Search for: All records

A multiplexed paper-based vertical flow assay integrated with machine learning was developed, achieving 89.5% accuracy in classifying COVID-19 immunity levels of individuals as unprotected,” protected, and infected within 20 minutes. 

To track COVID-19 immunity efficiently in point-of-care (POC) settings, we present a paper-based multiplexed vertical flow immunoassay (xVFA) along with a custom-designed serodiagnostic algorithm. During the development and testing of our algorithm, we utilized serum samples from individuals who had received mRNA-COVID-19 vaccines, tracking their antibody levels before and after each vaccine dose. By categorizing these samples based on their IgM and IgG levels into three categories (i.e., protected, unprotected, and infected), we trained and blindly evaluated a neural network-based algorithm for its inference accuracy. Leveraging this serodiagnostic algorithm, our cost-effective, paper-based xVFA platform swiftly measured the IgG and IgM levels from serum samples, facilitating the accurate monitoring of COVID-19 immunity levels. With its simple operation, scalability, and cost-effectiveness, our xVFA technology offers accessible COVID-19 serology testing to classify patients' immunity status rapidly. 

Monitoring the level of COVID-19 immunity within populations is crucial for understanding the disease trend and guiding public health policies, as SARS-CoV-2 remains a threat. Serological testing is essential in assessing immunity by detecting antibodies, IgG, and IgM, the immune system's response developed against infection or after vaccination. To rapidly and cost-effectively monitor COVID-19 immunity levels, we developed a paper-based multiplexed vertical flow immunoassay (xVFA) which detects the IgG and IgM levels in less than 20 mins, aiming to monitor the COVID-19 immunity levels of individuals longitudinally and categorize immune levels into three groups: protected, unprotected, and infected.