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A model of the renal cortex and medulla

dc.contributor.authorJacquez, John A.en_US
dc.contributor.authorCarnahan, Briceen_US
dc.contributor.authorAbbrecht, Peter Hermanen_US
dc.date.accessioned2006-04-17T16:13:54Z
dc.date.available2006-04-17T16:13:54Z
dc.date.issued1967en_US
dc.identifier.citationJacquez, John A., Carnahan, Brice, Abbrecht, Peter (1967)."A model of the renal cortex and medulla." Mathematical Biosciences 1(2): 227-261. <http://hdl.handle.net/2027.42/33390>en_US
dc.identifier.urihttp://www.sciencedirect.com/science/article/B6VHX-45H2G15-2H/2/59311c6f88757fc125062e49e0dfbe7cen_US
dc.identifier.urihttps://hdl.handle.net/2027.42/33390
dc.description.abstractMathematical models of the renal cortex and medulla are developed. The glomerular filtration rate is treated as an input parameter. The differential equations for transfer of water and solutes are developed under the assumption that the primary driving forces are the osmotic effect of the plasma proteins and the active transport of one solute across the walls of the nephric tubules. The system of simultaneous ordinary differential equations obtained for the stationary state for constant inputs in the medulla present a multiple-point boundary-value problem of some complexity.In the course of developing the model a number of problems were unearthed. First, it became apparent that the commonly accepted countercurrent exchange model for the medullary capillaries is not supported by anatomical studies and that a distributed capillary bed model might give a truer picture. Second, it became obvious that the information available on the sodium pump is as yet insufficient to let us decide unambiguously whether the pump is reversible or irreversible.en_US
dc.format.extent2644005 bytes
dc.format.extent3118 bytes
dc.format.mimetypeapplication/pdf
dc.format.mimetypetext/plain
dc.language.isoen_US
dc.publisherElsevieren_US
dc.titleA model of the renal cortex and medullaen_US
dc.typeArticleen_US
dc.rights.robotsIndexNoFollowen_US
dc.subject.hlbsecondlevelPublic Healthen_US
dc.subject.hlbsecondlevelStatistics and Numeric Dataen_US
dc.subject.hlbsecondlevelNatural Resources and Environmenten_US
dc.subject.hlbsecondlevelMathematicsen_US
dc.subject.hlbsecondlevelEcology and Evolutionary Biologyen_US
dc.subject.hlbsecondlevelBiological Chemistryen_US
dc.subject.hlbtoplevelSocial Sciencesen_US
dc.subject.hlbtoplevelScienceen_US
dc.subject.hlbtoplevelHealth Sciencesen_US
dc.description.peerreviewedPeer Revieweden_US
dc.contributor.affiliationumDepartment of Physiology, The Medical School, and Department of Biostatistics, The School of Public Health, The University of Michigan, Ann Arbor, Michigan, USAen_US
dc.contributor.affiliationumDepartment of Chemical Engineering, The Engineering School, and Department of Biostatistics, The School of Public Health, The University of Michigan, Ann Arbor, Michigan, USAen_US
dc.contributor.affiliationumDepartment of Physiology, The Medical School, The University of Michigan, Ann Arbor, Michigan, USAen_US
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/33390/1/0000789.pdfen_US
dc.identifier.doihttp://dx.doi.org/10.1016/0025-5564(67)90035-1en_US
dc.identifier.sourceMathematical Biosciencesen_US
dc.owningcollnameInterdisciplinary and Peer-Reviewed


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