In a new study, researchers have developed an extremely rapid diagnostic test that detects and identifies viruses in less than five minutes.
The method is able to differentiate with high accuracy SARS-CoV-2, the virus responsible for COVID-19, from negative clinical samples, as well as from other common respiratory pathogens such as influenza and seasonal human coronaviruses.
The research was conducted by scientists from Oxford University.
Working directly on throat swabs from COVID-19 patients, without the need for genome extraction, purification, or amplification of the viruses, the method starts with the rapid labeling of virus particles in the sample with short fluorescent DNA strands.
A microscope is then used to collect images of the sample, with each image containing hundreds of fluorescently-labeled viruses.
Machine-learning software quickly and automatically identifies the virus present in the sample.
This approach exploits the fact that distinct virus types have differences in their fluorescence labeling due to differences in their surface chemistry, size, and shape.
The scientists have worked with clinical collaborators to validate the assay on COVID-19 patient samples which were confirmed by conventional RT-PCR methods.
The researchers aim to develop an integrated device that will eventually be used for testing in sites such as businesses, music venues, airports, etc., to establish and safeguard COVID-19-free spaces.
They are currently working with Oxford University Innovation (OUI) and two external business/finance advisors to set up a spinout, and are seeking investment to accelerate the translation of the test into a fully integrated device to be deployed as a real-time diagnostic platform capable of detecting multiple virus threats.
They hope to incorporate the company by the end of the year, start product development in early 2021, and have an approved device available within six months of that time.
One author of the study is Professor Achilles Kapanidis.
The study was published on the preprint server MedRxiv.
Copyright © 2020 Knowridge Science Report. All rights reserved.
