Computational and Structural Biotechnology Journal

Protein expression within oncogenic or suppressive pathways is a hallmark indicator of oncogenesis. While traditional AI models in digital pathology attempt to predict singular proteins, there is a need to predict the downstream expression of proteins to indicate the propagation of signals. RNA expression provides novel information, but does not provide information about the downstream propagation of protein signals or whether those signals are functional. Using Reverse Phase Protein Array (RPPA) data with whole-slide images (WSIs) from the publicly available Cancer Genome Atlas Breast Adenocarcinoma dataset (TCGA-BRCA), we predict the expression of five key proteins identified from the apoptosis cascade, using DNA damage and repair (DDR) cascades as a biological control. Furthermore, we examine the performance of patch-level Vision Transformers (ViT) on the regression task, which was tested against the designed cellular-level ViT, CellRPPA. Our results demonstrate that patch-level vision transformers were unable to obtain statistically significant predictive results, achieving R-squared values {inverted exclamation} 0.1 for all folds. In addition, CellViT obtained R-squared values {inverted question} 0.1 in all five test folds. We also show that morphologically indicative cascades, such as the apoptosis cascade, provide significantly higher performance compared to the DDR cascade.

BackgroundPlacenta accreta spectrum (PAS) is characterized by abnormal trophoblastic invasion into the uterine myometrium and is a cause of postpartum hemorrhage and maternal death. Protease-activated receptor-1 (PAR-1) promotes various cellular actions, including invasion. Here, we analyzed the expression of PAR-1, platelet antigen, and fibrin in PAS. MethodsWe analyzed 49 PAS cases (placenta accreta vera [accreta vera], 31 cases; placenta increta [increta], 8 cases; placenta percreta [percreta], 10 cases, classified by the degree of placental villous invasion) and 33 control cases. We immunohistochemically examined the expression of PAR-1, platelet glycoprotein (GP) IIb/IIIa, and fibrin. ResultsThe frequency of previous cesarean section was higher in the increta and percreta groups than in the control and accreta vera groups. PAR-1 expression in placental villi was weak and limited in extent in control cases, whereas immunoreactivity and staining density increased in increta and percreta. Immunofluorescence revealed PAR-1 expression in cytotrophoblasts of placental villi and in aggregated platelets. PAR-1 expression scores in cytotrophoblasts increased significantly with the degree of villous invasion (accreta vera, increta, percreta) compared with controls. The immunopositive areas for GPIIb/IIIa and fibrin were significantly larger in PAS groups than in controls. Furthermore, the immunopositive areas for platelets and fibrin were positively correlated with the PAR-1 expression score. ConclusionThese results indicate that PAR-1 may play a role in placental villous invasion and that a thrombogenic placental environment may influence PAR-1 activation.

BackgroundPrescribing pharmaceutical agents during pregnancy requires a balance between maternal therapeutic needs and foetal risks. Quetiapine, an atypical antipsychotic, is prescribed in psychiatric disorders and increasingly used off-label in low-dosages (<200mg daily) for insomnia during pregnancy. This study aimed to investigate the possible relationship between using quetiapine in a low dosage during pregnancy and the neonatal outcome within the first three days after birth. MethodsA retrospective cohort study was conducted at the Radboud University Medical Centre (RadboudUMC), Nijmegen, The Netherlands. Liveborn neonates admitted to the RadboudUMC during the years 2017-2024 whose mothers used at least one dose of quetiapine ([&le;] 200mg daily) during pregnancy were included. Maternal characteristics and neonatal outcomes were analysed using SPSS, dose-dependent outcomes were also assessed. Results30 neonates were included. Of these, 7 (23.3%) were born preterm, 7 (23.3%) were small for gestational age (SGA), 6 (20.0%) were admitted to the neonatal intensive care unit (NICU) and 2 (6.3%) exhibited withdrawal symptoms. Most neonates were admitted to the hospital for 3 days. ConclusionThis study is the first to evaluate neonatal outcome after maternal low dose quetiapine in pregnancy in the Netherlands. We found relatively high rates of preterm birth, SGA and NICU admissions. As this was a single-centre study without a control group and with a small sample size in an academic population, results should be interpreted with caution. We recommend further prospective and multicentre studies including control groups.

