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PubMed2026

The Impact of Chatbot Type and Normative Messaging on Chatbot Usage Intention Based on the Health Technology Acceptance Model: Randomized Controlled Trial.

BACKGROUND: Digital health tools, such as health chatbots, may improve access to scalable health support, but adoption remains inconsistent. Existing models do not fully integrate technology acceptance factors with health motivation factors relevant to digital health use. OBJECTIVE: This study proposed and tested the health technology acceptance model and examined whether normative message framing and chatbot type were associated with health motivation, technology acceptance, and intention to use a health chatbot. METHODS: In October 2025, we conducted a 4 × 2 between-participants online experiment with 1000 US adults recruited from a nationally representative YouGov panel. Participants were randomized to 1 of 8 conditions varying norm message type (self-oriented, peer-oriented, expert-oriented, or family-oriented) and chatbot type (AI-powered or rule-based) in a cancer prevention and genetic risk information scenario. Outcomes included descriptive norms, injunctive norms, perceived susceptibility, perceived severity, perceived benefits, self-efficacy, perceived ease of use, trust, privacy concerns, and usage intention. Data were analyzed using a multivariate ANOVA with Bonferroni-adjusted post hoc tests and multiple linear regression. RESULTS: Peer-oriented and family-oriented messages produced higher usage intention than expert-oriented messages, and peer-oriented messages also increased descriptive norms, injunctive norms, self-efficacy, and trust. AI-powered chatbots were associated with higher usage intention (P=.02) and greater trust (P=.008) than rule-based chatbots. In regression analyses, the model explained 50.8% of the variance in usage intention. Usage intention was positively associated with descriptive norms (β=0.087; P=.003), injunctive norms (β=0.078; P=.009), perceived susceptibility (β=0.051; P=.03), perceived benefits (β=0.253; P<.001), and trust (β=0.33; P<.001), and negatively associated with perceived severity (β=-0.047; P=.049) and privacy concerns (β=-0.11; P<.001). Perceived ease of use and self-efficacy were not significant predictors. CONCLUSIONS: The health technology acceptance model was a useful framework for explaining the intention to use a health chatbot by combining technology acceptance and health motivation constructs. Both social design features and chatbot design features shaped adoption-related beliefs, with peer-oriented and family-oriented framing and AI-powered chatbots showing particular promise. Trust and privacy concerns remained central determinants of intended use.

Humans↗

PubMed2026

Suppression of AAV-Delivered Transgene Expression Using Artificial MicroRNAs Delivered by an Alternative AAV Serotype.

Adeno-associated virus (AAV) gene transfer vectors mediate long-term expression in nondividing cells, an advantage for treating chronic disorders. However, current platforms lack a way to selectively shut down transgene expression if adverse effects arise. To create an "off switch," we hypothesized that incorporating unique artificial microRNA (amiRNA) target sequences into an AAV expression cassette would allow subsequent suppression of transgene expression using a second AAV vector encoding the cognate amiRNA. We introduced 22-nt sequences absent from human and mouse transcriptomes into the 3' untranslated region (UTR) of a therapeutic AAV cassette. To identify optimal amiRNAs, two tandem copies of each amiRNA were cloned into the 3'UTR of an mCherry reporter gene. In vitro assessment of six amiRNA/target pairs using a dual luciferase assay identified four amiRNAs that efficiently suppressed reporter expression. Cells cotransfected with target site 3 (TS3) and amiRNA-T3B showed the greatest reduction in luciferase activity (80%, p < 0.0001) and were selected for further study. The "off-switch" system was then evaluated using an AAV5 therapeutic vector expressing a recombinant humanized anti-IgE monoclonal antibody (AAV5-TBG-anti-IgE-TS3), designed for long-term suppression of allergen-induced reactions. Co-transfection of HEK293T cells with anti-IgE-TS3 and amiRNA-T3B significantly reduced anti-IgE mRNA and protein levels relative to a control amiRNA (p < 0.0001). In vivo testing in Balb/c mice (n = 5) involved intravenous administration of AAV5-anti-IgE-TS3 (3.2 × 1010 gc), followed 4 weeks later by an AAVrh.10 amiRNA vector (AAVrh.10-TBG-amiRNA-T3B; 1 × 1011 gc). Control mice receiving only the therapeutic vector expressed 18.4 ± 13.8 µg/mL serum anti-IgE at 10 weeks. In contrast, mice receiving the amiRNA "off" vector showed marked suppression of anti-IgE (0.3 ± 0.15 µg/mL, p < 0.0001). These findings provide proof-of-concept that AAV-delivered amiRNAs can selectively switch off transgene expression, offering a strategy to improve the safety of AAV-mediated gene therapies.

