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PubMed2026

Postoperative complications and outcomes after surgical treatment for tophaceous gout: A systematic review and meta-analysis.

BACKGROUND: Surgical treatment remains necessary for selected patients with tophaceous gout, particularly when mechanical limitation, nerve compression, ulceration, infection, deformity, or failure of conservative treatment is present. However, postoperative outcomes after surgery for tophaceous gout have not been comprehensively quantified. This systematic review and meta-analysis evaluated postoperative complication profiles and recurrence burden after surgical treatment for tophaceous gout. METHODS: A systematic search of PubMed, Embase, Web of Science, and the Cochrane Library was conducted from database inception to March 25, 2026. Original studies reporting postoperative outcomes after surgical treatment for tophaceous gout were included. Pooled event rates with 95% confidence intervals (CIs) were calculated using a random-effects single-arm meta-analytic approach. Primary outcomes were postoperative infection, delayed wound healing, and recurrence. Secondary outcomes were reoperation, amputation, and overall complications. Functional outcomes were summarized descriptively. RESULTS: 18 retrospective studies were included. The pooled postoperative infection rate was 11.3% (95% CI 8.0%-15.7%), delayed wound healing 9.9% (95% CI 5.1%-18.2%), and recurrence 8.5% (95% CI 4.7%-14.9%). Secondary pooled rates were 9.7% (95% CI 5.5%-16.7%) for reoperation, 3.7% (95% CI 2.0%-6.8%) for amputation, and 24.0% (95% CI 14.0%-38.1%) for overall complications. Significant subgroup differences were identified only for infection according to anatomic site and intervention type. Sensitivity analyses showed that pooled estimates were robust. The certainty of evidence was very low for all outcomes. CONCLUSIONS: Surgical treatment for tophaceous gout is associated with measurable postoperative risks, particularly infection and overall complications. These findings support careful perioperative counseling, structured postoperative surveillance, integrated long-term urate-lowering management, and more standardized reporting of perioperative risk factors and postoperative outcomes in future surgical studies of tophaceous gout.

Humans↗

PubMed2026

Improving insurance deduction identification: a hybrid artificial intelligence model using machine learning and expert systems.

PURPOSE: Financial challenges in healthcare systems worldwide, especially in low- and middle-income countries like Iran, have increased hospitals' reliance on insurance reimbursements. Unrecognized insurance deductions often cause severe financial shortages, making efficient deduction management crucial. This study aimed to design a hybrid intelligent system for identifying and predicting insurance deductions by combining machine learning and expert system frameworks. DESIGN/METHODOLOGY/APPROACH: A mixed-methods design was applied in four stages. First, a scoping review identified the causes and patterns of insurance deductions. Second, interviews with 15 insurance experts produced a validated checklist and a dataset from inpatient billing records. Third, using the CRISP-DM methodology, machine learning algorithms were developed and tested in SPSS Modeler alongside a fuzzy expert system developed in MATLAB. Finally, the model was validated using the holdout method. FINDINGS: Four categories of deduction drivers were identified: service provision, registration errors, document submission issues, and revenue conversion processes. The CHAID decision tree outperformed other algorithms with a 99% precision rate and the lowest Mean Absolute Error (9.43). A brief assessment of potential overfitting was conducted to ensure that the CHAID model's high accuracy was interpreted cautiously and supported by the validation results. The fuzzy expert system with validated rules was adaptable for deduction classification, especially for cases unsuitable for quantitative modeling. ORIGINALITY/VALUE: The hybrid model improves detection and prevention of deductions, offering actionable insights for hospital administrators, insurers, and policymakers. Its implementation can enhance hospital information systems, streamline claims processing, and optimize revenue management amid financial constraints.

Machine Learning↗

PubMed2026

Comparative genomic epidemiology of food- and patient-derived diarrheagenic Escherichia coli from sentinel surveillance in Southeast China.

