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PubMed2026

Mul-PheG2P: decoupled learning and prediction-space fusion enables robust and interpretable multi-phenotype genomic prediction.

Genomic prediction of multiple phenotypes is crucial in modern plant breeding; however, existing methods struggle with negative transfer and lack interpretability, particularly across high-dimensional small-sample data and diverse species. To address this, we propose Mul-PheG2P, a novel paradigm based on decoupled learning and predictive space fusion. It employs a two-stage design: first training phenotype-specific encoders using genetic data, then decoupling phenotype-specific learning from cross-phenotype aggregation via an interpretable prediction layer. Mul-PheG2P outperforms existing methods across diverse crop datasets, including maize (Zea mays), wheat (Triticum aestivum), and tomato (Solanum lycopersicum). It provides a multi-scale interpretability chain: at the macro level, it quantifies phenotypic contributions via attention-based weighting; at the micro level, Integrated Gradients reveal the genetic basis of predictions. Notably, the model successfully identified the CCT (CONSTANS, CO-like, and TOC) motif regulating photoperiodism and the SQUAMOSA (SQUAMOSA promoter binding protein) promoter for inflorescence development, confirming its ability to capture functional biological mechanisms. These results highlight the high performance and interpretability of Mul-PheG2P, showcasing its value for low-cost, large-scale screening to advance precision breeding.

Phenotype↗

PubMed2026

Future promise, current clinical ambiguity: a systematic review of machine learning algorithm outputs predicting risk of cardiovascular disease.

OBJECTIVE: To examine whether the outputs of machine learning algorithms designed to predict risk of cardiovascular disease (CVD) address known deficiencies of the Framingham Risk Score (FRS) and improve risk estimates. METHODS: For this critical review, Medline, Embase and IEEE were searched from inception to 1 January 2025. Included were studies describing machine learning algorithms designed to specifically compare output of cardiovascular risk assessment with the FRS. Commentaries, letters, unpublished work or non-peer-reviewed papers were excluded.Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, two reviewers screened titles and abstracts independently, then populated a purpose-built data extraction form. A subsequent qualitative thematic analysis focused on algorithms' strengths, added value, potential harms, unintended consequences and equity implications.The main outcome assessed was whether, among healthy adults, the algorithm improved CVD risk prediction relative to the FRS. RESULTS: Of 707 studies retrieved, 29 met inclusion criteria. 23 reported improved predictive ability relative to the FRS. Most datasets and/or medical records used included sociodemographic predictors of CVD not included among FRS inputs. Some added costly diagnostic tests like CT angiography to FRS screening indicators. When they were defined, inputs and outcomes such as hypertension or myocardial infarction did not always adhere to FRS values. Statistical significance was generally taken as a proxy for clinical significance. Some algorithms overestimated the number at risk compared with the FRS without discussing whether that larger proportion might be at risk of overdiagnosis rather than CVD, while a few decreased the proportion found to be at risk. CONCLUSIONS: Use of artificial intelligence to improve accuracy of risk assessment for CVD demonstrates the technological capacity to merge known sociodemographic predictors with biologic variables and examine non-linear interactions among these. Still needed to achieve patient benefit is clinical insight, adherence to screening principles and cost-benefit assessment of inputs selected.

Humans↗

PubMed2026

Meta-PseU: A meta-classifier for robust prediction of RNA pseudouridine modification sites from long sequences.

BACKGROUND AND OBJECTIVES: Pseudouridine (Ψ) represents one of the most abundant and conserved RNA modifications. Ψ provides an additional hydrogen-bond donor that enhances RNA structural stability and modulates translation. It participates in diverse biological processes, including RNA-protein interactions, splicing, translational control, and stress responses. Aberrant pseudouridylation is implicated in cancer, neurodegenerative disorders, and autoimmune diseases. Despite its biological importance, experimental identification of Ψ sites remains time-consuming and costly, limiting the feasibility of transcriptome-wide profiling. Computational approaches have therefore become essential complements to experimental techniques. However, state-of-the-art machine-learning and deep-learning predictors often suffer from limited generalizability due to small training datasets. To overcome these issues, we aim at constructing new long-sequence datasets and developing a novel Ψ site predictor. METHODS: New long-sequence datasets were constructed as benchmarks for RNA Ψ-site prediction. The Ψ modification sites in RMBase 3.0 were mapped to the reference genomes across three species of human, mouse, and yeast, and the RNA sequences with a length of 201 were generated by extending the upstream and downstream from the mapped, central sites. To eliminate sequence redundancy, the sequences were clustered using CD-HIT with a 70% sequence identity threshold. We developed Meta-PseU, a logistic regression-based meta-classifier that considered 118 machine learning and deep learning classifiers. The datasets and programs are freely accessible at https://github.com/kuratahiroyuki/MetaPseU. RESULTS: By optimizing model configuration, we proposed the Meta-PseU model stacking 32 machine learning and deep learning classifiers out of 118 classifiers. Meta-PseU substantially improved model generalizability, overcoming a key limitation of existing approaches. It greatly outperformed state-of-the-art predictors and achieved increasing accuracy with increasing sequence length. CONCLUSIONS: Long-sequence datasets were newly constructed as benchmarks for RNA Ψ-site prediction. Meta-PseU offers a new framework for robust Ψ-site identification by using long sequences.

