The American journal of medicine
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This study aimed to compare flow-mediated dilation values between individuals with long COVID, individuals with myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS), and healthy age-matched controls to assess the potential implications for clinical management and long-term health outcomes. ⋯ The study demonstrates that both long COVID and ME/CFS patients exhibit similarly impaired endothelial function, indicating potential vascular involvement in the pathogenesis of these post-viral illnesses. The significant reduction in flow-mediated dilation values suggests an increased cardiovascular risk in these populations, warranting careful monitoring and the development of targeted interventions to improve endothelial function and mitigate long-term health implications.
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Machine learning has emerged as a significant tool to augment the medical decision-making process. Studies have steadily accrued detailing algorithms and models designed using machine learning to predict and anticipate pathologic states. The cardiac intensive care unit is an area where anticipation is crucial in the division between life and death. In this paper, we aim to review important studies describing the utility of machine learning algorithms to describe the future of artificial intelligence in the cardiac intensive care unit, especially in regards to the prediction of successful ventilatory weaning, acute respiratory distress syndrome, arrhythmia, and acute kidney injury.