Analysis of Lymphocyte Subpopulations and Cytokines in COVID-19-Associated Pneumonia and Community-Acquired Pneumonia
Background. The 2019 novel coronavirus SARS-CoV-2 caused large outbreaks of COVID-19 worldwide. COVID-19 resembles community-acquired pneumonia (CAP). Our aim was to identify lymphocyte subpopulations to distinguish between COVID-19 and CAP. Methods. We compared the peripheral blood lymphocytes and...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Journal of Immunology Research |
| Online Access: | http://dx.doi.org/10.1155/2021/6657894 |
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| author | Guohong Liu Xianghu Jiang Xiaojiao Zeng Yunbao Pan Haibo Xu |
| author_facet | Guohong Liu Xianghu Jiang Xiaojiao Zeng Yunbao Pan Haibo Xu |
| author_sort | Guohong Liu |
| collection | DOAJ |
| description | Background. The 2019 novel coronavirus SARS-CoV-2 caused large outbreaks of COVID-19 worldwide. COVID-19 resembles community-acquired pneumonia (CAP). Our aim was to identify lymphocyte subpopulations to distinguish between COVID-19 and CAP. Methods. We compared the peripheral blood lymphocytes and their subsets in 296 patients with COVID-19 and 130 patients with CAP. Parameters for independent prediction of COVID-19 were calculated by logistic regression. Results. The main lymphocyte subpopulations (CD3+CD4+, CD16+CD56+, and CD4+/CD8+ ratio) and cytokines (TNF-α and IFN-γ) of COVID-19 patients were significantly different from that of CAP patients. CD16+CD56+%, CD4+/CD8+ratio, CD19+, and CD3+CD4+ were identified as predictors of COVID-19 diagnosis by logistic regression. In addition, the CD3+CD4+counts, CD3+CD8+ counts, andTNF-α are independent predictors of disease severity in patients. Conclusions. Lymphopenia is an important part of SARS-CoV-2 infection, and lymphocyte subsets and cytokines may be useful to predict the severity and clinical outcomes of the disease. |
| format | Article |
| id | doaj-art-14862140c45a4b30a2e6c7ab303ebd13 |
| institution | Kabale University |
| issn | 2314-8861 2314-7156 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Immunology Research |
| spelling | doaj-art-14862140c45a4b30a2e6c7ab303ebd132025-02-03T01:24:47ZengWileyJournal of Immunology Research2314-88612314-71562021-01-01202110.1155/2021/66578946657894Analysis of Lymphocyte Subpopulations and Cytokines in COVID-19-Associated Pneumonia and Community-Acquired PneumoniaGuohong Liu0Xianghu Jiang1Xiaojiao Zeng2Yunbao Pan3Haibo Xu4Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan University, Wuhan, Hubei, ChinaDepartment of Laboratory Medicine, Zhongnan Hospital of Wuhan University, Wuhan University, Wuhan, Hubei, ChinaDepartment of Laboratory Medicine, Zhongnan Hospital of Wuhan University, Wuhan University, Wuhan, Hubei, ChinaDepartment of Laboratory Medicine, Zhongnan Hospital of Wuhan University, Wuhan University, Wuhan, Hubei, ChinaDepartment of Radiology, Zhongnan Hospital of Wuhan University, Wuhan University, Wuhan, Hubei, ChinaBackground. The 2019 novel coronavirus SARS-CoV-2 caused large outbreaks of COVID-19 worldwide. COVID-19 resembles community-acquired pneumonia (CAP). Our aim was to identify lymphocyte subpopulations to distinguish between COVID-19 and CAP. Methods. We compared the peripheral blood lymphocytes and their subsets in 296 patients with COVID-19 and 130 patients with CAP. Parameters for independent prediction of COVID-19 were calculated by logistic regression. Results. The main lymphocyte subpopulations (CD3+CD4+, CD16+CD56+, and CD4+/CD8+ ratio) and cytokines (TNF-α and IFN-γ) of COVID-19 patients were significantly different from that of CAP patients. CD16+CD56+%, CD4+/CD8+ratio, CD19+, and CD3+CD4+ were identified as predictors of COVID-19 diagnosis by logistic regression. In addition, the CD3+CD4+counts, CD3+CD8+ counts, andTNF-α are independent predictors of disease severity in patients. Conclusions. Lymphopenia is an important part of SARS-CoV-2 infection, and lymphocyte subsets and cytokines may be useful to predict the severity and clinical outcomes of the disease.http://dx.doi.org/10.1155/2021/6657894 |
| spellingShingle | Guohong Liu Xianghu Jiang Xiaojiao Zeng Yunbao Pan Haibo Xu Analysis of Lymphocyte Subpopulations and Cytokines in COVID-19-Associated Pneumonia and Community-Acquired Pneumonia Journal of Immunology Research |
| title | Analysis of Lymphocyte Subpopulations and Cytokines in COVID-19-Associated Pneumonia and Community-Acquired Pneumonia |
| title_full | Analysis of Lymphocyte Subpopulations and Cytokines in COVID-19-Associated Pneumonia and Community-Acquired Pneumonia |
| title_fullStr | Analysis of Lymphocyte Subpopulations and Cytokines in COVID-19-Associated Pneumonia and Community-Acquired Pneumonia |
| title_full_unstemmed | Analysis of Lymphocyte Subpopulations and Cytokines in COVID-19-Associated Pneumonia and Community-Acquired Pneumonia |
| title_short | Analysis of Lymphocyte Subpopulations and Cytokines in COVID-19-Associated Pneumonia and Community-Acquired Pneumonia |
| title_sort | analysis of lymphocyte subpopulations and cytokines in covid 19 associated pneumonia and community acquired pneumonia |
| url | http://dx.doi.org/10.1155/2021/6657894 |
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