New diagnosis test to detect Covid-19 in five min

New diagnosis test to detect Covid-19 in five min

London: Scientists from the Oxford University, UK, have developed a rapid Covid-19 test which can detect the coronavirus in less than five minutes, paving way for mass testing at airports and businesses.

Previous viral RNA tests took 1.5 to 2 hours to give a result. The medical device is able to identify the SARS-coV-2, the virus responsible for Covid-19, with high accuracy. The university aims to start product development of the testing device in early 2021 and have an approved device available in another six months.

“Unlike other technologies that detect a delayed antibody response or that require expensive, tedious and time-consuming sample preparation, our method quickly detects intact virus particles; meaning the assay is simple, extremely rapid, and cost-effective,” said Professor Achilles Kapanidis, Department of Physics, Oxford University.

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 on a fully integrated device that can be deployed as a real-time diagnostic platform capable of detecting multiple virus threats.

Working directly on throat swabs from Covid-19 patients, without the need for genome extraction, purification or amplification of the viruses, the method, published on the preprint server MedRxi, starts with the rapid labelling 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-labelled 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. Single-particle imaging combined with deep learning offers a promising alternative to traditional viral diagnostic methods, and has the potential for significant impact, according to the researchers.



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