Method Parameters' Impact on Mortality and Variability in Mouse Stroke Experiments: a Meta-analysis

Overview

Journal Sci Rep

Specialty Science

Date 2016 Feb 16

PMID 26876353

Citations 9

Authors

Edvin Ingberg

Hua Dock

Elvar Theodorsson

Annette Theodorsson

Jakob O Strom

Affiliations

Soon will be listed here.

Abstract

Although hundreds of promising substances have been tested in clinical trials, thrombolysis currently remains the only specific pharmacological treatment for ischemic stroke. Poor quality, e.g. low statistical power, in the preclinical studies has been suggested to play an important role in these failures. Therefore, it would be attractive to use animal models optimized to minimize unnecessary mortality and outcome variability, or at least to be able to power studies more exactly by predicting variability and mortality given a certain experimental setup. The possible combinations of methodological parameters are innumerous, and an experimental comparison of them all is therefore not feasible. As an alternative approach, we extracted data from 334 experimental mouse stroke articles and, using a hypothesis-driven meta-analysis, investigated the method parameters' impact on infarct size variability and mortality. The use of Swiss and C57BL6 mice as well as permanent occlusion of the middle cerebral artery rendered the lowest variability of the infarct size while the emboli methods increased variability. The use of Swiss mice increased mortality. Our study offers guidance for researchers striving to optimize mouse stroke models.

Citing Articles

Preclinical models of middle cerebral artery occlusion: new imaging approaches to a classic technique.

Sokolowski J, Soldozy S, Sharifi K, Norat P, Kearns K, Liu L Front Neurol. 2023; 14:1170675.

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Pinto R, Magalhaes A, Sousa M, Melo L, Lobo A, Barros P Front Neurol. 2022; 13:846735.

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A review of experimental models of focal cerebral ischemia focusing on the middle cerebral artery occlusion model.

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Meta-analysis of variation suggests that embracing variability improves both replicability and generalizability in preclinical research.

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