Development and validation of a cardiovascular risk prediction model for Sri Lankans using machine learning.

Introduction And Objectives: Sri Lankans do not have a specific cardiovascular (CV) risk prediction model and therefore, World Health Organization(WHO) risk charts developed for the Southeast Asia Region are being used. We aimed to develop a CV risk prediction model specific for Sri Lankans using machine learning (ML) of data of a population-based, randomly selected cohort of Sri Lankans followed up for 10 years and to validate it in an external cohort.

Material And Methods: The cohort consisted of 2596 individuals between 40-65 years of age in 2007, who were followed up for 10 years.

Severe early graft dysfunction post-heart transplantation: Two clinical trajectories and diastolic perfusion pressure as a predictor of mechanical circulatory support.

Background: Severe early graft dysfunction (EGD) is defined by mechanical circulatory support (MCS) <24 hours of heart transplantation (HT). We classified severe EGD based on timing of post-HT MCS: ''Immediate'' intra-operative vs ''Delayed'' post-operative MCS (after admission into intensive care unit (ICU) from operating theater). We hypothesized that (1) risk factors and clinical course differ between ''Immediate'' and ''Delayed'' MCS; and (2) diastolic perfusion pressure (DPP=diastolic blood pressure-central venous pressure) and Norepinephrine equivalents (NE=sum of vasopressor doses), as measures of vasoplegia are related to ''Delayed'' MCS.

Advancements in Textile-Based sEMG Sensors for Muscle Fatigue Detection: A Journey from Material Evolution to Technological Integration.

Textile-based surface electromyography (sEMG) electrodes have emerged as a prominent tool in muscle fatigue assessment, marking a significant shift toward innovative, noninvasive methods. This review examines the transition from metallic fibers to novel conductive polymers, elastomers, and advanced material-based electrodes, reflecting on the rapid evolution of materials in sEMG sensor technology. It highlights the pivotal role of materials science in enhancing sensor adaptability, signal accuracy, and longevity, crucial for practical applications in health monitoring, while examining the balance of clinical precision with user comfort.