Gender-associated difference following COVID-19 virus infection: Implications for thymosin alpha-1 therapy.

Gender influences clinical presentations, duration and severity of symptoms, and therapy outcome in coronavirus disease 2019 (COVID-19) infection. Whether the immune response to Tα1 treatment for SARS-CoV-2 differs between the sexes, and whether this difference explains the male susceptibility to COVID-19, is unclear. This study aimed to investigate the efficiency and safety of Tα1 treatment and provide a basis for practically identifying gender differences characteristics and features of COVID-19. One hundred twenty-seven patients had COVID-19 symptoms and tested COVID19-positive (female 42.52%) in Wuhan union hospital were enrolled for medication. They were randomly divided into groups Control and Tα1 intervention. Seventy-eight patients received a subcutaneous injection of 1.6 mg Tα1, based on supportive treatment for 15 days. The control group included untreated 49 COVID19 patients closely matched for gender and age and received regular supportive treatment. In this retrospective analysis, we found that COVID-19-infected males reported more symptoms than COVID-19-infected females. A high degree of gender differences-related variability was observed in CRP and PCT levels and the cell counts of many lymphocyte subpopulations in the COVID-19 patients after Tα1 intervention. Levels of CRP and IL-6 were higher in Tα1-treated male group than Tα1-treated female group, while the level of PCT was significantly lower in Tα1-treated male group. Gender differences may be a factor in sustaining COVID-19 immunity responded to Tα1, male and female show statistically significant differences in relevance to cytokine production associated with the development of a more significant number of symptoms. This leaves the question of identifying gender-specific risk factors to explain these differences.

Risk factors related to the severity of COVID-19 in Wuhan.

Objective: To evaluate the characteristics at admission of patients with moderate COVID-19 in Wuhan and to explore risk factors associated with the severe prognosis of the disease for prognostic prediction. Methods: In this retrospective study, moderate and severe disease was defined according to the report of the WHO-China Joint Mission on COVID-19. Clinical characteristics and laboratory findings of 172 patients with laboratory-confirmed moderate COVID-19 were collected when they were admitted to the Cancer Center of Wuhan Union Hospital between February 13, 2020 and February 25, 2020. This cohort was followed to March 14, 2020. The outcomes, being discharged as mild cases or developing into severe cases, were categorized into two groups. The data were compared and analyzed with univariate logistic regression to identify the features that differed significantly between the two groups. Based on machine learning algorithms, a further feature selection procedure was performed to identify the features that can contribute the most to the prediction of disease severity. Results: Of the 172 patients, 112 were discharged as mild cases, and 60 developed into severe cases. Four clinical characteristics and 18 laboratory findings showed significant differences between the two groups in the statistical test (P<0.01) and univariate logistic regression analysis (P<0.01). In the further feature selection procedure, six features were chosen to obtain the best performance in discriminating the two groups with a linear kernel support vector machine. The mean accuracy was 91.38%, with a sensitivity of 0.90 and a specificity of 0.94. The six features included interleukin-6, high-sensitivity cardiac troponin I, procalcitonin, high-sensitivity C-reactive protein, chest distress and calcium level. Conclusions: With the data collected at admission, the combination of one clinical characteristic and five laboratory findings contributed the most to the discrimination between the two groups with a linear kernel support vector machine classifier. These factors may be risk factors that can be used to perform a prognostic prediction regarding the severity of the disease for patients with moderate COVID-19 in the early stage of the disease.