Chinese Journal of Tissue Engineering Research ›› 2026, Vol. 30 ›› Issue (34): 9088-9094.doi: 10.12307/2026.865
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He Renda1, Ma Wei2, Sun Yongsi1, Mo Xueni2
Received:2025-10-29
Revised:2026-01-22
Online:2026-12-08
Published:2026-04-15
Contact:
Mo Xueni, PhD, Professor, Master’s supervisor, Development Planning Office of Guangxi University of Chinese Medicine, Nanning 530200, Guangxi Zhuang Autonomous Region, China
About author:He Renda, MS candidate, The First Clinical Medical College of Guangxi University of Chinese Medicine, Nanning 530200, Guangxi Zhuang Autonomous Region, China
Supported by:CLC Number:
He Renda, Ma Wei, Sun Yongsi, Mo Xueni. Future medical research on brain organoids: interdisciplinary training, bioengineering technologies, and optimized model maturity[J]. Chinese Journal of Tissue Engineering Research, 2026, 30(34): 9088-9094.
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2.1 脑类器官年发文量分析 如图 1所示:从2014-2024年,脑类器官领域的研究文献共计发表了1 324篇,整体呈现上升趋势。2014-2021年,文献数量从3篇迅速增长至218篇,显示出该领域的快速发展;2022-2023年,文献发表量趋于平稳,而2024年则再次出现显著增长,这表明脑类器官医学持续受到广泛关注,具有巨大的发展潜力。 2.2 脑类器官文献国家分布 根据脑类器官文献的国家共现分析结果(图2,3),2014-2024年间,共有64个国家在该领域发表了相关文献。其中,发文量排名前六的国家分别为美国(568篇)、中国(258篇)、德国(129篇)、韩国(117篇)、英国(76篇)和意大利(63篇)。进一步分析中介中心性,排名前六的国家依次为美国(0.4)、德国(0.24)、英国(0.17)、荷兰(0.11)、伊朗(0.08)和韩国(0.08)。中介中心性作为衡量节点影响力的重要指标,其值高于0.1表明该国家在合作网络中与其他节点联系紧密[8],具有较高的学术影响力。由此可见,美国和德国在脑类器官医学研究领域处于核心地位,研究成果对全球该领域的发展具有重要引领作用。尽管中国发文量较多,但中介中心性相对较低,这表明中国在该领域的核心枢纽作用仍有提升空间。相比之下,荷兰和伊朗等国的发文总量虽未跻身前列,却表现出较高的中介中心性,显示出这些国家在合作网络中扮演了不可或缺的桥梁作用。 2.3 脑类器官文献机构分布 发文量是衡量科研机构实力的重要指标之一,能够直观反映机构在特定领域的研究活跃度和影响力。如图4所示,全球共有372个机构参与了脑类器官领域的研究,其中发文量排名前十的机构依次为:加州大学系统(94篇)、哈佛大学(57篇)、中国科学院(44篇)、俄亥俄大学系统(39篇)、加州大学圣地亚哥分校(38篇)、哈佛大学医学附属机构(35篇)、约翰斯·霍普金斯大学(35篇)、马克斯·普朗克学会(31篇)、哈佛医学院(29篇)以及美国国立卫生研究院(NIH)(28篇)。从机构间的合作网络来看,绝大部分机构之间的连线较为紧密,表明该领域内机构间的合作交流频繁,形成了较为广泛的科研协作网络。 2.4 脑类器官文献作者分布与作者突现分析 基于作者共现分析(图5)的结果,N值为398,E值为503,表明脑类器官研究的广泛性与复杂性,同时网络密度为0.006 4,说明研究者之间的合作关系较为分散,未形成高度集中的核心群体。图中可以直观发现高发文量作者之间的合作频率较低,而低发文量作者与高发文量作者之间则表现出较为密切的合作关系。这一现象表明,研究团队内部形成了稳定的合作网络,但不同研究团队之间尚未建立广泛的合作关系。