Effectiveness of the User-Centered "Healthcare CEO" App for Patients With Type 1 Diabetes Transitioning From Adolescence to Early Adulthood: Protocol for a Randomized Controlled Trial.

Background: Young patients aged 16 to 25 years with type 1 diabetes (T1D) often encounter challenges related to deteriorating disease control and accelerated complications. Mobile apps have shown promise in enhancing self-care among youth with diabetes. However, inconsistent findings suggest that further evidence is necessary to confirm the effectiveness of app-based interventions.

Obesity reshapes regulatory T cells in the visceral adipose tissue by disrupting cellular cholesterol homeostasis.

Regulatory T cells (T) accumulate in the visceral adipose tissue (VAT) to maintain systemic metabolic homeostasis but decline during obesity. Here, we explored the metabolic pathways controlling the homeostasis, composition, and function of VAT T under normal and high-fat diet feeding conditions. We found that cholesterol metabolism was specifically up-regulated in ST2 VAT T subsets.

Trajectories and patterns of US counties' policy responses to the COVID-19 pandemic: A sequence analysis approach.

Background: It is increasingly recognized that policies played a role in mitigating or exacerbating health inequities during the COVID-19 pandemic. While US counties were particularly active in policymaking, limited work has characterized geographic and temporal variation in pandemic-era policymaking at the local level, a prerequisite for later studies examining the health effects of these policies. This paper fills this gap by characterizing county-level COVID-19-related policy trajectories over time using a novel national policy database and innovative methods.

StackDILI: Enhancing Drug-Induced Liver Injury Prediction through Stacking Strategy with Effective Molecular Representations.

Drug-induced liver injury (DILI) is a major challenge in drug development, often leading to clinical trial failures and market withdrawals due to liver toxicity. This study presents StackDILI, a computational framework designed to accelerate toxicity assessment by predicting DILI risk. StackDILI integrates multiple molecular descriptors to extract structural and physicochemical features, including the constitution, pharmacophore, MACCS, and E-state descriptors.