Dependovirus↗

PubMed2026

The Impact of Baseline Negative Emotions on Postoperative Quality of Life in Adolescent Idiopathic Scoliosis Patients: A 2-Year Follow-Up Study.

OBJECTIVE: Adolescent idiopathic scoliosis (AIS) is a three-dimensional spinal deformity that develops during puberty without a clear etiology. Beyond physical manifestations, AIS severely impacts adolescents' psychological and social well-being, leading to anxiety, depression, and low self-esteem. While advancements in surgical techniques have enhanced objective outcomes, existing studies on AIS have primarily focused on objective indices, with limited attention to the long-term impact of preoperative negative emotions on patient-reported subjective quality of life. METHODS: This was a retrospective cohort study. A total of 112 eligible AIS patients who underwent posterior spinal correction surgery between April and August 2023 were enrolled. Inclusion criteria included confirmed AIS, completion of 2-year follow-up, and informed consent; exclusion criteria included missing imaging/questionnaire data, comorbid psychiatric/neurological diseases, or prior spinal surgery. Patients were grouped using the Hospital Anxiety and Depression Scale (HADS) administered on admission. Quality of life was assessed preoperatively and 2 years postoperatively using the Scoliosis Research Society-22 (SRS-22, evaluating self-image, mental health, pain, function, treatment satisfaction) and Short Form 36 Health Survey (SF-36, assessing 8 physical and mental health dimensions). Statistical analysis was performed via SPSS, using independent t-tests, paired t-tests, Mann-Whitney U test, and chi-square test. p < 0.05 was considered significant. RESULTS: There were no significant differences in baseline characteristics (age, gender, BMI, surgical parameters, scoliosis type, preoperative/postoperative Cobb angles) between the two groups (all p > 0.05). Preoperatively, SRS-22 and SF-36 scores showed no inter-group differences (all p > 0.05). Postoperatively, the Negative Emotion Group had significantly lower scores in SRS-22 mental health (3.9 ± 0.3 vs. 4.5 ± 0.2) and treatment satisfaction (4.0 ± 0.3 vs. 4.6 ± 0.7), as well as SF-36 general health (68.6 ± 6.4 vs. 79.7 ± 13.3), role-emotional (61.3 ± 9.3 vs. 70.8 ± 9.7), and mental health (61.8 ± 14.3 vs. 68.9 ± 10.7) (all p < 0.05); no inter-group differences were observed in physical function-related dimensions. Both groups showed significant improvements in physical function-related dimensions postoperatively. The Non-Negative Emotion Group also exhibited significant improvements in SRS-22 self-image/pain and SF-36 bodily pain (all p < 0.05), while the Negative Emotion Group showed no significant improvements in these dimensions. CONCLUSIONS: Preoperative anxiety and depression do not affect the recovery of physical function in AIS patients after spinal correction surgery but significantly impede improvements in subjective quality of life dimensions, including mental health and treatment satisfaction. These findings highlight the need to integrate psychological assessment and targeted interventions into the perioperative management of AIS. Such a patient-centered approach will help optimize both physical and psychological outcomes, ultimately achieving comprehensive rehabilitation for AIS adolescents.

Humans↗

PubMed2026

Diagnostic performance of machine learning models for malignant and non-malignant pleural effusion: Systematic review and meta-analysis.

