Humankind is seeing Neptune’s rings in a whole new light thanks to the James Webb Space Telescope.
In an infrared image released September 21, Neptune and its gossamer diadems of dust take on an ethereal glow against the inky backdrop of space. The stunning portrait is a huge improvement over the rings’ previous close-up, which was taken more than 30 years ago.
Unlike the dazzling belts encircling Saturn, Neptune’s rings appear dark and faint in visible light, making them difficult to see from Earth. The last time anyone saw Neptune’s rings was in 1989, when NASA’s Voyager 2 spacecraft, after tearing past the planet, snapped a couple grainy photos from roughly 1 million kilometers away (SN: 8/7/17). In those photos, taken in visible light, the rings appear as thin, concentric arcs.
As Voyager 2 continued to interplanetary space, Neptune’s rings once again went into hiding — until July. That’s when the James Webb Space Telescope, or JWST, turned its sharp, infrared gaze toward the planet from roughly 4.4 billion kilometers away (SN: 7/11/22). Neptune itself appears mostly dark in the new image. That’s because methane gas in the planet’s atmosphere absorbs much of its infrared light. A few bright patches mark where high-altitude methane ice clouds reflect sunlight.
And then there are the ever-elusive rings. “The rings have lots of ice and dust in them, which are extremely reflective in infrared light,” says Stefanie Milam, a planetary scientist at NASA’s Goddard Space Flight Center in Greenbelt, Md., and one of JWST’s project scientists. The enormity of the telescope’s mirror also makes its images extra sharp. “JWST was designed to look at the first stars and galaxies across the universe, so we can really see fine details that we haven’t been able to see before,” Milam says.
Upcoming JWST observations will look at Neptune with other scientific instruments. That should provide new intel on the rings’ composition and dynamics, as well as on how Neptune’s clouds and storms evolve, Milam says. “There’s more to come.”
As people around the world marveled in July at the most detailed pictures of the cosmos snapped by the James Webb Space Telescope, biologists got their first glimpses of a different set of images — ones that could help revolutionize life sciences research.
The images are the predicted 3-D shapes of more than 200 million proteins, rendered by an artificial intelligence system called AlphaFold. “You can think of it as covering the entire protein universe,” said Demis Hassabis at a July 26 news briefing. Hassabis is cofounder and CEO of DeepMind, the London-based company that created the system. Combining several deep-learning techniques, the computer program is trained to predict protein shapes by recognizing patterns in structures that have already been solved through decades of experimental work using electron microscopes and other methods. The AI’s first splash came in 2021, with predictions for 350,000 protein structures — including almost all known human proteins. DeepMind partnered with the European Bioinformatics Institute of the European Molecular Biology Laboratory to make the structures available in a public database.
July’s massive new release expanded the library to “almost every organism on the planet that has had its genome sequenced,” Hassabis said. “You can look up a 3-D structure of a protein almost as easily as doing a key word Google search.”
These are predictions, not actual structures. Yet researchers have used some of the 2021 predictions to develop potential new malaria vaccines, improve understanding of Parkinson’s disease, work out how to protect honeybee health, gain insight into human evolution and more. DeepMind has also focused AlphaFold on neglected tropical diseases, including Chagas disease and leishmaniasis, which can be debilitating or lethal if left untreated. The release of the vast dataset was greeted with excitement by many scientists. But others worry that researchers will take the predicted structures as the true shapes of proteins. There are still things AlphaFold can’t do — and wasn’t designed to do — that need to be tackled before the protein cosmos completely comes into focus.
Having the new catalog open to everyone is “a huge benefit,” says Julie Forman-Kay, a protein biophysicist at the Hospital for Sick Children and the University of Toronto. In many cases, AlphaFold and RoseTTAFold, another AI researchers are excited about, predict shapes that match up well with protein profiles from experiments. But, she cautions, “it’s not that way across the board.”
