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Continuous tracking of COVID-19 patients' immune status.
International Immunopharmacology 2020 September 26
BACKGROUND: COVID-19 is threating human health worldwide. We aim to investigate the dynamic changes of immune status in COVID-19 patients with clinical evolution.
METHODS: Sixty-one COVID-19 patients (42 mild cases and 19 severe cases, 51 cases without secondary infection as non-infection group and 10 cases with secondary bacterial/fungal infection as infection group) and 52 healthy controls (HCs) were enrolled from our hospital. Leucocyte classification, lymphocyte subsets and cytokines were detected by full-automatic blood cell analyzer and flow cytometer, respectively.
RESULTS: Upon admission, eosinophils and lymphocyte subsets decreased significantly, while neutrophils, monocytes, basophils, IL-2, IL-6, IL-10 and IFN-γ increased significantly in COVID-19 patients compared to HCs. CD3+ T and DN (CD3+ CD4- CD8- ) cells appeared sustained decline, leucocytes, neutrophils and IL-10 showed sustained increase in severe group compared to mild group. Compared with the non-infection group, we observed a depletion of eosinophils, CD3+ T and CD4+ T cells, but leucocytes, neutrophils, IL-6 and IL-10 on the contrary in the infection group. Besides, in severe group of COVID-19 patients, DN cells were negatively correlated with IL-10, and DP (CD3+ CD4+ CD8+ ) cells were negatively correlated with IL-6. Lymphocytes, eosinophils, CD3+ T cells, CD4+ T cells, IL-6 and IL-10 all had great diagnostic efficacy (AUC, 0.905-0.975) for COVID-19. The laboratory indicators of COVID-19 patients with improved condition also showed a recovery trend with time.
CONCLUSIONS: The immune status of COVID-19 patients is different in each stage, and dynamic monitoring of related indicators can help predict the disease and may avoid cytokine storms.
METHODS: Sixty-one COVID-19 patients (42 mild cases and 19 severe cases, 51 cases without secondary infection as non-infection group and 10 cases with secondary bacterial/fungal infection as infection group) and 52 healthy controls (HCs) were enrolled from our hospital. Leucocyte classification, lymphocyte subsets and cytokines were detected by full-automatic blood cell analyzer and flow cytometer, respectively.
RESULTS: Upon admission, eosinophils and lymphocyte subsets decreased significantly, while neutrophils, monocytes, basophils, IL-2, IL-6, IL-10 and IFN-γ increased significantly in COVID-19 patients compared to HCs. CD3+ T and DN (CD3+ CD4- CD8- ) cells appeared sustained decline, leucocytes, neutrophils and IL-10 showed sustained increase in severe group compared to mild group. Compared with the non-infection group, we observed a depletion of eosinophils, CD3+ T and CD4+ T cells, but leucocytes, neutrophils, IL-6 and IL-10 on the contrary in the infection group. Besides, in severe group of COVID-19 patients, DN cells were negatively correlated with IL-10, and DP (CD3+ CD4+ CD8+ ) cells were negatively correlated with IL-6. Lymphocytes, eosinophils, CD3+ T cells, CD4+ T cells, IL-6 and IL-10 all had great diagnostic efficacy (AUC, 0.905-0.975) for COVID-19. The laboratory indicators of COVID-19 patients with improved condition also showed a recovery trend with time.
CONCLUSIONS: The immune status of COVID-19 patients is different in each stage, and dynamic monitoring of related indicators can help predict the disease and may avoid cytokine storms.
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