Breathalyzer to Detect Coronavirus Non-invasively

Prototype device that can non-invasively detect COVID-19 has been developed by a group of researchers.

Few people who have undergone nasopharyngeal swabs for coronavirus testing would describe it as a pleasant experience. The procedure involves sticking a long swab up the nose to collect a sample from the back of the nose and throat, which is then analyzed for SARS-CoV-2 RNA by the reverse-transcription polymerase chain reaction (RT-PCR).

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‘COVID-19 can be detected by people blowing into a breathalyzer. It has a 76% accuracy in distinguishing COVID-19 cases from controls and 95% accuracy in discriminating COVID-19 cases from lung infections.’
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Now, researchers reporting in ACS Nano have developed a prototype device that non-invasively detected COVID-19 in the exhaled breath of infected patients.

In addition to being uncomfortable, the current gold standard for COVID-19 testing requires RT-PCR, a time-consuming laboratory procedure. Because of backlogs, obtaining a result can take several days.

To reduce transmission and mortality rates, healthcare systems need quick, inexpensive and easy-to-use tests.

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Hossam Haick, Hu Liu, Yueyin Pan and colleagues wanted to develop a nanomaterial-based sensor that could detect COVID-19 in exhaled breath, similar to a breathalyzer test for alcohol intoxication.

Previous studies have shown that viruses and the cells they infect emit volatile organic compounds (VOCs) that can be exhaled in the breath.

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The researchers made an array of gold nanoparticles linked to molecules that are sensitive to various VOCs. When VOCs interact with the molecules on a nanoparticle, the electrical resistance changes.

The researchers trained the sensor to detect COVID-19 by using machine learning to compare the pattern of electrical resistance signals obtained from the breath of 49 confirmed COVID-19 patients with those from 58 healthy controls and 33 non-COVID lung infection patients in Wuhan, China.

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Each study participant blew into the device for 2-3 seconds from a distance of 1¬-2 cm. Once machine learning identified a potential COVID-19 signature, the team tested the accuracy of the device on a subset of participants.

In the test set, the device showed 76% accuracy in distinguishing COVID-19 cases from controls and 95% accuracy in discriminating COVID-19 cases from lung infections.

The sensor could also distinguish, with 88% accuracy, between sick and recovered COVID-19 patients.

Although the test needs to be validated in more patients, it could be useful for screening large populations to determine which individuals need further testing, the researchers say.

Source-Eurekalert