Predictions are more accurate for some proteins than for others. Erroneous predictions could leave some scientists thinking they understand how a protein works when really, they don’t. Painstaking experiments remain crucial to understanding how proteins fold, Forman-Kay says. “There’s this sense now that people don’t have to do experimental structure determination, which is not true.” Plodding progress Proteins start out as long chains of amino acids and fold into a host of curlicues and other 3-D shapes. Some resemble the tight corkscrew ringlets of a 1980s perm or the pleats of an accordion. Others could be mistaken for a child’s spiraling scribbles.
A protein’s architecture is more than just aesthetics; it can determine how that protein functions. For instance, proteins called enzymes need a pocket where they can capture small molecules and carry out chemical reactions. And proteins that work in a protein complex, two or more proteins interacting like parts of a machine, need the right shapes to snap into formation with their partners.
Knowing the folds, coils and loops of a protein’s shape may help scientists decipher how, for example, a mutation alters that shape to cause disease. That knowledge could also help researchers make better vaccines and drugs.
For years, scientists have bombarded protein crystals with X-rays, flash frozen cells and examined them under highpowered electron microscopes, and used other methods to discover the secrets of protein shapes. Such experimental methods take “a lot of personnel time, a lot of effort and a lot of money. So it’s been slow,” says Tamir Gonen, a membrane biophysicist and Howard Hughes Medical Institute investigator at the David Geffen School of Medicine at UCLA. Such meticulous and expensive experimental work has uncovered the 3-D structures of more than 194,000 proteins, their data files stored in the Protein Data Bank, supported by a consortium of research organizations. But the accelerating pace at which geneticists are deciphering the DNA instructions for making proteins has far outstripped structural biologists’ ability to keep up, says systems biologist Nazim Bouatta of Harvard Medical School. “The question for structural biologists was, how do we close the gap?” he says.
For many researchers, the dream has been to have computer programs that could examine the DNA of a gene and predict how the protein it encodes would fold into a 3-D shape.
Here comes AlphaFold Over many decades, scientists made progress toward that AI goal. But “until two years ago, we were really a long way from anything like a good solution,” says John Moult, a computational biologist at the University of Maryland’s Rockville campus.
Moult is one of the organizers of a competition: the Critical Assessment of protein Structure Prediction, or CASP. Organizers give competitors a set of proteins for their algorithms to fold and compare the machines’ predictions against experimentally determined structures. Most AIs failed to get close to the actual shapes of the proteins. Then in 2020, AlphaFold showed up in a big way, predicting the structures of 90 percent of test proteins with high accuracy, including two-thirds with accuracy rivaling experimental methods.
Deciphering the structure of single proteins had been the core of the CASP competition since its inception in 1994. With AlphaFold’s performance, “suddenly, that was essentially done,” Moult says.
Since AlphaFold’s 2021 release, more than half a million scientists have accessed its database, Hassabis said in the news briefing. Some researchers, for example, have used AlphaFold’s predictions to help them get closer to completing a massive biological puzzle: the nuclear pore complex. Nuclear pores are key portals that allow molecules in and out of cell nuclei. Without the pores, cells wouldn’t work properly. Each pore is huge, relatively speaking, composed of about 1,000 pieces of 30 or so different proteins. Researchers had previously managed to place about 30 percent of the pieces in the puzzle. That puzzle is now almost 60 percent complete, after combining AlphaFold predictions with experimental techniques to understand how the pieces fit together, researchers reported in the June 10 Science.
Now that AlphaFold has pretty much solved how to fold single proteins, this year CASP organizers are asking teams to work on the next challenges: Predict the structures of RNA molecules and model how proteins interact with each other and with other molecules.
For those sorts of tasks, Moult says, deep-learning AI methods “look promising but have not yet delivered the goods.”
Where AI falls short Being able to model protein interactions would be a big advantage because most proteins don’t operate in isolation. They work with other proteins or other molecules in cells. But AlphaFold’s accuracy at predicting how the shapes of two proteins might change when the proteins interact are “nowhere near” that of its spot-on projections for a slew of single proteins, says Forman-Kay, the University of Toronto protein biophysicist. That’s something AlphaFold’s creators acknowledge too.