Pseudouridine↗

PubMed2026

Extended Long-Term Effects of Cognitive and Aerobic Training on Cognitive Function in Patients With Stress-Related Exhaustion Disorder: A 4.5-Year Follow-Up of a Randomized Controlled Trial.

Stress-related conditions like clinical burnout and exhaustion disorder (ED) are associated with enduring cognitive problems. We have previously demonstrated that the addition of computerised cognitive training (CCT) and aerobic training (AT) to a multimodal rehabilitation programme (MMR) yielded greater improvements in cognitive function compared to MMR alone in patients with ED. These effects were maintained at the 1-year follow-up for CCT, but not for AT. Building on these findings, the present study examined the extended long-term effects of CCT and AT on cognitive function, psychological health, and work ability, 4.5 years after the interventions. Participants were recruited from a stress-rehabilitation clinic and 56 of the initial 132 participants returned for the 4.5-year follow-up. Assessments were conducted before (T1), immediately after (T2), 1-year after (T3) and 4.5-years after (T4) the interventions. Mixed model analyses assessed changes in the intervention groups relative to the control group from T1 to T4, with follow-up comparisons examining within-group stability of outcomes between T3 to T4. The primary outcome was cognitive performance on a global cognitive score. Secondary outcomes included domain-specific cognitive functioning, self-reported cognitive function, psychological health (burnout, depression, anxiety, and fatigue), and work ability. The analysis revealed sustained long-term effects on the global cognitive score, a trained updating task and episodic memory in the CCT group, with stable performance between one- and 4.5-year follow-up. The addition of AT did not yield any extended long-term effects on cognitive performance. Both groups showed extended long-term improvements in self-reported memory problems, although findings were mixed. Extended long-term improvements were observed on burnout for both groups, with additional effects on anxiety and work ability in the AT group. Notably, these effects were not present at the 1-year follow-up and are more plausibly explained by selective attrition rather than a delayed intervention effect. In conclusion, the result indicates that cognitive interventions such as CCT can have lasting positive effects on cognitive function in patients with ED, whereas the long-term psychological effects should be interpreted with caution. TRIAL REGISTRATION: ClinicalTrials.gov: NCT0073772.

Adult↗

PubMed2026

Diagnostic performance of machine learning models versus established risk stratification for intracranial aneurysm rupture: a systematic review and bivariate meta-analysis.

BACKGROUND: Machine learning (ML) models have been proposed to improve the discrimination of intracranial aneurysm rupture status beyond established clinical risk stratification tools. However, reported performance is heterogeneous and the relative contribution of model architecture and feature dominance remains unclear. METHODS: We performed a Preferred Reporting Items for Systematic Reviews and Meta-Analyses-diagnostic test accuracy systematic review and diagnostic meta-analysis of studies evaluating ML models for intracranial aneurysm rupture discrimination. PubMed, Embase and CENTRAL were searched to February 2026. Sensitivity and specificity were pooled using a bivariate random-effects model, with summary receiver operating characteristic curves generated across training, internal testing and external validation datasets. Models were compared with regression-based approaches and Population, Hypertension, Age, Size of aneurysm, Earlier subarachnoid haemorrhage, Site of aneurysm (PHASES) scores. Subgroup and meta-regression analyses explored associations between algorithm family and feature domain. RESULTS: Sixty-two retrospective cohorts (29 709 patients 209 models) met the inclusion criteria. In training datasets, pooled sensitivity and specificity for ML were 0.81 (95% CI 0.75 to 0.85) and 0.83 (0.80-0.86), with an area under the curve (AUC) of 0.878, exceeding PHASES (AUC 0.667). In testing datasets, ML retained higher discrimination (AUC 0.837) than regression models (0.806) and PHASES (0.646). In external validation, sensitivity was preserved (0.82), but specificity declined (0.66). Deep learning demonstrated the highest AUCs (training and testing). Incorporation of haemodynamic or radiomic features improved pooled discrimination relative to morphology alone. Evidence of small-study effects and mostly unclear Prediction Model Risk Of Bias Assessment Tool ratings were observed. CONCLUSIONS: ML approaches demonstrate higher pooled discrimination for aneurysm rupture status than conventional risk scores in retrospective datasets, but reduced external validation specificity and heterogeneity limit confidence for clinical translation. Prospective, externally validated, calibrated models are required before integration into routine cerebrovascular risk stratification.

