Visualizing research trends and identifying hotspots of herbal components for treating cardiovascular diseases: A bibliometric analysis from 2000 to 2023.

OBJECTIVE: The objective of this study was to investigate the global research trends in herbal medicine for the treatment of cardiovascular disease (CVD) from 2000 to 2023. A bibliometric approach was employed to analyze international collaborations, knowledge structures, emerging trends, and research frontiers. METHOD: The Web of Science (WOS) core collection was utilized as the database, employing the search formula (((TS = (traditional Chinese medicine)) OR TS = (Chinese herbal medicinal ingredient)) OR TS = (Chinese herbal medicinal constituent)) AND TS = (cardiovascular disease) to conduct the search. The search period spanned from January 1, 2000, to February 14, 2023, and the literature type included articles and reviews. RESULTS: A total of 1478 papers were included in the analysis after searching the WOS database and excluding conference proceedings, news articles, retractions, editorials, and letters. China demonstrated the highest number of publications, followed by the United States and Taiwan (China). The institution with the highest publications was the Chinese Academy of Medical Sciences. China, the United States, and India were the main countries involved in research in this field, and there was significant collaboration among them. The hotspots related to herbal components for treating cardiovascular diseases from 2000 to 2023 included systematic reviews, ischemic reperfusion injury, global burden, type 2 diabetes, and protection. CONCLUSION: This paper provides a reference for the future development of herbal research in cardiovascular aspects by revealing the current status, hotspots, and trends of global herbal research in cardiovascular factors over more than 20 years. Identification of potential collaborators and institutions can assist researchers in exploring new directions for future research and discovering new perspectives for potential collaborations in this field.

A clustering method of Chinese medicine prescriptions based on modified firefly algorithm.

This paper is aimed to study the clustering method for Chinese medicine (CM) medical cases. The traditional K-means clustering algorithm had shortcomings such as dependence of results on the selection of initial value, trapping in local optimum when processing prescriptions form CM medical cases. Therefore, a new clustering method based on the collaboration of firefly algorithm and simulated annealing algorithm was proposed. This algorithm dynamically determined the iteration of firefly algorithm and simulates sampling of annealing algorithm by fitness changes, and increased the diversity of swarm through expansion of the scope of the sudden jump, thereby effectively avoiding premature problem. The results from confirmatory experiments for CM medical cases suggested that, comparing with traditional K-means clustering algorithms, this method was greatly improved in the individual diversity and the obtained clustering results, the computing results from this method had a certain reference value for cluster analysis on CM prescriptions.