The AI trained to fold proteins by examining the contours of known structures. And many fewer multiprotein complexes than single proteins have been solved experimentally. Forman-Kay studies proteins that refuse to be confined to any particular shape. These intrinsically disordered proteins are typically as floppy as wet noodles (SN: 2/9/13, p. 26). Some will fold into defined forms when they interact with other proteins or molecules. And they can fold into new shapes when paired with different proteins or molecules to do various jobs.
AlphaFold’s predicted shapes reach a high confidence level for about 60 percent of wiggly proteins that Forman-Kay and colleagues examined, the team reported in a preliminary study posted in February at bioRxiv.org. Often the program depicts the shapeshifters as long corkscrews called alpha helices.
Forman-Kay’s group compared AlphaFold’s predictions for three disordered proteins with experimental data. The structure that the AI assigned to a protein called alpha-synuclein resembles the shape that the protein takes when it interacts with lipids, the team found. But that’s not the way the protein looks all the time.
For another protein, called eukaryotic translation initiation factor 4E-binding protein 2, AlphaFold predicted a mishmash of the protein’s two shapes when working with two different partners. That Frankenstein structure, which doesn’t exist in actual organisms, could mislead researchers about how the protein works, Forman-Kay and colleagues say. AlphaFold may also be a little too rigid in its predictions. A static “structure doesn’t tell you everything about how a protein works,” says Jane Dyson, a structural biologist at the Scripps Research Institute in La Jolla, Calif. Even single proteins with generally well-defined structures aren’t frozen in space. Enzymes, for example, undergo small shape changes when shepherding chemical reactions.
If you ask AlphaFold to predict the structure of an enzyme, it will show a fixed image that may closely resemble what scientists have determined by X-ray crystallography, Dyson says. “But [it will] not show you any of the subtleties that are changing as the different partners” interact with the enzyme.
“The dynamics are what Mr. AlphaFold can’t give you,” Dyson says.
A revolution in the making The computer renderings do give biologists a head start on solving problems such as how a drug might interact with a protein. But scientists should remember one thing: “These are models,” not experimentally deciphered structures, says Gonen, at UCLA.
He uses AlphaFold’s protein predictions to help make sense of experimental data, but he worries that researchers will accept the AI’s predictions as gospel. If that happens, “the risk is that it will become harder and harder and harder to justify why you need to solve an experimental structure.” That could lead to reduced funding, talent and other resources for the types of experiments needed to check the computer’s work and forge new ground, he says. Harvard Medical School’s Bouatta is more optimistic. He thinks that researchers probably don’t need to invest experimental resources in the types of proteins that AlphaFold does a good job of predicting, which should help structural biologists triage where to put their time and money.
“There are proteins for which AlphaFold is still struggling,” Bouatta agrees. Researchers should spend their capital there, he says. “Maybe if we generate more [experimental] data for those challenging proteins, we could use them for retraining another AI system” that could make even better predictions.
He and colleagues have already reverse engineered AlphaFold to make a version called OpenFold that researchers can train to solve other problems, such as those gnarly but important protein complexes.
Massive amounts of DNA generated by the Human Genome Project have made a wide range of biological discoveries possible and opened up new fields of research (SN: 2/12/22, p. 22). Having structural information on 200 million proteins could be similarly revolutionary, Bouatta says.
In the future, thanks to AlphaFold and its AI kin, he says, “we don’t even know what sorts of questions we might be asking.”
The answer to one of the greatest mysteries of the universe may come down to one of the smallest, and spookiest, particles.
Matter is common in the cosmos. Everything around us — from planets to stars to puppies — is made up of matter. But matter has a flip side: antimatter. Protons, electrons and other particles all have antimatter counterparts: antiprotons, positrons, etc. Yet for some reason antimatter is much rarer than matter — and no one knows why. Physicists believe the universe was born with equal amounts of matter and antimatter. Since matter and antimatter counterparts annihilate on contact, that suggests the universe should have ended up with nothing but energy. Something must have tipped the balance.
Some physicists think lightweight subatomic particles called neutrinos could point to an answer. These particles are exceedingly tiny, with less than a millionth the mass of an electron (SN: 4/21/21). They’re produced in radioactive decays and in the sun and other cosmic environments. Known for their ethereal tendency to evade detection, neutrinos have earned the nickname “ghost particles.” These spooky particles, originally thought to have no mass at all, have a healthy track record of producing scientific surprises (SN: 10/6/15).
