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 use...
| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2023-10-01
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| Series: | InfoMat |
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| Online Access: | https://doi.org/10.1002/inf2.12471 |
| _version_ | 1797650653920624640 |
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| author | 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 |
| author_facet | 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 |
| author_sort | Byungjoon Bae |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T16:04:31Z |
| format | Article |
| id | doaj.art-d86fdb9880e144668ae4d62cd554b671 |
| institution | Directory Open Access Journal |
| issn | 2567-3165 |
| language | English |
| last_indexed | 2024-03-11T16:04:31Z |
| publishDate | 2023-10-01 |
| publisher | Wiley |
| record_format | Article |
| series | InfoMat |
| spelling | doaj.art-d86fdb9880e144668ae4d62cd554b6712023-10-25T06:15:14ZengWileyInfoMat2567-31652023-10-01510n/an/a10.1002/inf2.12471Near‐sensor computing‐assisted simultaneous viral antigen and antibody detection via integrated label‐free biosensors with microfluidicsByungjoon Bae0Yongmin Baek1Jeongyong Yang2Heesung Lee3Charana S. S. Sonnadara4Sangeun Jung5Minseong Park6Doeon Lee7Sihwan Kim8Gaurav Giri9Sahil Shah10Geonwook Yoo11William A. Petri12Kyusang Lee13Department of Electrical and Computer Engineering University of Virginia Charlottesville Virginia USADepartment of Electrical and Computer Engineering University of Virginia Charlottesville Virginia USASchool of Electronic Engineering Soongsil University Seoul Republic of KoreaDepartment of Electrical and Computer Engineering University of Virginia Charlottesville Virginia USADepartment of Electrical and Computer Engineering University of Maryland College Park Maryland USADepartment of Chemical Engineering University of Virginia Charlottesville Virginia USADepartment of Electrical and Computer Engineering University of Virginia Charlottesville Virginia USADepartment of Electrical and Computer Engineering University of Virginia Charlottesville Virginia USADepartment of Electrical and Computer Engineering University of Virginia Charlottesville Virginia USADepartment of Chemical Engineering University of Virginia Charlottesville Virginia USADepartment of Electrical and Computer Engineering University of Maryland College Park Maryland USASchool of Electronic Engineering Soongsil University Seoul Republic of KoreaDivision of Infectious Diseases and International Health, Department of Medicine, School of Medicine University of Virginia Charlottesville Virginia USADepartment of Electrical and Computer Engineering University of Virginia Charlottesville Virginia USAAbstract 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.https://doi.org/10.1002/inf2.12471artificial intelligencebiosensoredge‐computingintegrationmicrofluidic |
| spellingShingle | 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 Near‐sensor computing‐assisted simultaneous viral antigen and antibody detection via integrated label‐free biosensors with microfluidics InfoMat artificial intelligence biosensor edge‐computing integration microfluidic |
| title | Near‐sensor computing‐assisted simultaneous viral antigen and antibody detection via integrated label‐free biosensors with microfluidics |
| title_full | Near‐sensor computing‐assisted simultaneous viral antigen and antibody detection via integrated label‐free biosensors with microfluidics |
| title_fullStr | Near‐sensor computing‐assisted simultaneous viral antigen and antibody detection via integrated label‐free biosensors with microfluidics |
| title_full_unstemmed | Near‐sensor computing‐assisted simultaneous viral antigen and antibody detection via integrated label‐free biosensors with microfluidics |
| title_short | Near‐sensor computing‐assisted simultaneous viral antigen and antibody detection via integrated label‐free biosensors with microfluidics |
| title_sort | near sensor computing assisted simultaneous viral antigen and antibody detection via integrated label free biosensors with microfluidics |
| topic | artificial intelligence biosensor edge‐computing integration microfluidic |
| url | https://doi.org/10.1002/inf2.12471 |
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