Food Poisoning Outbreaks Are Still A Problem. Powerful Tech Is Fighting Back.
With the pandemic at the forefront of everyone's minds, many people have wondered if food could be a source of coronavirus transmission. Luckily, that "seems unlikely," according to the CDC, but foodborne illnesses do still sicken a whopping 48 million people per year.
Whole genome sequencing is like "going from an eight-bit image—maybe like what you would see in Minecraft—to a high definition image."
In normal times, when there isn't a historic global health crisis infecting millions and affecting the lives of billions, foodborne outbreaks are real and frightening, potentially deadly, and can cause widespread fear of particular foods. Think of Romaine lettuce spreading E. coli last year— an outbreak that infected more than 500 people and killed eight—or peanut butter spreading salmonella in 2008, which infected 167 people.
The technologies available to detect and prevent the next foodborne disease outbreak have improved greatly over the past 30-plus years, particularly during the past decade, and better, more nimble technologies are being developed, according to experts in government, academia, and private industry. The key to advancing detection of harmful foodborne pathogens, they say, is increasing speed and portability of detection, and the precision of that detection.
Getting to Rapid Results
Researchers at Purdue University have recently developed a lateral flow assay that, with the help of a laser, can detect toxins and pathogenic E. coli. Lateral flow assays are cheap and easy to use; a good example is a home pregnancy test. You place a liquid or liquefied sample on a piece of paper designed to detect a single substance and soon after you get the results in the form of a colored line: yes or no.
"They're a great portable tool for us for food contaminant detection," says Carmen Gondhalekar, a fifth-year biomedical engineering graduate student at Purdue. "But one of the areas where paper-based lateral flow assays could use improvement is in multiplexing capability and their sensitivity."
J. Paul Robinson, a professor in Purdue's Colleges of Veterinary Medicine and Engineering, and Gondhalekar's advisor, agrees. "One of the fundamental problems that we have in detection is that it is hard to identify pathogens in complex samples," he says.
When it comes to foodborne disease outbreaks, you don't always know what substance you're looking for, so an assay made to detect only a single substance isn't always effective. The goal of the project at Purdue is to make assays that can detect multiple substances at once.
These assays would be more complex than a pregnancy test. As detailed in Gondhalekar's recent paper, a laser pulse helps create a spectral signal from the sample on the assay paper, and the spectral signal is then used to determine if any unique wavelengths associated with one of several toxins or pathogens are present in the sample. Though the handheld technology has yet to be built, the idea is that the results would be given on the spot. So someone in the field trying to track the source of a Salmonella infection could, for instance, put a suspected lettuce sample on the assay and see if it has the pathogen on it.
"What our technology is designed to do is to give you a rapid assessment of the sample," says Robinson. "The goal here is speed."
Seeing the Pathogen in "High-Def"
"One in six Americans will get a foodborne illness every year," according to Dr. Heather Carleton, a microbiologist at the Centers for Disease Control and Prevention's Enteric Diseases Laboratory Branch. But not every foodborne outbreak makes the news. In 2017 alone, the CDC monitored between 18 and 37 foodborne poison clusters per week and investigated 200 multi-state clusters. Hardboiled eggs, ground beef, chopped salad kits, raw oysters, frozen tuna, and pre-cut melon are just a taste of the foods that were investigated last year for different strains of listeria, salmonella, and E. coli.
At the heart of the CDC investigations is PulseNet, a national network of laboratories that uses DNA fingerprinting to detect outbreaks at local and regional levels. This is how it works: When a patient gets sick—with symptoms like vomiting and fever, for instance—they will go to a hospital or clinic for treatment. Since we're talking about foodborne illnesses, a clinician will likely take a stool sample from the patient and send it off to a laboratory to see if there is a foodborne pathogen, like salmonella, E. Coli, or another one. If it does contain a potentially harmful pathogen, then a bacterial isolate of that identified sample is sent to a regional public health lab so that whole genome sequencing can be performed.