Now researchers are building enormous detectors to find out if neutrinos could help solve the mystery of the universe’s matter. The Hyper-Kamiokande experiment in Hida City, Japan, and the Deep Underground Neutrino Experiment in Lead, S.D., will study neutrinos and their antimatter counterparts, antineutrinos. A difference in neutrinos’ and antineutrinos’ behavior might hint at the origins of the matter-antimatter imbalance, scientists suspect.
Watch the video below to find out how neutrinos might reveal why the universe contains, well, anything at all.
Face masks — the unofficial symbol of the COVID-19 pandemic — are leveling up.
A mask outfitted with special electronics can detect SARS-CoV-2, the virus that causes COVID-19, and other airborne viruses within 10 minutes of exposure, materials researcher Yin Fang and colleagues report September 19 in Matter.
“The lightness and wearability of this face mask allows users to wear it anytime, anywhere,” says Fang, of Tongji University in Shanghai. “It’s expected to serve as an early warning system to prevent large outbreaks of respiratory infectious diseases.” Airborne viruses can hitch a ride between hosts in the air droplets that people breathe in and out. People infected with a respiratory illness can expel thousands of virus-containing droplets by talking, coughing and sneezing. Even those with no signs of being sick can sometimes pass on these viruses; people who are infected with SARS-CoV-2 can start infecting others at least two to three days before showing symptoms (SN: 3/13/20). So viruses often have a head start when it comes to infecting new people.
Fang and his colleagues designed a special sensor that reacts to the presence of certain viral proteins in the air and attached it to a face mask. The team then spritzed droplets containing proteins produced by the viruses that cause COVID-19, bird flu or swine flu into a chamber with the mask.
The sensor could detect just a fraction of a microliter of these proteins — a cough might contain 10 to 80 times as much. Once a pathogen was detected, the sensor-mask combo sent a signal to the researchers informing them of the virus’s presence. Ultimately, the researchers plan for such signals to be sent to a wearer’s phone or other devices. By combining this technology with more conventional testing, the team says, health care providers and public health officials might be able to better contain future pandemics.
Getting out into society after a long isolation gets awkward. Ask the Pahrump poolfish, loners in a desert for some 10,000 years.
This hold-in-your-hand-size fish (Empetrichthys latos) has a chubby, torpedo shape and a mouth that looks as if it’s almost smiling. Until the 1950s, this species had three forms, each evolving in its own spring. Now only one survives, which developed in a spring-fed oasis in the Mojave Desert’s Pahrump Valley, about an hour’s drive west of Las Vegas.
Fish in a desert are not that weird when you take the long view (SN: 1/26/16). In a former life, some desert valleys were ancient lakes. As the region’s lakes dried up, fish got stuck in the remaining puddles. Various stranded species over time adapted to quirks of their private microlakes, and a desert-fish version of the Galapagos Islands’ diverse finches arose. “We like to say that Darwin, if he had a different travel agent, could have come to the same conclusions just from the desert,” says evolutionary biologist Craig Stockwell of North Dakota State University in Fargo.
The desert “island” where E. latos evolved was Manse Spring on a private ranch. From a distance, the spring looked “just like a little clump of trees,” remembers ecologist Shawn Goodchild, who is now based in Lake Park, Minn. The spot of desert greenery surrounded the Pahrump poolfish’s entire native range, about the length of an Olympic swimming pool.
By the 1960s, biologists feared the fish were doomed. The spring’s flow rate had dropped some 70 percent as irrigation for farms in the desert sucked away water. And disastrous predators arrived: a kid’s discarded goldfish. Conservation managers fought back, but neither poison nor dynamite wiped out the newcomers. And then in August of 1975, Manse Spring dried up.
Conservation managers had moved some poolfish to other springs, but the long-isolated species just didn’t seem to get the dangers of living with other kinds of fishes. The poolfish were easily picked off by predators in their new home.
