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Deprecated: Implicit conversion from float 267.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Engineering+(Beijing) 2020 ; 6 (10): 1178-1184 Nephropedia Template TP
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Clinical Significance of the Correlation between Changes in the Major Intestinal Bacteria Species and COVID-19 Severity #MMPMID33520333
Tang L; Gu S; Gong Y; Li B; Lu H; Li Q; Zhang R; Gao X; Wu Z; Zhang J; Zhang Y; Li L
Engineering (Beijing) 2020[Oct]; 6 (10): 1178-1184 PMID33520333show ga
Coronavirus disease 2019 (COVID-19) is a highly contagious infectious disease. Similar to H7N9 infection, pneumonia and cytokine storm are typical clinical manifestations of COVID-19. Our previous studies found that H7N9 patients had intestinal dysbiosis. However, the relationship between the gut microbiome and COVID-19 has not been determined. This study recruited a cohort of 57 patients with either general (n = 20), severe (n = 19), or critical (n = 18) disease. The objective of this study was to investigate changes in the abundance of ten predominant intestinal bacterial groups in COVID-19 patients using quantitative polymerase chain reaction (q-PCR), and to establish a correlation between these bacterial groups and clinical indicators of pneumonia in these patients. The results indicated that dysbiosis occurred in COVID-19 patients and changes in the gut microbial community were associated with disease severity and hematological parameters. The abundance of butyrate-producing bacteria, such as Faecalibacterium prausnitzii, Clostridium butyricum, Clostridium leptum, and Eubacterium rectale, decreased significantly, and this shift in bacterial community may help discriminate critical patients from general and severe patients. Moreover, the number of common opportunistic pathogens Enterococcus (Ec) and Enterobacteriaceae (E) increased, especially in critically ill patients with poor prognosis. The results suggest that these bacterial groups can serve as diagnostic biomarkers for COVID-19, and that the Ec/E ratio can be used to predict death in critically ill patients.