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PubMed2026

Synaptic vesicle glycoprotein 2A PET imaging in parkinsonian α-synucleinopathies: a systematic review.

Synaptic dysfunction is increasingly recognized as an early and biologically relevant component of α-synucleinopathies. However, conventional imaging biomarkers mainly assess dopaminergic dysfunction, glucose metabolism, or structural damage rather than presynaptic density itself. Synaptic vesicle glycoprotein 2A (SV2A) PET enables in vivo assessment of presynaptic terminal integrity and may provide complementary information in Parkinson's disease (PD), Parkinson's disease dementia/dementia with Lewy bodies (PDD/DLB), and multiple system atrophy (MSA). This systematic review synthesized the available evidence on SV2A-targeted PET in parkinsonian α-synucleinopathies, focusing on regional imaging patterns, clinical associations, longitudinal findings, and methodological determinants of interpretation. Seventeen reports were included. In PD, the most recurrent finding was reduced SV2A binding in the substantia nigra, although additional involvement of brainstem, caudate, striatal, thalamic, raphe, or cortical regions was reported in selected cohorts. In PDD/DLB, abnormalities appeared broader and more cortical, with evidence of association between cortical SV2A binding and cognitive performance. In MSA, one study suggested a distinct infratentorial and cerebellar pattern with potential relevance for phenotypic stratification. SV2A PET is a promising research biomarker for biological characterization of synucleinopathies. However, the field remains limited by small cohorts, methodological heterogeneity, variable quantification strategies, limited longitudinal evidence, and potential cohort overlap. Multicentre validation and harmonized protocols are required before clinical translation.

Humans↗
PubMed2026

Clinical Utility of Ultra-Widefield Swept-Source OCT for Intraocular Tumors: Comparison With Ultrasonography, SD-OCT, and MRI.

PURPOSE: To evaluate the clinical performance of ultra-widefield swept-source optical coherence tomography (UWF-OCT) in the assessment of choroidal tumors and to compare it with ultrasonography (US), spectral-domain (SD)-OCT, and magnetic resonance imaging (MRI). DESIGN: Retrospective diagnostic comparison. SUBJECTS: Thirty-nine eyes from 39 patients diagnosed with choroidal tumors at a single tertiary referral center. METHODS: This retrospective diagnostic comparison evaluated patients diagnosed with choroidal tumors at a single tertiary referral center between January 2023 and August 2025. All patients underwent UWF-OCT imaging at diagnosis. Tumor measurements obtained with UWF-OCT were compared with US, SD-OCT, and MRI. Comparative analysis among imaging modalities and predictors affecting UWF-OCT applicability was performed. MAIN OUTCOME MEASURES: Tumor thickness (mm) and largest basal diameter (LBD, mm) measurements, and complete measurability rate across different tumor size categories. RESULTS: Thirty-nine eyes from 39 patients (mean age 59.2 &#xb1; 16.9 years) were analyzed, including 27 choroidal melanomas (69.2%), 5 metastatic tumors (12.8%), 4 hemangiomas (10.3%), 2 osteomas (5.1%), and 1 (2.6%) indeterminate choroidal melanocytic lesion. UWF-OCT successfully measured both tumor thickness and largest basal diameter (LBD) in 100% (31/31) of small and medium choroidal tumors, substantially outperforming SD-OCT (complete measurement achieved in 63.6% of small tumors, and 0% of medium or large tumors). UWF-OCT measurements were systematically smaller than ultrasonography (thickness: -32.3%, P < .01; LBD: -11.1%, P < .01) and MRI (thickness: -29.2%, P < .01). Mushroom-shaped tumor morphology was the strongest negative predictor of UWF-OCT quality (OR = 0.015, 95% CI 0.001-0.196, P < .01). UWF-OCT's complete measurability was limited in large tumors (12.5%, 1/8). CONCLUSIONS: UWF-OCT provides precise, noninvasive, single-scan assessment of small-to-medium choroidal tumors with detailed structural visualization. It may be particularly useful for dome-shaped tumors, while multimodal imaging with US and MRI remains optimal for complex morphologies. Overall, UWF-OCT represents a valuable tool for diagnosis and treatment planning, with potential utility for longitudinal follow-up in choroidal tumor management.

Humans↗
PubMed2026

Second-Generation ELZA-sub400 Protocol: Individualized High-Fluence Cross-Linking for Ultra-Thin Keratoconus Corneas.

