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- All Subjects: Neuroscience
- Creators: Harrington Bioengineering Program
- Member of: Theses and Dissertations
This thesis project is the result of close collaboration with the Arizona State University Biodesign Clinical Testing Laboratory (ABCTL) to document the characteristics of saliva as a test sample, preanalytical considerations, and how the ABCTL utilized saliva testing to develop swift COVID-19 diagnostic tests for the Arizona community. As of April 2021, there have been over 130 million recorded cases of COVID-19 globally, with the United States taking the lead with approximately 31.5 million cases. Developing highly accurate and timely diagnostics has been an important need of our country that the ABCTL has had tremendous success in delivering. Near the start of the pandemic, the ABCTL utilized saliva as a testing sample rather than nasopharyngeal (NP) swabs that were limited in supply, required highly trained medical personnel, and were generally uncomfortable for participants. Results from literature across the globe showed how saliva performed just as well as the NP swabs (the golden standard) while being an easier test to collect and analyze. Going forward, the ABCTL will continue to develop high quality diagnostic tools and adapt to the ever-evolving needs our communities face regarding the COVID-19 pandemic.
commands designed to streamline post-processing of MRI images. Using this partnership, the Applied Neuroscience and Technology Lab at PCH has been able to complete production of a post-processing pipeline which integrates locally sourced smoothing techniques to help identify lesions in patients with evidence of Focal Cortical Dysplasia. The end result is a system in which a patient with epilepsy may experience more successful post-surgical results due to the
combination of a lesion detection mechanism and the radiologist using their trained eye in the presurgical stages. As one of the main points of this work is the global aspect of it, Barrett thesis funding was dedicated for a trip to London in order to network with other MELD project collaborators. This was a successful trip for the project as a whole in addition to this particular thesis. The ability to troubleshoot problems with one another in a room full of subject matter
experts allowed for a high level of discussion and learning. Future work includes implementing machine learning approaches which consider all morphometry parameters simultaneously.