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University of Chicago

ACS Nano, Sep 10 2024, 18 (36) | https://doi.org/10.1021/acsnano.4c02897 Deep Learning-Based Kinetic Analysis in Paper-Based Analytical Cartridges Integrated with Field-Effect Transistors

Hyun-June Jang ^1,2‡, Hyou-Arm Joung ^3‡, Artem Goncharov ³, Anastasia Gant Kanegusuku ⁴, Clarence W Chan⁵, Kiang-Teck Jerry Yeo^5,6, Wen Zhuang ^1,2, Aydogan Ozcan ^3,7,8,9*, Junhong Chen ^1,2*

¹Pritzker School of Molecular Engineering, University of Chicago, Chicago, Illinois 60637, United States.

²Chemical Sciences and Engineering Division, Physical Sciences and Engineering Directorate, Argonne National Laboratory, Lemont, Illinois 60439, United States.

³Department of Electrical and Computer Engineering, University of California, Los Angeles, California 90095, United States.

⁴Department of Pathology and Laboratory Medicine, Loyola University Medical Center, Maywood, Illinois 60153, United States.

⁵Department of Pathology, The University of Chicago, Chicago, Illinois 60637, United States.

⁶Pritzker School of Medicine, The University of Chicago, Chicago, Illinois 60637, United States.

⁷Department of Bioengineering, University of California, Los Angeles, California 90095, United States.

⁸California NanoSystems Institute (CNSI), University of California, Los Angeles, California 90095, United States.

⁹Department of Surgery, David Geffen School of Medicine, University of California, Los Angeles, California 90095, United States.

^‡H.J.J. and H.-A.J. contributed equally to this work.

^*Corresponding Authors : Aydogan Ozcan, Junhong Chen

Abstract

This study explores the fusion of a field-effect transistor (FET), a paper-based analytical cartridge, and the computational power of deep learning (DL) for quantitative biosensing via kinetic analyses. The FET sensors address the low sensitivity challenge observed in paper analytical devices, enabling electrical measurements with kinetic data. The paper-based cartridge eliminates the need for surface chemistry required in FET sensors, ensuring economical operation (cost < $0.15/test). The DL analysis mitigates chronic challenges of FET biosensors such as sample matrix interference, by leveraging kinetic data from target-specific bioreactions. In our proof-of-concept demonstration, our DL-based analyses showcased a coefficient of variation of <6.46% and a decent concentration measurement correlation with an r² value of >0.976 for cholesterol testing when blindly compared to results obtained from a CLIA-certified clinical laboratory. These integrated technologies have the potential to advance FET-based biosensors, potentially transforming point-of-care diagnostics and at-home testing through enhanced accessibility, ease-of-use, and accuracy.

논문정보

형식Research article
게재일2024년 09월 (BRIC 등록일 2024-09-13)
연구진국외 연구진
분야 바이오·의료융합 > 바이오센싱 및 나노바이오물질

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