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PubMed2026

Herd-level heterogeneity of antimicrobial resistance in commensal Escherichia coli: A nationwide high-throughput survey of Australian pig herds.

Antimicrobial resistance in commensal Escherichia coli provides a useful indicator for overall antimicrobial resistance burden. We applied this approach to assess antimicrobial resistance within and between commercial pig herds across Australia. A high-throughput robotic workflow was used to isolate 2730 E. coli colonies from rectal contents collected in 2022 from healthy slaughter pigs (n = 300) representing 30 herds (∼70% of national production). Up to 94 isolates per herd underwent antimicrobial susceptibility testing using the Robotic Antimicrobial Susceptibility Platform. Isolate- and herd-level antimicrobial resistance indices were calculated, weighting antimicrobials by their human health importance. Resistance to first-line agents was widespread: ampicillin 77% and tetracycline 79%. By contrast, resistance to critically important antimicrobials was rare (ciprofloxacin 0.11%; extended-spectrum cephalosporins 0.04%), and no clinical resistance to carbapenems or colistin was detected. Overall, 56.9% of isolates were multi-class resistant. Herd-level antimicrobial resistance within indices ranged from 1.51 to 5.76, revealing substantial between-herd heterogeneity. Three herds carried critically important antimicrobials-resistant isolates that would likely have been missed using conventional, lower-density sampling approaches. Whole-genome sequencing identified fluoroquinolone-resistant isolates belonging to ST10 and ST69 (both qnrS1), and ST744 (Quinolone Resistance Determining Region mutations plus blaCTX-M-27). By testing approximately tenfold more isolates than conventional surveys, we uncovered considerable antimicrobial resistance with heterogeneity within and between animals and herds, including farm-specific variability. This expanded sampling also enabled detection of critically important antimicrobial resistance at very low prevalence. In conclusion, high-throughput, high-density testing offers a practical early-warning system and herd-level benchmark to inform surveillance and targeted interventions.

Animals↗

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

Physiological and molecular responses of coelomocytes to low- to mid-frequency acoustic exposure in the sea urchin Strongylocentrotus intermedius.

Underwater noise is a widespread environmental pollutant in marine ecosystems, yet the effects of low- to mid-frequency acoustic exposure on immune physiology and molecular responses in echinoderms remain unclear. In this study, the sea urchin Strongylocentrotus intermedius was exposed to continuous pure-tone acoustic stimulation at 80, 125, 250, 500, 750, and 1000 Hz for 3 h. Results showed that acoustic exposure significantly affected redox homeostasis, energy metabolism, and immune function in S. intermedius coelomocytes: the antioxidant system and glutathione redox balance were altered, as indicated by increased superoxide dismutase (SOD) and catalase (CAT) activities, elevated reduced glutathione (GSH) content, and a higher GSH/GSSG ratio; glycolysis-related enzyme activities were enhanced, with increased pyruvate kinase (PK) activity under 125-500 Hz exposure and elevated hexokinase (HK) activity at 250 Hz; and immune function was impaired, as shown by increased coelomocyte mortality, reduced phagocytic activity, and inhibited acid phosphatase (ACP) and alkaline phosphatase (AKP) activities, whereas respiratory burst activity showed no significant change. Among all treatments, 250 Hz induced the most pronounced physiological responses. Transcriptomic analysis of coelomocytes from the 250 Hz group identified 663 differentially expressed genes, including 537 upregulated and 126 downregulated genes, mainly enriched in pathways related to apoptosis, phagosome, lysosome, glutathione metabolism, arachidonic acid metabolism, and carbohydrate metabolism. These findings indicate that low- to mid-frequency acoustic exposure can act as a physiological and molecular stressor to S. intermedius coelomocytes by affecting redox homeostasis, enhancing energy metabolism, and suppressing immune effector processes, with 250 Hz showing the strongest effect under the present exposure conditions. This study provides experimental evidence for evaluating the potential biological effects of low- to mid-frequency acoustic exposure on benthic echinoderms.

