Lori Tipton's life was a cascade of trauma that even a soap opera would not dare inflict upon a character: a mentally unstable family; a brother who died of a drug overdose; the shocking discovery of the bodies of two persons her mother had killed before turning the gun on herself; the devastation of Hurricane Katrina that savaged her hometown of New Orleans; being raped by someone she trusted; and having an abortion. She suffered from severe PTSD.
“My life was filled with anxiety and hypervigilance,” she says. “I was constantly afraid and had mood swings, panic attacks, insomnia, intrusive thoughts and suicidal ideation. I tried to take my life more than once.” She was fortunate to be able to access multiple mental health services, “And while at times some of these modalities would relieve the symptoms, nothing really lasted and nothing really address the core trauma.”
Then in 2018 Tipton enrolled in a clinical trial that combined intense sessions of psychotherapy with limited use of Methylenedioxymethamphetamine, or MDMA, a drug classified as a psychedelic and commonly known as ecstasy or Molly. The regimen was arduous; 1-2 hour preparation sessions, three sessions where MDMA was used, which lasted 6-8 hours, and lengthy sessions afterward to process and integrate the experiences. Two therapists were with her every moment of the three-month program that totaled more than 40 hours.
“It was clear to me that [the therapists] weren't going to heal me, that I was going to have to do the work for myself, but that they were there to completely support my process,” she says. “But the effects of MDMA were really undeniable for me. I felt embodied in a way that I hadn't in years. PTSD had robbed me of the ability to feel safe in my own body.”
Tipton doesn’t think the therapy completely cured her PTSD. “But when I completed the trial in 2018, I no longer qualified for the diagnosis, and I still don't qualify for the diagnosis today,” she told an April workshop on psychedelics as mental health treatment by the National Academies of Sciences, Engineering and Medicine, or NASEM.
Rick Doblin has been a catalyst behind much of the contemporary research into psychedelics. Prior to the DEA clamp down, the Boston psychotherapist had seen that MDMA and other psychedelics could benefit some of his patients where other measures had failed. He immediately organized efforts to question the drug rescheduling but to little avail. In 1986, he created the nonprofit Multidisciplinary Association for Psychedelic Studies (MAPS), which slowly laid the scientific foundation for clinical trials, including the one that Tipton joined, using psychedelics to treat mental health conditions.
Now, only slowly, have researchers been able to explore the power of these drugs to treat a broad spectrum of severely debilitating mental health conditions, including trauma, depression, and PTSD, where other available treatments proved inadequate.
“Psychedelic psychotherapy is an attempt to go after the root causes of the problems with just a relatively few administrations, as contrasted to most of the psychiatric drugs used today that are mostly just reducing symptoms and are meant to be taken on a daily basis,” Doblin said in a 2019 TED Talk. Most of these drugs can have broad effect but “some are probably more effective than others for certain conditions,” he added in a recent interview with Leaps.org. Comparative head-to-head studies of psychedelic therapies simply have not been conducted.
Their mechanisms of action are poorly understood and can vary between drugs, but it is generally believed that psychedelics change the activity of neurons so that the brain processes information differently, says Katrin Preller, a neuropsychologist at the University of Zurich. A recent important study in Nature Medicine by Richard Daws and colleagues used functional magnetic resonance imaging (fMRI) of the brain and found that “functional networks became more functionally interconnected and flexible after psilocybin treatment…implying that psilocybin's antidepressant action may depend on a global increase in brain network integration.”
Rosalind Watts, a clinical investigator at the Imperial College in London, believes there is “an overestimation of the importance of the drug and an underestimation of the importance of the [therapeutic] context” in psychedelic research. “It is unethical to provide the drug without the other,” she says. Doblin notes that “psychotherapy outcomes research demonstrates that the therapeutic alliance between the therapist and the patients is the single most predictive factor of outcomes. [It is] trust and the sense of safety, the willingness to go into difficult spaces” that makes clinical breakthroughs possible with the drug.