Diarrheagenic Escherichia coli (DEC) remains an important foodborne pathogen, yet long-term comparative genomic surveillance data jointly characterizing food-derived and patient-derived isolates remain limited. This surveillance-based comparative study integrated antimicrobial susceptibility testing and whole-genome sequencing to characterize diarrheagenic Escherichia coli isolates recovered from food and patient sources in Lishui, Southeast China, during 2018-2025, with emphasis on occurrence, resistance profiles, genomic backgrounds, and plasmid replicon-associated features. Antimicrobial susceptibility testing was performed for 258 selected isolates, and whole-genome sequencing was conducted for a curated analytical subset of 204 isolates. The sequenced subset was used for diversity-oriented comparative genomic analysis rather than for unbiased prevalence estimation of the entire DEC collection. EAEC predominated in both sources, although food-associated occurrence was heterogeneous across categories, with the highest recovery rate observed in raw meat. Patient-derived isolates showed a broader overall resistance burden, whereas food-derived isolates retained substantial resistance to tetracycline, chloramphenicol, and florfenicol. Phylogenetic analysis showed partial overlap in genomic backgrounds between food-derived and patient-derived isolates, while representative resistance determinants displayed both broadly distributed and lineage-enriched patterns. Replicon-based plasmid profiling identified 42 plasmid types, including 12 detected in both sources, with IncF-related replicons predominating among these shared profiles. Several food-derived isolates carried multiple plasmid replicon types that were also observed in patient-derived isolates. Overall, food-derived and patient-derived DEC showed partial overlap in genomic backgrounds, resistance determinants, and replicon-defined plasmid profiles within this surveillance setting, while retaining source-associated heterogeneity. These findings should be interpreted as surveillance-based comparative evidence rather than as evidence of direct source attribution or transmission.

Humans↗

PubMed2026

Driving Under the Influence of Cannabis Among U.S. Young Adults Who Use Cannabis: Evidence From the 2021-2024 National Survey on Drug Use and Health.

PURPOSE: To estimate the prevalence of driving under the influence of cannabis (DUIC) and identify associated factors among U.S. young adult drivers reporting past-year cannabis use. METHODS: This cross-sectional study analyzed pooled 2021-2024 National Survey on Drug Use and Health. The analytic data were restricted to drivers aged 18-25 years who reported past-year cannabis use (N = unweighted 17,141; weighted N = 10,814,381). The outcome was self-reported past-year DUIC. Independent variables included demographics, substance use, mental health, cannabis-related perceptions, and driving behaviors. These relationships were assessed by modified Poisson regression. RESULTS: The weighted prevalence of DUIC was 28.0%, representing approximately over three million young adults. DUIC prevalence increased with cannabis use frequency, from 2.51 times higher among those using cannabis 12-49 days (adjusted prevalence ratio [APR]: 2.51; 95% confidence interval [CI]: 2.29-2.75) to 3.63 times higher among those reporting use on 300-365 days (APR: 3.63; 95% CI: 3.60-3.76), compared with those using cannabis 1-11 days. Cannabis use disorder (APR: 2.34; 95% CI: 2.06-2.65), simultaneous alcohol and cannabis use (APR: 1.29; 95% CI: 1.28-1.30), and perceived easy cannabis availability (APR: 2.36; 95% CI: 2.33-2.39) were also associated with higher prevalence of DUIC. Nonenrollment in school and living in a state with a medical cannabis law were associated with lower DUIC prevalence. DISCUSSION: DUIC is highly prevalent among U.S. young adults who use cannabis, with a clear graded association across categories of cannabis use frequency. Public health interventions should address frequent use, cannabis use disorder, alcohol-cannabis co-use, and perceived cannabis availability.

Humans↗

PubMed2026

Artificial Intelligence for Diagnosing Meibomian Gland Dysfunction: A Systematic Review and Meta-Analysis of Diagnostic Test Accuracy Studies.

