Hana Diagnostics was launched in spring of 2020 with the immediate goal of commercializing an innovative diagnostic test from UCLA that provides novel functionalities to improve the accuracy of rapid testing.
CEO and Device Engineer
CTO and Assay Engineer
Co-Founder and Technology Transfer
Our rapid testing platform uniquely supports up to 100 independent simultaneous assay reactions within a compact plastic cassette . Compatible with large-scale rapid test fabrication , our assay can bring these multiplexed measurements to the point-of-care.
Our testing platform leverages a compact reader working with advanced calibration algorithms to accurately report measurement results of key clinical panels .
Lyme disease and associated tick-borne illnesses are on the rise . Current diagnostic testing is ineffective in the early stage of infection when treatment is most effective and when patients most seek care. By measuring a panel of IgM and IgG antibodies specific to Lyme disease, our rapid testing platform demonstrated greater diagnostic accuracy when compared to the CDC-recommended testing.
Read our publication below for more details on our clinical study:
H.-A. Joung et al., “Point-of-Care Serodiagnostic Test for Early-Stage Lyme Disease Using a Multiplexed Paper-Based Immunoassay and Machine Learning,” ACS Nano, vol. 14, no. 1, pp. 229–240, Jan. 2020, doi: 10.1021/acsnano.9b08151.
The hsCRP test is routinely ordered by physicians to assess the risk of cardio-vascular health, and has been shown to be a key decision making tool in deciding statin therapy for patients. The high degree of precision needed to reliably stratify patients into cardiac risk groups is a major challenge, prohibiting commercial hsCRP testing at the point-of-care. By leveraging multiplexed reactions to CRP along with neural-network based quantification algorithm, our rapid testing platform demonstrated accuracy and precision for the hsCRP test on par with FDA-guidelines.
Read our publication below for more details on our clinical study:
Z. S. Ballard et al., “Deep learning-enabled point-of-care sensing using multiplexed paper-based sensors,” npj Digital Medicine, vol. 3, no. 1, Art. no. 1, May 2020, doi: 10.1038/s41746-020-0274-y.
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