Statistical Dynamics of Flowing Red Blood Cells by Morphological Image Processing
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CitationHiggins, John M., David T. Eddington, Sangeeta N. Bhatia, and L. Mahadevan. 2009. Statistical Dynamics of Flowing Red Blood Cells by Morphological Image Processing. PLoS Computational Biology 5(2): e1000288.
AbstractBlood is a dense suspension of soft non-Brownian cells of unique importance. Physiological blood flow involves complex interactions of blood cells with each other and with the environment due to the combined effects of varying cell concentration, cell morphology, cell rheology, and confinement. We analyze these interactions using computational morphological image analysis and machine learning algorithms to quantify the non-equilibrium fluctuations of cellular velocities in a minimal, quasi-two-dimensional microfluidic setting that enables high-resolution spatio-temporal measurements of blood cell flow. In particular, we measure the effective hydrodynamic diffusivity of blood cells and analyze its relationship to macroscopic properties such as bulk flow velocity and density. We also use the effective suspension temperature to distinguish the flow of normal red blood cells and pathological sickled red blood cells and suggest that this temperature may help to characterize the propensity for stasis in Virchow's Triad of blood clotting and thrombosis.
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