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PubMed2026

Neuromodulation for Subjective Tinnitus: A Systematic Review and Meta-Analysis of Randomized Trials.

OBJECTIVE: To evaluate the effectiveness and safety of neuromodulation and bimodal stimulation for chronic subjective tinnitus in randomized controlled trials (RCTs). DATA SOURCES: PubMed/MEDLINE, Web of Science, and EMBASE (January 2015-December 2025) searched per PRISMA 2020. REVIEW METHODS: Adult RCTs (≥ 18 years) with chronic subjective tinnitus (> 3 months) assessing validated outcomes (THI, TFI, TQ) for neuromodulation/bimodal interventions vs. sham/controls. Two-stage screening, Cochrane RoB-2 risk-of-bias assessment. Random-effects meta-analyses (REML) were performed when ≥ 3 comparable trials were available; effects reported as standardized mean differences (SMD) with 95% CIs. Main Outcomes and measures included change in tinnitus severity (THI/TFI/TQ) while secondary outcomes included loudness (VAS/NRS), durability, and adverse events. RESULTS: Twenty-six RCTs (n = 1576) met criteria: tES (11; n = 372), rTMS (8; n = 432), acoustic coordinated reset (1; n = 100), vagus nerve stimulation (2; n = 90), and bimodal stimulation (4; n = 582). Meta-analysis showed a nonsignificant pooled effect for tDCS (SMD -0.36; 95% CI -0.75 to 0.02; I 2 = 51%) and rTMS (SMD -0.15; 95% CI -0.37 to 0.07; I 2 = 0%). Single-trial evidence for coordinated reset showed no advantage over broadband noise. VNS demonstrated modest benefits with safety concerns limited to implanted approaches. Bimodal stimulation yielded consistent, clinically meaningful reductions (often ≥ 10-20 points on THI/TFI), with durability up to 12 months. Adverse events were mild/transient across noninvasive modalities. CONCLUSIONS: Noninvasive neuromodulation appears safe with average benefits; among modalities, bimodal stimulation shows the most consistent and durable clinical improvements. Standardized, adequately powered RCTs with harmonized protocols and long-term follow-up are needed to refine targets and dosing.

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

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

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

Humans↗

PubMed2026

A pragmatic randomized controlled trial of self-directed online writing interventions for posttraumatic stress symptoms in a real-world digital setting.

Background: Public health and other large-scale crises, such as the COVID-19 pandemic, have intensified the global mental health burden, creating unprecedented demand for accessible interventions for posttraumatic stress symptoms (PTSS).Objective: We evaluated the feasibility and effectiveness of two self-directed online writing interventions embedded within China's WeChat ecosystem during the COVID-19 pandemic through a pragmatic randomised controlled trial.Methods: Between December 2021 and August 2022, 1,526 adults were screened for PTSS via a Tencent Medinfo Mini-Program. Eligible participants (n = 211) were randomised to Guided Narrative Technique-Writing (GNT-W, n = 100) or Expressive Writing (EW, n = 111). Both interventions comprised three self-directed daily writing sessions delivered entirely online without human support. Primary outcome was PTSD symptom severity (PTSD Checklist-Short), assessed at baseline, post-intervention, 2-week, and 1-month follow-ups.Results: While initial engagement followed typical digital health patterns (64.5% overall attrition), participants who initiated treatment showed strong adherence (77% completion). Both interventions were associated with significant within-group reductions in PTSS severity (GNT-W: b = -0.43, p = .023, d = -0.43; EW: b = -0.60, p = .001, d = -0.58), with no significant between-group difference (group × time: b = 0.18, p = .48). GNT-W did not confer additional benefit over EW protocol on PTSS severity.Conclusions: Both self-directed writing interventions were associated with within-group reductions in PTSS; without an inactive control condition, however, these changes cannot be firmly attributed to the interventions. GNT-W showed no advantage over the simpler EW protocol. These findings offer preliminary support for embedding scalable, low-barrier writing interventions in widely used digital platforms.Chinese Clinical Trial Registry: ChiCTR2000034836.

Humans↗

PubMed2026

Reassessment of the effect of oral l-arginine on blood pressure: A systematic review and meta-analysis based on ambulatory blood pressure monitoring.

