Recent Advances in SERS-based Biomedical Diagnostics of COVID-19

Abstract

Airborne viruses such as COVID-19 spread rapidly through the air, making them highly contagious. Therefore, there is an urgent need to develop a point-of-care diagnostic method that can perform quick and accurate on-site diagnosis. The reverse transcription polymerase chain reaction method, which involves the extraction and amplification of viral RNA and detection using fluorescence analysis, is the standard diagnostic method adopted worldwide. However, this method has a limitation in that it requires 3–4 h from preprocessing to detection, which makes on-site diagnosis challenging. By contrast, for immunological analysis, lateral flow assay kits that can be used individually for on-site self-testing have been commercialized. However, they have the disadvantage of low sensitivity, leading to falsenegative results in early stage patients or asymptomatic carriers with low viral concentrations. Various on-site diagnostic technologies for COVID-19 have been developed to address these issues using SERS detection and microdevice technologies. SERS detection has a higher sensitivity than that of fluorescence detection, and can overcome the false-negative problem. Further, miniaturization technologies using various microdevices enable the accurate and rapid on-site diagnosis of COVID19. This paper reviews the on-site diagnostic technologies that integrate SERSbased microdevices and portable Raman readers developed during the COVID-19 pandemic. Additionally, several machine learning techniques designed to enhance the accuracy of SERS-based detection results for COVID-19 are presented. The SERSbased microdevices and machine learning technologies introduced in this paper are evaluated as new diagnostic technologies that can respond to infectious diseases that may recur in the future.