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Gender-Equity Model for Liver Allocation using Artificial Intelligence (GEMA-AI) for waiting list liver transplant prioritization.
Clin Gastroenterol Hepatol
January 2025
Department of Computer Science and Numerical Analysis, University of Córdoba, Córdoba, Spain. Campus Universitario de Rabanales, Albert Einstein Building. Ctra. N-IV, Km. 396. 14071, Córdoba, Spain; Instituto Maimónides de Investigación Biomédica de Córdoba (IMIBIC), Córdoba, Spain. Av. Menéndez Pidal, s/n, Poniente Sur, 14004 Córdoba, Spain.
Background & Aims: We aimed to develop and validate an artificial intelligence score (GEMA-AI) to predict liver transplant (LT) waiting list outcomes using the same input variables contained in existing models.
Methods: Cohort study including adult LT candidates enlisted in the United Kingdom (2010-2020) for model training and internal validation, and in Australia (1998-2020) for external validation. GEMA-AI combined international normalized ratio, bilirubin, sodium, and the Royal Free Glomerular Filtration Rate in an explainable Artificial Neural Network.
Dietary patterns related to triglyceride glucose index and risk of type 2 diabetes: a large-scale cohort study.
Front Nutr
January 2025
Department of Nutrition and Food Hygiene, School of Public Health, Nantong University, Nantong, China.
Background: Triglyceride glucose (TyG) index has been proposed as a credible and simple surrogate indicator for insulin resistance. The primary aim of this study was to novelly examine the associations between dietary patterns reflecting variations in circulating TyG index and the risk of type 2 diabetes mellitus (T2DM).
Methods: This study included 120,988 participants from the UK Biobank, all of whom completed multiple 24-h dietary assessments.
Gradient disparities in allergy and the gut microbiome among rural, migrant, and urban populations across China.
World Allergy Organ J
January 2025
Department of Respiratory Medicine, Guangzhou Institute of Respiratory Health, China State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, The First Affiliated Hospital of Guangzhou Medical University, China.
Background: While much of the evidence linking the rapid urbanization and the increasing prevalence of allergen sensitization, but little is known regarding rural-to-urban migrants. The aim of this study was to identify the disparities in allergy, the gut microbiome and factors among native urban, migrating, and native rural Chinese.
Methods: We redesigned the dataset of the China Alliance of Research on Respiratory Allergic Disease secondary survey, and after stratified sampling, a subsample of 2422 subjects were enrolled for the analysis of a questionnaire, skin prick tests (SPT), and specific immunoglobulin E (sIgE) titer measurements against 8 common allergens.
F-FAPI-42 PET/CT for preoperatively identifying intrahepatic cholangiocarcinoma and hepatocellular carcinoma.
Quant Imaging Med Surg
January 2025
Department of Nuclear Medicine, Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
Background: Accurately differentiating hepatocellular carcinoma (HCC) from intrahepatic cholangiocarcinoma (ICC) is essential for therapeutic decision-making. This study aimed to explore the value of Fluor 18 (F)-conjugated fibroblast-activation protein inhibitor (FAPI-42) positron emission tomography-computed tomography (PET/CT) in distinguishing HCC from ICC preoperatively.
Methods: Patients with suspected intrahepatic lesions who underwent F-FAPI-42 PET/CT were retrospectively assessed and placed into an HCC group and an ICC group based on postoperative pathology.
External Validation of a 5-Factor Risk Model for Breast Cancer-Related Lymphedema.
JAMA Netw Open
January 2025
Institute of Medical Science, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada.
Importance: Secondary lymphedema is a common, harmful side effect of breast cancer treatment. Robust risk models that are externally validated are needed to facilitate clinical translation. A published risk model used 5 accessible clinical factors to predict the development of breast cancer-related lymphedema; this model included a patient's mammographic breast density as a novel predictive factor.
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