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Triglyceride-glucose index predicts adverse cardiovascular events in patients with H-type hypertension combined with coronary heart disease: a retrospective cohort study.
Cardiovasc Diabetol
January 2025
Department of Cardiology, Beijing Anzhen Hospital, Capital Medical University, Beijing, China.
Background: The triglyceride‒glucose index (TyG index) is a reliable surrogate for insulin resistance (IR) in individuals with type 2 diabetes mellitus and is associated with cardiovascular disease. Recent studies have reported that H-type hypertension is likewise a predictor of adverse events in patients with coronary heart disease (CHD). However, the relationship between the TyG index and prognosis in patients with H-type hypertension combined with CHD has not yet been reported.
View Article and Find Full Text PDFComparison of NoSAS score with STOP-Bang and Berlin scores in predicting difficult airway.
BMC Anesthesiol
January 2025
Department of Anesthesiology and Reanimation, Faculty of Medicine, Suleyman Demirel University, Operating Room, Floor:1, Cunur, Isparta, 32260, Turkey.
Background: This study aimed to compare the effectiveness of the NoSAS, STOP-Bang, and Berlin scoring systems, which are utilized to predict obstructive sleep apnea syndrome (OSAS), in forecasting difficult airway management. Additionally, the study sought to determine which of these scoring systems is the most practical and effective for this purpose.
Methods: Following the ethics committee approval, preoperative NoSAS, STOP-Bang, and Berlin scores were calculated for 420 patients aged 18 years and older who were scheduled for tracheal intubation.
Explore the factors related to the death of offspring under age five and appraise the hazard of child mortality using machine learning techniques in Bangladesh.
BMC Public Health
January 2025
Department of Statistics and Data Science, Jahangirnagar University, Dhaka, 1342, Bangladesh.
Background: Child mortality is a reliable and significant indicator of a nation's health. Although the child mortality rate in Bangladesh is declining over time, it still needs to drop even more in order to meet the Sustainable Development Goals (SDGs). Machine Learning models are one of the best tools for making more accurate and efficient forecasts and gaining in-depth knowledge.
View Article and Find Full Text PDFPredictive testing for Huntington's disease in a digital age; patient power with potential pitfalls.
Eur J Hum Genet
January 2025
City St. George's University, School of Health & Medical Sciences, London, UK.
Multi-modal framework for battery state of health evaluation using open-source electric vehicle data.
Nat Commun
January 2025
Department of Mechanical Engineering, National University of Singapore, Singapore, Singapore.
Accurate, practical, and robust evaluation of the battery state of health is crucial to the efficient and reliable operation of electric vehicles. However, the limited availability of large-scale, high-quality field data hinders the development of the battery management system for state of health estimation, lifetime prediction, and fault detection in various applications. In this work, to gain insights into underlying factors limiting battery management system performance in real-world vehicles, we analyze the operational data of 300 diverse electric vehicles over three years to understand the disparities between field data and laboratory battery test data and their effect on state of health estimation.
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