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UCLA Team Develops Point-of-Care Assay for Early-Stage Lyme Disease

NEW YORK — Researchers from the University of California, Los Angeles have developed a new handheld device they say can accurately diagnose Lyme disease in the earliest stages of infection more quickly and at a lower cost than existing methods.

Lyme disease is caused by the tick-borne spirochete Borrelia burgdorferi and infects roughly 300,000 individuals in the US every year. Early symptoms include a characteristic rash, fever, headaches, and chills, and if not treated with antibiotics in a timely manner, infection can lead to more severe symptoms such as lymphocytic meningitis, cranial neuropathy, facial nerve palsy, and arthritis. 

While Lyme disease tests exist, they are largely ineffective at diagnosing early-stage infection — defined as less than 30 days since the onset of symptoms or initial tick bite — due to the nascent immune response during this period, the test's developers write in a study published on Wednesday in ACS Nano. Additionally, the two-tiered serological testing approach recommended by the US Centers for Disease Control and Prevention, which involves a sensitive enzyme immunoassay or immunofluorescence assay followed by Western blot, is expensive at around $400 a test and has an extended sample-to-answer time of over 24 hours.

"The early stage is when patients are most likely to visit a hospital or clinic due to the onset of acute-phase symptoms like fever or rash," Aydogan Ozcan, the study's senior author, told 360Dx in an email. "Therefore, improving Lyme testing at the early stage can have a massive impact on administering a timely treatment for preventing prolonged symptoms."

To that end, the UCLA team developed a multiplexed vertical flow assay (xVFA) composed of a stack of functional paper layers that allows for the detection of antibodies for seven B. burgdorferi-specific antigens and a synthetic peptide in serum on a single sensing membrane. 

The assay can be operated in 15 minutes with material costs of $.42 per test, according to the researchers. After a sample is processed, the assay cassette is opened and the sensing membrane is imaged by a custom-designed mobile-phone-based reader. Computational analysis then quantifies the colorimetric signals on the sensing membrane through automated image processing, and a neural network is used to automatically infer a diagnosis from the multiplexed immunoreactions.

The scientists used a deep learning-based algorithm to select an optimal subset of antigen and peptide targets, then tested the assay using blinded serum samples from 25 early-stage Lyme disease patients and 25 controls. They found that the test achieved an area-under-the-curve (AUC) of .950 with sensitivity of 90.5 percent and specificity 87 percent.

The test's performance improved with batch-specific standardization and threshold tuning to achieve an AUC of .963, with a sensitivity of 85.7 percent and specificity of 96.3 percent.

"The multi-target and portable [point-of-care] nature of the computational xVFA make it uniquely suited for [Lyme disease] diagnostics, presenting major advantages in terms of time, cost, and performance," the researchers wrote in the paper.