The third inactivated vaccine booster dramatically enhanced SARS-CoV-2 antibody responses and did not influence the profile of prothrombotic antibody.

OBJECTIVE: The purpose of this study is to investigate the production of both SARS-CoV-2-specific antibodies and autoantibodies in serum following the third booster vaccination of the inactivated COVID-19 vaccine, and to study the effect of B cell subsets with CD27 and CD38 phenotypes in peripheral blood on antibody production. METHODS: Routine blood indexes, SARS-CoV-2 antibodies, platelet factor 4 and seven antiphospholipid antibodies were detected both before and 2 months after vaccination in the medical staff of the Zhongnan Hospital of Wuhan University. Peripheral blood B cell subtypes were detected prior to vaccination. RESULTS: Following immunization, the positive rate of anti-N-S1 IgG had increased from 24.8% to 91.3% and the average antibody concentration had increased by 11 times. The positive rate of NAb had increased from 24.8% to 91.3%, the average antibody concentration had increased by 12 times, and the primary increased anti-S1 IgG subtype was that of IgG1. Peripheral blood CD27+CD38+ B cells were positively correlated with antibody levels after vaccination and were a predictor of the antibody response. In addition, although some indicators showed slight absolute changes, the blood parameters and antiphospholipid antibodies of most volunteers were normal both before and after COVID-19 inactivated vaccine inoculation, and there was no statistical difference in abnormal rates either before or after inoculation. CONCLUSION: Antibodies in vivo were increased after vaccination with the inactivated vaccine, and IgG1 was the main subtype involved in response to the vaccine. Vaccination with the inactivated COVID-19 vaccine did not appear to affect thrombus-related autoantibodies. This article is protected by copyright. All rights reserved.

SARS-CoV-2-specific immune response in COVID-19 convalescent individuals.

We collected blood from coronavirus disease 2019 (COVID-19) convalescent individuals and investigated SARS-CoV-2-specific humoral and cellular immunity in these discharged patients. Follow-up analysis in a cohort of 171 patients at 4-11 months after the onset revealed high levels of IgG antibodies. A total of 78.1% (164/210) of the specimens tested positive for neutralizing antibody (NAb). SARS-CoV-2 antigen peptide pools-stimulated-IL-2 and -IFN-γ response can distinguish COVID-19 convalescent individuals from healthy donors. Interestingly, NAb survival was significantly affected by the antigen peptide pools-stimulated-IL-2 response, -IL-8 response, and -IFN-γ response. The antigen peptide pools-activated CD8+ T cell counts were correlated with NAb. The antigen peptide pools-activated natural killer (NK) cell counts in convalescent individuals were correlated with NAb and disease severity. Our data suggested that the development of NAb is associated with the activation of T cells and NK cells. Our work provides a basis for further analysis of the protective immunity to SARS-CoV-2 and for understanding the pathogenesis of COVID-19. It also has implications for the development of an effective vaccine for SARS-CoV-2 infection.

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 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.

Potential of Immune-Related Therapy in COVID-19.

At the beginning of 2020, a sudden outbreak of new coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), infections led to anxiety, panic, and crisis among people worldwide. The outbreak first occurred in Wuhan, China, in late December 2019 and then spread rapidly across the globe, thus becoming a major public health emergency. Although the current epidemic situation in China tends to be stable, coronavirus disease 2019 (COVID-19) continues to spread globally. At present, no specific therapeutic drugs and vaccines are available against COVID-19. Also, the pathogenesis of the SARS-CoV-2 is not fully clear. Human immunity is important in SARS-CoV-2 infection. Studies have shown that excessive inflammation caused by SARS-CoV-2 infection and subsequent induced uncontrolled cytokine storm are the main causes of disease deterioration and death of severe patients. Therefore, immune-related research is of great significance for the prevention, control, and prognosis of COVID-19. This study aimed to review the latest research on immune-related treatment of COVID-19.

Can routine laboratory tests discriminate SARS-CoV-2-infected pneumonia from other causes of community-acquired pneumonia?

BACKGROUND: The clinical presentation of SARS-CoV-2-infected pneumonia (COVID-19) resembles that of other etiologies of community-acquired pneumonia (CAP). We aimed to identify clinical laboratory features to distinguish COVID-19 from CAP. METHODS: We compared the hematological and biochemical features of 84 patients with COVID-19 at hospital admission and 221 patients with CAP. Parameters independently predictive of COVID-19 were calculated by multivariate logistic regression. The receiver operating characteristic (ROC) curves were generated and the area under the ROC curve (AUC) was measured to evaluate the discriminative ability. RESULTS: Most hematological and biochemical indexes of patients with COVID-19 were significantly different from patients with CAP. Nine laboratory parameters were identified to be predictive of a diagnosis of COVID-19. The AUCs demonstrated good discriminatory ability for red cell distribution width (RDW) with an AUC of 0.87 and hemoglobin with an AUC of 0.81. Red blood cell, albumin, eosinophil, hematocrit, alkaline phosphatase, and mean platelet volume had fair discriminatory ability. Combinations of any two parameters performed better than did the RDW alone. CONCLUSIONS: Routine laboratory examinations may be helpful for the diagnosis of COVID-19. Application of laboratory tests may help to optimize the use of isolation rooms for patients when they present with unexplained febrile respiratory illnesses.