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 The tip twirls around uncomfortably before it's withdrawn.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."
Artificial Intelligence is already helping to grow some of the food we eat.
The chef-farmers choose from among 45 types of herb and leafy-greens seeds, plop them into grow trays, and a few weeks later they pick and serve. While success is predicated on at least a small amount of these humans’ care, the systems are autonomously surveilled round-the-clock from Babylon’s base of operations. And artificial intelligence is helping to run the show.
Babylon piloted the use of specialized cameras that take pictures in different spectrums to gather some less-obvious visual data about plants’ wellbeing and alert people if something seems off.
Imagine consistently perfect greens and tomatoes and strawberries, grown hyper-locally, using less water, without chemicals or environmental contaminants. This is the hefty promise of controlled environment agriculture (CEA) — basically, indoor farms that can be hydroponic, aeroponic (plant roots are suspended and fed through misting), or aquaponic (where fish play a role in fertilizing vegetables). But whether they grow 4,160 leafy-green servings per year, like one Babylon farm, or millions of servings, like some of the large, centralized facilities starting to supply supermarkets across the U.S., they seek to minimize failure as much as possible.
Babylon’s soilless hydroponic growing system
Courtesy Babylon Micro-Farms
Here, AI is starting to play a pivotal role. CEA growers use it to help “make sense of what’s happening” to the plants in their care, says Scott Lowman, vice president of applied research at the Institute for Advanced Learning and Research (IALR) in Virginia, a state that’s investing heavily in CEA companies. And although these companies say they’re not aiming for a future with zero human employees, AI is certainly poised to take a lot of human farming intervention out of the equation — for better and worse.
Most of these companies are compiling their own data sets to identify anything that might block the success of their systems. Babylon had already integrated sensor data into its farms to measure heat and humidity, the nutrient content of water, and the amount of light plants receive. Last year, they got a National Science Foundation grant that allowed them to pilot the use of specialized cameras that take pictures in different spectrums to gather some less-obvious visual data about plants’ wellbeing and alert people if something seems off. “Will this plant be healthy tomorrow? Are there things…that the human eye can't see that the plant starts expressing?” says Amandeep Ratte, the company’s head of data science. “If our system can say, Hey, this plant is unhealthy, we can reach out to [users] preemptively about what they’re doing wrong, or is there a disease at the farm?” Ratte says. The earlier the better, to avoid crop failures.
Natural light accounts for 70 percent of Greenswell Growers’ energy use on a sunny day.
Courtesy Greenswell Growers
IALR’s Lowman says that other CEA companies are developing their AI systems to account for the different crops they grow — lettuces come in all shapes and sizes, after all, and each has different growing needs than, for example, tomatoes. The ways they run their operations differs also. Babylon is unusual in its decentralized structure. But centralized growing systems with one main location have variabilities, too. AeroFarms, which recently declared bankruptcy but will continue to run its 140,000-square foot vertical operation in Danville, Virginia, is entirely enclosed and reliant on the intense violet glow of grow lights to produce microgreens.
Different companies have different data needs. What data is essential to AeroFarms isn’t quite the same as for Greenswell Growers located in Goochland County, Virginia. Raising four kinds of lettuce in a 77,000-square-foot automated hydroponic greenhouse, the vagaries of naturally available light, which accounts for 70 percent of Greenswell’s energy use on a sunny day, affect operations. Their tech needs to account for “outside weather impacts,” says president Carl Gupton. “What adjustments do we have to make inside of the greenhouse to offset what's going on outside environmentally, to give that plant optimal conditions? When it's 85 percent humidity outside, the system needs to do X, Y and Z to get the conditions that we want inside.”
AI will help identify diseases, as well as when a plant is thirsty or overly hydrated, when it needs more or less calcium, phosphorous, nitrogen.
Nevertheless, every CEA system has the same core needs — consistent yield of high quality crops to keep up year-round supply to customers. Additionally, “Everybody’s got the same set of problems,” Gupton says. Pests may come into a facility with seeds. A disease called pythium, one of the most common in CEA, can damage plant roots. “Then you have root disease pressures that can also come internally — a change in [growing] substrate can change the way the plant performs,” Gupton says.
AI will help identify diseases, as well as when a plant is thirsty or overly hydrated, when it needs more or less calcium, phosphorous, nitrogen. So, while companies amass their own hyper-specific data sets, Lowman foresees a time within the next decade “when there will be some type of [open-source] database that has the most common types of plant stress identified” that growers will be able to tap into. Such databases will “create a community and move the science forward,” says Lowman.
In fact, IALR is working on assembling images for just such a database now. On so-called “smart tables” inside an Institute lab, a team is growing greens and subjects them to various stressors. Then, they’re administering treatments while taking images of every plant every 15 minutes, says Lowman. Some experiments generate 80,000 images; the challenge lies in analyzing and annotating the vast trove of them, marking each one to reflect outcome—for example increasing the phosphate delivery and the plant’s response to it. Eventually, they’ll be fed into AI systems to help them learn.
