There is a lot to be optimistic about regarding the new safe and highly effective vaccines, which are moving society closer toward the goal of close human contact once again.
To be clear, these vaccine candidates for COVID-19, both authorized and not yet authorized, are highly effective and safe. In fact, across all trials and sites, all six vaccines were 100% effective in preventing hospitalizations and death from COVID-19.
All Vaccines' Phase 3 Clinical Data
Complete protection against hospitalization and death from COVID-19 exhibited by all vaccines with phase 3 clinical trial data.
This astounding level of protection from SARS-CoV-2 from all vaccine candidates across multiple regions is likely due to robust T cell response from vaccination and will "defang" the virus from the concerns that led to COVID-19 restrictions initially: the ability of the virus to cause severe illness. This is a time of hope and optimism. After the devastating third surge of COVID-19 infections and deaths over the winter, we finally have an opportunity to stem the crisis – if only people readily accept the vaccines.
Amidst these incredible scientific advancements, however, public health officials and politicians have been pushing downright discouraging messaging. The ubiquitous talk of ongoing masks and distancing restrictions without any clear end in sight threatens to dampen uptake of the vaccines. It's imperative that we break down each concern and see if we can revitalize our public health messaging accordingly.
The first concern: we currently do not know if the vaccines block asymptomatic infection as well as symptomatic disease, since none of the phase 3 vaccine trials were set up to answer this question. However, there is biological plausibility that the antibodies and T-cell responses blocking symptomatic disease will also block asymptomatic infection in the nasal passages. IgG immunoglobulins (generated and measured by the vaccine trials) enter the nasal mucosa and systemic vaccinations generate IgA antibodies at mucosal surfaces. Monoclonal antibodies given to outpatients with COVID-19 hasten viral clearance from the airways.
Although it is prudent for those who are vaccinated to wear masks around the unvaccinated in case a slight risk of transmission remains, two fully vaccinated people can comfortably abandon masking around each other.
Moreover, data from the AztraZeneca trial (including in the phase 3 trial final results manuscript), where weekly self-swabbing was done by participants, and data from the Moderna trial, where a nasal swab was performed prior to the second dose, both showed risk reductions in asymptomatic infection with even a single dose. Finally, real-world data from a large Pfizer-based vaccine campaign in Israel shows a 50% reduction in infections (asymptomatic or symptomatic) after just the first dose.
Therefore, the likelihood of these vaccines blocking asymptomatic carriage, as well as symptomatic disease, is high. Although it is prudent for those who are vaccinated to wear masks around the unvaccinated in case a slight risk of transmission remains, two fully vaccinated people can comfortably abandon masking around each other. Moreover, as the percentage of vaccinated people increases, it will be increasingly untenable to impose restrictions on this group. Once herd immunity is reached, these restrictions can and should be abandoned altogether.
The second concern translating to "doom and gloom" messaging lately is around the identification of troubling new variants due to enhanced surveillance via viral sequencing. Four major variants circulating at this point (with others described in the past) are the B.1.1.7 variant ("UK variant"), B.1.351 ("South Africa variant), P.1. ("Brazil variant"), and the L452R variant identified in California. Although the UK variant is likely to be more transmissible, as is the South Africa variant, we have no reason to believe that masks, distancing and ventilation are ineffective against these variants.
Moreover, neutralizing antibody titers with the Pfizer and Moderna vaccines do not seem to be significantly reduced against the variants. Finally, although the Novavax 2-dose and Johnson and Johnson (J&J) 1-dose vaccines had lower rates of efficacy against moderate COVID-19 disease in South Africa, their efficacy against severe disease was impressively high. In fact J&J's vaccine still prevented 100% of hospitalizations and death from COVID-19. When combining both hospitalizations/deaths and severe symptoms managed at home, the J&J 1-dose vaccine was 85% protective across all three sites of the trial: the U.S., Latin America (including Brazil), and South Africa.
In South Africa, nearly all cases of COVID-19 (95%) were due to infection with the B.1.351 SARS-CoV-2 variant. Finally, since herd immunity does not rely on maximal immune responses among all individuals in a society, the Moderna/Pfizer/J&J vaccines are all likely to achieve that goal against variants. And thankfully, all of these vaccines can be easily modified to boost specifically against a new variant if needed (indeed, Moderna and Pfizer are already working on boosters against the prominent variants).
The third concern of some public health officials is that people will abandon all restrictions once vaccinated unless overly cautious messages are drilled into them. Indeed, the false idea that if you "give people an inch, they will take a mile" has been misinforming our messaging about mitigation since the beginning of the pandemic. For example, the very phrase "stay at home" with all of its non-applicability for essential workers and single individuals is stigmatizing and unrealistic for many. Instead, the message should have focused on how people can additively reduce their risks under different circumstances.
The public will be more inclined to trust health officials if those officials communicate with nuanced messages backed up by evidence, rather than with broad brushstrokes that shame. Therefore, we should be saying that "vaccinated people can be together with other vaccinated individuals without restrictions but must protect the unvaccinated with masks and distancing." And we can say "unvaccinated individuals should adhere to all current restrictions until vaccinated" without fear of misunderstandings. Indeed, this kind of layered advice has been communicated to people living with HIV and those without HIV for a long time (if you have HIV but partner does not, take these precautions; if both have HIV, you can do this, etc.).
Our heady progress in vaccine development, along with the incredible efficacy results of all of them, is unprecedented. However, we are at risk of undermining such progress if people balk at the vaccine because they don't believe it will make enough of a difference. One of the most critical messages we can deliver right now is that these vaccines will eventually free us from the restrictions of this pandemic. Let's use tiered messaging and clear communication to boost vaccine optimism and uptake, and get us to the goal of close human contact once again.
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.
