While living organ donor follow-up is mandated for 2 years in the USA, formal guidance on recovering associated costs of follow-up care is lacking. In this review, we discuss current billing practices of transplant programs for living kidney donor follow-up, and propose future directions for managing follow-up costs and supporting cost neutrality in donor care.Living donors may incur costs and financial risks in the donation process, including travel, lost time from work, and dependent care. In addition, adherence to the Organ Procurement and Transplantation Network (OPTN) mandate for US transplant programs to submit 6-, 12-, and 24-month postdonation follow-up data to the national registry may incur out-of-pocket medical costs for donors. Notably, the Centers for Medicare and Medicaid Services (CMS) has explicitly disallowed transplant programs to bill routine, mandated follow-up costs to the organ acquisition cost center or to the recipient’s Medicare insurance. We conducted a survey of transplant staff in the USA , which identified that the mechanisms for recovering or covering the costs of mandated routine postdonation follow-up at responding programs commonly include billing recipients’ private insurance (40%), while 41% bill recipients’ Medicare insurance. Many programs reported utilizing institutional allowancing (up to 50%), and some programs billed the organ acquisition cost center (25%). A small percentage (11%) reported billing donors or donors’ insurance.To maintain a high level of adherence to living donor follow-up without financially burdening donors, up-to-date resources are needed on handling routine donor follow-up costs in ways that are policy-compliant and effective for donors and programs. Development of a government-supported national living donor follow-up registry like the Living Donor Collective may provide solutions for aspects of postdonation follow-up, but requires transplant program commitment to register donors and donor candidates as well as donor engagement with follow-up outreach contacts after donation.The online version contains supplementary material available at 10.1007/s40472-022-00379-w. Living donor kidney transplantation not only offers the best treatment option to patients in need of renal replacement therapy but also provides cost-savings to the healthcare system, compared to both dialysis and deceased donor transplantation. A contemporary discrete event model analysis of a hypothetical cohort of 20,000 end-stage renal disease patients found that almost all living donor kidney transplants result in cost savings, ranging from $13,000 USD to $30,000 USD, and even higher risk transplants from HLA-incompatible donors were cost-effective .The follow-up of living organ donors has been a topic of long-standing controversy in the field of organ donation , in partIn this article, we review current understanding of the financial risks associated with living kidney donation, summarize the evolution of OPTN mandated living donor follow-up, describe how transplant programs manage the costs of routine living kidney donor (LKD) follow-up in contemporary practice, and propose future directions to improve financial neutrality for donors. This manuscript is a work product of the AST LDCOP.While living donor kidney transplantation is cost-saving for the healthcare system, it is notable that living donors incur costs in the donation process. In a recent systematic review of publications on donation-related expenses, Fu et al., found that LKDs incurred direct costs ranging between $900 USD to $19,900 USD in the period starting from evaluation to 1-year postdonation . In addiWhile the goal of the OPTN follow-up mandate is to support the medical safety of donation, it should be noted that currently, there is no formal policy for donors or programs to recuperate the costs of complying with this follow-up mandate. Practice variation across programs in terms of interpretation and use of the Centers for Medicare and Medicaid Services (CMS) cost report, and in access to other resources, can result in significant costs passed on to donors , 23. ComAlthough direct costs associated with OPTN-mandated postdonation medical follow-up are small compared to costs associated with living donor evaluation and surgery, managing these costs poses financial challenges for transplant programs and donors. Notably, CMS has explicitly disallowed billing donor follow-up costs to the Organ Acquisition Cost Center (OACC) on the Medicare Cost Report (MCR) or to a recipient’s Medicare .To help inform the community of current practices and guide discussions of effective strategies for managing follow-up costs, the AST LDCOP Financial Workgroup developed and conducted a contemporary survey of staff at US LKD programs on practices in handling living donor follow-up costs Table . The surEach transplant program was represented once in the primary analysis. Representative responses from programs with multiple respondents were selected using a hierarchical algorithm, similar to previous methods –30. For n = 18) of all active transplant programs; 49 were classified as having “lower” and 44 as having “higher” rates of follow-up, based on the median of 6-month follow-up compliance (83%).To assess possible relationships between program volume, follow-up rates, and billing practices, we examined Scientific Registry of Transplant Recipients (SRTR) data. For the representation of living donor recovery volume, we considered a period of 12 months before the beginning of the COVID-19 pandemic (March 2019–February 2020). For baseline follow-up rates, we considered complete 6-month follow-up rates  for living donations from October 2018 to September 2019, to allow time for reporting before the pandemic. Twenty-five programs were categorized as “smaller” and 68 as “larger” volume based on the median (Most survey respondents 82%) reported having standardized policies and procedures for handling living donor follow-up costs, which was true regardless of volume . Nearly one in five program respondents 17%) reported wanting clarity on cost management. The most commonly reported mechanism for recovering follow-up care costs was billing the recipient’s insurance, and notably, billing the recipient’s Medicare was as common as billing private insurance (41% vs. 40%). Examined by transplant program volume, billing the recipient’s Medicare was more common at smaller compared to larger volume programs (56% vs. 35%). A similar pattern was observed for billing the recipient’s private insurance (52% vs. 35%). Many programs reported utilizing institutional allowancing, which was reported more by larger compared to smaller volume programs (59% vs. 48%). Some programs reported accumulating follow-up costs on their OACC and then recording them on the MCR (25%). The practice of using the MCR tended to be more common at smaller than at larger volume programs (32% vs. 22%), and at programs with lower vs. higher follow-up performance (29% vs. 20%). Eleven percent of programs reported billing donors or donor insurance. Some reported use of charitable funds (12%), which was more common at centers with higher vs. lower follow-up performance (16% vs. 8%) (Table % reporte7% reportMost responding programs reported educating donor candidates on costs and coverage of routine follow-up (89%) but a minority reported they do not (11%). Education practices did not differ by either program volume or by rates of baseline follow-up. Donor education most commonly occurred at the time of evaluation 93%), while under one-half of programs educated at the time of follow-up 42%). Some programs also educated donors about follow-up care costs before surgery (38%) , institutional allowancing, and billing the MCR. These contemporary practice patterns differ from those identified in a 2007–2008 survey when billing donor insurance was the most common method . In two Some transplant programs may have misinterpreted the CMS manual and sought to recover LKD follow-up care costs via the MCR or recipient Medicare billing to fulfill the OPTN mandate and recover associated costs. Billing the recipient’s Medicare and using the MCR tend to occur more at smaller compared to larger volume programs, and at centers with lower vs. higher baseline follow-up performance. Thus, broader-reaching education for transplant programs is needed on billing practices that are compliant with the CMS manual. We recently outlined appropriate mechanisms for recovery of follow-up care costs , which iLiving donor candidates must be counseled in detail about the potential financial consequences of donation during the evaluation. Failing to counsel appropriately on financial risks may compromise the ability of donor candidates to provide informed consent. Living donors should also be educated on and assisted in accessing reimbursement programs for donation-associated costs, such as the US federally-funded Living Donor Assistance Center (NDLAC), and the National Kidney Registry’s “Living Donor Shield” when applicable , 37. We We also support the concept of national tracking of donor outcomes and posit that gaps in funding mechanisms may limit such efforts in the USA. Successful models to acquire information on living donor outcomes exist elsewhere, particularly in countries with socialized medicine. For example, all living donors in Switzerland are registered in the Swiss Organ Living Donor Health Registry, which collects information from general practitioners at 1-year after donation and biennially thereafter . AustralA path towards mitigating health and financial risks of living kidney donation includes maintaining a high level of adherence to complete living donor follow-up without financially burdening donors. We advocate for the revision of OACC policy to include follow-up costs as part of the commitment necessary for living donor care and safety, rather than solely for data collection. The SRTR Living Donor Collective may also provide a solution for long-term follow-up , 42, butSupplementary file1 (PDF 445 KB)Below is the link to the electronic supplementary material.