BACKGROUND: Accurately distinguishing malignant pleural effusion (MPE) from non-malignant pleural effusion is clinically important, but the generalisability and methodological quality of machine-learning (ML) models remain uncertain. METHODS: We searched eight databases to 23 April 2026. Diagnostic performance was pooled using random-effects and Reitsma bivariate models, and study quality was assessed using PROBAST+AI. RESULTS: Forty-two studies were included; 17 contributed to the AUC meta-analysis and 14 to the bivariate analysis. The pooled AUC was 0.90 (95 % CI 0.85-0.94; 95 % prediction interval 0.62-0.98), with sensitivity of 0.80 (95 % CI 0.77-0.83) and specificity of 0.87 (95 % CI 0.79-0.92). Only nine studies reported external, temporal or independent validation. Externally validated studies had a lower pooled AUC than studies without external validation (0.83 vs 0.92), with lower specificity observed in the two externally validated studies contributing sensitivity and specificity data. All 42 development assessments had high overall quality concerns, and all 42 model evaluations were judged at high risk of bias. CONCLUSIONS: ML models showed good apparent accuracy for distinguishing MPE from non-MPE, but the evidence was limited by substantial heterogeneity, high risk of bias and scarce external validation. The pooled estimates reflect the average performance of different selected models rather than the expected accuracy of a single clinical test. ML models should be regarded as adjuncts to existing diagnostic pathways until they are confirmed by rigorous multicentre prospective external validation and clinical-impact studies.

Humans↗

PubMed2026

Phase 1 Study Evaluating Gefurulimab Pharmacokinetics and Safety Following Delivery Via Autoinjector or Prefilled Syringe With Needle Safety Device in Healthy Adults.

PURPOSE: Gefurulimab, a novel dual-binding nanobody targeting complement component 5 (C5), is in clinical development for anti-acetylcholine receptor antibody-positive generalized myasthenia gravis. Gefurulimab has a low molecular weight, enabling subcutaneous (SC) self-administration by autoinjector (AI) or prefilled syringe with needle safety device (PFS-SD). We compared gefurulimab pharmacokinetic (PK) exposure and safety in healthy adults following a single SC dose administered by AI versus PFS-SD. METHODS: In this phase 1, open-label, randomized, parallel-group study (NCT06208488), healthy participants aged 18 to 65 years were stratified by weight and randomized equally to 1 of 6 combination groups of device and injection site (abdomen/thigh/upper arm). Participants received a single SC dose of gefurulimab on day 1 and were assessed throughout the 92-day evaluation period. Primary endpoints were PK parameters for each device: maximum observed concentration (Cmax) and area under the serum concentration-time curve (AUCinf, AUClast). PK across injection sites, pharmacodynamics, safety, immunogenicity, and device performance were also assessed. FINDINGS: Overall, 175 participants were randomized: AI (n = 87), PFS-SD (n = 88). Geometric least squares mean ratios (90% CI) comparing AI/PFS-SD for Cmax, AUCinf, and AUClast were 97.6% (94.5-100.8), 99.6% (96.1-103.3), and 98.8% (95.2‒102.6), respectively. Secondary analyses found no meaningful differences in PK parameters across injection sites. Serum-free C5 concentrations over time, treatment-emergent adverse event (TEAE) profiles, and antidrug antibody responses were similar between cohorts. Most TEAEs were mild; none led to study discontinuation. IMPLICATIONS: SC administration of gefurulimab by AI and PFS-SD was well tolerated with comparable exposure, meeting bioequivalence criteria.

Humans↗

PubMed2026

Insights into the fate and dynamics of antibiotic resistance in multidrug-resistant Bacillus cereus during in vitro simulated gastrointestinal digestion.

