绘制白细胞端粒长度研究的演变趋势图:文本挖掘研究。

IF 3.8 3区 医学 Q2 GENETICS & HEREDITY
Yuanjun Lyu, Hongjie Zhao, Guiping Zeng, Jia Yang, Qipeng Shao, Haiyang Wu
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引用次数: 0

摘要

背景:大量证据表明,测量白细胞端粒长度(LTL)是一种有用的工具,可被视为个人生物年龄的重要生物标志物,与许多慢性疾病相关。然而,迄今为止,人们对LTL领域的现状和未来发展还缺乏深入了解。因此,本研究旨在利用文献计量学方法总结该领域的知识结构、当前重点和新兴方向:从 Web of Science Core Collection 数据库中获取了 2000 年至 2022 年期间有关 LTL 的科学出版物。文献计量分析使用了几种文献计量工具,包括 CiteSpace、VOSviewer 和一个在线网站。主要评估包括调查主要撰稿人及其在国家/地区、机构和作者之间的合作关系,对作者、期刊和参考文献进行共引分析,检查参考文献突发情况,以及对关键词进行共现分析:结果:共发现 1818 篇论文,引用次数达 66,668 次。LTL的年发表量和引用量都呈现出明显的上升趋势。论文数量最多、H 指数值最高的国家是美国。加利福尼亚大学旧金山分校和阿维夫A分别被认定为该领域最有贡献的机构和作者。参考文献分析显示,纵向研究和亡羊补牢式随机研究是该领域近期最受关注的研究方法。关键词分析显示,LTL 领域最受关注的疾病是衰老、炎症、心血管疾病、内分泌疾病、神经和精神疾病以及癌症。此外,"慢性阻塞性肺病"、"门德尔随机化"、"肥胖"、"结直肠癌"、"国家健康与营养调查(NHNES)"、"端粒酶逆转录酶"、"妊娠 "等研究方向近来也日益受到关注,并有可能在可预见的未来发展成为研究热点:这是第一项全面概述长效语言研究的文献计量学研究。结论:这是第一份全面概述 LTL 研究的文献计量学研究报告,研究结果可为研究人员探索和应对 LTL 研究中当前和新出现的挑战提供有价值的参考。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Mapping the evolving trend of research on leukocyte telomere length: a text-mining study.

Background: Substantial evidence indicates that measuring leukocyte telomere length (LTL) is a useful tool that may be considered as a valuable biomarker of individual biological age, correlating with numerous chronic disorders. However, to date, there has been a lack of in-depth understanding regarding the current landscape and forthcoming developments in the LTL field. Therefore, this study aimed to utilize bibliometric methods to summarize the knowledge structure, current focus, and emerging directions in this field.

Method: Scientific publications on LTL spanning the period from 2000 to 2022 were acquired from the Web of Science Core Collection database. Several bibliometric tools including CiteSpace, VOSviewer, and an online website were utilized for bibliometric analysis. The primary evaluations encompassed investigating the major contributors and their collaborative relationships among countries/regions, institutions, and authors, conducting co-citation analyses of authors, journals, as well as reference, examining reference bursts, as well as performing co-occurrence analyses of keywords.

Results: There are 1818 papers with 66,668 citations identified. Both the annual publication and citation counts on LTL exhibited significant upward trends. The United States emerged as the most prominent contributor, as evidenced by the greatest volume of papers and the highest H-index value. University of California San Francisco and Aviv A were identified as the most productive institution and author in this domain, respectively. Reference analysis revealed that longitudinal study and mendelian randomization study are the most concerned research method in this field recently. Keywords analysis showed that the most concerned diseases in LTL fields were aging, inflammation, cardiovascular diseases, endocrine diseases, neurological and psychiatric diseases, and cancers. In addition, the following research directions such as "COPD", "mendelian randomization", "adiposity", "colorectal cancer", "National Health and Nutrition Examination Survey (NHNES)", "telomerase reverse transcriptase", "pregnancy" have garnered increasing attention in recent times and hold the potential to evolve into research foci in the foreseeable future.

Conclusion: This is the first bibliometric study that provides comprehensive overview of LTL research. The findings of this study could become valuable references for investigators to explore and address the current and emerging challenges in LTL research.

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来源期刊
Human Genomics
Human Genomics GENETICS & HEREDITY-
CiteScore
6.00
自引率
2.20%
发文量
55
审稿时长
11 weeks
期刊介绍: Human Genomics is a peer-reviewed, open access, online journal that focuses on the application of genomic analysis in all aspects of human health and disease, as well as genomic analysis of drug efficacy and safety, and comparative genomics. Topics covered by the journal include, but are not limited to: pharmacogenomics, genome-wide association studies, genome-wide sequencing, exome sequencing, next-generation deep-sequencing, functional genomics, epigenomics, translational genomics, expression profiling, proteomics, bioinformatics, animal models, statistical genetics, genetic epidemiology, human population genetics and comparative genomics.
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