[Multi-dimensional mining and analysis of core prescriptions in launched Chinese patent medicine for treatment of influenza].

Through the multi-dimensional mining and analysis of launched anti-influenza proprietary Chinese medicines,this paper explores the study of the prescriptions and pharmacodynamics of traditional Chinese medicines for influenza. We established a standardized database by collecting and excavating the launched Chinese patent medicines that clearly describe the treatment of influenza. Frequency analysis and association rules were used to analyze the frequency of Chinese patent medicines for the treatment of influenza in the aspects of dosage form,category and prescription drugs. The network module partitioning method was used to excavate the core drug combination for influenza. The relationship between functional nouns was used to construct a network of functional terminology and analyze the relationship between its main functions. The pharmacological characteristics quantitative method was used to analyze the pharmacological characteristics of three heat-clearing and detoxifying type Chinese patent medicines for influenza. This article shows the traditional Chinese medicine syndrome differentiation ideas and medication rules for influenza treatment in many aspects and from multiple perspectives,so as to provide a certain reference for the clinical application of proprietary Chinese medicines for influenza and the development of new influenza drugs.

CVSARRP: A framework to predict the risk of adverse to severe adverse reactions for 10855 diseases after COVID-19 vaccination.

COVID-19 vaccines greatly reduce the risk of infection with SARS-CoV-2. However, some people have adverse reactions after vaccination, and these can sometimes be severe. Gender, age, vaccines, and especially certain diseases histories are related to severe adverse reactions following COVID-19 vaccination. However, there are thousands of diseases and only some are known to be related to these severe adverse reactions. The risk of severe adverse reactions with other diseases remains unknown. Therefore, there is a need for predictive studies to provide improved medical care and minimize risk. Herein, we analyzed the statistical results of existing COVID-19 vaccine adverse reaction data and proposed a COVID-19 vaccine severe adverse reaction risk prediction method, named CVSARRP. The performance of the CVSARRP method was tested using the leave-one-out cross-validation approach. The correlation coefficient between the predicted and real risk is greater than 0.86. The CVSARRP method predicts the risk from adverse reactions to severe adverse reactions after COVID-19 vaccination for 10855 diseases. People with certain diseases, such as central nervous system diseases, heart diseases, urinary system disease, anemia, cancer, and respiratory tract disease, among others, may potentially have increased of severe adverse reactions following vaccination against COVID-19 and experiencing adverse events.