Dr. Andrew Brooks of RUCDR Infinite Biologics holds up a saliva sample.
The patient tilts back her head and winces as the long swab stick pushes six inches up her nose. The tip twirls around uncomfortably before it's withdrawn.
"Our saliva test can detect the virus in asymptomatic and pre-symptomatic cases."
A gloved and gowned healthcare worker wearing a face shield and mask tells the patient that she will learn whether she is positive for COVID-19 as soon as the lab can process her test.
This is the typical unpleasant scenario for getting a coronavirus test. But times are rapidly changing: Today, for the first time, the U.S. Food and Drug Administration cleared one company to sell saliva collection kits for individuals to use at home.
Scientists at the startup venture, RUCDR Infinite Biologics at Rutgers University in New Jersey, say that saliva testing offers an easier, more useful alternative to the standard nasal swab.
"Our saliva test can detect the virus in asymptomatic and pre-symptomatic cases," said Dr. Andrew Brooks, chief operating officer at RUCDR.
Another venture, Darwin BioSciences in Colorado, has separately developed an innovative method of testing saliva for the coronavirus that causes COVID-19.
Saliva testing can allow earlier detection to identify people who may not know they are contagious, say scientists at both companies. In addition, because patients spit into a tube or cup, saliva testing is safer for healthcare workers than taking swabs. This frees up scarce personal protective equipment (PPE) for use elsewhere. Nasal swabs themselves have been in scarce supply.
Saliva testing, if it becomes widespread, potentially could mean opening society sooner. The more ubiquitous testing becomes across the population, experts say, the more feasible it becomes for public health officials to trace and isolate contacts, especially of asymptomatic cases. Testing early and often will be essential to containing emerging hot spots before a vast outbreak can take root.
Darwin Biosceiences is preparing to seek an FDA Emergency Use Authorization (EUA) this month for its patented "CoVScreen" testing system, which potentially could be available to labs nationally by mid-summer.
Meanwhile, Infinite Biologics will now begin selling kits to consumers for home collection, upon order by a physician. The FDA said that the company's saliva test was as accurate as the nasal swab method used by health care professionals. An FDA summary documenting the company's data reported: "There was 100% positive and negative agreement between the results obtained from testing of saliva and those obtained from nasopharyngeal and oropharyngeal swabs."
The greatest scientific advantage, said Dr. Brooks, is that nasal and oral swabs only collect the surface area where the swab goes, which may not be the place with most viral load. In contrast, the virus occurs throughout a saliva sample, so the test is more trustworthy.
The lab at Rutgers can process 20,000 tests a day, with a 48-hour turnaround. They have 75,000 tests ready to ship now.
The Leap: Detecting Sickness Before You Feel It
"We wanted to create a device that could detect infections before symptoms appeared," explained Nicholas Meyerson, co-founder and CEO of Darwin.
For more than 300 years, he said, "the thermometer was the gold standard for detecting disease because we thought the first sign of illness was a fever. This COVID-19 pandemic has proven that not all pathogens cause a fever. You can be highly contagious without knowing it."
"The question is whether we can scale up fast enough to meet the need. I believe saliva testing can help."
Therefore, Meyerson and co-founder Sara Sawyer from the University of Colorado began to identify RNA biomarkers that can sense when a pathogen first enters a molecule and "sets off alarms." They focused on the nucleic acids concentrated in saliva as the best and easiest place to collect samples for testing.
"The isothermal reaction in saliva takes place at body or room temperature," he said, "so there's no need for complicated testing machinery. The chemical reaction can be read out on a paper strip, like a pregnancy test -- two stripes if you're sick, and one stripe if you're okay."
Before the pandemic, limited but successful human trials were already underway at CU in Boulder and at the CU Anschutz Medical Campus east of Denver. "This was our proof of concept," he said.
Darwin was founded in March and has secured enough venture capital to concentrate protype development on detecting the virus causing COVID-19. So far, said Meyerson, "Everything works."
A small double-blind test of 30 samples at CU produced 100 percent accuracy. "I'm not sure if that will hold true as we go into clinical trials," he said, "but I'm confident we will satisfy all the requirements for at least 95 percent clinical validation."
The specific "CoVStick" test strips will roll out soon, he said: "We hope before the second wave of the pandemic hits."
The broader saliva test-strip product from Darwin, "SickStick," is still one to two years away from deployment by the military and introduction into the consumer drugstore market for home use, said Meyerson. It will affordably and quickly detect a range of viral and bacterial infections.
An illustration of the "CoVStick."
(Darwin Biosciences)
A Potential Game Changer
Society needs widespread testing daily, said George Church, founding core faculty of the Wyss Institute for Biologically Inspired Engineering at Harvard University. Speaking at an online SynBioBeta webinar in April, he urged developing stockpiles of testing kits for home use.
As for any potential of false positives, Church said a much bigger risk is not having enough tests.
"Saliva testing is going to speed up the timeline for opening society a lot," said Meyerson. "People need to self-collect samples at home. A lot more people are going to be willing to spit into a tube than to push a swab six inches up their own nose."
Brooks, of Rutgers, addressed the big picture. "It's critical that we open society as soon as possible to minimize the economic impact of the pandemic. Testing is the surest and safest path. The question is whether we can scale up fast enough to meet the need. I believe saliva testing can help."
Giving robots self-awareness as they move through space - and maybe even providing them with gene-like methods for storing rules of behavior - could be important steps toward creating more intelligent machines.
Now roboticists are going a step further, training their creations to do even better: understand their own image in space and interact with humans like humans do. Today, there are already robot-teachers like KeeKo, robot-pets like Moffin, robot-babysitters like iPal, and robotic companions for the elderly like Pepper.
