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Oropuche Virus-An Emerging Pathogen With Escalating Risk for Outbreaks of Human Infection.

J Med Virol

January 2025

Department of Veterinary and Biomedical Sciences, College of Veterinary Medicine, University of Minnesota, Twin Cities, Minnesota, USA.

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The ability of environmental cues to trigger alcohol-seeking behaviours is thought to facilitate problematic alcohol use. Individuals' tendency to attribute incentive salience to cues may increase the risk of addiction. We sought to study the relationship between incentive salience and alcohol addiction using non-preferring rats to model the heterogeneity of human alcohol consumption, investigating both males and females.

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Inheritance of Imaging Parameters of Arrhythmic Risk in Mitral Valve Prolapse: A Pedigree Study.

Circ Cardiovasc Imaging

January 2025

Division of Cardiology, Department of Medicine, University of California, San Francisco (L.C., S.D., D.B., J.J.T., Q.F., L.T., A.H.R., R.J., S.H., H.H.H., Z.H.T., N.B.S., F.N.D.).

Background: A subset of patients with mitral valve prolapse (MVP), a highly heritable condition, experience sudden cardiac arrest (SCA) or sudden cardiac death (SCD). However, the inheritance of phenotypic imaging features of arrhythmic MVP remains unknown.

Methods: We recruited 23 MVP probands, including 9 with SCA/SCD and 14 with frequent/complex ventricular ectopy.

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Study Question: How accurately can artificial intelligence (AI) models predict sperm retrieval in non-obstructive azoospermia (NOA) patients undergoing micro-testicular sperm extraction (m-TESE) surgery?

Summary Answer: AI predictive models hold significant promise in predicting successful sperm retrieval in NOA patients undergoing m-TESE, although limitations regarding variability of study designs, small sample sizes, and a lack of validation studies restrict the overall generalizability of studies in this area.

What Is Known Already: Previous studies have explored various predictors of successful sperm retrieval in m-TESE, including clinical and hormonal factors. However, no consistent predictive model has yet been established.

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Objective: This research project aimed to identify and analyze the top 30 drugs most commonly associated with kidney stone formation using data from the U.S. Food and Drug Administration's Adverse Event Reporting System (FAERS) database.

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