ObjectivePreterm birth (PTB) is a leading cause of neonatal morbidity and mortality worldwide, with India alone contributing nearly 27% of the global PTB burden. Although alterations in the vaginal microbiome have been implicated in PTB, its association in the Indian context is underexplored. This study aimed to investigate the association of vaginal microbiome and PTB in Indian women at the time of delivery. Study designThe vaginal swabs were collected at the time of delivery from 72 women (31 term, 41 preterm) admitted to a tertiary care hospital in Western India. Microbial DNA was extracted, and the V3-V4 region of the 16S rRNA gene was sequenced. Community composition, alpha and beta diversity, and differential taxonomic abundance were assessed using bioinformatics pipelines. ResultsAt the time of delivery, there were no significant differences in alpha or beta diversity between term and preterm groups. Principal coordinate and unsupervised clustering analyses showed no group-wise segregation. The relative abundance of individual Lactobacillus species, including L. iners and L. helveticus, did not differ significantly between the two groups. However, a modest difference in the relative abundance of Streptococcus was observed between the two groups after adjustment. ConclusionThis study found no major microbial shifts in the vaginal microbiome associated with preterm birth in this cross sectional cohort of Indian women, suggesting that vaginal dysbiosis at the time of delivery may not be a principal driver of PTB in this population. These findings underscore the need for larger, longitudinal, and ethnically diverse studies using standardized methodologies better to understand the microbiomes role in PTB risk.

Cervical intraepithelial neoplasia grade 2 (CIN2) lesions show variable outcomes, and accurate prediction of regression remains a major clinical challenge. We developed an interpretable machine learning pipeline that integrates quantitative histological, clinical, and human papillomavirus (HPV) -genotyping data to predict lesion regression within one and two years. Using panoptic segmentation of routine hematoxylin and eosin (H&E) -stained biopsies, we extracted human-interpretable morphological and immune cell infiltration related features that capture the key histopathological characteristics of CIN2 and identified features that predicted lesion regression. Further, integrating these features to predictive clinical features achieved higher predictive accuracy than clinical variables alone. These findings demonstrate that quantitative, interpretable analysis of H&E histology of routine diagnostic biopsies contains relevant information that predicts the natural history of CIN2 lesions. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=121 SRC="FIGDIR/small/26344510v1_ufig1.gif" ALT="Figure 1"> View larger version (38K): org.highwire.dtl.DTLVardef@11735f5org.highwire.dtl.DTLVardef@d76e89org.highwire.dtl.DTLVardef@19a1d39org.highwire.dtl.DTLVardef@f48a01_HPS_FORMAT_FIGEXP M_FIG Created in BioRender. Lehtonen, O. (2026) https://BioRender.com/rlnkbkp C_FIG

To characterize sleeping posture, behaviour, and environment in healthy pregnant participants and their bed partners across multiple nights in the home setting during late pregnancy, we completed a manual review of overnight video recordings from a four-night, in-home, level 3 sleep apnea study. Sleeping postures were scored according to a thirteen-posture classification system to determine the cumulative time per night spent in each sleeping posture. Additional aspects of sleeping posture, behaviour, and environment were also assessed. Forty-one pregnant participants and 36 bed partners completed the study, contributing 168 nights of video. Significant differences were found between the pregnant participants and bed partners in cumulative time spent in each posture as well as frequency and duration of episodes spent in each posture. Pregnancy status, side of the bed, and presence of a pregnancy pillow, bed partner, shared bed sheets, and pets in the sleeping space had various effects on the time spent in each posture. Pregnant participants spent more time in transition postures (going-to-sleep, waking-to-void, returning-to-bed, and waking-in-the-morning) than bed partners. There was a moderately positive correlation in posture changes between pregnant participants and their bed partners. Pets significantly increased the number of posture changes per night for both groups. Pregnant participants had more absences and time absent from bed. Sleep in late pregnancy is characterized by an increased frequency and duration of episodes spent in a restricted number of sleeping postures and is impacted by the sleep environment. Modifying the sleeping environment may improve comfort, minimize disturbances, and benefit sleep. Statement of SignificanceSubjectively-recalled supine going-to-sleep posture in late pregnancy is associated with stillbirth and fetal growth restriction. Sleeping posture, however, is dynamic, and few studies provide comprehensive analyses of sleeping posture in pregnancy using objective measurements. This novel study used a gold-standard objective measure of sleeping posture, was conducted across multiple nights in the participants own homes, and accounts for usual sleeping behaviours and environment by including the participants bed partner. A critical remaining knowledge gap is whether an individuals nightly sleeping posture varies significantly across the third trimester. Future work should use nightly, continuous, and objective methods to measure sleeping posture across the entire third trimester to bridge this gap and investigate the relationship between sleeping posture and pregnancy outcomes.