Excitement and Challenges
Recurrent themes expressed at the NASEM workshop were exciting glimpses of the potential for psychedelics to treat mental health conditions combined with the challenges of realizing those potentials. A recent review paper found evidence that using psychedelics can help with treating a variety of common mental illnesses, but the paper could identify only 14 clinical trials of classic psychedelics published since 1991. Much of the reason is that the drugs are not patentable and so the pharmaceutical industry has no interest in investing in expensive clinical trials to bring them to market. MAPS has raised about $135 million over its 36-year history to conduct such research, says Doblin, the vast majority of it from individual donors and none from foundations.
The workshop participants’ views also were colored by the history of drug crackdowns and a fear that research might easily be shut down in the future. There was great concern that use of psychedelics should be confined to clinical trials with high safety and ethical standards, instead of doctors and patients experimenting on their own. “We need to get it right this time,” says Charles Grob, a psychiatrist at the UCLA School of Medicine. But restricting access to psychedelics will become even more difficult now that Oregon and several cities have acted to decriminalize possession and use of many of these drugs.
The experience with ketamine also troubled Grob. He is hoping to “mitigate the rush of rapid commercialization” that occurred with that drug. Ketamine technically is not a psychedelic though it does share some of their potentially euphoric properties. In 2019, soon after the FDA approved a form of ketamine with a limited label indication to treat depression, for profit clinics sprang up promoting off label use of the drug for psychiatric conditions where there was little clinical evidence of efficacy. He fears the same thing will happen when true psychedelics are made available.
If these therapies are approved, access to them is likely to be a problem. The drugs themselves are cheap but the accompanying therapy is not, and there is a shortage of trained psychotherapists. Mental health services often are not adequately covered by health insurance, while the poor and people of color suffer additional burdens of inadequate access. Doblin is committed to health care equity by training additional providers and by investigating whether some of the preparatory and integration sessions might be handled in a group setting. He says it is important that the legal aspects of psychedelics also be addressed so that patients “don't have to go underground” in order to receive this care.
One day in recent past, scientists at Columbia University’s Creative Machines Lab set up a robotic arm inside a circle of five streaming video cameras and let the robot watch itself move, turn and twist. For about three hours the robot did exactly that—it looked at itself this way and that, like toddlers exploring themselves in a room full of mirrors. By the time the robot stopped, its internal neural network finished learning the relationship between the robot’s motor actions and the volume it occupied in its environment. In other words, the robot built a spatial self-awareness, just like humans do. “We trained its deep neural network to understand how it moved in space,” says Boyuan Chen, one of the scientists who worked on it.
For decades robots have been doing helpful tasks that are too hard, too dangerous, or physically impossible for humans to carry out themselves. Robots are ultimately superior to humans in complex calculations, following rules to a tee and repeating the same steps perfectly. But even the biggest successes for human-robot collaborations—those in manufacturing and automotive industries—still require separating the two for safety reasons. Hardwired for a limited set of tasks, industrial robots don't have the intelligence to know where their robo-parts are in space, how fast they’re moving and when they can endanger a human.
Over the past decade or so, humans have begun to expect more from robots. Engineers have been building smarter versions that can avoid obstacles, follow voice commands, respond to human speech and make simple decisions. Some of them proved invaluable in many natural and man-made disasters like earthquakes, forest fires, nuclear accidents and chemical spills. These disaster recovery robots helped clean up dangerous chemicals, looked for survivors in crumbled buildings, and ventured into radioactive areas to assess damage.
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.”
The doctor will sniff you now? Well, not on his or her own, but with a device that functions like a superhuman nose. You’ll exhale into a breathalyzer, or a sensor will collect “scent data” from a quick pass over your urine or blood sample. Then, AI software combs through an olfactory database to find patterns in the volatile organic compounds (VOCs) you secreted that match those associated with thousands of VOC disease biomarkers that have been identified and cataloged.
