To develop and internally validate a machine-learning-based nomogram for predicting trifecta achievement in patients undergoing robot-assisted partial nephrectomy (RAPN). This retrospective single-center study included 426 patients who underwent RAPN between 2011 and 2025 years. Trifecta was defined as negative surgical margins, warm ischemia time ≤25 minutes, and absence of perioperative complications. Variable selection was performed using the Least Absolute Shrinkage and Selection Operator (LASSO) logistic regression. Selected predictors were incorporated into a multivariate logistic regression model to construct a nomogram. Model performance was assessed using discrimination, calibration, and decision-curve analysis (DCA) with internal bootstrap validation. Trifecta was achieved in 87.7% of patients. The total operative time, estimated blood loss, tumor location, collecting system entry, and need for perioperative blood transfusion were identified as independent predictors. The model demonstrated good discrimination (area under the curve = 0.792, 95% confidence interval: 0.738-0.845). Calibration was acceptable (bootstrap-corrected slope 0.91, intercept 0.13), and the DCA showed a higher net benefit than treat-all/none for thresholds of ∼0.10-0.75. This LASSO-based nomogram offers individualized and clinically interpretable prediction of trifecta achievement in RAPN. Integrating key surgical and tumor-related parameters has the potential to support perioperative decision-making and patient counseling. External validation through prospective multicenter studies is warranted to confirm its generalizability and facilitate clinical implementation.

Research on data-driven rapid nondestructive quality evaluation method, Calculus Bovis as an example.

Adolescence is a critical phase of rapid growth with long-term health implications. We aimed to examine the trends and associated factors of malnutrition among adolescents with diarrhoea in urban Bangladesh. We conducted a retrospective cross-sectional study and analysed data from 1597 adolescents aged 10-19 years enrolled in the Diarrhoeal Disease Surveillance System at icddr,b's Dhaka Hospital between 2012 and 2023. Nutritional status was defined using WHO standards. Trends were assessed using the Jonckheere-Terpstra test. Logistic regression models were developed after variable selection with least absolute shrinkage and selection operator. The pooled prevalence of stunting, thinness, and overweight across 2012-2023 was 473/1597 (29.6%), 388/1597 (24.3%), and 106/1597 (6.6%), respectively. Stunting declined significantly from 40.8% in 2012 to 25.7% in 2023, while thinness increased from 21.8% to 24.3%. In adjusted models, stunting was positively associated with age (aOR: 1.06; 95% CI: 1.02-1.10) and negatively with maternal post-primary education (aOR: 0.71; 95% CI: 0.53-0.94) and rich households (aOR: 0.66; 95% CI: 0.46-0.96). Thinness was less likely among females (aOR: 0.46; 95% CI: 0.35-0.60), older adolescents (aOR: 0.90; 95% CI: 0.86-0.94), and the richest households (aOR: 0.52; 95% CI: 0.30-0.90). Overweight was inversely associated with age (aOR: 0.92; 95% CI: 0.86-0.99) but increased with post-primary maternal education (aOR: 3.22; 95% CI: 2.09-4.96) and richest households (aOR: 2.72; 95% CI: 1.13-6.55). Adolescent stunting decreased while thinness increased over the 12-year period, highlighting a dual burden of malnutrition. Male adolescents and those from socioeconomically disadvantaged households remain particularly vulnerable to malnutrition. These findings underscore the need for integrated adolescent nutrition strategies within diarrhoeal disease management and urban health programmes in Bangladesh.

A nomogram prediction model for lumbar disc herniation recurrence after percutaneous endoscopic lumbar discectomy: a multicenter retrospective study.

ARDSMLpred: A machine learning model for predicting SCAP-associated ARDS based on MIMIC-IV database.

