Hui Shen1, Wei-Kai Zhu1, Zhi Lu2, Hai-Cheng Zhou3. 1. Department of Traditional Chinese Medicine, the First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning Province, 116011, China. 2. Department of Nuclear Medicine, the First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning Province, 116011, China. 3. Department of Endocrinology, the First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning Province, 116011, China. haichengzhou123@163.com.
Abstract
OBJECTIVE: To summarize current hotspots and predict the potential trends in traditional drugs of diabetes treatment for further research. METHODS: Publications on the application of traditional drugs in diabetes treatment were searched from PubMed without language limits. Highly frequent MeSH terms were identified through Bibliographic Item Co-Occurrence Matrix Builder (BICOMB). Biclustering analysis results were visualized utilizing the gCLUTO software. Finally, a strategic diagram was generated. RESULTS: Totally 2,386 relevant publications were obtained from PubMed on November 9th, 2018, and 69 highly frequent MeSH terms were identified. Biclustering analysis revealed that these highly frequent MeSH terms were classified into 7 clusters. After calculating the density and centrality of each cluster, strategy diagram was presented. Cluster 0 "Chinese medicine monomers such as antioxidant and hypoglycemic effects" was considered as the most potential research hotspot. CONCLUSIONS: In this study, we found 7 topics related to the application of traditional drugs in diabetes treatment. The molecular mechanisms of Chinese medicine monomers in diabetes could become a potential hotspot with high centricity and low density.
OBJECTIVE: To summarize current hotspots and predict the potential trends in traditional drugs of diabetes treatment for further research. METHODS: Publications on the application of traditional drugs in diabetes treatment were searched from PubMed without language limits. Highly frequent MeSH terms were identified through Bibliographic Item Co-Occurrence Matrix Builder (BICOMB). Biclustering analysis results were visualized utilizing the gCLUTO software. Finally, a strategic diagram was generated. RESULTS: Totally 2,386 relevant publications were obtained from PubMed on November 9th, 2018, and 69 highly frequent MeSH terms were identified. Biclustering analysis revealed that these highly frequent MeSH terms were classified into 7 clusters. After calculating the density and centrality of each cluster, strategy diagram was presented. Cluster 0 "Chinese medicine monomers such as antioxidant and hypoglycemic effects" was considered as the most potential research hotspot. CONCLUSIONS: In this study, we found 7 topics related to the application of traditional drugs in diabetes treatment. The molecular mechanisms of Chinese medicine monomers in diabetes could become a potential hotspot with high centricity and low density.
Entities:
Keywords:
Chinese medicine; MeSH term; bibliometrics; diabetes; hotspot; traditional drug