But even these reasonably intelligent creations still have huge limitations, some scientists think. “There are niche applications for the current generations of robots,” says professor Anthony Zador at Cold Spring Harbor Laboratory—but they are not “generalists” who can do varied tasks all on their own, as they mostly lack the abilities to improvise, make decisions based on a multitude of facts or emotions, and adjust to rapidly changing circumstances. “We don’t have general purpose robots that can interact with the world. We’re ages away from that.”
Robotic spatial self-awareness – the achievement by the team at Columbia – is an important step toward creating more intelligent machines. Hod Lipson, professor of mechanical engineering who runs the Columbia lab, says that future robots will need this ability to assist humans better. Knowing how you look and where in space your parts are, decreases the need for human oversight. It also helps the robot to detect and compensate for damage and keep up with its own wear-and-tear. And it allows robots to realize when something is wrong with them or their parts. “We want our robots to learn and continue to grow their minds and bodies on their own,” Chen says. That’s what Zador wants too—and on a much grander level. “I want a robot who can drive my car, take my dog for a walk and have a conversation with me.”
Columbia scientists have trained a robot to become aware of its own "body," so it can map the right path to touch a ball without running into an obstacle, in this case a square.
Jane Nisselson and Yinuo Qin/ Columbia Engineering
Today’s technological advances are making some of these leaps of progress possible. One of them is the so-called Deep Learning—a method that trains artificial intelligence systems to learn and use information similar to how humans do it. Described as a machine learning method based on neural network architectures with multiple layers of processing units, Deep Learning has been used to successfully teach machines to recognize images, understand speech and even write text.
Trained by Google, one of these language machine learning geniuses, BERT, can finish sentences. Another one called GPT3, designed by San Francisco-based company OpenAI, can write little stories. Yet, both of them still make funny mistakes in their linguistic exercises that even a child wouldn’t. According to a paper published by Stanford’s Center for Research on Foundational Models, BERT seems to not understand the word “not.” When asked to fill in the word after “A robin is a __” it correctly answers “bird.” But try inserting the word “not” into that sentence (“A robin is not a __”) and BERT still completes it the same way. Similarly, in one of its stories, GPT3 wrote that if you mix a spoonful of grape juice into your cranberry juice and drink the concoction, you die. It seems that robots, and artificial intelligence systems in general, are still missing some rudimentary facts of life that humans and animals grasp naturally and effortlessly.
How does one give robots a genome? Zador has an idea. We can’t really equip machines with real biological nucleotide-based genes, but we can mimic the neuronal blueprint those genes create.
It's not exactly the robots’ fault. Compared to humans, and all other organisms that have been around for thousands or millions of years, robots are very new. They are missing out on eons of evolutionary data-building. Animals and humans are born with the ability to do certain things because they are pre-wired in them. Flies know how to fly, fish knows how to swim, cats know how to meow, and babies know how to cry. Yet, flies don’t really learn to fly, fish doesn’t learn to swim, cats don’t learn to meow, and babies don’t learn to cry—they are born able to execute such behaviors because they’re preprogrammed to do so. All that happens thanks to the millions of years of evolutions wired into their respective genomes, which give rise to the brain’s neural networks responsible for these behaviors. Robots are the newbies, missing out on that trove of information, Zador argues.
A neuroscience professor who studies how brain circuitry generates various behaviors, Zador has a different approach to developing the robotic mind. Until their creators figure out a way to imbue the bots with that information, robots will remain quite limited in their abilities. Each model will only be able to do certain things it was programmed to do, but it will never go above and beyond its original code. So Zador argues that we have to start giving robots a genome.
How does one do that? Zador has an idea. We can’t really equip machines with real biological nucleotide-based genes, but we can mimic the neuronal blueprint those genes create. Genomes lay out rules for brain development. Specifically, the genome encodes blueprints for wiring up our nervous system—the details of which neurons are connected, the strength of those connections and other specs that will later hold the information learned throughout life. “Our genomes serve as blueprints for building our nervous system and these blueprints give rise to a human brain, which contains about 100 billion neurons,” Zador says.
If you think what a genome is, he explains, it is essentially a very compact and compressed form of information storage. Conceptually, genomes are similar to CliffsNotes and other study guides. When students read these short summaries, they know about what happened in a book, without actually reading that book. And that’s how we should be designing the next generation of robots if we ever want them to act like humans, Zador says. “We should give them a set of behavioral CliffsNotes, which they can then unwrap into brain-like structures.” Robots that have such brain-like structures will acquire a set of basic rules to generate basic behaviors and use them to learn more complex ones.
Currently Zador is in the process of developing algorithms that function like simple rules that generate such behaviors. “My algorithms would write these CliffsNotes, outlining how to solve a particular problem,” he explains. “And then, the neural networks will use these CliffsNotes to figure out which ones are useful and use them in their behaviors.” That’s how all living beings operate. They use the pre-programmed info from their genetics to adapt to their changing environments and learn what’s necessary to survive and thrive in these settings.
For example, a robot’s neural network could draw from CliffsNotes with “genetic” instructions for how to be aware of its own body or learn to adjust its movements. And other, different sets of CliffsNotes may imbue it with the basics of physical safety or the fundamentals of speech.
At the moment, Zador is working on algorithms that are trying to mimic neuronal blueprints for very simple organisms—such as earthworms, which have only 302 neurons and about 7000 synapses compared to the millions we have. That’s how evolution worked, too—expanding the brains from simple creatures to more complex to the Homo Sapiens. But if it took millions of years to arrive at modern humans, how long would it take scientists to forge a robot with human intelligence? That’s a billion-dollar question. Yet, Zador is optimistic. “My hypotheses is that if you can build simple organisms that can interact with the world, then the higher level functions will not be nearly as challenging as they currently are.”
