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PubMed2026

Antegrade dissection and re-entry vs retrograde strategy in chronic total occlusion percutaneous coronary intervention: Rationale and design of the ADRENALINE randomized study.

RATIONALE: While antegrade wiring (AW) is the most common initial strategy for chronic total occlusion (CTO) percutaneous coronary intervention (PCI), difficult CTO lesions frequently require either antegrade dissection and re-entry (ADR) or a retrograde strategy. Comparative data between ADR and the retrograde approach remain limited. DESIGN: The Antegrade Dissection vs Retrograde re-ENtry And Load of Interventionalist Effort (ADRENALINE) is a prospective, multicenter randomized study with a superiority design. It is planned to enroll 121 patients with difficult coronary CTO (J-CTO score ≥2) referred for CTO-PCI in accordance with the hybrid algorithm. Subjects undergoing successful AW will be included in the observational arm. Patients with failed or unattempted AW will be randomized 1:1 to ADR or retrograde CTO crossing strategy (n = 74). All patients will undergo pre- and postprocedural laboratory testing (including cardiac troponin T and creatine kinase-MB), cardiac magnetic resonance (CMR) for late gadolinium enhancement, and health status assessment by the Seattle Angina Questionnaire and the Rose Dyspnea Scale. The co-primary endpoints are total procedure time and successful guidewire crossing. Additionally, the relationship between different recanalization strategies and stress among interventional cardiologists will be explored. CONCLUSION: ADRENALINE is the first randomized study of ADR vs retrograde strategy for difficult CTO PCI, assessing procedural outcomes, CMR-detected myocardial infarction, and 3-month quality of life. ENROLMENT STATUS: The first patient was enrolled on July 29, 2025. As of June 14, 2026, 45 patients (26 randomized, 19 observational) of the planned 121 patients have been enrolled. TRIALS REGISTRATION: Clinicaltrials.gov: Identifier, NCT06878729.

Humans↗

PubMed2026

International study of coronary microvascular angina (iCorMicA): A registry-based diagnostic study and nested randomized trial.

BACKGROUND: Angina is a debilitating condition caused by coronary artery disease and microvascular dysfunction. Following coronary angiography angina and no obstructive coronary arteries is a common outcome, and women are disproportionately affected. The objectives are first, to assess causes of angina in patients undergoing invasive management; and second, to assess effects of coronary function test-guided management on clinical outcomes. METHODS: This is an international, multicenter, prospective, registry-based study and nested, randomized, controlled, triple-blind, and endpoint trial. Participants, community care providers, and outcomes assessors are masked. Consented participants enter the registry. Participants without obstructive coronary artery disease (luminal stenosis <50%, or fractional flow reserve >0.80) are eligible for randomization. Index of microcirculatory resistance (IMR; abnormal ≥25) and coronary flow reserve (CFR; abnormal <2.0; gray zone 2.0-2.5) are measured by bolus thermodilution, and results are disclosed (intervention) or not (control group) to the attending cardiologist. RESULTS: The primary outcome of the registry is the Seattle Angina Questionnaire summary score at baseline described by coronary artery disease status. Secondary outcomes include the prevalence of obstructive coronary artery disease, patient reported outcome measures and clinical outcomes. The primary outcome of the randomized trial is the within-individual change in Seattle Angina Questionnaire summary score at 12-months from baseline. Secondary outcomes include safety, diagnostic accuracy, patient reported outcome measures for quality of life, physical and psychological function, cardiovascular risk, clinical outcomes, health economics and mechanistic biomarkers. The first patient was screened on December 18, 2020 and the last patient was enrolled on June 30, 2026. Forty sites were included in the United Kingdom (n = 35), Republic of Ireland (n = 2), Holland (n = 2), and Poland (n = 1). In total, 1,483 participants were enrolled into the registry of whom 1,047 were randomized and 386 were not randomized (registry-only). CONCLUSION: This international, registry-based clinical trial will provide novel evidence on the natural history of angina and stratified therapy for angina with no obstructive coronary arteries. CLINICAL TRIAL REGISTRATION: https://clinicaltrials.gov/study/NCT04674449. UNIQUE IDENTIFIER: NCT04674449.

Humans↗

PubMed2026

Depression and amyloid-β across CSF, PET, and plasma biomarkers: a systematic review and meta-analysis.