Osteoporosis is a silent yet debilitating disease that often remains undetected until fractures occur. While early prediction is crucial, most studies combine male and female datasets to train a single model, introducing bias since osteoporosis risk and progression differ by gender. This study aims to develop gender-specific machine learning models that leverage longitudinal data to predict osteoporosis risk, providing tailored insights for men and women. Data were obtained from two large longitudinal cohorts: the Study of Osteoporotic Fractures (SOF) for women and the Osteoporotic Fractures in Men Study (MrOS) for men. Multiple ML algorithms were trained and evaluated for each sex, with model performance assessed using the area under the receiver operating characteristic curve (AUC-ROC). Among the tested models, the XGBoost model demonstrated the best performance for women, achieving an AUC-ROC of 0.93 using SOF data. For men, the Random Forest model achieved an AUC-ROC of 0.89 using MrOS data. Feature importance analysis identified sex-specific osteoporosis risk factors, underscoring the need for tailored prediction and management. By revealing male and female risk factors and reducing bias from combined datasets, the work advances personalized care and supports earlier, effective clinical intervention to prevent fractures and improve health outcomes.

BackgroundPreeclampsia is a leading cause of maternal morbidity and mortality in sub-Saharan Africa (SSA). Lead exposure remains widespread across the region, yet its contribution to preeclampsia risk has not been quantified at the population level. The World Health Organization (WHO, 2025) estimated that in 2023, there were approximately 182,000 maternal deaths in SSA (70 % of global deaths), and that approximately 16% of these deaths were due to hypertensive disorders of pregnancy (HDP) (Say et al, 2014). This implies that there were approximately 29,000 HDP-related maternal deaths annually in the SSA (with pre-eclampsia/eclampsia constituting a substantial fraction). MethodsWe synthesized maternal blood lead level (BLL) data from pregnancy biomonitoring studies conducted in SSA and supplemented these with soil-derived exposure scenarios representing severely contaminated settings. Using published meta-analytic evidence indicating a 1.6% increase in the odds of preeclampsia per 1 {micro}g/dL increase in maternal BLL (Poropat et al., 2018), we modelled relative odds and predicted absolute preeclampsia risk under a range of plausible baseline prevalence assumptions. Sensitivity analyses examined uncertainty related to exposure extrapolation and baseline preeclampsia prevalence, and population attributable fractions were estimated across baseline scenarios. ResultsBiomonitoring-derived maternal BLLs were associated with modest but consistent increases in predicted pre-eclampsia risk across baseline scenarios. Soil-derived exposure scenarios representing severe environmental contamination yielded sharply increasing predicted risks under uncapped extrapolation. Sensitivity analyses demonstrated that relative exposure-response gradients were robust to modelling assumptions, while absolute risk and attributable burden estimates varied with baseline prevalence. Central tendency maternal blood lead levels reported in sub-Saharan Africa are several-fold higher than those observed in contemporary biomonitoring programs in Europe, North America, East Asia, and Latin America. ConclusionMaternal lead exposure may contribute meaningfully to pre-eclampsia risk in sub-Saharan Africa, with modest effects at commonly observed exposure levels and potentially substantial effects in severely contaminated settings. These findings support the inclusion of environmental lead exposure prevention within broader maternal health strategies.

