Rapid Quantification of SARS-Cov-2 Spike Protein Enhanced with a Machine Learning Technique Integrated in a Smart and Portable Immunosensor
An IoT-WiFi smart and portable electrochemical immunosensor for the quantification of SARS-CoV-2 spike protein was developed with integrated machine learning features. The immunoenzymatic sensor is based on the immobilization of monoclonal antibodies directed at the SARS-CoV-2 S1 subunit on Screen-P...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-06-01
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| Series: | Biosensors |
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| Online Access: | https://www.mdpi.com/2079-6374/12/6/426 |
| _version_ | 1827661859978739712 |
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| author | Simone Fortunati Chiara Giliberti Marco Giannetto Angelo Bolchi Davide Ferrari Gaetano Donofrio Valentina Bianchi Andrea Boni Ilaria De Munari Maria Careri |
| author_facet | Simone Fortunati Chiara Giliberti Marco Giannetto Angelo Bolchi Davide Ferrari Gaetano Donofrio Valentina Bianchi Andrea Boni Ilaria De Munari Maria Careri |
| author_sort | Simone Fortunati |
| collection | DOAJ |
| description | An IoT-WiFi smart and portable electrochemical immunosensor for the quantification of SARS-CoV-2 spike protein was developed with integrated machine learning features. The immunoenzymatic sensor is based on the immobilization of monoclonal antibodies directed at the SARS-CoV-2 S1 subunit on Screen-Printed Electrodes functionalized with gold nanoparticles. The analytical protocol involves a single-step sample incubation. Immunosensor performance was validated in a viral transfer medium which is commonly used for the desorption of nasopharyngeal swabs. Remarkable specificity of the response was demonstrated by testing H1N1 Hemagglutinin from swine-origin influenza A virus and Spike Protein S1 from Middle East respiratory syndrome coronavirus. Machine learning was successfully used for data processing and analysis. Different support vector machine classifiers were evaluated, proving that algorithms affect the classifier accuracy. The test accuracy of the best classification model in terms of true positive/true negative sample classification was 97.3%. In addition, the ML algorithm can be easily integrated into cloud-based portable Wi-Fi devices. Finally, the immunosensor was successfully tested using a third generation replicating incompetent lentiviral vector pseudotyped with SARS-CoV-2 spike glycoprotein, thus proving the applicability of the immunosensor to whole virus detection. |
| first_indexed | 2024-03-10T00:17:04Z |
| format | Article |
| id | doaj.art-7d635f037fd3453686613040bfcaff66 |
| institution | Directory Open Access Journal |
| issn | 2079-6374 |
| language | English |
| last_indexed | 2024-03-10T00:17:04Z |
| publishDate | 2022-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biosensors |
| spelling | doaj.art-7d635f037fd3453686613040bfcaff662023-11-23T15:49:23ZengMDPI AGBiosensors2079-63742022-06-0112642610.3390/bios12060426Rapid Quantification of SARS-Cov-2 Spike Protein Enhanced with a Machine Learning Technique Integrated in a Smart and Portable ImmunosensorSimone Fortunati0Chiara Giliberti1Marco Giannetto2Angelo Bolchi3Davide Ferrari4Gaetano Donofrio5Valentina Bianchi6Andrea Boni7Ilaria De Munari8Maria Careri9Dipartimento di Scienze Chimiche, della Vita e della Sostenibilità Ambientale, Università di Parma, Parco Area delle Scienze 17/A, 43124 Parma, ItalyDipartimento di Scienze Chimiche, della Vita e della Sostenibilità Ambientale, Università di Parma, Parco Area delle Scienze 17/A, 43124 Parma, ItalyDipartimento di Scienze Chimiche, della Vita e della Sostenibilità Ambientale, Università di Parma, Parco Area delle Scienze 17/A, 43124 Parma, ItalyDipartimento di Scienze Chimiche, della Vita e della Sostenibilità Ambientale, Università di