Earlier this week, I attended a screening of You Can See Everything, the buzzy new documentary about the disgraced former biotech CEO Elizabeth Holmes. 

I went in not really knowing what to expect. I’d avoided spoilers (and won’t be giving anything away here, either!). I was wondering what new information this film could reveal, given how familiar many of us are with the Theranos story thanks to years of media coverage, the best-selling book Bad Blood, and the Hulu series The Dropout, starring Amanda Seyfried.

Like many others who have seen the film, I came away feeling unsettled. But not just about Holmes’s fraud and the somewhat disturbing interactions between the subjects. The film reignited a concern I’ve had about covering science for most of my career: Despite our best efforts, we never can actually see everything.

In case you’re rusty on the details, Elizabeth Holmes dropped out of Stanford University in 2003 to run Theranos, a diagnostics company she founded, when she was just 19. She went on to claim that her company had invented a way to run over 100 diagnostic tests on just a few drops of blood.

Holmes raised hundreds of millions of dollars for Theranos, which was valued at $9 billion in 2014. Forbes named her the youngest self-made female billionaire—she was worth $4.5 billion when she was just 30 years old.

Things started to unravel in 2015, when an investigation by the Wall Street Journal raised concerns by former Theranos employees that the tests didn’t work. Some also said that the company’s device had not been tested properly, and that blood samples had been analyzed on machines from other companies instead. MIT Technology Review named it one of the biggest technology failures of 2015.

Forbes had revised Holmes’s worth to $0 by 2016. That year, a damning government report said that the company’s lab was struggling with basic procedures and gave patients results even when they knew they could be wrong.

In 2018, Theranos, Holmes, and former company president Ramesh Balwani were charged with “massive fraud.” In 2022, she was sentenced to a little over 11 years in prison for defrauding investors. She began serving her sentence in May 2023.

It was around a month before this that filmmakers Nathan Fielder, Lance Oppenheim, and their colleagues began documenting Holmes and her partner, Billy Evans, at home with their two young children. I’m not giving anything away when I say it’s an extremely intimate portrayal—at least of how the two adults interact with Fielder, who is also a central character in the film.

As you might know if you’ve seen the trailer, there are plenty of super close-ups. I felt as if I spent most of the film staring straight into the faces of those people. And almost the whole way through, they all seemed to be trying to figure each other out. Holmes is known for her dishonesty, but she invited cameras into her home in an apparent bid to reframe the narrative and position herself as an honest person with an unusual mind. Will anyone believe her?

I couldn’t help feeling some empathy for Holmes, a mother we see preparing to leave her two kids for a lengthy prison sentence. And even though I have long known the facts, Holmes is clearly an effective manipulator. You can see her plant the seeds of doubt in Fielder’s mind. At times, I felt she was convincing me, sitting in my cinema seat. Maybe she really didn’t mean to lie?

But then I remembered how other journalists felt after being taken in by her claims. “I blew it,” Fortune’s Roger Parloff reflected in 2015, a year after profiling Holmes and her company. “I’m pretty sure she lied to me,” STAT’s Matthew Herper wrote recently, having interviewed Holmes in 2015. Even the former employee turned whistleblower Tyler Shultz, who knew the product didn’t work, said that he could “have a five-minute conversation with her and walk away feeling like my work was changing the world,” he said in 2022.

I didn’t cover Theranos in the 2010s. If I’d interviewed Holmes, would I have fallen for it all?

One of the early red flags in the Theranos case was the lack of published, peer-reviewed data. Much of the company’s work was classified as a “trade secret.” Scientists tend to publish their work in journals—a slow process that involves having the work evaluated by often anonymous experts in the field, who provide feedback, highlight questionable conclusions, and often ask for clarification. Peer review is meant to add rigor to the publication process. You should be able to trust published scientific papers.

Except that sometimes you can’t.

Scientists aren’t perfect. Some will exaggerate their results or inflate their importance. Some manipulate data to serve a hypothesis. Sometimes these infractions are caught during peer review, but not always. There are plenty of examples of scientific findings that could never be replicated by other labs and ended up collapsing under the weight of real evidence. Many papers ultimately end up with corrections. Some are withdrawn—10,000 research papers were retracted in 2023.

At any rate, there’s been a shift to sharing research in other ways. Today, many scientists opt to publish their work at preprint servers like arXiv, medRxiv, and bioRxiv before they submit them to established journals. Those outlets bypass peer review. So do company press releases, social media posts, and many conference presentations. And I get a lot of my stories from conversations and interviews.