BackgroundIt is known that cyclic menstruation imposes significant burdens on a large portion of the female population with a plethora of symptoms, including pelvic pain, dysmenorrhea, menorrhagia, and psychological distress. The effects are widespread, impacting the quality of life, ability to work and function, finances, and education. For the most part, previous studies have been surveys administered through social media channels such as pelvic pain and endometriosis societies, which, by definition, introduce bias. There is a need to evaluate the female population regarding female health issues in a less biased manner to assess the actual disease burden and response. MethodsA cross-sectional, IRB-approved study of the Nova Southeastern University undergraduate (2022) and graduate female population cohort was conducted using a self-administered, anonymous survey to assess the prevalence of menstrual pain and disability (menstrual burden). This included the use of prescription and non-prescription medications, self-help interventions, and cannabis use (either medicinal or recreational) for symptoms associated with menstruation. All students who identify as female at the university were invited via campus email to participate in this survey, hosted securely on the SurveyMonkey website. One initial invitation was sent, followed by two reminder invitations at 2-week intervals. A descriptive analysis was performed. Results14,024 email invitations were sent to the entire university population that identified as female to the registrar, with a response rate of 15.8% (similar to the response rate in most university surveys at this institution). 18.23% of the cohort reported bleeding that restricted the students ability to function, and 13.2% were so severely affected by menstrual symptoms that bed rest was required to cope adequately. Primary symptoms of bloating/swelling/constipation were reported in over 77%, mood swings/moodiness in approximately 74%, and pelvic or back pain was noted in over 70% of the respondents. In an assessment of treatment regimens, approximately 80% used over-the-counter medications, 55% reported using heating pads, 25% used oral contraceptive products, 29.6% engaged in exercise or meditation, and fully 14.76% used cannabis in its various forms as treatment adjuncts (in addition to other regimens). In the evaluation of multiple efficacies, the respondents reported that 72% of those that used OTC medications for relief found them very or moderately effective, 66.2% of those that used a heating pad found it very or moderately effective, 54.2% of those that used exercise and meditation found it very or moderately effective, and cannabis was found to be very or moderately effective by 82% of the cohort that reported using it as an adjunct. Unexpectedly, there were some racial/ethnic differences in disease burden, the types of treatment modalities accessed, and perceived effectiveness. There were minimal differences between age groups. ConclusionMenstruation in female college students represents a significant challenge for 40.2% of this South Florida population, causing moderately severe to extremely severe symptoms. This study supports previous findings by Schoep2 on the impact of menstruation on the quality of life of Dutch women and by Munros global systematic review3, which documented a significant menstrual burden and its implications for education. We found a high burden among college women when the entire female university population was invited to participate in this survey. This studys emphasis on the menstrual burden and its impact on quality of life expands on previous studies. Our results should pave the way for a policy review of how the menstrual burden is approached in university settings, particularly regarding efforts to encourage gender equality.

PurposeTo evaluate the predictive value of the thoracic spine-clavicle angle (TSCA) and the thoracic cage-clavicle angle (TCCA) for immediate postoperative shoulder balance. MethodsA total of 154 Lenke type 1 and 2 AIS patients who underwent corrective surgery in our hospital were included. The degree of clavicle angle (CA), thoracic spine tilt angle (TSTA), thoracic cage tilt angle (TCTA), proximal thoracic curve (PTC) Cobb angle, and main thoracic curve (MTC) Cobb angle were measured on standing full-length posteroanterior spine radiographs before and after surgery. Five parameters, TSCA, TCCA, correction rate of PTC, correction rate of MTC, relative PT/MT residual Cobb angle (RRCA), were calculated. Multinomial Logistic Regression was used to determine the risk factors of PSB. A p-value of less than 0.05 was considered statistically significant. ResultsFor TCCA, in group R (vs group B), pre-op right high (vs pre-op left high) (OR=8.102, 95%CI [1.650, 39.786], p=0.01) and RRCA (OR=1.015, 95%CI [1.004, 1.026], p=0.009) are risk factors. Pre-op left high (vs pre-op right high) (OR=0.123, 95%CI [0.025, 0.606], p=0.01) and correction rate of MTC (OR=0.886, 95%CI [0.809, 0.971], p=0.009) are protective factors. Correction rate of PTC shows no significant effect. In group L (vs group B), pre-op left high (vs pre-op right high) (OR=2.648, 95%CI [1.084, 6.469], p=0.033) is a risk factor. Pre-op right high (vs pre-op left high) (OR=0. 378, 95%CI [0. 155, 0.922], p=0. 033) is a protective factor. Correction rate of PTC, correction rate of MTC, and RRCA show no significant effect. ConclusionPreoperative shoulder balance, as defined by the TCCA, serves as an independent risk factor for PSB. Using postoperative balanced shoulders as the reference group, preoperative left shoulder high (vs right high) significantly increased the risk of postoperative left shoulder high, while significantly reducing the risk of postoperative right shoulder high. Preoperative right shoulder high (vs left high) significantly increased the risk of postoperative right shoulder high, while significantly reducing the risk of postoperative left shoulder high. The correction rate of MTC was an independent protective factor against postoperative right shoulder high, whereas RRCA was an independent risk factor for postoperative right shoulder high.

