Yonsei University · Medicine
Professor Sungha Park's research lab focuses on cardiovascular disease mechanisms, particularly the role of arterial stiffness, inflammation, and hemodynamic responses to environmental factors such as cold temperatures. The lab investigates molecular pathways involving angiotensin II, oxidative stress, and extracellular matrix remodeling, with an emphasis on the interplay between fibrosis, cytokine activation, and vascular pathology. Additionally, the lab applies statistical methods to survival analysis, developing advanced goodness-of-fit tests for censored data using entropy-based approaches. These interdisciplinary efforts bridge clinical cardiovascular research with biostatistical methodology.
Figures are computed from collected data and may differ slightly.
Increased arterial stiffness is an independent predictor of cardiovascular disease independent from blood pressure. Recent studies have shed new light on the importance of inflammation on the pathogenesis of arterial stiffness. Arterial stiffness is associated with the increased activity of angiotensin II, which results in increased NADPH oxidase activity, reduced NO bioavailability and increased production of reactive oxygen species. Angiotensin II signaling activates matrix metalloproteinases
Epidemiologic studies have consistently demonstrated an increased risk of cardiovascular disease during colder temperatures. Hemodynamic changes associated with cold temperature and an increase in thrombogenicity may both account for the increase in cardiovascular risk and mortality. Studies using both in-office and out-of-office BP measurements have consistently shown an elevation in BP during the colder seasons. The large difference in BP between cold and warm months may increase the incidence
We express the joint entropy of order statistics in terms of an incomplete integral of the hazard function, and provide a simple estimate of the joint entropy of the type II censored data. Then we establish a goodness of fit test statistic based on the Kullback-Leibler information with the type II censored data, and compare its performance with some leading test statistics. A Monte Carlo simulation study shows that the proposed test statistic shows better powers than some leading test statistics
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