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Computational Study on Use of Single-Point Analysis Method for Quantitating Local Cerebral Blood Flow in Mice

  • Michelle A. Puchowicz
  • Krishnan Radhakrishnan
  • Kui Xu
  • Danielle L. Magness
  • Joseph C. LaManna
Conference paper
Part of the Advances in Experimental Medicine and Biology book series (AEMB, volume 566)

Abstract

The benefits of a mouse model are efficiency and availability of transgenics/knockouts. Quantitation of cerebral blood in small animals is difficult because the cannulation procedure may introduce errors. The [14C]-iodoantipyrine autoradiography (IAP) method requires both the tissue concentration and the time course of arterial concentration of the [14C] radioactive tracer. A single point-analysis technique was evaluated for measuring blood flow in mice (30 g ± 0.3 g; n=11) by using computational models of sensitivity analysis, which quantitates relationships between the predictions of a model and its parameters. Using [14C]-IAP in conjunction with mathematical algorithms and assumed arterial concentration-versus-time profiles, cortical blood flow was deduced from single-point measurements of the arterial tracer concentration. The data showed the arterial concentration profile that produced the most realistic blood flows (1.6 ± 0.4; mean ± SD, ml/g/min) was a profile with a ramp time of 30 sec followed by a constant value over the remaining time period of 30 sec. Sensitivity analysis showed that the total experimental time period was a more important parameter than the lag period and the ramp period. Thus, it appears that the accuracy of the assumption of linearly increasing arterial concentration depends on the experimental time period and the final arterial [14C]-iodoantipyrine concentration.

Keywords

Ramp Time Arterial Concentration Local Cerebral Blood Flow Cortical Blood Flow Intraperitoneal Cavity 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media, Inc. 2005

Authors and Affiliations

  • Michelle A. Puchowicz
  • Krishnan Radhakrishnan
  • Kui Xu
  • Danielle L. Magness
  • Joseph C. LaManna

There are no affiliations available

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