Analysis of global trends and hotspots of skin microbiome in acne: a bibliometric perspective.

IF 4 3区 生物学 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Lanfang Zhang, Yuan Cai, Lin Li, Jie Hu, Changsha Jia, Xu Kuang, Yi Zhou, Zhiai Lan, Chunyan Liu, Feng Jiang, Nana Sun, Ni Zeng
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引用次数: 0

Abstract

Background: Acne is a chronic inflammatory condition affecting the hair follicles and sebaceous glands. Recent research has revealed significant advances in the study of the acne skin microbiome. Systematic analysis of research trends and hotspots in the acne skin microbiome is lacking. This study utilized bibliometric methods to conduct in-depth research on the recognition structure of the acne skin microbiome, identifying hot trends and emerging topics.

Methods: We performed a topic search to retrieve articles about skin microbiome in acne from the Web of Science Core Collection. Bibliometric research was conducted using CiteSpace, VOSviewer, and R language.

Results: This study analyzed 757 articles from 1362 institutions in 68 countries, the United States leading the research efforts. Notably, Brigitte Dréno from the University of Nantes emerged as the most prolific author in this field, with 19 papers and 334 co-citations. The research output on the skin microbiome of acne continues to increase, with Experimental Dermatology being the journal with the highest number of published articles. The primary focus is investigating the skin microbiome's mechanisms in acne development and exploring treatment strategies. These findings have important implications for developing microbiome-targeted therapies, which could provide new, personalized treatment options for patients with acne. Emerging research hotspots include skincare, gut microbiome, and treatment.

Conclusion: The study's findings indicate a thriving research interest in the skin microbiome and its relationship to acne, focusing on acne treatment through the regulation of the skin microbiome balance. Currently, the development of skincare products targeting the regulation of the skin microbiome represents a research hotspot, reflecting the transition from basic scientific research to clinical practice.

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来源期刊
Biodata Mining
Biodata Mining MATHEMATICAL & COMPUTATIONAL BIOLOGY-
CiteScore
7.90
自引率
0.00%
发文量
28
审稿时长
23 weeks
期刊介绍: BioData Mining is an open access, open peer-reviewed journal encompassing research on all aspects of data mining applied to high-dimensional biological and biomedical data, focusing on computational aspects of knowledge discovery from large-scale genetic, transcriptomic, genomic, proteomic, and metabolomic data. Topical areas include, but are not limited to: -Development, evaluation, and application of novel data mining and machine learning algorithms. -Adaptation, evaluation, and application of traditional data mining and machine learning algorithms. -Open-source software for the application of data mining and machine learning algorithms. -Design, development and integration of databases, software and web services for the storage, management, retrieval, and analysis of data from large scale studies. -Pre-processing, post-processing, modeling, and interpretation of data mining and machine learning results for biological interpretation and knowledge discovery.
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