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Integrated Computational Model Framework for the Study of Atherosclerosis

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A model for atherosclerosis

Atherosclerosis is a major cause of death in developed countries. To understand disease pathobiology and the underlying biological processes, a European study developed an integrated computational model framework.

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Atherosclerosis is a condition associated with the thickening of the artery wall due to plaque build-up and accumulation of white blood cells. This condition affects blood flow and modifies the interaction of blood components with the deformed arteries. Although a number of parameters such as LDL concentration influence atheroma formation, we lack an integrated model that unifies blood flow dynamics and arterial wall mechanics. In answer to this, scientists on the EU-funded ICOMATH (Integrated computational model framework for the study of atherosclerosis) project proposed a computational model framework that integrated these parameters to study the development of atherosclerosis. By focusing on LDL transport, they wanted to describe the biological events that trigger the atherogenic process and its progression. Acoustic radiation force of ultrasound and shear-based elastography were evaluated for their capacity to assess the viscoelastic behaviour of arterial walls and distinguish between normal and calcified arteries. Researchers used a theoretical model to study the radiation-force profile and generate sufficient spatial resolution. This method significantly improved the estimated local viscoelastic properties of the arterial wall. In another part of the project, researchers developed a 3-D finite-element-method (FEM)-based model to study the propagation of shear waves in viscoelastic media. At the same time, improved frequency-based algorithms dealt with noise reduction in the estimation of the viscoelastic properties of arterial walls. The same model enabled the investigation and the prediction of atherosclerotic plaque evolution. Collectively, implementation of the ICOMATH model could lead to a more accurate prediction of potential sites of plaque formation. This would enable prompt treatment and monitoring of individual patients.


Atherosclerosis, computational model, artery wall, LDL, FEM, viscoelastic media, algorithm

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