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Drug classes associated with the development of fulminant type 1 diabetes: a retrospective analysis using the FDA adverse event reporting system database.
Expert Opin Drug Saf
January 2025
Department of Endocrinology, Guang'anmen Hospital of China Academy of Chinese Medical Sciences, Beijing, China.
Background: Fulminant type 1 diabetes mellitus (FT1DM) is a severe subtype of type 1 diabetes characterized by rapid onset, metabolic disturbances, and irreversible insulin secretion failure. Recent studies have suggested associations between FT1DM and certain medications, warranting further investigation.
Objectives: This study aims to identify drugs associated with an increased risk of FT1DM using the FDA Adverse Event Reporting System (FAERS) database, evaluate reporting patterns, and provide actionable insights to reduce FT1DM occurrence and improve medication safety.
Use of Machine Learning to Predict the Incidence of Type 2 Diabetes Among Relatively Healthy Adults: A 10-Year Longitudinal Study in Taiwan.
Diagnostics (Basel)
December 2024
Department of Family Medicine, Taichung Veterans General Hospital, Taichung 407219, Taiwan.
: The prevalence of diabetes is increasing worldwide, particularly in the Pacific Ocean island nations. Although machine learning (ML) models and data mining approaches have been applied to diabetes research, there was no study utilizing ML models to predict diabetes incidence in Taiwan. We aimed to predict the onset of diabetes in order to raise health awareness, thereby promoting any necessary lifestyle modifications and help mitigate disease burden.
View Article and Find Full Text PDFConference 2024: Building an innovative scientific research ecosystem for microbiome and One Health.
Imeta
December 2024
Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen Chinese Academy of Agricultural Sciences Shenzhen China.
The Conference 2024 provides a platform to promote the development of an innovative scientific research ecosystem for microbiome and One Health. The four key components - Technology, Research (Biology), Academic journals, and Social media - form a synergistic ecosystem. Advanced technologies drive biological research, which generates novel insights that are disseminated through academic journals.
View Article and Find Full Text PDFPredictive properties of novel anthropometric and biochemical indexes for prediction of cardiovascular risk.
Diabetol Metab Syndr
December 2024
Department of Nutrition, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.
Objective: Our aim was to examine the correlation between CVDs and various anthropometric and biochemical indices in the Iranian population.
Methods: 9704 healthy individuals without CVD aged 35-65 were enrolled in our study. The anthropometric indices including Body Adiposity Index (BAI), Abdominal Volume Index (AVI), Body Roundness Index (BRI), Waist to Hip Ratio (WHR), Weight-adjusted Waist Index (WWI), Conicity Index (C-Index), A Body Shape Index (ABSI), Waist to Height Ratio (WHtR), Body Surface Area (BSA), Body Mass Index (BMI), Lipid Accumulation Product (LAP) and Visceral Adiposity Index (VAI) were calculated.
Predicting high sensitivity C-reactive protein levels and their associations in a large population using decision tree and linear regression.
Sci Rep
December 2024
Metabolic Syndrome Research Center, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.
High-sensitivity C-reactive protein (hs-CRP) is a biomarker of inflammation predicting the incidence of different health pathologies. In this study, we aimed to evaluate the association between hematological and demographic factors with hs-CRP levels using decision tree (DT) and linear regression (LR) modeling. This study was conducted on a population of 9704 males and females aged 35 to 65 years recruited from the Mashhad Stroke and Heart Atherosclerotic Disorder (MASHAD) cohort study.
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