Dogs can sniff out SARS-CoV-2 in urine samples with 96% accuracy, according to evidence from the draft study.
Are there no more swabs in your nose? Not quite, because the “dog test” is far from practical applications. This is because the dogs could only distinguish between positive and negative results in samples they trained with; they could not detect SARS-CoV-2 when they presented completely new samples.
It is known that dogs can sniff out odors that are specific to various diseases, and previous studies have shown that SARS-CoV-2 also has a strong signature that can detect dogs in saliva and sweat samples. According to the study, dogs have already been deployed to detect COVID-19 at a Dubai airport. But it was not known whether dogs could detect the virus in urine samples, where the virus load is usually lower, according to the study.
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To find out, a group of researchers first trained eight Labrador Retrievers and one Belgian Malaysian to recognize the odor of a synthetic substance known as the universal detection compound (UDC), which is an odor that is not naturally present in the environment does not occur. They placed the compound on one of the 12 ports of an ‘odor wheel’ and rewarded the dogs when they responded to the port with UDC.
After recognizing the UDC learning, the researchers used the scent wheel to train the dogs to respond to urine samples taken from SARS-CoV-2 positive patients. The samples were taken from seven individuals who tested positive for SARS-CoV-2 – two adults and five children, as well as six children with negative SARS-CoV-2 tests. During training, the dogs were given two scenarios, one in which the scent wheel contained the target odor in one port and a control or distraction odor in the other, and one in which the odor wheel contained all control or distraction odors. The virus was inactivated with heat or detergent to make it harmful to the dogs.
The researchers found that after three weeks of training, all dogs were able to identify positive SARS-CoV-2 samples with 96% accuracy on average. The overall specificity was 99%, meaning there were hardly any false positives; but the overall sensitivity was 68%, meaning there were some false negatives.
The reason for the lower sensitivity may be due to the strict way they did the tests; if the dogs once pass a gate with a positive sample without responding, it is missed as a mist, according to a statement.
“It’s not a simple thing for us dogs to do,” senior author Cynthia Otto, director of the University of Pennsylvania School of Veterinary Medicine Working Dog Center, said in the statement. “Dogs need to be specific about detecting the smell of the infection, but they need to generalize especially in the background odors from different people.”
Indeed, the dogs struggled a bit in the trials. They tended to distinguish between the actual patients’ scents and not their infection status, and they were also confused by a sample of a patient who had recently recovered from COVID-19 but had a negative result, according to the statement. . “The dogs kept responding to the monster, and we kept saying no,” Otto said in the statement. “But of course there was something else in the patient’s sample to which the dogs applied.”
But because the dogs were repeatedly trained on the same samples from the same patients, they could not generalize to completely new samples, which is the key to real-world applications.
In future studies, researchers should train dogs on different samples and not test the dogs repeatedly on samples from the same individuals, according to the statement. ‘Now the researchers are doing’ the T-shirt study ‘in which they train dogs to detect if someone is infected with SARS-CoV-2 and if someone is vaccinated based on the smells that people wore on shirts overnight.
“We are collecting far more samples in the study – hundreds or more – than we did in this first one, and we are hopeful that this will bring the dogs closer to what they encounter in a community environment,” Otto said.
The findings were published in the journal on April 14 PLOS Een.
Originally published on Live Science.