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URL: https://www.ox.ac.uk/news/2020-10-15-oxford-scientists-develop-extremely-rapid-diagnostic-test-covid-19
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Title: Oxford scientists develop extremely rapid diagnostic test for Covid-19 | University of Oxford
Description: Scientists from Oxford University’s Department of Physics have developed an extremely rapid diagnostic test that detects and identifies viruses in less than five minutes.
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Texto: Oxford scientists develop extremely rapid diagnostic test for Covid-19 | University of Oxford Skip to main content Home Home Admissions Undergraduate Graduate Continuing education Research Divisions Research impact Libraries Innovation and Partnership Support for researchers Research in conversation Public Engagement with Research News & Events Events Science Blog Arts Blog Oxford and coronavirus Oxford and Brexit News releases for journalists Filming in Oxford Find An Expert About Organisation Facts and figures Oxford people Oxford Access International Oxford Building Our Future Jobs 牛津大学 Search News & Events Events Regular events in the University Year Black History Month at Oxford Women Making History: Centenary Events First Animals Online University Events Office Science Blog Arts Blog Oxford and coronavirus Oxford and Brexit News releases for journalists Filming in Oxford Find An Expert The test uses a convolutional neural network to classify microscopy images of single intact particles of different viruses Credit: University of Oxford Published 15 Oct 2020 Share This Tweet Share on Facebook Share on LinkedIn Share on Reddit Home News Oxford scientists develop extremely rapid diagnostic test for Covid-19 Oxford scientists develop extremely rapid diagnostic test for Covid-19 Research Coronavirus Scientists from Oxford University’s Department of Physics have developed an extremely rapid diagnostic test that detects and identifies viruses in less than five minutes. The method, published on the preprint server MedRxiv , 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. 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 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. The test uses a convolutional neural network to classify microscopy images of single intact particles of different viruses Credit: University of Oxford The scientists have worked with clinical collaborators at the John Radcliffe Hospital in Oxford to validate the assay on COVID-19 patient samples which were confirmed by conventional RT-PCR methods. Professor Achilles Kapanidis , at Oxford’s Department of Physics , says: ‘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.’ DPhil student Nicolas Shiaelis , at the University of Oxford, says: ‘Our test is much faster than other existing diagnostic technologies; viral diagnosis in less than 5 minutes can make mass testing a reality, providing a proactive means to control viral outbreaks.’ Dr Nicole Robb , formerly a Royal Society Fellow at the University of Oxford and now at Warwick Medical School, says: ‘A significant concern for the upcoming winter months is the unpredictable effects of co-circulation of SARS-CoV-2 with other seasonal respiratory viruses; we have shown that our assay can reliably distinguish between different viruses in clinical samples, a development that offers a crucial advantage in the next phase of the pandemic.’ 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 6 months of that time. Read the preprint here: https://www.medrxiv.org/content/10.1101/2020.10.13.20212035v1 Latest Professor Nick Eyre appointed Oxford City Council scientific adviser 28 Oct 2020 Oxford University creates its 200th spinout company PhishAR 27 Oct 2020 Project for whole genome sequencing of extinct flightless birds 27 Oct 2020 Large tides may have been a key factor in the evolution of bony fish and tetrapods 23 Oct 2020 Global clinical trials of COVID-19 vaccine resume 23 Oct 2020 All news Share This Tweet Share on Facebook Share on LinkedIn Share on Reddit Connect with us iTunes Youtube Facebook Twitter LinkedIn Weibo Instagram Medium The Conversation Information About Oxford University Strategic plan Oxford's research Fees and funding Libraries Museums and collections Open days Oxford glossary Freedom of speech statement Statement on Modern Slavery Data privacy / GDPR Sport at Oxford Conferences at Oxford 牛津大学 Information For Prospective undergraduates Prospective graduate students Prospective Continuing Education students Prospective online/distance learning students Current Oxford students Current Oxford staff Oxford residents/Community Visitors/Tourists Media Alumni Teachers Parliamentarians Businesses/Partnerships Quick Links Contact search Jobs and vacancies Term dates Map Nexus365 email Giving to Oxford Oxford University Images © University of Oxford 2020 Contact us About this site Legal Privacy policy Cookie statement Accessibility Statement


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