UC Case No. 2020-375
SUMMARY
UCLA researchers in the Department of Electrical and Computer Engineering have developed a multiplexed paper-based and machine learning immunoassay using for Lyme disease that is efficient, low-cost, sensitive and specific.
BACKGROUND:
Point-of-care testing (POCT) utilizes rapid and simple diagnostics to enable efficient clinical decision making and care planning. There are significant unmet needs in POCTs for Lyme disease, which requires prompt diagnosis and treatment at an early stage in order to prevent long-term complications. Current test for Lyme disease requires two steps and suffers from high operating costs, slow turnaround time and mediocre sensitivity. The test also requires trained personnel and is prone to false positives. Improvements on POCTs for Lyme disease is needed to help patients in rural areas that have limited access to healthcare resources and increased exposure to infectious ticks.
INNOVATION:
The assay consists of vertical stacking of functional paper layers to allow repeated and consistent measurements. Spatially multiplexed sensing membranes in the paper layers contain Lyme specific antibodies that generate colorimetric signals when pathogen is present. The results can be analyzed within 20 minutes using a low-cost mobile phone reader that sends the image to a remote server to be rapidly evaluated via machine learning. This POCT is highly efficient, low cost, sensitive, specific, easy to use and robust, which makes it a perfect solution for rural populations.
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