No further biopsy, imaging test or procedures necessary for the diagnosis. According to some scientists, this is how diseases will be detected in the coming years.
All diseases alter the organic compounds found in the body and their odors. Volatolomics is an emerging branch of chemistry that uses the smell of gases emitted by breath, urine, blood, stool, tears or sweat to diagnose disease. When someone is sick, the normal biochemical process is disrupted, and this alters the makeup of the gas, including a change in odor.
“These metabolites show a snapshot of what’s going on with the body,” says Cristina Davis, a biomedical engineer and associate vice chancellor of Interdisciplinary Research and Strategic Initiatives at the University of California, Davis. This opens the door to diagnosing conditions even before symptoms are present. It’s possible to detect a sweet, fruity smell in the breath of someone with diabetes, for example.
Hippocrates may have been the first to note that people with certain diseases give off an odor but dogs provided the proof of concept. Scientists have published countless studies in which dogs or other high-performing smellers like rodents have identified people with cancer, lung disease or other conditions by smell alone. The brain region that analyzes smells is proportionally about 40 times greater in dogs than in people. The noses of rodents are even more powerful.
Take prostate cancer, which is notoriously difficult to detect accurately with standard medical testing. After sniffing a tiny urine sample, trained dogs were able to pick out prostate cancer in study subjects more than 96 percent of the time, and earlier than a physician could in some cases.
But using dogs as bio-detectors is not practical. It is labor-intensive, complicated and expensive to train dogs to bark or lie down when they smell a certain VOC, explains Bruce Kimball, a chemical ecologist at the Monell Chemical Senses Center in Philadelphia. Kimball has trained ferrets to scratch a box when they smell a specific VOC so he knows. The lab animal must be taught to distinguish the VOC from background odors and trained anew for each disease scent.
In the lab of chemical ecologist Bruce Kimball, ferrets were trained to scratch a box when they identified avian flu in mallard ducks.
Glen J. Golden
There are some human super-smellers among us. In 2019, Joy Milne of Scotland proved she could unerringly identify people with Parkinson’s disease from a musky scent emitted from their skin. Clinical testing showed that she could distinguish the odor of Parkinson’s on a worn t-shirt before clinical symptoms even appeared.
Hossam Haick, a professor at Technion-Israel Institute of Technology, maintains that volatolomics is the future of medicine. Misdiagnosis and late detection are huge problems in health care, he says. “A precise and early diagnosis is the starting point of all clinical activities.” Further, this science has the potential to eliminate costly invasive testing or imaging studies and improve outcomes through earlier treatment.
The Nose Knows a Lot
“Volatolomics is not a fringe theory. There is science behind it,” Davis stresses. Every VOC has its own fingerprint, and a method called gas chromatography-mass spectrometry (GCMS) uses highly sensitive instruments to separate the molecules of these VOCs to determine their structures. But GCMS can’t discern the telltale patterns of particular diseases, and other technologies to analyze biomarkers have been limited.
We have technology that can see, hear and sense touch but scientists don’t have a handle yet on how smell works. The ability goes beyond picking out a single scent in someone’s breath or blood sample. It’s the totality of the smell—not the smell of a single chemical— which defines a disease. The dog’s brain is able to infer something when they smell a VOC that eludes human analysis so far.
Odor is a complex ecosystem and analyzing a VOC is compounded by other scents in the environment, says Kimball. A person’s diet and use of tobacco or alcohol also will affect the breath. Even fluctuations in humidity and temperature can contaminate a sample.
If successful, a sophisticated AI network can imitate how the dog brain recognizes patterns in smells. Early versions of robot noses have already been developed.
With today’s advances in data mining, AI and machine learning, scientists are trying to create mechanical devices that can draw on algorithms based on GCMS readings and data about diseases that dogs have sniffed out. If successful, a sophisticated AI network can imitate how the dog brain recognizes patterns in smells.
