SARS-CoV-2 Surveillance in Indoor Air Using Electrochemical Sensor for Continuous Monitoring and Real-Time Alerts

Electrochemical Sensors and Biosensors for Identification of Viruses: A Critical Review

Due to their life cycle, viruses can disrupt the metabolism of their hosts, causing diseases. If we want to disrupt their life cycle, it is necessary to identify their presence. For this purpose, it is possible to use several molecular-biological and bioanalytical methods. The reference selection was performed based on electronic databases (2020-2023). This review focused on electrochemical methods with high sensitivity and selectivity (53% voltammetry/amperometry, 33% impedance, and 12% other methods) which showed their great potential for detecting various viruses. Moreover, the aforementioned electrochemical methods have considerable potential to be applicable for care-point use as they are portable due to their miniaturizability and fast speed analysis (minutes to hours), and are relatively easy to interpret. A total of 2011 articles were found, of which 86 original papers were subsequently evaluated (the majority of which are focused on human pathogens, whereas articles dealing with plant pathogens are in the minority). Thirty-two species of viruses were included in the evaluation. It was found that most of the examined research studies (77%) used nanotechnological modifications. Other ones performed immunological (52%) or genetic analyses (43%) for virus detection. 5% of the reports used peptides to increase the method's sensitivity. When evaluable, 65% of the research studies had LOD values in the order of ng or nM. The vast majority (79%) of the studies represent proof of concept and possibilities with low application potential and a high need of further research experimental work.

Real-time environmental surveillance of SARS-CoV-2 aerosols

Real-time surveillance of airborne SARS-CoV-2 virus is a technological gap that has eluded the scientific community since the beginning of the COVID-19 pandemic. Offline air sampling techniques for SARS-CoV-2 detection suffer from longer turnaround times and require skilled labor. Here, we present a proof-of-concept pathogen Air Quality (pAQ) monitor for real-time (5 min time resolution) direct detection of SARS-CoV-2 aerosols. The system synergistically integrates a high flow (~1000 lpm) wet cyclone air sampler and a nanobody-based ultrasensitive micro-immunoelectrode biosensor. The wet cyclone showed comparable or better virus sampling performance than commercially available samplers. Laboratory experiments demonstrate a device sensitivity of 77-83% and a limit of detection of 7-35 viral RNA copies/m³ of air. Our pAQ monitor is suited for point-of-need surveillance of SARS-CoV-2 variants in indoor environments and can be adapted for multiplexed detection of other respiratory pathogens of interest. Widespread adoption of such technology could assist public health officials with implementing rapid disease control measures.

Selective Electrochemical Detection of SARS-CoV-2 Using Deep Learning

COVID-19 has been in the headlines for the past two years. Diagnosing this infection with minimal false rates is still an issue even with the advent of multiple rapid antigen tests. Enormous data are being collected every day that could provide insight into reducing the false diagnosis. Machine learning (ML) and deep learning (DL) could be the way forward to process these data and reduce the false diagnosis rates. In this study, ML and DL approaches have been applied to the data set collected using an ultra-fast COVID-19 diagnostic sensor (UFC-19). The ability of ML and DL to specifically detect SARS-CoV-2 signals against SARS-CoV, MERS-CoV, Human CoV, and Influenza was investigated. UFC-19 is an electrochemical sensor that was used to test these virus samples and the obtained current response dataset was used to diagnose SARS-CoV-2 using different algorithms. Our results indicate that the convolution neural networks algorithm could diagnose SARS-CoV-2 samples with a sensitivity of 96.15%, specificity of 98.17%, and accuracy of 97.20%. Combining this DL model with the existing UFC-19 could selectively identify SARS-CoV-2 presence within two minutes.