Bacillus cereus, an important pathogen responsible for causing foodborne diseases worldwide, releases pore-forming enterotoxins, which target host epithelial cells, leading to osmotic lysis and ultimately manifesting as diarrheal syndrome. Moreover, some B. cereus strains carry antimicrobial resistance genes that confer multidrug resistance against a spectrum of antibiotics. Characterizing the survival traits of multidrug-resistant (MDR) B. cereus strains in the intestinal microenvironment is essential for developing targeted strategies to effectively manage diarrheal foodborne diseases caused by this pathogen. This study used whole-genome sequencing (WGS) to evaluate the pre- and post-digestion toxigenic potential, antimicrobial resistance profiles, and genetic diversity of MDR B. cereus strains isolated from food samples in Guangdong Province, China. The four B. cereus isolates investigated in this study exhibited a genetic diversity, as determined by multilocus sequence typing analysis of WGS data. All four isolates produced the diarrheal toxins Hbl, Nhe, and CytK to varying levels, indicative of their potential to cause outbreaks of foodborne diseases. Each of the four isolates exhibited resistance to more than three classes of antibiotics, fulfilling the criterion for multidrug resistance. At an initial concentration of 9 log colony-forming units (CFU)/mL, the intestinal concentration of these four isolates crossed the threshold required to induce widespread diarrhea in the general population. Under rice slurry protection, all tested isolates maintained intestinal concentration beyond the threshold when the initial concentration was increased to ≥8 log CFU/mL. Moreover, the upregulations of genes associated with acid tolerance, bile tolerance and stress response were observed in the surviving MDR B. cereus isolates. Digestion markedly altered the antibiotic resistance profiles of the MDR B. cereus isolates. In the absence of a food matrix, the MDR isolates lost their resistance to imipenem, meropenem, amoxicillin-clavulanic acid, and trimethoprim-sulfamethoxazole post-digestion and was influenced by the initial concentration of the strains. In the presence of food matrix rice slurry, the effects of digestion on the antibiotic resistance of MDR B. cereus isolates can be mitigated, enabling them to maintain their antibiotic resistance to the greatest extent. Most remarkably, after digestion, the isolates Bce055 and Bce166 exhibited newly emergent resistance to cefotetan and trimethoprim-sulfamethoxazole, respectively. Our findings clarify the fate of MDR B. cereus isolates in the gastrointestinal tract and inform the development of prevention and control strategies for foodborne diseases caused by this pathogen.

Drug Resistance, Multiple, Bacterial↗

PubMed2025

Systematic Review of Symptoms of Catatonia in Autism Spectrum Disorder.

Catatonia is a complex neuropsychiatric syndrome characterized by disturbances in mood, motor function, behavior and speech. It is increasingly recognized in individuals with autism spectrum disorder (ASD), although its identification remains challenging due to the overlapping clinical features of the two conditions. Shared characteristics, such as echophenomena, mannerisms, social indifference and repetitive behaviors can obscure accurate diagnosis. Although reports suggest a significant prevalence of catatonia among individuals with ASD, the condition remains poorly understood and frequently under recognized, leading to substantial diagnostic and treatment challenges. A systematic review was conducted to characterize the symptoms of catatonia in individuals with ASD. The literature search included peer-reviewed journal articles published in English from 1980 onward, focusing on studies examining co-occurring catatonia and ASD. A qualitative framework analysis was implemented to evaluate 45 peer-reviewed studies, with findings interpreted in relation to, and extending beyond, the diagnostic criteria for catatonia outlined in the International Classification of Diseases, 11th revision (ICD-11). The objective was to identify symptom patterns extending beyond current diagnostic frameworks and to support improved clinical recognition and diagnostic precision in ASD populations. The review identified six primary symptom clusters associated with catatonia in individuals with ASD: (1) psychomotor activity, (2) speech disturbances, (3) changes in behavior/skills/functions, (4) mental health symptoms, (5) physiological symptoms, and (6) symptoms related to arousal and awareness. Notably, several symptoms observed within these clusters are not currently included in the ICD-11 diagnostic criteria for catatonia. These additional symptoms include tics, motor compliance, incoherent speech, self-injury, impaired cognition, and appetite changes, suggesting a broader clinical presentation of catatonia in ASD populations than is presently captured in existing diagnostic frameworks. The findings of this review highlight the significance of enhancing clinicians' awareness and understanding of how catatonia manifests in individuals with ASD. Most notably, six symptom clusters, psychomotor changes, speech disturbances, behavioral and functional regression, affective and psychiatric symptoms, physiological symptoms, and arousal/awareness disturbances, were observed. Several symptoms identified in this review are not included in the current diagnostic criteria, and their recognition may facilitate in earlier identification and timely intervention, potentially preventing the severe consequences of untreated catatonia in this population.

Humans↗

PubMed2026

Orofacial Cleft Disparities in American Indian and Alaska Native Populations: A Systematic Review and Meta-Analysis.

