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2026-05-26 06:57:41

This story was originally published by Knowable Magazine.

At age 49, Jan Janisch-Hanzlik’s multiple sclerosis was destroying her freedom to live the life she wanted. She gave up her active nursing job for a desk role. Frequent falls made her afraid to carry her grandchildren. She had to move to a bigger house to make room for the wheelchair she feared she might end up needing full-time.

Even the best available medication wasn’t improving Janisch-Hanzlik’s symptoms, and she worried they’d only get worse. So when she learned about a trial of CAR T cell therapy at the University of Nebraska Medical Center in Omaha, close to the city of Blair where she lives, she phoned the clinic every other month until they were ready to enroll her as the first patient.

Originally designed to target and wipe out cancer by reprogramming the patient’s immune cells, CAR T is now being offered to patients in hundreds of clinical trials for autoimmune conditions like multiple sclerosis, lupus, Graves’ disease, vasculitis, and many others. The hope is that CAR T can duplicate the success it has demonstrated in a range of blood cancers by hunting down and eliminating cells that target the self in autoimmune diseases. This would essentially reset the body’s defenses to a state like the one that existed before the disease took hold.

But along with CAR T’s promise come risks, questions and challenges. There’s uncertainty about how well it will work for autoimmunity and how long any benefits might last, as well as what long-term side effects might arise. Janisch-Hanzlik knew this when she sat down to receive the experimental treatment on June 9, 2025; she felt a mix of hope and fear knowing that she would be spending the next week being monitored for side effects including dangerous inflammation.

In addition to her clinical expertise and desire to pioneer a new treatment, Janisch-Hanzlik’s two young grandchildren helped inspire her pursuit of a treatment with known risks and uncertain benefits. Because multiple sclerosis has a genetic component, Janisch-Hanzlik knew that they have an elevated chance of going through the same struggle she has. “I would want to be able to say I did everything that I possibly could to prevent them, or anyone else, from having something like this,” she says.

From Cancer to Autoimmunity

The first CAR T cancer treatment was approved by the Food and Drug Administration in 2017 for an aggressive form of leukemia. Since then, the powerful and intensive treatment has resulted in long-term remission for many cancer patients.

The basic premise of CAR T is to activate the power of key immune cells called T cells. T cells normally recognize other cells that have been infected by a virus or bacterium, or are otherwise abnormal, and either destroy them or recruit other parts of the immune system to do so.

In CAR T for cancer, scientists engineer those T cells to specifically hunt and destroy malignant cells. The technology got its start when cancer researchers figured out how to take out a patient’s own T cells, insert DNA instructions for a “chimeric antigen receptor,” or CAR, and put them back into the person’s circulation. The CAR, which sits on the T cell’s surface and latches on to a specific molecular partner on the surface of cancerous cells, activates the T cell to attack.

Today, CAR T cells are most commonly programmed to attack B cells, another key immune player. B cells are normally responsible for making antibodies, but in certain blood cancers, they proliferate out of control. By giving T cells a CAR that recognizes one of a couple of molecules unique to the B cell surface, the cells are reprogrammed to find and eliminate those cancerous cells.

B cells also are the central problem in many autoimmune conditions: They mistakenly make antibodies against normal tissues instead of against invading pathogens. So as CAR T began to succeed against B cell cancers, it didn’t take long for doctors to reason that CAR T therapy might also be able to wipe out bad B cells in people with autoimmunity.

A German team pioneered autoimmune CAR T in a woman with lupus, reporting positive results in 2021. Since then, that team and others have worked to translate the oncology success of CAR T to tackle a broad spectrum of autoimmune diseases.

“I think it’s a game changer,” says Amanda Piquet, an autoimmune neurologist at the University of Colorado Anschutz in Aurora. Piquet is evaluating CAR T therapy for a rare and poorly understood autoimmune condition called stiff person syndrome, with symptoms including muscle stiffness and painful spasms. There is no FDA-approved treatment. When she heard about a company called Kyverna that was testing CAR T cell therapy in the syndrome, she thought it was “a perfect opportunity.”

