Importance of H-FABP in Early Diagnosis of Acute Myocardial Infarction.

Background: Over the years, troponins have aced the para-clinical tests for confirming the diagnosis of acute myocardial infarction. However, the rise in their levels is entirely time-dependent, which can cause a delay in the initiation of treatment protocols. Heart fatty acid binding protein (H-FABP) can serve comparatively as a better biological marker for overcoming this flaw of troponins, as it is quickly released into the bloodstream once the myocardial injury occurs due to decreased blood supply.

Increased Susceptibility for Thromboembolic Events versus High Bleeding Risk Associated with COVID-19.

The infection with the SARS-CoV-2 virus is associated with numerous systemic involvements. Besides the severe respiratory injuries and cardiovascular complications, it became obvious early on that this disease carries an increased risk of thromboembolic events, but a higher propensity for bleedings as well. We researched the medical literature over significant PubMed published articles debating on the prevalence, category of patients, the moment of occurrence, and evolution of venous thromboembolism (VTE), but also of venous and arterial "in situ" thrombosis (AT), and hemorrhagic events as well.

Assessment of the Impact of Alcohol Consumption Patterns on Heart Rate Variability by Machine Learning in Healthy Young Adults.

Autonomic nervous system (ANS) dysfunction is present in early stages of alcohol abuse and increases the likelihood of cardiovascular events. Given the nonlinear pattern of dynamic interaction between sympathetic nervous system (SNS) and para sympathetic nervous system (PNS) and the complex relationship with lifestyle factors, machine learning (ML) algorithms are best suited for analyzing alcohol impact over heart rate variability (HRV), because they allow the analysis of complex interactions between multiple variables. This study aimed to characterize autonomic nervous system dysfunction by analysis of HRV correlated with cardiovascular risk factors in young individuals by using machine learning.