Whole genome sequencing can differentiate "virtually all" strains of foodborne pathogens, no matter the species, according to the FDA.
Whole genome sequencing is a method for reading the entire genome of a bacterial isolate (or from any organism, for that matter). Instead of working with a couple dozen data points, now you're working with millions of base pairs. Carleton likes to describe it as "going from an eight-bit image—maybe like what you would see in Minecraft—to a high definition image," she says. "It's really an evolution of how we detect foodborne illnesses and identify outbreaks."
If the bacterial isolate matches another in the CDC's database, this means there could be a potential outbreak and an investigation may be started, with the goal of tracking the pathogen to its source.
Whole genome sequencing has been a relatively recent shift in foodborne disease detection. For more than 20 years, the standard technique for analyzing pathogens in foodborne disease outbreaks was pulsed-field gel electrophoresis. This method creates a DNA fingerprint for each sample in the form of a pattern of about 15-30 "bands," with each band representing a piece of DNA. Researchers like Carleton can use this fingerprint to see if two samples are from the same bacteria. The problem is that 15-30 bands are not enough to differentiate all isolates. Some isolates whose bands look very similar may actually come from different sources and some whose bands look different may be from the same source. But if you can see the entire DNA fingerprint, then you don't have that issue. That's where whole genome sequencing comes in.
Although the PulseNet team had piloted whole genome sequencing as early as 2013, it wasn't until July of last year that the transition to using whole genome sequencing for all pathogens was complete. Though whole genome sequencing requires far more computing power to generate, analyze, and compare those millions of data points, the payoff is huge.
Stopping Outbreaks Sooner
The U.S. Food and Drug Administration (FDA) acquired their first whole genome sequencers in 2008, according to Dr. Eric Brown, the Director of the Division of Microbiology in the FDA's Office of Regulatory Science. Since then, through their GenomeTrakr program, a network of more than 60 domestic and international labs, the FDA has sequenced and publicly shared more than 400,000 isolates. "The impact of what whole genome sequencing could do to resolve a foodborne outbreak event was no less impactful than when NASA turned on the Hubble Telescope for the first time," says Brown.
Whole genome sequencing has helped identify strains of Salmonella that prior methods were unable to differentiate. In fact, whole genome sequencing can differentiate "virtually all" strains of foodborne pathogens, no matter the species, according to the FDA. This means it takes fewer clinical cases—fewer sick people—to detect and end an outbreak.
And perhaps the largest benefit of whole genome sequencing is that these detailed sequences—the millions of base pairs—can imply geographic location. The genomic information of bacterial strains can be different depending on the area of the country, helping these public health agencies eventually track the source of outbreaks—a restaurant, a farm, a food-processing center.
Coming Soon: "Lab in a Backpack"
Now that whole genome sequencing has become the go-to technology of choice for analyzing foodborne pathogens, the next step is making the process nimbler and more portable. Putting "the lab in a backpack," as Brown says.
The CDC's Carleton agrees. "Right now, the sequencer we use is a fairly big box that weighs about 60 pounds," she says. "We can't take it into the field."
A company called Oxford Nanopore Technologies is developing handheld sequencers. Their devices are meant to "enable the sequencing of anything by anyone anywhere," according to Dan Turner, the VP of Applications at Oxford Nanopore.
"The sooner that we can see linkages…the sooner the FDA gets in action to mitigate the problem and put in some kind of preventative control."
"Right now, sequencing is very much something that is done by people in white coats in laboratories that are set up for that purpose," says Turner. Oxford Nanopore would like to create a new, democratized paradigm.
The FDA is currently testing these types of portable sequencers. "We're very excited about it. We've done some pilots, to be able to do that sequencing in the field. To actually do it at a pond, at a river, at a canal. To do it on site right there," says Brown. "This, of course, is huge because it means we can have real-time sequencing capability to stay in step with an actual laboratory investigation in the field."
