Advances in stem cells treatment of diabetic wounds: A bibliometric analysis via CiteSpace

Abstract

Diabetes is a chronic medical condition that may induce complications such as poor wound healing. Stem cell therapies have shown promise in treating diabetic wounds with pre‐clinical and clinical studies. However, little bibliometric analysis has been carried out on stem cells in the treatment of diabetic wounds. In this study, we retrieved relevant papers published from January 1, 2003, to December 31, 2023, from Chinese and English databases. CiteSpace software was used to analyze the authors, institutions, and keywords by standard bibliometric indicators. Our analysis findings indicated that publications on stem cells in the treatment of diabetic wounds kept increasing. The most prolific author was Qian Cai (n = 7) and Mohammad Bayat (n = 16) in Chinese and English databases, respectively. Institutions distribution analysis showed that Chinese institutions conducted most publications, and the most prolific institution was the Chinese People's Liberation Army General Hospital (n = 9) and Shahid Beheshti University of Medical Sciences (n = 17) in Chinese and English databases, respectively. The highest centrality keyword in Chinese and English databases was “wound healing” (0.54) and “in vitro” (0.13), respectively. There were 8 and 11 efficient and convincing keyword clusters produced by a log‐likelihood ratio in the Chinese and English databases, respectively. The strongest burst keyword was “exosome” (strength 3.57) and “endothelial progenitor cells” (strength 7.87) in the Chinese and English databases, respectively. These findings indicated a direction for future therapies and research on stem cells in the treatment of diabetic wounds.

A bibliometric analysis and visualization of acupuncture and moxibustion therapy for herpes zoster and postherpetic neuralgia

“…The method was described in our previous study. 16,17 Briefly, the downloaded files from Chinese and English databases were imported into Citespace…”

“…The method was described in our previous study. 16 , 17 Briefly, the downloaded files from Chinese and English databases were imported into Citespace V6.3.R1. The parameters were set as follows: (1) Timespan: 2014–2023 (Slice Length = 1); (2) selection Criteria: g‐index ( k = 25); (3) the node type was set as author, institution, source, and keyword respectively; (4) choosing “Pathfinder” and “Pruning the merged network” for keyword analysis; while choosing “Pruning the sliced network” for other information.…”