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Background: Anti-tuberculosis drug-induced hepatic injury (ATDH) lacks specific diagnostic markers.The characteristics of gene polymorphisms have been preliminarily used for the risk classification of ATDH, and the activation of Pregnane X receptor/aminole-vulinic synthase-1/forkhead box O1 (PXR/ALAS1/ FOXO1) axis is closely related to ATDH. Therefore, we consider combining general clinical features of the electronic medical record, laboratory indications, and genetic features of key genes in this axis for predictive model construction to help early clinical diagnosis and treatment. Methods:The general characteristics derived from the Hospital Information System (HIS) medical record system, the biochemical tests and hematology tests were detected by Roche automatic biochemical immunoassay analyzer cobas8000 and Sysmex automatic hemocytometer XE2100. The single nucleotide polymorphisms (SNPs) genotyping work was conducted with a custom-designed 48-plex SNP scan ® TM Kit. A total of 746 cases were included which were divided into training set and validation set according to the ratio of 3:2 randomly. Taking the occurrence of confirmed ATDH as the outcome variable, lasso regression and logistic regression were used to identify the predictors preliminarily. alanine aminotransferase, aspartate aminotransferase, monocyte, uric acid, albumin, fever, the polymorphisms of rs4435111 (FOXO1) and rs3814055 (PXR) were chosen from all variables to combine the predictive model. The goodness of fit, predictive efficacy, discrimination, and consistency, and clinical decision curve analysis was used to assess the clinical applicability of the models.Results: The best model had a discriminant efficacy C-index of 0.8164, a sensitivity of 34.25%, specificity of 97.99%, a positive predictive value of 78.13% and negative predictive value of 87.69%, the two-tailed value of Spiegelhalter Z test of consistency test S:P =0.896, maximum absolute difference Emax =0.147, and average absolute difference Eave =0.017. In the validation set, performance was close. The clinical decision curve showed the clinical applicability of the prediction model when the prediction risk threshold was between 0.1 and 0.8.

To investigate psychological response of Chinese public during the regular prevention and control of Corona Virus Disease 2019 (COVID-19), and explore the relationship among income loss, social support and mental health.Five hundred twenty-six participants were randomly selected by snowball sampling method. Chinese version of Perceived Psychological Stress Scale, Perceived Social Support Scale, self-rating anxiety scale, and the PTSD Checklist for DSM-5 were used to measure the levels of psychological stress, social support, anxiety, and post-traumatic stress disorder (PTSD) symptoms. Demographic variables, income loss and income satisfaction during the outbreak period were also collected.The prevalence rate of anxiety, PTSD symptoms and stress problems were 19.8%, 23.8%, and 24.7% respectively. Multiple Regression Analysis illustrated that social support associated with stress, anxiety and PTSD after controlling demographic variables; for non-student samples, stress, anxiety, and PTSD were corelated with change in income and social support.During the regular prevention and control of COVID-19, social support might help reducing stress, anxiety, and PTSD symptoms. In addition to social support, change of income level was also an important factor for mental health. This study suggested the importance of maintaining a steady income after acute outbreak of COVID-19.

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