ObjectiveTo evaluate the prevalence, access to care, and health outcomes of orofacial clefts (OFCs) among American Indian and Alaska Native (AI/AN) populations through a systematic review and meta-analysis.DesignSystematic review and meta-analysis performed in accordance with PRISMA 2020 guidelines and registered with PROSPERO (CRD420251035364).SettingUS-based population registries, hospital databases, and institutional or community-level retrospective studies involving AI/AN populations.Patients and ParticipantsAI/AN individuals with OFCs compared with non-Hispanic White patients.InterventionsPrimary cleft lip and palate repair, secondary cleft-related procedures, and multidisciplinary cleft care.Main Outcome Measure(s)Prevalence of OFCs, timing of cleft surgery, discharge disposition, access to specialists, and qualitative determinants of disparities.ResultsEighteen studies including more than 1985 AI/AN patients were identified. Meta-analysis of 5 studies estimated a pooled OFC prevalence of 15 per 10 000 live births (95% confidence interval: 5-49), with substantial heterogeneity (I2 = 99.8%). Individual studies reported significantly higher OFC prevalence in AI/AN populations compared to non-Hispanic Whites (odds ratio range: 1.44-2.68). Geographic maldistribution of craniofacial-trained surgeons, increased odds of nonhome discharge, and delayed cleft palate repair were consistently observed barriers. Qualitative analyses highlighted structural inequities, perceived racism, and lack of culturally responsive care as major contributors to disparities.ConclusionsAI/AN populations face a disproportionately high burden of OFCs alongside structural barriers to timely, culturally competent care. Addressing these disparities requires community-engaged, multidisciplinary interventions that improve geographic access and integrate culturally responsive approaches to care.

Humans↗

PubMed2026

Can ChatGPT Replace Human Clinical Coders? A Comparative Study in Otology Billing.

OBJECTIVE: Evaluate the utility of the large language model (LLM), ChatGPT, for the analysis of operative notes and the generation of Current Procedural Terminology (CPT) codes in comparison to human clinical coders. STUDY DESIGN: CPT billing codes assigned by ChatGPT were compared to existing billing data. Otology practice within a tertiary academic center. METHODS: About 191 operative notes from a single surgeon (9/2022-10/2023) were analyzed. ChatGPT-3.5 and 4 models were prompted for CPT codes based on operative notes. Assessment included determining exact and partial match rates, sensitivity and specificity for targeted procedures, and work Relative Value Units (wRVU) differences between ChatGPT-generated and human-assigned codes. RESULTS: ChatGPT-3.5 achieved exact matches in 22% of cases and partial matches in 32%, while ChatGPT-4 achieved 14% exact and 33% partial matches. When cochlear implantation (CI) was excluded, performance dropped significantly. For CI, ChatGPT-3.5 demonstrated a sensitivity of 94% and specificity of 90%, while ChatGPT-4 showed a sensitivity of 96% and specificity of 92%. In contrast, performance on cartilage grafting was poor, with sensitivities of 4.2% for ChatGPT-3.5 and 0% for ChatGPT-4. ChatGPT-3.5 and 4 showed moderate CPT code matching accuracy among themselves, with slight agreement to human coders. Both models tended to underbill for wRVUs compared to human coders, with significant differences in the values generated. CONCLUSION: This study assessed ChatGPT's effectiveness in automating CPT code assignment for otologic surgeries. While the models achieved high sensitivity values for assigning codes related to cochlear implantation, both models struggled with complex cases, failed to apply modifiers, and often assigned fewer wRVUs. The findings highlight ChatGPT's potential in medical billing but indicate a need for further refinement.

Humans↗

PubMed2026

Nursing students' perspective of dignity: A systematic review.

AIM: This review synthesizes research on nursing students' perceptions of dignity shaped by education and clinical experiences. BACKGROUND: Respect for human dignity is central to nursing ethics. While dignity is well studied in patient care, less focus has been given to how nursing students perceive and experience dignity during their education. DESIGN: A systematic review of quantitative, qualitative, and mixed-methods studies. METHODS: A comprehensive search was conducted across the Medline, PubMed, ScienceDirect, Scopus, and Web of Science databases for English-language studies published from 2000 to 2025. Quality was assessed using the Joanna Briggs Institute Critical Appraisal Checklist and the Mixed Methods Appraisal. Tool. Data were synthesized thematically. Reporting followed PRISMA guidelines. RESULTS: A total of 24 articles were included. Students viewed patient dignity as linked to respect, privacy, and autonomy. Although students had solid theoretical knowledge, they encountered institutional and professional barriers in delivering dignified care. Supportive environments and participation in decision-making enhanced their sense of dignity. Finally, educational strategies such as role-playing, simulations, and empathy exercises enhanced emotional and psychosocial awareness. CONCLUSIONS: These findings indicate that dignity is crucial in shaping nursing students' professional identity and ethical awareness throughout their education. Reinforcing ethical values and dignity in nursing education is essential for high-quality care.

