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High-throughput processing of patient biosamples by next-generation sequencing and the comparison of molecular data with patient-level and sample-level clinical data require precise tracking and matching of sample identifiers throughout the biospecimen chain of custody and are critical to enabling robust interpretation of biomarker trial results. In addition to tracing individual steps in the sample and data processing workflows, bioinformatics solutions can be used to confirm that samples originate from the same patient. Here, the use of a bioinformatics workflow to identify matched samples originating from the same individual is showcased. The analysis workflow is suitable for any two or more pairs of NGS datasets to be compared and verified for patient sample origin. A scoring algorithm based on genome-wide comparisons of samples enables the user to determine whether two samples stem from the same individual. Specifically, single-nucleotide polymorphisms (SNPs) within selected linkage disequilibrium blocks are used to identify and compare samples. Threshold combinations for permissive and stringent selection of matched and mismatched samples were identified. The utility of this protocol was demonstrated through its application to the quality control and validation of clinical tumor tissue and blood samples, encompassing multiple omics modalities from over 2,000 patients.

Alzheimer's disease (AD) is a prevalent degenerative neurological disorder with limited treatment options. Prior studies reported specific metabolites and inflammatory proteins to be related to AD risk. However, the intricate relationship between inflammatory proteins, blood metabolites, and AD risk in European population remains unclear. Genetic instruments for 1,091 metabolites and 736 inflammatory proteins were derived from two recent comprehensive genome-wide association studies. Univariable Mendelian Randomization was employed to assess potential causal effects of metabolites on AD risk, potential effects of inflammatory proteins on metabolites, and effects of inflammatory proteins on AD risk. Multivariable MR (MVMR) was further applied to disentangle direct effects of proteins and metabolites on AD. Twelve metabolites were identified to be associated with AD risk, and 226 inflammatory proteins demonstrated likely to be causal effects on these 12 metabolites. Further examining the associations between such inflammatory proteins and AD risk revealed 22 associations for which the effect directions from inflammatory proteins to metabolites, from metabolites to AD risk, and from inflammatory proteins to AD risk were aligned, suggesting inflammatory protein - metabolite - AD risk pathway. MVMR further highlighted four trios in which the effect directions were consistent with the UVMR results, supporting a metabolite‑mediated pattern. This large‑scale genetic analysis highlights specific metabolites as direct contributors to AD risk and suggests that certain inflammatory proteins may influence AD primarily through downstream metabolic pathways. Our findings offer potential novel therapeutic targets for AD intervention.

Ischemic stroke poses a significant global health burden. Accurately identifying symptomatic carotid atherosclerotic plaques, beyond relying solely on stenosis degree, remains a critical challenge for precise stroke risk stratification. We aimed to develop and validate a deep learning radiomics (DLR) signature based on multicontrast MRI to identify symptomatic carotid plaques accurately. In this retrospective multicenter study, 409 carotid arteries from 355 patients with carotid atherosclerosis were enrolled (219 training, 95 internal validation, 95 external test). Deep learning (DL) and radiomics features were extracted and combined from automatically segmented plaque regions on multicontrast MRI. The optimized DLR signature derived from a 3-stage feature selection pipeline was leveraged to train diverse machine learning classifiers for robust identification of symptomatic carotid plaques. Model performance was evaluated using the area under the receiver operating characteristic curve (AUROC) and compared against clinical models, radiomics-only models, and DL-only models. Subgroup analysis across stenosis severities and comparison of MRI-based American Heart Association lesion types between DLR-defined risk groups were performed. The DLR model with logistic regression demonstrated excellent performance in identifying symptomatic plaques, achieving AUROCs of 0.975 (95% CI, 0.954-0.992), 0.933 (95% CI, 0.876-0.976), and 0.881 (95% CI, 0.807-0.939) in the training, internal validation, and external validation cohorts, respectively. It significantly outperformed the clinical model (AUROCs of 0.701, 0.749, 0.711; P < .05), radiomics-only model (AUROCs of 0.877, 0.839, 0.789; P < .05), and DL-only model (AUROCs of 0.948, 0.894, 0.845; P < .05 in training/external). Performance remained consistently high across stenosis severity subgroups (AUROCs of 0.895-0.982 for severe, 0.863-0.971 for mild-moderate stenosis). DLR-defined symptomatic groups showed significantly higher prevalence of complex type VI lesions (internal: 50.0% versus 14.8%, P < .001; external: 48.7% versus 20.7%, P = .004) and lower prevalence of predominantly calcified type VII lesions (external: 8.1% versus 43.1%, P < .001) compared with asymptomatic groups. The developed multicontrast MRI-based DLR signature provides a highly accurate and robust tool for the automated identification of symptomatic carotid plaques, underscoring its potential value as a noninvasive tool to guide personalized stroke prevention strategies.

