and performed differential expression analysis  and subs (limma . We perf[enrichr  R packag package ) of the p < 0.01) (Table p = 0.9) or age . When phenotypic data were available, cholesterol, LDL, HDL, and blood pressure values were higher in HFpEF patients compared to HFrEF, while NT-proBNP values were higher in HFrEF patients. Comorbidity burden measured by Elixhauser index was slightly lower in HFpEF than HFrEF patients, as previously reported [p < 0.001) or received an implantable cardioverter-defibrillator  less frequently than HFrEF patients, suggesting that the HFrEF cohort be a later stage of HF.The study population consisted of 29,047 patients with HF Fig. . Within reported . HFpEF pWe expected differences in the composition of comorbidity profiles between HF subtype cohorts. To quantify this variance, we applied MCA and estimated the variance associated with sub-cohort labels and clinical features Fig. A. DeviceNext, to explain and interpret the variance between HFpEF and HFrEF, we derived distinct comorbidity profiles for both cohorts. For this purpose, we fit random forest and elastic net classifier models with the 569 comorbidities as predictors to distinguish between HFpEF and HFrEF  was more diverse than the HFrEF profile (10 disease categories) and included comorbidities from the digestive disease, hematopoietic and neoplastic disease categories Fig. C. CardioIn conclusion, the observed variation in comorbidity profiles between HFpEF and HFrEF was analyzed by interpreting patient classifiers. The derived features captured known subtype associations such as typical etiologies of HF including hypertensive heart disease (with HFpEF) and ischemic heart disease (with HFrEF) but also more novel and understudied comorbidities associated with HFpEF such as breast cancer or rheumatoid arthritis with HFpEF.To analyze patterns of disease co-occurrence in the HF-patient cohort, we constructed a comorbidity network as previously described , 59–61).p < 0.01) , where two diseases are connected if they are phenotypically similar, and Morbinet , anotherJaccard index-based edge similarity of HFnet and Morbinet was 0.18 and of HFnet and HPOnet was 0.12 . Infectious and hematopoietic diseases had the highest median centrality scores , indicating that patients with diseases from these categories were typically suffering from many comorbidities. Diseases affecting the circulatory system had the highest prevalence as was expected with a HF centered cohort   compared to 40–59-year-old patients (n = 5973) comorbidity profiles. This could indicate a general increase of comorbidity burden with age or that with age come increasingly consistent comorbidity profiles between individuals. The 80 + cohort  had less similarity with DC1 and significantly more similarity with DC3, DC5, and DC9 profiles compared to 40–60-year-old patients. When comparing female and male patients, we found that DC6 and DC2 yielded the highest similarity differences, respectively. Comparing HFpEF with HFrEF patients, we found that DC1, DC2, DC6, and DC8 were more similar to HFpEF patients, while DC3, DC4, and DC5 were suggested to be similar to HFrEF patients. As DC1 and DC6 also captured sex-related comorbidity differences, we investigated further, whether DC6 diseases were more prevalent in HFpEF independent of sex. For this, we fit logistic regression models for each disease predicting HFpEF/HFrEF while controlling for sex  to DC similarity (9 dimensions) allowed us to capture differences among patient cohorts in regard to sex, age, and HF subtype in meaningful disease groups.Biomedical research has yielded significant knowledge of disease gene associations, which can be harnessed to extrapolate novel disease gene relationships. HFpEF is a comorbidity-driven syndrome and we hypothesized that the identified HFpEF comorbidity profile could be translated to a genetic profile consisting of recurrent genetic associations to these comorbidities. In this part of our study, we first integrated multiple biomedical databases to construct a cardiac specific multilayer disease and gene network. We then estimated the success of this network to recover known disease–gene associations and, finally, used the HFpEF comorbidity profile to identify the most commonly associated genes.To construct a gene network that reflected different hierarchies of gene function , we integrated multiple databases and represented gene–gene relationships as networks (“Methods”) Fig. A. To focp-value < 0.05). This is related to a biomedical research bias towards the investigation of a small number of genes [The presented HFhetnet