Humans↗

PubMed2026

Data-centric, robust, and explainable multimodal deep learning for clinical decision support: A systematic review.

PURPOSE: Multimodal deep learning is increasingly proposed for clinical decision support (CDS) under a "data-centric" framing that prioritizes label quality, missing-modality robustness, distribution shift, calibration, and explainability. Prior reviews have examined multimodal medical AI, CDS, and data-centric methods separately, but none address their intersection. We mapped the modalities, fusion strategies, and data-centric and explainability techniques used in this recent literature, quantified how often each is implemented rather than merely mentioned, assessed deployment-relevant evidence (external validation, clinical-outcome measurement, equity), and formally appraised study-level risk of bias. METHODS: Following the PRISMA 2020 statement (PROSPERO CRD420261427815; registered retrospectively), we screened 150 records and included primary, clinical, multimodal studies that applied machine or deep learning to a decision-support task and reported at least one quantitative result. Two reviewers screened and extracted data with consensus adjudication. Each study was coded against pre-specified operational definitions, separating implemented or empirically evaluated techniques from those only mentioned. Study-level risk of bias was assessed with PROBAST + AI. Synthesis was narrative. RESULTS: Thirty-one studies met inclusion; 30 (97%) were published between 2024 and 2026, with a median of three modalities (range 2-6), most commonly structured EHR (71%) and imaging (39%). Data-centric techniques were frequently reported (74-84% across label-noise, distribution-shift, calibration, missing-modality and class-imbalance handling; equity 61%). However, external validation was reported in only 4/31 studies (13%), a clinical or provider outcome in 3/31 (10%), and no study reported routine deployment. Overall risk of bias was high in 27/31 studies (87%), driven by the analysis domain. CONCLUSION: Within this recent, self-selected slice of the field, technical robustness and explainability techniques are widely reported but rarely validated out-of-distribution or against clinical outcomes, and the underlying evidence is at high risk of bias. Progress requires external multi-site validation, clinical-outcome measurement, formal bias appraisal, and adherence to AI reporting standards (e.g., TRIPOD + AI) before deployment can be justified.

Deep Learning↗

PubMed2026

Clinical Efficacy and Learning Curve of Far-Lateral Approach (FLA) in Uni-Portal Non-Coaxial Spinal Endoscopic Surgery (UNSES) in the Treatment of Lumbar Degenerative Diseases: A Prospective Study.

BACKGROUND: Uniportal non-coaxial spinal endoscopic surgery (UNSES) via far-lateral approach (FLA) is an innovative minimally invasive procedure for lumbar degenerative diseases, particularly far-lateral disc herniation and foraminal stenosis. However, complex lateral lumbar anatomy and strict endoscope-instrument coordination create a distinct learning curve that may compromise early surgical efficiency and safety. This study aimed to evaluate the efficacy and safety, quantify the learning curve, and to provide clinical guidance for the standardized promotion and application of this technology. METHODS: A total of 40 consecutive patients with lumbar degenerative diseases who underwent UNSES via FLA by a single surgeon between January 2025 and December 2025 were included. All data were analyzed using SPSS 26.0 statistical software (IBM, USA). Primary outcomes included operation time, blood loss, fluoroscopy frequency, and intraoperative complication rate. Secondary outcomes were VAS, ODI, and modified Macnab criteria at 1, 3, and 6 months postoperatively. The learning curve and the inflection point of the learning curve was determined using cumulative sum (CUSUM) analysis. The differences in clinical indicators between early and proficient stage were compared. RESULT: Operation time, blood loss, and fluoroscopy times decreased significantly with case accumulation (p < 0.05). CUSUM identified an inflection point at the 16th case, after which operation time stabilized at (55.3 ± 8.6) min, much shorter than the early phase (89.5 ± 10.3) min (p < 0.001). Before the 16th case, the curve was in an upward trend; after the 16th case, the curve tended to be flat, indicating the proficiency stage. Postoperative VAS and ODI improved significantly than those before surgery at each follow-up time (p < 0.05). There was no significant difference in postoperative VAS score and ODI between the two groups at each follow-up time point (p > 0.05). The total complication rate was 12.5% (5/40), were cured by conservative treatment. The total excellent-good rate was 90.0% (36/40). L5/S1 and Bertolotti's syndrome were independent factors affecting the learning curve. CONCLUSION: UNSES via FLA is a safe and effective minimally invasive technique for treating complex lumbar degenerative diseases. It has a certain learning curve, and the inflection point is about the 16th case. After mastering the key techniques such as anatomical positioning, endoscopic manipulation and hemostasis, the surgeon can gradually reach the proficiency stage, with significantly improved surgical efficiency and clinical efficacy, and controllable complications. This study provides a theoretical basis for the clinical training and technology promotion of UNSES via FLA.

