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. 2022 Jun;12(6):e899.
doi: 10.1002/ctm2.899.

Altered microRNA expression in severe COVID-19: Potential prognostic and pathophysiological role

Nathalie Garnier  1 , Kato Pollet  1   2 , Marie Fourcot  3 , Morgan Caplan  4 , Guillemette Marot  5 , Julien Goutay  4 , Julien Labreuche  6 , Fabrice Soncin  7   8 , Rabah Boukherroub  2 , Didier Hober  1 ; Lille COVID Research Network (LICORNE); Sabine Szunerits  2 , Julien Poissy  4   9 , Ilka Engelmann  1
Affiliations

Altered microRNA expression in severe COVID-19: Potential prognostic and pathophysiological role

Nathalie Garnier et al. Clin Transl Med. 2022 Jun.
No abstract available

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

FIGURE 1
FIGURE 1
miRNA expression in non‐severe compared to severe COVID‐19. (A) Number of miRNAs detected in nasopharyngeal swab specimens of severe (n = 20), non‐severe COVID‐19 (n = 21) and controls (n = 20). Box plots showing median and interquartile ranges of the number of expressed miRNAs in each group. (B) Box‐plots showing the expression of miRNAs that are differentially expressed between severe (S) and non‐severe (NS) COVID‐19 and control groups (Ctrl) in univariate analyses (panels Ba,b). Box‐plots showing the expression of miRNAs that discriminate severe and non‐severe COVID‐19 by the sPLS‐DA multivariate analysis (panels Bb,c). The miRNAs identified by both analyses are shown in panel Bb. The bars show the medians and the boxes the interquartile ranges of normalized miRNA expression. (C) Severe (S, triangles) and non‐severe COVID‐19 cases (NS, circles) are discriminated by miRNA expression by sPLS‐DA analysis
FIGURE 2
FIGURE 2
Receiver operator characteristic (ROC) analysis for the discrimination of severe and non‐severe COVID‐19. ROC curve and area under the ROC curve (AUC) values for the classification into severe and non‐severe COVID‐19 based on miRNA expression are shown for the three miRNAs identified in common by sPLS‐DA and univariate analysis
See this image and copyright information in PMC

References

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