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Genome-wide association studies (GWAS) in admixed populations such as African Americans (AA) have limited sample sizes, resulting in poor performance of polygenic risk scores (PRS). Based on the observations that many disease-causing genes are shared between AA and European ancestry (EA) populations, and some disease-causing variants are located within the boundaries of these genes, we proposed a novel gene-based PRS framework (PRSgene) by using variants located within disease-associated genes. Using the AA GWAS of alcohol use disorder (AUD) from the Million Veteran Program and the EA GWAS of problematic alcohol use as the discovery GWAS, we identified 858 variants from 410 genes that were AUD-related in both AA and EA. PRSgene calculated using these variants were significantly associated with AUD in three AA target datasets (P-values ranged from 7.61E-05 to 6.27E-03; Betas ranged from 0.15 to 0.21) and outperformed PRS calculated using all variants (P-values ranged from 7.28E-03 to 0.16; Betas ranged from 0.06 to 0.18). PRSgene were also associated with AUD in an EA target dataset (P-value = 0.02, Beta = 0.11). In AA, individuals in the highest PRSgene decile had an odds ratio of 1.76 (95% CI: 1.32-2.34) to develop AUD compared to those in the lowest decile. The 410 genes were enriched in 54 Gene Ontology biological processes, including ethanol oxidation and processes involving the synaptic system, which are known to be AUD-related. In addition, 26 genes were targets of drugs used to treat AUD or other diseases that might be considered for repurposing to treat AUD. Our study demonstrated that the gene-based PRS had improved performance in evaluating AUD risk in AA and provided new insight into AUD genetics.

Despite the success of pharmacovigilance studies in detecting signals of adverse drug events (ADEs) from real‐world data, the risks of ADEs in subpopulations warrant increased scrutiny to prevent them in vulnerable individuals. Recently, the case‐crossover design has been implemented to leverage large‐scale administrative claims data for ADE detection, while controlling both observed confounding effects and short‐term fixed unobserved confounding effects. Additionally, as the case‐crossover design only includes cases, subpopulations can be conveniently derived. In this manuscript, we propose a precision mixture risk model (PMRM) to identify ADE signals from subpopulations under the case‐crossover design. The proposed model is able to identify signals from all ADE‐subpopulation‐drug combinations, while controlling for false discovery rate (FDR) and confounding effects. We applied the PMRM to an administrative claims data. We identified ADE signals in subpopulations defined by demographic variables, comorbidities, and detailed diagnosis codes. Interestingly, certain drugs were associated with a higher risk of ADE only in subpopulations, while these drugs had a neutral association with ADE in the general population. Additionally, the PMRM could control FDR at a desired level and had a higher probability to detect true ADE signals than the widely used McNemar's test. In conclusion, the PMRM is able to identify subpopulation‐specific ADE signals from a tremendous number of ADE‐subpopulation‐drug combinations, while controlling for both FDR and confounding effects. [ABSTRACT FROM AUTHOR]

Objective Our objective is to develop and validate TrajVis, an interactive tool that assists clinicians in using artificial intelligence (AI) models to leverage patients' longitudinal electronic medical records (EMRs) for personalized precision management of chronic disease progression. Materials and Methods We first perform requirement analysis with clinicians and data scientists to determine the visual analytics tasks of the TrajVis system as well as its design and functionalities. A graph AI model for chronic kidney disease (CKD) trajectory inference named DisEase PrOgression Trajectory (DEPOT) is used for system development and demonstration. TrajVis is implemented as a full-stack web application with synthetic EMR data derived from the Atrium Health Wake Forest Baptist Translational Data Warehouse and the Indiana Network for Patient Care research database. A case study with a nephrologist and a user experience survey of clinicians and data scientists are conducted to evaluate the TrajVis system. Results The TrajVis clinical information system is composed of 4 panels: the Patient View for demographic and clinical information, the Trajectory View to visualize the DEPOT-derived CKD trajectories in latent space, the Clinical Indicator View to elucidate longitudinal patterns of clinical features and interpret DEPOT predictions, and the Analysis View to demonstrate personal CKD progression trajectories. System evaluations suggest that TrajVis supports clinicians in summarizing clinical data, identifying individualized risk predictors, and visualizing patient disease progression trajectories, overcoming the barriers of AI implementation in healthcare. Discussion The TrajVis system provides a novel visualization solution which is complimentary to other risk estimators such as the Kidney Failure Risk Equations. Conclusion TrajVis bridges the gap between the fast-growing AI/ML modeling and the clinical use of such models for personalized and precision management of chronic diseases. [ABSTRACT FROM AUTHOR]

