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PubMed2026

Molecular Landscape and Advanced Diagnostic Technologies for BRAF Mutations in Cancer: From Quantitative PCR and ddPCR to CRISPR-Based Platforms.

BRAF mutations are key oncogenic alterations across multiple malignancies, including melanoma, thyroid carcinoma, colorectal cancer, non-small cell lung cancer, glioma, and hairy cell leukemia. The most prevalent variant, BRAF-V600E, induces constitutive activation of the MAPK signaling pathway, promoting tumor progression and influencing therapeutic responsiveness. Accurate detection of BRAF alterations is therefore essential for molecular classification, prognostic assessment, treatment selection, and resistance surveillance. This review summarizes the molecular heterogeneity of BRAF mutations and critically evaluates current diagnostic methodologies. Conventional approaches such as allele-specific PCR and Sanger sequencing are compared with advanced quantitative platforms, including high-resolution melting analysis, droplet digital PCR, and next-generation sequencing, with emphasis on analytical sensitivity, mutation coverage, and clinical applicability. Emerging technologies such as CRISPR-based assays, rolling circle amplification systems, and nanoparticle-based biosensors and point-of-care diagnostic platforms are also discussed for their potential to enhance ultra-sensitive detection, particularly in liquid biopsy settings. These emerging tools are highlighted for their potential to enable ultra-sensitive, rapid, and decentralized mutation detection, particularly in liquid biopsy settings. Key challenges, including intratumoral heterogeneity, low allele-frequency variants, FFPE-associated artifacts, and clonal evolution under therapeutic pressure, are examined within a translational framework. In addition, we examine critical barriers to clinical implementation, including standardization, cost, and global accessibility of molecular diagnostics, and outline potential solutions through scalable technologies and decentralized testing strategies. We propose that optimal BRAF testing requires a mutation subclass-informed and clinically integrated strategy combining comprehensive baseline profiling with longitudinal molecular monitoring. Future diagnostic paradigms will likely integrate multi-omics data and artificial intelligence (AI)-assisted interpretation to refine precision oncology implementation. Looking forward, we propose that optimal BRAF testing will require integration of multi-omics profiling with AI-assisted interpretation, enabling automated variant classification, real-time clinical decision support, and improved prediction of therapeutic response and resistance.

Humans↗

PubMed2026

The tunica vaginalis flap as a rescue procedure in testicular torsion: Quantifying salvage rates with matched cohorts.

INTRODUCTION: Testicular torsion is the most common urological emergency in children, and the role of tunica albuginea fasciotomy with tunica vaginalis flap (TVF) in its treatment is controversial. The objective of this study was to evaluate the outcomes among patients undergoing TVF, standard orchiopexy (SO), and orchiectomy, with attention to symptom duration. METHODS: We performed a retrospective review of boys aged 1 month-18 years who underwent surgery for testicular torsion at a single centre from 2010 to 2024. Clinical, ultrasonographic, and operative variables were abstracted, and testicular salvage was defined as a follow-up volume ≥50% of the contralateral testis with blood flow. Propensity score matching for age, symptom duration, and parenchymal heterogeneity generated TVF-SO and TVF-orchiectomy cohorts. Salvage was further stratified by duration of symptoms (<6, 6-12, 12-24, >24 h). RESULTS: Among 157 patients, 31 (20%) underwent orchiectomy, 39 (25%) TVF, and 87 (55%) SO. Overall salvage was 54%, differing by procedure (SO 82%, TVF 36%, orchiectomy 0%; p < 0.001). In the TVF-SO matched cohort (n = 64), salvage was 38% for TVF and 59% for SO (p = 0.133). In the TVF-orchiectomy matched cohort (n = 38), salvage was 32% in the TVF group and 0% in the orchiectomy group (p = 0.02). Salvage after TVF declined steeply with ischemia time, with higher rates observed within 6 h of presentation. DISCUSSION: These findings suggest that TVF is used predominantly in high-risk torsion with adverse ultrasound features. When viewed descriptively, the TVF cohort showed lower follow-up viability than the SO cohort, but this difference must be interpreted in the context of the different intraoperative and preoperative risk profiles underlying procedure selection. We highlight TVF as a valuable additional consideration compared to outright orchiectomy. CONCLUSION: In this retrospective cohort, TVF was used in clinically severe torsion and was associated with follow-up viability in a subset of cases. These descriptive findings support further prospective study but should not be interpreted as evidence of equivalence or comparative benefit of TVF.

