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PubMed2026

Diabetic macular edema and GLP-1 receptor agonist use: a systematic review and meta-analysis.

BACKGROUND: Glucagon-like peptide-1 receptor agonists (GLP-1RAs) are widely used for type II diabetes and obesity because of their cardiometabolic benefits. However, concerns regarding potential ocular adverse effects, particularly diabetic macular edema (DME), have prompted the need to clarify their retinal safety. METHODS: A systematic review and meta-analysis study was conducted in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses and Meta-analysis of Observational Studies in Epidemiology statements (PROSPERO registration: CRD420251176164). MEDLINE (Ovid), EMBASE (Ovid), CENTRAL (Ovid), Web of Science, and PubMed were searched from inception to October 24, 2025. Randomized trials and observational cohort or case-control studies, including individuals with diabetes without baseline DME and exposed to GLP-1RAs were eligible. Two reviewers independently screened studies, extracted data, and assessed risk of bias using ROBINS-I. Certainty of evidence was evaluated using Grading of Recommendations, Assessment, Development, and Evaluation. Random-effects models were used to pool incidence proportions and hazard ratios (HRs). RESULTS: Thirteen retrospective cohort studies (2021-2025) using large real-world databases were included. Across 6 studies, the pooled proportion of incident DME among GLP-1RA users was 0.14 (95% CI: 0.07-0.23; I² = 99.8%). Compared with mixed antihyperglycemic therapies, GLP-1RA use was not associated with increased DME risk (pooled HR: 0.81, 95% CI: 0.52-1.26). GLP-1RAs were associated with a higher relative risk of DME compared with sodium-glucose cotransporter-2 inhibitors (HR: 1.50, 95% CI: 1.17-1.94) but not compared with dipeptidyl peptidase-4 inhibitors (HR: 0.90, 95% CI: 0.69-1.19). Evidence certainty was very low. CONCLUSION: Current low-certainty observational evidence does not support an overall increased risk of DME with GLP-1RA use. Prospective studies are needed to clarify comparative retinal safety.

Humans↗

PubMed2026

Clinical insights into catathrenia: A real-world analysis from a tertiary sleep center.

INTRODUCTION: Catathrenia is a rare sleep-related breathing disorder marked by groaning during prolonged expiration, often underrecognized or misdiagnosed as obstructive or central sleep apnoea (OSA or CSA) or parasomnia. Understanding its clinical and polysomnographic features is essential for accurate diagnosis and management. MATERIALS AND METHODS: We performed a retrospective observational study of adult patients diagnosed with catathrenia at Serviço de Medicina do Sono de Coimbra. Diagnosis was established by attended overnight polysomnography (PSG) with synchronised audio-video recording. Demographic data, symptoms, comorbidities, PSG variables, treatment modalities, and outcomes were reviewed. Catathrenia events were defined as deep inhalation followed by prolonged exhalation with monotonous groaning. RESULTS: Ten patients were included. Median age was 46 years (range 27-78), mostly female (70%). Common comorbidities included obesity (n = 4), depression (n = 2), Parkinson's disease (n = 1), and restless legs syndrome (n = 1). Six patients (60%) had concomitant obstructive sleep apnoea (OSA). Seven patients had excessive daytime sleepiness (Epworth Sleepiness Scale > 10). All catathrenia episodes occurred exclusively during REM sleep. Continuous positive airway pressure (CPAP) therapy was the most frequently used treatment and was associated with objective or subjective improvement in most patients. Two patients experienced spontaneous remission. CONCLUSION: Catathrenia remains underdiagnosed and can mimic other sleep disorders. Recognition of its REM-sleep predominance and PSG pattern is essential. Individualised treatment, often involving PAP therapy, may improve symptoms and patient outcomes.

Humans↗

PubMed2026

Compliance With Ecological Momentary Assessment Among Patients With Cancer: Systematic Review and Meta-Analysis.

