Objective:By analyzed the transmission patterns of 4 out of the 51 COVID-19 cluster cases in Shaanxi province to provide evidences for the COVID-19 control and prevention.Methods:The epidemiological data of RT-PCR test-confirmed COVID-19 cases were collected. Transmission chain was drawn and the transmission process was analyzed.Results:Cluster case 1 contained 13 cases and was caused by a family of 5 who traveled by car to Wuhan and returned to Shaanxi. Cluster case 2 had 5cases and caused by initial patient who participated family get-together right after back from Wuhan while under incubation period. Cluster case 3 contained 10 cases and could be defined as nosocomial infection. Cluster case 4 contained 4 cases and occurred in work place.Conclusion:Higher contact frequency and smaller places were more likely to cause a small-scale COVID-19 cluster outbreak, with potential longer incubation period. COVID-19 control strategies should turn the attention to infection prevention and control in crowded places, management of enterprise resumption and prevention of nosocomial infection.

Objective:To understand the incidence trend and epidemiological characteristics of COVID-19 in Shaanxi province.Methods:The incidence data of COVID-19 reported in Shaanxi as of 22 February, 2020 were collected for an epidemiological descriptive analysis.Results:A total of 245 confirmed cases of COVID-19 were reported in Shaanxi. Most cases were mild (87.76%). As time passed, the areas where confirmed cases were reported continued to increase. The case number in Xi’an was highest, accounting for nearly half of the total reported cases in the province. The epidemic pattern in Shaanxi had gradually shifted from imported case pattern to local case pattern, and the transmission of local cases was mainly based on family cluster transmission. The confirmed cases from different sources had caused the secondary transmission in Shaanxi. After February 7, the number of reported cases began to fluctuate and decrease stably, indicating a decrease-to-zero period.Conclusions:At present, the overall epidemic of COVID-19 in Shaanxi has gradually been mitigated. However, considering the approaching of return to work and study and the increasing of imported cases from other countries, the prevention and control of COVIS-19 in Shaanxi will face new challenges.

Objective:To investigate the relationship between triglyceride glucose index (TyG) and body inflammation.Methods:The data were obtained from a baseline survey in population in Xi'an in natural population cohort study in northwest China established in 2018-2019. Based on TG and FPG, TyG/TyG-BMI was constructed to reflect insulin resistance (IR) in the body, and systemic immune-inflammation index (SII) reflecting inflammation in the body was constructed using neutrophil, lymphocyte, and platelet counts. A logistic regression model was used to explore the relationship between the TyG and the SII.Results:A total of 11 491 subjects were included in the analysis. After adjusting for covariates, each unit increase in the TyG increased the risk of high SII by 21% ( OR=1.21, 95% CI:1.12-1.30). The risk of high SII in the group with the TyG in Q4 was 1.34 times higher than that in the group Q1 ( OR=1.34, 95% CI:1.18-1.52). Both sensitivity analysis and subgroup analysis further confirmed the stability of the association between the TyG and the SII. In the population with a BMI ranging from 18.5 to 23.9 kg/m 2, for every unit increase in the TyG as a continuous variable, the risk for high SII increased by 31% ( OR=1.31, 95% CI:1.18-1.45). As a categorical variable, the risk for high SII in the Q4 group was 1.52 times higher than that in the Q1 group ( OR=1.52, 95% CI:1.27-1.83). In a population with BMIs ranging from 24.0 to 27.9 kg/m 2, for every unit increase in the TyG as a continuous variable, the risk for high SII increased by 20% ( OR=1.20, 95% CI:1.07-1.35), and there was no significant difference when it was a categorical variable. Conclusions:The increase in IR is closely related to the development of inflammation in the body, and BMI may regulate their relationship. Early prevention of elevated IR levels before overweight or obesity may have a positive effect on the control of inflammation in the body.

OBJECTIVE To analyze influential factors for dabigatran exposure in elderly patients with non-valvular atrial fibrillation. METHODS The clinical information of 75 elderly patients diagnosed with non-valvular atrial fibrillation was collected from our hospital in Jan. 2019-Jun. 2020. One or two steady-state blood drug concentration samples were collected from each patient. NONMEM 7.2.0 software was used to establish a population pharmacokinetics model of dabigatran； the effects of different covariates on the apparent clearance of dabigatran were investigated， and the final model was verified by goodness of fit and Bootstrap method； NONMEM 7.2.0 software was used to analyze the drug exposure of ordinary elderly patients and elderly patients after taking dabigatran ester in different disease states. RESULTS Totally 122 blood concentration samples of dabigatran were collected. Advanced age， creatinine clearance and history of chronic heart failure were screened out as three significant covariates that influenced the clearance of dabigatran in elderly patients. The exposure of population with advanced age increased by about 50% compared with the general elderly， the exposure of population with history of chronic heart failure increased by nearly 30% compared with population without， and the exposure of population with moderate and severe renal injury increased by about 30% and 80% compared with mild. CONCLUSIONS Advanced age， renal injury and history of chronic heart failure are influential factors for elevated systemic exposure of dabigatran.