Abstract
It's been accuracy versus convenience and speed so far, but the next generation of smartphone-based COVID-19 tests are poised to alter that equation by adding affordability as well. In a recent report, the University of Arizona (UA) describes how a method they previously used for norovirus, an infection seen most notably on cruise ships, could be applied to COVID-19 detection. And, not surprisingly, it uses a smart phone.
“Our method can detect as low as 10-100 viral particles,” explains biomedical engineering professor Jeong-Yeol Yoon Ph.D., associate department head for biomedical engineering graduate affairs, who lead the UA work.
Jeong-Yeol Yoon Ph.D., (right), associate department head for biomedical engineering graduate affairs at University of Arizona adapted the technology for COVID-19 detection from one he had originally developed to detect norovirus.
The basis of technology, described in a 2019 paper published in the journal ACS Omega, is relatively simple. Users introduce antibodies with fluorescent beads to a water sample containing saliva. If enough particles of the pathogen are present in the sample, several antibodies attach to each pathogen particle. Under a microscope, the pathogen particles show up as little clumps of fluorescent beads, which the user can then count.
This advance enters a flourishing field. The vision is that students, or teachers or other campus staff, can take a COVID-19 test on the fly and then decide to self-quarantine as needed.
From the Gladstone Institute, UC Berkeley, and UCSF there is a technology for a CRISPR-based COVID-19 test that uses a smartphone camera to provide accurate results in under 30 minutes. This test uses the CRISPR-Cas13 protein, which directly binds and cleaves RNA segments, thereby eliminating the usual DNA conversion and amplification steps.
“It has been an urgent task for the scientific community to not only increase testing, but also to provide new testing options,” said Melanie Ott, M.D., Ph.D., director of the Gladstone Institute of Virology and one of the study leaders. “The assay we developed could provide rapid, low-cost testing to help control the spread of COVID-19.”
The smartphone-based system developed at the University of Arizona uses an onboard microscope to detect the presence of the SARS-CoV-2 virus in as little as 10 minutes.
Meanwhile, diagnostics company BD is developing a smart-phone-powered COVID-19 at-home antigen test as a complement to BD's Veritor system, a handheld test that was FDA approved last summer. BD is partnered with Scanwell Health, which has a smartphone platform. Scanwell's computer vision technology is aimed to mirror that of point-of-care and laboratory diagnostic systems. The BD Veritor System is used at the point of care to detect SARS-CoV-2 neucleoproteins from the virus. It can detect these in as little as 15 minutes.
The UA new smartphone-based COVID-19 test aims to combine the speed of an antigen test with the accuracy of a polymerase chain reaction (PCR) test. This test consists of a smartphone, a microscope and a piece of micro-fluidic paper—a wax-coated paper that guides the liquid sample to flow through specific channels. It's a saliva-based antigen test, but the components cost about $45, anyone who can use a cell phone should be able to use it, and results are available within about 10 minutes.
To create this test, researchers at UA are adapting the method they originally created to detect norovirus—the microbe famous for spreading on cruise ships—using a smartphone microscope.
“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 at UA. “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 process—adding beads to the sample, soaking a piece of paper in the sample, then taking a smartphone photograph of it under a microscope and counting the beads takes about 10 to 15 minutes. It involves the patient gargling, to take a spit/ water sample. It's so simple that Yoon says a nonscientist could learn how to do it by watching a brief video.
The team's latest research using water samples was done in collaboration with Kelly A. Reynolds, chair of the Department of Community, Environment at UA. This research was published in Nature Protocols.
The version of the technology described in the Nature Protocols paper makes further improvements, such as creating a 3D-printed housing for the microscope attachment and micro-fluidic paper chip. The paper also introduces a method called adaptive thresholding. Previously, researchers set a fixed value for what quantity of pathogen constituted a danger, which limited precision levels. The new version uses artificial intelligence to set the danger threshold and account for environmental differences, such as the type of smartphone and the quality of the paper.
The first over-the-counter, fully at-home diagnostic test for COVID-19 was the Ellume COVID-19 lateral flow antigen test, which detects fragments of proteins of the virus. It was approved in mid-December not long after the agency authorized the first prescription COVID-19 test for home use, and then the first non-prescription test system, where a self-collected sample is sent to a lab for reading. Since the start of the pandemic, the FDA has authorized more than 225 diagnostic tests for the virus, including more than 25 tests that allow for home sample collection.
The UA research team was led by Yoon, who is working with a large group of undergraduate and graduate students to develop the smartphone-based COVID-19 detection method. “We have about 85%-90% accuracy, we are working to optimize it to 95%,” Yoon says.
“I have a couple of friends who had COVID-19 that were super frustrated, because their PCR results were taking six or seven days, or they were getting false negatives from rapid antigen tests. But when they got the final PCR tests, they found out they had been sick, like they'd suspected,” said Katie Sosnowski, a biomedical engineering doctoral student who works in Yoon's lab. “It's really cool to be working on a detection platform that can get fast results that are also accurate.”