Effective diagnosis and treatment of rare genetic disorders requires the interpretation of a patient's genetic variants of unknown significance (VUSs). Today, clinical decision-making is primarily guided by gene-phenotype association databases and DNA-based scoring methods. Our web-accessible variant analysis pipeline, VUStruct, supplements these established approaches by deeply analyzing the downstream molecular impact of variation in context of 3D protein structure. VUStruct's growing impact is fueled by the co-proliferation of protein 3D structural models, gene sequencing, compute power, and artificial intelligence. Contextualizing VUSs in protein 3D structural models also illuminates longitudinal genomics studies and biochemical bench research focused on VUS, and we created VUStruct for clinicians and researchers alike. We now introduce VUStruct to the broad scientific community as a mature, web-facing, extensible, High-Performance Computing (HPC) software pipeline. VUStruct maps missense variants onto automatically selected protein structures and launches a broad range of analyses. These include energy-based assessments of protein folding and stability, pathogenicity prediction through spatial clustering analysis, and machine learning (ML) predictors of binding surface disruptions and nearby post-translational modification sites. The pipeline also considers the entire input set of VUS and identifies genes potentially involved in digenic disease. VUStruct's utility in clinical rare disease genome interpretation has been demonstrated through its analysis of over 175 Undiagnosed Disease Network (UDN) Patient cases. VUStruct-leveraged hypotheses have often informed clinicians in their consideration of additional patient testing, and we report here details from two cases where VUStruct was key to their solution. We also note successes with academic research collaborators, for whom VUStruct has informed research directions in both computational genomics and wet lab studies.

Canada has achieved near-universal adoption of electronic health records (EHRs) and yet interoperability, the secure exchange and use of health data across different systems and settings, remains limited. We aimed to describe the current state of EHRs in 10 provincial and 3 territorial jurisdictions in Canada and evaluate the maturity of their interoperability using a structured interoperability assessment model. We conducted an environmental scan of EHR use and interoperability across all provinces and territories using Canada Health Infoway documents and structured interviews with 23 subject matter experts. Using a rigorously designed interoperability maturity model, we evaluated jurisdictions across 4 enabler dimensions (governance, legislation and standards, incentives and capacity-building, and technical infrastructure) and 4 interoperability status dimensions (community EHRs, hospital EHRs, patient portals, and system analytics). We found that, although EHR adoption was high, maturity of EHR interoperability was low and uneven across Canada. Integrated EHR health data exchange was limited, and nearly all jurisdictions lacked EHR interoperability between hospitals, community specialists, and primary care. Data exchange between primary care and specialists, and between hospitals and community settings, was heavily dependent on fax (traditional or online) or mailed letters in every jurisdiction. Patient portal contents and system-level analytics using EHR data were underdeveloped nationally. No jurisdiction was advanced in all dimensions. Although most jurisdictions showed strength in at least 1 area, they also exhibited many areas for growth. We identified 8 key barriers to interoperability, each of which can be overcome. Canada has widespread EHR adoption, but maturity of EHR interoperability and the enabling conditions required for true interoperability are low and inconsistent across jurisdictions. Strengthening governance, legislation, standards, incentives, and technical infrastructure - supported by national legislation to mandate interoperability across different EHRs - will be essential to advancing connected care across Canada and realizing widespread benefits for patients, clinicians, and health systems.