Parma, Parco Area delle Scienze 17/A, 43124 Parma, ItalyDipartimento di Scienze Chimiche, della Vita e della Sostenibilità Ambientale, Università di Parma, Parco Area delle Scienze 17/A, 43124 Parma, ItalyDipartimento di Scienze Medico-Veterinarie, Università di Parma, Strada del Taglio 10, 43126 Parma, ItalyDipartimento di Ingegneria e Architettura, Università di Parma, Parco Area delle Scienze 181/A, 43124 Parma, ItalyDipartimento di Ingegneria e Architettura, Università di Parma, Parco Area delle Scienze 181/A, 43124 Parma, ItalyDipartimento di Ingegneria e Architettura, Università di Parma, Parco Area delle Scienze 181/A, 43124 Parma, ItalyDipartimento di Scienze Chimiche, della Vita e della Sostenibilità Ambientale, Università di Parma, Parco Area delle Scienze 17/A, 43124 Parma, ItalyAn IoT-WiFi smart and portable electrochemical immunosensor for the quantification of SARS-CoV-2 spike protein was developed with integrated machine learning features. The immunoenzymatic sensor is based on the immobilization of monoclonal antibodies directed at the SARS-CoV-2 S1 subunit on Screen-Printed Electrodes functionalized with gold nanoparticles. The analytical protocol involves a single-step sample incubation. Immunosensor performance was validated in a viral transfer medium which is commonly used for the desorption of nasopharyngeal swabs. Remarkable specificity of the response was demonstrated by testing H1N1 Hemagglutinin from swine-origin influenza A virus and Spike Protein S1 from Middle East respiratory syndrome coronavirus. Machine learning was successfully used for data processing and analysis. Different support vector machine classifiers were evaluated, proving that algorithms affect the classifier accuracy. The test accuracy of the best classification model in terms of true positive/true negative sample classification was 97.3%. In addition, the ML algorithm can be easily integrated into cloud-based portable Wi-Fi devices. Finally, the immunosensor was successfully tested using a third generation replicating incompetent lentiviral vector pseudotyped with SARS-CoV-2 spike glycoprotein, thus proving the applicability of the immunosensor to whole virus detection.https://www.mdpi.com/2079-6374/12/6/426machine learningSARS-CoV-2COVID-19electrochemical immunosensorIoT-WiFipoint of care testing |
| spellingShingle | Simone Fortunati Chiara Giliberti Marco Giannetto Angelo Bolchi Davide Ferrari Gaetano Donofrio Valentina Bianchi Andrea Boni Ilaria De Munari Maria Careri Rapid Quantification of SARS-Cov-2 Spike Protein Enhanced with a Machine Learning Technique Integrated in a Smart and Portable Immunosensor Biosensors machine learning SARS-CoV-2 COVID-19 electrochemical immunosensor IoT-WiFi point of care testing |
| title | Rapid Quantification of SARS-Cov-2 Spike Protein Enhanced with a Machine Learning Technique Integrated in a Smart and Portable Immunosensor |
| title_full | Rapid Quantification of SARS-Cov-2 Spike Protein Enhanced with a Machine Learning Technique Integrated in a Smart and Portable Immunosensor |
| title_fullStr | Rapid Quantification of SARS-Cov-2 Spike Protein Enhanced with a Machine Learning Technique Integrated in a Smart and Portable Immunosensor |
| title_full_unstemmed | Rapid Quantification of SARS-Cov-2 Spike Protein Enhanced with a Machine Learning Technique Integrated in a Smart and Portable Immunosensor |
| title_short | Rapid Quantification of SARS-Cov-2 Spike Protein Enhanced with a Machine Learning Technique Integrated in a Smart and Portable Immunosensor |
| title_sort | rapid quantification of sars cov 2 spike protein enhanced with a machine learning technique integrated in a smart and portable immunosensor |
| topic | machine learning SARS-CoV-2 COVID-19 electrochemical immunosensor IoT-WiFi point of care testing |
| url | https://www.mdpi.com/2079-6374/12/6/426 |
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