Bias in machine learning is a persistent challenge because it can create unfair outcomes, limit generalization, and reduce trust in real-world applications. A key source of this problem is shortcut learning, where models exploit signals linked to sensitive attributes, such as data source or collection site, instead of relying on task, relevant features. To tackle this, we propose the Deceptive Signal metric, a novel quantitative measure designed to assess the extent of a models reliance on hidden shortcuts during the learning process. This metric is derived via the Deceptive Bias Detection pipeline, which isolates shortcut dependence by contrasting model behavior under two controlled conditions: (1) Full Exclusion, where a sensitive subgroup is completely removed from training; and (2) Partial Exclusion, where the model has limited access to specific classes within the subgroup. By calculating the behavioral shift between these settings, the Deceptive Signal metric provides a concrete value representing the models proneness to learning task-irrelevant patterns. In experiments with the TCGA histopathology dataset, our metric successfully quantified strong dependencies on center-specific artifacts in models trained for cancer classification. Author summaryDeep learning models are becoming powerful tools in healthcare, but they often suffer from a critical vulnerability: they can get the right answer for the wrong reason. In medical imaging, an AI might correctly identify a tumor not by analyzing the tissue, but by recognizing irrelevant digital markers unique to the specific hospital or scanner that produced the image. This phenomenon, known as shortcut learning, makes AI systems appear accurate at first glance while remaining unreliable for real-world patient care. To solve this, our research moves beyond simple accuracy checks and introduces a specific quantitative metric for shortcut learning. We developed a testing framework that forces the model into controlled training scenarios, deliberately withholding specific "shortcut" information to see how the model reacts. By mathematically comparing the models behavior across these scenarios, we calculate a precise score that indicates the magnitude of the models dependence on irrelevant patterns. This metric allows to put a concrete number on a models trustworthiness and ensuring that medical decisions are driven by biology, not background noise.

Background & AimsThe increasing global prevalence of metabolic dysfunction-associated steatotic liver disease (MASLD) including metabolic dysfunction-associated steatohepatitis (MASH) creates an urgent need for objective methods of histopathological assessment. Conventional histological approaches are time-consuming and rely on interpreters experience. Therefore, the results obtained may suffer from high variability and only offer coarse categorisation. In this study, we propose a fully automated, deep-learning-based pipeline for the segmentation and characterisation of histological liver features for MASH/MASLD assessment. MethodsSegmentation was applied to H&E sections from 45 mice and 44 humans with MASH/MASLD. The method, which we named qHisto (quantitative histology), utilises the nnU-Net framework and quantifies key histological components of the MASH score, including macro- and microvesicular steatosis, fibrosis, inflammation, hepatocellular ballooning and glycogenated nuclei. Additionally, we characterized the tissue using novel features that are inaccessible through manual histology, such as the distribution of fat droplet sizes, aspect ratio of nuclei and heatmaps. ResultsqHisto parameters showed strong positive correlations with conventional histology scores (fat area R=0.91, inflammation density R=0.7, ballooning density R=0.49) and also with quantitative magnetic resonance imaging (fat area vs. hepatic fat fraction R=0.87). Our novel scores showed that deformation of nuclei is driven by large fat droplets rather than the overall amount of fat. ConclusionsA key advantage of our method is spatially resolved, precise histological quantification. These features provide a finely resolved assessment of disease severity than conventional categorical scoring. By automating time-consuming and repetitive readouts, qHisto improves standardisation and reproducibility of MASH/MASLD feature quantification and provides scalable, slide-wide readouts that can support histopathologists and enhance clinical assessment and therapeutic development. Impact and ImplicationsThe proposed method provides an objective, automatic tool for comprehensive, histological liver analysis of MASH/MASLD, which can be extended to other diseases and organs. By offering classic and novel quantitative parameters and scores, our method could support histologists in their daily routines and provide researchers with further insight into steatotic liver diseases.