OBJECTIVE: This meta-analysis aimed to evaluate the effect of oral l-arginine supplementation on ambulatory blood pressure (ABP). METHODS: A systematic search of PubMed, Cochrane Library, Embase, and Web of Science databases was conducted from their inception through March 1, 2026. Randomized controlled trials (RCTs) assessing the effects of oral l-arginine intervention were included. Outcome measures included 24-h systolic blood pressure (24h SBP), 24-h diastolic blood pressure (24h DBP), daytime systolic blood pressure (dSBP), daytime diastolic blood pressure (dDBP), nighttime systolic blood pressure (nSBP), and nighttime diastolic blood pressure (nDBP). Meta-analysis was performed using Stata 17.0. The weighted mean difference (WMD) was used as the effect size, and the results were pooled with 95% confidence intervals (CIs). RESULTS: A total of 5 RCTs comprising 202 participants were included. Meta-analysis results demonstrated that oral l-arginine significantly reduced 24h SBP (WMD = -4.23 mmHg, 95% CI [-5.87, -2.58]; P < 0.01) and 24h DBP (WMD = -3.04 mmHg, 95% CI [-4.48, -1.59]; P < 0.01). Significant reductions were also observed for dSBP (WMD = -4.16 mmHg, 95% CI [-5.90, -2.41]; P < 0.01) and dDBP (WMD = -4.25 mmHg, 95% CI [-5.85, -2.66]; P < 0.01). Furthermore, oral l-arginine significantly lowered nSBP (WMD = -5.70 mmHg, 95% CI [-7.81, -3.58]; P < 0.01) and nDBP (WMD = -4.18 mmHg, 95% CI [-6.27, -2.09]; P < 0.01). CONCLUSION: Oral l-arginine supplementation significantly reduces ABP. However, the number of included studies was limited, and further validation through additional relevant research is warranted.

Arginine↗

PubMed2026

Uric Acid-to-HDL Cholesterol Ratio is Associated with Hepatic Steatosis but Not Fibrosis in Nonobese Adults: A NHANES 2017-2020 Study.

BACKGROUND: Although metabolic dysfunction-associated steatotic liver disease (MASLD) has traditionally been regarded as a disease closely related to obesity, its prevalence is gradually rising in nonobese populations. The uric acid-to-high-density lipoprotein cholesterol ratio (UHR) is a novel metabolic biomarker that has been shown to be associated with MASLD. However, its role in nonobese individuals remains unclear. This study aimed to investigate the association between UHR and nonobese MASLD. METHODS: Data from the 2017 to 2020 National Health and Nutrition Examination Survey (NHANES) were analyzed. UHR was calculated as the serum uric acid (UA) divided by the high-density lipoprotein cholesterol (HDL). Liver steatosis (using controlled attenuation parameters, CAP) and fibrosis (using liver stiffness measurement, LSM) were evaluated through vibration-controlled transient elastography (VCTE). Multivariate linear regression was employed to evaluate associations. Nonlinear relationships were examined using smoothed curve fitting. RESULTS: This analysis included 3573 participants (47.41% male; aged 20-80 years). Log2-UHR was positively associated with CAP (β = 9.96, 95% CI: 6.93-12.98, P < 0.001) and MASLD prevalence (OR = 1.64, 95% CI: 1.38-1.95, P < 0.001). Subgroup analyses revealed significant interactions by sex, age, ethnicity, and smoking status (all P for interaction < 0.05), with stronger associations in females, adults aged 40-59 years, individuals of other Hispanic ethnicity, and never-smokers. Moreover, the association between log2-UHR and CAP was linear and positive (P < 0.01). No significant association was found between log2-UHR and LSM (P > 0.05). CONCLUSIONS: UHR is independently associated with hepatic steatosis but not fibrosis in nonobese US adults, suggesting its potential utility as an early risk assessment to tool. Large scale prospective studies are needed in the future to further validate the conclusions of this study.

Humans↗

PubMed2026

The mighty microproteins: from versatile cellular regulators to precision medicine therapeutics.