Humans↗

PubMed2026

Predicting ACL injury risk in athletes: A systematic review of machine learning-based models.

BACKGROUND: Early ACL injury risk identification in athletes is essential. This systematic review examines machine learning (ML) models for predicting ACL injuries, evaluating their methodological quality, performance, and reliability. METHOD: A comprehensive electronic search was conducted across PubMed, Scopus, Web of Science, and IEEE Xplore databases, supplemented by Google Scholar for grey literature, covering articles published between January 1, 2015, and August 30, 2025. Eligible studies were appraised using the Prediction Model Study Risk of Bias Assessment Tool (PROBAST) for methodological quality and risk of bias, and the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) guidelines for quality of evidence. RESULTS: Ten studies were included. PROBAST showed eight studies had moderate risk of bias and two low risk. TRIPOD found only two studies met quality criteria. ML models included logistic regression (n = 5), support vector machines (n = 4), k-nearest neighbor (n = 3), decision trees (n = 3), random forests (n = 5), neural networks (n = 2), linear discriminant analysis (n = 1), and pre-trained CNNs (n = 1). AUC ranged from 0.63 to 0.98. Accuracy (reported in six studies) ranged from 26% to 95%; however, these values should be interpreted with caution due to the absence of confidence intervals, lack of class imbalance handling, and limited external validation across studies. Tree-based ensemble methods such as random forest achieved competitive accuracy (74-86%), while SVM, a non-ensemble classifier, reported accuracy ranging from 71% to 95%; however, the highest values were obtained in studies with notably small sample sizes (n = 12 to n = 39), raising concerns about overfitting and generalizability. CONCLUSION: Current ML algorithms show promise for identifying athletes at high ACL injury risk and detecting relevant risk factors. Although study quality was generally satisfactory, future research should prioritize external validation and model interpretability to support clinical translation.

Humans↗

PubMed2026

PGR expression as a pharmacogenomic companion biomarker to GENE70-derived genomic risk in ER-positive/HER2-negative breast cancer.

BACKGROUND: The biology of the estrogen receptor-positive (ER+) and human epidermal growth factor receptor 2-negative (HER2-) breast cancers is heterogeneous even when they are categorized by their risk via genomics. Transcriptomic PGR expression reflects endocrine pathway activity and may provide complementary biological information within established GENE70-derived genomic-risk categories. Whether this molecular marker improves the biological interpretation of genomic-risk stratification beyond conventional clinicopathological assessment remains uncertain. OBJECTIVES: The aim of this study was to determine whether transcriptomic PGR expression provides complementary biological and prognostic information within reconstructed GENE70-derived genomic-risk categories and refines the characterization of endocrine-related tumour biology in ER-positive/HER2-negative breast cancer. METHODS: This study analysed publicly available transcriptomic and clinical data from three cohorts: METABRIC (discovery cohort), GSE96058/SCAN-B cohort (validation cohort) and TCGA-BRCA cohort (molecular validation cohort). The GENE70-derived genomic-risk score was reconstructed for each cohort using matched genes. Cox regression, Kaplan-Meier analysis and subgroup comparisons were used to assess relationships between PGR expression, clinicopathologic variables, molecular features and survival outcomes. RESULTS: Across the three independent cohorts, low transcriptomic PGR expression was consistently associated with higher GENE70-derived genomic risk, increased MKI67 expression, reduced ESR1 expression and enrichment of the Luminal B subtype. Survival findings differed between cohorts. In the discovery METABRIC cohort, transcriptomic PGR expression showed heterogeneous associations with survival, particularly within GENE70-derived high-risk subgroups, whereas the external GSE96058/SCAN-B validation cohort demonstrated consistent associations between low PGR expression and poorer overall survival in both the overall ER-positive/HER2-negative population and GENE70-derived high-risk subgroups. CONCLUSION: These findings suggest that transcriptomic PGR provides complementary biological and prognostic information within GENE70-derived genomic-risk categories. However, because treatment response was not evaluated in the present study, the findings should not be interpreted as evidence of predictive or pharmacogenomic utility and prospective studies incorporating treatment-response analyses are required before such applications can be established.

