Atraumatic Rhegmatogenous Retinal Detachment: Epidemiology and Association with Refractive Error in U.S. Armed Forces Service Members.

Purpose: To evaluate the incidence, refractive error (RE) association, and distribution of atraumatic rhegmatogenous retinal detachment (RRD) in U.S. military service members (SMs).

Characteristics of Resection Parameters in Robot-Assisted Total Knee Arthroplasty With the Ligament Balancing Workflow.

Objective: Robotic-assisted total knee arthroplasty (TKA) is a novel orthopedic technique. The workflow of robotic-assisted TKA is quite different from that of traditional manual TKA and may result incompletely different resection parameters. Understanding these parameters may help surgeons better perform robotic-assisted TKA.

Informatics strategies for early detection and risk mitigation in pancreatic cancer patients.

This review provides a comprehensive overview of the current landscape in pancreatic cancer (PC) screening, diagnosis, and early detection. This emphasizes the need for targeted screening in high-risk groups, particularly those with familial predispositions and genetic mutations, such as BRCA1, BRCA2, and PALB2. This review highlights the sporadic nature of most PC cases and significant risk factors, including smoking, alcohol consumption, obesity, and diabetes.

miRNA-125a regulates porcine oocyte maturation in vitro by targeting ADAR.

Follicular fluid extracellular vesicles are beneficial for in vitro oocyte maturation and development; however, their effect on the expression profiles of oocyte microRNAs (miRNAs) and the roles of related miRNAs are unknown. In this study, we aimed to investigate miRNA expression in mature oocytes cultured in follicular fluid extracellular vesicles and the effect of miRNA-125a (miR-125a) on oocyte maturation. The expression profiles of the miRNAs were determined by microRNA sequencing, followed by target gene prediction analysis.

Application of artificial intelligence in the health management of chronic disease: bibliometric analysis.

Background: With the rising global burden of chronic diseases, traditional health management models are encountering significant challenges. The integration of artificial intelligence (AI) into chronic disease management has enhanced patient care efficiency, optimized treatment strategies, and reduced healthcare costs, providing innovative solutions in this field. However, current research remains fragmented and lacks systematic, comprehensive analysis.