About: Evaluation of COVID-19 RT-qPCR test in multi-sample pools

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An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Evaluation of COVID-19 RT-qPCR test in multi-sample pools
Creator	Aharony, Noga Argoetti, Amir Berenbaum, Dina Gandali, Nagam Geffen, Yuval Halberthal, Michael Hashimshony, Tamar Kishony, Kuzli, Areen Mandel-Gutfreund, Yael Messer, Esther Shafran, Einat Szwarcwort-Cohen, Moran Tamar, Shaer Yelin, Idan
Source	MedRxiv
abstract	The recent emergence of SARS-CoV-2 lead to a current pandemic of unprecedented levels. Though diagnostic tests are fundamental to the ability to detect and respond, many health systems are already experiencing shortages of reagents associated with this test. Here, testing a pooling approach for the standard RT-qPCR test, we find that a single positive sample can be detected even in pools of up to 32 samples, with an estimated false negative rate of 10%. Detection of positive samples diluted in even up to 64 samples may also be attainable, though may require additional amplification cycles. As it uses the standard protocols, reagents and equipment, this pooling method can be applied immediately in current clinical testing laboratories. We hope that such implementation of a pool test for COVID-19 would allow expanding current screening capacities thereby enabling the expansion of detection in the community, as well as in close integral groups, such as hospital departments, army units, or factory shifts.
has issue date	2020-03-27(xsd:dateTime)
bibo:doi	10.1101/2020.03.26.20039438
has license	medrxiv
sha1sum (hex)	0ba71b8ced117cf824c3675bbbb5ae51c12719b9
schema:url	https://doi.org/10.1101/2020.03.26.20039438
resource representing a document's title	Evaluation of COVID-19 RT-qPCR test in multi-sample pools
resource representing a document's body	covid:0ba71b8ced117cf824c3675bbbb5ae51c12719b9#body_text
is schema:about of	named entity 'laboratory' named entity 'Correspondence' named entity 'correspondence' named entity 'R. K.' named entity 'data analysis' named entity 'Rambam Health Care Campus' named entity 'MEDICAL' named entity 'INTERPRETATION' named entity 'CORRESPONDENCE' named entity 'sample collection' named entity 'Bacteriology' named entity 'technion.ac.il' named entity 'experimental procedure' named entity 'CAMPUS' named entity 'LABORATORY' named entity 'rambam' named entity 'sample' named entity 'equally' named entity 'procedure' named entity 'authors' named entity 'data analysis' named entity 'set' named entity 'experimental' named entity 'EXPERIMENTAL PROCEDURE' named entity 'Bacteriology' named entity 'Rambam Health Care Campus' named entity 'THESE' named entity 'AUTHORS' named entity 'GOV' named entity 'SENT' named entity 'SAMPLE COLLECTION' named entity 'SET' named entity 'DATA ANALYSIS' named entity 'HEALTH CARE' named entity 'HEALTH' named entity 'BACTERIOLOGY'
is part of of	Evaluation of COVID-19 RT-qPCR test in multi-sample pools The recent emergence of SARS-CoV-2 lead to a current pandemic of unprecedented levels. Though diagnostic tests are fundamental to the ability to detect and respond, many health systems are already experiencing shortages of reagents associated with this test. Here, testing a pooling approach for the standard RT-qPCR test, we find that a single positive sample can be detected even in pools of up to 32 samples, with an estimated false negative rate of 10%. Detection of positive samples diluted in even up to 64 samples may also be attainable, though may require additional amplification cycles. As it uses the standard protocols, reagents and equipment, this pooling method can be applied immediately in current clinical testing laboratories. We hope that such implementation of a pool test for COVID-19 would allow expanding current screening capacities thereby enabling the expansion of detection in the community, as well as in close integral groups, such as hospital departments, army units, or factory shifts. covid:0ba71b8ced117cf824c3675bbbb5ae51c12719b9#body_text

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