Microproteins, are tiny proteins encoded by small open reading frame (sORF), translation of these non-canonical open reading frames (ncORFs) has been implicated in diverse biological processes and diseases. This review summarizes recent developments in the discovery, biogenesis, and functional characterization of microproteins, and their involvement in various disease, with special focus on their roles in cancer, cardiovascular, metabolic, neurodegenerative and immune-related disorders. We emphasize the regulation of key cellular pathways by microproteins, including mitochondrial homeostasis, apoptosis, metabolic reprogramming, and immune signaling, all of which affect disease initiation and progression. Emerging evidence also supports their potential as disease biomarkers and therapeutic candidates for precision medicine. Finally, the review critically discusses the current challenges including discrepancies in microprotein annotation, the limitations of ribosome profiling and proteogenomic approaches, the gap between computationally predicted and experimentally validated microproteins, and the need for rigorous orthogonal validation by means of CRISPR-based genome editing, ribosome release assays, mutational analysis, high-resolution mass spectrometry, and functional studies. Finally, we review recent development of AI-assisted ORF prediction, single-cell translatomics, spatial proteomics, and integrated multi-omics as emerging technologies reshaping. Microprotein discovery and functional annotation. Finally, we discuss the translational potential of microproteins and highlight the remaining challenges to clinical application, including peptide stability, pharmacokinetics, tissue-specific delivery, immunogenicity, and the need for rigorous preclinical and clinical validation. Together, this review provides an updated and critical overview of the rapidly evolving microprotein field and highlights future research priorities for translating these molecules into clinically useful biomarkers and precision therapeutics.

Microproteins↗

PubMed2026

Risk Factors and Predictive Model for Postoperative High Myopia in Children Undergoing Congenital Cataract Surgery With Intraocular Lens Implantation.

PURPOSE: To identify risk factors associated with the development of high myopia following congenital cataract surgery and to establish a robust predictive model. DESIGN: Retrospective clinical cohort study. SUBJECTS: This retrospective study included 106 pediatric patients who underwent congenital cataract surgery with primary IOL implantation (mean follow-up 8.19 years). The model was externally validated in an independent cohort of 72 patients with a mean follow-up of 7.83 years. METHODS: Preoperative and postoperative ocular biometric parameters were collected. Risk factors for postoperative high myopia were analyzed using Cox proportional hazards regression, which served as the basis for model construction. The predictive performance of the model was rigorously evaluated for discrimination and calibration. Discriminative ability was quantified using Harrell's C-index and the area under the receiver operating characteristic curve (AUC). Model calibration was assessed via calibration plots by comparing predicted probabilities with actual observed outcomes. Internal validation was performed using a bootstrapping method (500 iterations) to ensure model stability and adjust for potential overfitting. RESULTS: An initial postoperative refraction of <+0.75D, and a higher IOL Power to Axial length Ratio (IOL/AL ratio) were identified as significant risk factors for the development of postoperative high myopia. Shorter preoperative axial length was associated with a greater magnitude of postoperative myopic shift. The predictive model demonstrated robust performance, achieving a C-index of 0.711 (internal validation C-index: 0.713). The area under the receiver operating characteristic curve (AUC) values for predicting high myopia at 5 and 10 years were 0.858 and 0.745, respectively. Furthermore, calibration curves demonstrated excellent agreement between the predicted and observed outcomes throughout the follow-up period. In external validation, the model achieved a C-index of 0.825, 5-year AUC of 0.833, and 10-year AUC of 0.713. CONCLUSIONS: Our analysis established that initial postoperative refraction <+0.75D, and an elevated IOL/AL ratio are key determinants of high myopia risk following surgery. Shorter preoperative axial length was associated with a greater magnitude of postoperative myopic shift. This predictive framework provides clinicians with a practical tool to optimize preoperative IOL selection and identify high-risk infants who require vigilant myopia prevention and balanced amblyopia management.

Humans↗

PubMed2026

Applications of artificial intelligence in robot-assisted surgery: a systematic review.