ObjectiveTo analyse clinical factors associated with urinary albumin-to-creatinine ratio among women with preeclampsia. Design & MethodsA prospective cohort analysis was conceived at Amana referral regional hospital. Women with preeclampsia (exposed group) were compared with women without evidence of preeclampsia (control group) from any time after 20th week of pregnancy to at most 24-hours post-delivery. A simple random sampling with 1:1 matching was used to get study participants. Each participant was admitted for a duration of at least 24-hours at baseline for estimation of urinary-based albumin-creatinine ratio via 24-urine collection. Clinical Report Form was the main tool for data collection. A multivariable linear model was set for final analysis after appropriate linear model assumption validation. ResultsWe successfully recruited and prospectively analysed a total of 55440 women-hours of follow-up between January to June 2025. Systolic blood pressure (A.O.R.: 1.01, 95% C.I.: 1.00 - 1.77), diastolic blood pressure (A.O.R.: 1.12, 95% C.I.: 1.00 - 1.97), Caesarean delivery (A.O.R.: 1.19, 95% C.I.: 1.02 - 1.45), neonatal birth weight (A.O.R.: 2.0, 95% C.I.: 1.2 - 2.9) as well as newborns 5th minute Apgar score (A.O.R.: 1.07, 95% C.I.: 1.0 - 1.33) were factors significantly associated with maternal urinary-based albumin-to-creatinine ratio in this study population. ConclusionDiastolic blood pressure had a higher risk than systolic blood pressure to predict significant proteinuria. Newborns birth weight as well as 5th minute Apgar score were immediate outcomes associated with maternal significant proteinuria. RecommendationsFetal maternal calculators for predicting/prognosticating significant proteinuria among pregnant women with proteinuric preeclampsia are warranted.

Inflammatory resolution is essential for tissue health, yet its dynamics remain difficult to study in humans. Menstruation is a recurrent, non-pathological inflammatory process that provides a natural window into inflammation and repair. We developed and validated a standardized menstrual sampling and RNA-seq workflow, analyzing more than 1,000 samples from over 300 individuals. We show that menstrual transcriptomes are dominated by two major biological confounders: heterogeneous tissue composition and rapid temporal progression. We introduce tissue-aware transcriptional axes that quantify uterine enrichment and an Inflammatory Resolution Score (IRS) that positions samples along an inflammation-to-repair trajectory independent of tissue admixture. In healthy individuals, IRS defines a conserved resolution trajectory across early menstruation. Applying this framework to endometriosis and autoimmune disease reveals reproducible deviations from the healthy trajectory with distinct transcriptional programs and altered pathway coordination. Finally, we demonstrate translational relevance by developing a non-invasive endometriosis classifier grounded in resolution biology that generalizes across symptomatic populations and shifts following surgical intervention. This work establishes menstruation as a tractable human model system for quantifying inflammatory resolution and detecting disease-associated disruptions.

BackgroundUterine atony ([~]70%), lacerations ([~]20%) and placenta-related problems ([~]10%) are assumed main reasons for postpartum hemorrhage genesis. Coagulation components predictive for postpartum blood loss can be identified prepartum and before traditionally assumed main reasons are observed. ObjectivesTo better understand postpartum hemorrhage genesis, we prospectively researched prepartum clinical information, presence of assumed main reasons and peripartum coagulation changes in parturient women. Study designIn 676 women with vaginal deliveries, age, BMI, parity, gestation age, duration of second stage of labor and presence and type of assumed main reasons (uterine atony, lacerations and placenta-related problems) were recorded. Measured blood loss within 24h postpartum defined no, non-severe or severe PPH (<500ml, [&ge;]500ml to <1000ml, [&ge;]1000ml). Hemoglobin, platelet count, fibrinogen, factor II and factor XIII activity were measured at admission and 24-48h postpartum. ResultsOf 191 women developing postpartum hemorrhage, 53.9% did not show assumed main reasons (expected <5%, p<.001). Of 45 women with severe postpartum hemorrhage, 15.5% were without assumed main reasons (<5%, p<.001). Sole atony occurred less frequently than expected (8.2% in non-severe and 35.5% in severe PPH, p<.001). FXIII showed the largest decrease of coagulation factors by far, from no (-12%) to non-severe (-20%) and severe postpartum hemorrhage (-32%, p<.001). Duration of the second stage of labor was longer in women developing postpartum hemorrhage later on (71 vs. 46 minutes, p=.004), but was not different between women with or without assumed main reasons. ConclusionUterine atony frequency is low in non-severe postpartum hemorrhage, but progresses from non-severe to severe postpartum hemorrhage. It can thus not be the frequent reason for postpartum hemorrhage it is assumed to be, as all postpartum hemorrhages start as non-severe. A prolonged second stage of labor together with an ongoing (likely self-reinforcing) consumptive coagulopathy helps to explain postpartum hemorrhage genesis. FXIII is a prepartum predictor of postpartum blood loss and shows the most pronounced peripartum coagulation factor loss in any setting. This might allow to identify new treatment pathways.