I’ll admit I’ve looked at a scientific finding and thought: A scientist has just said they’ve done that. How can I be sure they really have?

I like to think that I hold myself to high journalistic standards here, as does everyone at MIT Technology Review. I would never publish a story about some new scientific development without doing my own form of peer review, and asking scientists—ideally people I know and trust—what they think of a new claim. 

But the film is a stark reminder of how easy it can be to fall for an appealing narrative. While most scientists are, I hope, pretty honest, and Holmes is certainly an outlier, we should all continue to interrogate the claims being fed to us. If you’ve seen the film, let me know what you think!

This article first appeared in The Checkup, MIT Technology Review’s weekly biotech newsletter. To receive it in your inbox every Thursday, and read articles like this first, sign up here.

I don’t remember much about Peppy, my first and only pet cat. I can conjure the sensation of stroking her while she purred, recall how she used to get stuck in trees (just like in cartoons!), and chuckle about the time she pooed in my baby sister’s cot. That’s about it.

What I do vividly remember, though, is the day she went missing. I was 10 years old, and I searched everywhere for her, scouting nearby fields and woods until sunset. I made posters. We never found her. I was devastated.

There was no way to get more time with Peppy, but almost 30 years later, pet owners are being offered an ever-expanding set of options to potentially prolong their years with their furry companions. The possibilities include drugs, cloning, and even cryonics. But is that a good thing?

There’s certainly a market for pet preservation. And a growing number of biotech companies have their sights set on pet longevity. Loyal, a company that is currently working on three potential longevity drugs for dogs, is trialing one designed to improve metabolic health in around 1,300 pet dogs across the US. Others are testing dog drugs that aim to combat both age-related diseases, including conditions like obesity and osteoarthritis, and aging itself. There’s even a company focused on longevity medicines for domesticated cats.

I don’t know how or when Peppy died. But if there had been a drug that might have protected her from diabetes or arthritis, there’s no doubt I would have tried to get my hands on it.

That’s about where my enthusiasm for pet life extension ends, though.

I’m far less interested in cloning, a relatively new option for people mourning a pet and seeking a replacement. In most cases, this technology involves taking the DNA of a pet, putting it into an egg cell from another animal of the same species, and growing the resulting embryo in a surrogate animal. It worked for Dolly the sheep, who was born the year before Peppy went missing. It has since worked for other mammals, including cattle, horses, and ferrets—and cats and dogs. In the US, a company called Viagen Pets offers to clone cats and dogs for $50,000 a go. This doesn’t result in an exact replica of the animal but a genetic twin.

Still, there are lots of reasons why a person might want to clone a pet. On its website, Viagen explicitly offers customers “a way to hold on.” Lori Kogan, a professor of clinical sciences at Colorado State University who has studied human-animal relationships for decades, doesn’t have a problem with the practice. She points out that while we have well-established rituals for coping with the loss of a person—such as funeral services, time off work, and memorial gifts—we don’t really have the same for pets. As a result, pet owners can experience what is sometimes called “disenfranchised grief,” she says. Having their feelings go unrecognized can feel isolating. If cloning a pet brings a person peace, she says, “who are we to judge?”

Fair enough. But I can’t help thinking that pet loss in childhood might be different. It’s often the first time a child confronts death. Losing Peppy was a formative experience, and distressing though it was, I like to think it helped prepare me for losses I experienced in adulthood. I wouldn’t want my own children to think that death is not final, and that individuals can be replicated or replaced.

Or, for that matter, revived, which is the promise behind pet cryonics. For a fee, a handful of companies offer to cool dead pets to extremely low temperatures and store them for long periods of time—even indefinitely. The idea is that at some point in the future, we’ll have invented incredible technologies that will not only bring your pet back to life but also cure whatever it died of. Alcor, a cryonics facility in Arizona, is currently storing the remains of hundreds of pets as well as people. It even recently announced it had cooled a small marsupial—its first pet sugar glider.

With cryonics, the chances of being reunited with your pet are bordering on zero. Even those who work in the field acknowledge that the stored people and animals are unlikely to be reanimated. As things stand, there is no way to really and truly bring any animal “back.”

I miss Peppy. But given the chance, would I stick her in a vat of liquid nitrogen? Or get another cat to give birth to a genetically identical animal? No, I would not.

RIP to the one and only Peppy.