Humans↗

PubMed2026

Reliability-aware hierarchical learning for Chagas disease screening from 12-lead ECGs: tackling label uncertainty and class imbalance.

Objective.Chagas disease, a neglected tropical disease (NTD) with significant cardiovascular impact, remains underdiagnosed in resource-limited regions. Electrocardiogram (ECG) screening offers a low-cost tool for detecting cardiac involvement, yet algorithm development is challenged by label noise, data scarcity, and the latent nature of infection. This study proposes a robust ECG-based screening framework that explicitly addresses these constraints.Approach.We introduce aReliability-Aware Hierarchical Learningstrategy that calibrates supervision according to data provenance, prioritizing serology-confirmed labels over noisy self-reports. To mitigate data scarcity, we compare a specialized convolutional neural network (CNN) trained from scratch with a transfer learning approach based on a Spatio-Temporal ECG foundation Model (FM). Performance is evaluated across varying data scales, and the representation structure is analyzed to interpret model behavior.Main results.On the official hidden test set of the George B. Moody PhysioNet/Computing in Cardiology Challenge 2025, our approach achieved a Challenge Score of 0.163. We observe that while the specialized CNN performs competitively in data-rich regimes, the FM exhibits superior robustness in extreme low-resource settings. Furthermore, performance reaches a plateau imposed by underlying disease physiology. Bimodal score distributions suggest that models distinguish established cardiomyopathy from indeterminate infection, which remains electrophysiologically indistinguishable from healthy controls.Significance.These findings clarify both the potential and intrinsic limits of ECG-based AI screening for NTD-associated cardiac involvement. Reliability-aware supervision and data-efficient transfer learning provide a practical framework toward scalable and clinically meaningful ECG screening systems in resource-constrained environments.

Humans↗

PubMed2026

Dynamic evolution of chaperone-mediated autophagy is associated with tumor microenvironment remodeling and prognostic stratification in lung adenocarcinoma: insights from single-cell transcriptomics, ensemble machine learning, and experimental validation.

BACKGROUND: Lung adenocarcinoma (LUAD) shows prognostic heterogeneity, and tumor-node-metastasis (TNM) staging is limited for individualized management. Chaperone-mediated autophagy (CMA) maintains proteostasis, but its role during adenocarcinoma in situ (AIS)-minimally invasive adenocarcinoma (MIA)-invasive adenocarcinoma (IAC) progression remains unclear. METHODS: Single-cell RNA sequencing (scRNA-seq) data from GSE189357 and bulk transcriptomes from The Cancer Genome Atlas (TCGA)-LUAD and Gene Expression Omnibus (GEO) cohorts were integrated. CMA activity, cell-cell communication, weighted gene co-expression network analysis (WGCNA), tumor-normal differential expression, machine-learning survival modeling, tumor microenvironment (TME) features, drug sensitivity, and EPC1 function were analyzed. RESULTS: CMA-high tumor epithelial cells increased from AIS (58.1%) to MIA (65.7%) but declined in IAC (44.4%; p < 0.001). CMA-low cells preferentially received fibroblast-derived extracellular matrix cues. A CMA-negatively correlated module identified 69 core genes. Random survival forest (RSF) performed best among 117 machine-learning combinations (mean concordance index > 0.873). High-risk patients had worse survival across cohorts, and the risk score was independently associated with overall survival (hazard ratio = 16.013, 95% confidence interval: 9.579-26.768, p < 0.001). High-risk tumors showed proliferative activation and M0 macrophage enrichment, whereas low-risk tumors showed stronger immune-related signaling. EPC1 overexpression suppressed malignant phenotypes in A549 cells. CONCLUSION: CMA dynamics are associated with stromal and immune remodeling during LUAD progression. A CMA-based model provides robust prognostic stratification and may offer a basis for future TME-guided studies.

Chaperone-mediated autophagy↗

PubMed2026

Diagnostic Performance of Machine Learning for Systemic Lupus Erythematosus: Systematic Review and Meta-Analysis.