Drug-induced liver injury (DILI) is an acute inflammatory liver disease caused not only by prescription and over-the-counter medications but also by health foods and dietary supplements. Typically, DILI patients recover once the causative substance is identified and discontinued. In contrast, autoimmune hepatitis (AIH) results from the immune-mediated destruction of hepatocytes due to a breakdown of self-tolerance mechanisms. Patients presenting with acute-onset AIH often lack characteristic clinical features, such as autoantibodies, and require prompt steroid treatment to prevent progression to liver failure. Liver biopsy currently remains the gold standard to differentiate acute DILI from AIH; however, general pathologists face significant diagnostic challenges due to overlapping histopathological features. This study integrates pathology expertise with deep learning-based artificial intelligence (AI) to differentiate DILI from AIH using histopathological images. Our AI model demonstrates promising classification accuracy (Accuracy 74%, AUC 0.81). This paper presents a detailed pathological analysis alongside AI methods, discusses the current model performance and limitations, and proposes directions for future improvements.

ObjectivesGiven the widespread use of period-tracking applications and evidence that some users rely on fertile-window predictions for pregnancy prevention, we aimed to quantify pregnancy risk arising from misclassification of biologically fertile days by period-tracking applications, and to compare this risk across calendar-based and basal body temperature (BBT)-supported period tracking and a digital contraceptive regulated as a medical device. MethodsWe conducted an observational analysis of cycles of mobile fertility application users who logged urinary luteinizing hormone (LH) tests. Biologically fertile days were defined using an LH-based reference fertile window (days -5 to 0 relative to ovulation). Three approaches were evaluated: a calendar-based period tracking application, a BBT-supported period tracking application, and a FDA-cleared digital contraceptive. Outcomes included day-specific frequency of fertile days misclassified as safe, cycle-level misclassification, and predicted pregnancy risk per cycle. Analyses were repeated in a subgroup of irregular cycles. Results543,167 menstrual cycles with a clear LH surge signature were included in the analysis. Calendar-based period tracking frequently misclassified fertile days as safe, with 67% of cycles containing at least one at-risk day and 25% containing at least one high-risk day. The mean predicted pregnancy risk per cycle was 22%, increasing to 65% in irregular cycles. BBT-supported period tracking reduced misclassification but remained associated with substantial risk (41% of cycles with at least one at-risk day; mean predicted pregnancy risk 9%). In contrast, the digital contraceptive showed consistently low misclassification (3% of cycles with any at-risk day and a mean predicted pregnancy risk of 0.5%). ConclusionsBoth calendar-based and BBT-supported period-tracking applications not intended for contraception frequently misclassify biologically fertile days and should not be considered reliable tools for pregnancy prevention. Regulated digital contraceptives demonstrate substantially lower pregnancy risk. Short condensationPeriod-tracking apps frequently misclassify fertile days as safe, including days with high pregnancy risk. In a large real-world analysis, both calendar- and BBT-supported trackers showed substantial risk, unlike digital contraception methods regulated as a medical device.

ObjectivePerimenopause is an under-recognized life stage that may be accompanied by complex and fluctuating symptoms. We aimed to quantify the prevalence of perimenopause uncertainty and explore the underlying drivers. MethodsWe conducted a mixed-methods study based on a cross-sectional survey of U.S. women aged 35 years and above (N=7,640). Closed-ended responses were analyzed to estimate the prevalence of perimenopause uncertainty with subgroup differences investigated by age and symptom severity. Content analysis of free-text responses (n=409) was conducted to identify the main uncertainty drivers. ResultsOverall, 34% of participants reported being unsure of their reproductive stage. Uncertainty peaked among those aged 40-44 (42%) and was highest among those with severe symptom burden (37%). The content analysis revealed three main uncertainty drivers. Symptom confusion and attribution was the most common (56%), reflecting difficulties interpreting bodily changes and distinguishing perimenopause from other causes. Knowledge gaps and information seeking accounted for 28% of responses, highlighting limited health literacy, assumptions about age, and active searches for evidence. Barriers to confirmation and care (16%) described dismissive healthcare encounters and reluctance to acknowledge perimenopause. Younger women (35-39 years) were more likely to cite knowledge gaps, while healthcare barriers peaked in the 40-44 age group. ConclusionPerimenopause uncertainty is a prevalent and clinically meaningful challenge. This uncertainty is conceptually distinct from illness-focused models: it is a universal transition with ambiguity and often lack of validation. Better symptom recognition and targeted communication is a crucial first step toward improving womens awareness and support during perimenopause.