is an assembly of the data-driven comorbidity relationships (HFnet) and six biomedical databases resulting in a total of 13,572 nodes and 181,529 edges . The rewired HFnet still performed better than random, which might be explained by (i) high edge density in the HFnet and (ii) the large size of the unaltered gene–gene and disease–gene network in comparison to the smaller HFnet.We performed this analysis by comparing the impact of three variations of the disease layer: (i) HFnet + HPOnet , (ii) only HFnet, and (iii) a rewired HFnet. Gene prediction worked best in the original HFhetnet  Fig. C. This pp-value < 0.01 for all metrics) with respiratory, neurological, genitourinary, and cardiovascular diseases performing best.Prediction success correlated weakly but significantly with gene set size .First, we confirmed important HFpEF phenotype characteristics including preserved ejection fraction, increased body weight, blood glucose levels, and blood pressure as well as diastolic dysfunction (increased E/e') , were identified by our network. XDH catalyzes the rate limiting step in purine metabolism producing uric acid  and prevIn general, HFpEF is likely to be a disease in which multiple genes and pathways contribute to the spectrum of phenotypes. Therefore, instead of using the disease–gene prediction to identify and validate individual genes, we have corroborated the overall effect of a spectrum of identified genes in murine gene expression data. While this provided additional evidence for the relevance of comorbidity-based gene prediction, further experimental validation is necessary to explore the functional role and reproducible validity of candidate genes. In real-world populations, it is likely that the genetic heterogeneity of the HFpEF syndrome will be influenced by the specific comorbidities that are well represented in each population. In previous disease–gene prediction studies, gene prediction was performed either by selecting multiple seed genes or single seed diseases , 130. WeIn addition, several data resources were generated in this study: (i) the HFpEF gene predictions, (ii) HFhetnet, and (iii) murine HFpEF transcriptome to help facilitate future efforts to understand HFpEF-related pathophysiology and benefit the research community.This study had important strengths and was subject to several limitations. An important strength of this study is that we analyzed clinical care data, which is a real-world representation of patients and therefore allowed us to perform a data-driven analysis of comorbidities in this patient population. However, as a result, this analysis is limited to the information captured (i) in our hospital system and (ii) at the hospital visits of a patient. Therefore, obtained results could be subject to some common biases found in medical record-derived data, such as non-random interaction with the health care system resulting in some patient populations having more data than others , incomplGiven these limitations, future studies are necessary to address the generalizability of our findings to other HF populations and to delineate different disease trajectories by considering the time of events. Nevertheless, our study recapitulated known HF comorbidity patterns, as discussed above, that could substantiate more novel comorbidity patterns identified in this work.Many open questions remain regarding HFpEF pathophysiology and genetics . InterdiIn our study, we found evidence for greater diversity of comorbidity profiles in patients with HFpEF compared to HFrEF. We further identified nine co-occurring disease groups which capture differences of disease prevalence regarding age, sex, and HF subtype. Here, we find that multimorbidity in HFpEF extends to disease clusters beyond typical HF comorbidities and includes rheumatoid, neoplastic, and gastrointestinal diseases. We further provided a biological interpretation of the HFpEF comorbidity profile, capturing overexpressed gene programs observed in murine HFpEF models. Oxidative stress, hypertrophy, cell differentiation, and fibrosis-related genes are recurrent patterns in genes associated to comorbidities of HFpEF and could constitute a link for the comorbid relationships of HFpEF resulting in a multiorgan disease state. Thus, our work highlights that comorbidity profiles are an important characteristic of HFpEF patients and should be incorporated into both clinical and genomic approaches to the study of HFpEF.Additional file 1: Supplementary Methods and Supplementary Figures S1-13. Methods: Processing of Comorbidities  and Other Clinical Predictors Patient classifier Comorbidity