Objective: To understand the clinical effectiveness and safety of Shufeng Jiedu Capsules combined with umifenovir (Arbidol) in the treatment of common-type COVID-19. Methods: A retrospective cohort study was used to analyze the case data of 200 inpatients diagnosed with common-type COVID-19 at Wuhan Hospital. Participants were divided into a control group and an experimental group. The control group was treated with Arbidol hydrochloride capsules while the experimental group was treated with combination Arbidol hydrochloride capsules and Shufeng Jiedu Capsules (SFJDC) for 14 days. Results: Defervescence was achieved more rapidly in the experimental group (P < 0.05). The white blood cell count and the lymphocyte percentage in the experimental group were higher than that of the control group (P < 0.05). CRP and IL-6 levels in the experimental group were significantly lower than those in the control group (P < 0.05). The proportion of chest CT studies showing resolution of pneumonia in the experimental group was significantly higher than that in the control group (P < 0.05). Conclusions: A treatment regimen of Shufeng Jiedu Capsules combined with Arbidol to treat commontype COVID-19, combining traditional Chinese and western allopathic medicine, improves time to recovery, has better clinical effectiveness, and is safe.

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Background Our aim was to explore whether a two-step hybrid machine learning model has the potential to discover the onset of depression in home-based older adults. Methods Depression data (collected in the year 2011, 2013, 2015 and 2018) of home-based older Chinese (n = 2,548) recruited in the China Health and Retirement Longitudinal Study were included in the current analysis. The long short-term memory network (LSTM) was applied to identify the risk factors of participants in 2015 utilizing the first 2 waves of data. Based on the identified predictors, three ML classification algorithms (i.e., gradient boosting decision tree, support vector machine and random forest) were evaluated with a 10-fold cross-validation procedure and a metric of the area under the receiver operating characteristic curve (AUROC) to estimate the depressive outcome. Results Time-varying predictors of the depression were successfully identified by LSTM (mean squared error =0.8). The mean AUCs of the three predictive models had a range from 0.703 to 0.749. Among the prediction variables, self-reported health status, cognition, sleep time, self-reported memory and ADL (activities of daily living) disorder were the top five important variables. Conclusions A two-step hybrid model based on “LSTM+ML” framework can be robust in predicting depression over a 5-year period with easily accessible sociodemographic and health information.