Humans↗

PubMed2026

From prediction to mechanism: Explainable AI uncovers plasma and CSF proteomic signatures of Alzheimer's disease.

Alzheimer's disease (AD) plasma and cerebrospinal fluid (CSF) proteomics can distinguish AD from cognitively normal controls, but the generalizability of machine learning performance and the recurrence of biological signals across datasets require cautious interpretation. We developed an explainable artificial intelligence framework spanning two fluids and four ADNI proteomic datasets, covering 2082 modality specific samples, all analysed internally within ADNI. Phase 1 analysed plasma using a 119 analyte NULISA and targeted UPENN panel (n = 727; 216 CE, 511 controls). Phase 2 extended the analysis to CSF using SOMAscan7k, TMT-MS and targeted SET2, with Elecsys Aβ42, Aβ40, total tau and p-tau181 as anchor biomarkers. Only SOMAscan was subject-independent relative to Phase 1 plasma; TMT-MS and SET2 overlapped with Phase 1 for 96.0% and 97.7% of subjects and therefore are not independent replication cohorts. Under subject-level splits with fold internal preprocessing, we compared Elastic Net, Explainable Boosting Machines and gradient boosted trees with SHAP-based explanations. Among the candidate pipelines, we selected the pipeline with the highest held-out test ROC AUC for each platform; the selected values were 0.927 in plasma and 0.954-0.973 across the three CSF datasets. Because the same held out test performance was used for pipeline selection and headline reporting, these are optimistically selected single-holdout estimates, not unbiased estimates of generalizable or clinical performance. Explanations identified five recurring biological axes within ADNI: cholinergic (ACHE), tau/14-3-3 (YWHAG, YWHAZ, YWHAB, YWHAE), neuro-axonal (NEFL, NEFH), microglial/complement (CHIT1, SMOC1, CHI3L1, C7, CFH) and synaptic (NPTXR, NPTX2, DLG4, SYT5, VSNL1, ELAVL2). CSF analyses showed synaptic vesicle-cycle enrichment (q = 2 × 10-6), and CSF YWHAG correlated strongly with total tau (ρ = 0.87). Cross-fluid directional concordance was modest overall (54-57%) but increased to 73-80% among mapped analyte/protein rows reaching q < 0.05 in CSF. These findings provide hypothesis-generating, internally supported evidence within ADNI. Independent external cohorts with locked pipelines are required to evaluate generalizable performance and biological reproducibility; the overlapping TMT-MS and SET2 analyses should not be interpreted as independent replication.

Alzheimer Disease↗

PubMed2026

Comparative effectiveness of torsemide vs furosemide in the management of heart failure patients: Win-ratio reanalysis of the TRANSFORM-HF trial.