profile assignment compared for effects of age, sex, time to HF diagnosis and time of recording Comorbidity network clustering Disease network comparison Disease prediction metrics Heart failure gene sets. Fig. S1. ICD10 code mapping. Fig. S2. Patient classifier training.Fig. S3. Parameter estimates from the patient classifier.Fig. S4. Time to HF and time of comorbidity profile assignment.Fig. S5. Comparison of comorbidities between HFpEF and HFrEF cohort.Fig. S6. HFnet overview.Fig. S7. Comparison of disease networks.Fig. S8. Comparison of centralities.Fig. S9. Comparison of clustering algorithms in the HFnet.Fig. S10. Leave-one-out cross validation of disease gene prediction.Fig. S11. HF subtype gene prediction.Fig. S12. L-NAME/HFD Phenotype data.Fig. S13. Myocardial gene expression in L-NAME/HFD. L-NAME/ HFD mouse model and RNA sequencing.Additional file 2: Supplementary Table 1. Manual disease code mappingsAdditional file 3: Supplementary Table 2. Centrality of nodes in HFnetAdditional file 4: Supplementary Table 3. Disease Cluster Composition of the HFnetAdditional file 5: Supplementary Table 4. Edge list of HFhetnetAdditional file 6: Supplementary Table 5. Predicted HFpEF and HFrEF genes


Advanced manufacturing technology and systems (AMTSs) combine the principles of mechanical engineering with design innovation to create products and processes that are better, faster and more precise . The corAdvanced manufacturing technology and systems cover a broad scope, involving manufacturing processes, machine tool design, system optimization, smart and flexible manufacturing, theoretical study and metrology ,4. From This Special Issue comprises 19 original papers concerning recent advances in the research and development of AMTSs. Specifically, many research fields are covered as follows: machining processes (six papers), micro-/nano precision fabrication (four papers), system optimization (eight papers) and a review (one paper). These typical studies reveal the recent advances in advanced manufacturing, which are briefly summarized as follows.Zhang et al.  studied


Cases resulting in death had a significantly higher probability of receiving rasburicase monotherapy than receiving no urate-lowering agents , or the allopurinol-rasburicase combination . Patients receiving allopurinol were less likely to need RRT compared to those not receiving it or those receiving rasburicase. In conclusion, current anecdotal evidence demonstrated that metastatic disease, especially in the liver and lungs, may be associated with STLS-related death compared to no metastatic status. Careful surveillance of high-risk cases within larger studies is essential to identify markers predicting morbidity or mortality.No systematic synthesis of all cases of spontaneous tumor lysis syndrome (STLS) in adult patients with solid tumors is available to date. Herein, we aim to recognize specific STLS characteristics and parameters related to a worse prognosis. We conducted a systematic search for randomized controlled trials, cohorts, case-control studies, and case reports. The primary endpoints were death and the need for renal replacement therapy (RRT) due to STLS. We estimated crude odds ratios (ORs) with 95% confidence intervals (95%CI) via univariate binary logistic regression. We included one cohort of 9 patients and 66 case reports of 71 patients [lung cancer 15(21.1%)]. Regarding the case reports, most patients [61(87.1%)] had metastatic disease [liver 46(75.4%)], developed acute kidney injury [59(83.1%)], needed RRT [25(37.3%)], and died due to STLS [36(55.4%)]. Metastatic disease, especially in the liver [The online version contains supplementary material available at 10.1007/s12032-023-02108-4. Tumor lysis syndrome (TLS) is a common oncological emergency and is caused by the massive lysis of tumor cells mediated by anticancer therapies or spontaneous cellular death in rapidly dividing tumors , 2. The In cases with solid tumors, the syndrome is usually induced by the administration of chemotherapeutic drug combinations, chemoembolization, radiotherapy, radiofrequency ablation (RFA), immune checkpoint inhibitors, monoclonal antibodies, or even corticosteroids - the latter more commonly reported in cases with hematological malignancies , 4, 6, 7To date, the vast majority of available evidence on STLS is derived from hematological malignancies , 9 whileThis scoping literature review was conducted in line with the extension for Scoping Reviews of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) . The proEligible were considered adult patients > 18 years) with non-hematological/solid