PURPOSE: To evaluate the safety and efficacy of a second-generation individualized corneal cross-linking (CXL) protocol (ELZA-sub400) using high-fluence UV-A irradiation in ultrathin ectatic corneas. DESIGN: Retrospective, single-center, consecutive interventional case series. METHODS: Twenty-nine eyes of 24 patients with progressive keratoconus or post-LASIK ectasia and a post-soak intraoperative thinnest stromal thickness <400 &#xb5;m were included. After epithelial removal and riboflavin soaking, continuous UV-A irradiation (365 nm) at 3 or 9 mW/cm&#xb2; was delivered with total fluence titrated up to 10 J/cm&#xb2; based on intraoperative ultrasound pachymetry and a previously published nomogram targeting an uncross-linked stromal margin of approximately 70 &#xb5;m above the endothelium. Outcomes were assessed at baseline and up to 12 months using corrected distance visual acuity (CDVA) and corneal parameters measured using Scheimpflug tomography and anterior segment OCT (AS-OCT) with Placido-based topography. The main outcome measure was the proportion of eyes without progression at 12 months, defined as <1.0 D increase in maximum keratometry (Kmax). Secondary outcomes included changes in CDVA, refraction, Kmax, stromal thickness, demarcation line depth, densitometry, and safety parameters. RESULTS: At 12 months, 22/29 eyes (76%; 95% CI, 57.9%-87.8%) met the nonprogression criterion. Mean change in Kmax was -0.77 &#xb1; 5.10 D (95% CI, -2.71 to 1.17; P = .418). Mean demarcation line-to-anterior stroma distance was 205 &#xb1; 64 &#xb5;m (95% CI, 180.7-229.3), and demarcation line-to-endothelium distance was 64 &#xb5;m (IQR, 49-152). All demarcation lines remained within the stromal layer; 15/29 eyes (51.7%) had a demarcation line located &#x2264;70 &#xb5;m from the endothelium. Median CDVA changed from 0.10 to 0.32 logMAR (P = .142). Minimum stromal thickness showed a median change of -4.0 &#xb5;m (P = .309). No significant change was observed in densitometry, and no eye developed deep stromal haze or endothelial decompensation. CONCLUSIONS: Second-generation ELZA-sub400 CXL halted ectasia progression in 76% of ultrathin corneas at 12 months and was associated with an acceptable short-term safety profile, including stromal-confined demarcation line formation and no observed endothelial decompensation. The numerical decline in spectacle CDVA observed in this severely affected cohort did not reach statistical significance but is clinically important and warrants confirmation in larger prospective studies.

Humans↗
PubMed2026

Safety of insulin eye drops in the treatment of open angle glaucoma: a randomized phase I clinical trial.

OBJECTIVE: The progression of glaucoma despite adequate intraocular pressure (IOP) control highlights the need for neuroprotective and neuroregenerative therapies. Preclinical studies suggest insulin promotes retinal ganglion cell survival and regeneration, but its safety in higher concentrations (100 and 500 units/mL), administered topically, has been poorly characterized in humans. We aim to assess the safety and tolerability of these two concentrations of insulin eye drops in patients with open-angle glaucoma (OAG). DESIGN: A phase I, randomized, double-blind, placebo-controlled, single-centre clinical trial. PARTICIPANTS: Patients with mild to moderate OAG were randomized 2:2:1 to receive once-daily topical insulin U-100, U-500, or placebo in 1 eye for 5 days, with follow-up visits at 1, 3, and 6 months. The primary safety outcomes include glycemia, serum potassium, ocular adverse events (AEs), and ocular tolerability scores. Secondary outcomes included IOP, best-corrected visual acuity (BCVA), retinal nerve fibre layer thickness, ganglion cell complex, visual field, and OCT angiography. RESULTS: Eighteen open-angle glaucoma patients were enrolled (mean age: 66.2 &#xb1; 10.1 years). No serious AEs related to insulin were observed. One asymptomatic, transient near-hypoglycemia event occurred in a fasting participant (3.9 mmol/L), with no recurrence after dietary adjustment. No significant changes were found in serum potassium, IOP, BCVA, visual fields, or OCT. Ocular symptoms in the insulin groups were limited to transient, mild burning sensation upon application. One participant experienced cystoid macular edema at 3 months, which was attributed to pre-existing ocular pathology. CONCLUSION: Topical insulin at 100 and 500 units/mL concentrations was well tolerated in patients for short-term use and did not result in significant systemic or ocular toxicity.

