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Buildout and integration of an automated high-throughput CLIA laboratory for SARS-CoV-2 testing on a large urban campus.
- Source :
-
SLAS technology [SLAS Technol] 2022 Oct; Vol. 27 (5), pp. 302-311. Date of Electronic Publication: 2022 Jun 17. - Publication Year :
- 2022
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Abstract
- In 2019, the first cases of SARS-CoV-2 were detected in Wuhan, China, and by early 2020 the first cases were identified in the United States. SARS-CoV-2 infections increased in the US causing many states to implement stay-at-home orders and additional safety precautions to mitigate potential outbreaks. As policies changed throughout the pandemic and restrictions lifted, there was an increase in demand for COVID-19 testing which was costly, difficult to obtain, or had long turn-around times. Some academic institutions, including Boston University (BU), created an on-campus COVID-19 screening protocol as part of a plan for the safe return of students, faculty, and staff to campus with the option for in-person classes. At BU, we put together an automated high-throughput clinical testing laboratory with the capacity to run 45,000 individual tests weekly by Fall of 2020, with a purpose-built clinical testing laboratory, a multiplexed reverse transcription PCR (RT-qPCR) test, robotic instrumentation, and trained staff. There were many challenges including supply chain issues for personal protective equipment and testing materials in addition to equipment that were in high demand. The BU Clinical Testing Laboratory (CTL) was operational at the start of Fall 2020 and performed over 1 million SARS-CoV-2 PCR tests during the 2020-2021 academic year.<br />Competing Interests: Declaration of interests The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.<br /> (Copyright © 2022 The Author(s). Published by Elsevier Inc. All rights reserved.)
Details
- Language :
- English
- ISSN :
- 2472-6311
- Volume :
- 27
- Issue :
- 5
- Database :
- MEDLINE
- Journal :
- SLAS technology
- Publication Type :
- Academic Journal
- Accession number :
- 35718332
- Full Text :
- https://doi.org/10.1016/j.slast.2022.06.003