BACKGROUND: Loop diuretics are widely used for managing congestion in patients with heart failure (HF). The TRANSFORM-HF trial is a multicenter randomized study that enrolled heart failure patients, comparing a strategy of torsemide vs furosemide. The time-to-event analysis demonstrated neutral effects on all-cause death at 30 months and the composite of all-cause death and first rehospitalization at 12 months. We evaluated whether a hierarchical win-ratio (WR) framework integrating mortality, recurrent hospitalization, and patient-reported health status provides additional interpretive insight. METHODS: This study is a secondary analysis of the pragmatic, multicenter, open-label, randomized TRANSFORM-HF trial, conducted across 60 US hospitals that randomized 2,859 patients hospitalized with HF to torsemide or furosemide. The primary 12-month hierarchical composite outcome was defined as (1) all-cause mortality, (2) recurrent all-cause hospitalizations, and (3) lack of improvement in the Kansas City Cardiomyopathy Questionnaire Clinical Summary Score (KCCQ-CSS). The primary statistical method was a WR analysis adjusting covariates via inverse probability weighting. Subgroup analyses evaluated potential heterogeneity across patient demographics and clinical characteristics. RESULTS: In the primary 12-month intention-to-treat analysis, the adjusted WR was 1.07 (95% CI, 0.98-1.16; P = .13), indicating no significant difference between torsemide and furosemide. A supplementary 30-month analysis with extended mortality follow-up yielded a similar estimate (adjusted WR, 1.06; 95% CI, 0.98-1.16; P = .14); hospitalization and KCCQ-CSS components were assessed through 12 months. As-treated sensitivity analyses were consistent with the neutral primary findings. Exploratory subgroup analyses were not adjusted for multiplicity and should be considered hypothesis-generating. CONCLUSIONS: The overall WR comparison between torsemide and furosemide showed no statistically significant difference in the primary 12-month analysis. The WR framework provided an interpretive decomposition across outcome domains but did not establish superiority of either loop diuretic strategy. All findings should be considered exploratory. TRIAL REGISTRATION: ClinicalTrials.gov, NCT03296813, https://clinicaltrials.gov/study/NCT03296813.

Aged↗

PubMed2026

Whole-Exome Sequencing in a Consanguinity-Enriched South Indian Retinitis Pigmentosa Cohort: Diagnostic Yield and Molecular Spectrum.

PURPOSE: To determine the molecular diagnostic yield, variant spectrum, inheritance architecture, and influence of consanguinity on whole-exome sequencing outcomes in a South Indian retinitis pigmentosa (RP) cohort. DESIGN: Prospective, registry-based cohort study. SUBJECTS: A total of 113 affected participants were enrolled through the Aravind Registry for Inherited Diseases of the Eye, including 109 unrelated probands and 4 affected relatives from already represented families. Primary analyses were restricted to the 109 unrelated probands. METHODS: Whole-exome sequencing was performed using a clinical exome workflow. Variants were interpreted using American College of Medical Genetics and Genomics/Association for Molecular Pathology criteria and cases were categorized as solved, possibly solved, inconclusive, or unsolved using prespecified inheritance-aware rules. MAIN OUTCOME MEASURES: Molecular diagnostic yield, distribution of implicated genes and variant classes, inheritance architecture, and diagnostic yield stratified by consanguinity status. RESULTS: Among the 109 unrelated probands, mean age at testing was 39.3 ± 14.1 years and 58.7% were male. Whole-exome sequencing identified 186 distinct rare variants across 92 inherited retinal disease genes, including 26 pathogenic and 33 likely pathogenic variants. A molecular diagnosis was established in 50 of 109 probands (45.9%), including 42 solved and 8 possibly solved cases; 45 (41.3%) were inconclusive and 14 (12.8%) remained unsolved, including 4 (3.7%) in whom no candidate variant was identified. EYS, USH2A, and ADGRV1 were the most frequently implicated genes. Autosomal recessive (AR) disease predominated (44/50, 88.0%). Consanguineous AR cases were exclusively homozygous (17/17); notably, 68.0% of nonconsanguineous AR cases were also homozygous (P = 0.013). Diagnostic yield was higher in consanguineous probands (51.4% vs. 41.7%), without reaching significance. Recurrent alleles included an established South Asian founder variant (MFSD8 c.1361T>C) and candidate founder alleles in EYS (c.4321C>T) and ADGRV1 (c.14329C>T). CONCLUSIONS: Whole-exome sequencing established a molecular diagnosis in nearly half of this South Indian RP cohort and revealed a predominantly recessive, homozygosity-enriched architecture shaped by consanguinity. These findings define a region-specific variant landscape to support clinical interpretation, genetic counseling, and future trial enrollment in this underrepresented population. FINANCIAL DISCLOSURES: The authors have no proprietary or commercial interest in any materials discussed in this article.