Early hearing detection and intervention (EHDI) is a crucial public health initiative focused on the prompt identification and management of hearing impairments in children, significantly impacting their communication abilities, cognitive development and overall well-being. Although the global acceptance of EHDI programmes is gradually expanding, delay in diagnosis and enrolment in early intervention continues to challenge optimal outcomes. Qualitative studies examining the barriers and facilitators within EHDI remain limited, particularly regarding the experiences of caregivers, healthcare providers and policymakers across various contexts. This systematic review and meta-synthesis of qualitative evidence aims to comprehensively identify and synthesise these barriers and facilitators from multiple stakeholder perspectives aiming to inform policies and strategies that improve EHDI programme effectiveness. A comprehensive search will be conducted in PubMed/MEDLINE, Web of Science, Scopus, Scientific Information Database and MagIran, from database inception to December 2025, with no language restrictions. The SPIDER framework will guide the inclusion of qualitative studies on barriers to and facilitators of EHDI (sample: children, parents and providers; phenomenon of interest: EHDI barriers/facilitators; design: qualitative; evaluation: experiences/perspectives; research type: qualitative). Quantitative-only studies, non-peer-reviewed sources and studies without accessible full texts will be excluded. Two reviewers will independently screen titles/abstracts/full texts, resolving disagreements via discussion or a third reviewer. Quality appraisal will be done using the Critical Appraisal Skills Programme checklist. Data synthesis will employ thematic content analysis. This systematic review and meta-synthesis was approved by the Resarch Ethics Committee of Tabriz University of Medical Sciences (approval number: IR.TBZMED.VCR.REC.1404.331). The committee determind that the study adheres to established etical standards, as it involes only the synthesis and analysis of previously published literature, with no direct interaction with human participants, n collection of primary data, and no involvement in clinical interventions. Therefore, the review dose not require informed of consent or ethical clearance beyond the institutional approval obtainad. Findings will be shared via peer-reviewed publication, conference presentations and targeted summaries for policymakers and clinicians.

The translation of big data analytics and artificial intelligence (AI) into clinical decision support systems (CDSSs) has advanced from proof of concept to real-world clinical practice. AI-informed CDSSs show measurable improvements in diagnostic accuracy, risk stratification, resource use, and patient outcomes compared to traditional models, offering the potential to assist clinicians in managing symptom complexity and uncertainty in health care delivery. Despite this potential, access to large amounts of high-quality and granular data remains one of the most significant bottlenecks to AI-enabled CDSSs. We argue that as health care systems increasingly adopt data-driven decision support, addressing the challenges of data accessibility and protection is essential to realizing the full potential of AI in clinical medicine. We use selected case examples of AI-informed CDSSs in oncology, organ transplantation, diabetic retinopathy, epilepsy, spinal cord injury, rare disease diagnosis, and emergency medicine to illustrate opportunities and challenges related to AI's potential to improve patient outcomes. We discuss public and semipublic, medical institutional and commercial, and government and national data sources that are currently available for the development of CDSSs and highlight the practical and ethical constraints associated with these data. We consider alternative data resources and ways in which health care systems can strengthen data ecosystems to increase AI-driven CDSS efficacy and implementation to improve patient outcomes.

The detrimental cycle of sarcopenic obesity (SO) significantly reduces quality of life in older adults, while the mechanisms are still unclear. We first analyzed the incidence of SO using the CHARLS database. We identified key genes by integrating differentially expressed genes, weighted gene co-expression network analysis, and targets of gut microbiota metabolites, refining the selection through machine learning methods (LASSO, XGBoost, SVM-REF, Random Forest). These genes were validated through single-cell sequencing, receiver operating characteristic analysis, and Muscle immunohistochemistry in a high-fat-diet (HFD) induced mouse model. Further analyses comprised immune infiltration profiling, pathway enrichment, and transcriptional regulation analysis. Additionally, we explored the relationships between key genes and autophagy, ferroptosis, and immunity responses. Finally, we predicted and evaluated potential therapeutic compounds via the CMap database and molecular docking. SO incidence in China increased significantly from 16.1% (2011) to 20.4% (2018). Machine learning identified ALDH1A3, CSF1R, and PHGDH as key genes. These genes were validated in external muscle single-cell datasets, demonstrating robust diagnostic performance with AUC values exceeding 0.72 across four independent GEO cohorts. Following an HFD intervention in mice, ALDH1A3 and CSF1R expression in muscle tissue was significantly upregulated, while PHGDH showed a consistent upward trend that did not reach statistical significance. Immune infiltration analysis revealed a significant increase in resting NK cells in both obesity and sarcopenia states. Functional enrichment analyses using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes linked the genes to transcriptional regulation pathways. The Cisbp_M4923 motif was identified as the most relevant transcription factor binding site. Finally, molecular docking simulations indicated stable binding of the top candidate compound, Birinapant, to the key gene targets. ALDH1A3, CSF1R, and PHGDH serve as potential co-morbid biomarkers for SO.

