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Background And Aim: Discriminating between idiosyncratic drug-induced liver injury (DILI) and autoimmune hepatitis (AIH) is critical yet challenging. We aim to develop and validate a machine learning (ML)-based model to aid in this differentiation.

Methods: This multicenter cohort study utilised a development set from Beijing Friendship Hospital, with retrospective and prospective validation sets from 10 tertiary hospitals across various regions of China spanning January 2009 to May 2023.

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Therapeutic hurdles persist in the fight against lung cancer, although it is a leading cause of cancer-related deaths worldwide. Results are still not up to par, even with the best efforts of conventional medicine, thus new avenues of investigation are required. Examining how immunotherapy, precision medicine, and AI are being used to manage lung cancer, this review shows how these tools can change the game for patients and increase their chances of survival.

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A planktonic population of bacteria can form a biofilm by adhesion and colonization. Proteins known as "adhesins" can bind to certain environmental structures, such as sugars, which will cause the bacteria to attach to the substrate. Quorum sensing is used to establish the population is dense enough to form a biofilm.

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Introduction: Schistosomiasis (Bilharzia), a neglected tropical disease caused by parasites, afflicts over 240 million people globally, disproportionately impacting Sub-Saharan Africa. Current diagnostic tests, despite their utility, suffer from limitations like low sensitivity. Polymerase chain reaction (PCR) and quantitative real-time PCR (qPCR) remain the most common and sensitive nucleic acid amplification tests.

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Multiparameter magnetic resonance imaging-based radiomics model for the prediction of rectal cancer metachronous liver metastasis.

World J Gastrointest Oncol

January 2025

Department of Hepatobiliary and Pancreaticosplenic Surgery, Jingzhou Hospital Affiliated to Yangtze University, Jingzhou 434100, Hubei Province, China.

Background: The liver, as the main target organ for hematogenous metastasis of colorectal cancer, early and accurate prediction of liver metastasis is crucial for the diagnosis and treatment of patients. Herein, this study aims to investigate the application value of a combined machine learning (ML) based model based on the multiparameter magnetic resonance imaging for prediction of rectal metachronous liver metastasis (MLM).

Aim: To investigate the efficacy of radiomics based on multiparametric magnetic resonance imaging images of preoperative first diagnosed rectal cancer in predicting MLM from rectal cancer.

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