Evaluation of the Levels of Peripheral CD3+, CD4+, and CD8+ T Cells and IgG and IgM Antibodies in COVID-19 Patients at Different Stages of Infection.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection affects the stimulatory levels of cellular-mediated immunity, which plays an essential role in controlling SARS-CoV-2 infection. In fact, several studies have shown the association of lymphopenia with severe COVID-19 in patients. The aim of this study is to investigate the response of the immune system, including cell-mediated immunity and antibody production, during different stages of SARS-CoV-2 infection. Peripheral blood and serum samples were collected from patients with moderate infection, patients under medication (hospitalized), patients who had recovered, and healthy individuals (n = 80). Flow cytometry analysis was performed on peripheral blood samples to determine the cellular immunity profile of each patient. The data showed a significant reduction in the levels of CD3+, CD4+, and CD8+ T cells and CD45+ cells in the moderate and under-medication groups, suggesting lymphopenia in those patients. Also, enzyme-linked immunosorbent assay (ELISA) was conducted on the serum samples to measure the levels of antibodies, including IgM and IgG, in each patient. The results revealed a significant increase in the levels of IgM in the moderate infection and under-medication patients, thus indicating the production of IgM during the first week of infection. Furthermore, changes in the levels of IgG were significantly detected among recovered patients, indicating therefore a remarkable increase during the recovery stage of SARS-CoV-2 infection and thus a strong humoral-mediated immunity. In summary, the results of this study may help us to understand the main role of the cellular immune responses, including CD3+, CD4+, and CD8+ T cells, against SARS-CoV-2 infection. This understanding might support the development of SARS-CoV-2 treatments and vaccines in the near future. IMPORTANCE Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged in late 2019 in China. This virus is a serious threat to people not only in China but also worldwide, where it has been detected in over 222 countries. It has been reported that ∼3.4% of SARS-CoV-2-infected patients have died. The significance of our study relies on the fact that an enzyme-linked immunosorbent assay and flow cytometry were used to measure the levels of antibodies and cellular immune response, respectively, from clinical samples of patients infected with SARS-CoV-2.

Combination of four clinical indicators predicts the severe/critical symptom of patients infected COVID-19.

BACKGROUND: Despite the death rate of COVID-19 is less than 3%, the fatality rate of severe/critical cases is high, according to World Health Organization (WHO). Thus, screening the severe/critical cases before symptom occurs effectively saves medical resources. METHODS AND MATERIALS: In this study, all 336 cases of patients infected COVID-19 in Shanghai to March 12th, were retrospectively enrolled, and divided in to training and test datasets. In addition, 220 clinical and laboratory observations/records were also collected. Clinical indicators were associated with severe/critical symptoms were identified and a model for severe/critical symptom prediction was developed. RESULTS: Totally, 36 clinical indicators significantly associated with severe/critical symptom were identified. The clinical indicators are mainly thyroxine, immune related cells and products. Support Vector Machine (SVM) and optimized combination of age, GSH, CD3 ratio and total protein has a good performance in discriminating the mild and severe/critical cases. The area under receiving operating curve (AUROC) reached 0.9996 and 0.9757 in the training and testing dataset, respectively. When the using cut-off value as 0.0667, the recall rate was 93.33 % and 100 % in the training and testing datasets, separately. Cox multivariate regression and survival analyses revealed that the model significantly discriminated the severe/critical cases and used the information of the selected clinical indicators. CONCLUSION: The model was robust and effective in predicting the severe/critical COVID cases.