BACKGROUND: Patients with cancer often experience substantial fluctuations in psychological states during disease management. Traditional research tools are limited in capturing these dynamic changes in real time, constraining clinicians' understanding of patients' true conditions. Ecological momentary assessment (EMA) enables high-frequency, real-time data collection, providing patient-reported data with greater ecological validity. However, the effectiveness of EMA studies critically depends on patient compliance, and reported compliance rates vary widely, with a lack of systematic quantitative synthesis. OBJECTIVE: This study aims to systematically review and quantitatively analyze compliance with EMA among patients with cancer, and to examine whether EMA design characteristics were associated with compliance. METHODS: Web of Science, PubMed, Embase, Cochrane Library, CINAHL, PsycINFO, CNKI, and Wanfang databases were searched for literature published up to April 30, 2026. Compliance was defined as completed prompts divided by delivered prompts. Single-group proportions were pooled using logit transformation and random-effects models with the Hartung-Knapp-Sidik-Jonkman adjustment. Prediction intervals were calculated to describe the expected distribution of compliance in future comparable settings. Subgroup analyses, univariable meta-regressions, leave-one-out sensitivity analyses, and tests for small-study effects were performed. Risk of bias was assessed using the Joanna Briggs Institute Critical Appraisal Checklist for Studies Reporting Prevalence Data, methodological reporting quality was assessed using a modified Checklist for Reporting EMA Studies, and certainty of evidence was evaluated using the Grading of Recommendations Assessment, Development, and Evaluation approach. RESULTS: Twenty-three studies involving 13,565 participants were included. The pooled compliance rate was 78.55% (95% CI 73.48%-82.87%), with a prediction interval of 48.59%-93.41%. Subgroup analyses identified no robust differences across study characteristics. Although study length showed a statistically significant subgroup test, the result was not stable after excluding singleton categories. Meta-regression analyses similarly found no significant linear associations for study length, prompts per day, items per prompt, or assessment window. Leave-one-out analyses showed that no single study drove the pooled estimate. Regarding the risk of bias, 2 studies were judged as low, while 21 were judged as moderate risk. Quality scores ranged from 6.5 to 9.0, and the certainty of evidence for the pooled compliance rate was rated as very low according to the Grading of Recommendations Assessment, Development, and Evaluation approach. CONCLUSIONS: Overall compliance with EMA among patients with cancer was moderate to high, suggesting that repeated real-world assessment may be feasible in oncology research settings. Nevertheless, the very high heterogeneity, wide prediction interval, and very low certainty of evidence indicate that compliance is context-dependent. The pooled estimate should therefore be interpreted as an approximate benchmark rather than a universal expected rate. Future oncology EMA studies should use standardized compliance denominators, report missing prompts transparently, and prospectively evaluate patient-centered design strategies that reduce burden while preserving data quality.

Humans↗

PubMed2026

Benchmarking large language models for extracting biobank-derived insights into health and disease.

Biobank-scale datasets such as the UK Biobank have become foundational resources for advancing biomedical discovery. Yet the complexity and heterogeneity of these resources, spanning genomics, imaging, clinical records, and metadata, pose substantial barriers to access and interpretation. Large Language Models (LLMs) offer a promising avenue for making such datasets more navigable through natural language interfaces. However, the extent to which current general-purpose LLMs can retrieve and synthesize biobank-specific insights has not yet been systematically evaluated. In this study, we present a reproducible, multi-metric evaluation framework to benchmark the capabilities of leading LLMs. We evaluated six leading large language models: Gemini 3 Pro, Claude Opus 4.5, Claude Sonnet 4.5, GPT-5.2, Mistral Large 2, and DeepSeek V3, on four benchmark tasks designed to assess biobank-related knowledge retrieval. We evaluate model performance across six dimensions (semantic accuracy, factual correctness, domain knowledge, reasoning quality, response depth, and biobank specificity) and assessed output consistency using curated UK Biobank references and a robust random baseline. All models outperformed the baseline by 2× to 3× , with strong statistical separation (p < 0.001), confirming meaningful biobank-specific knowledge retrieval. Gemini 3 Pro achieved the highest overall accuracy across tasks such as keyword synthesis, institution recognition, and topic inference, while Claude Sonnet 4.5 demonstrated the most uniform performance across evaluation dimensions. Our benchmark provides a rigorous framework for evaluating LLMs in biomedical settings. Using the UK Biobank as a real-world testbed, we highlight both the capabilities and limitations of current models, measuring their capacity to recall structured biomedical knowledge consistent with authoritative biobank metadata.

