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绘制自闭症谱系障碍生物标志物的结构:对最具影响力研究的综述

Mapping the structure of biomarkers in autism spectrum disorder: a review of the most influential studies.

作者信息

Jin Fang, Wang Zhidan

机构信息

School of Education Science, Jiangsu Normal University, Xuzhou, China.

出版信息

Front Neurosci. 2024 Dec 13;18:1514678. doi: 10.3389/fnins.2024.1514678. eCollection 2024.

DOI:10.3389/fnins.2024.1514678
PMID:39734494
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11671500/
Abstract

BACKGROUND

Autism spectrum disorder is a distinctive developmental condition which is caused by an interaction between genetic vulnerability and environmental factors. Biomarkers play a crucial role in understanding disease characteristics for diagnosis, prognosis, and treatment. This study employs bibliometric analysis to identify and review the 100 top-cited articles' characteristics, current research hotspots and future directions of autism biomarkers.

METHODS

A comprehensive search of autism biomarkers studies was retrieved from the Web of Science Core Collection database with a combined keyword search strategy. A comprehensive analysis of the top 100 articles was conducted with CiteSpace, VOSviewer, and Excel, including citations, countries, authors, and keywords.

RESULTS

The top 100 cited studies were published between 1988 and 2021, with the United States led in productivity. Core biomarkers such as genetics, children, oxidative stress, and mitochondrial dysfunction are well-established. Potential trends for future research may include brain studies, metabolomics, and associations with other psychiatric disorders.

CONCLUSION

This pioneering bibliometric analysis provides a comprehensive compilation of the 100 most-cited studies on autism, which not only offers a valuable resource for doctors, and researchers but shedding insights into current shortcomings and future endeavors. Future research should prioritize the application of emerging technologies for biomarkers, longitudinal study of biomarkers, and specificity of autism biomarkers to advance the precision of ASD diagnosis and treatment.

摘要

背景

自闭症谱系障碍是一种独特的发育状况,由遗传易感性和环境因素相互作用引起。生物标志物在理解疾病特征以进行诊断、预后和治疗方面起着关键作用。本研究采用文献计量分析来识别和综述关于自闭症生物标志物的100篇被引频次最高的文章的特征、当前研究热点及未来方向。

方法

通过组合关键词搜索策略,从科学引文索引核心合集数据库全面检索自闭症生物标志物研究。使用CiteSpace、VOSviewer和Excel对前100篇文章进行全面分析,包括被引频次、国家、作者和关键词。

结果

被引频次最高的100项研究发表于1988年至2021年之间,美国在研究产出方面领先。遗传学、儿童、氧化应激和线粒体功能障碍等核心生物标志物已得到充分确立。未来研究的潜在趋势可能包括脑研究、代谢组学以及与其他精神疾病的关联。

结论

这项开创性的文献计量分析提供了关于自闭症的100篇被引频次最高的研究的全面汇编,不仅为医生和研究人员提供了宝贵资源,还深入洞察了当前的不足和未来的努力方向。未来研究应优先考虑新兴技术在生物标志物方面的应用、生物标志物的纵向研究以及自闭症生物标志物的特异性,以提高自闭症谱系障碍诊断和治疗的精准度。

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本文引用的文献

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Leveraging AI for the diagnosis and treatment of autism spectrum disorder: Current trends and future prospects.利用人工智能进行自闭症谱系障碍的诊断和治疗:当前趋势和未来展望。
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Neuroplasticity of children in autism spectrum disorder.自闭症谱系障碍儿童的神经可塑性
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Neuroimaging genetics approaches to identify new biomarkers for the early diagnosis of autism spectrum disorder.
利用神经影像学遗传学方法识别自闭症谱系障碍早期诊断的新生物标志物。
Mol Psychiatry. 2023 Dec;28(12):4995-5008. doi: 10.1038/s41380-023-02060-9. Epub 2023 Apr 17.
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A bibliometric analysis of global research status and trends in neuromodulation techniques in the treatment of autism spectrum disorder.神经调节技术治疗自闭症谱系障碍的全球研究现状与趋势的文献计量分析
BMC Psychiatry. 2023 Mar 20;23(1):183. doi: 10.1186/s12888-023-04666-3.
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Machine learning for autism spectrum disorder diagnosis using structural magnetic resonance imaging: Promising but challenging.使用结构磁共振成像的机器学习用于自闭症谱系障碍诊断:前景广阔但也面临挑战。
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