Multivariate analysis of the combined cohort regarding the impact of donor’s rs2204985 genotype indicated different risk estimates in 10/10 and 9/10 HLA matched transplantations. A subanalysis on account of HLA incompatibility revealed that donor AA genotype in single HLA mismatched cases (n = 624) associated with significantly inferior overall-  and disease-free survival . This effect was driven by a combined higher risk of relapse incidence  and non-relapse mortality . This is the first study to explore the role of rs2204985 in a clinical uHSCT setting. Our data suggest that donor rs2204985 AA genotype in combination with single HLA mismatches may adversely impact post-HSCT outcome and should thus be avoided.A common genetic variant within the T cell receptor alpha (TCRA)-T cell receptor delta (TCRD) locus (rs2204985) has been recently found to associate with thymic function. Aim of this study was to investigate the potential impact of donor rs2204985 genotype on patient’s outcome after unrelated hematopoietic stem cell transplantation (uHSCT). 2016 adult patients were retrospectively analyzed. rs2204985 genotyping was performed by next generation sequencing,  Six decades ago allogeneic hematopoietic stem cell transplantation (HSCT) revolutionized the treatment of otherwise incurable hematopoietic disorders and utmost malignant ones , 2. The Thymus function is influenced by many factors but primarily by age and gender . ParticuAlthough strongly suspected, first significant evidence regarding the implication of genetic factors in thymic function and rate of involution came only a few years ago by Clave et al. . After aAs of today, there are no published data regarding the potential impact of donor’s rs2204985 genotype on the outcome of unrelated HSCT (uHSCT). We hypothesize, based on the findings of the aforementioned humanized mouse HSCT model , that thThis study included a total of 2016 adult patients with hematologic malignancies  who received their first unrelated HSC graft  or bone marrow (BM)) between 2000 and 2013 in a German transplant center. Sample size was based on a-priori sample size calculation. Patients not achieving complete remission were not included in the cohort due to potential confounding by their increased disease burden and poor prognosis. Stem cell donor searches for cooperating transplant centers were conducted by the search unit in Ulm.All clinical data were obtained from the German registry for stem cell transplantation (DRST), a subset of the EBMT ProMISe database for German patients. Patient consent was obtained for clinical data collection and registration in the EBMT database. Consent for histocompatibility testing in patients and donors was obtained upon initiation of the unrelated donor search. Treatment decisions along with follow up information from day 0, day 100 and yearly afterwards were collected by the cooperating transplant centers based on EBMT surveys (MED-AB-Survey). Missing data in the EBMT files was retrieved directly from the centers when possible. The study was approved by the ethical review board of the University of Ulm (project number 341/17).The disease status prior to transplantation was classified according to definitions previously used by the EBMT study group . MyeloabHigh resolution HLA-typing  for the gene loci HLA-A, -B, -C, -DRB1, -DQB1 and –DPB1 was readily available. Only transplant pairs with maximum one single mismatch for the loci HLA-A, -B, -C, -DRB1 and -DQB1 (i.e. 10/10 or 9/10 HLA-matched) were included in the study. HLA-DPB1 mismatches were checked for permissiveness by applying the T-cell epitope (TCE) algorithm as previously described .Genotyping of the rs2204985 in both patients and donors was performed by next generation sequencing (NGS) on an Illumina Miseq platform using DNA samples from unrelated donor search. The DNA sequence of the targeted intergenic region within the TCRA-TCRD locus for the design of the primers was retrieved from the NCBI SNP database .The olig- fwd 5'-3': GCCTGAATTTAGCAACTGGGAGGAG- rev 5'-3': GTTTCCCACTGAGGAGTTTGTCGGG. Sequencing data analysis was carried out by the open source program for statistical computing “R”, version 4.1.2 .Overall survival (OS), disease-free survival (DFS), non-relapse mortality (NRM), relapse, acute graft versus host disease (aGvHD) grade II-IV and chronic GvHD (cGvHD) were set as clinical outcome endpoints. Overall survival was defined as time to death from any cause or last follow-up. Disease-free survival was defined as time to treatment failure with death or relapse counting as events. Non-relapse mortality was defined as time from transplantation until any cause of death without previous relapse and disease relapse serving as competing risk. Relapse incidence was defined as time to the event of disease recurrence. This event was summarized by cumulative incidence estimate with death from other causes as the competing risk. The cumulative incidence of aGVHD grade II-IV, according to consensus grading , and cGVU-test for continuous variables. For OS and DFS survival, Kaplan-Meier analysis with log-rank testing was used. Comparison of cumulative incidence for NRM, aGvHD, cGvHD and relapse was done using competing risks analysis as proposed by Fine and Gray [p = 0.05. The open source program for statistical computing “R” (R Core Team), version 4.1.2 was used for all the statistical analyses.Statistical analysis of patient characteristics was performed by chi-squared test or fisher´s exact test for categorical and Mann-Whitney-and Gray . Cox’s pn = 519) of donors and 26.2% (n = 528) of patients carried the AA genotype. No differences regarding the donor rs2204985 genotype frequencies were observed between 10/10 and 9/10 HLA matched HSCTs. Furthermore, the distribution of other clinical predictors on account of donor rs2204985 genotype was similar within the 10/10 and the 9/10 HLA matched group, respectively. These data are summarized in Table The cohort consisted of 1392 10/10 (69.0%) and 624 9/10 (31.0%) HLA matched transplant pairs. With respect to rs2204985, three genotypes were identified . Overall 25.7% (p = 0.024) between patients transplanted with AA and AG/GG grafts, while no statistical significance was reached as to OS (long-rank p = 0.121) for the complete cohort. Nevertheless, the respective Kaplan-Meier curves were indicative of a slightly worse outcome correlating with the donor AA genotype (see Supplementary Data n = 624) revealed that donor AA genotype associated with markedly inferior OS  which are visually displayed as forest plots in Fig. p < 0.001. The respective Kaplan-Meier curves are presented in the Supplementary Data  compared to that of female ones . The results of these multivariate models are presented in Supplementary Table Regarding the effect of donor rs2204985 genotype on the primary outcome endpoints, weakly significant differences were observed in the DFS analysis (long-rank p = 0.048) and NRM . Similar results were seen in the multivariate analyses for the two respective endpoints. No association was found between donor rs2204985 genotype and risk of acute or chronic GvHD. The results of the univariate analyses for NRM and RI are displayed in Fig. Analysis of the secondary clinical endpoints NRM and RI revealed that the adverse effect of donor AA genotype on survival was driven by a combined higher risk of RI (1Y after HSCT: 29.3% vs 18.3%; 5Y after HSCT: 36.7% vs 29.9%, Optimal T cell immune reconstitution after HSCT is decisive for clinical success, as impaired thymic recovery has long been associated with increased risk of opportunistic infections, transplant-related morbidity and recurrence of primary disease . It was While analysis of the combined cohort did not confirm the initial hypothesis, subanalysis on account of HLA compatibility revealed that donor rs2204985 genotype was a significant predictive factor of outcome in single HLA mismatched cases. In contrast, no significant impact was identified on any outcome endpoint in the 10/10 HLA matched subgroup. One hypothesis for this difference observed between these two cohort subgroups could be that this effect becomes more relevant in an already endangered thymic milieu. It has been repeatedly reported that HLA mismatched HSCTs associate with a higher risk of GvHD and transplant-related mortality , 22. ThiAnother important finding of this study was that the detrimental effect of donor AA genotype appeared to be conferred by a combined higher risk of NRM and RI. Given that no differences regarding the incidence of GvHD were observed with respect to this parameter, it is reasonable to assume that higher infection rates may account for the increased NRM. Although, we have no complete data regarding infection incidence, the higher NRM and relapse rates observed in patients receiving AA grafts are consistent with the assumption that this genotype adversely impacts the T cell recovery. Last, GvHD and especially its chronic form have been found to correlate with severe T cell immunodeficiency –26. In oLimitations of this study constitute missing data regarding infection incidence rates as well as actual measurement of T cell reconstitution surrogate markers like sjTRECs or βTRECs in patients before and after HSCT. Missing data was present for CMV status (see Table In conclusion this is to our knowledge the first study to date investigating the potential role of the donor genetic determinant of thymopoiesis, rs2204985, in the outcome of patients receiving unrelated HSC grafts. Our data suggest that donor rs2204985 AA genotype in combination with single HLA mismatches may adversely affect the outcome of HSC transplanted patients and should therefore be avoided. Older male patients receiving single HLA-mismatched HSC grafts are expected to benefit the most from such optimized donor selection. It is of note that one in four unrelated donors of Caucasian origin is expected to carry the AA genotype. A weaker relapse and –presumably- infection control due to compromised T cell reconstitution as a result of the unfavorable donor AA genotype may account for these findings. Confirmatory studies in larger independent cohorts are warranted before final conclusions are drawn.Supplement


While reports on the long-term pathology in mismatched allografts have been focused on the donor and recipient body surface area, evidence is emerging to support donor-recipient age difference as an additional prognostic factor. Most reports are based on pediatric recipients receiving older/bigger allografts. Here, we describe three cases with age mismatch including two cases of adult patients receiving pediatric allografts and a third case of a younger patient receiving an allograft from an older donor exhibiting findings not described in extant literature. Each of these cases exhibits unique changes seen in mismatched donor-recipient size/age post-transplant pathology. These non-rejection changes should be suspected in cases of donor-recipient size/age mismatch. In cases of allograft function decline, a full biopsy workup, including electron microscopy, should be considered. Nonrejection-related histopathologic and ultrastructural changes exhibited in renal allografts in the setting of acute/chronic allograft dysfunction are of utmost importance to transplant surgeons, nephrologists, pathologists, and transplant care providers. These changes not only provide the patient care team insights into the health of the allograft, but they also direct patient care. The spectrum of nonrejection histopathologic changes in kidney allografts is a testament to the improvements in human leukocyte antigen (HLA) identification techniques and immunosuppressive therapies available to patients . AdditioThere is a growing body of literature surrounding the changes exhibited by allografts in the setting of donor-recipient size mismatch (i.e. the donor and recipient exhibit discrepancies in either body size or kidney size). Most research in this area has focused on the use of body surface area (BSA) or body mass index (BMI) to predict allograft outcomes and describe non-HLA donor mismatches -9. HowevBased on the experiences from our institution, we identified three cases of age mismatch exhibiting unique histopathologic and/or ultrastructural changes not previously described. Of these three cases, we describe two cases of adult patients receiving a pediatric kidney allograft and one where a pediatric patient received an adult allograft. Here, we describe these three cases and discuss the potential repercussions of this limited case series on the field as a whole.This article was previously presented as a meeting abstract at the 2022 American Society of Nephrology (ASN) Kidney Week Meeting on November 5, 2022.Case 1A 60-year-old female with a history of end-stage renal disease (ESRD) secondary to hypertensive nephrosclerosis (HNS) received bilateral kidneys en block from a five-day-old donor Table . The patCase 2A 33-year-old male with a history of ESRD secondary to HNS received one kidney from a 14-year-old female donor Table . No chanCase 3A 21-year-old female with ESRD of an unknown etiology received en block bilateral kidneys from a 74-year-old female donor Table . 1057 daNone of these patients exhibited T-cell or antibody-mediated rejection evident by minimal to mild T-cell infiltrate on biopsy and negative donor-specific antibodies (DSA) and C4d stain; immunostain for polyomavirus were negative and all other immunofluorescence stains were also negative for immune complex glomerulonephritis.In kidney allograft recipients, an acute decline in allograft function is worrisome and the etiologies for such a decline are broad including rejection and infection; however, in patients with a known donor-recipient size mismatch, other subtle structural changes to glomerular structures should be considered. In this limited case series, we described three unique donor-recipient kidney transplant scenarios all of which exhibited distinct histologic/ultrastructural findings and clinical outcomes.in vivo studies that the glomerular immaturity resolved in the months following transplantation [Additionally, this case series explores scenarios that are not common in clinical practice, like case 1. Prior studies exploring clinicopathologic findings in age/size mismatched donor-recipient pairs have found that transplants from younger donors were associated with the same if not better outcomes than older donor pairs ,11. Howeantation -15.The second case in this series brings up multiple donor-recipient factors that are not often explored. This case had limited data on the donor except for the age, sex, and size of the kidney. There was no donor BMI or BSA available which limits correlation with extant size mismatch literature, and though the age mismatch between this donor-recipient pair was not pronounced like the other cases, the donor was still classified as a pediatric donor which correlated with the suspected immaturity of the glomerular basement membrane. There was also a donor-recipient sex mismatch. Donor-recipient sex mismatch has previously been correlated with poorer outcomes ,17. AlthIn keeping with previously reported data, this study redemonstrated the previously reported data suggesting age, in particular advanced donor age, were associated with poorer outcomes . The mosTaken together, there are numerous factors that should be considered when assessing recipient allograft function in addition to rejection and infection. Though donor-recipient mismatches cannot be modulated in the post-transplant period, knowledge of the mismatch can help guide the interpretation of renal histologic and ultrastructural findings in the renal allograft. Additionally, this study highlights the importance of a thorough workup of acute renal allograft dysfunction, including histologic and ultrastructural analysis in kidney transplant recipients. This study also highlights the importance of further research into the pathophysiologic changes that occur in patients who have a donor-recipient age and/or size mismatch. Though this study is limited by its sample size, the findings are unique and warrant more large-scale exploration to determine the full range of histologic and ultrastructural findings in cases of age and/or size mismatches between donors and recipients.