Pig kidneys that were cooled to -150 °C and stored in a freezer for months have been successfully rewarmed and shown to work again for the first time, say scientists. They hope to replicate the findings with human organs.

The kidneys—which are similar in size to human ones—were left in a glass-like state, in one case for over a year. Once they had been rewarmed, tested in machines, and transplanted into pigs, the kidneys regained their color and made urine—key signs they were working.

“Ten years ago … I wouldn’t say I had high hopes it was actually going to work,” says Erik Finger, a transplant surgeon at the University of Minnesota who co-led the work. “After we did the last transplant, I walked out of the room and said, ‘This is a big deal.’” The research was shared at the preprint server bioRxiv and has not yet been peer-reviewed.

The kidneys aren’t perfect, and the researchers have yet to demonstrate that they can sustain life over the long term. But the results represent a milestone achievement, say others in the field.

“Thank God that someone’s actually managed to do it in one organ and prove that it works,” says Peter Kilbride, a cryobiologist and cofounder of Kyros Kairos, a company focused on improving funding for cryopreservation research, who was not involved in the research. “For years, people have been trying and failing to get to this step. Finally, we’ve got this breakthrough.”

“Their work is amazing and will enable us to truly, indefinitely bank organs,” says Korkut Uygun, a biomedical engineer and professor of surgery at Harvard Medical School, who was also not involved in the research. “It is a revolutionary time for transplantation.”

Buying time

Every year, tens of thousands of organ transplants take place in the US alone. These procedures are thought to have saved 2 million years of life in the last 25 years. But there’s plenty of room for improvement.

One major challenge is the lack of available organs. Even when an organ becomes available, it can be difficult to get it to the right recipient within 24 to 36 hours, which is the current limit for preserving human organs.

People who receive organ transplants must also take drugs that suppress their immune systems for the rest of their lives. These drugs are meant to stop their bodies from rejecting the new organs. But they come with side effects and increase the risk of infections and cancer.

The ability to bank organs for long periods of time holds the promise to address all these challenges. Doctors would have more time to match organs to recipients. They’d be able to limit the risk of organ rejection, too—potentially by preparing recipients, and their immune systems, beforehand.

“With real organ banking … instead of the patients waiting for the organ, it’s the organs that are waiting for the patients,” says Finger.

Scientists have been able to cool cells to ultra-low temperatures through a process called vitrification, which essentially turns them to something like glass. This procedure is already used to store eggs, sperm, and embryos. Vitrifying whole organs is much harder.

Some problems can be avoided with cryoprotectants—chemicals that work like antifreeze and prevent damaging ice crystals from forming. But these chemicals tend to be incredibly toxic. It’s also difficult to rapidly and uniformly cool large tissues, and just as hard to evenly rewarm an organ all the way through.

Working with John Bischof, a bioengineer at the University of Minnesota, Finger and colleagues have come up with a solution. Once an organ has been loaded with a cryoprotectant, they infuse it with iron oxide nanoparticles. These nanoparticles don’t do anything to the cells. But when it’s time to rewarm the organ, they can be activated with an electromagnetic field. 

When the radiofrequency of that field switches between two states, the particles also flip, realigning themselves with the field. That causes them to release heat throughout the organ. Adjusting the power of the field allows the team to control the rate of heating so that it gets faster once the organ reaches -120°C. 

The researchers successfully vitrified rat kidneys using this approach in work published in 2023. Back then, they were able to rewarm kidneys that had been kept in a freezer for up to 100 days before transplanting them into rats.

Since then, the team has been trying to achieve the same success with bigger organs. Pig kidneys are thought to be a good model for human ones—they’re about the same size and look similar. Rat kidneys weigh around one gram; pig kidneys are around 100.

It hasn’t been easy. The cryoprotectant used for vitrifying rat kidneys didn’t work as well in the pig organs. “We had to rediscover and modify the cocktail,” says Bischof. “That’s why it’s taken us three years.”

Deep-freezing kidneys

The first step in the procedure is to remove a kidney from a donor pig. Once it’s out, the kidney is flushed with a commonly used organ preservation solution to remove the blood, bagged, and temporarily laid on a bed of ice in a cooler so it can be taken over to the engineering lab.

There, team members unpack the kidney and hook it up to a device they’ve designed to control the organ’s temperature and pressure. Then they start to load it up with the cocktail of cryoprotectants, slowly enough to ensure an even spread through the organ but quickly enough to minimize any toxic effects. It takes around two hours.