Large Language Models↗

PubMed2026

NMR metabolomics and glycomics for cancer detection in patients with non-specific symptoms: a prospective observational cohort study.

BACKGROUND: Early cancer diagnosis in patients with non-specific symptoms is limited by the lack of discriminatory tests. Within the Oxfordshire Suspected CANcer (SCAN) pathway, exploratory biomarker work showed that serum 1H NMR-based metabolomics can identify cancer with high accuracy. SCAN2 evaluated whether integrating metabolomics with glycomics provides complementary molecular information and improves discrimination in a clinically complex, real-world population. METHODS: Serum from 369 SCAN patients (59 cancers) was analysed using AXINON® System-derived NMR metabolomics and HPLC-MS glycomics. Machine-learning models were trained to predict cancer status, with performance assessed by receiver operating characteristic (ROC) analysis of pooled cross-validated predictions. To place cancer risk in a broader clinical context, a second classifier modelling alternative non-cancer diagnosis was incorporated, and mean predicted probabilities from both models were jointly projected into a two-dimensional space, maintaining strict separation of training and test data. FINDINGS: In the full cohort, integration of glycomics with metabolomics achieved an AUC of 0.814 (95% CI 0.808-0.820). In a refined sub-cohort excluding major comorbidities and selected cancer types (32 cancers, 277 non-cancers), performance improved to an AUC of 0.884 (95% CI 0.879-0.890). Discriminatory features included cancer-associated biantennary fucosylated glycans alongside amino acid metabolites (glutamate, histidine) and lipoprotein-related measures. A classifier distinguishing metastatic from non-metastatic disease (n = 29 vs. 30) achieved an AUC of 0.80. Joint probability analysis in the full cohort preserved cancer-associated signatures across comorbidity burden, with projection-based classification achieving an accuracy of 89.2% (95% CI 85.7-92.6). INTERPRETATION: These findings validate the SCAN1 metabolomic signature in a more clinically complex cohort and indicate that integrating glycomics with metabolomics provides complementary biological information for cancer discrimination. Joint probability analysis provides an interpretable framework for cancer risk stratification within multimorbid diagnostic pathways, supporting the clinical potential of scalable multi-omics blood testing. FUNDING: EPSRC, EU Horizon 2020, Wellcome/MLSTF, Novo Nordisk Foundation.

Humans↗

PubMed2026

hypeR-GEM: connecting metabolite signatures to enzyme-coding genes via genome-scale metabolic models.

MOTIVATION: Enrichment analysis is a cornerstone of "omics" data interpretation, enabling researchers to connect analysis results to biological processes and generate testable hypotheses. Enrichment analysis in metabolomics poses distinct challenges for interpretation and multi-omics integration due to the lack of well-defined and consistent connections to well-curated gene-centered biological knowledge repositories. To address these challenges, we developed hypeR-GEM, a methodology and associated R package that adapts gene set enrichment analysis to metabolomics. hypeR-GEM leverages genome-scale metabolic models (GEMs) to infer reaction-based links between metabolites and enzyme-coding genes, enabling the mapping of metabolite signatures to gene signatures and their subsequent annotation via gene set enrichment analysis. RESULTS: We validated hypeR-GEM using paired metabolomics-proteomics and metabolomics-transcriptomics datasets by assessing whether genes mapped from metabolites significantly overlapped with differentially expressed proteins or transcripts. We further evaluated whether pathways enriched via hypeR-GEM-mapped genes corresponded to those derived from paired proteomic or transcriptomic data. In most datasets analyzed, both the predicted enzyme-coding genes and the associated enriched pathways showed significant concordance with independently derived omics signatures, supporting the utility and robustness of hypeR-GEM. Finally, we applied hypeR-GEM to the analysis of age-associated metabolic signatures from the New England Centenarian Study. The results revealed consistent enrichment of lipid-related pathways, aligning with the well-established role of lipid metabolism in aging, and highlighted additional pathways not captured in the metabolites' annotation, demonstrating hypeR-GEM's practical utility in a real-world use case. AVAILABILITY AND IMPLEMENTATION: The hypeR-GEM R package, documentation, and workflow examples are freely available at https://github.com/montilab/hypeR-GEM and archived at https://doi.org/10.5281/zenodo.20586748.