Animals↗

PubMed2025

Improving Community-Based Care for Adolescents with ADHD: a Randomized Controlled Trial of Artificial Intelligence-Assisted Fidelity Supports.

Cognitive-behavioral treatments (CBTs) for adolescents with ADHD demonstrate promise of long-term effects on outcome. However, their implementation in routine care community clinics faces barriers that impact quantity, efficiency, and quality of delivery, as well as client outcomes. This study is a randomized controlled trial designed to evaluate the impact of an AI-assisted service delivery model on therapist implementation of Supporting Teens' Autonomy Daily (STAND), a CBT blended with Motivational Interviewing (MI) for adolescents with ADHD. Adolescents with ADHD (N = 51), who were clients at three community mental health agencies, received treatment from 23 therapists. There was randomization of adolescents and therapists to AI-assisted or standard implementation supports. In addition to standard supports (i.e., training, standard facilitation resources, technical assistance, case supervision), AI-assisted support package included digitized facilitation resources housed in a clinical dashboard (Care4), feedback on content fidelity, and AI-generated feedback on MI implementation quality. The AI-assisted group was associated with more efficient treatment delivery and lower number of appointments attended by the adolescent. There was also a significant decrement in MI quality over time in the AI-assisted group compared to the standard support group. Feedback in focus groups indicated that therapists perceived a task-oriented mindset to be associated with receipt of the AI-assisted support package, leading therapists to prioritize efficiency over relational aspects of therapy. Following the results of this trial, a future, larger RCT should examine the impact of the AI-assisted implementation model on mental health outcomes and cost savings to organizations, third party payers, and clients. Trial registration number: NCT05135065; https://www.clinicaltrials.gov ; Registered September 2021.

Humans↗

PubMed2026

Development, feasibility, acceptability, and preliminary impact of NutriSOS®: A behavioral mobile app to promote sustainable diets.

The primary objective of this study was to describe the development of the NutriSOS® app and to evaluate its feasibility and acceptability for its use in the NutriSOS® Randomized Controlled Trial (RCT) to promote sustainable diets. A secondary objective was to explore preliminary changes in dietary and physical activity behaviors and environmental impact following app use. The NutriSOS® app integrates personalized dietary advice, educational content, self-monitoring, and social interaction features. A single-arm, pre-post pilot study was conducted in 37 young Mexican adults over four weeks. Feasibility, acceptability, quality, and usability were assessed using online surveys, alongside exploratory changes in dietary and physical activity behaviors, environmental indicators, and their association with perceived behavioral determinants. Feasibility and acceptability were high overall, with favorable responses reaching up to 100% in key components such as the nutritional guide and learning modules, and above 90% for messaging, registration, and design. Greater variability was observed in some sections, particularly the 24-h recall (41-86%). Reductions in red and processed meat and ultra-processed food consumption were observed (from 3 to 1 times/week, p < 0.01), with ∼60% decreases in their related environmental footprints (p < 0.01) and favorable self-reported behavioral determinants (p < 0.0001). Physical activity type and intensity changed (p < 0.05). These findings support NutriSOS® as a feasible and acceptable tool, while highlighting areas for refinement, particularly those related to the time and effort required for data entry, prior to its implementation in the NutriSOS® RCT, in which its effectiveness will be formally evaluated.

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

Spermidine and melatonin ameliorate heat stress-induced decline in sheep semen quality.