Large language models (LLMs) are increasingly used to generate patient-oriented medical information. In geriatrics, such information must balance accuracy, relevance, and safety, as older adults may be particularly susceptible to misleading or harmful advice. However, systematic evaluations of expert perceptions across multiple geriatric conditions remain limited. This study aimed to explore geriatricians' perceptions of the accuracy, relevance, and potential harm of LLM-generated patient information across common geriatric conditions and to examine variability and interrater agreement in expert ratings. In this cross-sectional expert rating study, 10 geriatricians evaluated 50 LLM-generated statements covering 5 geriatric conditions (sarcopenia, osteoporosis, urinary incontinence, depression, and dementia). Statements addressed diagnostic, etiological, prognostic, risk-related, and therapeutic aspects. Experts rated perceived accuracy, relevance, and potential harm using 5-point Likert scales. Rating distributions were summarized using medians and IQRs. The Kendall coefficient of concordance (W) was used exploratorily to assess agreement in the relative ordering of statements within predefined strata. Readability was assessed using Flesch-Kincaid Grade Level and Flesch Reading Ease. Expert ratings indicated high perceived accuracy (median 4.32, IQR 4.01-4.59 and perceived relevance (median 4.51, IQR 4.06-4.66), while perceived potential harm remained low (median 1.59, IQR 1.17-1.92). IQR values ranged from 0.00 to 1.38 with most values clustering below 0.5, indicating limited dispersion in expert ratings. Agreement in the relative ordering of statements varied across domains, with W values ranging from 0.27 to 0.62 (median 0.53, IQR 0.46-0.58), indicating moderate concordance. No statements combined low perceived accuracy with high perceived potential harm. Readability analysis indicated generally accessible language, with a median Flesch-Kincaid Grade Level of 8.3 (IQR 7.4-9.6) and a median Flesch Reading Ease score of 60.8 (IQR 50.1-66.9). LLM-generated patient information for common geriatric conditions was rated as largely accurate and relevant, with low potential harm in typical scenarios. Variability in expert emphasis and the exploratory nature of agreement analyses highlight the limitations of perception-based evaluation. Future studies should incorporate guideline-based validation, readability optimization, and patient-centered outcomes to more comprehensively evaluate the safety and suitability of LLM-generated information for geriatric patient education.

The implementation of artificial intelligence has been investigated in many aspects of cardiovascular disease. To develop deep learning models based on coronary angiograms to detect functionally significant coronary stenoses. A total of 610 frames from 122 coronary arteries that received pressure wire-based fractional flow reserve (FFR) assessment were analyzed. Deep learning models were developed for the segmentation and classification of coronary stenoses. Both internal and external validation of the deep learning models were performed. The mean FFR value was 0.84&#x2005;&#xb1;&#x2005;0.08. The artificial intelligence-based FFR was significantly correlated with wire-based FFR with an average correlation coefficient of 0.68 and a mean absolute error of 0.05. The diagnostic performance of artificial intelligence-based FFR versus wire-based FFR was accuracy 87.6%, F1 score = 83.6%, and recall = 81.1%. The artificial intelligence-based FFR showed good discriminative performance with an area under the receiver operating characteristic curve of 86.5% (95% CI: 79.3-93.6). The artificial intelligence-based FFR showed moderate agreement with pressure wire-based FFR and showed promising diagnostic performance in the internal cohort, although reduced performance was observed in external validation, warranting further refinement and multicenter validation.