Metabolomics↗

PubMed2026

Genomic science and the nurse educator's role: Promoting integration from curriculum to clinical practice.

BACKGROUND: Registered nurses and nurse educators play a critical role in preparing future clinicians to translate genomic discoveries into practice. However, emerging evidence suggests that both groups may lack sufficient knowledge and confidence in genomics, potentially limiting their ability to teach, mentor, and apply genomics in real-world settings. This gap is especially concerning in Aotearoa New Zealand, where the genomic literacy of nurse educators and clinicians remains underexplored. OBJECTIVE: This study aims to: (1) assess nurse educators' genomic literacy and confidence in teaching genomics; and (2) evaluate registered nurses' knowledge and confidence in applying and teaching genomics in clinical practice. DESIGN: Exploratory descriptive qualitative. SETTING: This study was conducted in the greater Auckland area. PARTICIPANTS: A total of 17 participants were recruited using purposive sampling to ensure a diverse range of perspectives across varying levels of teaching experience, disciplinary backgrounds, and exposure to genomic content. METHODS: Data were collected using semi-structured focus group interviews, a method well-suited for generating in-depth discussion and facilitating interaction among participants with shared professional interests. The collected data were analysed using thematic analysis methods. RESULTS: The findings offer insight into the preparedness of New Zealand's nursing workforce to engage with genomic-informed healthcare and inform strategies for integrating genomics into nursing curricula and continuing professional development. Given the interdisciplinary nature of genomic healthcare, these insights may also be relevant to other health professionals-including midwives, pharmacists, and allied health practitioners-who increasingly encounter genomic information in clinical practice and require foundational competencies to support patient care. CONCLUSION: Addressing this educational gap is critical to ensuring that nurses-key facilitators of patient care and public health-are equipped to deliver safe, equitable, and evidence-based genomic healthcare.

Humans↗

PubMed2026

Digital pathology, image analysis, and artificial intelligence in liver disease.

Advances in digital pathology, image analysis, and artificial intelligence (AI) are rapidly transforming how pathologists and researchers interact with tissue samples and enable the development of diagnostic tools that harness high-resolution whole-slide images; these advances are in turn creating new opportunities for research, education, and routine clinical care globally. Liver disease is no exception, and digital pathology and AI have many applications in the diagnosis of liver cancer and liver diseases and in the assessment and management of transplantation. Although quantitative image analysis techniques have been applied to liver disease in research settings for over 50 years, recent improvements in image resolution, data storage, and the availability of advanced AI methods such as deep learning have driven multiple exciting developments. In this Review, we summarise the advancements in digital pathology, image analysis, and AI in liver disease. Key challenges such as access to and the logistics of using digital solutions, quality issues, and appropriate guidance in research and clinical use are reviewed, along with potential solutions to these challenges in the context of liver pathology and liver disease. Digital technologies are well established in liver pathology research, and access in clinical practice is increasing, with potential to address current laboratory challenges. Further evaluation is required to assess real-world effectiveness, clinical safety, and implementation of AI tools in liver pathology.

Journal Article↗

PubMed2026

289th ENMC international workshop: assessing and managing emerging AAV related toxicities after gene therapy for neuromuscular disorders, 26 - 28 September 2025, Hoofddorp, The Netherlands.

Adeno-associated virus (AAV) mediated gene therapies has emerged as a potentially transformative treatment approaches for neuromuscular disorders, with two FDA-approved products now in widespread clinical use: onasemnogene abeparvovec (Zolgensma) for spinal muscular atrophy and delandistrogene moxeparvovec-rokl (Elevidys) for Duchenne Muscular Dystrophy. However, severe and occasionally fatal adverse events affecting vital organs, including the blood, liver, muscle, and heart, have emerged in both clinical trials and real-world post marketing settings. The 289th European NeuroMuscular Centre (ENMC) workshop convened 38 participants from patient advocacy groups, industry, and preclinical and clinical research groups to collaboratively review these toxicities, their underlying mechanisms, and potential mitigation and monitoring strategies. Discussions addressed the clinical spectrum and biological drivers of these events, the respective roles of innate and adaptive immunity, the contribution of specific vector characteristics as well as of the specific disease and recipient. The application of risk stratification and immunosuppressive regimens for prevention, monitoring, and management were considered. Emerging toxicities, including capillary leak syndrome, endothelial and dorsal root ganglia injuries, were reviewed alongside corresponding preclinical data from non-human primates. Participants agreed on the need to harmonize standard operating procedures, clinical guidelines, and data-sharing practices, and endorsed collaborative initiatives to proactively address critical gaps and unresolved key questions through a patient-centered framework.