"The timeliness of this information is critical," says Marc Allard, a senior biomedical research officer and Brown's colleague at the FDA. "The sooner that we can see linkages…the sooner the FDA gets in action to mitigate the problem and put in some kind of preventative control."
At the moment, the world is rightly focused on COVID-19. But as the danger of one virus subsides, it's only a matter of time before another pathogen strikes. Hopefully, with new and advancing technology like whole genome sequencing, we can stop the next deadly outbreak before it really gets going.
Awash in a fluid finely calibrated to keep it alive, a human eye rests inside a transparent cubic device. This ECaBox, or Eyes in a Care Box, is a one-of-a-kind system built by scientists at Barcelona’s Centre for Genomic Regulation (CRG). Their goal is to preserve human eyes for transplantation and related research.
In recent years, scientists have learned to transplant delicate organs such as the liver, lungs or pancreas, but eyes are another story. Even when preserved at the average transplant temperature of 4 Centigrade, they last for 48 hours max. That's one explanation for why transplanting the whole eye isn’t possible—only the cornea, the dome-shaped, outer layer of the eye, can withstand the procedure. The retina, the layer at the back of the eyeball that turns light into electrical signals, which the brain converts into images, is extremely difficult to transplant because it's packed with nerve tissue and blood vessels.
These challenges also make it tough to research transplantation. “This greatly limits their use for experiments, particularly when it comes to the effectiveness of new drugs and treatments,” said Maria Pia Cosma, a biologist at Barcelona’s Centre for Genomic Regulation (CRG), whose team is working on the ECaBox.
Eye transplants are desperately needed, but they're nowhere in sight. About 12.7 million people worldwide need a corneal transplant, which means that only one in 70 people who require them, get them. The gaps are international. Eye banks in the United Kingdom are around 20 percent below the level needed to supply hospitals, while Indian eye banks, which need at least 250,000 corneas per year, collect only around 45 to 50 thousand donor corneas (and of those 60 to 70 percent are successfully transplanted).
As for retinas, it's impossible currently to put one into the eye of another person. Artificial devices can be implanted to restore the sight of patients suffering from severe retinal diseases, but the number of people around the world with such “bionic eyes” is less than 600, while in America alone 11 million people have some type of retinal disease leading to severe vision loss. Add to this an increasingly aging population, commonly facing various vision impairments, and you have a recipe for heavy burdens on individuals, the economy and society. In the U.S. alone, the total annual economic impact of vision problems was $51.4 billion in 2017.
Even if you try growing tissues in the petri dish route into organoids mimicking the function of the human eye, you will not get the physiological complexity of the structure and metabolism of the real thing, according to Cosma. She is a member of a scientific consortium that includes researchers from major institutions from Spain, the U.K., Portugal, Italy and Israel. The consortium has received about $3.8 million from the European Union to pursue innovative eye research. Her team’s goal is to give hope to at least 2.2 billion people across the world afflicted with a vision impairment and 33 million who go through life with avoidable blindness.
Their method? Resuscitating cadaveric eyes for at least a month.
If we succeed, it will be the first intact human model of the eye capable of exploring and analyzing regenerative processes ex vivo. -- Maria Pia Cosma.
“We proposed to resuscitate eyes, that is to restore the global physiology and function of human explanted tissues,” Cosma said, referring to living tissues extracted from the eye and placed in a medium for culture. Their ECaBox is an ex vivo biological system, in which eyes taken from dead donors are placed in an artificial environment, designed to preserve the eye’s temperature and pH levels, deter blood clots, and remove the metabolic waste and toxins that would otherwise spell their demise.
Scientists work on resuscitating eyes in the lab of Maria Pia Cosma.
Courtesy of Maria Pia Cosma.
“One of the great challenges is the passage of the blood in the capillary branches of the eye, what we call long-term perfusion,” Cosma said. Capillaries are an intricate network of very thin blood vessels that transport blood, nutrients and oxygen to cells in the body’s organs and systems. To maintain the garland-shaped structure of this network, sufficient amounts of oxygen and nutrients must be provided through the eye circulation and microcirculation. “Our ambition is to combine perfusion of the vessels with artificial blood," along with using a synthetic form of vitreous, or the gel-like fluid that lets in light and supports the the eye's round shape, Cosma said.
