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Rapid Detection of SARS-CoV-2 RNA in Human Nasopharyngeal Specimens Using Surface-Enhanced Raman Spectroscopy and Deep Learning Algorithms.
Yang, Yanjun; Li, Hao; Jones, Les; Murray, Jackelyn; Haverstick, James; Naikare, Hemant K; Mosley, Yung-Yi C; Tripp, Ralph A; Ai, Bin; Zhao, Yiping.
  • Yang Y; School of Electrical and Computer Engineering, College of Engineering, The University of Georgia, Athens, Georgia30602, United States.
  • Li H; School of Microelectronics and Communication Engineering, Chongqing Key Laboratory of Bio-perception & Intelligent Information Processing, Chongqing University, Chongqing400044, P. R. China.
  • Jones L; Department of Infectious Diseases, College of Veterinary Medicine, The University of Georgia, Athens, Georgia30602, United States.
  • Murray J; Department of Infectious Diseases, College of Veterinary Medicine, The University of Georgia, Athens, Georgia30602, United States.
  • Haverstick J; Department of Physics and Astronomy, The University of Georgia, Athens, Georgia30602, United States.
  • Naikare HK; Department of Infectious Diseases, College of Veterinary Medicine, The University of Georgia, Athens, Georgia30602, United States.
  • Mosley YC; Tifton Veterinary Diagnostic and Investigational Laboratory, The University of Georgia, Athens, Georgia30602, United States.
  • Tripp RA; Department of Infectious Diseases, College of Veterinary Medicine, The University of Georgia, Athens, Georgia30602, United States.
  • Ai B; Tifton Veterinary Diagnostic and Investigational Laboratory, The University of Georgia, Athens, Georgia30602, United States.
  • Zhao Y; Department of Infectious Diseases, College of Veterinary Medicine, The University of Georgia, Athens, Georgia30602, United States.
ACS Sens ; 8(1): 297-307, 2023 01 27.
Article in English | MEDLINE | ID: covidwho-2185540
ABSTRACT
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.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Deep Learning / COVID-19 Type of study: Diagnostic study / Prognostic study Limits: Humans Language: English Journal: ACS Sens Year: 2023 Document Type: Article Affiliation country: Acssensors.2c02194

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Deep Learning / COVID-19 Type of study: Diagnostic study / Prognostic study Limits: Humans Language: English Journal: ACS Sens Year: 2023 Document Type: Article Affiliation country: Acssensors.2c02194