Physiogenomic representation of the most significant genetic associations found in the low carbohydrate group

See Figure 2 legend for details regarding individual patient genotypes (), the distribution of Δ%BF (), and the LOESS fit of the allele frequency () as a function of Δ%BF.<p><b>Copyright information:</b></p><p>Taken from "Physiogenomic comparison of human fat loss in response to diets restrictive of carbohydrate or fat"</p><p>http://www.nutritionandmetabolism.com/content/5/1/4</p><p>Nutrition & Metabolism 2008;5():4-4.</p><p>Published online 6 Feb 2008</p><p>PMCID:PMC2270845.</p><p></p

Distribution of baseline and change in percent body fat for LF (top) and LC (bottom) groups

The vertical axes () indicates the number of patients observed within a given 10% interval up to 60% (baseline, left panels) or within a given 2% or 5% interval (change, right panels) on the horizontal axes. Genotyping was not completed in 3 LF subjects and 7 LC subjects.<p><b>Copyright information:</b></p><p>Taken from "Physiogenomic comparison of human fat loss in response to diets restrictive of carbohydrate or fat"</p><p>http://www.nutritionandmetabolism.com/content/5/1/4</p><p>Nutrition & Metabolism 2008;5():4-4.</p><p>Published online 6 Feb 2008</p><p>PMCID:PMC2270845.</p><p></p

Multiscale interactome analysis coupled with off‑target drug predictions reveals drug repurposing candidates for human coronavirus disease

  The COVID-19 pandemic has highlighted the urgent need for the identification of new antiviral drug therapies for a variety of diseases. COVID-19 is caused by infection with the human coronavirus SARS-CoV-2, while other related human coronaviruses cause diseases ranging from severe respiratory infections to the common cold. We developed a computational approach to identify new antiviral drug targets and repurpose clinically-relevant drug compounds for the treatment of a range of human coronavirus diseases. Our approach is based on graph convolutional networks (GCN) and involves multiscale host-virus interactome analysis coupled to off-target drug predictions. Cell-based experimental assessment reveals several clinically-relevant drug repurposing candidates predicted by the in silico analyses to have antiviral activity against human coronavirus infection. In particular, we identify the MET inhibitor capmatinib as having potent and broad antiviral activity against several coronaviruses in a MET-independent manner, as well as novel roles for host cell proteins such as IRAK1/4 in supporting human coronavirus infection, which can inform further drug discovery studies.  </p