Consanguinity↗

PubMed2026

Clinical outcomes of Epstein-Barr virus infection/reactivation following CAR-T cell therapy: A systematic review.

BACKGROUND: Epstein-Barr virus (EBV) infection or reactivation is an emerging but underrecognized complication following chimeric antigen receptor T-cell (CAR-T) therapy and is likely associated with treatment-induced immune dysregulation. Data regarding its clinical impact remain limited. OBJECTIVE: To evaluate the reported occurrence, clinical manifestations, and outcomes of EBV infection or reactivation in adults undergoing CAR-T therapy. METHODS: A systematic review was conducted in accordance with the PRISMA 2020 guidelines. PubMed, Embase, and Cochrane CENTRAL were searched from inception to March 2025 for studies reporting EBV infection or reactivation after CAR-T therapy in adults. Due to limited and heterogeneous data, results were synthesized descriptively. RESULTS: Five studies comprising 80 patients were included (median age, 55 years; 52.6% male among patients with reported sex data [10/19]). Across the included studies, 11 EBV infection/reactivation events were identified among 80 described CAR-T recipients, representing 13.8% of the reported sample rather than a true incidence estimate. Among events with usable individualized timing data, the median interval from CAR-T infusion to EBV detection/reactivation was 9.8 months (approximate range, 1-44 months). Because EBV surveillance strategies and definitions were inconsistently reported across studies, this proportion should not be interpreted as a true incidence estimate. Four patients (36.4%) developed EBV-associated disease, including three cases of EBV-related lymphoproliferative disorder and one case of EBV-associated diffuse large B-cell lymphoma. Among seven patients with reported post-CAR-T treatment response, four achieved Complete Remission/ Continuous Complete Remission; treatment response should be interpreted separately from final survival status. Confirmed EBV-related mortality occurred in 2/11 patients with reported EBV infection/reactivation and in 2/4 patients with EBV-associated disease; all-cause mortality could not be reliably estimated because patient-level vital status could not be fully attributed to the EBV-reactivated subgroup. Reported toxicities predominantly consisted of low-grade cytokine-release syndrome; however, toxicity data were limited. CONCLUSION: Although infrequently reported, EBV infection or reactivation after CAR-T therapy may be associated with substantial morbidity and mortality among affected patients. However, the available evidence is limited by the small sample size, heterogeneous study designs, and inconsistent EBV surveillance practices.

Humans↗

PubMed2026

The landscape of pruning for large language models: A systematic review and unified taxonomy.

Confronting the inherent tension between the exceptional capabilities and the immense computational costs of Large Language Models (LLMs), pruning has become a crucial technique for achieving efficient deployment. However, a systematic analytical framework dedicated specifically to LLM pruning remains absent. In this paper, we aim to bridge this gap. We first elucidate the theoretical foundations that underpin the effectiveness of pruning, namely overparameterization and redundancy, and then propose a multidimensional taxonomy that organizes existing approaches along the axes of granularity, timing, and criteria. Building upon this unified perspective, we further analyze performance recovery mechanisms and the broader evaluation ecosystem, while also exploring forward-looking challenges such as interpretability, automation, and hardware-algorithm co-design. Through this comprehensive synthesis, we seek to provide an integrated and coherent analytical lens for advancing both research and practice in LLM pruning.

Large Language Models↗

PubMed2026

Analysis of deep learning techniques in computer-aided diagnosis for meniscus injuries: a systematic literature review.

