Rapid Detection of SARS-CoV-2 RNA in Human Nasopharyngeal Specimens Using Surface-Enhanced Raman Spectroscopy and Deep Learning Algorithms.

A rapid and cost-effective method to detect the infection of SARS-CoV-2 is fundamental to mitigating the current COVID-19 pandemic. Herein, a surface-enhanced Raman spectroscopy (SERS) sensor with a deep learning algorithm has been developed for the rapid detection of SARS-CoV-2 RNA in human nasopharyngeal swab (HNS) specimens. The SERS sensor was prepared using a silver nanorod array (AgNR) substrate by assembling DNA probes to capture SARS-CoV-2 RNA. The SERS spectra of HNS specimens were collected after RNA hybridization, and the corresponding SERS peaks were identified. The RNA detection range was determined to be 103-109 copies/mL in saline sodium citrate buffer. A recurrent neural network (RNN)-based deep learning model was developed to classify 40 positive and 120 negative specimens with an overall accuracy of 98.9%. For the blind test of 72 specimens, the RNN model gave a 97.2% accuracy prediction for positive specimens and a 100% accuracy for negative specimens. All the detections were performed in 25 min. These results suggest that the DNA-functionalized AgNR array SERS sensor combined with a deep learning algorithm could serve as a potential rapid point-of-care COVID-19 diagnostic platform.

Rapid and quantitative detection of respiratory viruses using surface-enhanced Raman spectroscopy and machine learning.

Rapid and sensitive pathogen detection is important for prevention and control of disease. Here, we report a label-free diagnostic platform that combines surface-enhanced Raman scattering (SERS) and machine learning for the rapid and accurate detection of thirteen respiratory virus species including SARS-CoV-2, common human coronaviruses, influenza viruses, and others. Virus detection and measurement have been performed using highly sensitive SiO2 coated silver nanorod array substrates, allowing for detection and identification of their characteristic SERS peaks. Using appropriate spectral processing procedures and machine learning algorithms (MLAs) including support vector machine (SVM), k-nearest neighbor, and random forest, the virus species as well as strains and variants have been differentiated and classified and a differentiation accuracy of >99% has been obtained. Utilizing SVM-based regression, quantitative calibration curves have been constructed to accurately estimate the unknown virus concentrations in buffer and saliva. This study shows that using a combination of SERS, MLA, and regression, it is possible to classify and quantify the virus in saliva, which could aid medical diagnosis and therapeutic intervention.

Silver nanotriangle array based LSPR sensor for rapid coronavirus detection.

A rapid, portable, and cost-effective method to detect the infection of SARS-CoV-2 is fundamental toward mitigating the current COVID-19 pandemic. Herein, a human angiotensin-converting enzyme 2 protein (ACE2) functionalized silver nanotriangle (AgNT) array localized surface plasmon resonance (LSPR) sensor is developed for rapid coronavirus detection, which is validated by SARS-CoV-2 spike RBD protein and CoV NL63 virus with high sensitivity and specificity. A linear shift of the LSPR wavelength versus the logarithm of the concentration of the spike RBD protein and CoV NL63 is observed. The limits of detection for the spike RBD protein, CoV NL63 in buffer and untreated saliva are determined to be 0.83 pM, 391 PFU/mL, and 625 PFU/mL, respectively, while the detection time is found to be less than 20 min. Thus, the AgNT array optical sensor could serve as a potential rapid point-of-care COVID-19 diagnostic platform.