Increased circulating level of interleukin-6 and CD8+ T cell exhaustion are associated with progression of COVID-19.

BACKGROUND: Coronavirus disease 2019 (COVID-19) is pandemic. It is critical to identify COVID-19 patients who are most likely to develop a severe disease. This study was designed to determine the clinical and epidemiological features of COVID-19 patients associated with the development of pneumonia and factors associated with disease progression. METHODS: Seventy consecutive patients with etiologically confirmed COVID-19 admitted to PLA General Hospital in Beijing, China from December 27, 2019 to March 12, 2020 were enrolled in this study and followed-up to March 16, 2020. Differences in clinical and laboratory findings between COVID-19 patients with pneumonia and those without were determined by the χ2 test or the Fisher exact test (categorical variables) and independent group t test or Mann-Whitney U test (continuous variables). The Cox proportional hazard model and Generalized Estimating Equations were applied to evaluate factors that predicted the progression of COVID-19. RESULTS: The mean incubation was 8.67 (95% confidence interval, 6.78-10.56) days. Mean duration from the first test severe acute respiratory syndrome coronavirus 2-positive to conversion was 11.38 (9.86-12.90) days. Compared to pneumonia-free patients, pneumonia patients were 16.5 years older and had higher frequencies of having hypertension, fever, and cough and higher circulating levels of neutrophil proportion, interleukin-6, low count (< 190/µl) of CD8+ T cells, and neutrophil/lymphocyte ratio. Thirteen patients deteriorated during hospitalization. Cox regression analysis indicated that older age and higher serum levels of interleukin-6, C-reactive protein, procalcitonin, and lactate at admission significantly predicted the progression of COVID-19. During hospitalization, circulating counts of T lymphocytes, CD4+ T cells, and CD8+ T cells were lower, whereas neutrophil proportion, neutrophil/lymphocyte ratio, and the circulating levels of interleukin-6, C-reactive protein, and procalcitonin were higher, in pneumonia patients than in pneumonia-free patients. CD8+ lymphocyte count in pneumonia patients did not recover when discharged. CONCLUSIONS: Older age and higher levels of C-reactive protein, procalcitionin, interleukin-6, and lactate might predict COVID-19 progression. T lymphocyte, especially CD8+ cell-mediated immunity is critical in recovery of COVID-19. This study may help in predicting disease progression and designing immunotherapy for COVID-19.

Study on the correlation between KCNJ11 gene polymorphism and metabolic syndrome in the elderly.

The aim of the study was to examine the correlation between KCNJ11 gene polymorphism and metabolic syndrome in elderly patients. From January 2014 to January 2015, 54 elderly patients with metabolic syndrome were enrolled in this study as the observation group. During the same period, 46 healthy elderly individuals were enrolled in this study as the control group. KCNJ11 gene polymorphism (rs28502) was analyzed using polymerase chain reaction-restriction fragment length polymorphism. The expression levels of mRNA in different genotypes were detected using FQ-PCR. ELISA was used to evaluate the KCNJ11 protein expression in different genotypes. KCNJ11 gene polymorphism and metabolic syndrome was studied by measuring the blood pressure levels in patients with different genotypes. Three genotypes of KCNJ11 gene in rs28502 were CC, CT and TT. The CC, CT and TT genotype frequencies in healthy population were 8.5, 9.2 and 82.2%, respectively, while the genotype frequencies in patients with metabolic syndrome were 42.4, 49.8 and 7.8%, respectively. There were significant differences between groups (P≤0.05). However, the genotype frequencies of C/T in healthy individuals and metabolic syndrome patients were 35.3 and 38.3%, respectively. There were no significant differences between groups (P>0.05). FQ-PCR results showed that the KCNJ11 mRNA expression levels in the control and observation groups had no significant differences (P>0.05). However, the results obtained from ELISA analysis revealed that KCNJ11 protein expression level in the observation group was significantly higher than that in the control group (P<0.05). In conclusion, KCNJ11 gene polymorphism is associated with metabolic syndrome in the elderly. Elderly patients with the CC and TT genotypes are more likely to develop metabolic syndrome.

Molecular characterization of Tb, a new approach for an ancient Brucellaphage.

Tb (Tbilisi), the reference Brucellaphage strain, was classified as a member of the Podoviridae family with icosahedral capsids (57 +/- 2 nm diameter) and short tails (32 +/- 3 nm long). Brucellaphage DNA was double stranded and unmethylated; its molecular size was 34.5 kilobase pairs. Some sequences were found through RAPD analysis, TA cloning technology, and structural proteins were observed by using SDS-PAGE. Thus, the results have laid the foundation for the wider use of Brucellaphage's basic mechanisms and practical applications.

[Development of a model for the diagnosis and risk classification on anthrax through artificial neural network].

OBJECTIVE: Based on data through clinical and epidemiological studies, a model regarding the diagnosis and risk classification on anthrax was developed by artificial neural network (ANN). The model could integrally diagnose anthrax cases, judge the risk tendency in time, and increase the ability of recognizing the anthrax accidents. METHODS: Clinical, laboratory and epidemiological data from anthrax cases was collected and analyzed. The important factors which could greatly influence the results on diagnosis and judgment was chosen and used as the neural units. Through the use of artificial neural network analytic method (back propagation, BP), an intelligent model on the diagnosis and risk classification was developed. RESULTS: Results from the multivariate analysis revealed that: 11 factors including incubation period, chest radiographic and microscopic findings, characteristics on professions etc. were associated with the judgment on the diagnosis and intensity of the epidemics. Through 500 times training on the neural network, the performance error decreased from 6.669 59 to 5.051 19 x 10(-11). The model was then validated. With 100% average correct rate, the predictive value was good. CONCLUSION: It was feasible to use the disease information to develop a diagnosis and risk classification model on anthrax by artificial neural network. With 100% average correct rate, the established model was valuable in practice.