Meniscus informatics is a growing subject of study in the healthcare industry. One of the major hindrances to the healthcare system's transformation is obtaining knowledge and meaningful information from complicated, high-dimensional and diverse sources. Modern biomedical research, for instance, has seen an increase in the use of complex, dissimilar, poorly documented, and generally unstructured electronic health records, imaging, sensor data and text, even after many current techniques have been used to extract more robust and useful elements from the data for analysis. New efficient standards for building end-to-end learning models from complex data are therefore needed. Therefore, the current study aims to examine the most recent research on the use of deep learning techniques for diagnosing meniscus tears and recommend creating comprehensive and meaningful interpretable structures that might benefit the healthcare industry. We also draw attention to shortcomings and the need for better technique development, and we provide new perspectives about this exciting new development in the field.

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↗

PubMed2026

Activity shapes large herbivores' ecological influences.

The ecological effects of large herbivores are shaped by their spatial and temporal patterns of activity (i.e. where, when and how intensely they use specific locations). When large herbivores' ecological influences are perceived to be undesirable, the traditional approach has been to reduce their population size. This numbers-first logic assumes that ecological effects scale primarily with abundance. We argue that this framing provides an incomplete understanding of large herbivores' ecological impacts. Using African elephants (Loxodonta africana) as a well-documented case study, we show that ecological effects on plants, animals and ecosystem processes correlate more with spatio-temporal patterns of activity than with population size. In large, open systems characterized by strong gradients of water availability, forage quality, shade and risk, elephants concentrate into predictable hotspots while relaxing activity elsewhere, generating localized impacts and opportunities for recovery. By contrast, in small, fenced or fragmented landscapes, where movements are constrained, and gradients are weak, spatial self-regulation breaks down, producing homogenized use and widespread ecological effects. We contend that understanding where, when and under what constraints herbivores use space provides a more general and mechanistic basis for interpreting ecological influence than abundance alone, with implications that extend beyond elephants to large herbivores globally.

Animals↗

PubMed2026

ORBIT: Oncogenic Representation Learning via Bi-Prototype Contrastive Learning in Hyperbolic Space for cancer driver gene identification.

Accurate identification of cancer driver genes is crucial for precision oncology but remains challenging due to the complexity of integrating heterogeneous data and modeling dynamic biological systems. To address these limitations, we propose ORBIT (Oncogenic Representation Learning via Bi-Prototype Contrastive Learning in Hyperbolic Space). Our framework synergistically fuses multi-omics profiles with functional network data using a context-adaptive graph reweighting mechanism to capture cancer-specific dynamics. The model employs a bi-prototype contrastive learning strategy within hyperbolic space, which aligns gene representations around distinct driver and non-driver semantic anchors while preserving the intrinsic hierarchy of biological networks. Comprehensive evaluations demonstrate that ORBIT achieves highly competitive stability in pan-cancer analysis while consistently outperforming state-of-the-art methods in cancer-specific predictions. Furthermore, functional enrichment analysis confirms that the model effectively segregates core cancer pathways, and drug sensitivity profiling validates the clinical relevance of the identified drivers. By integrating hyperbolic geometry with context-adaptive learning, ORBIT offers a robust and interpretable paradigm for precision medicine. The source codes and datasets are publicly accessible at https://github.com/spcho-dev/ORBIT.

Humans↗

PubMed2026

Proteomics in environmental pollution research: Advances, challenges, and future directions.

Environmental proteomics has emerged as a powerful approach for elucidating the molecular mechanisms underlying pollutant-induced biological effects. Although this field has developed rapidly, the systematic review of recent proteomics applications in environmental pollution research remains limited. This review explored the emerging roles of toxicoproteomics in biomarker discovery and mechanistic elucidation, as well as ecotoxicoproteomics in ecological risk assessment and bioremediation strategies. Here, we review the field, highlighting recent trends such as the integration of proteomics with genomics, transcriptomics, and metabolomics to provide a comprehensive view of biological responses to environmental stressors. We further discuss the growing application of artificial intelligence in improving proteomics data interpretation and accelerating biomarker discovery. In addition, recent technological advances in environmental proteomics are highlighted, including next-generation tissue microarray proteomics, nanoscale proteomics, single-cell proteomics, and spatial proteomics. Despite its potential, proteomics faces challenges, such as high operational costs, computational complexity in analysis, and technical limitations in low-abundance protein detection. We propose that the convergence of proteomics with artificial intelligence and multi-omics approaches offers promising solutions to these challenges, enhancing the practical application of proteomics in environmental monitoring and risk assessment.

