Design and characterization of tissue engineered substitutes rely on robust monitoring techniques that provide information regarding viability and function when exposed to various environmental conditions. In vitro studies permit the direct monitoring of cellular and construct changes because these substitutes remain accessible. However, upon in vivo implantation, changes in cell viability and function are often detected using indirect or invasive methods that make assessing temporal changes challenging. . Thus, the development of non-invasive monitoring modalities may facilitate improved tissue substitute design and, ultimately, clinical outcome.
The overall objective of this thesis was to establish a method to monitor and track cells and the cellular environment within a tissue engineered substitute in vitro and in vivo. This was accomplished via 31P NMR spectroscopy and through the incorporation of perfluorocarbon (PFC) emulsions for the monitoring of DO concentration by 19F NMR spectroscopy. The first aim of this thesis was to develop a method that tracked the state of cells and of the cellular environment within alginate constructs during perfusion studies in which the perfusing medium DO concentrations were changed over time or cells were exposed to a cytotoxic antibiotic. Due to challenges in acquiring DO concentration gradient information within beads, a second aim was to develop a mathematical model that would calculate gradients from experimentally acquired volume averaged DO concentrations; thus, significantly enhancing the robustness of tracking the alginate beads. Lastly, since the PFC emulsions used in the study may affect cell viability and function, a third aim was to characterize, experimentally and via modeling, the effect of several PFC emulsion concentrations on the encapsulated and #946;TC-tet cells.
| Identifer | oai:union.ndltd.org:GATECH/oai:smartech.gatech.edu:1853/14499 |
| Date | 06 April 2007 |
| Creators | Gross, Jeffrey David |
| Publisher | Georgia Institute of Technology |
| Source Sets | Georgia Tech Electronic Thesis and Dissertation Archive |
| Detected Language | English |
| Type | Dissertation |
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