Mapping the evolving trend of research on efferocytosis: a comprehensive data-mining-based study.

IF 6.1 3区 生物学 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Yanpeng Jian, Shijia Dong, Weijie Liu, Genfeng Li, Xiaoyu Lian, Yigong Wang
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引用次数: 0

Abstract

Background: Efferocytosis, the process by which apoptotic cells are recognized and removed by phagocytes, plays a critical role in maintaining tissue homeostasis and modulating inflammatory responses. Over recent decades, an increasing number of studies have investigated the molecular mechanisms and clinical implications of efferocytosis. This bibliometric analysis aims to map the evolving trends, identify key contributors, and outline emerging research themes in this field.

Methods: A comprehensive search was conducted in Web of Science database, to collect literature related to efferocytosis from 2006 to 2024. The dataset was analyzed using several tools such as CiteSpace and VOSviewer. Analyses included evaluation of publication trends, citation networks, keyword co-occurrence, and co-cited references. Key metrics such as the most prolific authors, top contributing countries, and major research clusters were identified to understand the field's evolution and interdisciplinary collaborations.

Results: The final dataset comprised 1549 scholarly works, consisting of 1166 original research articles and 383 review papers. The analysis revealed a steady increase in the number of publications concerning efferocytosis, particularly in the past decade. Geographically, China and the United States emerged as dominant contributors, representing over 64.4% of total publications. Among institutions, Harvard University demonstrated the highest research output in this field. Keyword analysis demonstrated the current research focus including molecular mechanisms and signaling regulation of efferocytosis, macrophage polarization and inflammatory modulation, pathological implications and therapeutic potential of efferocytosis in diseases. Inflammation, atherosclerosis, cardiovascular disease, myocardial infarction, and COPD are diseases that has received the most attention in this field. Several research topics including nanoparticle, neuroinflammation, fibrosis, immunometabolism, exosomes, apoptotic bodies, mesenchymal stem cells, aging, microglia, reactive oxygen species, CD47, lipid metabolism, immunotherapy, mitochondria, ferroptosis, may have great potential to be hot topics in the near future. Gene-focused investigations identified TNF, MERTK, IL10, LI6, and IL1b as the most extensively studied genetic elements in efferocytosis research.

Conclusions: This bibliometric study provides a comprehensive overview of the evolving research landscape in efferocytosis. These insights not only highlight the current milestones but also serve as a valuable guide for future research and policy-making aimed at harnessing efferocytosis for therapeutic innovations.

绘制出红细胞增生研究的发展趋势:一项基于数据挖掘的综合研究。
背景:Efferocytosis是凋亡细胞被吞噬细胞识别并清除的过程,在维持组织稳态和调节炎症反应中起着关键作用。近几十年来,越来越多的研究探讨了efferocytosis的分子机制和临床意义。这个文献计量分析的目的是绘制发展趋势,确定关键贡献者,并概述该领域的新兴研究主题。方法:全面检索Web of Science数据库,收集2006年至2024年与effocytosis相关的文献。使用CiteSpace和VOSviewer等工具对数据集进行分析。分析包括对出版趋势、引文网络、关键词共现和共被引文献的评估。确定了诸如最多产的作者、贡献最大的国家和主要研究集群等关键指标,以了解该领域的演变和跨学科合作。结果:最终数据集共收录学术著作1549篇,其中原创研究论文1166篇,综述论文383篇。分析显示,特别是在过去十年中,有关effocytosis的出版物数量稳步增加。从地理上看,中国和美国成为主要贡献者,占总出版物的64.4%以上。其中,哈佛大学在该领域的研究产出最高。关键词分析显示了当前的研究热点,包括efferocytosis的分子机制和信号调控、巨噬细胞极化和炎症调节、efferocytosis在疾病中的病理意义和治疗潜力。炎症、动脉粥样硬化、心血管疾病、心肌梗死和慢性阻塞性肺病是该领域最受关注的疾病。纳米粒子、神经炎症、纤维化、免疫代谢、外泌体、凋亡小体、间充质干细胞、衰老、小胶质细胞、活性氧、CD47、脂质代谢、免疫治疗、线粒体、铁凋亡等研究课题在不久的将来可能成为热点。以基因为中心的研究发现,TNF、MERTK、IL10、LI6和IL1b是在efferocytosis研究中研究最广泛的遗传因子。结论:这项文献计量学研究提供了一个全面的概述,不断发展的研究景观在effocytosis。这些见解不仅突出了当前的里程碑,而且为未来的研究和政策制定提供了有价值的指导,旨在利用efferocytosis进行治疗创新。
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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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