The number of studies on the variations of the branching of the TT is scarce, and those works that treat about the different types of the said trunk are oftentimes inconsistent. Therefore, the authors of the present study would like to propose a set of five types of TT, which were created based on observations of 41 computed tomography angiographies (82 TTs). To establish the anatomical variations, their prevalence, and morphometrical data regarding the TT and its branches, a retrospective study was performed. The results of 55 consecutive patients who underwent neck and thoracic computed tomography angiography (CTA) were analyzed. The analysis was performed on a total of 82 TTs of 41 patients, aged 15 to 82 years , of which 16 (39.0%) were females, and 25 (61.0%) were males. Initially, 11 types of variations were evaluated, of which types 1–4 constituted 89.0%. Furthermore, a new method of classification of the anatomical variations of the TTs has been established. In this study, the variety of the branching and morphology of the TT was presented, proposing its novel classification based on the five most commonly prevalent types. Types 1 and 2 were the most common, with a prevalence of 26.8% each. This work also provides physicians with crucial data about the morphology of the TT and its branches, which can surely be of use when performing endovascular or reconstructive procedures in the cervical region. The thyrocervical trunk (TT) arises from the anterosuperior aspect of the first part of the subclavian artery (SA), medial to the anterior scalene muscle. Anatomy textbooks describe the TT as having four branches, with the largest and most important being the inferior thyroid artery (ITA). The other branches of TT are the ascending cervical artery (ACA), the suprascapular artery (SSA), and the transverse cervical artery (TCA) . Additionally, a thorough analysis of the morphology of the trunk and its branches was conducted. The main goal of this paper was to provide an up-to-date extensive description of this highly variable structure and, with that knowledge, aid in diminishing potential surgical complications associated with the TT and its branches.The research protocol was submitted for assessment and approved by the Bioethical Committee of the Jagiellonian University, Cracow, Poland (1072.6120.51.2022). Further stages of the study were carried out in accordance with the approved guidelines.n = 25) were excluded due to significant artifacts. The remainder (n = 3) were excluded due to a significant lack of contrast filling the entire arterial system. Eventually, forty-one patients met the required criteria. All patients had their TTs visualized bilaterally, which gave a total of 82 TTs in the final analysis.To establish the anatomical variations, their prevalence, and morphometrical data regarding the TT and its branches, a retrospective study was performed. The results of 55 consecutive patients who underwent neck and thoracic computed tomography angiography (CTA) were analyzed in the Department of Radiology of the Jagiellonian University Medical College, Cracow, Poland, in March 2022. The result of each patient was analyzed bilaterally. Therefore, a total of 110 TTs were initially evaluated. Exclusion criteria were set as follows: (1) neck or/and thoracic trauma affecting the course of the TT and/or its initial branches, (2) significant artifacts that prevented accurate and precise imaging and/or measurement of the TT and/or its initial branches, (3) low-quality and illegible images, (4) significant lack of filling the whole arterial system with contrast and (5) significant influence of other structures, like anterior scalene muscle, on the course of the TT. Defects, which met the exclusion criteria, but included only one side of the CT, without interference with the contralateral side, did not disqualify the whole CT but only the affected side. Hence, of the initial 110, a total of 28 TTs were excluded to minimalize a potential bias. Of the 28, a great majority . The main CT imaging parameters were as follows: collimation/increment: 0.625/0.3 mm; tube current: 120 mAs; field of view: 210 mm; matrix size: 512 × 512.®, Guerbet, France). CT data acquisition was triggered using a real-time bolus-tracking technique  with the region of interest (ROI) placed in the ascending aorta. The CM was intravenously injected using a power injector at a flow rate of 5 mL/s. This was immediately followed by the injection of 40 mL of saline solution at the same flow rate. Following injection of CM and saline, image acquisition was automatically started with a 2 s delay when the attenuation trigger value reached a threshold of 120 Hounsfield units (HU). Scanning was performed in the caudocranial direction. The CTA examination was started at the level of the aortic arch up to the circle of Willis.All of the patients received intravenous administration of contrast material at a dose of 1 mL/kg (standard dose). A non-ionic contrast medium (CM) containing 350 mg of iodine per mL was used  software was used. 3-dimensional (3D) reconstructions of each scan were developed, employing a set of settings, severally adjusted to each scan. A volume rendering opacity range oscillated from 25 to 80 HU for the lower limit and up to 3070 HU for a higher limit. The range was individually adjusted to each TT after a visual investigation.At the beginning of each evaluation, the authors ensured that each TT, its branches, and its close anatomical area were fully visualized. Subsequently, each branch of the TT was identified by following its course. The direction of the TT and a set of its branches with their arrangement was evaluated and descriptively noted. Afterward, a set of measurements was executed on each TT. The literature lacks a clear and unequivocal definition of TT and how its length should be measured; thus, the authors decided that the TT length will be established as the shortest distance followed over the surface of the TT (from the SA to the branching off point of its first branch). Therefore, the authors recognize the TT as an arterial branch of the SA, which ends at the sprouting point of its first branch. Therefore, maximal diameter and ostial area were also measured at both the origin and endpoints of the TT. The distance between the TT and the vertebral artery (over the surface of the SA) was also established. Maximal diameters and ostial areas of each TTs branches were measured. Additionally, the shortest distance between each TT branch was set. Figure p < 0.05. U Mann–Whitney and Wilcoxon signed-rank tests were used to establish potential differences between groups. Spearman's rank correlation coefficient was used to determine possible correlations between the parameters.Statistical analysis was performed with SATISTICA v13.1 . Frequencies and percentages presented qualitative features. The Shapiro–Wilk test was used to assess the normal distribution. Quantitative characteristics were presented by medians and upper and lower quartiles , as well as means and standard deviation (SD), depending on the verified normality of the data. Statistical significance was defined as The analysis was performed on a total of 82 TTs of 41 patients, in age from 15 to 82 years old , of which 16 (39.0%) were female, and 25 (61.0%) were males. All subsequent results are presented in relation to the number of TTs instead of patients.The branching variation of each TT was deeply analyzed. Initially, 11 variations types were evaluated, of which types 1–4 constituted 89.0%. Therefore, in response to the literature lacks, a classification method of the TTs was set and consists of 5 different types. The five types were set as follows: (1) Type 1: TT originated from the SA. TT divided into SSA, which sprouted as the first branch, and a further common trunk for ITA and TCA; 2) Type 2: TT originated from the SA. TT divided into ITA, SSA, and TCA without a common trunk for any of the arteries; 3) Type 3: TT originated from the SA. TT divided into a further common trunk for SSA and TCA, which sprouted as the first branch, and an ITA; (4) Type 4: TT originated from the SA. TT divided into ITA and TCA; (5) Type 5: Any other arrangement. Figure  Type 2:  Type 3: 2 . The median maximal diameter at endpoint of the TT was found to be 3.8 mm . The median ostial area at endpoint of the TT was found to be 9.8 mm2 . According to the results of the sign tests, a statistically significant difference (p < 0.05) of both maximal diameters and ostial areas was established between the origin and endpoints of the TT. The detailed results are presented in Table The TT median maximal diameter at origin was 4.5 mm . The median TT ostial area at origin was 12.7 mmp = 0.02); (2) ostial area of the TT at origin (p < 0.001); (3) maximal diameter of the TT at endpoint (p < 0.001); (4) ostial area of the TT at endpoint (p < 0.001); (5) maximal diameter of the ITA, branching off from the TT (p < 0.001); (6) ostial area of the ITA, branching off from the TT (p < 0.001); (7) ostial area of the SSA, branching off from the TT (p = 0.02); (8) maximal diameter of the TCA, branching off from the TT (p = 0.02); (9) ostial area of the TCA, branching off from the TT (p < 0.001). There were no statistically significant differences in other groups. Detailed results concerning sex are gathered in Table Potential sexual dimorphism was also evaluated. Statistically significant differences between females and males were established regarding (1) maximal diameter of the TT at origin ( 0.001);  maximal p > 0.05) regarding patients’ side and direction of the TT in any category. Co-occurrence of the four main types was established and the results are gathered in Table Additionally, potential differences in the dimensions regarding the patients’ side and direction of the course of TT were analyzed. There were no statistically significant differences , (2) ostial area of the TT at origin , (3) distance between the vertebral artery and the TT , and (4) ostial area of the internal thoracic artery, branching off from the TT  were obtained. No other, statistically significant correlations were established.Potential correlations between the age of the patient and each dimension were analyzed. The correlations between patients’ age and (1) maximal diameter of the TT at origin . The TT consisted of the ITA, SSA, and TCA in types 1–3, showing that these branches quite consistently originate from the TT. However, type 4 (13.41%) represents a TT where the SSA does not originate from the said trunk but rather sprouts directly from the SA or other surrounding arterial entities. Interestingly, in some cases, the SSA was not found at all. Nevertheless, this type is relatively less prevalent than types 1–3. Anatomical textbooks describe the TT as giving off four branches; the ITA, TCA, SSA, and the ACA . Chen et al.  describeThe sternocleidomastoid flap has been a solid option for repairing defects of the floor of the mouth, mandible, and pharynx Ariyan . LeclereThe present study undoubtedly has some limitations. Although the size of the study group used in the current paper is the largest among imaging studies concerning the TT, larger population-based research is still warranted to discern the true prevalence of its variants. Additionally, radiological imaging only allows one to evaluate hemodynamically efficient arteries. Therefore, this can be a relatively big source of bias when assessing anatomical variations of the TT, and other arterial entities. The particular branches of the TT and their anastomosis with surrounding vascular structures should be further investigated in the future due to their potential clinical significance in endovascular and reconstructive procedures.In this study, the variety of the branching and morphology of the TT was presented, proposing its novel classification based on the five most commonly prevalent types. Types 1 and 2 were the most common, with a prevalence of 26.8% each. Type 1 consisted of a TT that gives off a SSA, and a common trunk between ITA and TCA. Type 2 presented a TT that gave off these three aforementioned branches directly, without a common trunk for any of them. This work also provides physicians with crucial data about the morphology of the TT and its branches, which can surely be of use when performing endovascular or reconstructive procedures in the cervical region.