Electrochemical nitrogen reduction reaction (NRR) is a promising route for realizing green and sustainable ammonia synthesis under ambient conditions. However, one of the major challenges of currently available Single-atom catalysts (SACs) is poor catalytic activity and low catalytic selectivity, which is far away from the requirements of industrial applications. Herein, first-principle calculations within the density functional theory were performed to evaluate the feasibility of a single Mo atom anchored on a g-C9N10 monolayer (Mo@g-C9N10) as NRR electrocatalysts. The results demonstrated that the gas phase N2 molecule can be sufficiently activated on Mo@g-C9N10, and N2 reduction dominantly occurs on the active Mo atom via the preferred enzymatic mechanism, with a low limiting potential of −0.48 V. In addition, Mo@g-C9N10 possesses a good prohibition ability for the competitive hydrogen evolution reaction. More impressively, good electronic conductivity and high electron transport efficiency endow Mo SACs with excellent activity for electrocatalytic N2 reduction. This theoretical research not only accelerates the development of NRR electrocatalysts but also increases our insights into optimizing the catalytic performance of SACs. [ABSTRACT FROM AUTHOR]

Background: Exercise is associated with improved survival, physical functioning, treatment tolerability, and quality of life in early-stage breast cancer. These same endpoints matter in metastatic breast cancer (MBC). Prior trials in MBC have found exercise to be not feasible or of limited benefit, possibly due to inclusion of patients with heterogeneous disease trajectories. Patients with MBC have variable disease trajectories and supportive care needs; those with indolent MBC have longer life expectancy, lower symptom burden and distinct priorities, and are well-positioned to participate in and benefit from an exercise program. The EMBody trial aims to determine the impact of a multimodal exercise intervention on cardiorespiratory fitness, physical function, body composition, and patient-reported outcomes, specifically in patients with stable, indolent MBC. Methods: Eligible patients have MBC with no evidence of disease progression on current therapy in the prior 12 months and cannot be receiving cytotoxic chemotherapy. The trial aims to enroll 100 patients, randomized 1:1 to the exercise intervention versus usual care, stratified by baseline function. The virtually-delivered exercise intervention arm achieves moderate intensity exercise with exercise physiologists 3 days/week for 16 weeks. The 60-minute sessions include aerobic, resistance, balance and stretching exercises. The exercise arm receives informational sessions on the role of exercise in cancer and principles of habit and self-efficacy. The primary endpoint is 16 week change in fitness on a ramp treadmill test between the exercise and control arms. Secondary endpoints include change in a physical function, muscle mass assessed by CT scans, and PROs of fatigue and quality of life. Exploratory analysis includes behavioral modifiers of exercise adherence and effectiveness and serologic measures of inflammatory, metabolic, and immune pathway biomarkers. Discussion: The EMBody trial evaluates exercise in a unique patient population with indolent, non-progressive MBC. Patients living with MBC experience similar symptom burden to those undergoing therapy for early-stage disease and the benefits achieved with exercise could be similarly impactful. This trial will contribute evidence to support expansion of exercise recommendations, among other survivorship care efforts, to those living with metastatic disease. Clinical trial information: NCT05468034. Trial registration: NCT05468034. Date of registration: 7/12/2022. [ABSTRACT FROM AUTHOR]