具体而言,发文量排名前三的作者分别为Knoblich, Juergen A(尤尔根·A·克诺布利希,13篇)、Song, Hongjun(宋洪军,12篇)和Arlotta, Paola(保拉·阿洛塔,9篇),这些作者在领域内具有较高的学术影响力。根据普赖斯定律(Price’s Law)计算得出核心作者最低发文量为2.701篇,取整后为3篇,得到脑类器官研究领域的核心作者人数为77人,占总作者人数的19.34%[9]。进一步进行作者突现分析(表1),研究识别出前12位突现作者,突现强度分布较为均衡。这一结果表明,脑类器官研究领域具有较高的主题多样性,研究热点分布均匀,且研究者的活跃程度相对一致。此外,研究热点的集中爆发期主要出现在2021-2024年,这一时间段的学术成果显著增加,表明脑类器官研究领域在近年来进入了快速发展的新阶段,这一发展趋势可能得益于技术进步、"
研究方法的创新以及跨学科合作的深化。 2.5 脑类器官文献共引分析 将脑类器官文献进行文献共引分析得到网络图谱,见图6,共引频次最高的6篇文章分别是Brain-Region-Specific Organoids Using Mini-bioreactors for Modeling ZIKV Exposure(使用微型生物反应器模拟 ZIKV 暴露的大脑区域特异性类器官,259次),Cell diversity and network dynamics in photosensitive human brain organoids(光敏人脑类器官中的细胞多样性和网络动力学,233次),An in vivo model of functional and vascularized human brain organoids(一种功能性和血管化人脑类器官的体内模型,232次),Individual brain organoids reproducibly form cell diversity of the human cerebral cortex(单个大脑类器官可重复地形成人类大脑皮质的细胞多样性,232次),Engineering of human brain organoids with a functional vascular-like system(具有功能性类血管系统的人脑类器官工程,204次),Assembly of functionally integrated human forebrain spheroids(功能整合的人类前脑球体的组装,186次),见表2[10-15],这些文献的高共引频次表明在脑类器官研究领域具有广泛认可度和重要的参考价值。 2.6 脑类器官关键词共现及聚类分析 将关键词、近义词合并后对数据进行共现分析,见图7,将出现频次排序前13位的关键词进行统计,见表3,通过聚类分析,得到Q值为0.505 5,S值为0.797 8的关键词聚类图,见图8,模块值Q和轮廓值S是衡量聚类结构合理性的2个重要指标,Q > 0.3说明聚类结构显著,S > 0.5表明聚类结果同质性较高[16],由此可见图8的聚类结构合理,代表性显著,根据关键词聚类图将近10年脑类器官领域的研究划分为以下10个主要研究主题:Parkinson’s disease(帕金森病),self-organization (自组织),blood-brain barrier (血脑屏障),in vitro expansion (体外扩增),cerebral cortex (大脑皮质),developmental neurotoxicity (发育性神经毒性),glioblastoma (胶质母细胞瘤),cells (细胞),consciousness (意识),drug discovery (药物发现)。 2.7 脑类器官文献关键词突现分析 对脑类器官文献关键词进行突现分析,见表4[17],表中pluripotent stem cells (多能干细胞)的突现强度最强,达到7.33,该方向在2017-2019年的热度较高,in vitro model(体外模型)、culture (培养)突现持续时间最长,表明in vitro model、culture发展潜力大,是该领域的长期研究热点,近年热点研究方向则为choroid plexus (脉络丛)、human cortical organoids (人类皮质类器官)以及microglia(小胶质细胞)。 2.8 脑类器官研究领域发展趋势 通过关键词时区图能快速把握领域的研究动态和趋势,如图9所示,节点N=290,连线E=1 493,密度为0.035 6,表明关键词之间存在显著关联但并非完全联通,2014-2017年,研究聚焦于基础技术,如induced pluripotent stem cells(多能干细胞诱导)、differentiation(分化)以及culture (培养),建立脑类器官生成的基础方法并使用动物模型验证技术可行性[10,18],self-organization "
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