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Statistical dynamics of flowing red blood cells by morphological image processing.

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.

SUBMITTER: Higgins JM

PROVIDER: S-EPMC2631282 | biostudies-literature | 2009 Feb

REPOSITORIES: biostudies-literature

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Statistical dynamics of flowing red blood cells by morphological image processing.

Higgins John M JM Eddington David T DT Bhatia Sangeeta N SN Mahadevan L L

PLoS computational biology 20090213 2

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-d ...[more]

PMID: 19214200

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Project description:The loss of the red blood cells (RBCs) deformability is related with many human diseases, such as malaria, hereditary spherocytosis, sickle cell disease, or renal diseases. Hence, during the last years, a variety of technologies have been proposed to gain insights into the factors affecting the RBCs deformability and their possible direct association with several blood pathologies. In this work, we present a simple microfluidic tool that provides the assessment of motions and deformations of RBCs of end-stage kidney disease (ESKD) patients, under a well-controlled microenvironment. All of the flow studies were performed within a hyperbolic converging microchannels where single-cell deformability was assessed under a controlled homogeneous extensional flow field. By using a passive microfluidic device, RBCs passing through a hyperbolic-shaped contraction were measured by a high-speed video microscopy system, and the velocities and deformability ratios (DR) calculated. Blood samples from 27 individuals, including seven healthy controls and 20 having ESKD with or without diabetes, were analysed. The obtained data indicates that the proposed device is able to detect changes in DR of the RBCs, allowing for distinguishing the samples from the healthy controls and the patients. Overall, the deformability of ESKD patients with and without diabetes type II is lower in comparison with the RBCs from the healthy controls, with this difference being more evident for the group of ESKD patients with diabetes. RBCs from ESKD patients without diabetes elongate on average 8% less, within the hyperbolic contraction, as compared to healthy controls; whereas, RBCs from ESKD patients with diabetes elongate on average 14% less than the healthy controls. The proposed strategy can be easily transformed into a simple and inexpensive diagnostic microfluidic system to assess blood cells deformability due to the huge progress in image processing and high-speed microvisualization technology.

Dataset Information

Statistical dynamics of flowing red blood cells by morphological image processing.

Publications

Statistical dynamics of flowing red blood cells by morphological image processing.

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