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Inflammation severity, rather than respiratory failure, is strongly associated with mortality of ARDS patients in high-altitude ICUs.
Front Physiol
January 2025
Centre de Recherche de l'Institute Universitaire de Cardiologie et de Pneumologie de Québec, Université Laval, Québec City, QC, Canada.
Introduction: In high-altitude cities located above 2,500 m, hospitals face a concerning mortality rate of over 50% among intensive care unit (ICU) patients with acute respiratory distress syndrome (ARDS). This elevated mortality rate is largely due to the absence of altitude-specific medical protocols that consider the unique physiological adaptations of high-altitude residents to hypoxic conditions. This study addresses this critical gap by analyzing demographic, clinical, sex-specific, and preclinical data from ICUs in Bogotá, Colombia (2,650 m) and El Alto, Bolivia (4,150 m).
View Article and Find Full Text PDFCharacterization of LTBP2 mutation causing mitral valve prolapse.
Eur Heart J Open
January 2025
Department of Medicine, Cardiovascular Precision Medicine Center, Hadassah Hebrew University Medical Center, P.O. Box 12000, 9112001 Jerusalem, Israel.
Aims: Mitral valve prolapse (MVP) is a common valvular disorder associated with significant morbidity and mortality, with a strong genetic basis. This study aimed to identify a mutation in a family with MVP and to characterize the valve phenotype in LTBP2 knockout (KO) mice.
Methods And Results: Exome sequencing and segregation analysis were performed on a large family with MVP.
Evaluation of Artificial Intelligence: Computer-aided Detection of Colorectal Polyps.
J Anus Rectum Colon
January 2025
Department of Endoscopy, Fukuoka University Chikushi Hospital, Chikushino, Japan.
Objectives: Colonoscopy is the gold standard for screening cancer and precancerous lesions in the large intestine. Recently, remarkable advances in artificial intelligence (AI) have led to the development of various computer-aided detection (CADe) systems for colonoscopy. This study aimed to evaluate the usefulness of AI for colonoscopy using CAD-EYE (Fujifilm, Tokyo, Japan) to calculate the adenoma miss rate (AMR).
View Article and Find Full Text PDFLarge annotated ultrasound dataset of non-alcoholic fatty liver from Saudi hospitals for analysis and applications.
Data Brief
February 2025
College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar.
This study presents a comprehensive ultrasound image dataset for Non-Alcoholic Fatty Liver Disease (NAFLD), addressing the critical need for standardized resources in AI-assisted diagnosis. The dataset comprises 10,352 high-resolution ultrasound images from 384 patients collected at King Saud University Medical City and National Guard Health Affairs in Saudi Arabia. Each image is meticulously annotated with NAFLD Activity Score (NAS) fibrosis staging and steatosis grading based on corresponding liver biopsy results.
View Article and Find Full Text PDFDevelopment of a multi-laboratory integrated predictive model for silicosis utilizing machine learning: a retrospective case-control study.
Front Public Health
January 2025
Department of Laboratory, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China.
Objective: Due to the high global prevalence of silicosis and the ongoing challenges in its diagnosis, this pilot study aims to screen biomarkers from routine blood parameters and develop a multi-biomarker model for its early detection.
Methods: A case-control study was conducted to screen biomarkers for the diagnosis of silicosis using LASSO regression, SVM and RF. A sample of 612 subjects (half cases and half controls) were randomly divided into training and test groups in a 2:1 ratio.
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