In March, Nano Research published a comprehensive review of volatolomics in health care authored by Haick and seven colleagues. The intent was to bridge gaps in the field for scientists trying to connect the biomarkers and sensor technology needed to develop a robot nose. This paper serves as a reference manual for the field that lists which VOCs are associated with what disease and the biomarkers in skin, saliva, breath, and urine.
Weiwei Wu, one of the co-authors and a professor at Xidian University in China, explains that creating a robotic nose requires the expertise of chemists, computer scientists, electrical engineers, material scientists, and clinicians. These researchers use different terms and methodologies and most have not collaborated before with the other disciplines. “The electrical engineers know the device but they don’t know as much about the biomarkers they need to detect,” Wu offers as an example.
This review is significant, Wu continues, because it can facilitate progress in the field by providing experts in all the disciplines with the basic knowledge needed to create an effective robot nose for diagnostic use. The paper also includes a systematic summary of the research methodology of volatolomics.
Once scientists build a stronger database of VOCs, they can program a device to identify critical patterns of specified diseases on a reliable basis. On a machine learning model, the algorithms automatically get better at diagnosing with each use. Wu envisions further tweaks in the next few years to make the devices smaller and consume less power.
A Whiff of the Future
Early versions of robot noses have already been developed. Some of them use chemical sensors to pick up smells in the breath or other body emission molecules. That data is sent through an electrical signal to a computer network for interpretation and possible linkage to a disease.
This electronic nose, or e-nose, has been successful in small pilot studies at labs around the world. At Ben-Gurion University in Israel, researchers detected breast cancer with electronic gas sensors with 95% accuracy, a higher sensitivity than mammograms. Other robot noses, called p-noses, use photons instead of electrical signals.
The mechanical noses being developed tap different methodologies and analytic techniques which makes it hard to compare them. Plus, the devices are intended for varying uses. One team, for example, is working on an e-nose that can be waved over a plate to screen for the presence of a particular allergen when you’re dining out.
A robot nose could be used as a real-time diagnostic tool in clinical practice. Kimball is working on one such tool that can distinguish between a viral and bacterial infection. This would enable physicians to determine whether an antibiotic prescription is appropriate without waiting for a lab result.
Davis is refining a hand-held device that identifies COVID-19 through a simple breath test. She sees the tool being used at crowded airports, sports stadiums and concert venues where PCR or rapid antigen testing is impractical. Background air samples are collected from the space so that those signals can be removed from the human breath measurement. “[The sensor tool] has the same accuracy as the rapid antigen test kits but exhaled breath is easier to collect,” she notes.
The NaNose, also known as the SniffPhone, uses tiny sensors boosted by AI to distinguish Alzheimer's, Crohn's disease, the early stages of several cancers, and other diseases with 84 to 98 percent accuracy.
Haick named his team’s robot nose, “NaNose,” since it is based on nanotechnology; the prototype is called the SniffPhone. Using tiny sensors boosted by AI, it can distinguish 23 diseases in human subjects with 84 to 98 percent accuracy. This includes early stages of several cancers, Alzheimer’s, tuberculosis and Crohn’s disease. His team has been raising the accuracy level by combining biomarker signals from both breath and skin, for example. The goal is to achieve 99.9 percent accuracy consistently so no other diagnostic tests would be needed before treating the patient. Plus, it will be affordable, he says.
Kimball predicts we’ll be seeing these diagnostic tools in the next decade. “The physician would narrow down what [the diagnosis] might be and then get the correct tool,” he says. Others are envisioning one device that can screen for multiple diseases by programming the software, which would be updated regularly with new findings.
Larger volatolomics studies must be conducted before these e-noses are ready for clinical use, however. Experts also need to learn how to establish normal reference ranges for e-nose readings to support clinicians using the tool.
“Taking successful prototypes from the lab to industry is the challenge,” says Haick, ticking off issues like reproducibility, mass production and regulation. But volatolomics researchers are unanimous in believing the future of health care is so close they can smell it.