Figure 1.
Hydrodynamic flow characteristics through a high-shear microfluidic device and stenosed mouse carotid artery. (A) Geometry of the microfluidic model with an experimental stenotic section. (B) In vivo experiments showing a stenotic region relative to flow direction and flow probe placement. (C) CFD simulations of the microfluidic device. Blood flowed from left to right. The color contour shows shear strain rate distribution. The solution showed a maximum shear strain rate of >3500 s−1 in the stenosis region, whereas upstream and downstream values were <1000 s−1. (D) CFD simulation in a stenosed mouse carotid artery. The large velocity gradient at the stenotic apex led to initial shear rates that exceeded 4000 s−1. (E-F) Thrombus growth was predicted by using a high-shear thrombosis model developed by Mehrabadi et al.45 Shear strain rate estimation is shown over time during high-shear thrombosis formation in the microfluidic assay and mouse carotid artery. Min and Max denote the lower and upper confidence limits of the model, respectively.

Hydrodynamic flow characteristics through a high-shear microfluidic device and stenosed mouse carotid artery. (A) Geometry of the microfluidic model with an experimental stenotic section. (B) In vivo experiments showing a stenotic region relative to flow direction and flow probe placement. (C) CFD simulations of the microfluidic device. Blood flowed from left to right. The color contour shows shear strain rate distribution. The solution showed a maximum shear strain rate of >3500 s−1 in the stenosis region, whereas upstream and downstream values were <1000 s−1. (D) CFD simulation in a stenosed mouse carotid artery. The large velocity gradient at the stenotic apex led to initial shear rates that exceeded 4000 s−1. (E-F) Thrombus growth was predicted by using a high-shear thrombosis model developed by Mehrabadi et al.45  Shear strain rate estimation is shown over time during high-shear thrombosis formation in the microfluidic assay and mouse carotid artery. Min and Max denote the lower and upper confidence limits of the model, respectively.

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