Aim: Several CAR-T cell therapies have received regulatory approval from both the US FDA and the EMA for the treatment of large B-cell lymphoma. However, direct comparative trials between CAR-T cell therapies are lacking, mainly due to different clinical development timelines and availabilities as well as substantial resource requirements and difficulties in recruiting sufficiently large and homogeneous cohorts from a highly pre-treated patient population. Consequently, indirect treatment comparisons (ITCs) play a critical role in evaluating the relative benefits of CAR-T cell therapies. However, ITCs are inherently susceptible to confounding, underscoring the importance of systematically identifying and appropriately adjusting for key prognostic factors, and treatment effect modifiers. Materials & methods: A systematic literature search was conducted in PubMed/MEDLINE, EMBASE and the Cochrane Central Register of Controlled Trials (CENTRAL) in November 2025. Database-specific search strategies using controlled vocabulary (MeSH and Emtree) were applied. Records were deduplicated prior to screening. Studies published in English or German were eligible. Two reviewers independently screened titles/abstracts and full texts using predefined criteria, with disagreements resolved by consensus. Results: A total of 27 publications met the inclusion criteria. Most studies used unanchored matching-adjusted indirect comparisons, followed by propensity score-based methods and network meta-analyses. The extent of covariate adjustment varied widely, ranging from no adjustment to extensive multivariable adjustment with up to 19 covariates. Commonly adjusted factors included demographics, disease severity, clinical status and treatment history. Efficacy outcomes most frequently assessed overall and progression-free survival and response rates, whereas safety outcomes were reported less consistently and were rarely covariate-adjusted, limiting comparative interpretation. Covariates were selected based on clinical expertise and/or literature review; however, no study provided a detailed description of the identification methodology. Conclusion: Although the selection of variables for adjustment frequently targeted recognized prognostic factors, the underlying processes lacked methodological transparency and were often constrained by data availability or undocumented expert opinion. Consequently, this resulted in substantial heterogeneity across studies. Notably, even fundamental covariates routinely required in health technology assessments, such as age, sex and disease severity, were inconsistently addressed, further limiting the comparability and robustness of the reported ITCs. To enhance the reliability and comparability of ITC results, standardized approaches for covariate identification and adjustment are urgently needed.

Numerous databases exist in the field of cardiac surgery across Europe, but none of them combine the entire captured data of all clinical specialties involved in the care process of patients, including anesthesia and perfusion. To fill this gap, the European Congenital Heart Surgeons Association (ECHSA) and the European Association for Cardiovascular Anesthesia and Intensive Care along with the European Board of Cardiovascular Perfusion propose to build a database module for Anesthesia and Perfusion data collected during congenital cardiac surgery and interventional procedures and coupling these data within the existing largest congenital cardiac surgery database in Europe-the ECHSA Congenital Cardiac Database (ECHSA-CCDB). This report will review the state of the development of this database module to date and propose an initial set of variables for collection. The report also outlines the methodology to be followed for the final selection of variables and sets a course for the start of data collection. The scope of procedures to be captured includes pediatric cardiac surgery operations with or without cardiopulmonary bypass and interventional cardiology procedures. Descriptive study/methods study. Congenital cardiac surgery/cardiology/anesthesiology. Congenital cardiac anesthesiologists, surgeons, and perfusionists. None. We discuss a literature review, survey, and panel meetings for the development of an anesthesia and perfusion variables module being integrated into ECHSA-Congenital Cardiac Database by identifying the first set of variables to be available for outcome research in surgical operations and transcatheter interventional cardiology procedures across Europe. This multisocietal collaboration will lead to the most comprehensive approach to patient outcome research in congenital cardiac surgery and Interventional Cardiology in Europe to date. The article describes the initial concept of the modules and the process steps used to identify candidate variables for anesthesia outcomes in the setting of congenital cardiac surgery.

Robust Bayesian high-dimensional variable selection and inference with the horseshoe family of priors

Post-traumatic stress disorder (PTSD) is associated with increased cardiovascular disease (CVD) morbidity and mortality, yet the biological mechanisms linking psychological trauma to cardiovascular risk remain incompletely understood. Disruptions in one-carbon metabolism, reflected by elevated homocysteine and altered folate and vitamin B12 levels, contribute to vascular inflammation and endothelial dysfunction and may represent a modifiable pathway linking PTSD to CVD. The purpose of this study was to examine relationships among physiological biomarkers (homocysteine, folate, vitamin B12), PTSD diagnosis, PTSD treatment participation, and CVD risk in a veteran population. A quantitative, comparative retrospective chart review was conducted among 279 U.S. veterans with documented homocysteine levels. CVD risk was categorized as high or low-to-moderate based on metabolic and vascular risk factors. Logistic regression, odds ratios, and chi-square analyses were used to examine predictors of CVD risk and elevated homocysteine. The Neuman Systems Model guided variable selection and interpretation. Elevated homocysteine was significantly associated with age, gender, race, systolic blood pressure, folate, vitamin B12, PTSD diagnosis (OR = 4.31, 95% CI [1.36-13.61]), and CVD risk (OR = 3.50, 95% CI [1.01-12.05]). Participation in PTSD treatment was significantly associated with homocysteine levels (OR = 6.43, 95% CI [2.02-20.45]). Findings support homocysteine as a clinically relevant biomarker linking PTSD and cardiovascular risk. The association between PTSD treatment and homocysteine suggests psychological interventions may influence biological pathways relevant to cardiovascular health, underscoring the value of biomarker-informed nursing assessment in trauma-exposed populations.

