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Statistical Dynamics of Flowing Red Blood Cells by Morphological Image Processing

Overview of attention for article published in PLoS Computational Biology, February 2009
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Title
Statistical Dynamics of Flowing Red Blood Cells by Morphological Image Processing
Published in
PLoS Computational Biology, February 2009
DOI 10.1371/journal.pcbi.1000288
Pubmed ID
Authors

John M. Higgins, David T. Eddington, Sangeeta N. Bhatia, L. Mahadevan

Abstract

Blood 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.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 104 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 4 4%
United Kingdom 2 2%
Norway 1 <1%
France 1 <1%
Italy 1 <1%
Unknown 95 91%

Demographic breakdown

Readers by professional status Count As %
Researcher 26 25%
Student > Ph. D. Student 21 20%
Student > Bachelor 11 11%
Student > Master 11 11%
Professor > Associate Professor 7 7%
Other 16 15%
Unknown 12 12%
Readers by discipline Count As %
Engineering 41 39%
Physics and Astronomy 13 13%
Agricultural and Biological Sciences 10 10%
Computer Science 7 7%
Medicine and Dentistry 4 4%
Other 12 12%
Unknown 17 16%