BACKGROUND: Early and accurate diagnosis of systemic lupus erythematosus (SLE) and its organ involvement is essential. Previous reviews of machine learning (ML) in SLE combined heterogeneous tasks and validation strategies and may have overinterpreted model performance. OBJECTIVE: This study evaluated the diagnostic performance of ML and deep learning (DL) models for 3 clinically distinct SLE-related tasks: SLE classification or diagnosis, lupus nephritis (LN) diagnosis, and neuropsychiatric systemic lupus erythematosus (NPSLE) discrimination. We also assessed methodological quality and certainty of evidence. METHODS: PubMed, Embase, Cochrane Library, Web of Science, and IEEE Xplore were searched from January 2014 to April 2026. Eligible peer-reviewed diagnostic accuracy studies developed or validated ML or DL models for 1 of the 3 prespecified tasks, used an accepted reference standard, and provided data for a 2×2 contingency table. Bivariate random-effects meta-analyses with the Hartung-Knapp-Sidik-Jonkman adjustment were used to pool sensitivity and specificity. We reported 95% prediction intervals (PIs), assessed risk of bias using the Quality Assessment of Diagnostic Accuracy Studies for Artificial Intelligence tool (QUADAS-AI; Viknesh Sounderajah [Imperial College London]), and evaluated certainty of evidence using the Grading of Recommendations Assessment, Development, and Evaluation framework for diagnostic test accuracy. RESULTS: Twenty-nine studies were included: 17 for SLE classification, 5 for LN diagnosis, and 7 for NPSLE discrimination. In the primary task-stratified analysis, pooled sensitivity was 0.91 (95% CI 0.86-0.94; 95% PI 0.56-0.99), and pooled specificity was 0.94 (95% CI 0.91-0.96; 95% PI 0.69-0.99), with low heterogeneity (I²=23.9% and 22.9%, respectively). DL models showed a sensitivity of 0.93 and specificity of 0.95, compared with 0.88 and 0.94 for traditional ML models. Certainty of evidence was high for most analyses but low for LN diagnosis because of inconsistency and imprecision. All studies were retrospective, and only 9 of 29 (31%) performed independent external validation. Overall risk of bias was high or unclear in 22 of 29 (75.9%) studies. No study reported model calibration, decision-curve analysis, or net clinical benefit. CONCLUSIONS: ML models showed promising diagnostic accuracy across 3 distinct SLE-related tasks, but wide PIs, limited external validation, and pervasive risk of bias restrict conclusions about real-world generalizability. Prospective multicenter studies with standardized tasks and reference standards, independent external validation, and formal assessment of calibration and clinical utility are required before clinical implementation.

Humans↗

PubMed2026

From fear to empowerment: the impact of employees AI awareness on workplace well-being - a new insight from the JD-R model.

PURPOSE: The primary purpose of the study was to explore the impact of health workers' awareness of artificial intelligence (AI) on their workplace well-being, addressing a critical gap in the literature. By examining this relationship through the lens of the Job demands-resources (JD-R) model, the study aimed to provide insights into how health workers' perceptions of AI integration in their jobs and careers could influence their informal learning behaviour and, consequently, their overall well-being in the workplace. The study's findings could inform strategies for supporting healthcare workers during technological transformations. DESIGN/METHODOLOGY/APPROACH: The study employed a quantitative research design using a survey methodology to collect data from 420 health workers across 10 hospitals in Ghana that have adopted AI technologies. The study was analysed using OLS and structural equation modelling. FINDINGS: The study findings revealed that health workers' AI awareness positively impacts their informal learning behaviour at the workplace. Again, informal learning behaviour positively impacts health workers' workplace well-being. Moreover, informal learning behaviour mediates the relationship between health workers' AI awareness and workplace wellbeing. Furthermore, employee learning orientation was found to strengthen the effect of AI awareness on informal learning behaviour. RESEARCH LIMITATIONS/IMPLICATIONS: While the study provides valuable insights, it is important to acknowledge its limitations. The study was conducted in a specific context (Ghanaian hospitals adopting AI), which may limit the generalizability of the findings to other healthcare settings or industries. Self-reported data from the questionnaires may be subject to response biases, and the study did not account for potential confounding factors that could influence the relationships between the variables. PRACTICAL IMPLICATIONS: The study offers practical implications for healthcare organizations navigating the digital transformation era. By understanding the positive impact of health workers' AI awareness on their informal learning behaviour and well-being, organizations can prioritize initiatives that foster a learning-oriented culture and provide opportunities for informal learning. This could include implementing mentorship programs, encouraging knowledge-sharing among employees and offering training and development resources to help workers adapt to AI-driven changes. Additionally, the findings highlight the importance of promoting employee learning orientation, which can enhance the effectiveness of such initiatives. ORIGINALITY/VALUE: The study contributes to the existing literature by addressing a relatively unexplored area - the impact of AI awareness on healthcare workers' well-being. While previous research has focused on the potential job displacement effects of AI, this study takes a unique perspective by examining how health workers' perceptions of AI integration can shape their informal learning behaviour and, subsequently, their workplace well-being. By drawing on the JD-R model and incorporating employee learning orientation as a moderator, the study offers a novel theoretical framework for understanding the implications of AI adoption in healthcare organizations.

Humans↗

PubMed2026

Distal versus proximal radial access for diagnostic cerebral angiography: comparative outcomes and learning curve analysis.