Heart rate turbulence (HRT) is a characteristic heart rate pattern triggered by a ventricular premature contraction (VPC). It can be used to assess autonomic function and health risk for various conditions, e.g., coronary artery disease or cardiomyopathy. While comparability is essential for scientific analysis, especially for research focusing on clinical application, the methodology of HRT still varies widely in the literature. Particularly, the ECG measurement and parameter calculation of HRT differs, including the calculation of turbulence slope (TS). In this article, we focus on common variations in the number of intervals after the VPC that are used to calculate TS (#TSRR) posing two questions: 1) Does a change in #TSRR introduce noticeable changes in HRT parameter values and classification? and 2) Do larger values of turbulence timing (TT) enabled by a larger #TSRR still represent distinct HRT? We compiled a free-access data set of 1,080 annotated long-term ECGs provided by Physionet. HRT parameter values and risk classes were determined both with #TSRR 15 and 20. A standard local tachogram was created by averaging the tachograms of only the files with the best heart rate variability values. The shape of this standard VPC sequence was compared to all VPC sequences grouped by their TT value using dynamic time warping (DTW) in order to identify HRT shapes. When calculated with different #TSRR, our results show only a little difference between the number of files with enough valid VPC sequences to calculate HRT (<1%) and files with different risk classes . In the DTW analysis, the difference between averaged sequences with a specific TT and the standard sequence increased with increasing TT. Our analysis suggests that HRT occurs in the early intervals after the VPC and TS calculated from late intervals reflects common heart rate variability rather than a distinct response to the VPC. Even though the differences in classification are marginal, this can lead to problems in clinical application and scientific research. Therefore, we recommend uniformly using #TSRR 15 in HRT analysis. With a simple point-of-care investigation of the heart rate, it is possible to estimate the condition and prognosis of patients. A possible method is HRT, which is a naturally occurring phenomenon that arises after a VPC : The chaMARS and CardioDay, which both have already been used for HRT analysis (Because of this dependency on the autonomic nervous system (ANS), HRT can be used as a marker for autonomic health . Studiesanalysis –15.For HRT, there are three main parameter values that can be calculated ” Consensus . HoweverUntil now, no study has analyzed the difference in HRT parameter values when calculated from different #TSRRs. A higher #TSRR increases the risk of artifacts and other arrhythmias to lie in the required calculation range which leads to an exclusion of the VPCSs . Conversely, with a lower #TSRR, these compromising intervals may lie outside of the needed calculation range for some VPC snippet, i.e. all RR intervals surrounding the VPC used for HRT calculations (VPCSs) which would make them shorter but valid sequences for HRT assessment. In consequence, a change in #TSRR can lead to a selection of different sets of VPCSs and, therefore, affect all HRT parameter values of a person.via the baroreflex, it is plausible that the reaction should arise without any delay. This means that the slope that represents the turbulence should arise first in direct proximity to the compI and second always after a similar time period. Accordingly, TS calculated from either only late intervals or intervals with widely differing indices may only describe random fluctuation rather than a reaction of the ANS. Because TT describes the localization of TS, it can be used to test this assumption.Since HRT is triggered by a VPC In this article, we analyze two hypotheses:There is a distinct difference in HRT parameters when calculating HRT with #TSRR 15 or 20.We test this on a large free-access data set from Physionet and compare the resulting HRT parameters and classes.Hypothesis 1: Persons with a high TT value or a high TT variability do not show HRT, but seemingly random fluctuations, i.e., heart rate variability (HRV).Hypothesis 2: via HRV parameters. This standard VPCS is then compared with sequences that have been averaged from VPCSs sorted for their respective TT value.We, therefore, create an averaged ideal standard VPCS (stVPCS) with distinct HRT by filtering the Physionet data set physionet.org   (https://CRAN.R-project.org/package=RHRT) and on github (https://github.com/VBlesius/RHRT). The default methodology of filtering, calculation, and classification is done as suggested in Blesius et al.  . RHRT prs et al. , which ms et al. . In cont.1 . RHRTstats package (v 4.1.1). RHRV version 4.2.6 was used to calculate HRV parameter values. For the Poincaré filter and the data preparation of the HRV calculation, we used the packages, geometry (v. 0.4.5), smoother (v. 1.1), and purrr (v. 0.3.4). To compare the stVPCS with the averaged VPCSs dtw (v. 1.22.3) was used. This package provides functions for DTW, which is an algorithm to compare similarities of two sequences: All points of the first sequence are matched to the points of the second one whereas points can be matched to multiple other points. The only limitation is that the first and last points have to be matched to each other, respectively, and mapped indices have to be increasing, meaning that there may not be overlapping matches. Dynamic time warping (DTW) can calculate a matching score, which was used in this analysis.Statistical differences between data sets were calculated with the RHRT and the settings numPostRRs = 15 (TSRR15) and numPostRRs = 20 (TSRR20). Additionally, we created a data set from the valid VPCs included in the analysis with #TSRR 20, but calculated HRT results with numPostRRs = 15 (TSRR15∩). This leads to a data set with identical VPCs but shorter VPCSs that allows comparison without considering filtering effects. We then calculated the arithmetic mean and SD of HRT parameter values and classified the data into HRT0-2 and HRTA-C. Depending on the number of files with enough valid VPCSs, either a Welch's unequal variances t-test or a paired Student's t-test (both with t.test of the stats package) were used to detect differences between the sets of parameter values.We assessed HRT of all files twice with the default parameters of The purpose of the analysis was to compare VPCSs sorted on the basis of TT with an stVPCS. Since HRT is most pronounced in persons without autonomic dysfunction as reviewed in Bauer et al. , we needAt first, all files that were shorter than 20 h or longer than 28 h were discarded. Since HRT is correlated with the circadian rhythm of the ANS, it should be calculated from measurements with a length as close as possible to 24 h or its multiple if procurable.Applying the following filters—especially the Poincaré filter—to full measurements can lead to the discarding of basically valid data due to temporary irregularities in heart rhythm. Furthermore, variability in the length of the measurements can lead to a bias in HRV parameter values . TherefoAs a next step, we used a filter that quantifies the data distribution within a Poincaré plot: this non-linear method of HRV analysis plots data points of a time series against their respective successors to visualize the beat-to-beat variability of RR intervals. Any pathology that affects the length of RR-intervals causes distinct patterns in the Poincaré plots. These patterns have been systematically analyzed and categorized by Esperer et al.  and were“island” patterns consisting of four or nine roundly shaped clusters that are connected to atrial tachycardia or atrial flutter, both with the atrioventricular block .“fan” patterns which look like broader spread torpedos or triangles and occur in persons with atrial fibrillation or multifocal atrial tachycardia .“lobe” patterns consisting of one central and several eccentric clusters which occur due to frequent VPCs or atrial premature contractions .For our analysis, we focused on filtering out Poincaré plots with island and fan patterns since they are specific for different kinds of atrial arrhythmia and atrioventricular block. This leaves torpedos, comets, and lobe patterns that show sinus rhythm or possible VPCs. Since high-frequent VPCs are an indicator for high risk –43, we fThe last filtering step is based on the HRV time and frequency domain of the data. We calculated the following HRV parameters with the RHRV package: SD of the averages of all normal sinus rhythm intervals (SDNN), SD of the averages of all normal sinus rhythm intervals in any 5 min segments (SDANN), triangular index, i.e., the total number of all normal sinus rhythm intervals divided by the maximum of the interval frequency distribution, square root of the mean of the squared successive differences between adjacent RR intervals (RMSSD), very low frequency power, low frequency power, high frequency power, and the ratio of low and high frequency power. For every parameter, all files were ranked for their HRV values, respectively: Files with a value that exceeded three times the interquartile range, i.e. the difference between the upper and lower quartiles (IQR), were considered to be outliers. If their values were greater or less than the median±3·IQR they were given the penalty score “–1.” For all HRV parameters, high values were assumed to be better, only for triangular index lower values were scored higher. Accordingly, the files were sorted by their HRV value and the best 20% received the score “1,” while the remaining received “0.” After this scoring process for every HRV parameter, the scores were summed up for every file, leading to possible scores from –8  to 8 . On the basis of the scores, the highest ranking 20% of the files were used to create the stVPCS.RHRT package. All HRT calculations were done with the default settings of the RHRT package except for “numPostRRs” (#TSRR) for which we used 20 intervals because the longer range is the maximum of commonly used #TSRR and provided more intervals for later comparisons. For each file, the averaged VPCS was used as the basis to calculate an overall averaged VPCS (stVPCS).Heart rate turbulence of all top ranking files was calculated with the RHRT package except “numPostRRs” for which we used 30 intervals to ensure a wide range of possible TT values.We calculated the HRT parameter values of every file in our databases with the default settings of the via DTW, we tested two methods: First, we matched the standard sequence dynamically to the averaged sequences with the default step pattern “symmetric2” of the dtw function. Second, we removed the leading intervals of the standard sequence before the TT to receive a sequence that only consists of the intervals that shape the TS and all following