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Optimisation of a Mouse Model of Cerebral Ischemia-Reperfusion to Address Issues of Survival and Model Reproducibility and Consistency.

Authors :
Liu, Zhenqian
Chen, Mo
Duan, Xu
Zhai, Yujia
Ma, Bin
Duan, Zuowei
Xu, Jiang
Liu, Haiyan
Source :
Computational Intelligence & Neuroscience; 7/6/2022, p1-10, 10p
Publication Year :
2022

Abstract

Middle cerebral artery occlusion (MCAO) induced brain ischemia-reperfusion model in Mice is essential for understanding the pathology of stroke and investigating potential treatments, in which a variety of methods may be employed to block the middle cerebral artery (MCA), the most common being through the insertion of a monofilament; however, in vivo ischemia-reperfusion models are associated, particularly in mice, with high variability in lesion volume and high mortality. We aimed to optimise a mouse model of cerebral ischemia-reperfusion, addressing issues of mouse survival, model reproducibility, and consistency. The model was optimised in two ways: first, insert the monofilament directly through the internal carotid artery rather than through the external or common carotid artery, and second, by extending the length of the silicone coating on the monofilament, the length of the silicone coating enables embolization of the beginning of the middle cerebral artery, as well as the anterior cerebral artery and part of the posterior communicating artery. Results: We assessed various parameters, including blood flow changes in the middle cerebral artery, stability of the infarct area, correlation between infarct volume percentages and neurological deficit scores, mortality, weight changes, and wellbeing. We found that optimisation of the surgical procedure may improve mouse wellbeing and reduce mortality, through reduced weight loss and decrease the variability. In conclusion, we suggest that the optimisation of the model is superior for the study of both short and long-term outcomes of ischemic stroke. These results have considerable implications on stroke model selection for researchers. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
16875265
Database :
Complementary Index
Journal :
Computational Intelligence & Neuroscience
Publication Type :
Academic Journal
Accession number :
157837736
Full Text :
https://doi.org/10.1155/2022/7594969