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Developing a Better SARS-CoV-2 Diagnostic Tool Using RT-LAMP Technology

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dc.contributor.advisor Gelber, Cohava
dc.contributor.author Kwon, Yongjun
dc.creator Kwon, Yongjun
dc.date 2021-11-27
dc.date.accessioned 2022-05-16T17:24:44Z
dc.date.available 2022-05-16T17:24:44Z
dc.identifier.uri http://hdl.handle.net/1920/12861
dc.description.abstract SARS-CoV-2 belongs to the betacoronavirus genus, and is closely related to severe acute respiratory syndrome coronavirus (SARS-CoV). Since its emergence in Wuhan province, China in December of 2019, from a suspected bat or pangolin origin, the pathogen has spread rapidly, with millions of cases reported on every continent. Currently, the World Health Organization reports 236 million confirmed cases globally, with an estimated case fatality rate of approximately 2.0%. Significantly higher mortality rates are observed in elderly patients, immunocompromised patients, and patients with other preexisting conditions, such as cardiovascular disease, cancer and diabetes. Given the exceptional transmissibility and relatively high mortality rate, the development of simple, robust yet accessible diagnostics are of utmost importance to public health. As such, both myself and my colleagues have developed a SARS-CoV-2 rapid diagnostic kit suitable for use in point of care settings. This kit is for the in vitro qualitative detection of the SARS-CoV-2 RNA in nasopharyngeal swabs, oropharyngeal swabs, and saliva collected from individuals with or without ongoing symptoms. Here, I will describe computational approaches used for developing the components included in this kit, the workflow, and will share the discoveries both I and my colleagues made to develop the novel SARS-CoV2 diagnostic kit. en_US
dc.language.iso en en_US
dc.subject SARS-CoV-2 en_US
dc.subject rapid diagnostic en_US
dc.subject in silico study en_US
dc.subject automated diagnostic en_US
dc.title Developing a Better SARS-CoV-2 Diagnostic Tool Using RT-LAMP Technology en_US
dc.type Thesis en_US
thesis.degree.name Master of Science in Bioinformatics en_US
thesis.degree.level Master's en_US
thesis.degree.discipline Bioinformatics en_US
thesis.degree.grantor George Mason University en_US


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