Chinese Journal of Tissue Engineering Research ›› 2024, Vol. 28 ›› Issue (34): 5487-5493.doi: 10.12307/2024.838
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Luo Weidong, Zou Lihua, Huang Da
Received:
2023-12-19
Accepted:
2024-01-18
Online:
2024-12-08
Published:
2024-03-14
Contact:
Huang Da, Master, Lecturer, School of Physical Education, Jiangxi University of Technology, Nanchang 330098, Jiangxi Province, China
About author:
Luo Weidong, PhD, Associate professor, School of Physical Education, Jiangxi University of Technology, Nanchang 330098, Jiangxi Province, China
CLC Number:
Luo Weidong, Zou Lihua, Huang Da. Visual analysis of hot topics in concussion field by finite element method: Improvements in brain injury models, test methods and protective devices[J]. Chinese Journal of Tissue Engineering Research, 2024, 28(34): 5487-5493.
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2.2 学科分布特征 人类社会发展遇到的重大科学问题常需要多个学科的共同协作才得以攻破。学科交叉的方向一般是自然科学前沿探索研究的新方向,是自然科学发展新的增长点,易于产生重大突破[7]。脑震荡作为一个较为复杂的头部损伤,涉及众多学科领域的知识整合。有限元法因其高效、精确及便捷等优势广泛应用于工业,医学等方向。根据Web of Science核心集数据系统文献的学科统计分析可知215篇文献共涉及47个学科方向,其中载文量排名前10位的学科分别是生物医学工程、生物物理学、运动科学、临床神经学、神经科学、工程机械、计算机科学跨学科应用、重症监护医学、工程多学科及材料科学。其中117篇来自生物医学工程、35篇来自生物物理学、33篇来自运动科学、30篇来自临床神经学。通过学科交叉研究,为有限元法运用于脑震荡领域的研究提供了新的研究研究思路与视野,以及奠定的基础,见表1。"
2.3 主要高产作者、国家、机构分布 表2列出了该领域发文量排名前10位的作者、机构、国家。发文量最高的作者是来自渥太华大学的Gilchrist,M博士,发文量为43篇;排名第2位的也是来自渥太华大学的Hoshizaki,TB,发文量也是43篇;排名第3位也是来自渥太华大学Post,A博士,发文量为33篇。根据赖普思定律,核心作者最低发文量N=0.749√Mmax (Mmax为最高产作者发文量)。Mmax为43,计算得出N=5,发文量达到5篇以上即为该领域的核心作者。627位作者中共计有39位作者发文量大于5篇,共计126篇,占发文总量的58.6%。核心作者占总发文量的50%以上则表明该领域的核心作者团队形成,因此在该领域已有核心作者团队形成。发文量最高的机构为来自加拿大的渥太华大学,发文量为48篇;排名第2位的是来自爱尔兰的都柏林大学,发文量为43篇;排名第3位的是来自美国的弗吉尼亚大学,发文量为22篇。发文量最多的国家是美国,发文量为120篇;加拿大排名第2位发文量为64篇;第3位是爱尔兰,发文量为47篇。"
2.4 关键词共现、聚类和爆发分析结果 关键词是对研究内容的高度提炼,作为文章主题的概括和研究重点的凝练。关键词共现分析是研究一篇文献的核心部分,是识别有限元法在脑震荡领域研究热点的重要方法,其可用来分析研究领域内热点的分布情况,其机制是通过计算关键词在所有收录文献的标题和摘要中重复出现的次数来识别关键词的重要性[6]。关键词聚类的作用是展示相关领域的主要的热点研究方向,通过关键词聚类能够挖掘到有限元法在脑震荡领域的研究热点问题[5]。 在Cite Space 6.2.R4软件功能与参数设置区的Node Type选择Keyword。分析时间为2010-2023年,默认1年为1次切割。阈值选项选择“g-index”,k=25。得到频次排名前20的高频关键词,见下表3。之后在运动图像窗口点击Timeline,形成了节点N=287,E=1 795,Q值=0.379 8 > 0.3,Mean Modularity值=0.731 6的关键词时间线图谱,见图2。共计11个聚类群,见下表4。"