malignancies developing laboratory STLS (LSTLS) as per Cairo-Bishop criteria  . The lab years wiWe included studies that reported on parameters belonging to at least one of the 4 outcome axes:Tumor-related data ;STLS-related characteristics including potentially triggering agents , clinical presentation  , 2;Prognosis of STLS . Death due to STLS (yes/no) was defined as death during admission for STLS and caused by the syndrome’s manifestations or direct complications.We considered death or the need for RRT due to STLS as primary outcomes and the rest as secondary.Published randomized controlled trials (RCTs), prospective/retrospective cohorts, case-control studies, case series, or case reports written in English, German, or French were considered eligible.https://www.rayyan.ai/). Three independent reviewers  initially screened the records for relevance based on title and abstract only. Disagreements were resolved through consensus or by a fourth reviewer’s (MP) decision. Relevant articles were then assessed for eligibility by the same three independent reviewers with inconsistencies having been again addressed through discussion until consensus or decision by a fourth author (MP). References of the included articles were also searched, as per the snowball procedure, to further investigate cases not referred to as “spontaneous” by the authors. In case of overlapping/duplicate samples within the same analysis, the study with the most comprehensive report on the outcomes of interest was selected.A systematic literature search of the PubMed and Scopus databases and the CENTRAL registry  was conducted using the following algorithm: “Tumor Lysis Syndrome” AND spontaneous. A preliminary search was conducted on May 1, 2022. We updated the search up to October 17, 2022, to not exclude any recently published eligible studies. The obtained records were imported into EndNote 20 and underwent semiautomatic deduplication . DedupliThree blinded reviewers  extracted data from the eligible articles in a predesigned Excel spreadsheet. Discrepancies were resolved through discussion until consensus. Items for which data were sought and extracted are presented in Supplementary methods.Within the scoping nature of this review, the risk of bias assessment of the eligible studies was not considered obligatory . Since mOR) were estimated by univariate binary logistic regression with “death due to STLS”, and “need for RRT” serving as the dependent variables and one of the rest of the investigated variables being, each time, used as the predictor variable  if their distributions were skewed, while categorical variables were presented as frequencies (%). The normality of continuous variables was examined by applying the Shapiro-Wilk test. Descriptive statistics of the examined variables were calculated for the overall sample and the sample of each one of the different primary tumor sites. Inferential statistics using the entire sample were restricted to the primary outcomes. Means of independent samples were compared using the independent-samples t-test if data were normally distributed; if not, the Mann-Whitney U Test was performed . Chi-squvariable . Multivavariable , 19. A sThe literature search yielded 414 records. Following semiautomatic deduplication and title-abstract screening, 127 reports were assessed for eligibility. Of these, 54 were excluded and 73  male, mean (SD) age of 63 13), median  Charlson Comorbidity Index (CCI) on the admission of 7 ], three (33.3%) had a primary lung tumor while the rest was diagnosed with gastric, colon and endometrial adenocarcinomas, esophageal squamous cell carcinoma, quadriceps myxoid liposarcoma and a tumor of unknown primary site . AKI was3, medianΤhe most common primary tumor sites in individually reported patients  were lung [15 21.1%)], colon [6 (8.5%)], and liver [5 (7.0%)]. The authors suggested increased age, large bulky masses [median  largest transverse dimension of 8.9 cm ], metastatic disease, recent biopsy, dehydration, infection, preexisting kidney dysfunction or urinary obstruction, use of nephrotoxic drugs or medications that inhibit uric acid excretion, and increased baseline levels of WBC, phosphorus, potassium, uric acid or LDH as predisposing factors for the spontaneous emergence of TLS in adults with solid tumors. Forty (58%) patients had concurrent diagnosis of STLS and their underlying tumor. Most patients [61 (87.1%)] had developed metastatic disease, with the liver [46 (75.4%)] being the most reported site, followed by lung [22 (36.1%)] and bone involvement [16 (26.2%)]. At the time of presentation, seven (10.6%) had LSTLS