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Infectious diseases, including bacterial, fungal, and viral, have once again gained urgency in the drug development pipeline after the recent COVID-19 pandemic. Tuberculosis (TB) is an old infectious disease for which eradication has not yet been successful. Novel agents are required to have potential activity against both drug-sensitive and drug-resistant strains of Mycobacterium tuberculosis (Mtb), the causative agent of TB.

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Risk Prediction of Liver Injury in Pediatric Tuberculosis Treatment: Development of an Automated Machine Learning Model.

Drug Des Devel Ther

January 2025

Department of Pharmacy, The Affiliated Changsha Central Hospital, Hengyang Medical School, University of South China, Changsha, 410004, People's Republic of China.

Purpose: Drug-induced liver injury (DILI) is one of the most common and serious adverse drug reactions related to first-line anti-tuberculosis drugs in pediatric tuberculosis patients. This study aims to develop an automatic machine learning (AutoML) model for predicting the risk of anti-tuberculosis drug-induced liver injury (ATB-DILI) in children.

Methods: A retrospective study was performed on the clinical data and therapeutic drug monitoring (TDM) results of children initially treated for tuberculosis at the affiliated Changsha Central Hospital of University of South China.

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Extraction and quantitation of fentanyl in exhaled breath condensate using a magnetic dispersive solid phase based on graphene oxide and covalent organic framework composite and LC-MS/MS analysis.

J Chromatogr B Analyt Technol Biomed Life Sci

January 2025

Food and Drug Safety Research Center, Tabriz University of Medical Sciences, Tabriz, Iran; Research Center of New Material and Green Chemistry, Khazar University, 41 Mehseti Street, Baku AZ1096, Azerbaijan. Electronic address:

Free fentanyl is responsible for its pharmacological effects, but its total concentration is typically determined for therapeutic drug monitoring purposes. Determination of fentanyl concentration can help reduce the prescribed doses, leading to fewer side effects and increased effectiveness. Therefore, predicting free drug concentration in pharmaceutical research is crucial.

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ChatGPT-Assisted Machine Learning for Chronic Disease Classification and Prediction: A Developmental and Validation Study.

Cureus

December 2024

Department of Medicine, Medical Teaching Institution (MTI) Hayatabad Medical Complex, Peshawar, PAK.

Background Chronic diseases such as chronic kidney disease (CKD), chronic liver disease (CLD), tuberculosis (TB), dementia, and heart disease are global health concerns of significant importance, representing major causes of morbidity and mortality worldwide. Early diagnosis and interventions are critical to improve patient outcomes and reduce healthcare costs. Methods This prospective observational study analyzed clinical data from 270 patients (calculated using G*Power 3.

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Liver injury in tuberculosis patients, associated with noncompliance with treatment, is further exacerbated by viral hepatitis, which not only directly harms the liver but also increases susceptibility to drug-induced liver injury. The aim of this study was to analyze the associated risk factors for viral hepatitis in tuberculosis patients. This systematic review and meta-analysis adhere to the PRISMA 2020 statement, and the protocol has been registered with PROSPERO (CRD42023477241).

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