The scientists use this novel setup with the eye submersed in its medium to keep the organ viable, so they can test retinal function. “If we succeed, we will ensure full functionality of a human organ ex vivo. It will be the first intact human model of the eye capable of exploring and analyzing regenerative processes ex vivo,” Cosma added.
A rapidly developing field of regenerative medicine aims to stimulate the body's natural healing processes and restore or replace damaged tissues and organs. But for people with retinal diseases, regenerative medicine progress has been painfully slow. “Experiments on rodents show progress, but the risks for humans are unacceptable,” Cosma said.
The ECaBox could boost progress with regenerative medicine for people with retinal diseases, which has been painfully slow because human experiments involving their eyes are too risky. “We will test emerging treatments while reducing animal research, and greatly accelerate the discovery and preclinical research phase of new possible treatments for vision loss at significantly reduced costs,” Cosma explained. Much less time and money would be wasted during the drug discovery process. Their work may even make it possible to transplant the entire eyeball for those who need it.
“It is a very exciting project,” said Sanjay Sharma, a professor of ophthalmology and epidemiology at Queen's University, in Kingston, Canada. “The ability to explore and monitor regenerative interventions will increasingly be of importance as we develop therapies that can regenerate ocular tissues, including the retina.”
Seemingly, there's no sacred religious text or a holy book prohibiting the practice of eye donation.
But is the world ready for eye transplants? “People are a bit weird or very emotional about donating their eyes as compared to other organs,” Cosma said. And much can be said about the problem of eye donor shortage. Concerns include disfigurement and healthcare professionals’ fear that the conversation about eye donation will upset the departed person’s relatives because of cultural or religious considerations. As just one example, Sharma noted the paucity of eye donations in his home country, Canada.
Yet, experts like Sharma stress the importance of these donations for both the recipients and their family members. “It allows them some psychological benefit in a very difficult time,” he said. So why are global eye banks suffering? Is it because the eyes are the windows to the soul?
Seemingly, there's no sacred religious text or a holy book prohibiting the practice of eye donation. In fact, most major religions of the world permit and support organ transplantation and donation, and by extension eye donation, because they unequivocally see it as an “act of neighborly love and charity.” In Hinduism, the concept of eye donation aligns with the Hindu principle of daan or selfless giving, where individuals donate their organs or body after death to benefit others and contribute to society. In Islam, eye donation is a form of sadaqah jariyah, a perpetual charity, as it can continue to benefit others even after the donor's death.
Meanwhile, Buddhist masters teach that donating an organ gives another person the chance to live longer and practice dharma, the universal law and order, more meaningfully; they also dismiss misunderstandings of the type “if you donate an eye, you’ll be born without an eye in the next birth.” And Christian teachings emphasize the values of love, compassion, and selflessness, all compatible with organ donation, eye donation notwithstanding; besides, those that will have a house in heaven, will get a whole new body without imperfections and limitations.
The explanation for people’s resistance may lie in what Deepak Sarma, a professor of Indian religions and philosophy at Case Western Reserve University in Cleveland, calls “street interpretation” of religious or spiritual dogmas. Consider the mechanism of karma, which is about the causal relation between previous and current actions. “Maybe some Hindus believe there is karma in the eyes and, if the eye gets transplanted into another person, they will have to have that karmic card from now on,” Sarma said. “Even if there is peculiar karma due to an untimely death–which might be interpreted by some as bad karma–then you have the karma of the recipient, which is tremendously good karma, because they have access to these body parts, a tremendous gift,” Sarma said. The overall accumulation is that of good karma: “It’s a beautiful kind of balance,” Sarma said.