PURPOSE: To identify, appraise, and synthesize the performance of artificial intelligence-based meibography reading as compared with human graders in diagnosing meibomian gland dysfunction. METHODS: We followed Cochrane methodology and reporting guidelines for diagnostic test accuracy reviews. To assess potential risk of bias and applicability, we used a modified Quality Assessment of Diagnostic Accuracy Studies-2 checklist. We applied bivariate logistic models to estimate summary sensitivity and specificity when appropriate and used the GRADE framework to rate the certainty of the evidence. RESULTS: We identified 14 eligible studies involving 5511 predominantly middle-aged participants (average age: 27-55 years) who were primarily female (≥54.5%). A total of 18,926 meibography images were obtained through noncontact infrared (11 studies) or in vivo confocal microscopy (three studies). Two studies reported external validation of deep learning models, 12 reported internally validated models, and one reported both. All but one study had high risk of bias in at least one domain; 12 studies raised high or intermediate concern about applicability. Based on three external evaluations, the summary sensitivity and specificity for diagnosing meibomian gland dysfunction from normal glands were 97.5% (95% confidence interval: 77.5%-99.8%) and 85.5% (95% confidence interval: 47.3%-97.5%). Sources of heterogeneity in internally validated models included study population, case mix, and others. The overall evidence was very low to low certainty because of imprecision, high risk of bias, and concerns about applicability. CONCLUSIONS: Artificial intelligence-based meibography grading appears less accurate than human graders. Future studies should adopt rigorous designs, including a more diverse participant pool (or image set), and external validation.

Humans↗

PubMed2026

Meniscal preservation in the age of biologics: toward a quantitative decision algorithm for personalized repair.

BACKGROUND: Despite advances in arthroscopic repair and biologic augmentation, surgical indication for meniscal tears remains heterogeneous. No standardized framework currently integrates biomechanical, clinical, and biological determinants to guide repair versus resection. PURPOSE: To develop a quantitative decision model-the Meniscal Preservation Score (MPS)-that unifies biomechanical and biological evidence to stratify reparability potential and standardize treatment selection in meniscal surgery. METHODS: A systematic evidence synthesis conducted in accordance with PRISMA 2020 reporting standards of studies published from 2000 to 2025 in PubMed, Embase, and Scopus identified key determinants of meniscal healing. Five consistent predictors-patient age, vascularity, tear morphology, associated pathology, and activity profile-were weighted through a two-round modified Delphi consensus among ten experienced knee surgeons. The resulting 0-9-point MPS was incorporated into a stepwise decision tree linking lesion morphology, biological context, and surgical strategy. Conceptual validation used 50 simulated cases and a retrospective cohort of 45 patients to test agreement between algorithm recommendations and expert surgical decisions. RESULTS: The MPS achieved 86% concordance with expert judgment in simulation and 84% agreement in clinical validation. In this retrospective exploratory cohort, cases in which surgical management was concordant with MPS recommendations demonstrated higher mean IKDC scores at 24 months and lower observed reoperation rates. These findings should be interpreted as associative rather than causal, as treatment allocation was not controlled and discordant cases may have represented inherently more complex pathology. CONCLUSION: The MPS represents an evidence-informed decision-support framework designed to systematize reparability assessment. While exploratory analyses suggest structural coherence with expert reasoning, prospective implementation and external validation are required before clinical adoption as a predictive tool. LEVEL OF EVIDENCE: conceptual model with exploratory validation.

Humans↗

PubMed2026

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

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

Humans↗

PubMed2026

The potential of clustering methods for pre-test triage in sleep medicine: A systematic review.