The study she led, which reported preliminary results in December 2025, tested a single dose of CAR T in 26 people. Before the treatment, many participants struggled with a slow, mechanical gait, and 12 used assistive devices such as walkers and canes. Most patients were able to walk faster by 16 weeks post-treatment, and eight no longer needed their assistive devices for short distances. In April, the company reported that all 26 patients, as of their last follow-up appointment four to 12 months out from the therapy, were no longer using any other immunotherapies.

Risks and Uncertainties

Despite such striking results, reprogramming the immune system is no simple matter. In early treatment of cancer patients, CAR T cells produced life-threatening side effects, as outlined in a 2026 article in the Annual Review of Medicine. As CAR T cells attack their targets, the associated inflammation can cause symptoms like high fevers and low blood pressure. If that inflammation reaches the brain, it can cause additional problems such as confusion and drowsiness.

Fortunately, physicians now have a decade’s worth of experience recognizing and treating these problems. “They’re certainly reversible and don’t cause long-term damage most of the time,” says Emily Littlejohn, a rheumatologist at the Cleveland Clinic.

Physicians and patients also must contend with decreased immunity as both a side effect of the treatment and its desired outcome. In CAR T treatment, doctors typically use powerful chemotherapy drugs to temporarily reduce the body’s immune cell population to make room for the new, engineered cells, leaving patients temporarily immunosuppressed. And if the treatment works, it will decimate B cell populations. Patients can be vulnerable to infections for up to a year after treatment, says Littlejohn.

These effects are manageable with preventive antibiotics, antivirals, and vaccines. Patients also retain antibodies that their B cells made before the treatment, which provide residual protection for a few months. And for reasons that are not yet fully understood, CAR T seems to leave older B cells, which provide immune memory of past infections, intact in some cases. One study found that autoimmune patients treated with CAR T still made antibodies for diseases they’d been previously vaccinated against, like chicken pox and measles. These are signs that the treatment did not completely return the immune system to its factory settings.

When evaluating CAR T risk, it’s important to consider that many existing treatments for autoimmune disease also suppress the immune system for as long as a person takes them, experts note.

But there are other possible CAR T risks for autoimmune patients. In February, FDA officials published a paper endorsing CAR T’s potential in autoimmunity but warning of “unpredictable long-term toxicity.” CAR T treatment for cancer, the authors noted, has been linked to diverse long-term issues such as Parkinson’s disease. There have also been cases where the bioengineered cells themselves turned malignant, causing new, T cell-based cancers.

Causing a secondary cancer may be an acceptable risk when treating a life-threatening cancer, but probably not for autoimmunity, says Matt Lunning, medical director for gene and cellular therapy at Nebraska Medicine in Omaha. How to balance the risk between the impacts of an autoimmune disease, which can range widely in severity, and the difficult-to-quantify risk of future side effects or cancers remains a major open question.

Researchers are already working on second- and third-generation versions of CAR T that they expect to be safer for both cancer and autoimmunity. For example, James Howard, a neuromuscular neurologist at the University of North Carolina at Chapel Hill, is testing a technology from a company called Cartesian Therapeutics that encodes the CAR using molecules of mRNA, the short-lived genetic messenger used in Covid-19 vaccines, instead of long-lasting DNA. The CAR T cells should wipe out B cells for only as long as the mRNA persists, then lose their B cell-targeting abilities. With no chance for genetically modified T cells to hang around long-term, there should be no cancer risk.

Another plus of Cartesian’s approach: Physicians infuse these T cells in sufficient numbers that they don’t need to reproduce in the patient’s body, which Howard thinks reduces risk for inflammation. In a recent trial, 15 people with autoimmune diseases received the Cartesian CAR T treatment; two-thirds saw their symptoms improve and none suffered long-term serious side effects.

Treating CAR T Sticker Shock

Beyond side effects, the other major challenge facing CAR T therapy is its price tag, which reaches hundreds of thousands of dollars including hospital stays, cell engineering, and other expenses.

The treatment would likely be cheaper, and simpler, if scientists could eliminate the need for personalized engineering of each patient’s own cells and instead use donor cells, or if they could cut out the step of engineering and growing the cells in a laboratory. Lunning says he is eyeing up-and-coming procedures that would modify a person’s T cells within their own body ­instead of doing the genetic engineering in a lab.

