The team of researchers, led by biomedical engineering professor Jeong-Yeol Yoon, adapted a method they'd developed to detect noroviruses to detect the novel coronavirus as well.
The test uses a mouth rinse method developed by Michael Worobey, head of the university's Department of Ecology and Evolutionary Biology, which has people receiving the test gargle and spit sterile saltwater into a cup instead of using a swab. The method seems to be more sensitive than the standard swab method, with the rinse method detecting the virus in about 20% more patients than the swab method, according to the University of Arizona.
The research by the team at the university was published in Nature Protocols on Friday.
"We've outlined it so that other scientists can basically repeat what we did and create a norovirus-detecting device," said Lane Breshears, a biomedical engineering doctoral student in Yoon's lab. "Our goal is that if you want to adapt it for something else, like we've adapted it for COVID-19, that you have all the ingredients you need to basically make your own device."
The smartphone-based test uses a smartphone, a simple microscope and a piece of microfluidic paper (a wax-coated paper that guides the liquid sample to flow through specific channels). The components cost about $45, ending up cheaper than other testing methods.
To conduct the test, users introduce antibodies with fluorescent beads to a potentially contaminated water sample. If enough pathogen particles are present, several antibodies attach to each particle. Under the microscope, the particles show up as clumps of fluorescent beads, which the user can then count. The entire process takes about 10 to 15 minutes and could even be done by a nonscientist trained by a brief video.
The test method published on Friday has a number of improvements over the original version of the method published in 2019, including a 3D-printed housing for the microscope attachment and microfluidic paper chip. Additionally, while there had been a fixed value for what quantity of pathogen constituted a danger, the new method uses artificial intelligence to determine the threshold based on environmental differences, such as the type of smartphone and the quality of paper.
Pending approval by the university's institutional review board, students will have the option to provide written consent for their test samples to be run through the new testing method as well. The test could eventually be deployed to student hubs with a representative at each hub trained to use the testing device.The test could provide a solution to the difficulties faced worldwide with quickly and accurately testing workers, tourists and medical professionals and could allow for more sectors of the economy to open in a safer manner.A number of quick testing solutions have been developed by multiple companies and countries, but often require complex, professional training in order to be used. The method developed by the University of Arizona team would make testing easy and accessible for almost anyone, even those without any prior training.
"Adapting a method designed to detect the norovirus – another highly contagious pathogen – is an outstanding example of our researchers pivoting in the face of the pandemic," said University of Arizona President Robert C. Robbins. "This promising technology could allow us to provide fast, accurate, affordable tests to the campus community frequently and easily. We hope to make it a regular part of our 'Test, Trace, Treat' strategy, and that it will have a broader impact in mitigating the spread of the disease."
Yoon's team is also working on developing an even simpler method which allows slightly more room for error, using the same technology but with just an app and a microfluidic chip stamped with a QR code. The idea is based on a 2018 paper they published in Chemistry-A European Journal.
"Unlike the fluorescent microscope technique, where you get the chip into just the right position, you just take a snapshot of the chip," said biomedical engineering master's student Pat Akarapipad. "No matter the angle or distance the photo is taken from, the smartphone app can use AI and the QR code to account for variances and run calculations accordingly."
The method does not require any training and could allow civilians without any training to test their own samples easily.