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.

痤疮皮肤微生物组的全球趋势和热点分析:文献计量学视角。
背景:痤疮是一种影响毛囊和皮脂腺的慢性炎症。最近的研究揭示了痤疮皮肤微生物组研究的重大进展。缺乏对痤疮皮肤微生物组研究趋势和热点的系统分析。本研究运用文献计量学方法,对痤疮皮肤微生物群的识别结构进行深入研究,识别热点趋势和新兴课题。方法:我们从Web of Science Core Collection中检索关于痤疮皮肤微生物组的文章进行主题搜索。采用CiteSpace、VOSviewer和R语言进行文献计量学研究。结果:本研究分析了来自68个国家1362个机构的757篇文章,其中美国在研究方面处于领先地位。值得注意的是,南特大学(University of Nantes)的布里吉特·德雷姆萨诺(Brigitte drsamno)是这一领域最多产的作者,发表了19篇论文,共被引用334次。关于痤疮皮肤微生物组的研究成果不断增加,其中《实验皮肤病学》是发表文章最多的期刊。主要重点是研究皮肤微生物组在痤疮发展中的机制和探索治疗策略。这些发现对开发微生物组靶向治疗具有重要意义,可以为痤疮患者提供新的个性化治疗选择。新兴的研究热点包括皮肤护理、肠道微生物组和治疗。结论:本研究结果表明,皮肤微生物群及其与痤疮的关系是一个蓬勃发展的研究兴趣,重点是通过调节皮肤微生物群平衡来治疗痤疮。目前,针对皮肤微生物群调控的护肤品开发是一个研究热点,反映了从基础科学研究向临床实践的过渡。
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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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