The DOI matches its Crossref record; 73 of the 73 references it cites were found in a registry or an index.
Molecular diagnostic technologies for COVID-19: Limitations and challenges
Open accessAbstract
BACKGROUND: To curb the spread of the COVID-19 (coronavirus disease 2019) pandemic, the world needs diagnostic systems capable of rapid detection and quantification of the novel coronavirus (SARS-CoV-2). Many biomedical companies are rising to the challenge and developing COVID-19 diagnostics. In the last few months, some of these diagnostics have become commercially available for healthcare workers and clinical laboratories. However, the diagnostic technologies have specific limitations and reported several false-positive and false-negative cases, especially during the early stages of infection. AIM: This article aims to review recent developments in the field of COVID-19 diagnostics based on molecular technologies and analyze their clinical performance data. KEY CONCEPTS: The literature survey and performance-based analysis of the commercial and pre-commercial molecular diagnostics address several questions and issues related to the limitations of current technologies and highlight future research and development challenges to enable timely, rapid, low-cost, and accurate diagnosis of emerging infectious diseases.
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Cited by
False Negative Rate of COVID-19 PCR Testing: A Discordant Testing Analysis
Kanji, J. N., Zelyas, N., MacDonald, C., Pabbaraju, K., Khan, M. N., Prasad, A., Hu, J., Diggle, M., Berenger, B. M., & Tipples, G. (2021) · Virology Journal · doi:10.1186/s12985-021-01489-0 · Cited by 207
Mbiine, R., Nakanwagi, C., Lekuya, H. M., Aine, J., Hakim, K., Nabunya, L., & Tomusange, H. (2021) · JMIR Formative Research · doi:10.2196/27521 · Cited by 3
Metadata
- Authors
- Afzal, A.
- Venue
- Journal of Advanced Research
- Publisher
- Elsevier BV
- Published
- 2020-11
- Volume
- 26
- Pages
- 149-159
- ISSN
- 2090-1224 (ISSN-L)
- 2090-1232
- OpenAlex
- W3047349135
- Cited by
- 363
- References
- 73
- FWCI
- 8.69
- Type
- journal article
- Language
- en
- Open access
- gold
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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