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

Yanjun Yang1, Hao Li2, Les Jones3

  • 1School of Electrical and Computer Engineering, College of Engineering, The University of Georgia, Athens, Georgia30602, United States.

ACS Sensors
|December 23, 2022
PubMed
Summary

A novel surface-enhanced Raman spectroscopy (SERS) sensor combined with deep learning rapidly detects SARS-CoV-2 RNA in 25 minutes. This cost-effective method shows high accuracy for potential COVID-19 point-of-care diagnostics.

Keywords:
SARS-CoV-2 detectiondeep learningmachine learningrecurrent neural network (RNN)silver nanorod arraysurface-enhanced Raman scattering (SERS)

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Area of Science:

  • Nanotechnology and Spectroscopy
  • Biomedical Diagnostics
  • Artificial Intelligence in Healthcare

Background:

  • Rapid and cost-effective detection of SARS-CoV-2 is crucial for managing the COVID-19 pandemic.
  • Existing diagnostic methods may face limitations in speed, cost, or accessibility for point-of-care applications.

Purpose of the Study:

  • To develop a rapid and cost-effective sensor for detecting SARS-CoV-2 RNA in human nasopharyngeal swab specimens.
  • To integrate a deep learning algorithm for accurate classification of positive and negative COVID-19 samples.

Main Methods:

  • Fabrication of a surface-enhanced Raman spectroscopy (SERS) sensor using a silver nanorod array (AgNR) substrate functionalized with DNA probes.
  • Detection of SARS-CoV-2 RNA via hybridization with DNA probes on the AgNR substrate, followed by SERS spectral analysis.
  • Development and application of a recurrent neural network (RNN)-based deep learning model for classifying SERS spectra.

Main Results:

  • The SERS sensor demonstrated a detection range of 10^3-10^9 copies/mL for SARS-CoV-2 RNA.
  • The RNN deep learning model achieved an overall accuracy of 98.9% in classifying 160 specimens (40 positive, 120 negative).
  • Blind testing on 72 specimens yielded 97.2% accuracy for positive and 100% accuracy for negative samples, with a total detection time of 25 minutes.

Conclusions:

  • The DNA-functionalized AgNR array SERS sensor coupled with an RNN deep learning algorithm offers a promising approach for rapid COVID-19 detection.
  • This integrated platform has the potential to serve as a rapid point-of-care diagnostic tool for SARS-CoV-2 infections.
  • The high accuracy and speed of this method contribute to advancements in infectious disease diagnostics.