To characterize applications of artificial intelligence (AI) in robot-assisted surgery, summarize technical and clinical performance, and assess the quality of the available evidence. PubMed, Web of Science Core Collection, and Scopus were searched for English-language journal articles published from 1 January 2020 through 31 October 2025. Randomized, observational, model-development, validation, and feasibility studies evaluating AI in robot-assisted surgery or closely related image-guided minimally invasive workflows were eligible. Two reviewers independently performed study selection, data extraction, and risk-of-bias assessment. Owing to heterogeneity in surgical procedures, AI tasks, analytical units, validation strategies, and outcomes, findings were synthesized descriptively without statistical pooling. The review was registered in the International Prospective Register of Systematic Reviews (CRD420251175699). Seventeen studies were included: seven clinical prediction or decision-support studies, eight intraoperative recognition, segmentation, or image-guided studies, and two training or workflow studies. Five prediction studies reported area-under-the-curve values of 0.74-0.95. Technical studies reported F1 or Dice scores of 0.525-0.995 and task-specific accuracies of 0.840-0.998. Two randomized studies suggested benefits for personalized suturing feedback and automated camera control, but neither established improved patient outcomes. Only one study had low overall risk of bias; the remaining studies were at high or unclear risk or raised some concerns. AI applications in robot-assisted surgery show promise for prediction, intraoperative perception, training, and workflow support. Evidence primarily demonstrates technical feasibility rather than established clinical effectiveness. Independent multicenter validation and prospective evaluation of patient, educational, and workflow outcomes are required before widespread implementation.

Robotic Surgical Procedures↗

PubMed2026

Automated CEAP Classification of Venous Duplex Reports Using Multimodal Artificial Intelligence.

OBJECTIVE: To develop and internally validate a prototype multimodal artificial intelligence system for automated CEAP (Clinical, Etiological, Anatomical and Pathophysiological) classification of venous duplex ultrasound (VDUS) reports, integrating natural language processing of free-text components with computer vision analysis of hand-drawn anatomical diagrams. METHODS: Single centre retrospective observational study using routinely collected clinical data. One thousand consecutive venous duplex ultrasound reports from Cambridge University Hospitals NHS Foundation Trust, UK (July 2024 - May 2025) were labelled according to the CEAP classification, excluding the Etiological component, which could not be reliably determined from duplex reports alone. Transfer learning was applied using ClinicalBERT for text and MobileNetV3 for diagrammatic data. Clinical classes were predicted from request line text. Text- and image-based pathophysiological models were developed for four anatomical territories (Great Saphenous Vein, Small Saphenous Vein, Deep system, Perforators), combined using late fusion with probability averaging. RESULTS: The clinical CEAP model achieved accuracy of 0.91, macro-F1 of 0.82, and macro-AUC of 0.98. Pathophysiological prediction varied, with text models broadly outperforming image models. Fusion yielded heterogeneous benefits, improving SSV performance but reducing Deep system accuracy. The performance of the final pathophysiological CEAP fusion models varied across anatomical territories: accuracy ranged from 0.70-0.92 and macro-AUC from 0.80-0.92. CONCLUSION: This study demonstrates the feasibility of automated CEAP classification from VDUS reports. Despite class imbalance affecting minority class predictions, the strong discriminatory performance validates this multimodal ML model for extracting clinically meaningful information from real-world data. This approach offers potential, pending external validation, to streamline vascular services through automated triage and guideline-compliant decision making.

Artificial intelligence↗

PubMed2026

Efficacy of CagriSema for Reaching Anthropometric Treatment Targets and Cardiometabolic Outcomes: A Secondary, Post hoc Analysis of REDEFINE 1.

AIMS: To assess the added value of absolute anthropometric targets alongside percentage weight loss in the clinical management of obesity. MATERIALS AND METHODS: The phase 3a, 68-week REDEFINE 1 trial randomised adults without diabetes with BMI ≥ 30 kg/m2, or ≥ 27 kg/m2 with ≥ 1 obesity-related complication, to once-weekly CagriSema 2.4 mg/2.4 mg, semaglutide 2.4 mg, cagrilintide 2.4 mg, or placebo, plus lifestyle intervention. This secondary, post hoc analysis assessed the proportions of participants achieving BMI < 27 kg/m2 and/or WHtR < 0.53 targets, and percentage weight loss by anthropometric target. The association between the proportion of participants maintaining or achieving normalisation of four cardiometabolic outcomes: normoglycemia (glycated haemoglobin < 5.7% and fasting plasma glucose < 5.6 mmol/L); blood pressure (< 130/80 mmHg); triglycerides (< 1.7 mmol/L); lipids (high-density lipoprotein cholesterol ≥ 1.3 mmol/L [female] or ≥ 1.0 mmol/L [male]) and reaching anthropometric targets or change in body weight (%) was also assessed. RESULTS: The proportion of participants achieving both BMI < 27 kg/m2 and WHtR < 0.53 targets at week 68 was 30.3%, 19.1%, 9.0%, and 3.3% in participants who received CagriSema, semaglutide, cagrilintide, or placebo respectively. Both BMI and WHtR performed similarly as indicators for all four cardiometabolic outcomes. At more stringent cut-off values, anthropometric targets were better measures than percentage weight loss. CONCLUSIONS: The proportion of participants achieving anthropometric targets was greater with CagriSema Versus other treatments. These findings support further validation of BMI and WHtR anthropometric treatment targets and indicate a potential for indicating amelioration of clinical outcomes in obesity and a greater emphasis on target-based treatment strategies.