Researchers are even farther along with a version of CAR T that uses healthy donors as a source of T cells. These could then be used by many patients in an “off-the-shelf” approach. It’s a method that has its own challenges, because of the immune mismatch between donor and patient cells that would lead them to attack each other. This problem can be overcome with additional genetic modifications to the donated T cells that prevent recipient and donor systems from recognizing each other as foreign, says Bing Du, an immunologist at East China Normal University in Shanghai who’s among many researchers working on this approach. Du estimates a lab could make CAR T cells for more than 1,000 patients from a single donor’s blood cells, at significant cost savings.

This kind of off-the-shelf CAR T therapy is what Janisch-Hanzlik of Nebraska received, under Lunning’s care, in 2025. The study organizers at TG Therapeutics expect to complete their research in early 2029.

Janisch-Hanzlik ended up sailing through the follow-up without side effects. A couple of months after the infusion, she was watching TV when she noticed she no longer needed special glasses to correct double vision. She started forgetting to bring her cane when moving about her house or going grocery shopping; she didn’t need it. Now, nearly a year since the treatment, she rarely falls and no longer requires a daily, three-hour nap. She recently enjoyed a trip to the Grand Canyon and looks forward to spending more time with her grandchildren.

She does still have symptoms, including weakness in her right leg, numbness and tingling in her feet, and difficulty finding the right word when speaking. She asks her doctors if they think she’s going to get better, stay the same or get worse again.

“I have been told so many times, ‘We don’t know, you’re the first. We’re just going to have to wait and see,’” she says. “I definitely am thankful for every day I have.”

This article originally appeared in Knowable Magazine, an independent journalistic endeavor from Annual Reviews. Sign up for the newsletter.

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2026-05-23 22:00:00

The post This Week’s Awesome Tech Stories From Around the Web (Through May 23) appeared first on SingularityHub.

2026-05-23 05:32:42

As the AI boom accelerates, governments and utilities are struggling to keep pace with the industry’s huge energy demands. New figures suggest data centers now consume about 6 percent of electricity in the US, raising concerns about grid capacity and environmental impacts.

Data centers have always been energy-hungry, but the AI explosion is causing computing demand to skyrocket. The biggest data centers now consume as much electricity as small cities and are proliferating at breakneck speed.

A new report from the International Data Center Authority (IDCA) finds that the total power draw of all these facilities has now hit 67.7 gigawatts—a 36 percent jump over two years. The US alone accounts for 29.2 gigawatts of that total, roughly 43 percent of global consumption.

“Our real-time data shows that many very large AI factories are coming into operation, spiking up total US consumption,” Mehdi Paryavi, CEO and founder of IDCA, told Data Center Knowledge. “The US now devotes 6 percent of its total electricity to data centers.”

That could be a significant milestone, as the report warns that “significant community and political pushback starts to occur in nations once their data center footprints have reached the 5 percent consumption level of national grids.” The US isn’t alone—the UK is now using 5.8 percent of its electricity to power data centers, and in Germany, the figure has hit 9.5 percent.

Opposition is growing.

Hundreds of state-level bills to regulate data centers have been introduced, according to the report. In Maine, the legislature passed a bill that would have barred construction of data centers bigger than 20 megawatts until 2027. Maine’s governor, Janet Mills, vetoed the bill, and the legislature failed to override the veto. But Mills later signed an executive order forming a council to investigate the impact of data centers in the state, with recommendations due in early 2027.

Local planners are also refusing to issue new permits due to energy scarcity. For example, developers in Northern Virginia’s Data Center Alley, a region already densely packed with the facilities, will have to wait until 2032 to launch new projects.

Water usage is an equally important concern in many areas. The vast majority of data centers rely on water-cooled chillers or evaporative cooling towers that can consume millions of gallons daily. A single large facility can potentially draw as much water as 6,500 households. Modern AI facilities increasingly use more modern closed-loop liquid cooling systems that require minimal ongoing water use, but these account for a small proportion of the overall data center fleet.