intervals. The averaged sequences were cut accordingly and shortened to fit the standard sequence. The standard sequence was matched to all averaged sequences index by index with the dtw step pattern rigid.We extracted the RR intervals in a VPCS following the compI (postRRs) of every averaged VPCS, grouped them based on their respective TT, and calculated an averaged postRRs sequence for every TT. For the next step of matching the postRRs of the stVPCS to every averaged postRRs sequence As a measure of the variability of TT within a file, we calculated the SD of TT (TTSD) and the Pearson correlation coefficient for TT and TTSD.The number of files that included enough VPCs to calculate HRT was similar with #TSRR 15 (870 files) and #TSRR 20 (862). Similarly, the number of files sorted in different HRT classes were similar with #TSRR 15 and 20 for HRT0-2  as well as HRTA-C . Of the 862 files from which HRT parameters could be calculated in both analyzes, 43 and 55 files were differently classified into HRT classes HRT0-2 and HRTA-C, respectively refer to , 3.p = 4.9·10−4) and 0.4 , respectively. The most differing values of the unpaired t-tests were the TT values of TSRR15 and TSRR20 with CI –0.5 to 0.03 and p = 0.08. The p-values of all other unpaired t-tests ranged from 0.71 to 0.99 with differences of the arithmetic means between 0.001 and 0.043. A noticeable difference is the high number of TT values that were NR with #TSRR 20 (79) compared to both #TSRR 15 analyzes .When comparing the HRT parameters of TSRR15, TSRR20, and TSRR15∩ , the most influenced parameter is TT with 5.47 ± 2.38 (TSRR15∩) and 5.75 ± 3 (TSRR20) refer to . The TO Of the 1,080 annotation files included in the analysis, 70 files were shorter than 20 h and 1 file longer than 28 h. Thus, they were excluded.Of the 1,009 remaining files, 652 were removed through the Poincaré filter, leaving 357 files.After HRV parameter calculation and averaging 33 of the files contained at least one outlier. The median score of the files was 1 with a minimum of –7 and a maximum of 7.From the best 20% (71 files), HRT was calculated. In 24 files, no or too few valid VPCSs could be found. While most of the remaining 47 files showed a distinct HRT pattern refer to , some diRHRT package. When adding TT to the classification, 40 files had HRT class HRTA and 7 files HRTB, whereas the classification from 10 files  are marked as unreliable.After classification, 41 of the files used for the stVPCS had HRT class HRT0, 5 files had HRT1, and 1 file HRT2. Of these files, 7  are marked as not reliable by the Because stVPCS should be used for comparison as the ideal HRT shape, it is important that it shows a pronounced reaction to the VPC and low risk HRT parameters. The sequence averaged from all 47 VPCSs showed a distinct HRT pattern . The sequences with TT 2 to 4 considerably line with the stVPCS, while the sequences flatten out continuously with rising TT.Analogously to the comparison with the full sequences, the averaged VPCS with TT 3 matched the best with the stVPCS after cutting. The difference of the VPCS of TT 1 to the best sequence is similar to the analysis without cutting (full VPCSs: Diffp < 0.005, refer to The TT and TTSD within a file were significantly correlated (ρ = 0.26, In our analysis of 1,080 files, only 8 could additionally be classified when using a lower #TSRR. Furthermore, there were 43 and 55 files that changed classification due to #TSRR in the classification systems HRT0-2 and HRTA-C, respectively. The switches were both within HRT classes as well as between an HRT class and NR. Interestingly, a high number of these files switched from an HRT class when calculated with #TSRR 15 to NR with #TSRR 20, meaning that a higher #TSRR leads to more variability in the data.The same can be seen for TT values, where a higher amount of values was NR with #TSRR 20. This can be explained by the majority of the files with not reliable TT values showing a very shallow tachogram in visual analysis. With no distinct IL increase, random fluctuations have a stronger influence on the location of the steepest slope, thus increasing the variability of TT. Furthermore, longer VPCSs lead to a higher number of possible TT values and therefore higher variability. This high variability combined with a lower number of VPCSs results in non-significant results in the reliability check and, thus, a higher number of files with NR TT.The only HRT parameter with distinctly differing values is TT which is to be expected with higher #TSRR. This leads to the differences in classification being marginal with less than 1% more classifiable files and 5–6% files changing the resulting classes. However, in clinical settings, even small numbers of patients that cannot be classified or are differently classified based on methodological variances are unfavorable-especially if this could be avoided by uniformly adjusting one parameter.The stVPCS received through our pipeline shows a distinct HRT pattern with the HRT classes of HRT0 and HRTA, which imply the least possible risk. The tachogram of our stVPCS is similar to tachograms showing characteristic HRT patterns in reviews , 19, 22.Apart from TT 1, the tachograms with low TTs showed a similar pattern to the stVPCS (check the Nevertheless, TTSD is lower with lower TT values meaning that in persons with low TT the fastest slope occurs in a narrower range. Again, a narrower range implies a steady underlying mechanism that causes turbulence within a distinct time interval while a high fluctuation of TT values within a person suggests randomness. Therefore, TTSD may possibly be used as a measurement for the reliability of TT as well as TS and nTS.Our data suggest, that only measurements with TT 2 to approximately 6 show distinct HRT. This can be seen in the DTW plots in combination with the median values of the HRT parameters grouped by TT. We recommend visually inspecting all measurements with TT 1 or higher than 7 to ensure the validity of the HRT parameters. Admittedly, manual visual inspection introduces unpreventable human error and, thus, variability to the analyzes, which should be avoided wherever possible. Therefore, DTW may be a method to ensure a reliable reaction to the VPC by comparing the progress of the tachogram of a person to a standard tachogram established from a healthy peer group. Additionally, stVPCS could be generated for different pathological conditions, which would enable using HRT not only for risk assessment but also as part of diagnostics. Possibly, DTW could replace the original HRT parameters, because it analyzes the tachogram as a whole instead of reducing it to selective parameters that can be biased as seen in this study with TS. Using DTW for HRT analysis needs establishing the mentioned stVPCSs through a sufficiently large data set with fitting health conditions and with respect to factors influencing HRT like age or circadian rhythm . A similOur first hypothesis suggested a distinct difference in HRT values when calculated with different #TSRRs. Although we could show a difference in the number of assessable HRT values and HRT classes, the differences are not as distinct as we expected with < 1% and 5–6% affected files, respectively. However, no variable risk assessment is obstructive in clinical diagnostics, especially if the results obtained from the same person vary solely based on a difference in methodology. Consequentially, the question remains which of the commonly used #TSRR are optimal for the analysis.Therefore, our second hypothesis tackled the question of whether high TT or TTSD values do not show actual HRT but random fluctuation. The tachograms of the files with different parameter values based on #TSRR show, that these differences are mainly based on variability due to different sets of VPCSs used for calculation instead of actual HRT at the end of the VPCSs. Furthermore, the comparison of the stVPCS with averaged VPCSs grouped by TT verifies that with increasing TT the response to the VPCs decreases considerably. The same result is implied by HRT values passing their respective thresholds to an increasing degree with rising TT.While some of the files included in the stVPCS derive from NSR2DB that is defined as subjects with “normal sinus rhythm,” the vast majority of files belong to the CRISDB, LTSTDB, LTAFDB, and CHF2DB that include ECGs of persons after myocardial infarction, with ST-segment anomalies, atrial fibrillation, and congestive heart failure, respectively. Therefore, it is probable that files were included from persons with diagnosed pathologies that are not visible in the used autonomic markers and may bias our results. It would be interesting to repeat the analysis with data from healthy subjects to examine a possible difference in the stVPCSs.Due to the lack of meta-data in the user databases, we did not analyze any influence of medication on #TSRR. To our knowledge, a temporal change in the HRT response has not been studied so far. The focus of HRT research rather lies on the strength of the response than its delay. The same goes for any response of the baroreflex: Baroreflex sensitivity has been shown to change with antihypertensive medication , 48, butIt is important to mention that our results allow conclusions about the behavior of the autonomic marker but not its predictive power. Since HRT is a risk marker for major adverse cardiac events, analysis without meta-data about the outcome of the studied patients can only be the first step and must be verified with appropriate clinical data.We recommend using #TSRR 15 for HRT analysis. The lower number of valid intervals results in a higher amount of VPCSs that can be used in the analysis as well as discarding of intervals that show random fluctuation instead of HRT. Therefore, it leads to more reliable data.https://physionet.org/content/chfdb/1.0.0/; https://physionet.org/content/chf2db/1.0.0/; https://physionet.org/content/crisdb/1.0.0/; https://physionet.org/content/excluded/1.0.0/; https://physionet.org/content/ltafdb/1.0.0/; https://physionet.org/content/ltdb/1.0.0/; https://physionet.org/content/ltstdb/1.0.0/; https://physionet.org/content/nsrdb/1.0.0/; https://physionet.org/content/nsr2db/1.0.0/; https://physionet.org/content/sddb/1.0.0.Publicly available datasets were analyzed in this study. This data can be found at: Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.VB conceived the project, implemented the analysis pipeline and performed all analyzes and literature research, created all figures and tables, and wrote the initial draft of the manuscript and revised it. CS provided technical advice in the implementation of the analysis pipeline. CS, GE, and AD reviewed the manuscript. AD supervised the project. All authors contributed to the article and approved the submitted version.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.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.


