中国神经再生研究(英文版)

中国神经再生研究(英文版) ›› 2019, Vol. 14 ›› Issue (10): 1823-1832.doi: 10.4103/1673-5374.257535

• 原著:脑损伤修复保护与再生 • 上一篇    下一篇

人体神经干细胞主题趋势和知识结构的映射：2013-2017年文献的定量和共词双向分析

  

  • 出版日期:2019-10-15 发布日期:2019-10-15
  • 基金资助:

    国家自然科学基金(81471308)；中国干细胞临床研究项目(CMR-20161129-1003)；中国大连创新技术基金(2018J11CY025)

Mapping theme trends and knowledge structures for human neural stem cells: a quantitative and co-word biclustering analysis for the 2013–2018 period

Wen-Juan Wei 1, 2, Bei Shi 3, Xin Guan 1, 2, Jing-Yun Ma 1, 2, Ya-Chen Wang 1, 2, Jing Liu 1, 2   

  1. 1 Stem Cell Clinical Research Center, the First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning Province, China
    2 National Joint Engineering Laboratory, the First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning Province, China
    3 Department of Physiology, China Medical University, Shenyang, Liaoning Province, China
  • Online:2019-10-15 Published:2019-10-15
  • Contact: Jing Liu, PhD, liujing.dlrmc@hotmail.com
  • Supported by:

    This study was supported by the National Natural Science Foundation of China, No. 81471308 (to JL); the Stem Cell Clinical Research Project in China, No. CMR-20161129-1003 (to JL); the Innovation Technology Funding of Dalian in China, No. 2018J11CY025 (to JL).

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摘要：

神经干细胞存在于神经系统中，具有自我更新及多向分化潜能，近年来已成为临床上修复中枢神经系统损伤的最具潜力的种子细胞。然而迄今为止，尚未见应用文献计量学方法对神经干细胞相关研究的知识结构及发展趋势进行分析的报道。文章选取PubMed数据库的文献数据为研究对象，以MeSH主题词“神经干细胞”为检索词，获取2013-2018年共2742篇文献。利用书目共现分析系统(BICOMB)对检索结果的相关信息进行提取和统计，生成78个高频主题词，并构建词篇矩阵和共现矩阵。利用gCLUTO软件对词篇矩阵进行双聚类分析并可视化，进一步利用GraphPad Prism 5软件和Ucinet 6.0软件绘制战略坐标图及社会网络图。分析结果显示，近6年来针对神经干细胞的研究可划分为5个主流的研究方向，其中与神经干细胞的细胞学和生理学相关的研究已完善成熟，而神经干细胞在临床应用、组织工程、代谢及信号通路、病理学及病毒学等领域的研究仍不成熟。未来的新兴热点将集中在神经干细胞治疗脑卒中、帕金森病、神经保护剂、脑肿瘤及microRNA遗传学、Notch受体、塞卡病毒、神经嵴和胚胎干细胞等方面。文章为神经干细胞学科前沿和知识基础的探索提供了有益参考，并提出观测科学活动特征的新角度。

orcid: 0000-0002-0493-296X (Jing Liu)

关键词: 人类神经干细胞, PubMed, 文献计量学分析, 双语分析, 协同词分析, 战略图分析, 社会网络分析, 热点研究主题, 主题趋势, 知识结构, 神经再生

Abstract:

Neural stem cells, which are capable of multi-potential differentiation and self-renewal, have recently been shown to have clinical potential for repairing central nervous system tissue damage. However, the theme trends and knowledge structures for human neural stem cells have not yet been studied bibliometrically. In this study, we retrieved 2742 articles from the PubMed database from 2013 to 2018 using “Neural Stem Cells” as the retrieval word. Co-word analysis was conducted to statistically quantify the characteristics and popular themes of human neural stem cell-related studies. Bibliographic data matrices were generated with the Bibliographic Item Co-Occurrence Matrix Builder. We identified 78 high-frequency Medical Subject Heading (MeSH) terms. A visual matrix was built with the repeated bisection method in gCLUTO software. A social network analysis network was generated with Ucinet 6.0 software and GraphPad Prism 5 software. The analyses demonstrated that in the 6-year period, hot topics were clustered into five categories. As suggested by the constructed strategic diagram, studies related to cytology and physiology were well-developed, whereas those related to neural stem cell applications, tissue engineering, metabolism and cell signaling, and neural stem cell pathology and virology remained immature. Neural stem cell therapy for stroke and Parkinson’s disease, the genetics of microRNAs and brain neoplasms, as well as neuroprotective agents, Zika virus, Notch receptor, neural crest and embryonic stem cells were identified as emerging hot spots. These undeveloped themes and popular topics are potential points of focus for new studies on human neural stem cells.

Key words: nerve regeneration, human neural stem cells, PubMed, bibliometric analysis, biclustering analysis, co-word analysis, strategic diagram analysis, social network analysis, hot research topics, mapping theme trends, knowledge structures, neural regeneration