自2010年以来文献的高频关键词为concussion(脑震荡),traumatic brain injury(创伤性脑损伤),injury(损伤),professional football(职业橄榄球),acceleration(加速度),brain injury(头部损伤),biomechanics (生物力学),finite element model(有限元模型)。从中心性来看创伤性脑损伤、职业橄榄球、生物力学、头部损伤、有限元模型、橄榄球、有限元分析等节点发挥着重要枢纽作用。脑震荡是创伤性脑损伤的一个子集,在学术术语上经常交替使用[1]。除去与研究主题直接相关的关键词,综合关键词使用频率与中心性分析,橄榄球、头部损伤、生物力学、有限元模型及有限元建模可以作为关键词共现网络的关键节点。 Mean Silhouette(平均轮廓)值是用来衡量网络同质性的指标,取值范围为(-1-1)越接近1反映网络同质性越高,聚类的主题越明确,聚类分类中文献的相关性越接近。Mean Silhouette值为0.7时聚类结果具有高度性的,在0.5以上,可以认为聚类结果是合理的[3]。通过关键词聚类得到11个聚类群,见表4。11个聚类群的平均轮廓值均大于0.5,因此11个聚类结果是合理的。#0聚类群finite element(有限元)的关键词数量最多为39个,年份为2014年。#1聚类群helmet(头盔)关键词数量为37个,年份为2017年。#2聚类群finite element method(有限元模型)关键词数量为34个,年份为2015年。#3聚类群finite element analysis(有限元分析)关键词数量为33个,年份为2018年。#4聚类群finite element modeling(有限元建模)关键词数量为31个,年份为2014年。#5聚类群convolutional neural network(卷积神经网络)关键词数量为29个,年份为2018年。#6聚类群collegiate football players(大学橄榄球运动员)关键词数量为24个,年份为2014年。#7聚类群neurodegeneration (神经退化)关键词数量为17个,年份为2019年。#8 behavioral assessments(行为评估)关键词数量为14个,年份为2013年。#9聚类群brain injury (头部损伤)关键词数量14个,年份为2015年。#10聚类群personal protection equipment(个人防护装备)关键词数量为8个,年份为2010年。 通过对关键词进行爆发探测,发现16个爆发关键词,如图3。划分为两个阶段:第一阶段(2015-2018年),该阶段的关键词分别为impact(撞击)、finite element modeling(有限元建模)、kinematics(运动学)、head injury(脑损伤)、acceleration(加速度)、Head impact(头部撞击)、deformation(变形);第二阶段(2019-2021年),该阶段的关键词为maximum principal strain(最大主应变)、Diffuse axonal injury(弥散性轴索损伤)、sports(运动)、Professional football(职业橄榄球)、impact(撞击)、concussion(脑震荡)、responses(反应)、model(模型)、exposure(暴露)。"
2.5.1 文献高被引分析 通常在某个领域高被引文献被看作是该研究领域的理论基础,共被引是指2篇文献共同出现在第3篇施引文献的参考文献目录中,则2篇文献形成共被引分析[3]。通常在某个领域高被引文献被看作是该研究领域的理论基础,知识基础是一个有利于进一步明晰研究前沿本质的概念。如果把研究前沿定义为一个研究领域的发展状况,那么研究前沿的引文就形成了相应的知识基础[8]。 对共被引排名前10 的文献展开深入分析,表5列出了有限元在脑震荡领域从2010-2023年共被引次数排名前10 的文献[9-18],这些文献是该领域的核心文献,高被引次数在一定程度上说明了这些研究成果为该领域研究提供了参考,学术影响较大,也在某种程度上反映了此领域的研究的知识基础。 2.5.2 文献共被引聚类分析 为清晰地呈现有限元在脑震荡领域研究基础理论知识群,对共被引文献进行聚类分析,结果发现10个有价值的聚类群[9-14,19-39],见表6,图4。最大的聚类群为#0(藏本模型),平均年份为2019年;第2大聚类群为#1(预先估计),平均年份为2012年;第3位大聚类群为#4(撞击生物力学),平均年份为2011年。"
2.5.3 最具影响力文献分析 通过文献共被引爆发探测共检测出25篇文献,如图5所示。根据文献爆发时间分为两个阶段:第一阶段2011-2016年,共计10篇参考文献。该阶段内爆发性最高的文献是HASIJA等[40]于2013年发表,被引爆发年份发生在2016-2018年,主要研究脑损伤标准的制定。爆发性排第2位的是HERNANDEZ[10](2015),爆发年份发生在2016-2020年,主要发现了6DOF标准比仅3DOF平移和仅3DOF旋转标准更能预测损伤。胼胝体的峰值主应变(6DOF FE标准)是最强的预测指标,其次是两个标准分别是旋转测量,峰值旋转加速度大小和头部撞击功率(HIP)。排名第3位的是KIMPARA[13](2012),爆发年份为2014-2017年,主要研究旋转运动学引起的轻度创伤性脑损伤预测标准。第二阶段为2017-2021年,共计15篇参考文献。爆发性最强的是GABLER[11](2018),爆发年份为2020-2023年,其发现了一个新的度量标准,通用脑损伤标准,能够更好地预测脑应变反应的度量。排名第2位的是SANCHEZ[12](2019),爆发年份为2020-2023年,其修正了用于确定脑震荡风险的数据,并指出在许多重建中改变了头部角运动和大脑应变反应。排名第3位的是GABLER[9](2019),爆发年份为2020-2023年,该研究描述了一种新的脑损伤指标,称为弥漫性轴突多轴综合评估,是最大脑劳损的最佳预测指标。"
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