ABSTRACT
As one of the "Three Drugs Three Prescriptions" anti-COVID-19 traditional Chinese medicine, Jinhua Qinggan granules (JHQG) has been proved to have clear clinical effects. With complex medicinal flavors and ingredients, there is no systematic research report on chemical composition in vivo or in vitro. An ultrahigh pressure liquid chromatography-quadrupole-time of flight mass spectrometry (UPLC-QTOF/MS) method was developed in this study to identify the components of the anti-COVID-19 traditional Chinese medicine JHQG granules. Analyze the collected rat plasma samples after administration and explore the exposed components in rats within 8 hours after intragastric administration. Preliminary pharmacokinetic analysis was then performed on this basis. Through UPLC-QTOF/MS analysis and verification by standard products, a total of 77 chemical components in JHQG formula have been identified, among which 22 compounds were highly exposed in vivo, mainly derived from three medicinal materials of honeysuckle, scutellaria and forsythia. Through the assessment of the blood drug concentration by the compartment model, 6 PK parameters of 4 high-exposure chemical components have been obtained, clarifying the metabolic characteristics of the main exposed components in JHQG briefly. The method is simple, efficient, sensitive and accurate and provides research basis to the clarification of the pharmacodynamics material basis and mechanism of JHQG, which has certain reference significance for the basics and applications research of the traditional Chinese medicine prescriptions in fighting the SARS-CoV-2.
ABSTRACT
Objective: To establish a new model for the prediction of severe outcomes of COVID-19 patients and provide more comprehensive, accurate and timely indicators for the early identification of severe COVID-19 patients. Methods: Based on the patients' admission detection indicators, mild or severe status of COVID-19, and dynamic changes in admission indicators (the differences between indicators of two measurements) and other input variables, XGBoost method was applied to establish a prediction model to evaluate the risk of severe outcomes of the COVID-19 patients after admission. Follow up was done for the selected patients from admission to discharge, and their outcomes were observed to evaluate the predicted results of this model. Results: In the training set of 100 COVID-19 patients, six predictors with higher scores were screened and a prediction model was established. The high-risk range of the predictor variables was calculated as: blood oxygen saturation <94%, peripheral white blood cells count >8.0×10(9), change in systolic blood pressure <-2.5 mmHg, heart rate >90 beats/min, multiple small patchy shadows, age >30 years, and change in heart rate <12.5 beats/min. The prediction sensitivity of the model based on the training set was 61.7%, and the missed diagnosis rate was 38.3%. The prediction sensitivity of the model based on the test set was 75.0%, and the missed diagnosis rate was 25.0%. Conclusions: Compared with the traditional prediction (i.e. using indicators from the first test at admission and the critical admission conditions to assess whether patients are in mild or severe status), the new model's prediction additionally takes into account of the baseline physiological indicators and dynamic changes of COVID-19 patients, so it can predict the risk of severe outcomes in COVID-19 patients more comprehensively and accurately to reduce the missed diagnosis of severe COVID-19.