Humans↗

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↗

PubMed2026

Redox Rewiring in Nicotine-Driven Gastric Carcinogenesis: Uncovering ROS-Dependent Oncogenic Circuits.

SIGNIFICANCE: Nicotine from tobacco products, secondhand smoke, and emerging delivery systems remains a major but underappreciated driver of gastric carcinogenesis (GC). Although reactive oxygen species (ROS) have long been implicated in tumor biology, current models incompletely explain how chronic nicotine selectively reprograms gastric epithelial signaling. This review advances the concept of redox rewiring, whereby nicotine establishes a persistent oxidative state that orchestrates multiple oncogenic programs via spatially compartmentalized NOX signaling. RECENT ADVANCES: We synthesize evidence for a unified model wherein nicotine activates nAChR/β-AR signaling, Ca2+ influx, PKC, and compartmentalized NOX-derived ROS to generate distinct oncogenic outputs. Beyond the established NOX/ROS/NF-κB/MAPK-driven IL-8 and MMP-9 axes, we integrate emerging evidence into three interconnected modules governing EMT/metastasis (ABL1/STAT3/COX-2/periostin), survival/chemoresistance (ERK/GLI1/Bcl-2), and invasion/immune evasion (miR-21/PDCD4). Collectively, these circuits suggest that ROS function not merely as damaging byproducts but as spatially organized signaling mediators dictating tumor behavior. CRITICAL ISSUES: A major challenge is distinguishing established mechanisms from incompletely validated models. The three proposed axes are testable hypotheses requiring experimental validation. Most data derive from in vitro studies with nonphysiologic nicotine concentrations, and artifacts from nonspecific ROS probes are common. Compensatory pathway activation and multi-target effects of natural products remain underexplored. FUTURE DIRECTIONS: We outline a precision-redox oncology roadmap linking pathway-specific biomarkers, mechanistically matched natural products, and biomarker-enriched trials. Priorities include genetic validation of the three axes, time-resolved ROS imaging, and pulsed natural product regimens. By reframing nicotine-driven GC as adaptive redox network remodeling, this review provides a framework for prevention, stratification, and next-generation therapy. Antioxid. Redox Signal. 00, 000-000.

gastric cancer↗

PubMed2026

Perceptions of Pharmacogenomic Testing Among People With Treatment Resistant Depression: Legitimization as a Facilitator of Acceptance.

Pharmacogenomic testing for psychiatric medications has been proposed as both an early intervention to optimize treatment response, and for use among patients who have tried multiple medications without symptom remission. Therefore, this testing may be particularly salient to the subset of individuals with major depressive disorder for whom depression has been labeled as "treatment resistant". Understanding the impact of this diagnostic label on illness identity and attitudes towards new therapies is important as genomic technology expands and rates of depression increase. We sought to explore perceptions and attitudes towards pharmacogenomic testing among individuals who had received a diagnosis of treatment resistant depression. We conducted a qualitative study with a constructivist orientation. Participants were recruited from a larger genomic research study and interviewed by phone or video call. We took an inductive approach to coding guided by reflexive thematic analysis. Themes were then organized into a relational framework following principles of interpretive description. Twelve individuals were interviewed. Key themes included internalized acceptance/hopelessness, and external validation/frustration, which were cyclically interconnected. These themes were situated within a larger framework illustrating the ways that illness identity and modifying factors such as relief of guilt, social support, pharmacogenomic testing and depressive symptoms can either facilitate acceptance and validation or contribute to feelings of hopelessness and frustration. Though participants expressed some skepticism around its effectiveness, pharmacogenomic testing may contribute to the shift towards acceptance and validation by legitimizing individuals' experiences with lack of treatment response. Genetic counselors and other healthcare providers should be aware of the complex balance between hope and frustration underlying conversations around pharmacogenomic testing, and factors that are more likely to foster self-acceptance.