For the Jews, Christians, and Muslims who believe in the physical resurrection of the body that will be made new in an afterlife, the already existing body is sacred since it will be the basis of a new refashioned body in an afterlife.---Omar Sultan Haque.
With that said, Sarma believes it is a fallacy to personify or anthropomorphize the eye, which doesn’t have a soul, and stresses that the karma attaches itself to the soul and not the body parts. But for scholars like Omar Sultan Haque—a psychiatrist and social scientist at Harvard Medical School, investigating questions across global health, anthropology, social psychology, and bioethics—the hierarchy of sacredness of body parts is entrenched in human psychology. You cannot equate the pinky toe with the face, he explained.
“The eyes are the window to the soul,” Haque said. “People have a hierarchy of body parts that are considered more sacred or essential to the self or soul, such as the eyes, face, and brain.” In his view, the techno-utopian transhumanist communities (especially those in Silicon Valley) have reduced the totality of a person to a mere material object, a “wet robot” that knows no sacredness or hierarchy of human body parts. “But for the Jews, Christians, and Muslims who believe in the physical resurrection of the body that will be made new in an afterlife, the [already existing] body is sacred since it will be the basis of a new refashioned body in an afterlife,” Haque said. “You cannot treat the body like any old material artifact, or old chair or ragged cloth, just because materialistic, secular ideologies want so,” he continued.
For Cosma and her peers, however, the very definition of what is alive or not is a bit semantic. “As soon as we die, the electrophysiological activity in the eye stops,” she said. “The goal of the project is to restore this activity as soon as possible before the highly complex tissue of the eye starts degrading.” Cosma’s group doesn’t yet know when they will be able to keep the eyes alive and well in the ECaBox, but the consensus is that the sooner the better. Hopefully, the taboos and fears around the eye donations will dissipate around the same time.
As Our AI Systems Get Better, So Must We
As the power and capability of our AI systems increase by the day, the essential question we now face is what constitutes peak human. If we stay where we are while the AI systems we are unleashing continually get better, they will meet and then exceed our capabilities in an ever-growing number of domains. But while some technology visionaries like Elon Musk call for us to slow down the development of AI systems to buy time, this approach alone will simply not work in our hyper-competitive world, particularly when the potential benefits of AI are so great and our frameworks for global governance are so weak. In order to build the future we want, we must also become ever better humans.
The list of activities we once saw as uniquely human where AIs have now surpassed us is long and growing. First, AI systems could beat our best chess players, then our best Go players, then our best champions of multi-player poker. They can see patterns far better than we can, generate medical and other hypotheses most human specialists miss, predict and map out new cellular structures, and even generate beautiful, and, yes, creative, art.
A recent paper by Microsoft researchers analyzing the significant leap in capabilities in OpenAI’s latest AI bot, ChatGPT-4, asserted that the algorithm can “solve novel and difficult tasks that span mathematics, coding, vision, medicine, law, psychology and more, without needing any special prompting.” Calling this functionality “strikingly close to human-level performance,” the authors conclude it “could reasonably be viewed as an early (yet still incomplete) version of an artificial general intelligence (AGI) system.”
The concept of AGI has been around for decades. In its common use, the term suggests a time when individual machines can do many different things at a human level, not just one thing like playing Go or analyzing radiological images. Debating when AGI might arrive, a favorite pastime of computer scientists for years, now has become outdated.
We already have AI algorithms and chatbots that can do lots of different things. Based on the generalist definition, in other words, AGI is essentially already here.
Unfettered by the evolved capacity and storage constraints of our brains, AI algorithms can access nearly all of the digitized cultural inheritance of humanity since the dawn of recorded history and have increasing access to growing pools of digitized biological data from across the spectrum of life.
Once we recognize that both AI systems and humans have unique superpowers, the essential question becomes what each of us can do better than the other and what humans and AIs can best do in active collaboration. The future of our species will depend upon our ability to safely, dynamically, and continually figure that out.