The report suggests that some of this negative reaction is also self-inflicted. Developers routinely use locally registered entities with generic names that obscure who is actually behind a project, leading to a lack of trust in local communities.

“Before being swept along by the enthusiasm of tech billionaires whose profits depend on this expansion, we should pause and ask ourselves whether it’s worth the price,” Greenpeace UK’s chief scientist Doug Parr told the Guardian in response to the findings.

“We need more transparency about the amount of water and energy used by data centers, proper environmental impact assessments, and a ban on new polluting plants being built to power AI.”

It’s not only new projects putting strain on the grid though. The report found that an estimated 13 percent of US cloud consumption, totaling more than 3 gigawatts, comes from so-called “zombie” workloads—abandoned test environments and unused applications that continue to draw power without doing any useful work.

In addition, there are thousands of smaller data centers embedded in corporate buildings and regional offices drawing considerable amounts of power. These are often missed by consumption estimates that typically focus on large hyperscale campuses, but the IDCA says they account for at least 15 percent of total data center power consumption, in part because they are considerably less efficient than their larger counterparts.

The problems are only likely to get worse though, as tech companies show no signs of slowing down. Annual global data center spending is approaching $1 trillion, with up to $700 billion anticipated in the US alone in 2026, the report notes.

Whether grids will be able to absorb all that new capacity, and how hard local communities fight back against developments, may well end up being a deciding factor in whether the AI boom keeps rolling or fizzles out.

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2026-05-22 08:37:05

Uncovering nature’s secrets is no easy task. The daily life of a scientist is often grueling, frustrating, and—perhaps surprisingly—boring as they repeat experiments over and over.

Here’s where AI could lend a hand. This week, two studies offer a glimpse into a future where AI and scientists bounce ideas off each other and collaborate on projects to benefit humanity.

Both systems rely on large language models in end-to-end scientific discovery. They read through existing literature, generate hypotheses, suggest relevant experiments, and analyze and interpret the data for scientists to evaluate. The researchers then give the AI feedback, and the cycle begins again.

One of the systems, called Robin, was instructed to find drugs for a common eye condition. Developed by FutureHouse, a non-profit that builds AI systems to automate research in biology and other scientific fields, Robin quickly homed in on candidates. According to the team, the AI slashed research time 200-fold compared to scientists working alone.

The other system is Google DeepMind’s Co-Scientist. With human guidance, Co-Scientist found already approved drugs that could be repurposed for a type of leukemia within hours. It also surfaced promising targets for liver scarring. The system wasn’t tested in-house; it was distributed to other teams to integrate into their particular fields and workflows.

AI companies are racing to design agents that automate scientific discovery. But both teams stress their systems are collaborators, not replacements. Scientists crafted each project’s vision, checked the agent’s output, and guided its work, like a professor tutoring a bright student.

“These projects represent a significant step forwards,” wrote the editorial team at Nature, where both studies were published. “But for all the ‘wow’ factor, it is crucial to bear in mind that the AI systems were not working alone.”

Nobelist Pursuit

Scientists have a complex relationship with AI.

Nobel Prize-winning protein-prediction models have helped researchers make progress on previously undruggable targets, especially in complex diseases like cancer. Scientists are increasingly asking chatbots for help coding, writing articles, and even inspiring new ideas.

But the problem of AI slop in science is worsening: The bots are polluting scientific literature. Tens of thousands of articles in 2025 contained faulty references hallucinated by AI. Some scientists are uncomfortable with AI’s notoriously hefty energy consumption and worry over-reliance could erode cognition, judgment, and creativity. In a phenomenon called the “illusions of understanding,” AI solutions make us overestimate what we know.

Love or hate it, AI’s impact on research is growing. In the past few years, multi-agent systems, some with sophisticated reasoning abilities, are beginning to break complex problems into solvable chunks and “self-reflect” on their output.

Robin and Co-Scientist showcase this power in a cornerstone of scientific discovery: Suggesting novel, rigorous, and testable ideas when faced with real-world problems such as drug discovery.