Humans↗

PubMed2026

Food-derived extracellular vesicles as delivery platforms for medicine-food homology components in metabolic syndrome.

Diet-induced obesity and associated metabolic syndromes have become major global public health challenge, highlighting the urgent need for safe and effective strategies. Recently, food-derived extracellular vesicles (FDEVs) have garnered increasing attention as natural nanocarriers due to their excellent biocompatibility and specific targeted delivery capabilities. FDEVs can efficiently deliver medicine-food homology components (MFHCs) to precisely regulate lipid metabolism, inflammatory responses, and insulin sensitivity, thereby improving obesity and its metabolic abnormalities. This systematic review summarizes recent advances in the use of FDEVs as delivery vehicles for MFHCs to suppress diet-induced obesity and metabolic syndrome, with a particular focus on the underlying molecular mechanisms, including signaling pathway regulation and cellular metabolic remodeling. In addition, the clinical translational potential and industrial application prospects of FDEVs are evaluated, and key challenges related to preparation techniques, safety assessment, and large-scale production are discussed. By integrating current evidence, this review aims to provide theoretical framework and future perspectives for the development of FDEVs as a novel targeted delivery platform and treatment of metabolic diseases.

Extracellular Vesicles↗

PubMed2026

Cationic porphyrin covalent organic framework reinforced hydroxypropyl methylcellulose films for photodynamic-photothermal sterilization and food preservation.

Microbial contamination in food necessitates effective antimicrobial packaging. While cellulose-based packaging materials suffer from limited antimicrobial efficacy, lack of active functionality, and susceptibility to inducing microbial resistance. To address these challenges, this study synthesized a cationic porphyrin-based covalent organic framework (Por-ICOF) as a multimodal photosensitizer. Por-ICOF was uniformly dispersed via non-covalent interaction within hydroxypropyl methylcellulose (HPMC), creating an HPMC/Por-ICOF composite film. This integration enhanced mechanical strength (increased by 26%), hydrophobicity (WCA 71°), and gas barrier properties (OP reduced by 42%, WVP reduced by 36%). Under visible light, the HPMC/Por ICOF film superior absorption generated reactive oxygen species (ROS) and photothermal effects, inactivating 99.2% of Escherichia coli and 99.95% of Staphylococcus aureus within 20 min. The composite film exhibited excellent biocompatibility and effectively extended the shelf life of strawberries. This cationic modification strategy for cellulose-based films offers a novel avenue for the design of high-performance antimicrobial food packaging materials.

Food Preservation↗

PubMed2026

Socket motility assessment of anophthalmic sockets: a systematic review.

PURPOSE: Systematically review and categorize the methods used to assess socket and prosthetic motility in anophthalmic patients following enucleation or evisceration. METHODS: A systematic review was conducted in accordance with PRISMA guidelines. PubMed, Embase, Web of Science, and Scopus were searched from inception through September 2024. Studies reporting qualitative or quantitative assessments of motility in anophthalmic sockets or ocular prostheses were included. Motility assessment methods were categorized as qualitative (descriptive or graded clinical evaluation) or quantitative (numerical measurements in millimeters, degrees, or objective tracking systems). RESULTS: Thirty-five studies encompassing 1,819 patients met inclusion criteria. Nineteen studies used qualitative assessment methods, including subjective observation, graded scales based on cardinal gaze positions, or comparison with the contralateral eye. Sixteen studies employed quantitative techniques, such as the Kestenbaum limbus test, Lister perimeter measurements, conjunctival or limbal markings, photographic image analysis, infrared eye-tracking systems, and magnetic search-coil technology. Considerable heterogeneity was observed in measurement techniques, reporting standards, timing of assessment, and distinction between socket and prosthetic motility. CONCLUSIONS: Substantial variability exists in the methods used to assess motility in anophthalmic sockets, limiting comparability across studies. Establishing standardized, feasible, and reproducible assessment approaches may improve outcome reporting and facilitate meaningful comparisons in future oculoplastic research.

