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Mouse model of ischemic acute kidney injury: technical notes and tricks

Open access
Wei, Qingqing, Dong, Zheng·2012·American Journal of Physiology-Renal Physiology

Abstract

Renal ischemia-reperfusion leads to acute kidney injury (AKI), a major kidney disease associated with an increasing prevalence and high mortality rates. A variety of experimental models, both in vitro and in vivo, have been used to study the pathogenic mechanisms of ischemic AKI and to test renoprotective strategies. Among them, the mouse model of renal clamping is popular, mainly due to the availability of transgenic models and the relatively small animal size for drug testing. However, the mouse model is generally less stable, resulting in notable variations in results. Here, we describe a detailed protocol of the mouse model of bilateral renal ischemia-reperfusion. We share the lessons and experiences gained from our laboratory in the past decade. We further discuss the technical issues that account for the variability of this model and offer relevant solutions, which may help other investigators to establish a well-controlled, reliable animal model of ischemic AKI.

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Crossref · 10.1152/ajprenal.00352.2012

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Metadata

Authors
Wei, Q., & Dong, Z.
Venue
American Journal of Physiology-Renal Physiology
Published
2012-12-01
Volume
303
Issue
11
Pages
F1487-F1494
ISSN
  • 1522-1466 (ISSN-L)
  • 1931-857X
OpenAlex
W2160807970
Cited by
303
References
2
FWCI
6.55
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Mouse model of ischemic acute kidney injury: technical notes and tricks. https://app.cochat.ai/papers/10.1152/ajprenal.00352.2012
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Wei, Q., & Dong, Z. (2012). Mouse model of ischemic acute kidney injury: technical notes and tricks. American Journal of Physiology-Renal Physiology. https://doi.org/10.1152/ajprenal.00352.2012

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.