Humans↗

PubMed2026

Effects of digital health-based exercise interventions on older adults with sarcopenia: Systematic Review and Meta-Analysis of Randomized Controlled Trials.

BACKGROUND: Sarcopenia, the progressive loss of muscle mass and function, impairs independence in older adults. Digital health exercise interventions are scalable solutions for older adults with sarcopenia. This systematic review and meta-analysis aimed to synthesize the evidence on their efficacy in populations with clinically diagnosed sarcopenia and identify influential intervention characteristics associated with treatment outcomes. METHODS: We systematically searched PubMed, EMBASE, Web of Science, Cochrane Library, CINAHL, CNKI, and Wanfang on May 20, 2026, with no date restrictions. We included randomized controlled trials involving adults aged ≥60 with sarcopenia receiving digital exercise interventions. Two reviewers independently screened studies, extracted data, and assessed risk of bias; analyses were performed using R and Review Manager. RESULTS: Fourteen trials (n = 927) were included. Digital interventions showed potential improvements in muscle mass (MD = 0.25, 95%CI:0.03-0.46, 95% PI:-0.36 to 0.85), muscle strength (MD = 2.14, 95% CI:1.18-3.11, P < 0.001), balance ability (SMD = 0.31, 95% CI:0.12-0.51, P = 0.001), walking performance (SMD = 0.55, 95% CI:0.21-0.89, 95% PI:-0.70 to 1.79), and physical function (SMD = 0.89, 95% CI:0.08-1.71, 95% PI:-2.33 to 4.12), but not quality of life (SMD = 0.08, 95% CI:-0.19 to 0.35, P = 0.53). Exploratory subgroup analyses suggested that factors such as supervision, program duration, and measurement tools may influence outcomes; however, formal tests for subgroup differences were generally non-significant, and consistent patterns across all metrics were not observed. CONCLUSION: Digital exercise interventions show potential for managing sarcopenia in older adults, though the very low to moderate certainty of evidence indicates that true effects may differ substantially from observed estimates. This review explores potential roles of intervention design, supervision, and multimodal delivery. Future research should adopt rigorous designs and longer follow-up to validate results and enhance clinical application. TRIAL REGISTRATION: PROSPERO CRD420251135174.

Humans↗

PubMed2026

Exploratory proteomic and metabolomic profiling of pleural effusions identifies histone H4 and alanine as promising complementary markers for pleural tuberculosis.

The diagnosis of pleural tuberculosis (Pl-TB) remains challenging. Histopathological analysis and pathogen detection in pleural biopsies are informative but limited. We investigated differentially expressed proteins and metabolites in pleural effusions from patients with Pl-TB, malignancies, and other pathologies. A proteomic analysis of pooled pleural effusions identified 45 proteins exclusively detected or upregulated in Pl-TB samples, many linked to infectious processes. Conversely, 18 proteins were uniquely found or upregulated in malignant pleural effusions, mainly associated with detoxification and hemostasis. To validate these findings, we employed targeted proteomics in individual samples. Eight proteins were validated: S100-A9, histone H4, insulin-like growth factor-binding protein 2, fibrinogen beta chain, ficolin-3, immunoglobulin heavy constant alpha 1, sulfhydryl oxidase 1, and histidine-rich glycoprotein. Additionally, NMR-based metabolomics identified 13 metabolites with differential abundance between Pl-TB and non-TB samples. Notably, N-acetyl-glycoprotein and the branched-chain amino acids, alanine and lysine differed between groups. Proteomic and metabolomic analyses revealed distinct molecular profiles between Pl-TB and non-TB patients, despite intra-group variability. To address this, we applied classification models. Histone H4 and alanine consistently emerged as discriminative features. Overall, this study provides novel insights into the molecular landscape of Pl-TB. The combined quantification of proteins and metabolites may improve differential diagnosis, although should be further validated in larger, independent cohorts before clinical application.

Humans↗

PubMed2026

Integrative genomic and transcriptomic analyses identify key regulators of skin pigmentation in Larimichthys crocea.

