Microfluidic Diagnostic Technologies for Home-based Healthcare
Paul Yager, Professor, University of Washington
For over a century, testing of human samples for acute and chronic diseases has been performed in centralized laboratories by trained technicians or now by large robotic instruments capable of batch processing hundreds of tests. Since 2008 the Yager lab at UW has, under support of NIH, NSF, DARPA, the US Army and DTRA, focused on low-cost point-of-care biomedical diagnostics using two-dimensional porous networks (“paper microfluidics") for ultra-low-cost point-of-care pathogen identification. Novel approaches to both on-device nucleic acid amplification and sensitive protein detection were developed and reduced to practice. In the last decade there has been increasing interest in wearable sensors, and continuous monitoring. The COVID-19 pandemic opened up markets for rapid home testing for viruses, and exposed many people to the long-term possibilities for medical testing at home. Like many of our colleagues, for the last 2 years (under support of WRF and an Emergent Ventures Rapid Grant), our lab has pivoted to address the pandemic by focusing on a respiratory pathogen panel that incudes SARS-CoV-2. The goal is a rapid semiquantitative validated highly-sensitive multiplexed nucleic acid test using isothermal amplification that can be stored for months at room temperature, but deliver results to an untrained home user (and perhaps also to public health authorities) from a nasal swab within 30 minutes. We will show our latest results. The ability to detect and quantify a wide range of pathogens within an hour of sample acquisition opens up a range of health monitoring opportunities. By coupling low-cost disposables with an optical reader, it is possible to have a home-based system to detect a wide of range of conditions beyond acute infections, allowing putting an integrated system for health maintenance in the home (or any POC setting) at low cost. This approach is being commercialized by a new company, UbiDX.
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