Flurry of Ideas

Both systems use large language models to create AI agents that work semi-independently on different parts of a problem.

FutureHouse’s Robin, for example, was tasked with finding a treatment for a dry-eye disorder that’s a common cause of blindness. The agents scoured troves of scientific literature, including hundreds of thousands of open source papers, patents, and clinical trial data.

Rather than inventing a drug from scratch, the team asked Robin to repurpose existing drugs, a common strategy for speeding treatments to patients, and one particularly well suited to AI.

Robin can “consider tens of thousands of biological mechanisms…that could address the underlying cause of that disease,” study author Sam Rodriques, founder and CEO of FutureHouse, told Nature.

Armed with that knowledge, Robin took the role of research lead and recruited other AI agents to design lab experiments around potential drug candidates. In what the team called a “tournament of ideas,” the agents debated hypotheses, weighed evidence from previous studies, and selected the best for testing. The system then suggested experiments for validation.

Human scientists took over from there. They ran the suggested experiments and fed the results into another AI agent specializing in data analysis. After several iterations, Robin flagged ripasudil—a drug approved for glaucoma—as a promising candidate. The drug acts on immune cells, instead of eye cells, and hadn’t been explored for the condition. Early cell experiments were promising.

Co-Scientist works similarly but also incorporates DeepMind’s earlier experience building game-playing AI models. Faced with a scientific challenge, its agents have time to evolve hypotheses, test their reasoning, and rank ideas by plausibility and novelty.

DeepMind first released the AI in early 2025 to a small group of researchers. It’s been used by independent teams studying liver scarring, neurodegenerative diseases, and aging.

At Stanford University, for example, Gary Peltz used the system to find three promising drugs for chronic liver disease. Two worked well in the lab. One, to his surprise, was already FDA-approved for another disease. “When I saw that it was really quite striking. I kind of fell off my chair,” he said.

Beyond drug discovery, Co-Scientist has also worked on decades-old biological mysteries, like why many bacterial species share the same cluster of genes to resist antibacterial drugs. Scientists have wrestled with the problem for years; the AI system reached the same conclusion in days.

Inspiration Galore

To be clear, none of the AI-suggested drug candidates have been fully vetted. Even therapies that look promising in early cell experiments often fail once tested in the body.

Still, there’s little doubt that AI is already inspiring eureka moments.

One early Co-Scientist user, Clare Bryant who studies infectious disease at the University of Cambridge, was surprised when the system flagged a protein she’d missed. The protein intersected with biological processes she was already investigating to fight pathogens. “I spent the rest of the week itching to get back to the lab” to test the theory, she said.

Both teams took care to limit AI hallucination, where systems confidently present false or misleading information. Co-Scientist, for example, includes an internal “review board” that tests hypotheses against existing evidence to keep them grounded in reality. Meanwhile, Robin uses a built-in brake that restricts it to established knowledge and limits irrational leaps in logic.

The AI systems are already over a year old, and the field moves fast. Newer systems, such as Edison’s Kosmos, target the entire drug development pipeline. Yet even as the tools grow more sophisticated, researchers continue to stress that human oversight is essential.

“Human messiness, curiosity, and playfulness have fueled countless discoveries, and helped to inform society’s ethical frameworks,” wrote Nature’s editorial team. “AI systems might offer greater efficiency in some instances, but we don’t yet know whether greater efficiency equates to greater insight.”

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2026-05-20 04:20:06

Hydrogen gas produced by geological processes beneath Earth’s surface has been touted as a promising clean energy source. A new study provides the first solid evidence that it could be a practical and commercially viable option for decarbonizing the grid.

Hydrogen is an energy-dense fuel that produces only water when burned. But today, the vast majority of industrial hydrogen is manufactured using fossil fuels in an energy intensive process, negating its green credentials. While there’s hope renewable energy could one day power the process and provide a reliable source of green hydrogen, that technology is still a long way from commercial viability.

Recently though, there has been growing excitement about the possibility of vast natural hydrogen reserves stored deep underground. Several large deposits have been discovered and estimates suggest that trillions of tons of the gas could be sitting beneath our feet.