Lab tests of fake fish-murder scenes may help explain why. For instance, researchers tainted aquarium water with pureed fish bits. In an expected reaction, fathead minnows (Pimephales promelas) freaked at traces of dead minnow drifting through the water and huddled low in the tank. The Pahrump poolfish in water tainted with blender-whizzed skin of their kind just kept swimming around the upper waters as if corpse taint were no scarier than tap water. Literally. Stockwell and colleagues can say that because they ran a fear test with nonscary dechlorinated tap water. Poolfish didn’t huddle then either, the team reports in the Aug. 31 Proceedings of the Royal Society B.
Then, however, Stockwell and a colleague were musing about some rescued poolfish in cattle tanks when nearby dragonflies caught the researchers’ attention.
Before dragonflies mature into shimmering aerial marvels, the young prowl underwater as violent predators. In moves worthy of scary aliens in a sci-fi movie, many dragonfly nymphs can shoot their jaws out from their head to scoop up prey, including fish eggs and fish larvae. With young dragonflies prowling a pool’s bottom and plants, poolfish moving up the water column “would be a good way to reduce their risk,” Stockwell says. Testing of that idea has begun.
Fish that people thought were foolishly naïve may just be savvy in a different way. Especially after isolation in a desert with dragons.
The massive Tonga eruption generated a set of planet-circling tsunamis that may have started out as a single mound of water roughly the height of the Statue of Liberty.
What’s more, the explosive eruption triggered an immense atmospheric shock wave that spawned a second set of especially fast-moving tsunamis, a rare phenomenon that can complicate early warnings for these oft-destructive waves, researchers report in the October Ocean Engineering.
As the Hunga Tonga–Hunga Ha’apai undersea volcano erupted in the South Pacific in January, it displaced a large volume of water upward, says Mohammad Heidarzadeh, a civil engineer at the University of Bath in England (SN: 1/21/22). The water in that colossal mound later “ran downhill,” as fluids tend to do, to generate the initial set of tsunamis. To estimate the original size of the mound, Heidarzadeh and his team used computer simulations, as well as data from deep-ocean instruments and coastal tide gauges within about 1,500 kilometers of the eruption, many of them in or near New Zealand. The arrival times of tsunami waves, as well as their sizes, at those locations were key pieces of data, Heidarzadeh says.
The team analyzed nine possibilities for the initial wave, each of which was shaped like a baseball pitcher’s mound and had a distinct height and diameter. The best fit to the real-world data came from a mound of water a whopping 90 meters tall and 12 kilometers in diameter, the researchers report.
That initial wave would have contained an estimated 6.6 cubic kilometers of water. “This was a really large tsunami,” Heidarzadeh says.
Despite starting out about nine times as tall as the tsunami that devastated the Tohoku region of Japan in 2011, the Tongan tsunamis killed only five people and caused about $90 million in damage, largely because of their remote source (SN: 2/10/12).
Another unusual aspect of the Tongan eruption is the second set of tsunamis generated by a strong atmospheric pressure wave.
That pressure pulse resulted from a steam explosion that occurred when a large volume of seawater infiltrated the hot magma chamber beneath the erupting volcano. As the pressure wave raced across the ocean’s surface at speeds exceeding 300 meters per second, it pushed water ahead of it, creating tsunamis, Heidarzadeh explains. Along many coastlines, including some in the Indian Ocean and Mediterranean Sea, these pressure wave–generated tsunamis arrived hours ahead of the gravity-driven waves spreading from the 90-meter-tall mound of water. Gravity-driven tsunami waves typically travel across the deepest parts of the ocean, far from continents, at speeds between 100 and 220 meters per second. When the waves reach shallow waters near shore, the waves slow, water stacks up and then strikes shore, where destruction occurs.
Pressure wave–generated tsunamis have been reported for only one other volcanic eruption: the 1883 eruption of Krakatau in Indonesia (SN: 8/27/83).
Those quicker-than-expected arrival times — plus the fact that the pressure-wave tsunamis for the Tongan eruption were comparable in size with the gravity-driven ones — could complicate early warnings for these tsunamis. That’s concerning, Heiderzadeh says.
