ABSTRACT
The outbreak of COVID-19 coronavirus disease around the end of 2019 has become a pandemic. The preferred method for COVID-19 detection is the real-time polymerase chain reaction (RT-PCR)-based technique; however, it also has certain limitations, such as sample-dependent procedures with a relatively high false negative ratio. We propose a safe and efficient method for screening COVID-19 based on Raman spectroscopy. A total of 177 serum samples are collected from 63 confirmed COVID-19 patients, 59 suspected cases, and 55 healthy individuals as a control group. Raman spectroscopy is adopted to analyze these samples, and a machine learning support-vector machine (SVM) method is applied to the spectrum dataset to build a diagnostic algorithm. Furthermore, 20 independent individuals, including 5 asymptomatic COVID-19 patients and 5 symptomatic COVID-19 patients, 5 suspected patients, and 5 healthy patients, were sampled for external validation. In these three groups-confirmed COVID-19, suspected, and healthy individuals-the distribution of statistically significant points of difference showed highly consistency for intergroups after repeated sampling processes. The classification accuracy between the COVID-19 cases and the suspected cases is 0.87 (95% confidence interval [CI]: 0.85-0.88), and the accuracy between the COVID-19 and the healthy controls is 0.90 (95% CI: 0.89-0.91), while the accuracy between the suspected cases and the healthy control group is 0.68 (95% CI: 0.67-0.73). For the independent test dataset, we apply the obtained SVM model to the classification of the independent test dataset to have all the results correctly classified. Our model showed that the serum-level classification results were all correct for independent test dataset. Our results suggest that Raman spectroscopy could be a safe and efficient technique for COVID-19 screening.
ABSTRACT
Corona Virus Disease 19 (COVID-19) pandemic has created an alarming situation across the globe. Varieties of diagnostic protocols are being developed for the diagnosis of COVID-19. Many of these diagnostic protocols however, have limitations such as for example unacceptable no of false-positive and false-negative cases, particularly during the early stages of infection. At present, the real-time (quantitative) reverse transcriptase-polymerase chain reaction (RT-PCR) is considered the gold standard for COVID-19 diagnosis. However, RT-PCR based tests are complex, expensive, time consuming and involve pre-processing of samples. A swift, sensitive, inexpensive protocol for mass screening is urgently needed to contain this pandemic. There is urgent need to harness new powerful technologies for accurate detection not only of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) but also combating the emergence of pandemics of new viruses as well. To overcome the current challenges, the authors propose a diagnostic protocol based on Surface-enhanced Raman Spectroscopy (SERS) coupled with microfluidic devices containing integrated microchannels functionalized either with vertically aligned Au/Ag coated carbon nanotubes or with disposable electrospun micro/nano-filter membranes. These devices have the potential to successfully trap viruses from diverse biological fluids/secretions including saliva, nasopharyngeal, tear etc. These can thus enrich the viral titre and enable accurate identification of the viruses from their respective Raman signatures. If the device is successfully developed and proven to detect target viruses, it would facilitate rapid screening of symptomatic as well as asymptomatic individuals of COVID-19. This would be a valuable diagnostic tool not only for mass screening of current COVID-19 pandemic but also in viral pandemic outbreaks of future.