RESUMO
Acute coronary syndrome is commonly associated with traditional cardiovascular risk factors such as smoking, hypertension, diabetes, and hyperlipidemia. Myocardial infarction in a young person presents a significant challenge because its etiology is least likely associated with atherosclerosis. Polycythemia vera refers to one of the rare causes of myocardial infarction, which involves enhanced erythrocyte levels, leukocytosis, thrombocytosis, splenomegaly, and a greater chance of vascular occlusion due to clotting in coronary arteries. A 22-year-old male from Pakistan, Asia without typical risk factors, presented with severe chest pain. Electrocardiography indicated acute inferior wall myocardial infarction, and streptokinase was administered. Subsequent investigations confirmed polycythemia vera. Treatment with hydroxyurea and aspirin was initiated, whereas normal coronary arteries in CT coronary angiogram were observed. This case highlights polycythemia vera's rare role in young individuals' heart attacks without known risk factors, emphasizing the need for early detection and specialized treatments involving hematologists to prevent future thrombotic episodes.
RESUMO
: It is widely accepted that cellular requirements and environmental conditions dictate the architecture of genetic regulatory networks. Nonetheless, the status quo in regulatory network modeling and analysis assumes an invariant network topology over time. In this paper, we refocus on a dynamic perspective of genetic networks, one that can uncover substantial topological changes in network structure during biological processes such as developmental growth. We propose a novel outlook on the inference of time-varying genetic networks, from a limited number of noisy observations, by formulating the network estimation as a target tracking problem. We overcome the limited number of observations (small n large p problem) by performing tracking in a compressed domain. Assuming linear dynamics, we derive the LASSO-Kalman smoother, which recursively computes the minimum mean-square sparse estimate of the network connectivity at each time point. The LASSO operator, motivated by the sparsity of the genetic regulatory networks, allows simultaneous signal recovery and compression, thereby reducing the amount of required observations. The smoothing improves the estimation by incorporating all observations. We track the time-varying networks during the life cycle of the Drosophila melanogaster. The recovered networks show that few genes are permanent, whereas most are transient, acting only during specific developmental phases of the organism.
