Statistical Dynamics of Flowing Red Blood Cells by Morphological Image Processing
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 a...
| Principais autores: | , , , |
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| Formato: | Artigo |
| Idioma: | en_US |
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Public Library of Science
2010
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| Acesso em linha: | http://hdl.handle.net/1721.1/52608 https://orcid.org/0000-0002-1293-2097 |
| _version_ | 1826198689094303744 |
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| author | Higgins, John M. Eddington, David T. Bhatia, Sangeeta N. Mahadevan, L. |
| author2 | Harvard University--MIT Division of Health Sciences and Technology |
| author_facet | Harvard University--MIT Division of Health Sciences and Technology Higgins, John M. Eddington, David T. Bhatia, Sangeeta N. Mahadevan, L. |
| author_sort | Higgins, John M. |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T11:08:16Z |
| format | Article |
| id | mit-1721.1/52608 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T11:08:16Z |
| publishDate | 2010 |
| publisher | Public Library of Science |
| record_format | dspace |
| spelling | mit-1721.1/526082022-10-01T01:31:56Z Statistical Dynamics of Flowing Red Blood Cells by Morphological Image Processing Higgins, John M. Eddington, David T. Bhatia, Sangeeta N. Mahadevan, L. Harvard University--MIT Division of Health Sciences and Technology Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Bhatia, Sangeeta N. Eddington, David T. Bhatia, Sangeeta N. Mahadevan, L. 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. 2010-03-16T12:59:22Z 2010-03-16T12:59:22Z 2009-02 2008-07 Article http://purl.org/eprint/type/JournalArticle 1553-7358 http://hdl.handle.net/1721.1/52608 Higgins 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.1000288 19214200 https://orcid.org/0000-0002-1293-2097 en_US http://dx.doi.org/10.1371/journal.pcbi.1000288 PLoS Computational Biology Creative Commons Attribution http://creativecommons.org/licenses/by/2.5/ application/pdf Public Library of Science PLoS |
| spellingShingle | Higgins, John M. Eddington, David T. Bhatia, Sangeeta N. Mahadevan, L. Statistical Dynamics of Flowing Red Blood Cells by Morphological Image Processing |
| title | Statistical Dynamics of Flowing Red Blood Cells by Morphological Image Processing |
| title_full | Statistical Dynamics of Flowing Red Blood Cells by Morphological Image Processing |
| title_fullStr | Statistical Dynamics of Flowing Red Blood Cells by Morphological Image Processing |
| title_full_unstemmed | Statistical Dynamics of Flowing Red Blood Cells by Morphological Image Processing |
| title_short | Statistical Dynamics of Flowing Red Blood Cells by Morphological Image Processing |
| title_sort | statistical dynamics of flowing red blood cells by morphological image processing |
| url | http://hdl.handle.net/1721.1/52608 https://orcid.org/0000-0002-1293-2097 |
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