The next step is to infuse the organ with the iron oxide nanoparticles, which are pumped through the organ for around seven to eight minutes, says Finger. Then it’s time to cool the organ by pumping cold nitrogen vapor into a chamber housing it.

It’s a delicate process, and the team must ensure that the organ is cooling all the way through. At the same time, they must lower the kidney’s temperature as quickly as possible to prevent ice from forming. “Oftentimes there are multiple people doing different things to make sure that things don’t go awry,” says Bischof.

Once the organ reaches the vitrified state at around -120 °C, it “behaves like a glass,” says Finger. “At that point, you can stay stable for long periods of time—we think indefinitely.” The team then pauses the cooling process to ensure that the organ is glassy throughout. Otherwise, it could crack.

After the pause, if everything goes well, the kidney is ultimately cooled to -150 °C. Once it’s in the freezer, it can stay there for days, weeks, or even months. The team has kept a small number of kidneys banked for over a year.

Pink right up

The real test of these kidneys comes when they’re taken out of the freezer, rewarmed, and flushed of their chemicals and nanoparticles. Can they support life? So far, the team has tested some of them in a couple of different ways.

In one set of experiments, the researchers put rewarmed kidneys on perfusion devices—systems that are already used in transplant surgeries both to provide organs with nutrients and to help doctors assess them while they’re out of the body.

In another set of experiments, Finger and his colleagues transplanted rewarmed kidneys into pigs and monitored them for two hours. In both experiments, the rewarmed kidneys seemed to recover, says Finger. “They pink right up,” he says. 

COURTESY OF THE RESEARCHERS

There are lots of ways to measure the function of a kidney, but one key test is whether or not the organ can make urine. By this measure, the rewarmed kidneys did well, although they produced around two-thirds of the amount that would normally be expected. “They produce urine … within a minute or so,” says Finger. “We know that they work.” 

Most of the rewarmed kidneys had been banked for days or weeks. But one had been in the freezer for just over a year. When Finger and his colleagues rewarmed it and put it on a perfusion machine, they found that it produced urine “immediately.” “It actually looked better than many of the [other] kidneys,” says Finger. “We’re comfortable now saying we can store them for a year and rewarm them with excellent function.”

“It’s a very big deal to go a year,” says Kevin Myer, president and CEO of LifeGift, an organ procurement organization based in Texas. Being able to store organs for that length of time would transform organ transplantation. He envisions banks of organs that could be used on demand in emergency cases where people can’t wait, such as for some drug overdoses or gunshot wounds.

Final hurdles

But the procedure isn’t ready for prime time just yet. As well as producing less urine than expected, the rewarmed kidneys also showed signs of injury. The damage seems to be caused by the cryoprotectant chemicals, say the researchers.

“This is one big breakthrough that solves half the problem,” as Kilbride puts it.

The team has shown that the rewarmed kidneys can regain blood flow, which is “very important,” says Greg Fahy, a cryobiologist who is considered a pioneer in the field. But he’s worried about the extent of the damage to the organs. “We can’t tell from these experiments whether these kidneys might have supported life or not.”

But Fahy, who is chief scientific officer at the biotech companies Intervene Immune and 21st Century Medicine (where he is also executive director), acknowledges that it’s not clear how these kidneys might fare once they’re inside a living animal for the long term. 

Finger also hopes to find ways to repair the damage seen in the kidneys. Other research has shown that the health of transplant organs can improve while they’re on perfusion machines. And damaged kidneys might undergo some level of self-repair once they’re in a body. At any rate, human kidneys may cope better with the process than pig kidneys, says Finger.

The next step will be to test the kidneys in pigs over longer periods of time. The team, along with many other researchers in the field, will continue the search for less toxic cryoprotectants, too. Finger, Bischof, and their colleagues are also planning experiments with human kidneys.

Finger is optimistic. “We’re going to see that there’s injury, and it’s not perfect … but now we’ve got to the point where it works,” he says. “These are big, historic milestones in our field, and we’re excited about it.”

This week my colleague Antonio Regalado had an interesting update on GLP-1 weight-loss drugs. According to research presented at an aging meeting in Boston, these drugs seem to affect at least some measures of biological age.

Overweight and diabetic people who take GLP-1s have a biological age around two to three years younger than similar individuals who take a placebo, according to findings from Eli Lilly and Novo Nordisk, two pharmaceutical companies that make the drugs.

