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Cardiovascular diseases are the leading cause of death globally and in Brazil. The provision of quality healthcare faces challenges due to resource scarcity and unequal distribution, particularly affecting rural areas. Telehealth strategies have shown potential to address these challenges by improving access to specialised care.

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Left ventricular systolic dysfunction (LVSD) and its severity are correlated with the prognosis of cardiovascular diseases. Early detection and monitoring of LVSD are of utmost importance. Left ventricular ejection fraction (LVEF) is an essential indicator for evaluating left ventricular function in clinical practice, the current echocardiography-based evaluation method is not avaliable in primary care and difficult to achieve real-time monitoring capabilities for cardiac dysfunction.

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A murine model of induced myocarditis and cardiac dysfunction.

Microbiol Spectr

January 2025

Department of Molecular and Comparative Pathobiology, Johns Hopkins University, School of Medicine, Baltimore, Maryland, USA.

Unlabelled: is a protozoan parasite that causes human and animal African trypanosomiases (HAT and AAT). Cardiac symptoms are commonly reported in HAT patients, and intracardiac parasites with accompanying myocarditis have been observed in both natural hosts and animal models of infection. Despite the importance of as a cause of cardiac dysfunction and the dramatic socioeconomic impact of African trypanosomiases in sub-Saharan Africa, there are currently no reproducible murine models of associated cardiomyopathy.

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Obesity is associated with abnormal repolarization manifested by QT interval prolongation, and oxidative stress is an important link between obesity and arrhythmias. However, the underlying electrophysiological and molecular mechanisms remain unclear. The aim of this study is to evaluate the role of obesity in potassium current in ventricular myocytes and the potential mechanism of NADPH oxidase 2 (Nox2).

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Medical datasets are vital for advancing Artificial Intelligence (AI) in healthcare. Yet biases in these datasets on which deep-learning models are trained can compromise reliability. This study investigates biases stemming from dataset-creation practices.

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