Aged↗
PubMed2026

Multimodal alignment improves generalizability of genomic biomarker prediction in computational pathology.

Computational pathology models that use digitized histopathology whole-slide images have the potential to become a cost-effective and scalable alternative to molecular assays for the prediction of genomic biomarkers, a key task in precision oncology. However, as new genomic biomarkers are discovered or quantified, large, labeled datasets must be prospectively collected to train new models. To address this challenge, we developed multimodal alignment for biomarker learning and generalization (MARBLE), a multimodal contrastive pretraining strategy that integrates structured biomarker knowledge into representation learning of histopathology images. MARBLE aligns histopathology-derived representations with representations of genomic biomarkers generated by a large language model (LLM) and a protein language model (PLM). This biologically informed alignment enables data-efficient generalization to novel, out-of-distribution biomarkers. Using the MSK-IMPACT cohort of over 40,000 patients across multiple biomarker panel versions, we design experiments grounded in real-world data to demonstrate the value of our proposed approach.

CP: computational biology↗
PubMed2026

Implicit and explicit statistical learning in reading: Evidence from a randomized controlled-learning study and computational modeling.

A key challenge in reading acquisition is understanding how learners extract the complex probabilistic mappings between print, meaning, and sound. Statistical learning (SL) theory offers a mechanistic account of how such mappings are acquired, whether implicitly through exposure or explicitly through instruction. We conducted a randomized controlled-learning study in Chinese, a writing system characterized by multiple sub-lexical regularities linking orthography, semantics, and phonology. Ninety-five 2nd-3rd graders with or at risk for dyslexia were randomly assigned to one of three groups: an implicit-SL training group exposed to repeated lexical and sublexical orthography-semantics-phonology associations, an explicit-SL training group receiving the same input plus explicit instruction on the sublexical print-sound mapping, and a no-SL control group. Both SL groups outperformed controls on the characters they were trained on, as well as on untrained characters that required generalization. However, only the explicit group demonstrated abstraction of print-sound mapping to novel items. Neural network simulations further revealed distinct mechanisms supporting implicit and explicit SL, consistent with a dual-system account of reading acquisition. Together, these findings (1) clarify how implicit and explicit learning distinctly support the discovery of statistical structure in written language and (2) underscore the implicit-explicit dual learning mechanism underlying reading acquisition.

Humans↗
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

Computational metabolomics at scale: from open data to insight.

Metabolomics data are currently generated at scale thanks to the evolution of technologies that have led to marked improvements in the number of metabolites detected, spanning all chemical classes. These data are increasingly submitted to public repositories for data reuse, integration, and interpretation. Despite the availability of public resources and associated computational tools, the field still lacks a widely adopted, consistent data and analytics infrastructure capable of transforming this wealth of information into scientific insight. Indeed, the metabolomics field is just now scratching the surface of being able to harness the power of new computational technologies. In this review, we summarize discussions from the "Dagstuhl-Seminar 24181 Computational Metabolomics: Towards Molecules, Models, and their Meaning" with a focus on public data availability, open data standards, data and knowledge integration, and education. Our goal is to raise awareness and adoption of the latest open science resources while highlighting key areas needing further development.

Metabolomics↗
PubMed2026

Association of the Charlson Comorbidity Index With 1-Year Outcomes in Patients With Macular Edema Secondary to Retinal Vein Occlusion.