Proteomics↗

PubMed2026

Quantitative assessment of the fingerprint evidential value using machine learning.

Fingerprints as physical evidence have long supported criminal investigation and adjudication. In practice, however, fingerprint identification relies mainly on examiners' experience. Furthermore, expert opinions tend to be categorical, even though the opinions with the same conclusion could differ substantially in evidential strength. To quantitatively assess fingerprint evidential value, this study proposes a machine learning-based framework as an interpretable decision-support tool. A lightweight residual one-dimensional convolutional neural network was constructed, incorporating channel recalibration and a similarity-driven attention mechanism to learn adaptive contribution weights for different matched minutiae (minutiae for short). Controlled experiments revealed that the predicted evidential value increased with the number of minutiae and was significantly influenced by the quality of minutiae. With 10 minutiae, the mean predicted scores were 4.49, 7.00, and 9.09 for blurred, moderately blurred, and clear minutiae, respectively. Multiple regression analysis indicated that replacing a pair of blurred minutiae with a pair of clear minutiae increased the score by 0.492, whereas replacing it with a pair of moderately blurred minutiae increased the score by only 0.216. By mapping predicted scores to graded levels of evidential strength, the framework contributes to a paradigm shift from categorical expert opinions to graded ones, helping courts evaluate fingerprint evidence more scientifically.

Humans↗

PubMed2026

Direct and spillover hospitalisation patterns during climate hazards across regions of different health-system resilience levels in China: a nationwide retrospective analysis.

BACKGROUND: Health-system resilience serves as a key contributor in mitigating adverse health impacts during climate hazards. However, quantitative insights into resilience-associated health-care utilisation patterns and targeted adaptation policies remain scarce. We aimed to capture the spatiotemporal health impacts in disaster-exposed counties and their neighbouring counties in China during storms, floods, tropical cyclones, and blizzards or winter storms; understand the association between health-system resilience metrics and hazard-attributable hospitalisations; and develop evidence-based adaptation policies towards climate extremes. METHODS: In this retrospective, observational analysis of county-level aggregated hospitalisation data, we used a propensity score matching-difference-in-differences framework to assess the spatiotemporal changes of nine types of disease-specific hospitalisations in both disaster-exposed and neighbouring regions during storms, floods, tropical cyclones, and blizzards in China. We quantified the relative importance and health gains of health-system metrics during such hazards through random forest approach with interpretable partial dependence plots to derive evidence-based adaptation recommendations. FINDINGS: We included hospitalisation data from Jan 1, 2016 to Dec 31, 2023. In this period, 3241 county-hazard event combinations and 41 747 482 hospitalisations were recorded across 955 Chinese counties. The disaster-exposed regions experienced an initial decline in hospitalisation rates, followed by admission surges after disasters. For example, infectious disease admissions decreased by 11·92% (95% CI -10·53 to -13·31) during the flood-active period but increased by 7·68% (6·46-8·91) after 1-2 weeks of floods. Neighbouring zones were also affected through spillover effects, with infectious disease admissions increasing by 3·18% (1·76-4·61) after 1-2 weeks of the floods. Cardiovascular disease, injuries, infectious, respiratory, and mental disorders were more sensitive across all regions. Particularly for disaster-exposed counties, cardiovascular hospitalisations increased by 14·31% (7·34-21·29) during the tropical cyclone-active period. Notably, compared with low-resilience counties, high-resilience counties were associated with 19·48-30·03% smaller hazard-related relative changes in hospitalisation rates during the hazard-active period and 27·07-31·08% smaller hazard-related relative changes in hospitalisation rates in post-hazard periods. For instance, during the storm-active period, the increase in respiratory hospitalisations was 7·21% (0·67-13·75) in high-resilience counties versus 12·13% (5·20-19·05) in low-resilience counties. Health workforce (relative importance 14·58% during the hazard-active period and 13·80% during the post-hazard period) and service delivery (14·10% during the hazard-active period and 14·17% during the post-hazard period) were identified as key contributors of health-system resilience. Empirical synergistic effects were observed when combining interventions during the post-hazard period, with the combined effect of service delivery (individual contribution 8%) and workforce (individual contribution 4%) exceeding the sum of their individual contributions (16% reduction in cumulative excess admissions) by 33%. INTERPRETATION: Climate hazards are associated with substantial changes in hospitalisation rates in both disaster-exposed and neighbouring regions. Health-system resilience is essential in addressing disaster-health challenges. Targeted adaptation interventions should be context-appropriate and threshold-aware, thereby maximising the public health benefits relative to resilience-oriented investments in health systems. FUNDING: Gates Foundation and the National Natural Science Foundation of China.

