The efficiency of artificial intelligence for management and clinical decision-making in the identification of patients with undiagnosed HCV infection (Intelligen-C strategy).

Introduction: Artificial intelligence (AI) allows the optimization of diagnostic processes for hepatitis C virus (HCV) patients. Our objective was to evaluate the clinical, economic, and management benefits of an AI-based clinical decision support system (Intelligen-C strategy).

Methods: The Intelligen-C strategy consisted of (1) a retrospective phase (Dec 2013-Sep 2021), in which medical records were reviewed to search for anti-HCV-positive and/or HCV-RNA-positive patients lost in the system, and (2) a prospective phase (Feb 2022-Jan 2023), in which automated screening (40-70 years) and routine testing for risk factors were performed in patients who were admitted to the emergency department or were hospitalized.

Hypothalamic SIRT1-mediated regulation of the hormonal trigger of ovulation and its repression in energy deficit.

Female reproduction is highly sensitive to body energy stores; persistent energy deficit, as seen in anorexia or strenuous exercise, is known to suppress ovulation via ill-defined mechanisms. We report herein that hypothalamic SIRT1, a key component of the epigenetic machinery that links nutritional status and puberty onset via modulation of Kiss1, plays a critical role in the control of the preovulatory surge of gonadotropins, i.e.

Fast and accurate prediction of drug induced proarrhythmic risk with sex specific cardiac emulators.

In silico trials for drug safety assessment require many high-fidelity 3D cardiac simulations to predict drug-induced QT interval prolongation, which is often computationally prohibitive. To streamline this process, we developed sex-specific emulators for a fast prediction of QT interval, trained on a dataset of 900 simulations. Our results show significant differences between 3D and 0D single-cell models as risk levels increase, underscoring the ability of 3D modeling to capture more complex cardiac responses.

Optimization of Clozapine Treatment: Study of Variables Affecting Response in Uruguayan Patients With Schizophrenia.

Purpose/background: Clozapine is the recommended drug for treatment-resistant schizophrenia. Drug response could be affected by numerous factors such as age, sex, body mass index, co-medication, consumption of xanthine-containing beverages, smoking, and genetic variants of the enzymes involved in clozapine metabolism (CYP1A2, CYP3A4, and, to a lesser extent, CYP2C19 and CYP2D6). This study evaluated genetic and nongenetic variables that may affect clozapine plasma concentrations in Uruguayan patients with schizophrenia.