The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Dual-Functional Plasmonic Photothermal Biosensors for Highly Accurate Severe Acute Respiratory Syndrome Coronavirus 2 Detection
Open accessAbstract
Abstract The ongoing outbreak of the novel coronavirus disease (COVID-19) has spread globally and poses a threat to public health in more than 200 countries. Reliable laboratory diagnosis of the disease has been one of the foremost priorities for promoting public health interventions. The routinely used reverse transcription polymerase chain reaction (RT-PCR) is currently the reference method for COVID-19 diagnosis. However, it also reported a number of false-positive or -negative cases, especially in the early stages of the novel virus outbreak. In this work, a dual-functional plasmonic biosensor combining the plasmonic photothermal (PPT) effect and localized surface plasmon resonance (LSPR) sensing transduction provides an alternative and promising solution for the clinical COVID-19 diagnosis. The two-dimensional gold nanoislands (AuNIs) functionalized with complementary DNA receptors can perform a sensitive detection of the selected sequences from severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) through nucleic acid hybridization. For better sensing performance, the thermoplasmonic heat is generated on the same AuNIs chip when illuminated at their plasmonic resonance frequency. The localized PPT heat is capable to elevate the in situ hybridization temperature and facilitate the accurate discrimination of two similar gene sequences. Our dual-functional LSPR biosensor exhibits a high sensitivity toward the selected SARS-CoV-2 sequences with a lower detection limit down to the concentration of 0.22 pM and allows precise detection of the specific target in a multigene mixture. This study gains insight into the thermoplasmonic enhancement and its applicability in the nucleic acid tests and viral disease diagnosis.
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Cites
Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China
Huang, C., Wang, Y., Li, X., Ren, L., Zhao, J., Hu, Y., Zhang, L., Fan, G., Xu, J., Gu, X., et al. (2020) · The Lancet · doi:10.1016/s0140-6736(20)30183-5 · Cited by 48,884
Self-citationA novel coronavirus from patients with pneumonia in China, 2019
Zhu, N., Zhang, D., Wang, W., Li, X., Yang, B., Song, J., Zhao, X., Huang, B., Shi, W., Lu, R., et al. (2020) · New England Journal of Medicine · doi:10.1056/nejmoa2001017 · Cited by 28,244
Cryo-EM structure of the 2019-nCoV spike in the prefusion conformation
Wrapp, D., Wang, N., Corbett, K. S., Goldsmith, J. A., Hsieh, C.-L., Abiona, O., Graham, B. S., & McLellan, J. S. (2020) · Science · doi:10.1126/science.abb2507 · Cited by 8,897
Xie, X., Zhong, Z., Zhao, W., Zheng, C., Wang, F., & Liu, J. (2020) · Radiology · doi:10.1148/radiol.2020200343 · Cited by 1,960 · cited 2×
“10 In addition, the current RT-PCR-based detection methods demand high manpower and long processing time, which may not be able to provide the capacity to test all the suspected cases during full-scale outbreaks.”
BackgroundViral load of SARS-CoV-2 in clinical samples.
Pan, Y., Zhang, D., Yang, P., Poon, L. L. M., & Wang, Q. (2020) · The Lancet Infectious Diseases · doi:10.1016/s1473-3099(20)30113-4 · Cited by 1,562
Label-Free Plasmonic Biosensors for Point-of-Care Diagnostics: A Review
Soler, M., Huertas, C. S., & Lechuga, L. M. (2019) · Expert Review of Molecular Diagnostics · doi:10.1080/14737159.2019.1554435 · Cited by 236
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Diagnostics for SARS-CoV-2 infections
Kevadiya, B. D., Machhi, J., Herskovitz, J., Oleynikov, M. D., Blomberg, W. R., Bajwa, N., Soni, D., Das, S., Hasan, M., Patel, M., et al. (2021) · Nature Materials · doi:10.1038/s41563-020-00906-z · Cited by 833
COVID-19 diagnosis —A review of current methods
Yüce, M., Filiztekin, E., & Özkaya, K. G. (2021) · Biosensors and Bioelectronics · doi:10.1016/j.bios.2020.112752 · Cited by 757
Toward Nanotechnology-Enabled Approaches against the COVID-19 Pandemic
Weiss, C., Carriere, M., Fusco, L., Capua, I., Regla-Nava, J. A., Pasquali, M., Scott, J. A., Vitale, F., Unal, M. A., Mattevi, C., et al. (2020) · ACS Nano · doi:10.1021/acsnano.0c03697 · Cited by 601
Fabiani, L., Saroglia, M., Galatà, G., De Santis, R., Fillo, S., Luca, V., Faggioni, G., D'Amore, N., Regalbuto, E., Salvatori, P., et al. (2021) · Biosensors and Bioelectronics · doi:10.1016/j.bios.2020.112686 · Cited by 434
Diagnostic methods and potential portable biosensors for coronavirus disease 2019
Cui, F., & Zhou, H. S. (2020) · Biosensors and Bioelectronics · doi:10.1016/j.bios.2020.112349 · Cited by 373
Jain, S., Nehra, M., Kumar, R., Dilbaghi, N., Hu, T., Kumar, S., Kaushik, A., & Li, C.-Z. (2021) · Biosensors and Bioelectronics · doi:10.1016/j.bios.2021.113074 · Cited by 357
Nanotechnology-based disinfectants and sensors for SARS-CoV-2
Talebian, S., Wallace, G. G., Schroeder, A., Stellacci, F., & Conde, J. (2020) · Nature Nanotechnology · doi:10.1038/s41565-020-0751-0 · Cited by 334
Flexible Plasmonic Biosensors for Healthcare Monitoring: Progress and Prospects
Song, L., Chen, J., Xu, B. B., & Huang, Y. (2021) · ACS Nano · doi:10.1021/acsnano.1c07176 · Cited by 186
Wavelength-multiplexed hook nanoantennas for machine learning enabled mid-infrared spectroscopy
Ren, Z., Zhang, Z., Wei, J., Dong, B., & Lee, C. (2022) · Nature Communications · doi:10.1038/s41467-022-31520-z · Cited by 132
Azali, M. A., Mohamed, S., Harun, A., Hussain, F. A., Shamsuddin, S., & Johan, M. F. (2022) · Journal of Genetic Engineering and Biotechnology · doi:10.1186/s43141-022-00368-7 · Cited by 13
Metadata
- Authors
- Qiu, G., Gai, Z., Tao, Y., Schmitt, J., Kullak-Ublick, G. A., & Wang, J.
- Venue
- ACS Nano
- Publisher
- American Chemical Society (ACS)
- Published
- 2020-04-13
- Volume
- 14
- Issue
- 5
- Pages
- 5268-5277
- ISSN
- 1936-0851 (ISSN-L)
- 1936-086X
- OpenAlex
- W3015500613
- Cited by
- 1,059
- References
- 6
- FWCI
- 25.89
- Type
- journal article
- Language
- en
- Open access
- hybrid
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