OBJECTIVE: To determine the predictive value of the Charlson Comorbidity Index (CCI) for outcomes in patients with macular edema secondary to retinal vein occlusion (RVO). DESIGN: Retrospective clinical cohort study. SUBJECTS: Patients seen between 2013 and 2023 at the Cole Eye Institute, Cleveland Clinic, were included. All patients were >18, diagnosed with RVO (International Classification of Diseases (ICD)-9 and 10 codes), had a complete CCI score, and had at least 1 year of ophthalmic follow-up data after their first intravitreal injection (baseline). Patients with ocular surgery, trauma, or panretinal photocoagulation were excluded. METHODS: Age-adjusted CCI scores were calculated for each patient from chart review. For patients with bilateral RVO, one eye was selected randomly. Patients were stratified into tertiles by CCI distribution: tertile 1 (CCI 0-5; mean 3.4), tertile 2 (age-CCI 4.1-6; mean 4.9), and tertile 3 (CCI &#x2265; 8; mean 9.6). Multivariable linear regression was performed to determine the predictive value of CCI and other covariates on visual and anatomical outcomes. MAIN OUTCOME MEASURES: Best-corrected visual acuity (BCVA) and central subfield thickness (CST) at 1-year follow-up. RESULTS: A total of 972 patients met all criteria, with an average age-adjusted CCI score of 6.2. Each one-point increase in CCI predicted 0.38 fewer letters in BCVA at follow-up (P < .001). Baseline BCVA was a significant predictor of follow-up BCVA in all tertiles (P < .001). In the third tertile, each one-point increase in CCI was associated with a 0.72 letter reduction in follow-up BCVA (P < .001). For CST, baseline CST was strongly predictive of final CST (P < .001), while CCI was only significant in the first tertile, where each point increase in CCI predicted a 13.7 &#xb5;m increase in CST (P = .02). In RVO subtype interaction models, the age-adjusted CCI &#xd7; CRVO interaction was not statistically significant for either 1-year BCVA (P = .269) or 1-year CST (P = .695). CONCLUSIONS: Higher CCI scores are significantly associated with worse visual outcomes in patients with RVO, particularly in the combined population and most comorbid patients (tertile 3). CCI was significantly associated with higher (thicker) CST only among the least comorbid patients (tertile 1).

Humans↗
PubMed2026

Community-driven advances in computational mass spectrometry: The perspective of EuBIC-MS members.

Advances in data acquisition, artificial intelligence, and integrative bioinformatics are driving the rapid evolution of computational mass spectrometry, and in turn, transforming modern proteomics, metabolomics, and lipidomics. These developments have greatly increased the scale and complexity of mass spectrometry data, underscoring the importance of evolving accurate, transparent, efficient and reproducible data processing workflows. Addressing these challenges requires collaborative innovation that brings together expertise in software engineering, statistics, and biology. The European Bioinformatics Community for Mass Spectrometry (EuBIC-MS), an initiative of the European Proteomics Association (EuPA), fosters a culture of open, community-driven development through its biennial Developers Meetings and Winter Schools. This commentary summarizes the scientific background and outcomes of the EuBIC-MS Developers Meeting 2025, which took place in Novacella, Italy. Three keynote presentations highlighted major frontiers in the field: deep proteome and phosphoproteome profiling, text mining for protein-protein interaction extraction, and scalable proteomics for AI-driven drug discovery. Seven community-selected hackathons addressed emerging challenges such as single-cell proteomics data analysis, FAIR metadata extraction, deep learning frameworks, R-Python interoperability, and DIA validation. Together, these efforts demonstrate the potential for scientific and technical innovation to arise from open collaboration, and highlight how community-driven initiatives can accelerate progress in computational mass spectrometry. SIGNIFICANCE: Modern proteomics increasingly depends on computational advances to translate complex, high-dimensional data into biological knowledge. The EuBIC-MS Developers Meeting 2025 exemplifies how community-driven collaboration can directly accelerate this process by bringing together experts from bioinformatics, statistics, and experimental proteomics to co-develop open, interoperable, and reproducible analytical tools. By fostering shared software frameworks, transparent benchmarking, and collaborative problem solving, the EuBIC-MS community helps ensure that technological innovation translates into reliable biological insights. This collaborative model strengthens the foundation for quantitative, system-level understanding of proteomes and establishes a sustainable path for integrating artificial intelligence and next-generation data acquisition into routine biological discovery. This commentary shows some current highlights in the field of computational mass spectrometry and community-based approaches undertaken during the most recent Developers Meeting to solve these challenges. The approaches discussed and initiated during the meeting - ranging from deep proteome profiling and phosphosite mapping to text mining, single-cell data analysis, and FAIR metadata extraction - address key bottlenecks that currently limit the biological interpretability and comparability of proteomics data.

Mass Spectrometry↗
PubMed2026

Linked-color imaging with computer-aided detection and the proximal adenoma miss rate: a randomized tandem trial.