BACKGROUND AND PURPOSE: Distal transradial access (dTRA) is an alternative to proximal transradial access (pTRA) for neuroangiography, but comparative real-world data and evidence on its early learning curve remain limited. We compared procedural performance and access-site complications between dTRA and pTRA and evaluated the early learning curve of dTRA. METHODS: We retrospectively analyzed 470 diagnostic cerebral angiography procedures, representing 421 unique patients, performed via radial access at a single center between January 2025 and February 2026, including 237 dTRA and 233 pTRA procedures. Baseline characteristics, including age, sex, body mass index (BMI) category, aortic arch type, and antiplatelet/anticoagulant use, procedural performance, and clinically assessed access-site events were compared between groups. Radial artery occlusion (RAO) was assessed by postoperative bedside pulse examination and confirmed with Doppler ultrasound when clinical findings were uncertain. Multivariable logistic regression was used to evaluate predictors of RAO, persistent bleeding or repeated compression, hand edema, and a composite access-site event endpoint. Because repeated procedures occurred in a subset of patients and event counts were limited, first-procedure sensitivity analysis and analyses of infrequent outcomes were interpreted cautiously. The dTRA learning process was assessed in the first 100 dTRA cases performed by a single operator using multivariable regression, cumulative sum (CUSUM) analysis, segmented trend analysis, and phase-based comparisons. RESULTS: Baseline characteristics were comparable between groups, including age, male sex, BMI category, aortic arch type, and antiplatelet/anticoagulant use. Compared with pTRA, dTRA was associated with more puncture attempts (3.0 [2.0-4.0] vs 2.0 [1.0-3.0], P < 0.001), longer puncture time (2.0 [1.0-5.0] vs 2.0 [1.0-3.0] min, P = 0.003), lower first-pass success (19.4% vs 35.2%, P < 0.001), and a higher crossover rate (11.4% vs 6.0%, P = 0.037). However, dTRA was associated with a lower clinically assessed RAO rate (2.5% vs 7.7%, P = 0.011). On multivariable analysis, pTRA was independently associated with higher odds of RAO (OR 3.27, 95% CI 1.26-8.49, P = 0.015) and the composite access-site event endpoint (OR 3.12, 95% CI 1.55-6.28, P = 0.001). Similar findings were observed in a sensitivity analysis restricted to the first procedure per patient. In the first 100 dTRA cases, cumulative dTRA experience was independently associated with shorter total procedure time (beta = -0.074 min/case, P = 0.009), while CUSUM and moving-average analyses suggested that the major learning effect occurred within approximately the first 10-15 cases. CONCLUSIONS: In this retrospective single-operator cohort, dTRA was associated with lower clinically assessed RAO than pTRA despite greater access difficulty. The early learning effect was mainly reflected in shorter total procedure time. These findings support the feasibility of dTRA but should be interpreted cautiously given the study's observational design and limited anatomical data.

Humans↗

PubMed2025

Social Responsiveness as a Mediator in Adapted Cognitive Behavioral Therapy for Autistic Youth with Maladaptive and Interfering Anxiety.

Numerous adaptations to interventions have been included in cognitive behavioral therapy (CBT) for autistic youth. This study examines the degree to which CBT adapted to the social needs of autistic youth confers significant benefit by promoting social responsiveness. A secondary analysis was conducted on a multisite randomized clinical trial (Wood et al. in JAMA Psychiatry 77:474-483, 2020) comparing adapted CBT with standard-of-practice CBT and treatment-as-usual (TA). Autistic youth (N = 167; aged 7-13) with maladaptive and interfering anxiety participated. The adapted CBT (BIACA) uses a modular format, with an emphasis on supplementing common CBT practice elements (reframing and graded exposure) with social skill supports. The primary outcome measure was the Pediatric Anxiety Rating Scale. Social responsiveness was assessed with the Social Responsiveness Scale, Second Edition. Participants' general mental health was assessed as a secondary outcome using the Brief Problem Checklist. Mediation was tested using the SPSS PROCESS macro. Analyses suggested that the effect of adapted CBT on anxiety was mediated by its effects on social responsiveness, with a statistically significant indirect effect. Youth randomly assigned to adapted CBT exhibited better overall mental health at posttreatment compared to those randomized to the other conditions, and this effect was also mediated by improved social responsiveness. CBT adapted to address some of the social needs of autistic youth may enhance mental health outcomes by supporting social responsiveness, perhaps increasing the ease and effectiveness with which some youth can navigate potentially stressful situations such as entering and participating in group activities.

Humans↗

PubMed2026

Online Social Anxiety in the Digital Age: Transitions, Predictors, and Mental Health Associations in Emerging Adulthood.