Alzheimer's disease is increasingly defined by biomarker evidence of amyloid-β and tau pathology, sharpening questions about whether late-life depression contributes to, or instead reflects, this pathology. We conducted a systematic review and meta-analysis of studies published between 2000 and 2025 that compared amyloid-β biomarkers in adults with and without depression, with depression defined by validated clinical diagnoses or symptom rating scales. Twenty-four studies were included, spanning three biomarker sources: cerebrospinal fluid, positron emission tomography imaging, and plasma. Across all sources, the pooled difference in amyloid-β burden between depressed and non-depressed individuals was small and clustered near zero, indicating only a weak, statistically non-significant tendency toward higher amyloid in depression. When the three sources were examined separately, each yielded a similar near-null result, although between-study heterogeneity was considerable for cerebrospinal fluid and plasma and moderate for imaging. Importantly, a prespecified subgroup analysis showed that imaging results diverged by quantification method: studies using the simpler standardized uptake value ratio clustered around zero, whereas the smaller group of studies using kinetic distribution volume ratio modelling showed a significant positive association, suggesting that methodological choices critically influence the observed relationship. Taken together, these findings indicate that depression is not consistently accompanied by greater amyloid-β burden across widely used biomarker platforms. The distribution volume ratio signal nonetheless raises the possibility of subtle associations that cruder methods may obscure, and suggests that depression may shape Alzheimer's disease trajectories more by modifying the clinical impact of amyloid than by altering its amount.

Humans↗

PubMed2026

Cognitive-metabolic relationship in temporal lobe epilepsy: A systematic review.

OBJECTIVE: To summarize the current literature on neurometabolic dysfunction identified through brain imaging and its cognitive correlates in temporal lobe epilepsy (TLE). BACKGROUND: Cognitive decline contributes to chronic disability in TLE. The pathophysiology of cognitive decline in TLE is poorly understood, limiting therapeutic advances. Characterizing metabolic changes in patients with TLE and cognitive impairment may identify biomarkers and inform new treatment strategies. DESIGN/METHODS: We conducted a systematic review of five major databases, gathering studies published through December 2024, in accordance with PRISMA guidelines. We included all observational studies describing associations between metabolic imaging findings and cognitive measures in TLE. RESULTS: Of 1449 reports, 38 met the inclusion criteria, encompassing 1161 patients with TLE aged 5-66 years. Twenty-two studies applied fluorodeoxyglucose (18F-FDG) positron emission tomography (PET) to assess interictal brain glucose metabolism. Two studies utilized PET with other tracers to assess more specific metabolic aspects. Fourteen studies used proton magnetic resonance spectroscopy (1H-MRS) to quantify local concentrations of brain metabolites. Impairment of verbal memory was consistently associated with left temporal lobe metabolite changes. Non-memory cognitive impairments correlated with changes in glucose metabolism, N-acetylaspartate, and gamma-aminobutyrate in both temporal and extratemporal areas. CONCLUSION: 18F-FDG PET remains the most widely used imaging modality to assess cognitive-metabolic correlates in TLE, while other PET tracers and 1H-MRS are potentially underexplored. Verbal memory impairment correlates robustly with left temporal dysmetabolism. Cognitive impairment in TLE is multifaceted and correlates with measurable changes in metabolism in both temporal and extratemporal regions. While our synthesis was restricted by some methodological limitations, these neurometabolic signatures may hold promise as potential biomarkers for identifying risk of cognitive decline and highlight avenues for future research.

Humans↗

PubMed2026

Retinal microstructural alterations as early phenotypes of depression in radiogenomics analysis.

BACKGROUND: With the increasing prevalence of depression, there is an urgent clinical need for early screening in depression. The retina offers a promising window for early screening in depression due to its rapid, non-invasive, objective, eye-brain correlated characteristics, but previous research has yielded conflicting alterations in retinal microstructure in depression. METHODS: We screened retinal optical coherence tomography and brain magnetic resonance imaging data in the UK Biobank to enroll 23,225 participants for retinal study of depression occurrence, and 1475 participants for the eye-brain association study. We also used genetic data (ID: ebi-a-GCST90014267 and ukb-d-20,448) from the Integrative Epidemiology Unit Open Genome-Wide Association Study for Mendelian randomization analysis. We used Cox regression to assess the association between retinal microstructure and depression risk, Mendelian randomization to infer causality, and mediation analysis to explore retina-brain pathway association. RESULTS: The Cox regression analysis showed that retinal ganglion cell-inner plexiform layer (GCIPL) thickness remained a significant predictor of depression. The Mendelian randomization analysis indicated a positive statistical association between GCIPL thickness and depression. Moreover, there was a significant positive correlation (all p < 0.001) between the volume of specific depression-related brain regions and the GCIPL thickness. Adjusting for age, sex, and head size, the mediation analysis provided preliminary evidence for a potential anatomical pathway linking retinal GCIPL thickness to depression-related brain regions through primary visual cortex and secondary visual cortex volumes. CONCLUSION: Thickened retinal GCIPL is a potential early phenotype of depression and has a potential association pathway with depression-related brain regions using a radiogenomics approach.

