The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to rapidly generate personalized data regarding immune health and exposure to SARS-CoV-2. Detection of SARS-CoV-2 exposure is urgently needed to understand viral spread, conduct contact tracing, provide public health recommendations, prepare for hospitalization or critical care emergencies, and safely reduce the need for social distancing. The proposed system will use new technologies to measure chemicals in blood to understand how COVID-19 evolves after exposure. This approach offers improved speed, accuracy, ease of use, cost, and ability to deploy in communities where clinical resources may not be readily accessible. Machine learning can be used to study population-level data to understand the relationship between immune health and COVID-19 severity. The dataset developed herein can improve the reliability of early signs of severe pathological COVID-19 progression, improving both quality of care and efficiency for public health use.
This Small Business Innovation Research (SBIR) Phase I project will enable the development of a remote or home-based self-monitoring system to identify anti-SARS-CoV-2 antibodies (IgG and IgM) and inflammatory biomarkers (CRP and IL-6) from finger-stick blood after viral exposure. Specifically, this technology combines serial serology, lateral flow immunochromatographic assays, and novel app-enabled spectrophotometry to evaluate immune health during the course of infection. This approach will provide novel information and data for large-scale analysis and mitigation measures.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
