Searching the RRID Resource Information Network
Are you sure you want to leave this community? Leaving the community will revoke any permissions you have been granted in this community.
Smruthi Karthikeyan, Greg Humphrey 2020. Automated high throughput viral concentration from wastewater using the KingFisher Flex platform. protocols.io https://dx.doi.org/10.17504/protocols.io.bptemnjeCopy Citation Copied
URL: https://dx.doi.org/DOI:10.17504/protocols.io.bptemnje
Authors: Smruthi Karthikeyan, Greg Humphrey
Group: Coronavirus Method Development Community
Summary: Large-scale wastewater surveillance has the ability to greatly augment the tracking of infection dynamics especially in communities where the prevalence rates far exceed the testing capacity. However, current methods for viral detection in wastewater are severely lacking in terms of scaling up for high throughput. In the present study, we employed an automated magnetic-bead based concentration approach for viral detection in sewage that can effectively be scaled up for processing 24 samples in a single 40-minute run. The method compared favorably to conventionally used methods for viral wastewater concentrations with a limit of detection of 8.809 viral gene copies/ml from input sample volumes as low as 10ml and can enable the processing of over 100 wastewater samples in a day.
Affiliations: University of California, San Diego, University of California, San Diego
External URL: https://www.medrxiv.org/content/10.1101/2020.11.16.20232900v1
Version: 1
Publication Date: 2020
Expand AllComing soon.
Check
for resource mentions.
Coming soon.
Source:
The NIDDK Information Network (dkNET) serves the needs of basic and clinical investigators by providing seamless access to large pools of data and research resources relevant to the mission of The National Institute of Diabetes Digestive and Kidney Diseases (NIDDK). dkNET is hosted at University of California San Diego, and is supported by NIH NIDDK grant U24DK097771.
Email: info_at_ dknet.org