Humans↗

PubMed2026

A systematic review and meta-analysis of OCT-based ophthalmic changes in amyotrophic lateral sclerosis.

BACKGROUND: Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease marked by motor decline and respiratory failure. Optical coherence tomography (OCT), a non-invasive imaging technique, has been explored for detecting retinal structural changes that may reflect neurodegeneration in ALS. While some studies report thinning of retinal layers, findings remain inconsistent. Therefore, a meta-analysis is needed to clarify the extent of retinal involvement and the potential of OCT as a biomarker in ALS. METHODS: A systematic literature search was conducted across PubMed, EMBASE, and Cochrane databases for studies published between 2010 and May 2025. Study quality was assessed using the Newcastle-Ottawa Scale (NOS), and publication bias was evaluated through funnel plot asymmetry and Egger's test. Pooled effect sizes were calculated using random-effects models to account for between-study heterogeneity, and differences in OCT parameters between ALS patients and healthy controls were expressed as standardized mean differences (SMD) with 95% confidence intervals (CI). Statistical heterogeneity was quantified using the I2 statistic. RESULTS: A total of 17 studies were included in the present meta-analysis. The primary unadjusted global model demonstrated significant reduction of retinal nerve fibre layer (RNFL) thickness in ALS patients compared to controls (unadjusted SMD = -0.295, 95% CI: -0.522, -0.068). Upon applying a Design Effect variance inflation model to address fellow-eye non-independence, the pooled estimate remained robustly significant across a conservative range of intraclass correlations (SMD ranged from -0.256 to -0.249). Subgroup analyses revealed that RNFL thinning was particularly pronounced in spinal-onset ALS (SMD = -0.54, 95% CI: (-0.98, -0.10). When studies were stratified by the region of conduct, RNFL and macular thinning reached statistical significance only within the non-Asian subgroup, though the formal test for subgroup differences was not significant. CONCLUSION: This meta-analysis demonstrates significant bilateral RNFL thinning in ALS, with relative preservation of the Inner Nuclear Layer and Ganglion Cell Layer - Inner Plexiform Layer, supporting retinal neurodegeneration as a feature of this multisystem disorder. SYSTEMATIC REVIEW REGISTRATION: PROSPERO identifier CRD420251076035.

Humans↗

PubMed2026

Ciliochoroidal Effusion and Regional Scleral Thickening in Peripapillary Pachychoroid Syndrome.

PURPOSE: To assess anterior scleral thickness and the presence of ciliochoroidal effusion (CE) in eyes with peripapillary pachychoroid syndrome (PPS), and to compare the results with a cohort of healthy age-matched controls. DESIGN: Retrospective cross-sectional study METHODS: A total of 20 eyes from 12 patients diagnosed with PPS and 30 eyes from 15 healthy control subjects. All participants underwent a comprehensive ophthalmic examination, including spectral-domain optical coherence tomography with enhanced depth imaging (EDI-OCT) and anterior segment OCT (AS-OCT). Choroidal thickness was measured at predefined macular and peripapillary locations, while anterior scleral thickness was assessed 6 mm posterior to the scleral spur in 4 quadrants. Ciliochoroidal effusion was evaluated qualitatively using AS-OCT. Comparisons between groups were performed using linear mixed models with Bonferroni correction (scleral thickness corrected P < .010) RESULTS: The mean age of PPS patients was 75.6 ± 9.8 years, and 16% were females. Anterior scleral thickness was significantly greater in the temporal quadrant in PPS eyes compared to controls (396.85 ± 74.97 µm vs 331.13 ± 62.65 µm: P = .007). Ciliochoroidal effusion was detected in 60% of PPS eyes, predominantly in the superior and temporal sectors, whereas no effusion was observed in healthy controls (P < .001). Eyes with CE exhibited a thicker mean scleral thickness and a thicker subfoveal choroidal thickness compared to eyes without effusion (P < .05). CONCLUSION: Eyes with PPS demonstrated increased temporal scleral thickness and a high prevalence of CE. These findings suggest that scleral characteristics may represent a predisposing anatomical factor in PPS, although the precise pathophysiological mechanisms remain to be elucidated.

Humans↗

PubMed2026

Association Between 24-Hour Blood Pressure and Rates of Retinal Nerve Fiber Layer Progression in Glaucoma: The Vascular Imaging in Glaucoma Study.