Sleep disorders exhibit substantial heterogeneity, and traditional classifications may not fully capture clinically relevant subtypes. Clustering techniques can identify patient subgroups that improve phenotypic characterization and may support personalized management. This systematic review evaluated the application of clustering in sleep medicine, with particular focus on its potential use as a pre-test triage tool prior to formal sleep testing. PubMed/MEDLINE, Embase, Web of Science, and Scopus were searched to February 2025. Eligible studies applied clustering to classify sleep disorders in adults. Two reviewers independently conducted screening, data extraction, and risk-of-bias assessment using QUADAS-2. The protocol was registered on PROSPERO. Fifty-one studies (1983-2025) were included, predominantly focused on obstructive sleep apnea (OSA) (n = 38, 74%). Hierarchical clustering (n = 20) and K-means clustering (n = 14) were the most frequently used techniques. Internal validation was reported in only 18% of studies, and external validation was reported in only 1 study. Seven studies relied exclusively on baseline clinical, demographic, or questionnaire data, representing pre-test scenarios, whereas most incorporated polysomnography-derived variables, limiting their applicability to early clinical stratification. Hierarchical clustering was the most commonly applied method; however, the overall lack of validation limits confidence in the robustness and clinical applicability of identified phenotypes. The potential role of clustering as a pre-test triage strategy remains largely unexplored, as most studies focused on post-diagnostic phenotyping and were affected by incorporation bias. Future research should prioritize pre-test clinical variables, rigorously validate internally and externally, and adopt standardized methodological and reporting practices to facilitate clinical translation.

Humans↗

PubMed2026

Urinary Small Extracellular Vesicle DNA as a Biomarker for the Non-Invasive Diagnosis of Bladder Cancer.

Existing diagnostic technologies for bladder cancer (BC) suffer from low sensitivity, low specificity, or a lack of validation. Therefore, validated, non-invasive diagnostic biomarkers with high sensitivity and specificity for early detection of BC are needed to complement and improve upon the limitations of existing diagnostic methods. We used low-pass whole genome sequencing (LP-WGS) technology to detect copy number variations (CNVs) in small extracellular vesicle (sEV) DNA isolated from urine samples of patients. Based on these results, we constructed and validated a diagnostic model to differentiate between benign and malignant bladder lesions. We conducted a receiver operating characteristic analysis and calculated the area under the curve (AUC) to evaluate the performance of the diagnostic model. The urine sEV-DNA LP-WGS data revealed CNV differences between benign and malignant samples. The diagnostic model achieved an AUC of 0.953, a sensitivity of 86.7%, and a specificity of 100% in the training cohort and an AUC of 0.985, a sensitivity of 90%, and a specificity of 100% in the validation cohort. Even at the lowest coverage depth of 0.01X, the performance of the diagnostic model remained relatively robust. Notably, the performance of this diagnostic model surpassed that of the biomarker neuron-specific enolase (sensitivity: 85.7% vs. 64.3%; specificity: 100% vs. 87.5%) and urinary cytology (sensitivity: 100% vs. 66.7%; specificity: 100% vs. 94.1%). Our study demonstrates that urine sEV-DNA exhibits high discriminatory power in distinguishing between benign and malignant bladder lesions, making it a promising tool for auxiliary diagnosis of BC.

Humans↗

PubMed2026

Metabolomic Signatures of Inflammation in Chronic Kidney Disease.