So far, those estimates have been almost entirely theoretical, based largely on near-surface measurements, proxy data, and extrapolation rather than direct observations. Studies have also typically brushed over the complexities of storing and distributing hydrogen gas, which needs to be kept at high pressure or extremely cold temperatures.

A new study, published in PNAS, firms up the numbers. The authors track the release of natural hydrogen over an 11-year period from a mine in Canada and conclude the site produces enough hydrogen to generate 4.7 million kilowatt-hours of energy annually. That’s enough to power a few hundred homes or an industrial facility and suggests the most promising approach to natural hydrogen could be to use it where you find it, they say.

“We present an alternative vision for the hydrogen economy that can address some of the current challenges arising from the focus to date, that has been largely based on transportation of hydrogen over long distances from source location to markets,” the authors write. “Calculations from this study site show that the amount of locally generated energy has economic value for both industries and communities located on hydrogen-producing rock.”

The new study focused on the Kidd Creek mine near Timmins, Ontario where researchers had collected 11 years of hydrogen discharge data from 35 boreholes between two and three kilometers below the surface.

The authors found that, on average, these boreholes were pumping out between 1 and 3 liters (0.04 to 0.1 cubic feet) of the gas per minute. Across all of Kidd Creek’s nearly 15,000 boreholes, the researchers estimate the site releases more than 140 tons of hydrogen per year.

The hydrogen at Kidd Creek is primarily produced through a process called serpentinization, in which water reacts with iron-containing minerals deep in the crust. More than 70 percent of the continental crust has the potential for this kind of hydrogen generation, the researchers say, suggesting the mine, and its hydrogen output, may be far from unique.

Since the gas is already being vented during routine mining, capturing it would require relatively modest investment, the researchers say.  And hydrogen isn’t the only resource on offer. Sites that produce hydrogen also tend to release methane and helium at predictable rates.

Based on the amount of hydrogen at Kidd Creek, the researchers estimate the site is probably producing 4,200 tons of methane and 140 to 280 tons of helium. The latter could be particularly valuable, given its critical role in cryogenic technologies. With recent supply crunches, further exacerbated by the Iran war, prices have been in the range of $100,000 per ton.

Capturing the gas isn’t always simple, the authors note. Underground microbes can consume it before extraction. It may also require significant investment after capture to separate the gases.

But many communities sitting on hydrogen-producing rock may have a valuable renewable energy source just beneath their feet. And the economic case for exploiting it is looking increasingly solid.

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2026-05-18 22:00:00

Our brains keep on whirling long after we drift off to sleep.

Each night, the hippocampus, a major hub for learning, replays experiences from the previous day and etches them into memory. And even in deep sleep, neurons in sensory regions of the brain spark with activity when they receive new stimuli, like sounds.

This raises a provocative question: How much is consciousness required to make sense of the world around us?

A new study suggests the unconscious brain can handle far more than simple sensory cues. Recording electrical activity from patients under general anesthesia, a team at Baylor College of Medicine and collaborators found the hippocampus continued processing sounds, words, and speech while patients listened to alternating tones and podcast clips.

Groups of neurons shifted their activity depending on the type of word spoken—nouns or verbs, for example—and predicted the next word in sentences.

“Our findings show that the brain is far more active and capable during unconsciousness than previously thought,” study author Sameer Sheth said in a press release. “Even when patients are fully anesthetized, their brains continue to analyze the world around them.”

Scientists have long thought that language processing, a complex computation, relied on awareness. Anesthesia disrupts large-scale communication across the brain, seemingly making complex language processing impossible. But the new findings suggest that even as global brain dynamics break down, some local circuits retain the ability to process sophisticated information—and, at least for storytelling, predict what comes next.

To be clear, it doesn’t mean that participants were secretly awake. Whether the brain retains local processing power during sleep, coma, or other states of unconsciousness is also up for debate.

But “this work pushes us to rethink what it means to be conscious,” said Sheth. “The brain is doing much more behind the scenes than we fully understand.”

Lights Out

We slip into unconsciousness every night. The brain shifts gears.