BackgroundReverse total shoulder arthroplasty (rTSA) is an increasingly common surgical procedure often performed to treat pain related to glenohumeral osteoarthritis or to rotator cuff arthropathy. Although surgical outcomes are generally excellent, recent evidence has found that postoperative pain ([&ge;] 3/10) two years following surgery is reported by an estimated 18% of patients. Recently, the NIH Acute-to-Chronic Pain Signatures program recommended longitudinal studies using select biomarkers to describe and predict individual patient responses to surgery. These data are not yet available for rTSA procedures. MethodsThis was a longitudinal cohort study performed at a single academic medical center. Twenty participants undergoing rTSA surgery were included, recruited from a tertiary hospital system in the southern United States. The first objective of this study was to describe changes in general pain intensity (Numerical Pain Rating Scale), widespread body pain, anxiety (General Anxiety Disorder-7), depression (Patient Health Questionnaire-9), neuropathic pain symptoms (painDETECT), and quantitative sensory testing from baseline to 6 weeks following rTSA. The second objective was to identify the baseline demographic and pain-related factors associated with 6-week postsurgical improvements in pain intensity. ResultsFrom before to after surgery, our cohort demonstrated significant improvement in shoulder pain intensity, widespread body pain, PainDETECT score, and temporal summation magnitude measured at the surgical deltoid. Degree of 6-week pain intensity improvement was associated with baseline pain intensity (F=18.79, p=0.0004) and temporal summation magnitude of the tibialis anterior (F=5.06, p=0.0380). ConclusionsPain intensity, location, nature, and mechanism can serve as biomarkers of the short-term postsurgical changes that can be expected following rTSA. Baseline pain intensity and temporal summation magnitude of the tibialis anterior were associated with the degree of pain improvement, suggesting their use for preoperative risk assessment. Future research should evaluate whether these 6-week biomarker changes are associated with the development of chronic postoperative pain at longer durations after surgery. Level of EvidenceLevel I, Prognostic Study

ObjectivesTo assess if maternal stress is higher in pregnancies with congenital heart disease (CHD) compared to low-risk pregnancies and if maternal stress is associated with placental microstructure and function. To explore if CHD alters the relationship between maternal stress and placental measures. MethodsIn this prospective observational study, 27 participants carrying a fetus with CHD and 42 participants with typical low-risk pregnancies underwent 1-2 combined diffusion{square}T2* relaxation placental MRIs from 20 weeks gestation (GA) and completed the Edinburgh Postnatal Depression Scale and State Trait Anxiety Inventory [43 male fetuses, median (IQR) GA at assessment 30.86 weeks (27.43-34.00), interval between assessments 6.00 weeks (4.86-7.14)]. 98 complete placental MRI and maternal stress datasets were available. Generalized Estimating Equations were used for analyses. ResultsHigher trait anxiety was associated with higher placental apparent diffusion coefficient (p=0.023) adjusting for CHD, sex, GA at assessment, GA at assessment2, state anxiety, depressive symptoms and previous mental health treatment. Maternal state anxiety (p=0.005) and depressive symptoms (p=0.046) were higher in pregnancies with CHD adjusting for GA at assessment and previous mental health treatment. CHD did not alter these relationships (p>0.119). ConclusionsMaternal proneness to anxiety, measured with the trait anxiety inventory, is associated with increased diffusivity in the placenta, which may reflect altered microstructural maturation. Mothers with fetal CHD show more depressive symptoms and feelings of anxiety and may benefit from screening for elevated maternal stress. The findings contribute to a growing body of research regarding the influence of prenatal stress on placental development. HighlightsO_LIMaternal stress and placental MRI data acquired in pregnancies with and without CHD C_LIO_LIMaternal trait anxiety is associated with increased placental diffusivity C_LIO_LIMaternal state anxiety and depressive symptoms are higher in fetal CHD C_LIO_LIState anxiety and depressive symptoms not associated with placental MRI measures C_LIO_LICHD did not moderate relationships between placental MRI measures and stress C_LI