Adaptive immune response↗

PubMed2026

Methylation profiling in CNS tumor diagnostics: a single-centre real-world experience from Central Europe.

Genome-wide DNA methylation profiling has transformed neuro-oncology by providing an objective, machine learning-based taxonomy that mitigates interobserver variability and refines the histo-molecular criteria of the current WHO classification. We evaluate the real-world diagnostic performance and clinical utility of this modality in a prospective, consecutively accrued three-year cohort of 291 central nervous system (CNS) tumors across a mixed adult-pediatric population. Successful profiling was completed in 95.9% of cases. Using the Epignostix classifier, a high-confidence diagnostic match (calibrated score [CS] ≥ 0.84) was achieved in 70.3% of analyzable samples, while 26.5% returned lower-confidence scores (≥ 0.3 to < 0.84) and only 3.2% remained completely unclassifiable (CS < 0.3). When integrated into a comprehensive diagnostic framework, methylation profiling provided clinically useful results in 81.1% of cases, establishing diagnoses in 70 cases submitted for molecular subclassification and resolving diagnostic uncertainty or prompting major revisions in 149 histologically challenging tumors. Within truly ambiguous lesions, integration of methylome data dictated tumor grade modifications in 38.8% of cases (upgrading in 29.4% and downgrading in 9.4%), shifting patient risk stratification. Crucially, over half (52.7%) of the lower-confidence cases yielded meaningful clinical integration when supported by histomorphology and ancillary genetic or immunohistochemical markers, demonstrating that rigid score cutoffs should not dictate assay failure. Discrepant or misleading classifications occurred in 1.9%. Updating bioinformatic pipelines from version 11b4 to 12.8 rescued multiple ambiguous entries, increasing overall clinical utility to 84.1%. These findings demonstrate that integrating computational epigenomics with classical neuropathology enhances diagnostic precision, while highlighting the ongoing need for careful clinical-pathological correlation.

Central nervous system tumors↗

PubMed2026

Assessing perinatal depression identifying abilities among maternal and child health workers in rural China using smartphone-based virtual patients: a multi-center cross-sectional study.

OBJECTIVE: To assess rural maternal and child health (MCH) workers' virtual patients (VPs)-assessed performance in identifying perinatal depression (PND) using smartphone-based VPs, and to identify factors associated with this performance in rural Hunan, China. METHODS: A multicentre cross-sectional study was conducted in Hunan Province, China. A standardized questionnaire collected demographic and work-related characteristics of rural MCH workers. Smartphone-based VPs were used to assess PND identification performance in a simulated clinical scenario. An overall score ≥60 was used as a prespecified operational benchmark across consultation, ancillary assessment, diagnosis, management, and health education domains. Data were analyzed using SPSS 26.0. RESULTS: A total of 375 rural MCH workers participated, yielding an effective response rate of 90.4%. Only 25.9% met the prespecified operational benchmark for VP-assessed PND identification performance. The mean accuracy scores for consultation, ancillary assessment, diagnosis, management, and health education were 94%, 48%, 64%, 58%, and 74%, respectively. Complete consultation accuracy was higher among MCH workers from township health centers than among those from county-level MCH hospitals. MCH workers aged 18-39 years showed higher odds of complete diagnostic accuracy for PND than those aged ≥40 years. CONCLUSIONS: Smartphone-based VP assessment was feasible in rural MCH settings and revealed suboptimal PND identification performance. Mobile VPs may help identify frontline performance gaps and inform targeted training, but further validation against real-world clinical performance, or standardized patient encounters is needed before large-scale implementation. These findings may support targeted capacity-building for rural MCH workers and more equitable perinatal mental health care.