Compared to when we’re awake and alert, the mind’s activity patterns change dramatically. The hippocampus reactivates neurons involved in recent learning, rapidly replaying their activity patterns to strengthen neural connections. Elsewhere, the brain generates short bursts of electrical activity called sleep spindles, which shut off communication between regions necessary for processing new information from the outside world. These unique electrical signals are crucial for sorting new experiences and integrating them into long-term memory.

The brain is clearly busy during unconsciousness, but it also seems largely sealed off from its surroundings. Over the past two decades, however, scientists have increasingly realized the sleeping brain remains surprisingly alert.

In one study, volunteers repeatedly exposed to unfamiliar sounds during sleep were able to identify them after waking up. In another, participants hearing their own names or angry voices triggered brain activity even in deep sleep, a phenomenon called “sentinel processing.”

Scientists have also recorded directly from the brains of people with epilepsy, who had electrodes implanted to pinpoint the source of seizures. The researchers confirmed that the auditory cortex—the first region involved in processing sound—lit up with activity, but it appeared disconnected with regions responsible for interpreting meaning.

Similar patterns emerged under other states of unconsciousness. After receiving propofol, a common drug used to induce general anesthesia, patients still showed activity in their auditory cortex, but information relay to higher regions involved in cognition seemed to break down.

Or did it?

“The brain has developed such amazing, sophisticated mechanisms for doing all these complex tasks all day long, that it can do some of these things even without us being aware,” Sheth told Nature. They decided to take another look.

Someone’s Home

The team focused on the hippocampus, best known as the brain’s memory center. Linking it to language processing seems like a stretch. But mounting evidence suggest the hub is responsible for far more than memory. It may also help organize information more broadly, from the mapping of physical spaces to watching other unfolding events like language.

It’s still a niche idea, said Sheth. But the hippocampus could play a much broader role in structuring the world around us—even without awareness. “How is the world organized? The hippocampus may be part of that as well,” he said.

To test the idea, the team recruited seven people undergoing epilepsy surgery. While they were under propofol anesthesia, the team inserted tiny probes into the hippocampus. Called Neuropixels, the implants are thinner than a human hair but packed with over a thousand sensors that eavesdrop on the electrical chatter of hundreds of neurons at once.

The team first played repetitive beeps to three participants, occasionally interrupted by random boops at a different pitch. In the beginning, neurons were indifferent to the oddball sounds. But within 10 minutes, their activity levels showed they were getting better at separating the unexpected tones from the normal ones.

“They learned over time to pay more attention to oddball sounds,” even while the person was fully unconscious, said Sheth.

A second test took things further. The team played 10-minute snippets from The Moth Radio Hour, a storytelling podcast featuring speakers from all walks of life, each with distinct intonations, turns of phrases, and accents.

Across the recordings, specific groups of hippocampal neurons responded to different linguistic features. Some were attuned to uncommon words like “cosmos.” Others tracked grammatical structure, responding differently to nouns, verbs, or adjectives.

The neurons also cared about semantic meaning, or the relationships between words. For example, they seemed to recognize that “cat” is conceptually closer to “dog” than an unrelated word like “pen.” The hippocampus also seemed to anticipate upcoming words based on the context of a sentence, with activity patterns similar to those seen in the awake brain.

“We are always making predictions about what we’re about to hear next,” said Sheth. Even under anesthesia, these neurons appeared to keep track of the narrative, indicating a “very sophisticated form of processing of the natural speech that they’re listening to.”

Despite intense neural activity, patients didn’t remember any of the podcast stories upon waking. Still, traces of the experience may have lingered unconsciously. In future studies, the team plans to test for this by exposing unconscious participants to different podcasts then later asking which ones feel familiar. They also want to explore whether the hippocampus processes stories told in unfamiliar languages.

The findings are preliminary, drawn from a small group of people under one type of anesthetic. The sleeping or comatose brain may work differently. But the work could help scientists decipher brain activity in people with severe traumatic brain injuries in a vegetative state. It could also guide the development of implants to rewire damaged neural circuits to other parts of the brain and reboot communication.

“Maybe the most important thing is what can we do about this,” said Sheth. For someone who’s unconscious, “can we bring them back?”

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