Exponential Random Graph Models (ERGMs) are a powerful and flexible framework for modeling network data. A fundamental challenge in ERGM estimation is the correct specification of the (sufficient) statistics that define the model structure. This paper addresses the problem of variable selection in ERGMs by making use of LASSO, a penalized estimation technique that shrinks some parameter estimates to zero, effectively selecting relevant variables. While LASSO is well established in standard regression settings, its application to ERGMs remains less explored. Here, we demonstrate how LASSO can be employed in the ERGM framework to perform variable selection and propose a ranking procedure to assess the relevance of candidate model terms.

Interpretable forecasting of carbon allowance returns using multi-scale trend-aware network with dynamic variable selection and squeeze-excitation

The early response effect, defined as a reliable symptomatic improvement during the initial phase of treatment, is the most robust predictor of recovery following eating disorder treatment. This study aimed to investigate which symptom domains mostly influence the early response effect. Data from N=232 adult patients (90.8% females; mean age=29.97, SD=10.67) treated in an outpatient eating disorder psychotherapy service were randomly partitioned into training (N=161) and test (N=71) samples. A Bayesian network model was developed in the training sample, modelling early changes (sessions 1-4) and interactions among symptoms measured by the Eating Disorder Examination Questionnaire (EDE-Q). A variable selection approach was applied to include only the most important variables in the model (i.e., reliable predictors of recovery). The trained model was externally validated by applying it to predict post-treatment recovery status in the test sample. Prediction accuracy was evaluated using the AUC statistic. The model identified a network of six interrelated eating disorder symptoms which were the most important predictors of recovery. The model was reliable in predicting recovery status and showed good generalisability to a test sample (training AUC=0.81 vs. test AUC = 0.77). Early changes in six areas (ranked by importance) reliably predict recovery after therapy: [1] avoidance of body exposure; [2] feelings of 'fatness'; [3] preoccupation with food, eating or calories; [4] fear of losing control over eating; [5] dissatisfaction with body shape; [6] dietary rules. The identification of early response domains associated with eventual recovery could help to inform targeted interventions strategies for patients with eating disorders. Future replication is warranted in more diverse and larger samples, including the applicability of these findings to different diagnostic groups.

Establishment of prediction equations for available energy values of sorghum in broilers using machine learning and stepwise regression methods.

Following therapeutic advancements, recompensation has gained increasing recognition in patients on waitlist for liver transplantation (LT). Identifying key predictors of waitlist removal because of improvement can enhance prognostication and resource allocation. We hence examined predictors of improvement-related waitlist removal using a machine learning-based approach with data from the United Network for Organ Sharing database. In this retrospective cohort study, adult LT waitlist candidates from 2000 to 2025 in the United Network for Organ Sharing registry were included. A random survival forest model was applied to examine key predictors associated with improvement-related waitlist removal, while accounting for death and LT as competing risks. Variable importance (VIMP) measure and minimal depth were used to guide variable selection. Model performance was evaluated using the concordance index, Brier scores, and time-dependent area under the curve. The cohort included 127 978 individuals listed for LT. Eight thousand four hundred ninety-three (6.6%) were delisted because of clinical improvement. The random survival forest model demonstrated strong performance and discriminatory ability overall at 1, 5, and 15 y (concordance index was 0.777, 0.771, and 0.781; time-dependent area under the curve was 0.78, 0.78, and 0.80). Brier scores were reduced relative to the reference. Strong predictors of recovery highlighted in both VIMP and minimal depth-based assessments of VIMP included diagnosis, age, and serum albumin. Identified variables could inform the development of robust predictive models to guide individualized decision-making for LT. With further validation and integration into clinical workflows, such models could enhance prognostication of patient trajectory on the LT waitlist and facilitate appropriate resource allocation.