Humans↗

PubMed2026

Data-centric, robust, and explainable multimodal deep learning for clinical decision support: A systematic review.

PURPOSE: Multimodal deep learning is increasingly proposed for clinical decision support (CDS) under a "data-centric" framing that prioritizes label quality, missing-modality robustness, distribution shift, calibration, and explainability. Prior reviews have examined multimodal medical AI, CDS, and data-centric methods separately, but none address their intersection. We mapped the modalities, fusion strategies, and data-centric and explainability techniques used in this recent literature, quantified how often each is implemented rather than merely mentioned, assessed deployment-relevant evidence (external validation, clinical-outcome measurement, equity), and formally appraised study-level risk of bias. METHODS: Following the PRISMA 2020 statement (PROSPERO CRD420261427815; registered retrospectively), we screened 150 records and included primary, clinical, multimodal studies that applied machine or deep learning to a decision-support task and reported at least one quantitative result. Two reviewers screened and extracted data with consensus adjudication. Each study was coded against pre-specified operational definitions, separating implemented or empirically evaluated techniques from those only mentioned. Study-level risk of bias was assessed with PROBAST + AI. Synthesis was narrative. RESULTS: Thirty-one studies met inclusion; 30 (97%) were published between 2024 and 2026, with a median of three modalities (range 2-6), most commonly structured EHR (71%) and imaging (39%). Data-centric techniques were frequently reported (74-84% across label-noise, distribution-shift, calibration, missing-modality and class-imbalance handling; equity 61%). However, external validation was reported in only 4/31 studies (13%), a clinical or provider outcome in 3/31 (10%), and no study reported routine deployment. Overall risk of bias was high in 27/31 studies (87%), driven by the analysis domain. CONCLUSION: Within this recent, self-selected slice of the field, technical robustness and explainability techniques are widely reported but rarely validated out-of-distribution or against clinical outcomes, and the underlying evidence is at high risk of bias. Progress requires external multi-site validation, clinical-outcome measurement, formal bias appraisal, and adherence to AI reporting standards (e.g., TRIPOD + AI) before deployment can be justified.

Deep Learning↗

PubMed2026

Beyond Photometric Consistency: Addressing Loss Insensitivity to Depth Noise in Endoscopic Estimation via Error Calibration.

Self-supervised monocular depth estimation in endoscopy is fundamentally constrained by the ill-posed nature of photometric supervision. In this work, we identify a critical yet overlooked cause of this ambiguity: the inherent insensitivity of photometric loss to depth noise. To overcome this intrinsic limitation, we propose Depth Error Calibration Learning (DECL), a two-stage framework that suppresses prediction variance and mitigates residual errors in self-supervised depth estimation. In Stage I (Variance Reduction), a cyclic depth generation strategy produces multiple depth hypotheses for the input image. The per-pixel empirical variance is quantified and integrated into a dedicated variance loss term, which penalizes inconsistent predictions and encourages the network to generate more stable and reliable depth estimates. In Stage II (Bias Calibration), an image-conditioned diffusion model refines the Stage-I depth prior and mitigates structured residuals through iterative denoising, thereby improving geometric accuracy and global consistency. Extensive experiments on three public endoscopic datasets demonstrate that DECL achieves consistent improvements over representative self-supervised monocular depth estimation methods under the evaluated protocols. Moreover, ablation studies on two representative backbones indicate that DECL is not restricted to a single network implementation, while broader validation on additional backbone families remains necessary. The source code is publicly available at https://github.com/DavidLuBit/EndoDenoising.

Journal Article↗

PubMed2026

Closed-loop insulin delivery for glycaemic control in hospitalised and perioperative adults: A systematic review and meta-analysis of randomised controlled trials.

We evaluated whether closed-loop insulin delivery improves glycaemic control in hospitalised and perioperative adults. PubMed/MEDLINE, Embase, CENTRAL, and ClinicalTrials.gov were searched from inception to 29 June 2026 for randomised controlled trials comparing closed-loop or automated insulin delivery with usual care or conventional insulin therapy. Random-effects meta-analyses were conducted; risk of bias was assessed using RoB 2 and certainty of evidence using GRADE. Seven trials involving 375 analysed participants were included. Closed-loop insulin delivery increased time in target glucose range by 23.91 percentage points (95% CI 19.40 to 28.43; I2 = 0%) and reduced mean glucose by 1.79 mmol/L (95% CI 1.06 to 2.53 lower; I2 = 36.3%); certainty was moderate for both outcomes. Two trials involving 69 participants reported compatible participant-level data for clinically significant hyperglycaemia, and both estimates favoured closed-loop insulin delivery, although the evidence was exploratory and imprecise. No severe hypoglycaemic events occurred in either group, precluding reliable estimation of comparative safety. Closed-loop insulin delivery may improve glycaemic process measures, but larger pragmatic trials are needed to establish clinical benefits, safety, and implementation feasibility.