One way to address the issue would be to install instruments that measure atmospheric pressure with the deep-sea equipment already in place to detect tsunamis, says Hermann Fritz, a tsunami scientist at Georgia Tech in Atlanta.
With that setup, scientists would be able to discern if a passing tsunami is associated with a pressure pulse, thus providing a clue in real time about how fast the tsunami wave might be traveling.
It was the juice that tipped him off. At lunch, Ícaro de A.T. Pires found the flavor of his grape juice muted, flattened into just water with sugar. There was no grape goodness. “I stopped eating lunch and went to the bathroom to try to smell the toothpaste and shampoo,” says Pires, an ear, nose and throat specialist at Hospital IPO in Curitiba, Brazil. “I realized then that I couldn’t smell anything.”
Pires was about three days into COVID-19 symptoms when his sense of smell vanished, an absence that left a mark on his days. On a trip to the beach two months later, he couldn’t smell the sea. “This was always a smell that brought me good memories and sensations,” Pires says. “The fact that I didn’t feel it made me realize how many things in my day weren’t as fun as before. Smell can connect to our emotions like no other sense can.”
As SARS-CoV-2, the virus responsible for COVID-19, ripped across the globe, it stole the sense of smell away from millions of people, leaving them with a condition called anosmia. Early in the pandemic, when Pires’ juice turned to water, that olfactory theft became one of the quickest ways to signal a COVID-19 infection. With time, most people who lost smell recover the sense. Pires, for one, has slowly regained a large part of his sense of smell. But that’s not the case for everyone. About 5.6 percent of people with post–COVID-19 smell loss (or the closely related taste loss) are still not able to smell or taste normally six months later, a recent analysis of 18 studies suggests. The number, reported in the July 30 British Medical Journal, seems small. But when considering the estimated 550 million cases and counting of COVID-19 around the world, it adds up.
Scientists are searching for ways to hasten olfactory healing. Three years into the COVID-19 pandemic, researchers have a better idea of how many people are affected and how long it seems to last. Yet when it comes to ways to rewire the sense of smell, the state of the science isn’t coming up roses.
A method called olfactory training, or smell training, has shown promise, but big questions remain about how it works and for whom. The technique has been around for a while; the coronavirus isn’t the first ailment to snatch away smell. But with newfound pressure from people affected by COVID-19, olfactory training and a host of other newer treatments are now getting a lot more attention.
The pandemic has brought increased attention to smell loss. “If we have to provide a silver lining, COVID is pushing the science at a speed that’s never happened before,” says Valentina Parma, an olfactory researcher and assistant director of the Monell Chemical Senses Center in Philadelphia. “But,” she cautions, “we are really far from a solution.”
Nasal attack Compared with sight or hearing, the sense of smell can seem like an afterthought. But losing it can affect people deeply. “Your world really changes if you lose the sense of smell, in ways that are usually worse,” Parma says. The smell of a baby’s head, a buttery curry or the sharp salty sea can all add emotional meaning to experiences. Smells can also warn of danger, such as the rotten egg stench that signals a natural gas leak.
As an ear, nose and throat doctor, Pires recalls a deaf patient who lost her sense of smell after COVID-19 and enrolled in a clinical trial that he and colleagues conducted on smell training. She worked in a perfumery company — her sense of smell was crucial to her job and her life. “At the first appointment, she said, with tears in her eyes, that it felt like she wasn’t living,” Pires recalls.
Unlike the cells that detect color or sound, the cells that sense smell can replenish themselves. Stem cells in the nose are constantly pumping out new smell-sensing cells. Called olfactory sensory neurons, these cells are dotted with molecular nets that snag specific odor molecules that waft into the nose. Once activated, these cells send messages through the skull and into the brain.
Because of their nasal neighborhood, olfactory sensory neurons are exposed to the hazards of the environment. “They may be covered with a little layer of mucus, but they’re sitting out there being constantly bombarded with bacteria and viruses and pollutants and who knows what else,” says Steven Munger, a chemosensory neuroscientist at the University of Florida College of Medicine in Gainesville.
