Near‐sensor computing‐assisted simultaneous viral antigen and antibody detection via integrated label‐free biosensors with microfluidics
Byungjoon Bae,
Yongmin Baek,
Jeongyong Yang,
Heesung Lee,
Charana S. S. Sonnadara,
Sangeun Jung,
Minseong Park,
Doeon Lee,
Sihwan Kim,
Gaurav Giri,
Sahil Shah,
Geonwook Yoo,
William A. Petri,
Kyusang Lee
Affiliations
Byungjoon Bae
Department of Electrical and Computer Engineering University of Virginia Charlottesville Virginia USA
Yongmin Baek
Department of Electrical and Computer Engineering University of Virginia Charlottesville Virginia USA
Jeongyong Yang
School of Electronic Engineering Soongsil University Seoul Republic of Korea
Heesung Lee
Department of Electrical and Computer Engineering University of Virginia Charlottesville Virginia USA
Charana S. S. Sonnadara
Department of Electrical and Computer Engineering University of Maryland College Park Maryland USA
Sangeun Jung
Department of Chemical Engineering University of Virginia Charlottesville Virginia USA
Minseong Park
Department of Electrical and Computer Engineering University of Virginia Charlottesville Virginia USA
Doeon Lee
Department of Electrical and Computer Engineering University of Virginia Charlottesville Virginia USA
Sihwan Kim
Department of Electrical and Computer Engineering University of Virginia Charlottesville Virginia USA
Gaurav Giri
Department of Chemical Engineering University of Virginia Charlottesville Virginia USA
Sahil Shah
Department of Electrical and Computer Engineering University of Maryland College Park Maryland USA
Geonwook Yoo
School of Electronic Engineering Soongsil University Seoul Republic of Korea
William A. Petri
Division of Infectious Diseases and International Health, Department of Medicine, School of Medicine University of Virginia Charlottesville Virginia USA
Kyusang Lee
Department of Electrical and Computer Engineering University of Virginia Charlottesville Virginia USA
Abstract Precise diagnosis and immunity to viruses, such as severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) and Middle East respiratory syndrome coronavirus (MERS‐CoV) is achieved by the detection of the viral antigens and/or corresponding antibodies, respectively. However, a widely used antigen detection methods, such as polymerase chain reaction (PCR), are complex, expensive, and time‐consuming Furthermore, the antibody test that detects an asymptomatic infection and immunity is usually performed separately and exhibits relatively low accuracy. To achieve a simplified, rapid, and accurate diagnosis, we have demonstrated an indium gallium zinc oxide (IGZO)‐based biosensor field‐effect transistor (bio‐FET) that can simultaneously detect spike proteins and antibodies with a limit of detection (LOD) of 1 pg mL–1 and 200 ng mL–1, respectively using a single assay in less than 20 min by integrating microfluidic channels and artificial neural networks (ANNs). The near‐sensor ANN‐aided classification provides high diagnosis accuracy (>93%) with significantly reduced processing time (0.62%) and energy consumption (5.64%) compared to the software‐based ANN. We believe that the development of rapid and accurate diagnosis system for the viral antigens and antibodies detection will play a crucial role in preventing global viral outbreaks.
