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Date of Award
Gregory W. Auner
Raman spectroscopy is experiencing a resurgence of interest due to the ubiquitous use of lasers, ever growing computing power, and machine learning. Brain tumor imaging and tumor edge detection is one area where Raman spectroscopy can assist the neurosurgeon in critical decisions. We first demonstrate the ability of Raman spectroscopy to map the difference in grey and white matter in red green blue color maps that match the neuropathologist’s demarcation of the tissue. Secondly, we exhibit the ability of Raman spectroscopy paired with machine learning to identify the difference between normal tissue, solid tumor, infiltrating tumor, and necrosis in freshly excised human brain tissue. This label free technique has the potential to provide the surgeon with real-time results to aid in surgical decisions.
The brain is the most complex organ in the human body with trillions of synaptic connections communicating via electrical activity. To improve the complexity of GBM models we propose to combine GBM cells with neurons on a microconnectome. The study of the connections in the brain is dubbed connectomics. At present there lacks an in vitro method to method to study simple connectomes on a chip. Herein we propose several simple connectomes on a chip with electrodes for stimulating and recording the neurons. The neural chip connectome architecture is based on a bifurcating fractal pattern which represents the hierarchical nature of connections within the brain. Models of the titanium electrodes suggest that using this material will have no adverse effect on the cell culture. Further we demonstrate that the device is biocompatible.
Broadbent, Brandy, "Raman Spectroscopy Applications In Brain Tumors & Preliminary Look Into Fractal Scaffold For Gbm Model" (2018). Wayne State University Dissertations. 2015.