Humans↗

PubMed2026

Major cardiovascular event risk of advanced therapies in inflammatory bowel diseases: systematic review and meta-analysis.

BACKGROUND: Patients with chronic immune-mediated disorders (IMIDs), including inflammatory bowel disease (IBD), are at increased risk of cardiovascular disease. While advanced therapies show cardioprotective effects in other IMIDs, their impact on major adverse cardiovascular events (MACE) in IBD remains unclear. We conducted a meta-analysis of randomized controlled trials (RCTs) and observational studies evaluating MACE risk with advanced therapies in IBD. METHODS: Systematic search of PubMed, Embase, and Cochrane Central Register of Controlled Trials identified 43 studies (36 RCTs, including 9 long-term follow-up (LTF) studies, and 7 observational studies) published between 2002 and 2024. Primary analyses estimated odds ratios (OR) for MACE comparing advanced therapy to placebo, with secondary analyses stratifying studies by drug class and length of follow-up. Sensitivity analyses were conducted using alternative methods to account for zero-event data. RESULTS: Placebo-controlled RCTs showed a nonsignificant trend toward reduced MACE risk (OR 0.60; 95% CI 0.24-1.51), with similar findings across sensitivity analyses accounting for sparse and zero-event data. Class-specific trends suggested lower MACE risk with IL-12/IL-23 inhibitors (OR 0.35; 95% CI 0.05-2.21), JAK inhibitors (OR 0.57; 95% CI 0.16-2.06), and a potential increase with Anti-TNF agents (OR: 3.04; 95% CI 0.31-29.47), though none reached statistical significance. LTF studies showed consistent findings. Observational studies suggested lower MACE risk with Anti-TNF therapies (OR 0.29; 95% CI 0.21-0.40), but not with IL-12/IL-23 (OR 4.41; 95% CI 0.49-39.28) or JAK inhibitors (OR 1.57; 95% CI 0.86-2.84). CONCLUSION: Advanced therapies did not demonstrate a clear increase or decrease in cardiovascular risk in IBD. The discrepancies between RCTs and observational studies underscore the urgent need for rigorous-designed observational research with long-term follow-up to evaluate the real-world impact of advanced therapies on MACE risk.

Humans↗

PubMed2026

Associations between smart infusion pump-electronic health record interoperability and healthcare outcomes: A systematic review.

OBJECTIVE: This study synthesized available evidence on the associations between smart infusion pump-electronic health record (EHR) interoperability and healthcare outcomes. METHODS: A systematic review of PubMed, CINAHL, Embase, and Scopus databases identified 901 records, which were imported into Rayyan® for duplicate removal, independent screening by three reviewers, and resolution of discrepancies. Eligible studies were peer-reviewed, data-driven, and reported associations between smart infusion pump-EHR interoperability and healthcare outcomes. Studies focused solely on technical validation or interoperability prototypes were excluded. A backward citation search identified additional studies. Two reviewers independently extracted and cross-validated study characteristics using standardized templates. Methodological quality was assessed with the Joanna Briggs Institute Critical Appraisal Tools. RESULTS: Twenty records of 14 full-text studies and 6 conference proceedings were included. Most records reported positive associations between smart infusion pump-EHR interoperability and outcomes related to safety (e.g., medication administration errors, safety-reported events, pump alerts, and compliance with interoperability and drug library), operational efficiency (e.g., programming and documentation time and technical issues), financial performance (e.g., charges captured, and cost avoided), and user experience domains. Most studies used observational designs, reflecting real-world interoperability implementations, where controlling confounding factors is challenging. Limited reporting of baseline characteristics, pump type, and sample sizes limited comparability across studies. CONCLUSIONS: Smart infusion pump-EHR interoperability was associated with improvements in patient safety, efficiency, charge capture, and user experience, with variable findings across studies. Future research should use rigorous methodologies and standardized measures, examine relationships across outcome domains, assess limitations of pump-EHR interoperability, and evaluate underexplored outcomes, including team communication, cognitive workload, and AI-enabled pumps. IMPLICATIONS FOR CLINICAL PRACTICE: Interoperability should be viewed as a component of a broader sociotechnical system, in which technology, user, workflow, clinical content, and organizational practices collectively determine overall effectiveness.