The yellow body coloration of large yellow croaker (Larimichthys crocea) constitutes a crucial economic trait, yet its underlying genetic regulatory mechanisms remain poorly understood. This study systematically elucidated the molecular basis of body color variation by integrating genome resequencing and skin transcriptome analyses, combined with the contextual analysis of key pigmentation-related genes and phenotypic histological validation. 200 phenotyped individuals (including yellow-selected lines, F1 progeny, and normal control groups, all derived from a well-characterized aquaculture stock) identified 39 significantly associated SNPs (-log₁₀(P) ≥ 6), mapping to multiple candidate genes. These genes were significantly enriched in pathways related to pigment deposition (GO:0033059), melanosome organization (GO:0032438), melanogenesis, and tyrosine metabolism. Cross-developmental stage transcriptome analysis revealed 2395 differentially expressed genes (DEGs). Multi-omics integration identified eight overlapping candidate genes, including tyrp1, slc45a2, oca2, and dgat2, among which tyrp1 was prioritized for in-depth validation based on its core regulatory role in eumelanin synthesis, significant SNP association signal, and consistent downregulation in transcriptomic data. Experimental validation demonstrated that the g.895C > T mutation in exon 2 of tyrp1b was strongly significantly associated with the yellow phenotype: the frequency of mutant genotypes (TT/CT) reached 92.86%in the yellow-selected group, whereas the control group exclusively exhibited the wild-type genotype (CC). qPCR confirmed significantly downregulated tyrp1b expression in the skin of yellow individuals, consistent with the transcriptome trend. Histological and stereomicroscopic observations of skin tissues further validated the physiological basis of the yellow phenotype, revealing a significant reduction in melanophore number and abnormal melanosome morphology in yellow-phenotype individuals, accompanied by increased xanthophore density. These results suggest that tyrp1b mutation is strongly associated with the yellow phenotype. However, the presence of a wild-type CC individual in the yellow group indicates that this mutation is not strictly required for yellow coloration, suggesting that other genetic or environmental factors may also contribute to the phenotype, Additionally, downregulation of the carotenoid metabolism gene bco2 coupled with upregulation of xdh, together with the functional changes of slc45a2 and oca2, may synergistically promote xanthophore pigment deposition, contributing to the yellow phenotype. As melanin synthesis in large yellow croaker relies on the conserved tyrosinase pathway and transporter proteins, mutations in associated genes (tyrp1b, slc45a2, oca2) represent a primary underlying cause for the loss of melanin-based coloration and transition to a yellow phenotype in L. crocea. These findings provide key molecular targets and a theoretical foundation for molecular breeding of body color in this species, and also enrich the understanding of xanthism regulatory mechanisms in teleosts.

Animals↗

PubMed2026

Plasma proteomics reveal SERPINA1 and CD59 as candidate biomarkers for COVID-19 severity stratification and prognosis prediction.

BACKGROUND: COVID-19 has been closely associated with coagulation abnormalities. However, existing biomarkers, including D-dimer and fibrin degradation products (FDP), exhibit limited accuracy in stratifying disease severity and predicting long-term clinical outcomes. OBJECTIVES: This study aimed to use proteomic analysis to identify plasma biomarkers associated with COVID-19 severity and prognosis, and validate their predictive utility for mortality and thromboembolic complications. METHODS: Plasma proteomic profiles were analyzed across three COVID-19 severity classes. Differential expression analysis and functional analysis were performed. Clustering analysis was used to identify proteins correlated with disease severity. Candidate biomarkers were validated in an independent cohort. Predictive performance of the biomarkers for mortality, sepsis and venous thromboembolism was evaluated using bootstrap-corrected ROC analyses and multivariable regression analyses. RESULTS: Proteomic analysis revealed progressive involvement of the coagulation and complement pathway with increasing disease severity. SERPINA1 and CD59 were identified as candidate biomarkers and exhibited significantly higher plasma levels in severe cases. Bootstrap-corrected ROC analyses demonstrated strong predictive performance: SERPINA1 achieved AUCs of 0.775 and 0.924 for 30-day and 12-month mortality, and CD59 achieved AUCs of 0.720 for sepsis; the combined model further improved prediction of 12-month mortality (AUC 0.946) and sepsis (AUC 0.904), outperforming D-dimer and FDP. Multivariable regression confirmed their independent prognostic value. CONCLUSION: This exploratory study identifies SERPINA1 and CD59 as candidate prognostic biomarkers in COVID-19, highlighting the role of coagulation and complement-related pathways in disease severity and warranting further prospective validation.

Humans↗