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https://www.selleckchem.com/products/nhwd-870.html Epistasis analysis elucidates the effects of gene-gene interactions (G×G) between multiple loci for complex traits. However, the large computational demands and the high multiple testing burden impede their discoveries. Here, we illustrate the utilization of two methods, main effect filtering based on individual GWAS results and biological knowledge-based modeling through Biofilter software, to reduce the number of interactions tested among single nucleotide polymorphisms (SNPs) for 15 cardiac-related traits and 14 fatty acids. We performed interaction analyses using the two filtering methods, adjusting for age, sex, body mass index (BMI), waist-hip ratio, and the first three principal components from genetic data, among 2,824 samples from the Ludwigshafen Risk and Cardiovascular (LURIC) Health Study. Using Biofilter, one interaction nearly met Bonferroni significance an interaction between rs7735781 in XRCC4 and rs10804247 in XRCC5 was identified for venous thrombosis with a Bonferroni-adjusted likelihood raraits and to improve precision medicine capability. COVID-19 is a pandemic disease and questions rise about the coronavirus 2 (Sars-CoV-2) effect on nervous system. This involvement could help explaining the pathogenesis of this condition and lead to novel therapeutic approaches. To assess the occurrence of neurological symptoms in COVID-19 patients during the Italian pandemic outbreak, as reported by physicians. In the early days of pandemic emergence we developed an online survey open to all Italian clinicians involved in the diagnosis and management of COVID-19 patients. The survey was structured in three sections, with nine different items concerning the presence of different specific clinical abnormalities. Each item was graded from "absent" to "severe" in a 4-point Likert's scale. Likert's scale data were analyzed by studying the distribution of responses by using medians and bar charts-relative frequencie

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