The outbreak of coronavirus disease 2019 , caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has attracted increasing worldwide attention. Cases of liver damage or dysfunction (mainly characterized by moderately elevated serum aspartate aminotransferase levels) have been reported among patients with COVID-19. However, it is currently uncertain whether the COVID-19related liver damage/dysfunction is due mainly to the viral infection per se or other coexisting conditions, such as the use of potentially hepatotoxic drugs and the coexistence of systemic inflammatory response, respiratory distress syndromeinduced hypoxia, and multiple organ dysfunction. Based on the current evidence from case reports and case series, this

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Mild therapeutic hypothermia is considered standard care in the treatment of patients resuscitated from cardiac arrest. With increasingly more frequent concomitant use of platelet-inhibiting drugs, clinicians must be cognizant of the ramifications of hypothermia on platelet function as part of hemostasis. The effects of hypothermia on platelet function have been studied for more than 50 years, but the results are inconsistent and may be related to the circumstances during which hypothermia is achieved. This review summarizes current knowledge of platelet function during hypothermia and the impact on hemostasis.

This is the first meta-analysis investigating the relationship between NAFLD and CVD independent of the impact of DM. Our findings suggested that NAFLD increases the risk of CVD in populations with comparable DM profiles. Diabetic patients diagnosed with NAFLD might benefit from a more early cardiovascular risk assessment, thereby reducing CVD morbidity and mortality.

Big data clinical research typically involves thousands of patients and there are numerous variables available. Conventionally, these variables can be handled by multivariable regression modeling. In this article, the hierarchical cluster analysis (HCA) is introduced. This method is used to explore similarity between observations and/or clusters. The result can be visualized using heat maps and dendrograms. Sometimes, it would be interesting to add scatter plot and smooth lines into the panels of the heat map. The inherent R package does not provide this function. A series of scatter plots can be created using package, and then background color of each panel is mapped to the regression coefficient by using custom-made panel functions. This is the unique feature of the package. Dendrograms and color keys can be added as the legend elements of the lattice system. The package provides some useful functions for the work.