without CSTLS, 32 (48.5%) had CSTLS which was classified as mild as per Cairo-Bishop grading , and 27  had severe CSTLS. STLS resulted in AKI in the majority [59 (83.1%)] of individuals, while arrhythmias, seizures, and symptomatic hypocalcemia were prevalent in only three (4.2%), one (1.4%), and two (2.8%) cases, respectively. For the reduction of uric acid levels, most patients were administered either allopurinol [18 (27.3%)] or rasburicase monotherapy [18 (27.3%)], whilst the rest received no treatment [15 (22.7%)], or combinations of rasburicase with allopurinol [14 (21.2%)] or febuxostat [1 (1.5%)]. Patients had generally an unfavorable prognosis with high rates of need for RRT [25 (37.3%)], death related to STLS [36 (55.4%)], and all-cause death [52 (80.0%)]. Recurrence was rare (2.9%), with only 2 relevant incidents being recorded , it was significantly associated with the need for RRT . Patients receiving allopurinol were less likely to require RRT compared to those not receiving this medication  or those receiving rasburicase . Notably, patients to which RRT was applied had significantly higher LDH levels at the STLS diagnosis than those not reaching RRT  or requirement for RRT . A possible explanation of this observation could be that rasburicase was reserved for patients with a more severe course of STLS and was possibly applied at the point of clinical deterioration , 74, 92.Finally, our study indicated a significant association of Cairo-Bishop CSTLS severity at the time of its presentation with the requirement for RRT but not with death due to the syndrome. Considering that renal complications of STLS might be more frequent than manifestations such as uncontrollable seizures or life-threatening arrhythmias, the Cairo-Bishop grade of CSTLS at the time of presentation might have been predominantly determined by SeCr levels . As moreAs of the time of writing, this is the largest review trying to synthesize all available data systematically and separately on adult patients with solid tumors developing STLS. We have attempted not only to describe the characteristics of these patients, or the parameters related to the syndrome but also to investigate which of these parameters may be associated with the need for RRT and STLS-caused death. The latter has not been addressed by previous studies in this subset of patients. Our findings may generate hypotheses in terms of prognostic factors of TLS in solid tumors and guide future clinical research.However, our study also has certain limitations. Firstly, our small sample size has introduced a higher probability of type I error, leading to underpowered analyses, and not allowing to safely fit any multiple regression models. Secondly, the included population was highly heterogenous, consisting of various cancer histology, primary sites, and disease stages. Since a subgroup analysis by primary sites would be underpowered, we used the total sample investigating the effect on STLS-caused death and the need for RRT. Although we searched for all possible study designs, our findings were derived from case reports which are considered very low-quality evidence. Data from case reports are collected in a retrospective manner, and are, therefore, accompanied by a high risk for selection or confounding biases and subjectivity . In addiThe synthesis of evidence on adult patients with solid tumors developing STLS demonstrated that metastatic disease, especially in the liver or lungs, and administration of rasburicase may be both associated with increased likelihood of death due to the syndrome compared to patients without any diagnosed metastasis and those receiving no urate-lowering treatment or the allopurinol-rasburicase combination, respectively. The need for RRT was found lower in patients receiving allopurinol than those not receiving allopurinol or those receiving rasburicase. The observed linkage between rasburicase and worse prognosis may be partially attributable to the potential selection of patients with a worse course of the syndrome to receive this specific agent. The CSTLS severity on its presentation was significantly associated with the need for RRT but not with death due to the syndrome. Based on all these findings, larger studies addressing the limitations of current literature and investigating the potential of underdiagnosis or underreporting of STLS in solid tumors are needed to compare the efficacy and safety of different management protocols and to identify unexplored triggering factors and predictors of worse prognosis.Below is the link to the electronic supplementary material.Supplementary Material 1


