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Cited by 1,754

The DOI matches its Crossref record; 96 of the 96 references it cites were found in a registry or an index.

Diagnosing COVID-19: The Disease and Tools for Detection

Open access
Udugama, Buddhisha, Kadhiresan, Pranav, Kozlowski, Hannah N., Malekjahani, Ayden, Osborne, Matthew, Li, Vanessa Y. C., Chen, Hongmin, Mubareka, Samira, Gubbay, Jonathan B., Chan, Warren C. W.·2020·ACS Nano

Abstract

COVID-19 has spread globally since its discovery in Hubei province, China in December 2019. A combination of computed tomography imaging, whole genome sequencing, and electron microscopy were initially used to screen and identify SARS-CoV-2, the viral etiology of COVID-19. The aim of this review article is to inform the audience of diagnostic and surveillance technologies for SARS-CoV-2 and their performance characteristics. We describe point-of-care diagnostics that are on the horizon and encourage academics to advance their technologies beyond conception. Developing plug-and-play diagnostics to manage the SARS-CoV-2 outbreak would be useful in preventing future epidemics.

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Registry

Crossref · 10.1021/acsnano.0c02624

Cites

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Metadata

Authors
Udugama, B., Kadhiresan, P., Kozlowski, H. N., Malekjahani, A., Osborne, M., Li, V. Y. C., Chen, H., Mubareka, S., Gubbay, J. B., & Chan, W. C. W.
Venue
ACS Nano
Published
2020-03-30
Volume
14
Issue
4
Pages
3822-3835
ISSN
  • 1936-0851 (ISSN-L)
  • 1936-086X
OpenAlex
W3012991084
Cited by
1,754
References
96
FWCI
45.81
Type
journal article
Language
en
Open access
bronze

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CoChat. (2026). Citation check: Diagnosing COVID-19: The Disease and Tools for Detection. https://app.cochat.ai/papers/10.1021/acsnano.0c02624
The paper
Udugama, B., Kadhiresan, P., Kozlowski, H. N., Malekjahani, A., Osborne, M., Li, V. Y. C., Chen, H., Mubareka, S., Gubbay, J. B., & Chan, W. C. W. (2020). Diagnosing COVID-19: The Disease and Tools for Detection. ACS Nano. https://doi.org/10.1021/acsnano.0c02624

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.