Exactly how SARS-CoV-2 damages the smell system isn’t clear. But recent studies suggest the virus’s assault is indirect. The virus can infect and kill nose support cells called sustentacular cells, which are thought to help keep olfactory neurons happy and fed by delivering glucose and maintaining the right salt balance. That attack can inflame the olfactory epithelium, the layers of cells that line parts of the nasal cavity.
Once this tissue is riled up, the olfactory sensory neurons get wonky, even though the cells themselves haven’t been attacked. After an infection and ensuing inflammation, these neurons slow down the production of their odor-catching nets, a decrease that could blind themselves to odor molecules, scientists reported in the March 17 Cell.
With time, the inflammation settles down, and the olfactory sensory neurons can get back to their usual jobs, researchers suspect. “We do think that for post-viral smell disorders, the most common way to recover function is going to be spontaneous recovery,” Munger says. But in some people, this process doesn’t happen quickly, if ever.
That’s where smell training comes in.
A nose workout One of the only therapies that exists, smell training is quite simple — a good old-fashioned nose workout. It involves deeply smelling four scents (usually rose, eucalyptus, lemon and cloves) for 30 seconds apiece, twice a day for months.
In one study, 40 people who had smell disorders came away from the training with improved smelling abilities, on average, compared with 16 people who didn’t do the training, olfactory researcher Thomas Hummel and his colleagues reported in the March 2009 Laryngoscope.
Since then, the bulk of studies has shown that the method helps between 30 and 60 percent of the people who try it, says Hummel, of Technische Universität Dresden in Germany. His view is that the method can help some people, “but it does not work in everybody.”
One of the nice things is that there are no harmful side effects, Hummel says. That’s “the charming side of it.” But to do the training correctly takes discipline and stamina. “If you don’t do it regularly, and you give up after 14 days, this is futile,” he says.
Pires in his recent trial had hoped to speed up the process, which usually takes three months, by adding four more odors to the regimen. For four weeks, 80 participants received either four or eight smells. Both groups improved, but there was no difference between the two groups, the researchers reported July 21 in the American Journal of Rhinology & Allergy.
It’s not known how the technique works in the people it seems to help. It could be that it focuses people’s attention on faint smells; it could be stimulating the growth of replacement cells; it could be strengthening some pathways in the brain. Data from other animals suggest that such training can increase the number of olfactory sensory neurons, Hummel says.
Overall, this nose boot camp may be a possible approach for people to try, but big questions remain about how it works and for whom, Munger says. “In my view, it’s very important to be up front with patients about the very real possibility this therapy may not lead to a restoration of smell, even if they and their doctor feel it is worth trying,” he says. “I am not trying to discourage people here, but I also think we need to be very careful not to give unwarranted promises.”
Smell training doesn’t come with harmful biological side effects, but it can induce frustration if it doesn’t work, Parma says. In her practice, “I have been talking to a lot of people who say, ‘I did it every day for six months, twice a day for 10 minutes. I met in groups with other people, so we kept each other accountable, and I did that for six months. And it didn’t work for me.’” She adds, “I would want to address the frustration that this induces in patients.”
Beyond training Other potential treatments are coming under scrutiny, such as steroids, omega-3 supplements, growth factors and vitamins A and E, all of which might encourage the recovery of the nasal epithelium.
More futuristic remedies are also in early stages of research. These include epithelial transplants designed to boost olfactory stem cells, treatments with platelet-rich plasma to curb inflammation and promote healing, and even an “electronic nose” that would detect odor molecules and stimulate the brain directly. This cyborg-smelling system takes inspiration from cochlear implants for hearing and retinal implants for vision. For many people, the sense of smell is appreciated only after it’s gone, Parma says, an apathy that’s illustrated in stark terms by a recent study of about 400 people. The vast majority of respondents — nearly 85 percent — would rather give up their sense of smell than sight or hearing. About 19 percent of respondents said they would prefer to give up their sense of smell than their cell phone. The survey results “dramatically illustrate the negligible value people place on their sense of smell,” researchers wrote in the March Brain Sciences.
Even as a doctor who treats people with smell loss, Pires has a newfound fondness for a good whiff. “Having lost it for a while made me appreciate it even more.”