Literature DB >> 24491495

Effects of mechanical properties and atherosclerotic artery size on biomechanical plaque disruption - mouse vs. human.

Laurent M Riou1, Alexis Broisat1, Catherine Ghezzi1, Gérard Finet2, Gilles Rioufol2, Ahmed M Gharib3, Roderic I Pettigrew4, Jacques Ohayon5.   

Abstract

Mouse models of atherosclerosis are extensively being used to study the mechanisms of atherosclerotic plaque development and the results are frequently extrapolated to humans. However, major differences have been described between murine and human atherosclerotic lesions and the determination of similarities and differences between these species has been largely addressed recently. This study takes over and extends previous studies performed by our group and related to the biomechanical characterization of both mouse and human atherosclerotic lesions. Its main objective was to determine the distribution and amplitude of mechanical stresses including peak cap stress (PCS) in aortic vessels from atherosclerotic apoE(-/-) mice, in order to evaluate whether such biomechanical data would be in accordance with the previously suggested lack of plaque rupture in this model. Successful finite element analysis was performed from the zero-stress configuration of aortic arch sections and mainly indicated (1) the modest role of atherosclerotic lesions in the observed increase in residual parietal stresses in apoE(-/-) mouse vessels and (2) the low amplitude of murine PCS as compared to humans. Overall, the results from the present study support the hypothesis that murine biomechanical properties and artery size confer less propensity to rupture for mouse lesions in comparison with those of humans.
Copyright © 2014 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Artery size; Human; Mechanical properties; Mouse; Plaque disruption

Mesh:

Substances:

Year:  2014        PMID: 24491495      PMCID: PMC4698166          DOI: 10.1016/j.jbiomech.2014.01.020

Source DB:  PubMed          Journal:  J Biomech        ISSN: 0021-9290            Impact factor:   2.712


  44 in total

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2.  Putative murine models of plaque rupture.

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3.  Predicting Coronary Stenosis Progression Using Plaque Fatigue From IVUS-Based Thin-Slice Models: A Machine Learning Random Forest Approach.

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