Roughly 20 different boPAG members are known but their distribution patterns and degree of glycosylation during pregnancy are not well characterized. The objective of the present study was the development of a parallel reaction monitoring-based assay for the profiling of different boPAGs during pregnancy and after gestation. Furthermore, we investigated the effects of N-glycosylation on our analytical results. BoPAGs were purified from cotyledons of four different pregnancy stages. The assay detects 25 proteotypic peptides from 18 boPAGs in a single run. The highest abundances were found for boPAG 1 in both, glycosylated and deglycosylated samples. Strongest effects of glycosylation were detected during mid and late pregnancy as well as in afterbirth samples. Furthermore, we identified different boPAG-clusters based on the observed relative protein abundances between glycosylated and deglycosylated samples. A linkage between the impact of glycosylation and potential N-glycosylation sites or phylogenetic relation was not detected. In conclusion, the newly developed parallel reaction monitoring-based assay enables for the first time a comprehensive semi-quantitative profiling of 18 different boPAGs during pregnancy and post-partum on protein level, thereby investigating the influence of glycosylation. The results of this study provide new and important starting points to address further research on boPAGs to better understand their physiological role during pregnancy and for the development of new pregnancy detection tests.Bovine pregnancy-associated glycoproteins ( PAGs) are expressed in trophoblast cells of the placenta of species within the Cetartiodactyla order and secrete in maternal blood and milk [Bovidae [Pregnancy-Associated Glycoproteins  belong to the ancient group [To date, in cattle roughly 20 different PAGs and related paralogs are known, with largely varying temporal and spatial expression patterns during gestation , 9, 10. nt group , 9, 11. nt group .The ancient boPAGs are thought to be active aspartic proteinases, whereas modern boPAGs have lost their catalytic activity due to amino acid substitutions within the binding sites , 11, 13.a-antigen  could be identified as the most abundant N-glycan in boPAGs expressed in binucleate cells [a-antigen in the beginning (before day 30 of gestation) and to the end of pregnancy causes a higher serum half-life of boPAGs at these timepoints [An unusual feature of boPAGs is their high degree of glycosylation. The expected molecular mass of the protein core without posttranslational modifications and after the removal of the signal sequence is around 37 kDa , 16. Thete cells . This N-te cells , 19, 20.mepoints . Howeverm/z ranges and known retention time windows, e.g. a set of predefined tryptic peptides as surrogates for the proteins of interest [PRM) is a targeted method where all product ions of mass-selected peptides are monitored in parallel with one ion injection and full scan mass analysis [During the last years, new mass spectrometers and data acquisition schemes for the characterization and quantification of proteins have been developed –24. In tinterest , 26. Theinterest , 26, 27.analysis , 24. Thianalysis . Furtheranalysis , 28.The aim of the present study is the development and validation of a multiplexed PRM assay for boPAGs in order to allow for the first time a comprehensive semi-quantitative profiling of boPAGs during pregnancy and post-partum. Furthermore, we investigate glycosylated and deglycosylated samples to detect possible effects of protein modifications on the results obtained using our methods. The establishment of a new method for the characterization of boPAGs on protein level will enable future studies investigating the physiological role of boPAGs during pregnancy and developing new pregnancy detection tests.The study is in accordance with the German legal and ethical requirements of appropriate animal procedures. Animals were not purposely euthanized for this study. Tissue samples were taken during the conventional slaughter process.FPLC). A scheme of the different FPLC steps can be found in S1 Fig in The detailed workflow of tissue collection and subsequent protein purification is described in Krebs et al., 2021 . In briePNGaseF) digestion to remove N-gylcans according to the manufacturer’s instructions . Therefore, 10 μg of protein extract were denatured for 10 min at 100°C in glycoprotein denaturing buffer 10x  and LiChrosolv H2O  (if necessary) in a 10 μl reaction volume. After cooling for 5 min on ice, the samples were centrifuged for 10 sec at full speed . Following this, 100 IU PNGaseF, 2 μl GlycoBuffer 2 (10x) , 2 μl 10% NP-40  and 6 μl LiChrosolv H2O were added to the samples to reach a total reaction volume of 20 μl. After incubation for 1 h at 37°C, deglycosylated protein samples were used for in gel digestion followed by mass spectrometric analysis.Aliquots of protein samples from chromatography analysis were subjected to Peptide-N-Glycosidase F (2O (glycosylated samples). In addition, NuPAGE LDS sample buffer 4x  was added to all samples, followed by an incubation for 10 min at 70°C prior loading.Purified protein samples from homogenized cotyledonary tissue of all pregnancy stages were either deglycosylated with PNGase F or left glycosylated. All samples were diluted to a final concentration of 1 μg protein/lane in either deglycosylation buffer (deglycosylated samples) or with dd H3H8O) [Gel electrophoresis was performed using NuPAGE 4–12% Bis-Tris 1.0 mm gradient gels . Gels were run at 80 V until the samples were migrated out of the wells. Afterwards the voltage was increased to 150 V to finish the run. Molecular weight standard (Protein Marker VI (10–245) prestained, Applichem, Germany) was run simultaneously. Gels were either stained with Coomassie Brilliant Blue or proteins were transferred onto nitrocellulose membrane  in transfer buffer  . The tra2O and stained with Ponceau S solution (0.1% Ponceau diluted in 5% CH3COOH) to check the protein transfer quality. Afterwards Ponceau stain was rinsed off with washes of Tris-buffered saline containing Tween 20 (TBST) .After transfer, the nitrocellulose membrane was briefly rinsed with ddHRT) for 30 min, washed three times with TBST, and probed with different boPAG antisera at a concentration of 13 μg antibody/membrane. Polyclonal boPAG antisera were produced using seven boPAG-fractions of different pregnancy stages from chromatography. A detailed description of the polyclonal antibody production is given in Krebs et al. (2021) [The membranes were blocked with 1% BSA in TBST at room temperature (. (2021) . Afterwa2O used in these experiments were purchased from Merck, Germany in LiChrosolv hypergrade.Acetonitrile and H2O, followed by incubation for 10 min at 70°C prior to loading. Gel electrophoresis was performed on a NuPAGE 4–12% Bis-Tris 1.0 mm gradient gel in an 8x8 cm vertical unit  and stained with Coomassie Brilliant Blue for visualization. For an initial shotgun analysis to determine sample purity and detect PRM peptide candidates, gels were run full distance and cut into 15 equidistant slices irrespective of staining. For the final PRM assays sample were run ~1 cm into the gel for purification purposes only and excised as a single band. Gel slices were washed 5 min at room temperature under shaking. Afterwards, the supernatant was discarded and 100% acetonitrile  added to the gel pieces. After incubation for 15 min at room temperature on a shaker (750 rpm), the supernatant was again discarded and the gel pieces were dried in a vacuum centrifuge .Both glycosylated samples from chromatography and deglycosylated samples from PNGaseF digestion were subjected to in-gel digestion with trypsin (EC 3.4.21.4). Therefore, all samples were diluted to a final concentration of 10 μg protein/lane in NuPAGE LDS sample buffer 4x, NuPAGE Sample Reducing Agent 10x  and H4H10O2S2 in 100 mM NH4HCO3, 50 min, 56°C). Following centrifugation, the supernatant was removed and the gel pieces dried using 100% acetonitrile again. Alkylation was then achieved with iodoacetamide . After removal of the supernatant, gel pieces were washed again with 100 mM NH4HCO3. Samples were dried using 100% acetonitrile again twice, and the gel pieces dried in a vacuum centrifuge. Endoprotease lysis was performed overnight at 37°C using modified porcine trypsin  in digestion buffer  at an enzyme-to-protein ratio of 1:50.Reduction was performed by incubation with dithiothreitol . 80 μl neat acetonitrile were added and further incubated 15 min at 37°C. The supernatants were transferred to fresh tubes, the gel pieces incubated again with 5% aqueous formic acid , centrifuged, and incubated again after addition of 65 μl neat acetonitrile. Subsequently, the gel pieces were centrifuged and the supernatants pooled with supernatants from the first extraction. Resulting peptide solutions were dried in a vacuum centrifuge and stored at -20°C until further use.2O2 in H2O) to a nominal concentration of 100 fmol/μl, and spiked with 100 fmol/μl Escherichia coli ß-galactosidase (EC 3.2.1.23) tryptic digest to avoid adsorption effects [Prior to MS analysis, peptide mixtures were reconstituted in loading buffer , and AGC target of 1*10e6 and a maximum fill time of 60 ms. Up to the 12 most abundant peptide precursors of charge states 2 to 5 above a 2*10e4 intensity threshold were then sequentially isolated at 2.0 FWHM isolation width, fragmented with nitrogen at a normalized collision energy setting of 25%, and the resulting product ion spectra recorded at a resolution setting of 17,500 FWHM, and AGC target of 2*10e5 and a maximum fill time of 60 ms. Selected precursor m/z values were then excluded for the following 15 s. Two technical replicates per sample were acquired.For initial protein identification, samples were enriched on a self-packed reversed phase-C18 precolumn  and separated on an analytical reversed phase-C18 column  using a 30 min linear gradient of 5–35% acetonitrile/0.1% formic acid (v:v) at 300 nl/min). The eluent was analyzed on a Q Exactive hybrid quadrupole/orbitrap mass spectrometer  equipped with a FlexIon nanoSpray source and operated under Excalibur 2.4 software using a data-dependent acquisition method. Each experimental cycle was of the following form: one full MS scan across the 350–1600 For PRM assays, samples were analyzed on a nanoflow chromatography system (Eksigent nanoLC425) hyphenated to a hybrid triple quadrupole-TOF mass spectrometer (TripleTOF 5600+) equipped with a Nanospray III ion source  and controlled by Analyst TF 1.7.1 software build 1163 . Peptides dissolved in loading buffer were enriched on a micro pillar array trapping column  and separated on an analytical micro pillar array column  using a 30 min linear gradient of 5–35% acetonitrile/0.1% formic acid (v:v) at 300 nl/min.m/z 350–1250 accumulated for 150 ms at a resolution of 30,000 FWHM, and up to 12 MS/MS scans of m/z 180–1600 accumulated for 150 ms at a resolution of 17,500 FWHM and a precursor isolation width of 0.7 FWHM, resulting in a total cycle time of 2.1 s. Precursors were chosen from a retention-time encoded list of peptide m/z values of interest, which were selected for MS/MS in a time window of RT±4 min above a threshold MS intensity of 125 cps. MS/MS activation was achieved by CID using nitrogen as a collision gas and the manufacturer’s default rolling collision energy settings. Two technical replicates per sample were acquired.Targeted LC/MS/MS analysis was performed using a Top12 parallel reaction monitoring acquisition consisting of a MS survey scan of FDR) of 0.01, respectively, using a forward-and-reverse decoy database approach. Afterwards, protein identification results were imported into Scaffold version 5.0.0  [Protein and peptide identification was achieved using MASCOT Software version 2.4   or MaxQure, USA)  for analPRM data were analyzed using Skyline version 20.1.0.76  . In Skylm/z values between 450 and 800.PRM for qualitative measurements of boPAGs in the protein digests were performed targeting at least one prototypic peptide for each boPAG . SelecteAll experimental results were statistically analyzed with R 3.6.1 . Peptides with a high coefficient of variation (CV) of their measured peak area between two technical replicates (CV > 30%) were not considered for further analysis. The relative protein abundances (expressed in percentage) of each boPAG (glycosylated and deglycosylated) in each pregnancy stage were calculated by the ratio of its peptide peak area to the total peptide peak area from all boPAGs of each pregnancy