Paper-Based Computational Multi-Peptide Optical Sensor for Point-of-Care Testing of Lyme Disease

Hyou-Arm Joung; Rajesh Ghosh; Artem Goncharov; Kevin Ngo; Barath Palanisamy; Marcell Veszpremi; Elizabeth J. Horn; Paul M. Arnaboldi; Raymond J. Dattwyler; Omai B. Garner; Dino Di Carlo; Dino Di Carlo; Aydogan Ozcan; Aydogan Ozcan; Aydogan Ozcan

doi:10.1364/FIO.2022.JTu7B.4

Frontiers in Optics + Laser Science 2022 (FIO, LS)
Technical Digest Series (Optica Publishing Group, 2022),
paper JTu7B.4
•https://doi.org/10.1364/FIO.2022.JTu7B.4

Paper-Based Computational Multi-Peptide Optical Sensor for Point-of-Care Testing of Lyme Disease

Hyou-Arm Joung, Rajesh Ghosh, Artem Goncharov, Kevin Ngo, Barath Palanisamy, Marcell Veszpremi, Elizabeth J. Horn, Paul M. Arnaboldi, Raymond J. Dattwyler, Omai B. Garner, Dino Di Carlo, and Aydogan Ozcan

Frontiers in Optics 2022

Rochester, New York United States
17–20 October 2022
ISBN: 978-1-957171-17-3

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Joint Postdeadline Papers Session II (JTu7B)

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H. Joung, R. Ghosh, A. Goncharov, K. Ngo, B. Palanisamy, M. Veszpremi, E. J. Horn, P. M. Arnaboldi, R. J. Dattwyler, O. B. Garner, D. Di Carlo, and A. Ozcan, "Paper-Based Computational Multi-Peptide Optical Sensor for Point-of-Care Testing of Lyme Disease," in Frontiers in Optics + Laser Science 2022 (FIO, LS), Technical Digest Series (Optica Publishing Group, 2022), paper JTu7B.4.

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Abstract

We report a paper-based multi-peptide optical sensor processed by a neural network for point-of-care testing of Lyme disease. Using clinical serum samples, we demonstrate 91.7% sensitivity and 100% specificity without cross-reactivity with other diseases.

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