Despite their benefits, digital health tools often face adoption barriers because of the digital divide. Identifying the fundamental user skills required to effectively navigate these tools and the usability barriers is essential to addressing disparities in use. This study aimed to identify the skill and usability barriers to using digital health tools. This study included English-, Spanish-, or Cantonese-speaking patients, aged &#x2265;50 years, who received care at an urban safety net health system in the United States. Participants completed a survey examining sociodemographic characteristics and digital health tool use and were observed and video recorded as they navigated four digital health care tasks: (1) launch a video visit, (2) visit a health website through a URL, (3) log in to the patient portal, and (4) sign up for a patient portal account. Participants who could not independently perform the tasks received additional support. Tasks were conducted in English, while instructions and additional assistance were provided in each participant's preferred language. Video recordings were thematically coded to identify the fundamental skills needed for effective digital tool use and usability barriers in the design of digital tools. We examined whether task independence was associated with participant demographics and thematic categories using Kruskal-Wallis, &#x3c7;2, and Fisher exact tests. In total, 74% (34/46), 52% (31/60), 71% (44/62), and 70% (43/61) of participants (N=64) independently completed digital tasks 1, 2, 3, and 4, respectively. Older age, minoritized races and ethnicities, non-English language preference, lower educational attainment, access to cellular data only or no internet access, and lack of a portal account were associated with a higher likelihood of requiring assistance or being unsuccessful at completing each task (P<.001, except for older age [P=.004]). The qualitative coding of video recordings identified 3, 4, and 6 categories of typing, navigation, and human-computer interaction (HCI) skills, respectively, as fundamental skills required to independently complete digital tasks. &#x3c7;2 and Fisher exact tests indicated significant associations between most typing, navigation, and HCI categories and independent task completion. We coded usability barriers as one of 6 learnability challenges or 3 operability challenges. This study identified that independent use of digital health tools requires fundamental typing, navigation, or HCI skills as well as high usability of digital tools. The inclusion of 4 different digital tasks added specificity to the type of skills and usability considerations necessary to ensure accessibility of digital health tools to diverse older adults. This study underscores the need for vendors to cocreate digital health tools with historically excluded end users in mind. As health care systems expand digital tool adoption, they must distinguish fundamental skill gaps from usability barriers, as each may require different intervention strategies.

Multimorbidity, the coexistence of 2 or more chronic conditions, has been linked to cognitive aging and Alzheimer's disease (AD) and AD-related dementias, yet the mechanisms remain unclear. We aimed to examine the associations of multimorbidity with cognition and biomarkers across multiple mechanistic pathways. We cross-sectionally analyzed 3,808 dementia-free participants (mean age 64.9&#x2009;&#xb1;&#x2009;8.5&#x2009;years, 62% female) from the Health and Aging Brain Study: Health Disparities. Multimorbidity burden was assessed using a latent construct derived from chronic conditions identified through objective measures, medical history, and self-report. A latent factor score for cognition was estimated using confirmatory factor analysis and neuropsychological tests. Using linear and logistic regression, we examined the associations of multimorbidity burden with biomarkers of AD (positron emission tomography [PET] amyloid, plasma &#x3b2;-amyloid 42/40, and phosphorylated tau [p-tau] measures), neurodegeneration (cortical thickness, hippocampal volume, and plasma neurofilament light and total tau), and cerebral small vessel disease (SVD) (magnetic resonance imaging white matter hyperintensities, cerebral microbleeds, and lacunes). Greater multimorbidity burden was associated with worse cognition and biomarkers of AD (PET amyloid standardized uptake value ratios and positivity, p-tau181, and p-tau217), neurodegeneration (neurofilament light, total tau, cortical thickness, and hippocampal volume), and SVD (white matter hyperintensity volume and presence of lacune and cerebral microbleeds). Among dementia-free individuals, higher multimorbidity burden was associated with biomarkers for greater AD pathology, neurodegeneration, and SVD. These findings support a more holistic approach to managing chronic disease burden, which has the potential to reduce overall pathophysiological burden and delay cognitive decline. ANN NEUROL 2026.