Humans↗

PubMed2026

Thrombus Metabolism-Based Molecular Subtyping for Prognostic Risk Stratification in Acute Ischemic Stroke: A Preliminary Study.

AIMS: To preliminarily characterize metabolic molecular subtypes of cerebral thromboemboli and evaluate their clinical significance in anterior circulation acute ischemic stroke due to large vessel occlusion (AIS-LVO). METHODS: Untargeted metabolomics was performed on thromboemboli retrieved from 36 patients with anterior circulation AIS-LVO using ultra-performance coupled liquid chromatography with quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF-MS). Unsupervised hierarchical clustering was employed to identify distinct metabolic molecular subtypes, and their associations with stroke etiology, radiographic severity, and functional outcomes were analyzed. RESULTS: Two distinct thrombus metabolic molecular subtypes (C1 and C2) were identified based on 12 metabolites significantly associated with both short-term (7-day ∆NIHSS) and long-term (90-day mRS) functional outcomes. The C1 subtype, predominantly cardioembolic, exhibited enhanced lipid metabolism, whereas the C2 subtype, primarily atherothrombotic, demonstrated increased folate metabolism. Patients with C1 thromboemboli presented more severe admission ischemic lesions (as indicated by ASPECTS) and experienced poorer short-term and long-term outcomes. A six-metabolite signature derived from LASSO regression was identified for exploratory discrimination of thrombus metabolic subtypes, etiological subtypes, and 90-day outcomes. CONCLUSION: This preliminary exploratory study identifies two metabolically distinct thrombus molecular subtypes with clinical implications in anterior circulation AIS-LVO, providing a novel basis for risk stratification and personalized secondary prevention and warrants further investigation.

Humans↗

PubMed2026

Fundamentals of pacemakers ECG interpretation - part 2.

BACKGROUND: Modern pacemakers incorporate arrhythmia-response algorithms, ventricular pacing minimization protocols, and safety mechanisms that generate ECG patterns indistinguishable from pathological AV block, sensing malfunction, or device-mediated tachycardia. Failure to recognize these algorithm-driven signatures leads to unnecessary interventions, misdiagnosis, and inappropriate device reprogramming. This manuscript is the second in a two-part series on pacemaker ECG interpretation. METHODS: We conducted a narrative review of peer-reviewed literature and device-specific documentation on algorithm-driven ECG behavior, synthesizing evidence across arrhythmia recognition, upper rate physiology, ventricular pacing minimization, mode switching, safety mechanisms, and hysteresis algorithms. RESULTS: Pacemaker-mediated tachycardia produces regular paced wide-complex tachycardia locked at the upper tracking rate, initiated by any event with retrograde VA conduction. Ventricular tachycardia is identified by QRS morphology diverging from the known paced pattern, absent pacing spikes, and AV dissociation. Upper rate Wenckebach behavior mimics Mobitz type I AV block; 2:1 upper rate response mimics second-degree AV block. Ventricular pacing minimization algorithms produce isolated nonconducted P waves and prolonged AV intervals that simulate pathological conduction disease. Mode switching causes abrupt rate drops misidentified as output failure. Ventricular safety pacing generates a conspicuously short, fixed AV interval. Three discrete pacing artifacts reflect AV-sequential cardiac resynchronization therapy (CRT), ventricular safety pacing in CRT, or His-bundle pacing with backup RV output. Rate and AV hysteresis produce pauses and wandering AV intervals mimicking oversensing or Wenckebach periodicity. CONCLUSIONS: Recognizing algorithm-driven ECG patterns requires knowledge of device timing intervals and refractory periods, which lets clinicians distinguish programmed behavior from true malfunction or cardiac arrhythmia.

Humans↗