Hyperandrogenism is a key pathological characteristic of polycystic ovary syndrome (PCOS). Tumor necrosis factor α (TNF-α) is both an adipokine and a chronic inflammatory factor, which has been proven to be involved in the pathologic process of PCOS. This study aimed to determine how TNF-α affects glucose uptake in human granulosa cells in the presence of high testosterone concentration.  KGN cell line was treated with testosterone and TNF-α alone or co-culture combination for 24 hr, or starved for 24 hr. Quantitative real-time polymerase chain reaction (qPCR) and western blot were performed to measure glucose transporter type 4 (GLUT4) message RNA (mRNA) and protein expression in treated KGN cells. Glucose uptake and GLUT4 expression were detected by immunofluorescence (IF). Furthermore, western blot was performed to measure the contents in the nuclear factor kappa-B (NF-κB) pathway. Meantime, upon addition of TNF-α receptor II (TNFRII) inhibitor or Inhibitor of nuclear factor kappa-B kinase subunit beta (IKKβ) antagonist to block the TNFRII-IKKβ-NF-κB signaling pathway, the glucose uptake in KGN cells and GLUT4 translocation to cytomembrane were detected by IF, and related proteins in TNFRII-IKKβ-NF-κB were detected by western blot.The glucose uptake in Testosterone + TNF-α group was lowered significantly, and Total GLUT4 mRNA and proteins were reduced significantly. GLUT4 translocation to cytomembrane was tarnished visibly; concurrently, the phosphorylated proteins in the TNFRII-IKKβ-NF-κB signaling pathway were enhanced significantly. Furthermore, upon addition of TNFRII inhibitor or IKKβ inhibitor to block the TNFRII-IKKβ-NF-κB signaling pathway, the glucose uptake of treated granulosa cells was improved. TNFRII and IKKβ antagonists may improve glucose uptake in granulosa cells induced by TNF-α by blocking the TNFRII-IKKβ-NF-κB signaling pathway under high androgen conditions. Polycystic ovary syndrome (PCOS), one of the most common metabolic and endocrine disorders in women of reproductive age, is characterized by hyperandrogenism, obesity, ovulatory infertility, polycystic ovaries, low-grade chronic inflammation, and insulin resistance (IR) . In womein vitro study was designed to evaluate the effect of TNF-α on GCs with hyperandrogenism. In this study, KGN cells were cultured with TNF-α or testosterone or TNF-α and testosterone to examine whether the TNF-α affected glucose uptake in GCs via the classic TNF-α inflammatory pathway  in the presence of high levels of androgen. Several researchers found that the level of TNF-α protein in the follicular fluid (FF) of PCOS patients was significantly elevated -19. GCs Cell culture 2 at 37 ℃.The KGN cell line, a human granulosa-like tumor cell line, was provided by Zhao Yuanyuan, PhD. KGN cells were maintained in 6-cm Petri dishes and cultured in DMEM/F12 -Dulbecco’s Modified Eagle medium  supplemented with 10% fetal bovine serum (FBS) (EVERY GREEN), penicillin (100U/l), and streptomycin (100 U/l) (Solarbio Life Science), in a humidified incubator (Thermo Fisher Scientific.) containing 5% COCell culture After adhesion, cells were divided into groups for 24 hr culture with 28.84 ng/ml (100 nM) testosterone alone , 100 ng/ml TNF-α  alone, or these two reagents for co-culture. Cells were divided into four groups based on cell culture: T group (culture with testosterone), α group (culture with TNF-α), T+α group (co-culture with testosterone and TNF-α), and con group (the blank control group: starved for 24 hr).Addition of inhibitors1) Except for the con group, the TNFRII antagonist Qiangke (Celgene Biopharma) was administered to the three culture groups (200 ng/ml) for 24 hr. Then, the cells were collected for subsequent analysis. 2) A phosphorylated IKKB competitive antagonist IMD0354: 1151.01 ng/ml(3 µM)  was used to add testosterone and TNF-α for co-culture of GCs for 0 hr, 1 hr, 2 hr, 4 hr, 6 hr, and 12 hr. After IMD0354 preconditioning for 30 min, testosterone and TNF-α were administered for 0 hr, 1 hr, 2 hr, 4 hr, 6 hr, and 12 hr. In the absence of IMD0354 preconditioning, testosterone, and TNF-α were administered for 0 hr, 1 hr, 2 hr, 4 hr, 6 hr, and 12 hr, and then the cells were collected for subsequent analysis.Total RNA extraction and quantitative real-time polymerase chain reaction (qPCR)−ΔΔCT method, and β-actin was used as a reference gene. Total RNA was isolated from KGN cells starved for 24 hr (the con group), treated with testosterone or TNF-α alone or in combination for 24 h, extracted with TRIzol reagent (Solarbio Life Sciences), and reverse transcribed into cDNA with HifairII 1st strand cDNA