BACKGROUND AND AIMS: Linked-color imaging (LCI) aids the detection and characterization of lesions. Computer-aided detection (CADe) systems have been introduced to improve lesion detection during colonoscopy. Although several studies have been reported regarding LCI, few have investigated the combination of LCI and CADe. This study aimed to evaluate the efficacy of LCI with CADe colonoscopy compared to conventional white-light colonoscopy. METHODS: A single-center, randomized tandem trial was conducted. Participants referred for first-time colonoscopy after fecal immunochemical test (FIT)-positive, asymptomatic screening, or surveillance colonoscopy were randomized (1:1) to undergo CADe-assisted colonoscopy of LCI or white-light imaging (WLI) in the right side of the colon. The primary outcome was adenoma miss rate (AMR) in the right side of the colon. Secondary outcomes included polyp miss rate (PMR), diminutive adenoma miss rate (dAMR), sessile serrated lesion miss rate (SSLMR), advanced adenoma miss rate, advanced neoplasia miss rate, flat-type lesion miss rate (FMR), and the differences in miss rates based on expertise. RESULTS: Among 232 randomized participants, 209 were analyzed (LCI/CADe: 102; WLI: 107). AMR (WLI: 39% vs LCI/CADe: 20%; P = .001), PMR (42% vs 18%; P < .001), and dAMR (42% vs 21%; P = .003) were significantly lower in the LCI/CADe arm, particularly among experts. SSLMR (46% vs 0%), advanced AMR (30% vs 0%), advanced neoplasia miss rate (25% vs 0%), and FMR (27% vs 5.6%) were lower in LCI/CADe, although without statistical significance. CONCLUSIONS: Compared to conventional colonoscopy, LCI with CADe colonoscopy resulted in a statistically significant decrease, especially in AMR. (UMIN 000050685).

Humans↗
PubMed2026

Effect of Baseline ASPECTS on Tenecteplase Efficacy Before Thrombectomy in Acute Large-Vessel Occlusion Stroke: A Post Hoc Analysis of the BRIDGE-TNK Randomized Trial.

BACKGROUND AND OBJECTIVES: The impact of ischemic extent on the efficacy and safety of intravenous thrombolysis before thrombectomy remains uncertain. The aim of this study was to evaluate whether the baseline ischemic extent, measured by the Alberta Stroke Program Early Computed Tomography Score (ASPECTS), modifies outcomes of intravenous tenecteplase administered before endovascular thrombectomy. METHODS: This was a post hoc analysis of the BRIDGE-TNK (thrombectomy with vs without rhTNK-tPA in stroke) trial, conducted across China from May 2022 to September 2024. We compared the efficacy and safety of intravenous tenecteplase plus thrombectomy vs thrombectomy alone in acute large-vessel occlusion stroke patients within 4.5 hours of last known well, stratified by baseline ASPECTS (<8 vs 8-10). The outcomes included 90-day functional independence (modified Rankin Scale score of 0-2), 48-hour symptomatic intracranial hemorrhage (sICH), and 90-day mortality. Regression models incorporating a treatment-by-ASPECTS interaction term were used for analysis. RESULTS: Among 550 patients, 241 (43.8%) had ASPECTS <8 (median [interquartile range, IQR] age, 69 [61-77] years; 56.4% male) and 309 had ASPECTS 8-10 (median [IQR] age, 70 [61-77] years; 59.5% male). The rate of functional independence was significantly higher in the tenecteplase plus thrombectomy group than in the thrombectomy-alone group in the ASPECTS <8 subgroup (adjusted risk ratio [aRR], 1.67; 95% CI 1.18-2.35), but not in the ASPECTS 8-10 subgroup (aRR, 0.99; 95% CI 0.84-1.17; pinteraction = 0.007). Rates of sICH did not differ significantly between treatment groups in either ASPECTS subgroups (ASPECTS <8: 10.0% vs 11.2%; ASPECTS 8-10: 7.5% vs 2.8%; pinteraction = 0.11). Ninety-day mortality was comparable between treatment groups in the ASPECTS <8 subgroup, but numerically higher with tenecteplase plus thrombectomy in the ASPECTS 8-10 subgroup (aRR = 1.89, 95% CI 0.99-3.61, pinteraction = 0.04). DISCUSSION: In this exploratory post hoc analysis, a signal of benefit was observed in patients with ASPECTS <8 who received intravenous tenecteplase before thrombectomy, whereas no functional improvement and possible safety concerns were seen in those with ASPECTS 8-10. Prospective confirmation in randomized trials is required before practice change. TRIAL REGISTRATION INFORMATION: ClinicalTrials.gov; Unique identifier: NCT04733742.

