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Human miRNA signature accurately identifies influenza and SARS-CoV-2 infection in a ferret model.

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posted on 2021-07-28, 17:25 authored by Ryan J. Farr, Christina L. Rootes, Louise C. Rowntree, Thi H. O. Nguyen, Luca Hensen, Lukasz Kedzierski, Allen C. Cheng, Katherine Kedzierska, Gough G. Au, Glenn A. Marsh, Seshadri S. Vasan, Chwan Hong Foo, Christopher Cowled, Cameron R. Stewart

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.

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