Solute concentration in the kidney-- II. input-output studies on a central core model
dc.contributor.author | Foster, David | en_US |
dc.contributor.author | Jacquez, John A. | en_US |
dc.contributor.author | Daniels, Eugene | en_US |
dc.date.accessioned | 2006-04-07T16:32:44Z | |
dc.date.available | 2006-04-07T16:32:44Z | |
dc.date.issued | 1976 | en_US |
dc.identifier.citation | Foster, David, Jacquez, John A., Daniels, Eugene (1976)."Solute concentration in the kidney-- II. input-output studies on a central core model." Mathematical Biosciences 32(3-4): 337-360. <http://hdl.handle.net/2027.42/21902> | en_US |
dc.identifier.uri | http://www.sciencedirect.com/science/article/B6VHX-45G0HGB-M/2/553463fe8854baa3eda7df5037ca8b8a | en_US |
dc.identifier.uri | https://hdl.handle.net/2027.42/21902 | |
dc.description.abstract | Input-output studies have been carried out on a central core model of the renal medulla of the rabbit. The results emphasize the importance of having a nontransported solute such as urea. Even though only salt was actively transported, in the thick AHL, urea had to be present for a concentration gradient to be built up. Inflow into the DHL was shown to be a major determinant of the concentration process. The concentration increased with inflow into DHL up to a maximum value; with further increase in inflow into DHL, the concentration obtained decreased. Extrapolating from these studies and what is known of the role of flow in vasa rectae we argue that independent control of afferent and efferent arteriolar resistance of juxtamedullary nephrons could play a major role in control of the concentration of urine. | en_US |
dc.format.extent | 1198071 bytes | |
dc.format.extent | 3118 bytes | |
dc.format.mimetype | application/pdf | |
dc.format.mimetype | text/plain | |
dc.language.iso | en_US | |
dc.publisher | Elsevier | en_US |
dc.title | Solute concentration in the kidney-- II. input-output studies on a central core model | en_US |
dc.type | Article | en_US |
dc.rights.robots | IndexNoFollow | en_US |
dc.subject.hlbsecondlevel | Public Health | en_US |
dc.subject.hlbsecondlevel | Statistics and Numeric Data | en_US |
dc.subject.hlbsecondlevel | Natural Resources and Environment | en_US |
dc.subject.hlbsecondlevel | Mathematics | en_US |
dc.subject.hlbsecondlevel | Ecology and Evolutionary Biology | en_US |
dc.subject.hlbsecondlevel | Biological Chemistry | en_US |
dc.subject.hlbtoplevel | Social Sciences | en_US |
dc.subject.hlbtoplevel | Science | en_US |
dc.subject.hlbtoplevel | Health Sciences | en_US |
dc.description.peerreviewed | Peer Reviewed | en_US |
dc.contributor.affiliationum | The Department of Physiology, The Medical School, The University of Michigan, Ann Arbor, Michigan 48109, USA | en_US |
dc.contributor.affiliationum | The Department of Physiology, The Medical School, The University of Michigan, Ann Arbor, Michigan 48109, USA | en_US |
dc.contributor.affiliationum | The Department of Physiology, The Medical School, The University of Michigan, Ann Arbor, Michigan 48109, USA | en_US |
dc.description.bitstreamurl | http://deepblue.lib.umich.edu/bitstream/2027.42/21902/1/0000309.pdf | en_US |
dc.identifier.doi | http://dx.doi.org/10.1016/0025-5564(76)90120-6 | en_US |
dc.identifier.source | Mathematical Biosciences | en_US |
dc.owningcollname | Interdisciplinary and Peer-Reviewed |
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