Heat stress impairs reproductive performance in sheep through endocrine disruption and oxidative stress. This study evaluated the protective effects of spermidine (SPD) and melatonin (MT) supplementation on semen quality in Dorper rams during summer. Twenty-four rams were randomly assigned to a control group, an SPD group (5 mg/kg, dietary supplementation), or an MT group (60 mg, subcutaneous implantation) and treated for 60 days. The temperature-humidity index (THI) was monitored throughout the experimental period. Compared with the control group, MT significantly reduced serum cortisol concentration on day 30 (P < 0.05), whereas no significant differences were observed at the other sampling time points. Serum testosterone, spermidine, and melatonin concentrations remained unchanged throughout the study (P > 0.05). SPD supplementation significantly increased ejaculate volume on day 35 and sperm motility on day 42 (P < 0.05), whereas MT did not significantly affect these parameters. Neither treatment reduced the overall sperm abnormality rate. However, both SPD and MT significantly decreased the proportion of acephalic and decaudated sperm on day 56 (P < 0.05). Neither treatment significantly affected pregnancy rate, delivery rate, or the expression of PMFBP1 and SUN5 proteins in semen (P > 0.05). Regarding oxidative stress, MT significantly downregulated CAT protein expression (P < 0.05), whereas SPD significantly reduced MDA content and SOD1 protein expression (P < 0.05); MT showed similar but non-significant trends for these two markers (P > 0.05). Collectively, these findings demonstrate that SPD and MT exert distinct protective effects against heat stress, with SPD improving selected semen quality traits and both treatments reducing sperm head-tail separation, although these benefits did not translate into improved reproductive performance.

Animals↗

PubMed2026

Respiratory-onset peripartum cardiomyopathy: a systematic review of diagnostic pitfalls and clinical outcomes.

INTRODUCTION: Peripartum cardiomyopathy (PPCM) may initially present with prominent respiratory symptoms that resemble primary pulmonary disease, particularly in late pregnancy and the early postpartum period. In clinical practice, this presentation often triggers alternative diagnostic pathways, introducing delay at a time when rapid cardiac assessment is critical. Although respiratory-dominant presentations are repeatedly described across case-based and observational reports, they have not been systematically examined as a distinct diagnostic pathway within the PPCM literature. CONTENT: This PRISMA-guided systematic review synthesized evidence relating to respiratory-onset presentations of PPCM. Major databases and registers were searched comprehensively. Following screening of 589 records and full-text assessment of 145 reports, 49 studies met inclusion criteria. Twenty studies were qualitatively prioritized for narrative synthesis using ROBIS-informed methodological appraisal. Evidence was examined across diagnostic misclassification patterns, cardiopulmonary mechanisms, differential diagnoses, investigative strategies, and acute and longitudinal management considerations. SUMMARY: Respiratory-led presentations were commonly misattributed to asthma, pneumonia, pulmonary embolism, or perioperative causes, with diagnostic delay frequently reported. Across heterogeneous study designs, cardiogenic pulmonary edema with left-ventricular systolic dysfunction emerged as a recurring unifying mechanism. Early use of echocardiography, natriuretic peptides, and targeted imaging consistently aided differentiation from primary respiratory pathology. Severe clinical deterioration was often described in the context of delayed recognition. OUTLOOK: Respiratory-onset PPCM represents a high-risk diagnostic pathway rather than a discrete disease entity. Prospective registries, standardized diagnostic algorithms, and closer integration of obstetric and cardiopulmonary care are needed to refine early recognition and improve maternal outcomes.

Humans↗

PubMed2026

USleep: efficacy of app-based audio interventions to improve sleep disturbance in working adults, a multi-arm randomized controlled trial.