For all the enthusiasm surrounding this technology, it’s not without downsides. Training just one AI system can emit over 250,000 pounds of carbon dioxide, according to MIT Technology Review. AI could also be used “to enhance environmental benefit for CEA and optimize [its] energy consumption,” says Rozita Dara, a computer science professor at the University of Guelph in Canada, specializing in AI and data governance, “but we first need to collect data to measure [it].”
The chef-farmers can choose from 45 types of herb and leafy-greens seeds.
Courtesy Babylon Micro-Farms
Any system connected to the Internet of Things is also vulnerable to hacking; if CEA grows to the point where “there are many of these similar farms, and you're depending on feeding a population based on those, it would be quite scary,” Dara says. And there are privacy concerns, too, in systems where imaging is happening constantly. It’s partly for this reason, says Babylon’s Ratte, that the company’s in-farm cameras all “face down into the trays, so the only thing [visible] is pictures of plants.”
Tweaks to improve AI for CEA are happening all the time. Greenswell made its first harvest in 2022 and now has annual data points they can use to start making more intelligent choices about how to feed, water, and supply light to plants, says Gupton. Ratte says he’s confident Babylon’s system can already “get our customers reliable harvests. But in terms of how far we have to go, it's a different problem,” he says. For example, if AI could detect whether the farm is mostly empty—meaning the farm’s user hasn’t planted a new crop of greens—it can alert Babylon to check “what's going on with engagement with this user?” Ratte says. “Do they need more training? Did the main person responsible for the farm quit?”
Lowman says more automation is coming, offering greater ability for systems to identify problems and mitigate them on the spot. “We still have to develop datasets that are specific, so you can have a very clear control plan, [because] artificial intelligence is only as smart as what we tell it, and in plant science, there's so much variation,” he says. He believes AI’s next level will be “looking at those first early days of plant growth: when the seed germinates, how fast it germinates, what it looks like when it germinates.” Imaging all that and pairing it with AI, “can be a really powerful tool, for sure.”
Researchers from the University of Cambridge have laid the foundations for growing synthetic embryos that could develop a beating heart, gut and brain.
The organoid revolution
In 1981, scientists discovered how to keep stem cells alive. This was a significant breakthrough, as stem cells have notoriously rigorous demands. Nevertheless, stem cells remained a relatively niche research area, mainly because scientists didn’t know how to convince the cells to turn into other cells.
Then, in 1987, scientists embedded isolated stem cells in a gelatinous protein mixture called Matrigel, which simulated the three-dimensional environment of animal tissue. The cells thrived, but they also did something remarkable: they created breast tissue capable of producing milk proteins. This was the first organoid — a clump of cells that behave and function like a real organ. The organoid revolution had begun, and it all started with a boob in Jello.
For the next 20 years, it was rare to find a scientist who identified as an “organoid researcher,” but there were many “stem cell researchers” who wanted to figure out how to turn stem cells into other cells. Eventually, they discovered the signals (called growth factors) that stem cells require to differentiate into other types of cells.
For a human embryo (and its organs) to develop successfully, there needs to be a “dialogue” between these three types of stem cells.
By the end of the 2000s, researchers began combining stem cells, Matrigel, and the newly characterized growth factors to create dozens of organoids, from liver organoids capable of producing the bile salts necessary for digesting fat to brain organoids with components that resemble eyes, the spinal cord, and arguably, the beginnings of sentience.
Synthetic embryos
Organoids possess an intrinsic flaw: they are organ-like. They share some characteristics with real organs, making them powerful tools for research. However, no one has found a way to create an organoid with all the characteristics and functions of a real organ. But Magdalena Żernicka-Goetz, a developmental biologist, might have set the foundation for that discovery.
Żernicka-Goetz hypothesized that organoids fail to develop into fully functional organs because organs develop as a collective. Organoid research often uses embryonic stem cells, which are the cells from which the developing organism is created. However, there are two other types of stem cells in an early embryo: stem cells that become the placenta and those that become the yolk sac (where the embryo grows and gets its nutrients in early development). For a human embryo (and its organs) to develop successfully, there needs to be a “dialogue” between these three types of stem cells. In other words, Żernicka-Goetz suspected the best way to grow a functional organoid was to produce a synthetic embryoid.
As described in the aforementioned Nature paper, Żernicka-Goetz and her team mimicked the embryonic environment by mixing these three types of stem cells from mice. Amazingly, the stem cells self-organized into structures and progressed through the successive developmental stages until they had beating hearts and the foundations of the brain.
“Our mouse embryo model not only develops a brain, but also a beating heart [and] all the components that go on to make up the body,” said Żernicka-Goetz. “It’s just unbelievable that we’ve got this far. This has been the dream of our community for years and major focus of our work for a decade and finally we’ve done it.”
If the methods developed by Żernicka-Goetz’s team are successful with human stem cells, scientists someday could use them to guide the development of synthetic organs for patients awaiting transplants. It also opens the door to studying how embryos develop during pregnancy.
This article originally appeared on Big Think, home of the brightest minds and biggest ideas of all time.