Emerging evidence suggests that genetic variations contribute to individual differences in concussion recovery. A polygenic score (PGS) can capture the cumulative effect of multiple genetic variants, providing a partial explanation for individual differences in recovery times. In this study, our primary objective was to investigate the association between a PGS and recovery time following sport- or military activity-related concussion to better understand the genetic influences on recovery and inform personalized treatment strategies. Genome-wide genotyping data from collegiate athletes and military cadets with confirmed sport- or military activity-related concussions were obtained from the CARE Consortium study (N = 4659). A genome-wide association study (GWAS) was conducted on the discovery cohort (n = 768 samples) to identify single-nucleotide polymorphisms (SNPs) associated with time to return-to-play initiation (RTPi) after concussion using log-logistic accelerated failure time (AFT) models, adjusted for demographic and injury-related factors, and the first five principal components of genetic ancestries. Linkage disequilibrium-clumped SNPs with the lowest p values were selected and further refined using a robust variable selection method under a multivariable AFT model, resulting in the final set of SNPs. The PGS was constructed from this final set of SNPs and validated in an independent validation cohort (n = 262 samples) for time to RTPi. The validation was extended to the biologically relevant slow RTPi outcome (≥ 14days) using a logistic regression model. We also tested the PGS for association with time to return-to-play (RTP) and slow RTP (≥ 28days) as secondary outcomes. A Gene Ontology (GO) enrichment analysis was performed on the selected SNPs to identify relevant biological processes. The PGS was constructed using 49 SNPs. We found that higher PGS was significantly associated with a longer time to RTPi after concussion (p = 0.001), where each standard deviation increase in the PGS was associated with a 12% longer time to RTPi. The PGS was significantly associated with a slow RTPi recovery (≥ 14days), with each standard deviation increase corresponding to an 82% higher odds of delayed recovery (p = 0.002). No significant associations were observed between PGS and RTP outcomes. GO enrichment analysis highlighted synaptic mechanisms related to organization, signaling, and neuronal repair, which are critical for restoring neural communication during the healing process. Higher PGS is significantly associated with longer recovery time after concussion. This finding provides novel genetic insights for developing personalized treatment strategies to reduce the burden of prolonged recovery from concussion injury.

Multiple osteochondromas (MO) is a rare, inherited disorder characterized by multiple benign bone tumors. Although pain and fatigue are commonly encountered in clinical practice, their impact on children with MO remains largely unaddressed in literature. This study aimed to (1) assess fatigue and pain levels in children with MO, (2) compare fatigue severity with healthy peers, and (3) identify variables associated with fatigue and pain. In this cross-sectional study, 230 children (4-18yrs.) with MO were invited to complete validated, age-specific online questionnaires addressing fatigue (Checklist Individual Strength [CIS], visual analogue scale [VAS-fatigue]), pain (VAS), and psychosocial factors. Fatigue scores were compared to reference data from healthy peers using one-sample t-tests. The International Classification of Functioning, disability and health framework was used for a-priori selection of potential independent variables for multivariable regression analysis. A total of 134 children participated. Fatigue was reported by 89.6% (VAS-fatigue = 3.17 ± 2.63), with CIS scores significantly higher than healthy controls (CIS-MO = 66.32 ± 24.63 vs. CIS-healthy peers = 55.07 ± 20.99, p = 0.012). Pain was reported by 75.4% (VAS = 2.41 ± 2.42). For fatigue, both CIS and VAS-fatigue were associated with pain and emotional/behavioral problems (CIS-model adj. R2 = 0.524, p < 0.001; VAS-fatigue model adj. R2 = 0.576, p < 0.001). CIS was also associated with depressive symptoms (CDI, p = 0.019). The pain regression model explained 45% of the variance (p < 0.001), and pain was significantly associated with functional disability and VAS-fatigue (p < 0.001). Fatigue, alongside pain, is highly prevalent and significantly increased in children with MO compared to healthy peers. Psychosocial factors are closely associated with both fatigue and pain, underscoring the importance of multidisciplinary disease management. Not applicable.

