Human miRNA signature accurately identifies influenza and SARS-CoV-2 infection in a ferret model.
A, Detection of SARS-CoV-2 viral genomic RNA in the retroperitoneal lymph node (blue), nasal wash (orange), oral swab (green), and turbinate tissue (red) of infected ferrets (n = 20, swab and wash samples taken from every ferret at each time point, tissue samples were analysed from the 4 euthanized ferrets at each time point). Data is presented as log10 copies per g of tissue or ml of sample. B, Final metrics of the trained logistic regression model to identify uninfected or SARS-CoV-2 infected ferrets. Dotted line is a perfect score (or 100%). Error bars are 95% CI for 1,000 random assessments. C, Decision boundary graph showing the logistic regression decision point (solid black line) and the probability a sample is infected with SARS-CoV-2 (blue to red shading). Datapoints are uninfected (circles, n = 11) and SARS-CoV-2 infected (crosses, n = 20) ferrets. D, Final metrics of the trained linear support vector classifier model to identify uninfected, influenza A (H1N1) virus, or SARS-CoV-2 infected ferrets. Dotted line is a perfect score (or 100%). Error bars are 95% CI for 1,000 random assessments. As ROC AUC is a measure of binary classification (two groups) it is omitted here. E, Decision boundary graph showing the linear support vector classifier decision points and predicted groups: uninfected (blue, n = 11), influenza A (H1N1) virus infected (light blue, n = 11) or SARS-CoV-2 infected (red, n = 20) ferrets.