An IoT-WiFi smart and portable electrochemical immunosensor for the quantification of SARS-CoV-2 spike protein integrated with machine learning features was developed. The immunoenzymatic sensor is based on the immobilization of monoclonal antibodies directed to SARS-CoV-2 S1 subunit on Screen-Printed Electrodes functionalized with gold nanoparticles, the analytical protocol involving a single-step sample incubation. Immunosensor performance was assessed by validation carried out in viral transfer medium, which is commonly used for de-sorption 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, ML algorithm can be easily integrated into the developed cloud-based portable Wi-Fi device. 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.

Rapid Quantification of SARS-Cov-2 Spike Protein Enhanced with a Machine Learning Technique Integrated in a Smart and Portable Immunosensor / Fortunati, Simone; Giliberti, Chiara; Giannetto, Marco; Bolchi, Angelo; Ferrari, Davide; Donofrio, Gaetano; Bianchi, Valentina; Boni, Andrea; De Munari, Ilaria; Careri, Maria. - In: BIOSENSORS. - ISSN 2079-6374. - 12:6(2022). [10.3390/bios12060426]

Rapid Quantification of SARS-Cov-2 Spike Protein Enhanced with a Machine Learning Technique Integrated in a Smart and Portable Immunosensor

Fortunati, Simone
Investigation
;
Giliberti, Chiara
Formal Analysis
;
Giannetto, Marco
Writing – Original Draft Preparation
;
Bolchi, Angelo
Resources
;
Ferrari, Davide
Resources
;
Donofrio, Gaetano
Validation
;
Bianchi, Valentina
Formal Analysis
;
Boni, Andrea
Conceptualization
;
De Munari, Ilaria
Formal Analysis
;
Careri, Maria
Project Administration
2022

Abstract

An IoT-WiFi smart and portable electrochemical immunosensor for the quantification of SARS-CoV-2 spike protein integrated with machine learning features was developed. The immunoenzymatic sensor is based on the immobilization of monoclonal antibodies directed to SARS-CoV-2 S1 subunit on Screen-Printed Electrodes functionalized with gold nanoparticles, the analytical protocol involving a single-step sample incubation. Immunosensor performance was assessed by validation carried out in viral transfer medium, which is commonly used for de-sorption 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, ML algorithm can be easily integrated into the developed cloud-based portable Wi-Fi device. 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.
Rapid Quantification of SARS-Cov-2 Spike Protein Enhanced with a Machine Learning Technique Integrated in a Smart and Portable Immunosensor / Fortunati, Simone; Giliberti, Chiara; Giannetto, Marco; Bolchi, Angelo; Ferrari, Davide; Donofrio, Gaetano; Bianchi, Valentina; Boni, Andrea; De Munari, Ilaria; Careri, Maria. - In: BIOSENSORS. - ISSN 2079-6374. - 12:6(2022). [10.3390/bios12060426]
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11381/2925592
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