PURPOSE: Low systemic blood pressure (BP) has been implicated as a risk factor for glaucoma progression. The purpose of this study was to investigate the association between 24-hour BP and rates of retinal nerve fiber layer (RNFL) loss in eyes with primary open-angle glaucoma. DESIGN: Prospective cohort study. PARTICIPANTS: Seventy-nine eyes from 42 subjects with glaucoma (mean age, 68.5 ± 7.6 years) enrolled in the Vascular Imaging in Glaucoma Study at the Bascom Palmer Eye Institute. METHODS: Participants underwent 24-hour ambulatory BP monitoring at baseline. Follow-up evaluations were conducted at 4-month intervals and included ophthalmic examination, BP measurement, and peripapillary RNFL thickness measurement with spectral-domain optical coherence tomography. The association between BP and RNFL loss over time was assessed using linear mixed-effects models adjusted for age, sex, race, baseline RNFL thickness, central corneal thickness, and intraocular pressure. MAIN OUTCOME MEASURES: The effect of baseline 24-hour mean arterial pressure (MAP), systolic BP (SBP), and diastolic BP (DBP) on the rate of average RNFL loss over time. RESULTS: Eyes underwent an average of 13 ± 3 optical coherence tomography exams over 43 ± 10 months of follow-up. The mean rate of RNFL loss was -0.34 ± 0.64 µm/y (median: -0.32; interquartile range: -0.66 to -0.04 µm/y). After adjusting for confounding factors, every 10 mm Hg lower in 24-hour minimum MAP, SBP, and DBP was associated with -0.542 µm/y (P < .001), -0.360 µm/y (P = .003), and -0.458 µm/y (P = .008) faster RNFL loss, respectively. Eyes in the lowest quartile of average 24-hour MAP (81-90 mm Hg) and minimum 24-hour DBP (35-47 mm Hg) experienced significantly faster progression compared to those in the highest quartile, with differences of -0.68 µm/y (P = .017) and -0.63 µm/y (P = .030), respectively. CONCLUSIONS: Lower systemic BP, especially minimum MAP, SBP, and DBP measured by 24-hour ambulatory BP monitoring, is associated with faster rates of RNFL loss in primary open-angle glaucoma eyes. 24-hour BP monitoring may help predict glaucoma patients at greater risk of progression.

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 ± 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 µm were included. After epithelial removal and riboflavin soaking, continuous UV-A irradiation (365 nm) at 3 or 9 mW/cm² was delivered with total fluence titrated up to 10 J/cm² based on intraoperative ultrasound pachymetry and a previously published nomogram targeting an uncross-linked stromal margin of approximately 70 µm 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 ± 5.10 D (95% CI, -2.71 to 1.17; P = .418). Mean demarcation line-to-anterior stroma distance was 205 ± 64 µm (95% CI, 180.7-229.3), and demarcation line-to-endothelium distance was 64 µm (IQR, 49-152). All demarcation lines remained within the stromal layer; 15/29 eyes (51.7%) had a demarcation line located ≤70 µm 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 µm (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

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

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 kcal/mol, Wogonin (-9.3 kcal/mol) and Xanthohumol (-8.1 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↗

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

Applications of quantum AI in brain disorder diagnosis: A systematic review.

BACKGROUND AND OBJECTIVE: Brain disorder diagnosis and prediction remain challenging because neuroimaging, electrophysiological, behavioral, and multimodal data are high-dimensional, noisy, heterogeneous, and limited by small clinical cohorts. This systematic review synthesised applications of quantum artificial intelligence (QAI) for brain disorder diagnosis, prediction, detection, and monitoring. METHODS: Following PRISMA guidelines, studies published from 2016 to 13 January 2026 were retrieved from Scopus, Web of Science, and IEEE Xplore. After screening, 36 studies met the eligibility criteria and were qualitatively analysed according to disorder category, data modality, QAI method, implementation setting, validation strategy, and performance. RESULTS: At the broader disease-group level, neurodegenerative disorders were the most frequently investigated, followed by mental health and psychiatric disorders. At the individual level, Parkinson's disease and schizophrenia were the leading applications, followed by depression, anxiety, Alzheimer's disease, and stress-related tasks. MRI-based modalities were the most frequently used data source, followed by multimodal data and EEG. Methodologically, primary QAI approaches were dominated by quantum neural and QDL architectures, followed by quantum-inspired optimization or feature-selection methods and quantum-kernel/conventional QML classifiers. Qiskit/IBM Quantum and PennyLane were the most frequently reported quantum software frameworks. However, most studies relied on simulators, classical quantum-inspired implementations, or unclear implementation settings, with limited real-hardware evaluation. CONCLUSIONS: QAI shows emerging potential for brain disorder analysis, particularly through hybrid quantum-classical learning, quantum neural architectures, quantum-kernel methods, and quantum-inspired optimization. Nevertheless, current evidence remains preliminary and requires larger datasets, subject-level and external validation, fair classical benchmarking, noise-resilient circuits, real quantum hardware evaluation, explainability, and clinical validation.

Humans↗