Synthesis SuperMix for qPCR . After removing the remaining DNA according to the instructions, the reverse transcription mix was incubated at 42 °C for 2 min, 25 °C for 5 min, 42 ℃ for 30 min, and 85 ℃ for 5 min. The samples were quantified by qPCR with Hieff SYBR green qPCR master mix  on a qTOWER3 G RT–PCR system  with specific primers, as listed in Western blotting Western blotting was performed to examine the levels of TNF-α receptor II (TNFRII), I-kappa-β kinase (IKKβ), phosphorylated-IKKβ (p-IKKβ), nuclear factor-kappa B p65 (TP65), phosphorylated-NF-κB p65 (p-P65), and glucose transporter-4 (GLUT4). Total protein was extracted by RIPA lysis buffer (Beyotime Institute of Biotechnology) supplemented with complete phosphorylated protease inhibitor cocktail tablets  at a 1:100 dilution. Protein concentration was determined by a Nanodrop 2000 . Equal amounts  of total protein were separated by 10% SDSPAGE and transferred onto 0.22 polyvinylidene difluoride (PVDF) membranes . The membranes were blocked with 5% nonfat milk diluted in 0.01 M Tris-buffered saline supplemented with Tween 20 (TBST) for 1 hr at room temperature (RT) and probed with primary antibodies against TNFRII , IKKβ , p-IKKβ , TP65 , p-P65 , GLUT4 , and βactin  in 0.01 M TBST with or without 5% nonfat dry milk overnight at 4 °C. Then, the membranes were incubated with horseradish peroxidaseconjugated secondary antibodies at 1:5,000 dilution  for 1 hr at RT. Immunoreactive protein bands were visualized by enhanced chemiluminescence  on a multifunctional imaging system. Blots were scanned and quantified with image analysis software (VILBER LOURMAT FUSION FX7 EDGE). All densities of the target protein band were normalized to β-actin.GLUT4 immunofluorescence (IF) analysisAfter treatment, the medium was removed, and the cells were rinsed with 1× PBS for 30 sec at RT, incubated with 2 ml of 4% paraformaldehyde  for 10 min at RT, and rinsed 3 times at RT with 1× PBS for 30 sec. Then, 60 µl of primary antibody against GLUT4  diluted in 5% normal goat serum  in 1× PBS was applied to the coverslips, which were incubated overnight at 4 ℃. The group without primary antibodies served as the negative control group. The specimens were incubated with 60 µl of secondary antibody  diluted in 5% NGS/1× PBS for 1 hr at RT. Coverslip slides were sealed with one drop of antifading mounting medium (with DAPI)  for 2 hr at RT. Green fluorescence intensity was measured with an FV-ASW system . The average grey value was determined with Cellsens.Glucose uptake After KGN cells were treated, the maintenance medium was replaced with 400 μl of serum-free DMEM/F12 for a 30-min preincubation in a 37 °C incubator. The cells were washed two times with precooled PBS. The fluorescent glucose analog 34226ng/ml (100 μM) 2-NBDG  and 580.77 ng/ml (100nM) insulin  were added and incubated for 1 hr, and then the cells were isolated (for protein and mRNA extraction). 2-NBDG uptake was terminated by removing the medium, and the cells were washed twice with precooled PBS. Some cells were trypsinized and resuspended in 400 μl of precooled PBS, while the remaining cells were stained with DAPI  and observed with an Olympus confocal microscope . The average grey values analysis was conducted using Cellsens.Statistical analysis post hoc Duncan test. SPSS 21.0  was used for data analysis, and P<0.05 was considered statistically significant, while P<0.01 was considered statistically dramatic.The data are presented as the mean±SD. Statistical analysis was carried out by GraphPad Prism . Levene’s test was used to test the normality of data distribution. Differential analysis was conducted by one-way analysis of variance (ANOVA), followed by the Glucose uptake of GCs coculture with TNF-α under high testosterone was lowered significantlyP<0.05). 2-NBDG uptake in the groups that were cultured with TNF-α alone or testosterone and TNF-α was drastically decreased compared with the blank control group (P<0.01). In addition, intracellular green fluorescence decreased with cotreatment.The effects of each treatment on glucose uptake were assessed by a glucose fluorescence substitute (2-NBDG)-based assay. 