Humans↗

PubMed2026

Effects of short-bout accumulated exercise on postprandial metabolism in adults: A systematic review and meta-analysis.

OBJECTIVE: To systematically evaluate the acute effects of short-bout accumulated exercise (SBAE) interrupting prolonged sedentary behaviour on postprandial glucose, insulin, and triglycerides in adults. METHODS: Systematic searches in PubMed, Web of Science, Embase, Cochrane Library, CINAHL, SPORTDiscus, and CNKI (inception to 10 December 2025) identified randomised crossover trials comparing SBAE (≤10 min/bout, inter-bout interval ≥30 min or adequate recovery) with continuous sedentary behaviour. Outcomes included postprandial glucose, insulin, and triglyceride AUCs. Standardised mean differences (SMD) were pooled using random-effects models. RESULTS: Thirty-one publications reporting data from 29 independent cohorts involving 573 unique participants (mean age 47.8 ± 20.3 years; 47.5% female; mean BMI 29.0 ± 5.1 kg/m²) were included. Compared with continuous sedentary behaviour, SBAE significantly reduced glucose AUC (SMD = -0.53, 95% CI: -0.71 to -0.34, P < 0.001) and insulin AUC (SMD = -0.58, 95% CI: -0.85 to -0.31, P < 0.001), but not triglyceride AUC (SMD = -0.17, 95% CI: -0.48 to 0.15, P = 0.306).Exploratory subgroup analyses showed statistically significant reductions in glucose and insulin for walking and for inter-bout intervals < 60 min, but not for standing alone or intervals ≥ 60 min. A statistically significant insulin-lowering effect was observed in obese individuals. CONCLUSION: SBAE acutely improves postprandial glucose and insulin control. Exploratory subgroup analyses showed statistically significant effects for walking and for inter-bout intervals < 60 min, but these comparisons are observational and no formal interaction test was conducted. These findings provide preliminary evidence for acute SBAE in sedentary populations, though long-term health effects and real-world generalisability require further investigation.

Adult↗

PubMed2026

Temporal shifts in gyrA mutation types and sublineage replacement in ST11 Salmonella enterica serovar Enteritidis over a decade (2014-2023): A genomic epidemiological study in Guangxi, China.

The overuse or abuse of antibiotics drives the global health threat of antimicrobial resistance. Although bans on certain veterinary antibiotics, such as colistin, have proven effective, the impact of fluoroquinolone stewardship on the evolution of the foodborne pathogen Salmonella enterica serovar Enteritidis (S. Enteritidis) remains unclear. Here, we conducted a decade-long (2014-2023) retrospective longitudinal genomic epidemiological analysis of 441 ST11 S. Enteritidis isolates from Guangxi, China, alongside a global reference dataset of 4297 genomes. Our aim was to elucidate the effect of real-world antibiotic stewardship on the shift of gyrA point mutations and lineage distribution. Surveillance identified three global epidemic clade sublineages (GEC-L2, L3, L4), with the multidrug-resistant GEC-L4 (i.e., GC-c or MMC2), characterized by the gyrA mutation with amino acid substitution D87Y, being domestically dominant (70.07%, 309/441). Following China's 2016 ban on the veterinary use of critical fluoroquinolones, the proportion of the highly resistant GEC-L4 sublineage decreased continuously (from 86.84% in 2017 to 56.00% in 2023), while the less resistant GEC-L3 sublineage (i.e., GC-b or MMC1), mainly characterized by gyrA D87G, increased simultaneously (from 13.16% to 44.00%). This phenomenon might be attributed to the fact that the GEC-L4 sublineage exhibited a higher fitness cost compared with the GEC-L3 sublineage, as confirmed by the competition assay. A Random Forest Model validated that the gyrA mutation with amino acid substitution D87Y was the paramount feature for these sublineages' identification. In contrast, global data showed a continuous increase in gyrA mutations (from 8.63% in 2006 to 68.85% in 2024), primarily D87Y (from 1.44% to 31.15%) and D87N (from 4.32% to 22.95%), correlating with rising average fluoroquinolone consumption. This study provides direct genomic evidence that national-level antibiotic stewardship can drive the replacement of highly resistant sublineages with moderately resistant ones. These findings offer crucial scientific evidence for evaluating the impact of antibiotic management policies and inform strategies for the rational use of antimicrobials.

