The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Development and evaluation of a rapid CRISPR-based diagnostic for COVID-19
Open accessAbstract
Background The recent outbreak of infections by the 2019 novel coronavirus (2019-nCoV), the third zoonotic CoV has raised great public health concern. The demand for rapid and accurate diagnosis of this novel pathogen brought significant clinical and technological challenges. Currently, metagenomic next-generation sequencing (mNGS) and reverse-transcription PCR (RT-PCR) are the most widely used molecular diagnostics for 2019-nCoV. Methods 2019-nCoV infections were confirmed in 52 specimens by mNGS. Genomic information was analyzed and used for the design and development of an isothermal, CRISPR-based diagnostic for the novel virus. The diagnostic performance of CRISPR-nCoV was assessed and also compared across three technology platforms (mNGS, RT-PCR and CRISPR) Results 2019-nCoVs sequenced in our study were conserved with the Wuhan strain, and shared certain genetic similarity with SARS-CoV . A high degree of variation in the level of viral RNA was observed in clinical specimens. CRISPR-nCoV demonstrated a near single-copy sensitivity and great clinical sensitivity with a shorter turn-around time than RT-PCR. Conclusion CRISPR-nCoV presents as a promising diagnostic option for the emerging pathogen.
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Early transmission dynamics in Wuhan, China, of novel coronavirus-infected pneumonia
Li, Q., Guan, X., Wu, P., Wang, X., Zhou, L., Tong, Y., Ren, R., Leung, K. S. M., Lau, E. H. Y., Wong, J. Y., et al. (2020) · New England Journal of Medicine · doi:10.1056/nejmoa2001316 · Cited by 16,575
Self-citationFirst case of 2019 novel coronavirus in the United States
Holshue, M. L., DeBolt, C., Lindquist, S., Lofy, K. H., Wiesman, J., Bruce, H., Spitters, C., Ericson, K., Wilkerson, S., Tural, A., et al. (2020) · New England Journal of Medicine · doi:10.1056/nejmoa2001191 · Cited by 6,030
CRISPR-Cas12a target binding unleashes indiscriminate single-stranded DNase activity
Chen, J. S., Ma, E., Harrington, L. B., Da Costa, M., Tian, X., Palefsky, J. M., & Doudna, J. A. (2018) · Science · doi:10.1126/science.aar6245 · Cited by 4,507
Coronavirus Pathogenesis and the Emerging Pathogen Severe Acute Respiratory Syndrome Coronavirus
Weiss, S. R., & Navas-Martin, S. (2005) · Microbiology and Molecular Biology Reviews · doi:10.1128/mmbr.69.4.635-664.2005 · Cited by 1,256
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Amplification-free detection of SARS-CoV-2 with CRISPR-Cas13a and mobile phone microscopy
Fozouni, P., Son, S., Díaz de León Derby, M., Knott, G. J., Gray, C. N., D’Ambrosio, M. V., Zhao, C., Switz, N. A., Kumar, G. R., Stephens, S. I., et al. (2021) · Cell · doi:10.1016/j.cell.2020.12.001 · Cited by 1,047
Esbin, M. N., Whitney, O. N., Chong, S., Maurer, A., Darzacq, X., & Tjian, R. (2020) · RNA · doi:10.1261/rna.076232.120 · Cited by 477
From Diagnosis to Treatment: Recent Advances in Patient-Friendly Biosensors and Implantable Devices
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Metadata
- Authors
- Hou, T., Zeng, W., Yang, M., Chen, W., Ren, L., Ai, J., Wu, J., Liao, Y., Gou, X., Li, Y., Wang, X., Su, H., Gu, B., Wang, J., & Xu, T.
- Venue
- PLOS Pathogens
- Publisher
- Public Library of Science (PLoS)
- Published
- 2020-08-27
- Volume
- 16
- Issue
- 8
- Pages
- e1008705
- ISSN
- 1553-7366 (ISSN-L)
- 1553-7374
- OpenAlex
- W3081392899
- Cited by
- 239
- References
- 4
- FWCI
- 6.40
- Type
- journal article
- Language
- en
- Open access
- gold
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- Hou, T., Zeng, W., Yang, M., Chen, W., Ren, L., Ai, J., Wu, J., Liao, Y., Gou, X., Li, Y., Wang, X., Su, H., Gu, B., Wang, J., & Xu, T. (2020). Development and evaluation of a rapid CRISPR-based diagnostic for COVID-19. PLOS Pathogens. https://doi.org/10.1371/journal.ppat.1008705
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