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Yang Y, Li H, Jones L, Murray J, Haverstick J, Naikare HK, Mosley YYC, Tripp RA, Ai B, Zhao Y. Rapid Detection of SARS-CoV-2 RNA in Human Nasopharyngeal Specimens Using Surface-Enhanced Raman Spectroscopy and Deep Learning Algorithms. ACS Sens 2023; 8:297-307. [PMID: 36563081 PMCID: PMC9797020 DOI: 10.1021/acssensors.2c02194] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2022] [Accepted: 12/14/2022] [Indexed: 12/24/2022]
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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Affiliation(s)
- Yanjun Yang
- School of Electrical and Computer Engineering, College
of Engineering, The University of Georgia, Athens,
Georgia30602, United States
| | - Hao Li
- School of Microelectronics and Communication
Engineering, Chongqing Key Laboratory of Bio-perception & Intelligent Information
Processing, Chongqing University, Chongqing400044, P.
R. China
| | - Les Jones
- Department of Infectious Diseases, College of Veterinary
Medicine, The University of Georgia, Athens, Georgia30602,
United States
| | - Jackelyn Murray
- Department of Infectious Diseases, College of Veterinary
Medicine, The University of Georgia, Athens, Georgia30602,
United States
| | - James Haverstick
- Department of Physics and Astronomy, The
University of Georgia, Athens, Georgia30602, United
States
| | - Hemant K. Naikare
- Department of Infectious Diseases, College of Veterinary
Medicine, The University of Georgia, Athens, Georgia30602,
United States
- Tifton Veterinary Diagnostic and Investigational
Laboratory, The University of Georgia, Athens, Georgia30602,
United States
| | - Yung-Yi C. Mosley
- Department of Infectious Diseases, College of Veterinary
Medicine, The University of Georgia, Athens, Georgia30602,
United States
- Tifton Veterinary Diagnostic and Investigational
Laboratory, The University of Georgia, Athens, Georgia30602,
United States
| | - Ralph A. Tripp
- Department of Infectious Diseases, College of Veterinary
Medicine, The University of Georgia, Athens, Georgia30602,
United States
| | - Bin Ai
- School of Microelectronics and Communication
Engineering, Chongqing Key Laboratory of Bio-perception & Intelligent Information
Processing, Chongqing University, Chongqing400044, P.
R. China
| | - Yiping Zhao
- Department of Physics and Astronomy, The
University of Georgia, Athens, Georgia30602, United
States
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Ngo HT, Jin M, Trick AY, Chen FE, Chen L, Hsieh K, Wang TH. Sensitive and Quantitative Point-of-Care HIV Viral Load Quantification from Blood Using a Power-Free Plasma Separation and Portable Magnetofluidic Polymerase Chain Reaction Instrument. Anal Chem 2023; 95:1159-1168. [PMID: 36562405 DOI: 10.1021/acs.analchem.2c03897] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Point-of-care (POC) HIV viral load (VL) tests are needed to enhance access to HIV VL testing in low- and middle-income countries (LMICs) and to enable HIV VL self-testing at home, which in turn have the potential to enhance the global management of the disease. While methods based on real-time reverse transcription-polymerase chain reaction (RT-PCR) are highly sensitive and quantitatively accurate, they often require bulky and expensive instruments, making applications at the POC challenging. On the other hand, although methods based on isothermal amplification techniques could be performed using low-cost instruments, they have shown limited quantitative accuracies, i.e., being only semiquantitative. Herein, we present a sensitive and quantitative POC HIV VL quantification method from blood that can be performed using a small power-free three-dimensional-printed plasma separation device and a portable, low-cost magnetofluidic real-time RT-PCR instrument. The plasma separation device, which is composed of a plasma separation membrane and an absorbent material, demonstrated 96% plasma separation efficiency per 100 μL of whole blood. The plasma solution was then processed in a magnetofluidic cartridge for automated HIV RNA extraction and quantification using the portable instrument, which completed 50 cycles of PCR in 15 min. Using the method, we achieved a limit of detection of 500 HIV RNA copies/mL, which is below the World Health Organization's virological failure threshold, and a good quantitative accuracy. The method has the potential for sensitive and quantitative HIV VL testing at the POC and at home self-testing.