Emergency department presents a distinctive challenge for implementation infection prevention and control (IPC), due to their complex and dynamic environment, diverse patient population, and unknown carrier status. The objective was to assess the compliance with a number of IPC practices among a group of healthcare workers (HCWs) working in the emergency department. An observational cross-sectional study was conducted at a large emergency department at a tertiary care hospital between 2018 and 2023. Data were gathered during observation sessions using a standardized IPC observation form. Observers were either experienced IPC professionals or trained medical students. Out of 123,947 HCW-specific practices observed, 85,542 (69.0%) were compliant and out of 41,650 unit-specific practices observed, 38,355 (92.1%) were compliant. The compliance was highest in the competence of acute respiratory infection procedures (97.3%), followed by isolation precautions (97.0%), housekeeping (96.8%), disposal of sharps (96.8%), waste management (94.5%), donning and doffing of personal protective equipment (PPE, 72.9%), use of PPE (72.3%), hand hygiene (67.2%), patient sitters (64.1%), and disinfection of medical equipment (61.2%). Nurses across all units had much better compliance than other professions. There were >&#x2009;10% differences in the compliance across the units, with higher compliance in mainly pediatric compared with adult units. The compliance was highest during the COVID-19 pandemic years. There is considerable variability in implementation of IPC at the emergency department, by practice, profession, unit, and pandemic time. The findings underscore the importance of strategies to improve disinfection of medical equipment, hand hygiene, and adherence of patient sitters.

The gut microbiome of pigs is a complex microbial ecosystem critical to host health and agricultural productivity. While amplicon sequencing studies have expanded our understanding of this community, a lack of standardized data and metadata often hinders comparative analysis and data reuse. To address this challenge, we present the Pig Gut Microbiome Dataset (PGMD, version 1.0), a comprehensive resource developed through the systematic selection of publications, extensive manual curation of associated metadata, and standardized reprocessing of raw amplicon sequencing data. This initial release integrates 202 publications (encompassing 207 16S rRNA gene sequencing data BioProjects), comprising 12,336 samples, collected from 22 countries. The dataset encompasses 52 host species and 3,028 taxonomic groups. Samples are systematically categorized by research topics and host phenotypes, enabling users to explore microbial community composition, identify differentially abundant taxa across experimental conditions and phenotypes, investigate six core phenotype-associated microbial clades, and determine dominant taxa across four key growth stages. PGMD significantly enhances the standardization and integration of pig gut microbiome data, serving as a valuable resource for research towards precision feeding and improved animal health. All data in the dataset are hosted and available in figshare https://doi.org/10.6084/m9.figshare.25911745.v1.

Workload and psychological stress, which industrial workers perceived as stressors, affected their performance after they were exposed to the work content. A model simulating the stress-performance of a working individual is a beneficial tool for work task design and production management, enabling long-term Human Resource Development. Current models and concepts lack the construction of work-content components, human centricity, and the mechanism of stress transformation and effect; therefore not able to reproduce subtle human behaviors. This paper formulates this problem with a multi-disciplinary literature review, and proposes a conceptual qualitative system dynamics model to simulate the stress and performance of workers in a given work environment and conditions. By replicating the changes in work content with associated effects, human-centric solutions and interventions can be designed. A use case in the Vensim environment with different simulated scenarios returned behavior and tendency in the outputs that aligned with phenomena reported in relevant studies. The model enables analysis of human factors in complex manufacturing systems, especially the effect of work content on individual workload-stress perception, benefiting future development of Human Digital Twins. This research calls for experiments and clinical trials to strengthen the existing associations between model factors and more effort for developing realistic mechanisms for modeling human factors in Industry 5.0.

