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dc.contributor.authorHiggins, John M.
dc.contributor.authorEddington, David T.
dc.contributor.authorBhatia, Sangeeta N.
dc.contributor.authorMahadevan, L.
dc.date.accessioned2010-03-16T12:59:22Z
dc.date.available2010-03-16T12:59:22Z
dc.date.issued2009-02
dc.date.submitted2008-07
dc.identifier.issn1553-7358
dc.identifier.urihttp://hdl.handle.net/1721.1/52608
dc.description.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.en
dc.language.isoen_US
dc.publisherPublic Library of Scienceen
dc.relation.isversionofhttp://dx.doi.org/10.1371/journal.pcbi.1000288en
dc.rightsCreative Commons Attributionen
dc.rights.urihttp://creativecommons.org/licenses/by/2.5/en
dc.sourcePLoSen
dc.titleStatistical Dynamics of Flowing Red Blood Cells by Morphological Image Processingen
dc.typeArticleen
dc.identifier.citationHiggins JM, Eddington DT, Bhatia SN, Mahadevan L (2009) Statistical Dynamics of Flowing Red Blood Cells by Morphological Image Processing. PLoS Comput Biol 5(2): e1000288. doi:10.1371/journal.pcbi.1000288en
dc.contributor.departmentHarvard University--MIT Division of Health Sciences and Technologyen_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Scienceen_US
dc.contributor.approverBhatia, Sangeeta N.
dc.contributor.mitauthorEddington, David T.
dc.contributor.mitauthorBhatia, Sangeeta N.
dc.contributor.mitauthorMahadevan, L.
dc.relation.journalPLoS Computational Biologyen
dc.eprint.versionFinal published versionen
dc.identifier.pmid19214200
dc.type.urihttp://purl.org/eprint/type/JournalArticleen
eprint.statushttp://purl.org/eprint/status/PeerRevieweden
dspace.orderedauthorsHiggins, John M.; Eddington, David T.; Bhatia, Sangeeta N.; Mahadevan, L.en
dc.identifier.orcidhttps://orcid.org/0000-0002-1293-2097
mit.licensePUBLISHER_CCen
mit.metadata.statusComplete


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