Literature DB >> 32910461

The dynamics of immune response in COVID-19 patients with different illness severity.

Bo Zhang1, Daoyuan Yue1, Yun Wang1, Feng Wang1, Shiji Wu1, Hongyan Hou1.   

Abstract

This study aimed to analyze the dynamic changes of lymphocyte subsets and specific antibodies in coronavirus disease 2019 (COVID-19) patients with different illness severity. The amounts of lymphocyte subsets and the levels of immunoglobulin M (IgM) and IgG antibody were retrospectively analyzed in 707 COVID-19 cases. The amounts of lymphocyte subsets were significantly decreased with the increased severity of illness and the levels of IgM and IgG were lower in critical cases than severe and moderate cases. In deceased patients, the lymphocytes subsets were significantly lower than recovered patients. However, the relationship between the levels of IgM and IgG and the amounts of lymphocyte subsets were not significantly correlated. During different stages of COVID-19, the total T cell, CD4+ T cell, and CD8+ T cell counts were gradually recovered to the normal levels in severe and critical groups but the changing trend was relatively stable in the moderate group. The production of IgM and IgG antibodies were delayed in critical groups but also could reach the peak levels at one month after illness onset and decreased to background levels. To detect the kinetics of lymphocytes and antibodies has important clinical value in predicting the illness severity and understanding the pathogenesis of COVID-19.
© 2020 Wiley Periodicals LLC.

Entities:  

Keywords:  COVID-19; SARS-CoV-2; antibody; illness severity; lymphocyte subsets

Year:  2020        PMID: 32910461     DOI: 10.1002/jmv.26504

Source DB:  PubMed          Journal:  J Med Virol        ISSN: 0146-6615            Impact factor:   2.327


  10 in total

Review 1.  Severe Acute Respiratory Syndrome Coronavirus 2: Manifestations of Disease and Approaches to Treatment and Prevention in Humans.

Authors:  Michael E Watson; Kengo Inagaki; Jason B Weinberg
Journal:  Comp Med       Date:  2021-09-17       Impact factor: 0.982

2.  T-Cell Subsets and Interleukin-10 Levels Are Predictors of Severity and Mortality in COVID-19: A Systematic Review and Meta-Analysis.

Authors:  Amal F Alshammary; Jawaher M Alsughayyir; Khalid K Alharbi; Abdulrahman M Al-Sulaiman; Haifa F Alshammary; Heba F Alshammary
Journal:  Front Med (Lausanne)       Date:  2022-04-28

Review 3.  Antibody Responses in COVID-19: A Review.

Authors:  Mateo Chvatal-Medina; Yorjagis Mendez-Cortina; Pablo J Patiño; Paula A Velilla; Maria T Rugeles
Journal:  Front Immunol       Date:  2021-04-15       Impact factor: 7.561

4.  scCODA is a Bayesian model for compositional single-cell data analysis.

Authors:  M Büttner; J Ostner; C L Müller; F J Theis; B Schubert
Journal:  Nat Commun       Date:  2021-11-25       Impact factor: 14.919

Review 5.  Rapid testing for coronavirus disease 2019 (COVID-19).

Authors:  Alexander Biby; Xiaochuan Wang; Xinliang Liu; Olivia Roberson; Allya Henry; Xiaohu Xia
Journal:  MRS Commun       Date:  2022-01-20       Impact factor: 2.935

6.  Data-driven multi-scale mathematical modeling of SARS-CoV-2 infection reveals heterogeneity among COVID-19 patients.

Authors:  Shun Wang; Mengqian Hao; Zishu Pan; Jinzhi Lei; Xiufen Zou
Journal:  PLoS Comput Biol       Date:  2021-11-24       Impact factor: 4.475

7.  Enhanced cell deconvolution of peripheral blood using DNA methylation for high-resolution immune profiling.

Authors:  Lucas A Salas; Ze Zhang; Devin C Koestler; Rondi A Butler; Helen M Hansen; Annette M Molinaro; John K Wiencke; Karl T Kelsey; Brock C Christensen
Journal:  Nat Commun       Date:  2022-02-09       Impact factor: 14.919

Review 8.  Kinetics of severe acute respiratory syndrome coronavirus 2 infection antibody responses.

Authors:  Yajie Lin; Jiajie Zhu; Zongming Liu; Chaonan Li; Yikai Guo; Ying Wang; Keda Chen
Journal:  Front Immunol       Date:  2022-08-05       Impact factor: 8.786

9.  Immune cell profiling and antibody responses in patients with COVID-19.

Authors:  Mitra Rezaei; Shima Mahmoudi; Esmaeil Mortaz; Majid Marjani
Journal:  BMC Infect Dis       Date:  2021-07-05       Impact factor: 3.090

10.  Deep learning identifies antigenic determinants of severe SARS-CoV-2 infection within T-cell repertoires.

Authors:  John-William Sidhom; Alexander S Baras
Journal:  Sci Rep       Date:  2021-07-12       Impact factor: 4.379

  10 in total

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