Opioid overdose remains a leading cause of preventable death in the United States. Existing approaches to identify individuals at elevated risk rely on imprecise rule-based criteria that misclassify patients' risk of this serious health outcome. Machine learning (ML) algorithms can help improve prediction performance and can be combined with electronic health record (EHR) interventions to reduce overdose risk. The Machine Learning Prediction and Reducing Overdoses With EHR Nudges (mPROVEN) clinical trial integrates a validated ML overdose risk model with behavioral economics-informed EHR nudges to test whether the combination improves evidence-based prescribing behaviors associated with lower overdose risk and, ultimately, reduces overdose among elevated-risk patients. mPROVEN is a pragmatic cluster randomized controlled trial conducted in primary care practices within a large multistate integrated health system. Eligible patients are adults (&#x2265;18 years) identified by the ML algorithm as having elevated overdose risk and seen at a primary care visit during the study period. Primary care practices serve as the unit of randomization and will be randomized into three arms: (1) usual care; (2) elevated risk flag only, where clinicians see a noninterruptive EHR flag indicating elevated overdose risk; and (3) elevated risk flag + nudges, in which active choice and accountable justification alerts are embedded within the EHR in addition to the elevated risk flag. The trial will enroll a target cohort of 800 patients for the primary analysis. The intervention period is 4 months (or until the study ends, whichever occurs later). The primary outcome is a 3&#x2011;point composite measure of safer opioid prescribing at 4 months, awarding 1 point each for active naloxone prescription, average opioid dosage of 50 morphine milligram equivalents per day or less, and absence of opioid-benzodiazepine overlap. Secondary outcomes include the composite outcome at 6 months, individual score components, and all-cause and overdose-specific emergency department or inpatient visits. Outcomes will be compared across study arms using an intention&#x2011;to&#x2011;treat approach with linear mixed&#x2011;effects models accounting for clinic-level clustering. Funded by the National Institutes of Health, in June 2022, enrollment began on March 10, 2025. Enrollment for the primary analysis cohort (n=798) was completed in May 2025 with additional participants enrolled for secondary analyses through December 2025 (n=1662). Primary cohort analyses began in January 2026, and results are expected by mid-2027. The mPROVEN study is among the first pragmatic randomized controlled trials to integrate ML&#x2011;based opioid overdose risk prediction with behavioral nudges within a large health system EHR. By combining advances in data science and behavioral economics, the study aims to reduce opioid overdose risk in primary care using a scalable and low-touch intervention to address a high-priority public health issue. ClinicalTrials.gov NCT06806163; https://clinicaltrials.gov/study/NCT06806163. DERR1-10.2196/94007.

Curiosity plays a fundamental role in human learning, development, and motivation, and emerging evidence suggests that reduced curiosity is linked to poorer mental health outcomes, including depressive symptoms (DS). However, to date, the majority of curiosity research relies on self-report assessments and thus risks biased reporting. Virtual reality (VR), a novel tool increasingly used within mental health research and treatment, might represent a potent tool for offering ecologically valid insights into curiosity-driven behaviors while circumventing issues related to self-report assessments, including demand characteristics and recall bias. The study aimed to enhance the assessment of curiosity by using a novel VR environment and to examine its relevance to DS. Specifically, we tested 2 hypotheses using a novel VR environment: first, that curiosity, as assessed through spontaneous exploratory interactions and behaviors in VR, positively correlates with self-reported curiosity, and second, that VR-based curiosity is inversely associated with DS. This exploratory study used an observational design that included 100 volunteers. All participants completed self-reported assessments of DS and curiosity before engaging in a novel VR scenario. Although progression in the virtual environment required solving cognitive tasks, these were embedded as structural elements rather than framed as the primary objective. Instead, participants' free explorations and interactions with objects formed the basis for the 4 curiosity metrics used in this study. After VR exposure, participants completed a questionnaire assessing cybersickness symptoms. Hypothesis 1 was not supported, as only one curiosity metric, namely object interactions, was positively associated with one aspect of curiosity relating to motivation to seek new knowledge and experiences. Further, diminishing significance after correction for multiple testing warranted caution. Results relating to hypothesis 2 indicated partial support, in that object interaction was significantly associated with DS while controlling for age, sex, and cybersickness levels. Sensitivity analyses showed no associations between object interactions and self-reported anxiety and stress symptoms. VR may be a potent tool for assessing exploratory behaviors in a controlled, yet ecologically valid, environment that avoids issues related to self-report. However, whether such motivations translate to established curiosity constructs warrants further research. This study also provided preliminary insights into how assessing exploratory interactions in VR may be a promising avenue that could enhance the understanding of the etiology and assessment of DS-particularly its early stages.

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