RATIONALE & OBJECTIVE: Inflammation is associated with adverse kidney, cardiovascular, and mortality outcomes. Investigation of the metabolic milieu as it relates to inflammation may provide important insights into these disease processes. STUDY DESIGN: Prospective cohort. SETTING & PARTICIPANTS: African American Study of Kidney Disease and Hypertension (AASK), Atherosclerosis Risk in Communities (ARIC) study, and Boston Kidney Biopsy Cohort (BKBC) participants with available metabolomics and inflammatory protein data. PREDICTORS: Baseline blood levels of 718 metabolites. OUTCOMES: Baseline and longitudinal changes in blood levels of tumor necrosis factor receptors 1 and 2 (TNFR1, TNFR2), tumor necrosis factor-alpha (TNF-α), interferon-gamma (IFN-γ), interleukins 6, 8, and 10 (IL-6, IL-8, IL-10), uromodulin (UMOD), and epidermal growth factor (EGF). ANALYTICAL APPROACH: Multivariable linear regression and linear mixed-effects models. RESULTS: Among 491 AASK participants (mean age 54 years; 37% women; mean glomerular filtration rate, 45 mL/min/1.73 m2), 367 cross-sectional associations between metabolites and inflammatory proteins were significant after correction for multiple comparisons. The direction of association was mostly positive for TNFR1 (97%), TNFR2 (97%), IL-8 (77%), and IL-10 (100%); negative for UMOD (80%) and EGF (97%); and variable for TNF-⍺, IFN-γ, and IL-6. Pathways were distinct for several inflammatory proteins (eg, tryptophan metabolism for TNFR2). Forty-five associations between metabolites and longitudinal change in inflammatory proteins were identified. Notable metabolites included tigylcarnitine and N 2,N 5-diacetylornithine, which were associated with 2-year increases in TNFR1 and/or TNFR2, and 1,5-anhydroglucitol, where lower levels were associated with decreases in UMOD. In ARIC (n = 3,773) and BKBC (n = 413), replication of cross-sectional associations was excellent for TNFR1 (ARIC 83%; BKBC 85%) and TNFR2 (ARIC 64%; BKBC 79%) but poor for IL-8 (ARIC 3%; BKBC 3%). LIMITATIONS: Metabolite data limited to baseline visit; potential for residual confounding. CONCLUSIONS: Using an untargeted approach, multiple metabolites were cross-sectionally and longitudinally associated with inflammatory proteins in persons with chronic kidney disease.

Chronic kidney disease↗

PubMed2026

Are Adverse Childhood Experiences Associated with Metabolic Syndrome in Patients with Severe Mental Illness?

BACKGROUND: Patients with severe mental disorders (SMD) are at substantially elevated risk for metabolic syndrome (MetS), contributing to excess cardiovascular morbidity and premature mortality. Adverse childhood experiences (ACEs) have been associated with dysregulation of metabolic pathways, yet their contribution to MetS risk in SMD remains poorly understood. OBJECTIVE: This study aimed to investigate the association between ACEs and MetS in outpatients with bipolar disorder (BD) and schizophrenia (SZ) in clinical remission and to identify independent and incremental predictors of MetS using a hierarchical analytical framework. METHODS: This cross-sectional study included 140 outpatients with SMD (96 with BD and 44 with SZ) in clinical remission, recruited from a university hospital in Eastern Turkey. MetS was defined according to NCEP-ATP III criteria, and ACEs were assessed using the Turkish version of the Adverse Childhood Experiences Scale (ACE-TR). Hierarchical and multivariable logistic regression analyses were performed to examine factors associated with MetS. RESULTS: MetS was highly prevalent in this sample (46.4%). ACE-TR total score was independently and consistently associated with MetS across all hierarchical models (odds ratio [OR] range: 1.68-1.77), with each one-unit increase conferring approximately 71% higher odds in the fully adjusted model (OR = 1.71; 95% confidence interval [CI] 1.26-2.32; P = 0.001). The number of hospitalizations was the only other independently associated variable (OR = 1.19; 95% CI 1.02-1.39). Sexual abuse (16.9% vs. 2.7%; P = 0.004), emotional neglect (63.1% vs. 30.7%; P < 0.001), and physical neglect (30.8% vs. 14.7%; P = 0.022) were significantly more prevalent in the MetS group. ACE-TR total score was positively correlated with waist circumference and triglyceride levels. CONCLUSION: The strong and consistent association between ACEs and MetS underscores the importance of trauma-informed care models in psychiatric practice, where metabolic comorbidity remains a leading cause of premature mortality.

Humans↗

PubMed2026

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

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

Humans↗

PubMed2026

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

Diagnostic utility of high-risk HPV polymerase chain reaction-based testing in head and neck FNA specimens with indeterminate cytomorphology.

