Near-sensor computing-assisted simultaneous viral antigen and antibody detection via integrated label-free biosensors with microfluidics

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.