stage. This is a common method, which is also described in the literature , 39. RelThe shotgun MS proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE  partner In this study we developed a multiplexed PRM assay for boPAGs with the aim to provide an overview of the relative abundances of different boPAGs on protein level in the course of pregnancy and after parturition.BoPAGs were purified from cotyledons from different gestation stages by FPLC. A detailed description of the purification process can be found in Krebs et al. (2021) . A schemUpon gel electrophoresis, boPAG-fractions isolated from the cotyledonary tissue gave one major band at around 67 kDa in the native form and at approximately 37 kDa after deglycosylation . These aFor an initial overview of the purity and composition of the different protein fractions, the glycosylated and deglycosylated samples were subjected to in-gel trypsin digestion followed by shotgun-MS analysis. From the data, a total of 9 different boPAGs  could be identified in the samples of the respective gestation stages . BoPAG 1 was the most abundant boPAG in all pregnancy stages and showed a significant difference (P < 0.001) in mean relative abundance compared to the other investigated boPAGs. Furthermore, boPAG 18 showed significantly higher relative abundances (P < 0.001) in comparison to boPAG 2, boPAG 3, boPAG 4, boPAG 5, boPAG 6, boPAG 7, boPAG 8, boPAG 9, boPAG 10, boPAG 11, boPAG 14, boPAG 15, boPAG 17, boPAG 20, boPAG 21 and boPAG 16 (P = 0.005). BoPAG 16 had a higher level of mean relative abundance (P < 0.001) compared to boPAG 2, boPAG 3, boPAG 4, boPAG 5, boPAG 6, boPAG 7, boPAG 8, boPAG 9, boPAG 10, boPAG 11, boPAG 14, boPAG 17, boPAG 20, boPAG 21 and boPAG 15 (P = 0.005) throughout the period of gestation studied. The additionally observed differences between boPAGs did not reach statistical significance. Nevertheless, we detected distribution patterns in regard to boPAG concentrations at different stages in the course of pregnancy. The first group  showed nearly equal relative mean abundances in early, mid and late pregnancy and decreased levels of protein post-partum. A second group  displayed highest levels of relative mean abundances at early and late pregnancy stage in comparison to the levels observed at mid pregnancy and after gestation. BoPAG 17 and boPAG 18 exhibited a profile with highest levels during mid and late pregnancy. The last set comprised boPAG 10, boPAG 16 and boPAG 20. Within this group, we detected highest levels of relative abundance in the afterbirth sample.The possible effects of glycosylation on mass spectrometry analyses are well known. Their complexity and associated physical properties can lead to an overall poorer detection of glycosylated proteins or peptides , 44–47. We subjected six samples purified from cotyledonary tissues at different pregnancy stages to enzymatic deglycosylation. The success of the Peptide-N-Glycosidase F (PNGase F) treatment was verified by gel electrophoresis . The obsANOVA pointed out significant differences in the relative abundance of boPAGs (P < 0.001). Again, boPAG 1 was the most abundant boPAG at all pregnancy stages and showed significant differences (P < 0.001) in mean relative abundance compared to the other investigated boPAGs. Furthermore, boPAG 16 showed significantly higher relative abundances in comparison to boPAG 2 (P < 0.001), boPAG 3 (P = 0.009), boPAG 4 (P = 0.006), boPAG 5 (P = 0.005), boPAG 6 (P = 0.01), boPAG 7 (P < 0.001), boPAG 8 (P = 0.008), boPAG 9 (P = 0.006), boPAG 10 (P = 0.003), boPAG 11 (P = 0.04), boPAG 14 (P = 0.007), boPAG 17 (P = 0.007), boPAG 20, (P < 0.001) and boPAG 21 (P = 0.008). The observed differences between other boPAGs did not reach statistical significance.Nevertheless, mean relative boPAG levels could be assigned to different groups of nearly identical distribution patterns. BoPAG 1, boPAG 7 and boPAG 14 showed highest relative levels in mid pregnancy compared to the other pregnancy stages. Another group  showed nearly equal relative mean abundances during gestation and after parturition. BoPAG 2, boPAG 3, boPAG 4, boPAG 17 and boPAG 21 had highest levels of mean relative abundances during early and late pregnancy compared to their levels during mid pregnancy and post-partum. Some boPAGs, such as boPAG 4, boPAG 6, boPAG 8 and boPAG 18 showed decline in relative abundances in the course of pregnancy with highest levels reached at early pregnancy stage. Two boPAGs (boPAG 4 and boPAG 8) had increased levels in the post-partum sample. The last set comprised boPAG 10, boPAG 16 and boPAG 20 which exhibited highest levels of relative mean abundances in the afterbirth sample.Overall, the mean Total Area Fragment between glycosylated and deglycosylated samples increased by 58.5%. Distributions of the relative proportion of boPAG abundances at the different pregnancy stages for glycosylated samples and deglycosylated samples are shown in Furthermore, we observed changes in abundances and distribution patterns depending on the glycosylation status of the samples. The levels of boPAG 1 were significantly increased and the levels of boPAG 18 were significantly decreased in the deglycosylated samples during pregnancy and postpartum in comparison to the glycosylated samples (P < 0.001). The additionally observed differences in the abundance of other glycosylated and deglycosylated boPAGs did not reach statistical significance. Nevertheless, we discovered changes in the distribution pattern of the different boPAGs. The above mentioned boPAG 1 had increased relative abundances in deglycosylated samples during mid and late pregnancy compared to the glycosylated ones. BoPAG 2 exhibited a nearly equal distribution among glycosylated and deglycosylated samples with a slight increase in relative abundances in deglycosylated samples. BoPAG 3, boPAG 9 and boPAG 21 displayed similar distribution pattern between glycosylated and deglycosylated samples with a small decrease in relative abundances in deglycosylated samples. BoPAG 4, boPAG 6 and boPAG 8 showed a major change in their distribution pattern which was dependent on the glycosylation state of the samples. For those proteins, in the glycosylated samples highest abundances were observed in early and late pregnancy. Upon deglycosylation they exhibited decreasing levels throughout gestation. BoPAG 7 and boPAG 14 displayed distribution pattern in glycosylated samples which resembled the profile of boPAG 4, boPAG 6 and boPAG 8. However, in the deglycosylated samples they showed an increase in relative abundances during mid pregnancy. An opposite effect was monitored in regard to boPAG 17. Of note, boPAG 16 and boPAG 20 showed a decrease in relative abundance during mid pregnancy and a slight increase during late pregnancy when comparing glycosylated with deglycosylated samples. Overall, boPAG 18 had lower abundances in deglycosylated samples than in glycosylated ones. Another set of boPAGs comprised boPAG 5, boPAG 10, boPAG 11 and boPAG 15. These proteins displayed no glycosylation-dependent difference in their distribution.We observed major differences in the percentage change of Total Area Fragment values from peptides between deglycosylated and glycosylated samples during mid and late pregnancy as well as post-partum . One-wayThese findings are further supported by the results of our Western blot analysis -method from two placental extracts (obtained from cotyledons of 18 cm and 40 cm crown-rump fetuses) [There are only a few studies that give insights into the protein level of different boPAGs –19, 21. fetuses) , 18. No fetuses) . Taken tfetuses)  and GreeThe study of Touzard et al. (2013) investigated three different boPAG profiles  during gestation using Western blot analysis [In summary, the comparison of our findings with the few existing proteomic studies on boPAGs shows that the developed PRM assay provides reliable and comparable results. Furthermore, we were able to expand the existing knowledge of previous protein analysis studies on boPAGs due to the advantages of PRM.The available body of literature shows that boPAGs can have up to six potential N-glycosylation sites and that the degree of N-glycosylation seems to be the major factor in boPAG molecular mass , 12, 16.Enzymatic digestion of all samples with PNGase F resulted in changes of relative boPAG abundances and of their distribution pattern. The overall mean of the Total Area Fragment between glycosylated and deglycosylated samples increased by 58.5%. This increase in Total Area Fragment was not equally distributed over the different boPAGs and pregnancy stages. In our study, the largest influence of glycosylation was detectable in mid and late pregnancy samples as well as post-partum. These results were further verified by immunoblotting. As already described, the detected band signals in all investigated samples shifted their apparent molecular weights after PNGase F treatment , 16, 19 As a result, we were able to divide the boPAGs into groups. One group consisting of eight boPAGs  showed only minor differences with either an increase or a decrease between the two sample types within a pregnancy stage in a range of 30%. It seems likely that these differences arose from the normal variability of the measurement, especially for the boPAGs with a low abundance. The change in the distribution pattern of the relative protein abundances between glycosylated and deglycosylated samples in this set of boPAGs originate in the greater increase in the Total Area Fragment of the other boPAG group within the different pregnancy stages. It seems quite likely that the boPAGs of this group are not heavily glycosylated in any pregnancy stage.The other group of boPAGs  showed an increase in the Total Area Fragment between glycosylated and deglycosylated samples by 35.1%-205.6%. It seems that the second set of boPAGs have a higher or more complex degree of glycosylation during gestation compared to the first set and the deglycosylation process leads to a better detection of boPAGs in this group .Given these data we asked if there is any correlation between group belonging and the number of potential glycosylation sites or the distance between potential glycosylation sites and the monitored proteotypic peptides. After sequence analysis , we coulThe PRM assay developed and described in this study provides a practical and efficient method that promises to be a powerful tool for further research on boPAGs. With this assay it is possible to compare protein abundances between cotyledons and intercotyledonary chorion during gestation and at term. In combination with a stepwise deglycosylation of the samples this might improve our knowledge of the mechanisms behind the observed changes of boPAG glycosylation. Furthermore, the PRM assay can be adapted to blood and milk samples. Accurate quantification of boPAGs in these body fluids is achievable by any of the commonly used stable isotope labelling techniques , 48, 53.In conclusion, we developed a PRM assay for the determination of the relative protein abundances of 18 different boPAGs during pregnancy and after calving. To our knowledge, this is the first study which addresses the detection of the different boPAGs in parallel in the time of pregnancy and afterbirth samples on protein level, thereby investigating the influence of glycosylation.The detected boPAG distribution pattern in glycosylated samples confirmed the results of other proteomic studies. Highest degrees of glycosylation appeared in mid and late pregnancy samples as well as in afterbirth samples. Additionally, we identified a group of boPAGs that seems not heavily glycosylated in any pregnancy stage. A linkage between the impact of glycosylation and potential N-glycosylation sites or phylogenetic relation was not detected. The PRM assay itself and the results of this study give new starting points to address further research on boPAGs to better understand the physiological role during pregnancy and achieve a real knowledge of these proteins and their posttranslational modifications. For these reasons, the designed assay shall be improved and applied for the detection of individual boPAGs in maternal blood and milk in the near future. This improvement will also allow us to efficiently screen higher numbers of animals. Reliable identification and quantification will be ensured by the use of labelled synthetic peptides.S1 Raw images(PDF)Click here for additional data file.S1 File(PDF)Click here for additional data file.S2 File(PDF)Click here for additional data file.S3 File(PDF)Click here for additional data file.