2-NBDG uptake in KGN cells of the T group decreased significantly compared with the blank control group , P<0.05.ol group , P<0.01.GLUT4 level of GCs cultured with TNF-α under high testosterone was lowered significantlyP<0.05), as well as in α or T+α groups, as compared with the con group (P<0.01). In the meantime, GlUT4 protein expression was significantly decreased in α group vs con group and T+α group vs T group (P<0.05). In addition, the expression in T+α group was drastically decreased compared with the con group (P<0.01). GlUT4 protein is a glucose transporter that is embedded in the cell membrane. When Glut4 migrates from the cytoplasm to the cell membrane, glucose uptake is increased. We measured the GLUT4 immunofluorescence intensity in the cytomembrane after TNF-α or testosterone or TNF-α and testosterone were added to KGN cells. The results showed that when the KGN cell line was cocultured with TNF-α and testosterone, the presentation of GLUT4 on the cytomembrane was gradually reduced , and those expressions were increased drastically in the T+α group compared with those in the T group and the blank control group respectively (P﹤0.01). p-P65 expression was drastically higher in the T+α group than in the α group, T group, and blank control group (P﹤0.01). These results indicated that the inflammatory pathways induced by TNF-α, the NF-κB signaling pathway, were visibly amplified in a high testosterone environment , especially in the T+α group which was cultured with a TNFRII inhibitor Qiangke. These results suggest that the TNFRII inhibitor could reduce the phosphorylated protein levels of NF-κB signaling pathway factors that were activated by TNF-α, particularly in a high-testosterone environment. In the presence of a TNFRII inhibitor QiangKe, the expression of GLUT4 on the cytomembrane was increased significantly. (P<0.05). Then when a TNFRII inhibitor QiangKe was added, 2-NBDG uptake in treated cells was increased visibly (P<0.01).Blocking TNFRII with a TNF-α receptor II inhibitor (Qiangke) on KGN cells cultured with testosterone, TNF-α or cocultured with testosterone and TNF-α, the protein level of TNFRII (TNFRII/β-actin), p-IKKβ (p-IKKβ/ IKKβ/β-actin), and p-P65(p-P65/TP65/β-actin) were all reduced , P<0.01,icantly. ; P<0.05. visibly ; P<0.01.Effect of IKKβblocking on KGN cellscultured with TNF-α and high-level testosteroneP<0.05). In the presence of a high testosterone environment, inflammatory responses induced by TNF-α could be significantly reduced by pretreating with IKKβ inhibitor. When the cells were pretreated with an IKKβ inhibitor (IMD0354), intracellular green fluorescence was evidently increased compared with that of cells that were treated with TNF-α and testosterone for 4 hr, 6 hr, and 12 hr (P<0.01).IMD0354, as one of selectivity IKKβ inhibitors, could generate a blocking effect on the NF-κB signaling pathway. After pretreatment with IMD0354 (IKKβ inhibitor) for 30 min, KGN cells were stimulated with TNF-α (100 ng/ml) and testosterone (28.84 ng/ml) for 0 hr, 1 hr, 2 hr, 4 hr, 6 hr, and 12 hr. The level of p-P65 in cells that were pretreated with IMD0354 for 4 hr, 6 hr, and 12 hr was lower than in treated cells, and these results were statistically significant ; P<0.05.nd 12 hr ; P<0.01.PCOS is characterized by hyperandrogenism, obesity, IR, and ovulatory dysfunction. As a heterogeneous hormone-imbalance gynecological disorder, the estimated prevalence of PCOS is approximately 4–21% among adolescents and reproductive-aged women, depending on the definition criteria of PCOS , 26. A lTNF-α exerts a wide range of biological activities and is a “master regulator” of immune and inflammatory responses. A high The experimental results indicated that the glucose uptake of GCs could be regulated via TNF-α-TNFRII-IKKB-NF-κBp65, the major TNF-α signaling pathway. Chronic inflammation is a typical characteristic of PCOS, in which TNF-α plays a pivotal role . TNF-α hOur experimental results showed that highly increased TNF-α might be an important factor leading to the dysfunction of ovarian GCs, especially in the presence of pathologically enhanced androgen. In the meantime, we attempted to use anti-TNF-α to improve the function of GCs. Our results also revealed that anti-TNF-α or blocking the NF-κB pathway could increase the glucose uptake in GCs. However, this study has several limitations. It included a relatively small number of experiments on other cell lines and healthy control cell lines. Therefore, additional experiments need to be performed on other cell lines.DX S and XH W designed the experiments; DX S performed experiments and collected data; DX S and XH W discussed the results and strategy; XH W supervised, directed, and managed the study; DX S and XH W approved the final version to be published.Natural Science Foundation of Hebei Province (Grant No. H2019106051) and the project of Shijiazhuang Science and Technology Bureau  (No.211460513).This study was supported by the The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.The authors declare that they have no competing interests.