Background: By using network pharmacology and molecular docking technology, we have explored the mechanism of action of Sanqi in the treatment of endometriosis (EMS), in order to provide reference for clinical studies of Chinese medicine treatment of Ems and Chinese medicine pharmacology. Methods: There are 123 intersecting targets between the active ingredients of Sanqi and disease targets. In the Protein-Protein Interaction network, Jun proto-oncogene, AP-1 transcription factor subunit, tumor necrosis factor, interleukin 6, etc., are the core proteins. The top 20 genes ranked by degree have been analyzed according to the Kyoto Encyclopedia of Genes and Genomes pathway and Gene Ontology analysis, and 20 pathways have been identified. Results: On the Kyoto Encyclopedia of Genes and Genomes pathway, the most important part is the phosphatidylinositol 3’-kinase-Akt signaling pathway, and on the Gene Ontology pathway, it is the Heme binding. The top 3 targets docked to quercetin have a certain affinity when it is docked to their degree value. Among the chemical components of Sanqi, quercetin has the most targets, suggesting that it may play a major role in the treatment of EMS. Conclusion: The results of molecular docking provide further evidence of the potential role of Sanqi for EMS. Overall, our study provides a new direction for the treatment of EMS and provides the basis for Sanqi as a drug for the treatment of EMS.

Background: In reports of adverse reactions following vaccination with the coronavirus disease 2019(COVID-19) vaccines, there have been fewer reports of concern for menstrual disorders in female. Objective: Our study employed Vaccine Adverse Event Reporting System (VAERS) to investigate and analyze the relationship between COVID-19 Vaccines and menstrual disorders in female. Methods: We collected reports of menstrual disorders in VAERS from July 2, 1990 to November 12, 2021, and performed a stratified analysis. The potential relationship between COVID-19 vaccine and reports of menstrual disorders was evaluated using the Reporting Odds Ratio (ROR) method. Results: A total of 14,431 reports of menstrual disorders were included in the study, and 13,118 were associated with COVID-19 vaccine. The ROR was 7.83 (95% confidence interval [95%CI]: 7.39–8.28). The most commonly reported event was Menstruation irregular (4998 reports), and a higher percentage of female aged 30–49 years reported menstrual disorders (42.55%) after exposure to COVID-19 Vaccines. Both for all reports of menstrual disorders (ROR = 5.82; 95%CI: 4.93–6.95) and excluding reports of unknown age (ROR = 13.02; 95%CI: 10.89–15.56),suggest that female age may be associated with menstrual disorders after vaccination with the COVID-19 Vaccines. Conclusion: There is a potential safety signal when the COVID-19 vaccine is administered to young adult female (30–49 years old), resulting in menstrual disorders in. However, due to the well-known limitations of spontaneous reporting data, it is challenging to explicity classify menstrual disorders as an adverse event of the COVID-19 Vaccines, and reports of adverse reactions to COVID-19 Vaccines in this age group should continue to be tracked.

Background: In recent years, clinical studies have found that there is a close relationship between osteoporosis and polycystic ovary syndrome. However, there are few literature on the pathogenesis of osteoporosis and polycystic ovary syndrome. In order to clarify their common pathogenic mechanism and provide potential targets for drugs to regulate them at the same time, bioinformatics methods are used to explore, so as to provide a new direction for the study of the relationship between diseases in the future. Methods: To screen the targets of osteoporosis and polycystic ovary syndrome by Genecards, Online Mendelian Inheritance in Man databases and Therapeutic Target Database to take the intersection of the two mappings and upload the intersection targets to the STRING database to construct protein-protein interaction network; to screen the core targets by degree value and import them to Metascape database for Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analysis; and finally, to construct the visualization network of core targets and pathways by Cytoscape software. Ethical approval and informed consent of patients are not required because the data used in this study is publicly available and does not involve individual patient data or privacy. Results: The core targets of polycystic ovary syndrome and osteoporosis were insulin gene, insulin-like growth factor 1, CTNNB1, serine/threonine kinase 1, signal transducer and activator of transcription 3, LEP, etc. The biological processes involved include the regulation of protein phosphorylation, cell proliferation and differentiation, hormone endocrine, reproductive system and skeletal system. The related pathways were concentrated in Foxo signaling pathway, HTLV-I infection, PI3K-AKT signaling pathway, MAPK signaling pathway and AGE-RAGE signaling pathway in diabetic complications. Conclusions: There is a close relationship between osteoporosis and polycystic ovary syndrome in terms of target and molecular mechanism. This study used bioinformatics to clarify their targets and mechanisms, providing potential targets for drugs to regulate both diseases simultaneously and providing new directions to explore the relationship between the diseases.