Role of balance and physical fitness in assessing the effectiveness of cardiac rehabilitation on functional recovery

The aim was to build a risk score (RS) for gastric and esophageal cancer (GEC) based on self-reported information as a first step to develop a risk-adapted screening modality for GEC or precursor lesions in a non-high incidence region in the framework of the RISC-GAP project. Data from 375,280 participants aged 50 years and older in the UK Biobank project were used. The outcome was incident esophageal or gastric cancer. Various variables, including sociodemographic data, medical conditions, medication, lifestyle factors and diet, were initially considered. To be able to use the RS as a screening tool in the general population, only variables that can be determined by self-report were selected. For variable selection, we used COX regression models with LASSO penalization; the main criterion was 5- and 10-years AUC. The final score included the following eight variables: sex, age, smoking status, drinking status, body mass index, history of esophagitis, medication with gastric acid inhibitors and surgery in the stomach/esophagus area. 10-fold cross-validation revealed a discrimination of 0.740 (5-year AUC) and 0.724 (10-year AUC), respectively. High-risk individuals were defined as those with a 10-year cancer risk of 1% or more (around 6% of the study population). The RS allows a reasonable discrimination of individuals with an elevated risk of gastric or esophageal cancer. In further steps of the RISC-GAP project it will be evaluated whether selection of a high-risk population can be further improved by additional clinical and biomarker information.

We propose a Bayesian kernel machine regression (BKMR) framework for count outcomes with dynamic spatiotemporal dependence. The proposed model, termed Negative Binomial BKMR with spatiotemporal effects (NB-BKMR), integrates (i) a negative binomial likelihood to accommodate overdispersion, (ii) a kernel-based exposure–response surface for complex mixtures, (iii) hierarchical group-wise variable selection and (iv) a dynamic spatiotemporal random effect structure based on a Leroux conditional autoregressive (CAR) prior evolving over time. Posterior inference is conducted in a fully Bayesian framework using Polya-Gamma data augmentation. Through simulation studies, under varying nonlinear exposure–response functions, correlation structures, and spatiotemporal dependence patterns, we show that NB-BKMR yields well-calibrated uncertainty quantification and robust identification of dominant mixture drivers, even when exposures are highly correlated. An application to the U.S. state-level traffic fatality counts (1982–1988) illustrates how the model uncovers nonlinear effects and interactions among socioeconomic and behavioral predictors while improving predictive performance relative to generalized additive models with spatiotemporal smooths. This work extends existing BKMR methodology by unifying mixture modeling, count outcomes, and dynamic spatial dependence in a single coherent framework, with particular relevance for areal public health surveillance data.

Intrinsically disordered proteins (IDPs) play critical roles in cellular signaling and regulation, yet their dynamic conformational landscapes make them difficult to characterize experimentally and computationally. Phosphorylation, one of the most common post-translational modifications, frequently occurs within intrinsically disordered regions and can modulate protein structure and function. Enhanced sampling molecular dynamics methods offer a potential route to more efficiently explore the diverse conformations accessible to IDPs, but systematic comparisons of their performance sampling the conformational landscape of such systems remain limited. Here, we evaluate the conformational sampling of the intrinsically disordered β-catenin17-48 peptide in both its nonphosphorylated and phosphorylated states using three enhanced sampling approaches: Gaussian-accelerated molecular dynamics (GaMD), metadynamics (METAD), and weighted ensemble simulations (WESTPA), in comparison with conventional molecular dynamics simulations. Two collective variables (CVs) were explored to guide sampling: the ϕ dihedral angles of the phosphorylation sites Ser33 and Ser37 and the end-to-end distance of the peptide. We found that different enhanced sampling methods explored distinct regions of conformational space rather than converging to a single ensemble, with GaMD largely overlapping with unbiased simulations. Notably, METAD and WESTPA more readily accessed conformational regions not observed in unbiased simulations. Analysis of the combined conformational ensembles identified intermediate conformations connecting the nonphosphorylated and phosphorylated states, which are preferentially sampled in simulations employing adaptive strategies. Additionally, the Ser33/Ser37 ϕ angle CV more effectively captures phosphorylation-dependent conformational shifts than the end-to-end distance metric. Together, these results highlight how both the choice of enhanced sampling strategy and the selection of collective variables influence the exploration of IDP conformational landscapes.