With these ever-larger datasets, rapidly increasing computing and memory power, and new and better algorithms, our AI systems will keep getting better faster than most of us can today imagine. These capabilities have the potential to help us radically improve our healthcare, agriculture, and manufacturing, make our economies more productive and our development more sustainable, and do many important things better.
Soon, they will learn how to write their own code. Like human children, in other words, AI systems will grow up. But even that doesn’t mean our human goose is cooked.
Just like dolphins and dogs, these alternate forms of intelligence will be uniquely theirs, not a lesser or greater version of ours. There are lots of things AI systems can't do and will never be able to do because our AI algorithms, for better and for worse, will never be human. Our embodied human intelligence is its own thing.
Our human intelligence is uniquely ours based on the capacities we have developed in our 3.8-billion-year journey from single cell organisms to us. Our brains and bodies represent continuous adaptations on earlier models, which is why our skeletal systems look like those of lizards and our brains like most other mammals with some extra cerebral cortex mixed in. Human intelligence isn’t just some type of disembodied function but the inextricable manifestation of our evolved physical reality. It includes our sensory analytical skills and all of our animal instincts, intuitions, drives, and perceptions. Disembodied machine intelligence is something different than what we have evolved and possess.
Because of this, some linguists including Noam Chomsky have recently argued that AI systems will never be intelligent as long as they are just manipulating symbols and mathematical tokens without any inherent understanding. Nothing could be further from the truth. Anyone interacting with even first-generation AI chatbots quickly realizes that while these systems are far from perfect or omniscient and can sometimes be stupendously oblivious, they are surprisingly smart and versatile and will get more so… forever. We have little idea even how our own minds work, so judging AI systems based on their output is relatively close to how we evaluate ourselves.
Anyone not awed by the potential of these AI systems is missing the point. AI’s newfound capacities demand that we work urgently to establish norms, standards, and regulations at all levels from local to global to manage the very real risks. Pausing our development of AI systems now doesn’t make sense, however, even if it were possible, because we have no sufficient ways of uniformly enacting such a pause, no plan for how we would use the time, and no common framework for addressing global collective challenges like this.
But if all we feel is a passive awe for these new capabilities, we will also be missing the point.
Human evolution, biology, and cultural history are not just some kind of accidental legacy, disability, or parlor trick, but our inherent superpower. Our ancestors outcompeted rivals for billions of years to make us so well suited to the world we inhabit and helped build. Our social organization at scale has made it possible for us to forge civilizations of immense complexity, engineer biology and novel intelligence, and extend our reach to the stars. Our messy, embodied, intuitive, social human intelligence is roughly mimicable by AI systems but, by definition, never fully replicable by them.
Once we recognize that both AI systems and humans have unique superpowers, the essential question becomes what each of us can do better than the other and what humans and AIs can best do in active collaboration. We still don't know. The future of our species will depend upon our ability to safely, dynamically, and continually figure that out.
As we do, we'll learn that many of our ideas and actions are made up of parts, some of which will prove essentially human and some of which can be better achieved by AI systems. Those in every walk of work and life who most successfully identify the optimal contributions of humans, AIs, and the two together, and who build systems and workflows empowering humans to do human things, machines to do machine things, and humans and machines to work together in ways maximizing the respective strengths of each, will be the champions of the 21st century across all fields.
The dawn of the age of machine intelligence is upon us. It’s a quantum leap equivalent to the domestication of plants and animals, industrialization, electrification, and computing. Each of these revolutions forced us to rethink what it means to be human, how we live, and how we organize ourselves. The AI revolution will happen more suddenly than these earlier transformations but will follow the same general trajectory. Now is the time to aggressively prepare for what is fast heading our way, including by active public engagement, governance, and regulation.
AI systems will not replace us, but, like these earlier technology-driven revolutions, they will force us to become different humans as we co-evolve with our technology. We will never reach peak human in our ongoing evolutionary journey, but we’ve got to manage this transition wisely to build the type of future we’d like to inhabit.
Alongside our ascending AIs, we humans still have a lot of climbing to do.