China↗

PubMed2026

Predicting training outcomes for developmental dyslexia from EEG data.

Developmental dyslexia (DD) is characterised by lower-than-average reading abilities and is diagnosed in approximately 10% of individuals. The societal barriers may limit professional fulfilment and psychological wellbeing of individuals with DD, calling for the development of effective interventions to counteract them. As DD is associated with challenges in both phonological and visuo-attentional domains, different longitudinal training approaches were developed to strengthen them. However, they require a considerable amount of personal, social and economic resources and the outcomes may vary depending on individual differences in behavioural and neurophysiological functionality. Hence, predicting training outcomes might help in developing personalised treatment protocols and optimising the use of resources. In the present work we applied machine learning to resting-state EEG to predict longitudinal training outcomes in adults with DD enrolled in a randomized clinical trial. In particular, one group received a visuo-attentional training combined with transcranial alternating current stimulation (tACS), another group received visuo-attentional training with sham/placebo stimulation, and the third group received a phonological training with sham/placebo stimulation. The improvement in text reading speed was associated with spectral power in low-beta and individual frequencies in the alpha (IAF) and beta (IBF) bands, while the improvement in pseudoword reading was associated with IBF. The findings highlight the potential of capturing neural markers of treatment responsiveness in DD. Future studies should focus on the generalisability of predictive models to real-world settings, while investigating whether specific EEG markers predict responsiveness to distinct remediation protocols, thus supporting the development of personalised interventions.

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

2024-2025 BNT162b2 KP.2 COVID-19 full season vaccine effectiveness from vaccine registries linked to administrative claims in two states: A cohort study in non-immunocompromised adults.

BACKGROUND: Data on effectiveness of COVID-19 vaccinations during the 2024-2025 respiratory season are limited, particularly among those with underlying medical conditions (UMC). We estimated BNT162b2 KP.2 vaccine effectiveness (VE) against COVID-19-associated hospital admission, emergency department (ED), and urgent care (UC) visits in two U.S. states. METHODS: Retrospective cohort study of non-immunocompromised adults living in Louisiana or California, with ≥1 year prior continuous enrollment in insurance plans contributing to the HealthVerity claims database beginning August 22, 2024. The effectiveness of BNT162b2 KP.2 vaccine (2024-2025 formulation, hereafter referred to as BNT162b2), measured as a time-varying exposure against hospital admission, ED, or UC encounters with International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) code U07.1 was calculated as 1 - adjusted hazard ratio using Cox proportional hazard models adjusted for age group, sex, state, insurance payor, presence or absence of UMCs, and pre-index healthcare utilization. Stratifications included those aged 65 years and older, those aged 18-64 years with UMCs, and those aged 18-64 years without UMCs. RESULTS: The cohort included 6,256,421 individuals (93% California, 7% Louisiana); 330,565 (5%) received the BNT162b2 vaccine. Vaccinated individuals were older and had more comorbidities, wellness visits, and prior influenza vaccination. Overall, 66% of the study population had ≥1 UMC; the most prevalent conditions were obesity (25%), history of immunocompromised conditions (23%), and mental health conditions (19%). COVID-19-related encounter rates for ED, UC or hospitalization were lower among vaccinated compared to unvaccinated persons (25.1 vs 36.3 per 100,000 person-months). Among all adults, VE was 37% against hospitalization, 12% against ED/UC encounters, and 16% against ED/UC/hospitalization encounters. Results were similar across age groups and UMCs. CONCLUSIONS: BNT162b2 provided protection against COVID-19-associated outcomes of ED, UC or hospitalization among non-immunocompromised U.S. adults, including those with UMCs, over the course of the 2024-2025 respiratory virus season, supporting continued vaccine recommendations. REGISTRATION: This study was posted on clinicaltrials.gov prior to analyses (NCT06923137).

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