基于数据挖掘的血管性痴呆动物模型特点分析

摘要/Abstract

摘要： 目的分析血管性痴呆(vascular dementia,VD)实验动物模型造模要素,为建立稳定可靠的血管性痴呆动物模型实验研究提供参考依据。方法以“血管性痴呆+动物模型”为关键词,对各大数据库中2011年10月1日—2021年10月1日发表的关于血管性痴呆的实验研究文献进行检索。随后,对文献中涉及的造模方法、指标检测等内容进行统计分析,探究其统计学关系。结果在筛选的148篇有效文献中,VD 造模动物主要选用雄性Wistar 大鼠及雄性SD大鼠,体质量多在 200~300 g之间,造模方法以永久性结扎双侧颈总动脉为主,尼莫地平常被用作阳性对照药物,检测指标主要包括Morris水迷宫测试以评估动物行为学变化,以及氧化应激、胆碱能神经指标和脑海马组织病理状态等,以反映VD动物模型的病理生理状态。结论未来应着眼于优化造模方法,提高造模成功率,降低实验动物死亡率。努力建立起体现中西医临床病证特点的血管性痴呆动物模型,以推动相关研究的深入发展。

关键词: 血管性痴呆, 数据挖掘, 动物模型

Abstract: Objective To analyze the modeling elements of experimental animal models of vascular dementia, and to provide reference for the establishment of stable and reliable animal models of vascular dementia experimental research. Methods With "vascular dementia + animal model" as the theme, the experimental studies on vascular dementia were searched from major databases from January 1, 2011 to October 1, 2021, respectively. Statistical analysis was carried out on related modeling methods and index detection in literature. Results A total of 148 valid literatures were screened. Male Wistar rats and male SD rats were mostly selected for VD model animals, and their body weight ranged from 200g to 300g. Permanent ligation of bilateral common carotid arteries was usually used for modeling. Morris water maze was used to detect animal behavior changes, oxidative stress and cholinergic nerve indexes, and pathological state of brain and hippocampal tissues, which reflected the pathophysiological state of VD animal models. Conclusion In the future, we should focus on optimizing the modeling method to improve the success rate of modeling and reduce the mortality rate.To establish the animal model of vascular dementia with Chinese and western medicine.

Key words: vascular dementia, data mining, animal model

中图分类号:

R749.13

刘霁, 孙鸿运, 官磊瑶, 郭冬丽. 基于数据挖掘的血管性痴呆动物模型特点分析[J]. 中华养生保健, 2025, 43(2): 77-81.

LIU Ji, SUN Hong-yun, GUAN Lei-yao, GUO Dong-li. Characteristic Analysis of Vascular Dementia Animal Model Based on Data Mining[J]. ZHONGHUA YANGSHENG BAOJIAN, 2025, 43(2): 77-81.

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