BACKGROUND: Fine-needle aspiration (FNA) is critical in the initial diagnosis of many high-risk human papillomavirus (HR-HPV)-associated, metastatic oropharyngeal squamous cell carcinomas. Updated guidelines recommend HR-HPV-specific polymerase chain reaction (PCR) analysis over p16 immunohistochemistry on FNA specimens because p16 performs poorly on cytology material. PCR-based assays on liquid cytology material have demonstrated excellent analytic performance; however, the diagnostic utility of a positive HR-HPV PCR result in specimens with indeterminate cytomorphology remains uncharacterized. METHODS: The authors retrospectively identified 279 head and neck FNA specimens that had paired HR-HPV PCR testing on residual liquid cytology material over a 5-year period. The positive predictive value for histopathologically confirmed squamous cell carcinoma on surgical follow-up was calculated within each cytologic interpretive category. RESULTS: The HR-HPV PCR results were positive in 50.2% of specimens, negative in 40.9%, and indeterminate in 9.0%. The HR-HPV positivity rate ranged from 0% in specimens categorized as negative for malignancy to 57.3% in cytologically positive specimens, with 19.0%, 41.2%, and 50.0% positivity in the atypical, suspicious, and nondiagnostic categories, respectively. Among cytologically indeterminate specimens with positive HR-HPV PCR results (n = 14), the positive predictive value was 100% (95% confidence interval, 78.5%-100.0%). Blinded slide review additionally identified 15 cytologically positive specimens in which the definitive malignant interpretation depended substantially on HR-HPV positivity; all 15 were confirmed as squamous cell carcinoma. CONCLUSIONS: A positive HR-HPV PCR result on liquid cytology material carries a positive predictive value of 100% for malignancy in cytologically indeterminate head and neck FNA specimens. These findings support integrating HR-HPV PCR analysis into routine cytologic interpretation with the potential to upgrade some indeterminate specimens to malignant when HR-HPV is detected, expediting definitive treatment and sparing patients additional, invasive sampling.

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

Prognostic effect of serum glial fibrillary acidic protein and neurofilament light chain for predicting progression independent of relapse activity in multiple sclerosis: A systematic review.

BACKGROUND: Progression independent of relapse activity (PIRA) is increasingly appreciated as one of the important factors contributing to disability accumulation in MS. sGFAP and sNfL could represent markers reflecting two separate biological processes related to relapse-independent progression in MS. OBJECTIVE: To perform a systematic review of the literature on blood GFAP and/or NfL measured in relation to PIRA or other similar relapse-independent progression endpoints in people with MS. METHODS: PubMed, Scopus, and Web of Science databases were searched from inception to 1 June 2026. The eligible studies were original human studies measuring blood GFAP and/or NfL concentrations in serum, plasma, or any other type of blood-derived material and assessing PIRA, PIRMA, CDP/CDW without relapses, relapse-free EDSS progression, non-inflammatory progression, or comparable relapse-independent disability worsening outcomes. Methodological quality was assessed according to the Newcastle-Ottawa scale and the QUIPS instrument for bias detection in the body of evidence on prognostic factors. Due to heterogeneity of outcomes, biomarker measurements and effect estimates, results were synthesized qualitatively rather than quantitatively. RESULTS: After removing duplicates, 1206 records were screened, followed by full-text review of 120 reports. A total of 18 reports were included. Overall, sGFAP was associated more frequently with PIRA or PIRA-like disability progression, particularly in cohorts with suppressed or limited overt inflammatory activity. Evidence for sNfL was more variable and context-dependent: several studies reported associations with PIRA-like or relapse-independent disability worsening when acute inflammatory activity was absent, suppressed, or analytically separated, whereas other studies reported negative or inconclusive findings. Negative or inconclusive results were reported by several articles, particularly when broad outcomes were evaluated or the study population was small. CONCLUSION: Blood GFAP and NfL give complementary but non-interchangeable information concerning PIRA in MS patients. The existing evidence base does not allow us to perform meta-analysis because of heterogeneity in terms of outcomes, standardization of biomarkers, and treatment context. Further prospective investigations with uniform criteria will be necessary for their use as biomarkers of PIRA in clinical settings.

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↗