BACKGROUND: Online social anxiety (OSA), a multidimensional form of social evaluative anxiety in online social contexts, disproportionately affects emerging adults who constitute the largest active group of media users and face heightened psychological sensitivity due to growing pressures and immature sociocognitive regulation during the transition to adulthood. However, its heterogeneity, transitions, and longitudinal associations with mental health outcomes remain underexplored. METHODS: This study utilized data from two waves of a three-wave longitudinal survey, with 849 Chinese participants (Meanage = 21.6 years; 50.4 percent female) assessed at 4-month intervals. Individuals were classified using latent profile analysis and the stability and changes of profiles were assessed via latent transition analysis (LTA). Multinomial logistic regressions were conducted separately at baseline and follow-up to identify correlates of profile membership. Predictors of profile transitions were examined using manual three-step LTA models, and associations between latent transition patterns and follow-up mental health outcomes were examined using BCH-LTA distal outcome analyses controlling for the corresponding baseline symptom level. RESULTS: Four profiles of OSA were identified: low, privacy-sensitive, moderate-high, and high OSA. Extreme profiles (low/high OSA) showed high stability (80.4 percent and 79.1 percent), while privacy-sensitive OSA exhibited the lowest stability (55.1 percent). Profile memberships were influenced by social-cognitive biases and digital interaction, particularly fear of negative evaluation and online interpersonal trust, whereas profile transitions were mainly associated with anxiety. Transitions toward less severe OSA profiles were generally associated with better subsequent mental health, whereas transitions toward more severe profiles corresponded to poorer outcomes, particularly for offline social anxiety. CONCLUSION: OSA was heterogeneous in its manifestation, severity and transitions. Personalized and early interventions targeting profile-specific vulnerabilities are critical to prevent the worsening of OSA and mitigate its psychological burden.

Humans↗

PubMed2026

Integrative machine learning and transcriptomic analysis reveals molecular mechanisms underlying low survival rate in larval Chinese Bahaba (Bahaba taipingensis).

Chinese Bahaba (Bahaba taipingensis) is a Class I protected marine fish endemic to China. Low larvae survival during artificial breeding severely hinder population recovery. To investigate the molecular mechanism of high mortality in larval fish, this study performed RNA-seq on liver from naturally deceased (ND) and mass-dead (MD) individuals, combined with least absolute shrinkage and selection operator (LASSO) regression and random forest (RF) algorithms to screen for core signature genes. A total of 873 differentially expressed genes (DEGs) were identified, including 112 upregulated and 761 downregulated genes. GO and KEGG enrichment analyses revealed significant enrichment in amino acid metabolism disorders, one‑carbon folate pool impairment, PPAR signaling abnormalities, ECM-receptor interaction, focal adhesion pathway, indicating widespread metabolic suppression accompanied by extracellular matrix remodeling and signaling disturbances in the livers of MD fish. MAD pre-filtering combined with dual machine learning algorithms yielded 18 robust core signature genes, among which SLC38A4, MMP1, FADD, FKBP5, and APOB were consistently identified as high-frequency core genes by both algorithms. SLC38A4 exhibited the highest importance score in the RF model and was significantly downregulated, making it the primary molecule distinguishing ND from MD phenotypes. ROC curve analysis showed that both models achieved an AUC of 1.000 (95% CI lower bound: 0.610), confirming the precise discriminatory ability of the core genes. GSEA further demonstrated significant enrichment of this core gene set in ND samples. This study provides the first systematic elucidation of the molecular mechanisms underlying liver dysfunction in low survival rate B. taipingensis, characterized by amino acid transport impairment, metabolic reprogramming, and structural remodeling, offering theoretical foundations for health assessment, early mortality risk warning, and artificial breeding conservation of this species.

Animals↗

PubMed2026

A flipped classroom approach compared with low-interactive online learning for pediatric pain management knowledge and instructional motivation in nursing students: A randomized controlled study.

AIM: This study aimed to compare a flipped classroom approach with low-interactive online learning in terms of nursing students' questionnaire-assessed pediatric pain management knowledge and instructional motivation. BACKGROUND: Pain management in children is a critical and multidimensional nursing responsibility. However, limited curricular time and opportunities for applied learning may restrict nursing students' preparedness in this area. Structured and interactive instructional formats, such as the flipped classroom, may support knowledge acquisition and motivation in pediatric nursing education. METHODS: This study employed a parallel-group randomized controlled trial design with a 1:1 allocation ratio. Eighty-eight third-year prelicensure nursing students were randomized to either the flipped classroom group (n = 44) or the low-interactive online learning group (n = 44). Due to attrition (2 intervention, 2 control), analyses included 42 participants per group (n = 84 in total). Data were collected between February and July 2022 using the Pediatric Pain Management Knowledge Scale for Nursing Students and the Instructional Materials Motivation Survey. This study was prospectively registered at ClinicalTrials.gov (Identifier: NCT07129044). RESULTS: At baseline, the groups were comparable in terms of knowledge and learning motivation. Following the intervention, the flipped classroom group demonstrated greater improvements in questionnaire-assessed pediatric pain management knowledge and instructional motivation than the low-interactive online learning group. Although scores declined from post-test to the three-month follow-up, they remained above baseline in the flipped classroom group. CONCLUSIONS: Within the context of this course, the flipped classroom approach was associated with greater improvement in questionnaire-assessed pediatric pain management knowledge and instructional motivation than low-interactive online learning. The findings should be interpreted as proximal educational outcomes rather than evidence of improved clinical competence or durable long-term effectiveness. Further studies using objective performance-based outcomes and longer follow-up periods are needed.