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Affiliation(s)
- Hoan T Ngo
- Department of Mechanical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, United States
| | - Mei Jin
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, United States
| | - Alexander Y Trick
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, United States
| | - Fan-En Chen
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, United States
| | - Liben Chen
- Department of Mechanical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, United States
| | - Kuangwen Hsieh
- Department of Mechanical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, United States
| | - Tza-Huei Wang
- Department of Mechanical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, United States
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, United States
- Institute for NanoBioTechnology, Johns Hopkins University, Baltimore, Maryland 21218, United States
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Gao X, Liu X, Zeng Y, Zhang Q, Zhang B, Zou G. Spectrum-Resolved Electrochemiluminescence to Multiplex the Immunoassay and DNA Probe Assay. Anal Chem 2022; 94:15801-15808. [DOI: 10.1021/acs.analchem.2c03579] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Affiliation(s)
- Xuwen Gao
- School of Chemistry and Chemical Engineering, Shandong University, Jinan 250100, China
| | - Xiancheng Liu
- Shenzhen Lifotronic Technology Co., Ltd, Nanshan District, Shenzhen 518055, China
| | - Ying Zeng
- Shenzhen Lifotronic Technology Co., Ltd, Nanshan District, Shenzhen 518055, China
| | - Qingqing Zhang
- Shenzhen Lifotronic Technology Co., Ltd, Nanshan District, Shenzhen 518055, China
| | - Bin Zhang
- School of Chemistry and Chemical Engineering, Shandong University, Jinan 250100, China
| | - Guizheng Zou
- School of Chemistry and Chemical Engineering, Shandong University, Jinan 250100, China
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Jiang W, Ji W, Zhang Y, Xie Y, Chen S, Jin Y, Duan G. An Update on Detection Technologies for SARS-CoV-2 Variants of Concern. Viruses 2022; 14:v14112324. [PMID: 36366421 PMCID: PMC9693800 DOI: 10.3390/v14112324] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Revised: 10/15/2022] [Accepted: 10/20/2022] [Indexed: 01/18/2023] Open
Abstract
Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is responsible for the global epidemic of Coronavirus Disease 2019 (COVID-19), with a significant impact on the global economy and human safety. Reverse transcription-quantitative polymerase chain reaction (RT-PCR) is the gold standard for detecting SARS-CoV-2, but because the virus's genome is prone to mutations, the effectiveness of vaccines and the sensitivity of detection methods are declining. Variants of concern (VOCs) include Alpha, Beta, Gamma, Delta, and Omicron, which are able to evade recognition by host immune mechanisms leading to increased transmissibility, morbidity, and mortality of COVID-19. A range of research has been reported on detection techniques for VOCs, which is beneficial to prevent the rapid spread of the epidemic, improve the effectiveness of public health and social measures, and reduce the harm to human health and safety. However, a meaningful translation of this that reduces the burden of disease, and delivers a clear and cohesive message to guide daily clinical practice, remains preliminary. Herein, we summarize the capabilities of various nucleic acid and protein-based detection methods developed for VOCs in identifying and differentiating current VOCs and compare the advantages and disadvantages of each method, providing a basis for the rapid detection of VOCs strains and their future variants and the adoption of corresponding preventive and control measures.
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Affiliation(s)
- Wenjie Jiang
- Department of Epidemiology, College of Public Health, Zhengzhou University, Zhengzhou 450001, China
| | - Wangquan Ji
- Department of Epidemiology, College of Public Health, Zhengzhou University, Zhengzhou 450001, China
| | - Yu Zhang
- Department of Epidemiology, College of Public Health, Zhengzhou University, Zhengzhou 450001, China
| | - Yaqi Xie
- Department of Epidemiology, College of Public Health, Zhengzhou University, Zhengzhou 450001, China
| | - Shuaiyin Chen
- Department of Epidemiology, College of Public Health, Zhengzhou University, Zhengzhou 450001, China
- Henan Key Laboratory of Molecular Medicine, Zhengzhou University, Zhengzhou 450001, China
- Correspondence: (S.C.); (Y.J.); (G.D.); Tel.: +86-13523408394 (S.C.); +86-0371-67781453 (Y.J.); +86-0371-67789797 (G.D.)
| | - Yuefei Jin
- Department of Epidemiology, College of Public Health, Zhengzhou University, Zhengzhou 450001, China
- Correspondence: (S.C.); (Y.J.); (G.D.); Tel.: +86-13523408394 (S.C.); +86-0371-67781453 (Y.J.); +86-0371-67789797 (G.D.)
| | - Guangcai Duan
- Department of Epidemiology, College of Public Health, Zhengzhou University, Zhengzhou 450001, China
- Henan Key Laboratory of Molecular Medicine, Zhengzhou University, Zhengzhou 450001, China
- Correspondence: (S.C.); (Y.J.); (G.D.); Tel.: +86-13523408394 (S.C.); +86-0371-67781453 (Y.J.); +86-0371-67789797 (G.D.)
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