STUDY OBJECTIVES: To evaluate the efficacy of three categories of standalone, audio-based sleep interventions (Bedtime Stories, Sleep Sounds, Sleep Skills) delivered via mental health application (MHapp) in improving sleep among working adults with sleep disturbance. METHODS: A multi-arm, parallel randomized controlled trial was conducted. Adults with self-reported sleep disturbances were recruited online and randomly allocated to Bedtime Stories, Sleep Sounds, Sleep Skills, or digital control. Participants completed self-report questionnaires on sleep disturbance and other related outcomes at baseline (t0) and after the 4-week intervention (t1). The primary analysis followed an intention-to-treat approach using mixed-effects models. RESULTS: A total of 495 working adults (mean age = 32.7 years; 55.8% female) were randomized. For sleep disturbance (primary outcome), the between-group Hedges' g effect sizes were very small and not statistically significant (Bedtimes stories vs. control: g = 0.12, 95% CI -0.13 to 0.37, Sleep Sounds vs. control: g = 0.14, 95% CI -0.11 to 0.39, Sleep Skills 0.07, 95% CI -0.07 to 0.29), with slightly greater reductions in sleep disturbance for the intervention groups than control. The same pattern was observed for sleep-related impairment, mental health, well-being, and pre-sleep arousal. CONCLUSION: Audio-based sleep interventions delivered via a MHapp did not demonstrate superior efficacy over a digital control condition in reducing self-reported sleep disturbance among working adults. Although safe and well-tolerated, their use as standalone treatments for sleep disturbance is not supported by these findings. Future research should explore effectiveness in real-world settings, including user content choice across categories, and use objective sleep measures. CLINICAL TRIAL REGISTRATION: Registered at https://www.isrctn.com/ under "Evaluating the efficacy of audio-based digital tools to improve sleep on the Unmind workplace well-being platform"; https://www.isrctn.com/ISRCTN13426045; registration number: 13426045.

Humans↗

PubMed2026

Microbial allies in a cotton pest: A descriptive account of associated microbiota dynamics in Dysdercus cingulatus across development.

BACKGROUND: Hemipteran insects harbour several symbiotic partners, mainly bacteria, which play pivotal roles for hosts like dietary provision, support overall physiology, xenobiotic degradation and manipulate/regulate behaviour. Most of these symbionts usually reside and operate from the digestive tracts of the animals. Cotton is one of the major cash crops in India and Dysdercus cingulatus (D. cingulatus) though a secondary pest, is causing significant destruction of cotton bolls, poor lint quality and reduce oil content of seeds. Premature opening of cotton bolls often leads to bacterial and fungal infections, thus resulting in extensive economic loss worldwide. D. cingulatus is a hemimetabolous insect that comprises of developmental stages like egg, nymph (5 instar stages), and adult. The present work explored the ontogeny specific diversity in the associated microbiota and predicted their probable functional inputs in D. cingulatus. RESULTS: The data obtained using 16S rRNA gene sequencing (NovaSeq 6000) revealed presence of members of Proteobacteria (65.83%), Firmicutes (24%), Actinobacteria (10%) phyla throughout the ontogeny of D. cingulatus. Highest alpha diversity of these symbiotic bacteria was recorded in the third instar nymphs in contrast to rest of the developmental stages. Among all the observed genera, Stenotrophomonas, Hungatella and Glutamicibacter were predominant from egg to adult stages. MicFunPred, a tool used for predicting the probable functional inputs of these symbionts, hinted at their probable stage specific contribution in crucial biochemical pathways such as polyketide biosynthesis, ascorbate/aldarate metabolism, pentose phosphate and glyoxylate cycles, steroid hormone and peptidoglycan biosynthesis, and glycolysis/pyruvate metabolism. CONCLUSIONS: The primary investigations on the ontogenetic composition and diversity of associated microbiota, suggest dynamic shifts in D. cingulatus, concurrent with their probable functions/roles in the host development and metabolism. To the best of our knowledge, this is the first report on symbiotic microbiota variation across the developmental stages of D. cingulatus that provides preliminary descriptive observations that may guide future functional and experimental investigations into microbiota-based pest management.

Animals↗

PubMed2026

Integrated miRNA-mRNA profiling reveals candidate regulatory relationships associated with high-fat diet-induced muscle lipid deposition in black seabream (Acanthopagrus schlegelii).