The purpose of this study was to investigate whether tibial tuberosity–posterior cruciate ligament (TT-PCL) distance is representative of the true lateralization of tibial tuberosity in isolation and its influence on the accuracy of the Akagi line and medial third of the tibial tuberosity (MTTT).A total of 135 osteoarthritis patients with varus knees who undergoing computed tomography scans were enrolled to establish three-dimension models of the knees. Tibial width (TW), tibial tuberosity lateralization (TTL), posterior cruciate ligament lateralization (PCLL), knee rotation angle (KRA) and tibial rotational axes were measured and investigated their correlations with TT-PCL distance. Based on the analysis of receiver operating characteristic (ROC) curve, the influence of TT-PCL distance on the distributions of mismatch angles of tibial rotational axes was investigated with a safe zone (-5° to 10°).r = 0.493; P < 0.001) and TTL  which was different with PCLL  and KRA . All tibial rotational axes were significantly positively correlated with TT-PCL distance (P < 0.001). The mismatch angles between the vertical line of the surgical epicondylar axis (SEA) and the Akagi line and MTTT were -1.7° ± 5.3° and 7.6° ± 5.6° respectively. In terms of the optimal cut-off value of 19 mm for TT-PCL distance, the Akagi line applied as tibial rotational axis ensures 87.3% of the positions of tibial components within the safe zone when TT-PCL distance > 19 mm, and MTTT ensures 83.3% when TT-PCL distance ≤ 19 mm.TT-PCL distance was in significantly positive correlation with TW (TT-PCL distances cannot reflect the true lateralization of tibial tuberosity in isolation but can aid in the combination of the Akagi line and MTTT in varus knees. The patients with TT-PCL distance > 19 mm are recommended to reference the Akagi line for tibial rotational alignment. MTTT is recommended to the patients with TT-PCL distance ≤ 19 mm. The study will aid surgeons in deciding which reference may be used by measuring TT-PCL distance using a preoperative CT. Rotational alignment of tibial component is an important factor in determining the success of total knee arthroplasty. Rotational misalignment between femoral components and tibial components may cause various postoperative complications such as patellofemoral joint problems, gait abnormalities, polyethylene wear, stiff knees, or anterior knee pain –5. ThereThe consensus of femoral rotational alignment has been achieved by now where the surgical epicondylar axis (SEA), the posterior femoral condyle axis, and the Whiteside line have been shown to be reproducible and accurate as femoral rotational axes. Among them, the SEA which is the line connecting the prominent point of lateral femoral epicondyle and the medial femoral epicondylar sulcus moreover constitutes the flexion–extension axis of the knee . As for The lateralization of the tibial tuberosity has been assessed by tibial tuberosity-trochlear groove (TT-TG) distance to adjust the choice of anatomical landmarks for tibial rotational alignment , 14. HowSeitlinger et al.  proposedTherefore, the purpose of this study was to investigate: (1) whether TT-PCL distance in varus knees represents the true lateralization of tibial tuberosity in isolation; (2) how TT-PCL distance influences the accuracy of tibial rotational axes.n = 7) or with severe bone defects on the proximal tibia (n = 12); (2) hemophilic arthritis or rheumatoid arthritis (n = 4); (3) a history of knee trauma or infection (n = 3). At last, one hundred and thirty-five patients including 82 female and 53 male with varus deformities were enrolled in this study. The demographic data such as age, gender, hip-knee-ankle (HKA) angle and body mass index  of the 135 patients are listed in Table One hundred and sixty-one osteoarthritis (Kellgren and Lawrence grade 3 or 4) patients with varus deformities who performed computed tomographic (CT) examination prior to primary TKA at our institution from February 2021 to March 2022 were investigated. The patients who met the following criteria were excluded from this study:(1) the patients whose anatomical landmarks cannot be accurately identified . The parameters were as the following: 512 × 512 matrix, 120 kV, 320 mA, 0.867 mm thickness, 0.867 mm skip between slices, and field of view of 14 cm × 14 cm. All lower limbs of the patients were in the neutral position and fully extended without internal or external rotation during CT scanning. Then the preoperative CT data was saved in DICOM  format from the picture archiving and communication system (PACS) of our hospital. After that, all imaging data was imported into 3D reconstruction software  with initial processing. At last, a CAD software program  was applied to reconstruct 3D knee models.Three planes were defined on the 3D model of the knee to mark the anatomical landmarks correctly Fig. . The plaEach anatomical landmark for rotational alignment was marked in the 3D model of the knee Fig. . The proIn the 3D model of the tibia, three anatomical landmarks were defined to establish three tibial rotational axes. The midpoint of the insertion of the posterior cruciate ligament, the medial border of the patellar tendon’s tibial attachment and the medial third of the tibial tuberosity were drawn and projected into the plane A. Then two tibial rotational axes were established: the Akagi line, the one between the midpoint of the posterior cruciate ligament insertion and the medial border of the patellar tendon’s attachment, and the line between the midpoint of the posterior cruciate ligament insertion and the medial third of the tibial tuberosity (MTTT) Fig. .The mismatch angles between the Akagi line and MTTT and the AP axis were measured by two experienced physicians respectively. The internal rotational angles of tibial rotational axes relative to the AP axis were recognized as negative values with the external rotational angles as positive values.In this study, the TT-PCL distance was measured in the 3D model of the knee according to previous studies , 19. TheIn this study, the position of the tibial tuberosity relative to the medial–lateral dimension of proximal tibia was assessed using the tibial tuberosity lateralization according to the method of Tensho et al. . The widThe relative relationship between the distal femur and the proximal tibia represented the knee rotation which was assessed by the torsional angle of the femur and tibial posterior condyle. A line tangent to the posterior border of the femoral posterior condyles which was defined as the femoral posterior condyle line (fPCL) was made in the femoral 3D models and projected in the plane B. The angle between the fPCL and the tPCL was the knee rotation angle (KRA) Fig. .Fig. 4knAll statistical analyses were processed by SPSS statistical software . The normality of all data measured by two independent researchers was tested by the Kolmogorov–Smirnov normality test. The measurements were performed again one month after the initial measurements by one of the physicians to assess the intraclass correlation coefficients (ICC). The sample of the study was calculated to be at least 132 cases using G*Power 3.1 .p value < 0.05 was considered to be statistically significant.At first, the correlations between TT-PCL with tibial anatomical parameters and the mismatch angles of the Akagi line and MTTT were analyzed using the Pearson correlation coefficient. The r-value of the correlation coefficient was classified as the following: strong (≥ 0.7), moderate (0.4–0.7), or mild (0.2–0.4). Then, the t-test was used to compare the mismatch angle between the Akagi line and MTTT and the AP axis, respectively. The distributions of the mismatch angles of the Akagi line and MTTT with the safe zone (-5°-10°) were counted. Thirdly, ROC-curve analysis was used to identify the cut-off points of the TT-PCL distance for the Akagi line and MTTT according to the distributions of the mismatch angles with the safe zone (-5°-10°), respectively. The cut-off values of TT-PCL for the Akagi line and MTTT were calculated by the Youden’s index. An optimal cut-off value of TT-PCL was determined in the combined use of the Akagi line and MTTT for tibial rotational alignment. A All data from both physicians was tested for consistency prior to the analysis. Both Intra- and interobserver ICC values were greater than 0.8, indicating high confidence in the measurements. Table r = 0.547, p < 0.001) and TW. TT-PCL distance was significantly and mildly correlated with PCLL . There was no significant association between TT-PCL distance and KRA. The Akagi line and MTTT were in the moderately strong and significant correlation with TT-PCL distance.The correlations between TT-PCL distance and tibial anatomical parameters and tibial rotational axes are shown in Table P < 0.001). The cases which were inside the safe zone were 99 cases (73.3%) and 95 cases (70.4%) in the patients when the Akagi line and MTTT were referenced as tibial rotational axes. Based on the analysis of ROC curve, the cut-off point of TT-PCL distance was 16.46 with an area under curve (AUC) of 0.73 (95% CI 0.62–0.83) for the Akagi line and 22 with an AUC of 0.75 (95% CI 0.64–0.85) for MTTT  TT-PCL distances cannot reflect the true lateralization of tibial tuberosity in isolation.2) As the TT-PCL distance increases, the rotational alignment axes for tibial component will tend to be externally rotated.3) It is recommended to apply 19mm as the cut-off value for TT-PCL distance to adjust the choice of tibial rotational axes. The Akagi line can be referred when TT-PCL distance>19 mm and MTTT can be referred when TT-PCL distance≤19 mm.Despite the advances of the surgical techniques and the designs for components in total knee arthroplasty, there are still approximately 20% of the osteoarthritis patients dissatisfied with their clinical outcomes . It has In order to install tibial components in the appropriate position on the horizontal plane, many anatomical landmarks have been proposed to construct tibial rotational axes. Insall et al.  first foOne important finding of this study was that there were positive relationships between TT-PCL distance and the mismatch angle of tibial rotational axes relative to the SEA, though TT-PCL distance in varus knees is affected not only by the lateralization of tibial tuberosity, but also by the lateralization of posterior cruciate ligament insertion and proximal tibial width. In this study, the mean TT-PCL distance was 19.2 ± 4.3 mm, which was smaller than the previous measurement based on 3D models . An optiThis study has the following limitations: First, this study investigated the effect of the position of tibial tuberosity on the accuracy of tibial rotational alignment in Eastern population based on 3D reconstruction technology. The findings of the study may not be applicable to western populations. Second, this study enrolled the OA patients with varus knees excluding those with valgus knees. The influences of the valgus deformity of the knees on tibial rotation alignment and TT-PCL distance were not taken into consideration. At the same time, the flexion constraints leading to tibial internal rotation was neglected in this study which might have influence on the accuracy of the measurements. Third, this study derived the cut-off values of TT-PCL distance to reference different anatomical landmarks based on a safe zone (-5°-10°) generalized from previous studies –26 but dTT-PCL distances cannot reflect the true lateralization of tibial tuberosity in isolation but can aid in the combination of the Akagi line and MTTT in varus knees. The patients with TT-PCL distance > 19 mm are recommended to reference the Akagi line for tibial rotational alignment. MTTT is recommended to the patients with TT-PCL distance ≤ 19 mm. The study will aid surgeons in deciding which reference may be used by measuring TT-PCL distance using a preoperative CT.