Humans↗

PubMed2026

Association between cumulative social disadvantage, as measured by the social determinants of health score, and epilepsy: a cross-sectional study.

BACKGROUND: Social determinants of health (SDoH) shape access to care, health behaviors, and long-term outcomes, yet their cumulative relationship with epilepsy has not been well quantified. This study examined whether a composite SDoH score was associated with epilepsy in adults. METHODS: This cross-sectional study used data from the National Health and Nutrition Examination Survey 2013-2018. The SDoH score ranged from 0 to 8 and summarized eight unfavorable social conditions. Epilepsy was identified using medication-based ascertainment. Survey-weighted logistic regression models were applied to evaluate the association between SDoH score and epilepsy. Restricted cubic spline, subgroup, sensitivity, and receiver operating characteristic analyses were also performed. RESULTS: A total of 13,119 participants were included, of whom 114 had epilepsy. Participants with epilepsy had a higher mean SDoH score than those without epilepsy (3.41 ± 0.24 vs. 2.35 ± 0.06, P < 0.001). In the fully adjusted model, each 1-point increase in SDoH score was associated with 31% higher odds of epilepsy (OR 1.31, 95% CI 1.16-1.48). Compared with the low-score group (0-2), the adjusted odds ratios were 2.09 (95% CI 1.06-4.15) for scores of 3-5 and 2.67 (95% CI 1.34-5.33) for scores of 6-8. Spline analysis showed a significant overall association without evidence of nonlinearity. Adding SDoH components to demographic variables improved model discrimination (AUC 0.731 vs. 0.589, P for difference <0.001). CONCLUSION: Greater cumulative social disadvantage, as reflected by the SDoH score, was associated with higher odds of epilepsy.

Humans↗

PubMed2026

Beyond predictive performance: A systematic review and critical methodological appraisal of AI/ML and conventional modelling strategies in breast, colorectal, and pancreatic Cancer.

BACKGROUND: Predictive modelling for cancer risk, treatment-related complications, and survival is central to precision oncology. Conventional logistic regression (LR) and Cox proportional hazards (CoxPH) regression remain widely used but are limited when modelling nonlinear interactions, high-dimensional imaging features, and multimodal clinical-metabolic predictors. Artificial intelligence (AI) and machine learning (ML) methods offer expanded capability through automated feature extraction, ensemble learning, and flexible survival modelling, but the evidence on when AI/ML adds value over conventional models across cancer sites and predictive tasks remains fragmented. OBJECTIVE: To systematically evaluate the methodological performance, validation strategies, and translational limitations of AI/ML models compared with conventional statistical models in published predictive-modelling studies for breast, colorectal, or pancreatic cancer. METHODS: PubMed, Scopus, and Web of Science were searched for studies published between January 2019 and March 2025. Two reviewers independently conducted title-and-abstract screening, full-text eligibility assessment, and PROBAST risk-of-bias assessment. Sixty-five studies (n = 907,567 participants) were narratively synthesised by cancer site, predictive task, model family, comparator, validation strategy, predictor modality, and calibration or explainability reporting. RESULTS: The 65 studies comprised breast cancer (n = 35), colorectal cancer (n = 21), and pancreatic cancer (n = 9). AI/ML superiority over LR and CoxPH was task- and data-dependent. CNN- and U-Net-based models predominated in imaging and body-composition tasks, tree-based ensembles consistently outperformed LR for tabular perioperative complication prediction, and CoxPH remained competitive, and in the largest pancreatic risk study, superior to XGBoost (C-index 0.802 vs 0.723) in well-structured datasets. PROBAST analysis-domain risk was moderate in 54 of 65 studies (83%), driven by limited external validation, sparse calibration reporting (11/65), and few decision-curve analyses (7/65). CONCLUSION: AI/ML adds the most methodological value in imaging-derived feature extraction and nonlinear perioperative prediction, while conventional regression remains preferable in large, structured datasets with linear predictors. Clinical translation requires standardised body-composition definitions, external validation, calibration assessment, decision-curve analysis, and explainability, in line with TRIPOD+AI and CLAIM standards.

Humans↗