High-fat diets are increasingly used in aquaculture due to their protein-sparing effects; however, the post-transcriptional regulatory mechanisms of fish muscle in response to high-fat diets (HFD) remain unclear. In this study, juvenile black seabream were fed either a normal-fat diet (NFD) or a HFD to investigate the miRNA-mRNA regulatory network associated with diet-induced muscle lipid deposition. Oil Red O staining and biochemical analysis showed that high-fat diet feeding markedly increased lipid droplet accumulation and crude lipid content in muscle, indicating significant induction of muscle lipid deposition. Integrated mRNA and miRNA expression profiling revealed substantial transcriptomic and post-transcriptional responses to high-fat diet challenge. A total of 271 differentially expressed genes were identified, including 120 upregulated and 151 downregulated genes. Through combined target prediction and expression correlation analysis, thirteen candidate inverse miRNA-mRNA relationships were subsequently identified, and RT-qPCR supported the expression patterns of selected miRNAs and mRNAs. These pairs included miR-499-x-dmgdh, miR-499-y-gatm, miR-727-y-ass1, miR-4649-x-foxo4, miR-9129-z-myl7, and several novel miRNA-mediated interactions involving adk, chst11, lypla2, frem2, kcnc4, wars1, bag2, and capn2. Functional analysis suggested that these regulatory pairs were mainly associated with metabolic adaptation, structural remodeling, and cellular stress responses. In particular, gatm, dmgdh, ass1, and adk were associated with energy metabolism-related processes, including pathways previously linked to Ampk regulation, whereas myl7, frem2, and kcnc4 may contribute to muscle structural maintenance and excitability regulation. Overall, this study provides candidate miRNA-mRNA regulatory relationships potentially involved in high-fat diet-induced muscle lipid deposition and adaptive remodeling in black seabream, offering a basis for future functional studies on muscle metabolism and quality regulation in marine fish.

Animals↗

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

Predictive Models for Hypoglycemia Risk in Haemodialysis Patients With Diabetic Kidney Disease: Systematic Review and Meta-Analysis.

AIM: To provide evidence for selecting and developing reliable clinical assessment tools for hypoglycemia in diabetic kidney disease patients during haemodialysis. DESIGN: Review. METHODS: Systematic searches were performed in 9 Chinese and English databases to collect literature regarding the development of hypoglycemia risk prediction models in haemodialysis patients with diabetic kidney disease. Two reviewers independently performed literature screening, data extraction, risk-of-bias assessment, and applicability evaluation. The Prediction Model Risk of Bias Assessment Tool was used to assess the risk of bias and applicability of the included studies. Meta-analysis was conducted using R software. DATA SOURCES: CNKI, Wanfang, VIP, CBM, PubMed, Cochrane Library, EMbase, Web of Science, and CINAHL. The search period covered from the establishment date of each database to December 2025. RESULTS: Six studies, comprising six prediction models, were included. Two studies performed internal validation, and three conducted external validation. All models reported the area under the curve, ranging from 0.813 to 0.866, and calibration measures. Four studies were rated as having a high risk of bias, while all six demonstrated good overall applicability. The meta-analysis showed that the pooled AUC value of the six studies was 0.846 (95% CI: 0.823-0.867). CONCLUSION: Research on hypoglycemia risk prediction models in haemodialysis patients with diabetic kidney disease remains in the developmental stage. Although the included prediction models exhibited satisfactory apparent discriminatory ability and clinical applicability, most of the original studies suffered from a high risk of bias and lacked adequate validation. The true predictive performance and clinical application value of these models remain to be further verified. Accordingly, routine and unconditional clinical application is not recommended at this stage. Future studies should include more high-quality, multicenter external validation and develop models with high generalizability, favourable clinical applicability, and robust predictive performance to facilitate early identification of hypoglycemia risk in this population. IMPACT: This study systematically evaluated the hypoglycemia risk prediction models for diabetic kidney disease patients during haemodialysis, and the research on hypoglycemia risk prediction models for maintenance haemodialysis patients during dialysis is still in the development stage. This study provides a reference for clinical medical staff to select or develop hypoglycemia risk prediction and assessment tools for diabetic kidney disease patients during haemodialysis. REPORTING METHOD: This study was conducted in accordance with the relevant guidelines of the EQUATOR Network and followed the TRIPOD-SRMA Checklist. PATIENT OR PUBLIC CONTRIBUTION: No patient or public contribution. TRIAL REGISTRATION: PROSPERO: CRD420251243352.

Humans↗

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

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↗