Humans↗
PubMed2026

In silico identification of DNMT1 inhibitors from the PlantCyc database through computational approach to assess the anti-cancer potential of nutraceutical compounds in breast cancer.

Breast cancer accounts for a disproportionate share of global cancer-related deaths, with 670,000 fatalities and 2.3 million new diagnoses recorded in women during 2022 alone. Existing treatment modalities carry considerable toxicity burdens, and resistance to available agents remains an unresolved clinical problem. DNA methyltransferase 1 (DNMT1), the enzyme chiefly responsible for maintaining genome-wide methylation patterns during DNA replication, has been mapped out as a high-value target in breast cancer because its dysregulation silences tumour suppressor genes through promoter hypermethylation. The present work involves hierarchical in silico workflow to screen 4549 plant-derived compounds from the PlantCyc database (v16.0.3) against the human DNMT1 catalytic domain (PDB ID: 4WXX). Ten top-scoring compounds were taken forward for molecular docking via AutoDock Vina; Quercetin and Kaempferol both recorded the highest binding affinities at -9.5&#x202f;kcal/mol, Wogonin (-9.3&#x202f;kcal/mol) and Xanthohumol (-8.1&#x202f;kcal/mol) also emerged as strong binders. Pharmacokinetic evaluation using ADMET-AI confirmed that all 10 compounds met Lipinski's rule of five, with human intestinal absorption values at or above 0.98. Wogonin and Xanthohumol were selected for a 100 ns all-atom molecular dynamics (MD) simulation in GROMACS due to their well-rounded ADMET profiles and limited existing data on their specific interactions with DNMT1 in breast cancer. Across all measured trajectory metrics, backbone RMSD, residue fluctuation, radius of gyration, solvent-accessible surface area, and intermolecular hydrogen bond count, Wogonin formed a more stable, compact complex. These findings suggest that Wogonin and Xanthohumol are non-toxic nutraceutical candidates suitable for DNMT1 targeted epigenetic therapy, with computational foundation strong enough to facilitate future in vitro and in vivo validation work.

Humans↗
PubMed2026

A systematic review of human avoidance learning: Cognition, computation, and methods.

Avoidance behaviour is fundamental for survival but can become maladaptive in clinical conditions. A large body of literature has accumulated on the dynamics of human avoidance learning. However, current theories and overviews do not provide an exhaustive account of this evidence. In this systematic review, we identify N = 116 studies on human avoidance learning. We analyse these studies with the goal of distilling robust empirical phenomena as a basis for theory-building, and examine their diagnostic value in differentiating between competing theories. We find that the evidence is difficult to reconcile with foundational two-factor and classical safety-signal accounts, and most strongly supports expectancy- and inference-based views, in which avoidance responses are selected with respect to represented consequences. At the same time, no current framework provides a complete account of the evidence: several findings point to an additional role for operant valuation, Pavlovian influences, and contextual or latent-state control over the expression of avoidance. Methodologically, we observe that the problem setting in the most common experimental paradigms is radically simpler than real-world avoidance and therefore unlikely to expose the limits of inferential or reflective mechanisms. Consequently, we argue that paradigms with greater computational demands and more realistic action affordances are required to identify the mechanisms underlying avoidance learning. Collectively, these insights provide a foundation for theoretical refinement, computational modelling, and methodological innovation, with implications for advancing interventions targeting maladaptive avoidance.

Humans↗
PubMed2026

Bioactive peptides for meat quality and preservation: Integrating peptidomics and computational screening.

