Efficient and timely testing has taken center stage in the management, control, and monitoring of the current COVID-19 pandemic. Simple, rapid, cost-effective diagnostics are needed that can complement current polymerase chain reaction-based methods and lateral flow immunoassays. Here, we report the development of an electrochemical sensing platform based on single-walled carbon nanotube screen-printed electrodes (SWCNT-SPEs) functionalized with a redox-tagged DNA aptamer that specifically binds to the receptor binding domain of the SARS-CoV-2 spike protein S1 subunit. Single-step, reagentless detection of the S1 protein is achieved through a binding-induced, concentration-dependent folding of the DNA aptamer that reduces the efficiency of the electron transfer process between the redox tag and the electrode surface and causes a suppression of the resulting amperometric signal. This aptasensor is specific for the target S1 protein with a dissociation constant ( K D ) value of 43 ± 4 nM and a limit of detection of 7 nM. We demonstrate that the target S1 protein can be detected both in a buffer solution and in an artificial viral transport medium widely used for the collection of nasopharyngeal swabs, and that no cross-reactivity is observed in the presence of different, non-target viral proteins. We expect that this SWCNT-SPE-based format of electrochemical aptasensor will prove useful for the detection of other protein targets for which nucleic acid aptamer ligands are made available.

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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.