Journal Article↗

PubMed2026

Ensemble DNA methylation clock demonstrates Immune-metabolic aging signatures associated with mortality.

Aging is a multifactorial process that is best described in terms of the progressive acquisition of multiple layers of phenotypic changes, such as epigenetic modifications, inflammation, and metabolic dysregulation. DNA methylation clocks have been extensively used to construct epigenetic clocks based on the DNAm profiles that can be used to estimate biological age and predict age-associated outcomes. Nevertheless, the vast majority of clocks constructed so far have been based on linear models, which are unlikely to fully account for the heterogeneity and non-linearity of survival-related DNAm signatures. In this work, we constructed a heterogeneous stacked ensemble survival model based on DNAm data obtained from the Framingham Heart Study. We first identified 190 CpG loci using elastic net Cox regression and subsequently constructed a survival prediction model based on the fusion of five complementary survival models by means of a neural network meta-learner. The prediction power of the survival model was evaluated in an external validation cohort, where we observed strong performance for predicting all-cause mortality that significantly exceeded PhenoAge and was statistically comparable to GrimAge. These performance estimates were derived in cohorts of European ancestry and externally validated in postmenopausal women aged 50-79 years, and should therefore be interpreted as applicable only to demographically similar populations.

Humans↗

PubMed2026

Beyond species trees: pervasive gene flow limits phylogenomic resolution in the diversification of Juniperus from the Qinghai-Tibet Plateau.

Understanding how lineages diversify despite persistent ancestral polymorphism and recurrent gene flow remains a central challenge in evolutionary biology. Juniperus distributed across the Qinghai-Tibet Plateau provide an ideal system for addressing this question because repeated geological uplift and climatic oscillations have likely promoted cycles of lineage divergence, range shifts, and secondary contact. Here, we combined approximately 1.08 million genome-wide SNPs from 164 individuals representing thirteen Juniperus lineages with phylogenomic datasets comprising 3,381 nuclear single-copy genes and nearly complete plastomes. We detected extensive phylogenomic discordance and cytonuclear incongruence across genomic datasets. Topology weighting, coalescent simulations, quartet-based tests, and analyses of gene flow and reticulation collectively support the interpretation that these patterns were shaped by the combined effects of prolonged incomplete lineage sorting and gene flow during lineage diversification. Ecological niche analyses further provide a spatial and climatic context in which environmentally similar lineages may have had greater opportunities for secondary contact during historical range shifts. Collectively, our results reveal that the evolutionary history of Qinghai-Tibet Plateau Juniperus is characterized by reticulate diversification rather than strictly bifurcating evolution, and demonstrate how genome-wide discordance can provide biological insights into the evolutionary processes underlying lineage diversification.

Gene Flow↗