Bioactive peptides generated from meat proteins, fermented meat products, and slaughter by-products have attracted increasing attention as functional molecules for improving meat quality and preservation. In meat systems, peptides can be produced through endogenous postmortem proteolysis, microbial fermentation, gastrointestinal digestion, or controlled enzymatic hydrolysis of underutilized animal by-products. These peptides are closely associated with key meat science endpoints, including postmortem tenderization, oxidative stability, color retention, flavor development, microbial inhibition, and the valorization of processing by-products. However, although high-resolution peptidomics has greatly expanded the identification of meat-derived peptide sequences, their translation into practical meat applications remains limited by matrix interactions, processing stability, sensory constraints, safety concerns, and insufficient validation in real meat systems. This review synthesizes recent advances in meat-related peptidomics and computational screening, including sequence-based prediction, machine learning, molecular docking, molecular dynamics, stability assessment, and safety-oriented filtering. Particular attention is given to how these approaches can prioritize peptides with antioxidant, antimicrobial, flavor-modulating, and preservation-related functions under meat-specific technological constraints. By integrating peptide generation pathways, mass spectrometry-based identification, in silico prioritization, and meat quality endpoints, this review proposes a stage-gated framework for translating meat-derived bioactive peptides from discovery to application. Future research should strengthen matrix-specific validation, standardized peptidomic reporting, and safety assessment to support the use of bioactive peptides in meat quality improvement, clean-label preservation, and circular utilization of meat industry by-products.

Animals↗
PubMed2026

Reinforcement learning-based dynamic ensemble for missense variant effect prediction and tiered prioritization of VUS.

BACKGROUND: Accurate classification of missense variants remains a challenging task despite major advances in genomics. Numerous computational models have been developed to assist in variant classification, but often require repeated integration and benchmarking efforts. Ensemble methods have been proposed to overcome the limitations of single predictors, but mostly rely on fixed, predefined weights that constrain their ability to capture interactions among predictive signals. METHODS: We present GenixRL, a dynamic ensemble framework that reformulates model fusion as a reinforcement learning optimization problem. GenixRL uses a Q-learning agent to learn a policy that dynamically weights the probabilistic outputs of complementary predictors, including BayesDel (addAF and noAF), ClinPred, and MetaRNN. Replacing static weighting with policy learning allows GenixRL to adaptively identify optimal weightings and substantially improve classification accuracy. RESULTS: In benchmark evaluation against 25 state-of-the-art predictors, GenixRL achieved an AUROC of 0.9644 on an independent ClinVar dataset. On saturation genome editing assays for BRCA1 and BRCA2, GenixRL achieved the best performance and ranked highest on 14 of 17 clinically significant genes in a zero-shot evaluation. Applied to uncertain and conflicting ClinVar variants, GenixRL enabled tiered, evidence-based prioritization of hundreds of thousands of variants as likely pathogenic or pathogenic with high confidence, supported by orthogonal population evidence from gnomAD. CONCLUSION: GenixRL advances pathogenicity prediction for missense variants and provides an adaptive ensemble that sorts variants of uncertain significance into tiered candidates for expert curation and functional validation.

Mutation, Missense↗
PubMed2026

AI echo INSIGHT study: A prospective blinded randomized trial of artificial intelligence echocardiogram interpretation.

BACKGROUND: Transthoracic echocardiography (TTE) is the most commonly performed cardiac imaging modality with over 30 million studies annually. Demand for timely expert interpretation continues to outpace capacity, creating diagnostic delays and inter-observer variability that impact patient care. Recent research has suggested computer vision artificial intelligence (AI) models can generate accurate preliminary comprehensive TTE reports, however, prospective evaluation is needed to determine whether AI-assisted TTE interpretation can improve clinician efficiency while preserving diagnostic accuracy. METHODS: AI ECHO INSIGHT is a prospective randomized blinded clinical trial conducted at Kaiser Permanente Northern California that will evaluate 1200 historical TTE studies (1000 consecutive unselected studies plus 200 with moderate or greater valvular disease) interpreted using three workflows: (1) AI-generated preliminary report finalized by a blinded cardiologist (AI-assisted); (2) cardiologist-generated preliminary report finalized by a blinded cardiologist (cardiologist-assisted); and (3) sonographer-generated preliminary report finalized by a blinded cardiologist (sonographer-assisted). The primary outcome is the rate of substantial change between preliminary and final reports, comparing the AI-assisted workflow to the pooled cardiologist-assisted and sonographer-assisted workflows. Secondary outcomes include cardiologist interpretation time for report finalization, superiority testing for diagnostic accuracy, and reporting consistency. CONCLUSION: AI ECHO INSIGHT is a prospective randomized blinded clinical trial evaluating the clinical impact of AI-assisted TTE interpretation on diagnostic accuracy, cardiologist efficiency, and reporting consistency in real-world echocardiography workflows. TRIAL REGISTRATION: ClinicalTrials.gov registration number NCT07229300.

Humans↗