I’ve heard people in the aging field describe GLP-1s as longevity drugs for a while now (even if they only benefit people who are overweight or diabetic in the first place). This latest announcement comes after we’ve seen a raft of other benefits associated with taking these drugs. But there are also some weird side effects.

So far, GLP-1 agonist drugs—which repress hunger—have been approved for treating diabetes, chronic kidney disease, and obesity, and they can also be prescribed for some people who are overweight or at risk of certain cardiovascular diseases. But there is research to suggest they might also help people with substance-use disorders, and they’ve been investigated for potential benefits in neurodegenerative disease and cancer.

And a lot of people are taking them. According to a recent Gallup poll, 11% of Americans are taking a GLP-1 drug for weight loss, up from 3% in 2024. And 15% of Americans say they’ve used one for weight loss at some point.

They can be incredibly effective. But, like any drug, they can come with side effects. And we’re still figuring out exactly what those might be, and how widespread they are.

The most common side effects are gastrointestinal—nausea, vomiting, diarrhea, and constipation. The drugs have also been linked to pancreatitis and gallstone disease. 

Some claim that, in rare cases, the drugs might cause damage to nerves behind the eye. Dozens of people are suing Novo and Eli Lilly after developing a condition that can lead to sudden vision loss. They’re claiming that their symptoms were brought on by the GLP-1 drugs that these companies manufacture. Both companies dispute the claims. Research into the link is mixed.

We’re still getting to grips with the effects of weight-loss drugs on hair and nails, too. When Savanna Vidal at the George Washington University School of Medicine and Health Sciences in Washington, DC, and colleagues assessed electronic health data from 67 US health-care organizations, they found that GLP-1 use was associated with an increased risk of hair loss. It’s still not thought to be common, however.

And at a conference in Austria last week, another group presented the results of a smaller study suggesting that 66% of GLP-1 users reported at least one nail disorder—in some cases involving nail detachment. Nail conditions were also fairly common among people who didn’t take the drugs, though. The authors don’t claim that the drugs are causing nails to fall out (as I’m sure 11% of the American population will be relieved to hear).

The side effects might look different in children, but there’s not much data on those at the moment. Still, pediatric side effects will be something to watch given that, between 2019 and 2026, there was a 310-fold increase in GLP-1 prescriptions for eight- to 11-year-old children with obesity in the US. Child prescriptions are still rare, but they’ve been rising.

On Wednesday, the World Health Organization released its first guidelines on managing obesity in children and adolescents. Despite the scale of the issue—the WHO estimates that 70 million children aged five to nine were living with obesity in 2024—the organization explicitly says it does not recommend pharmacological treatment for kids under 10.

“This recommendation accounts for the lack of evidence on both benefits and the potential adverse events and consequences of the different medications and/or their active ingredients on normal growth, development, physiological parameters, and mental health,” the guidelines state.

I get press releases about GLP-1s on an almost daily basis. There’s been a ton of research into these drugs, and there are probably hundreds of other studies underway. There will likely be plenty more on this from The Checkup as the evidence evolves.

This article first appeared in The Checkup, MIT Technology Review’s weekly biotech newsletter. To receive it in your inbox every Thursday, and read articles like this first, sign up here.

This week, I officially signed up for an unusual competition. One that rewards competitors for getting younger.

I recently turned 40, and I don’t need reminding that both time and my chronological age only tick forward. But this game is focused on competitors’ biological ages—figures that are meant to provide a better way to measure the age-related health of our organs and bodies.

Over a six-month period, around 500 of us will try to reverse our biological age as measured in a bunch of different ways. There’s even a leaderboard! The winners will include the person who shows the greatest difference between their chronological and biological age, as well as the person who manages to reverse their biological age the most.

The competition is the brainchild of the neuroscientist and physician Christin Glorioso, who is also founder and CEO of NeuroAge Therapeutics. The company offers tools to track brain health and aging and is running a trial to find out if biological brain age can predict the onset of Alzheimer’s disease.

Glorioso says she has several goals for the Younger contest. The first, she tells me, is to “explain to the world what aging clocks are.” Regular Checkup readers will be familiar with these tools, but for the uninitiated, they are basically designed to measure biological age.

Many measure chemical signatures that form a layer on top of our DNA and seem to change with age. But others might involve looking at proteins or lipids in blood. Some are designed to measure functional health, while others predict when a person will die. There are more than a hundred clocks out there, and they probably each capture a specific aspect of the biological process of aging.

These clocks are proving very useful for scientists, who use them to study aging and development in humans and many other species. But using them to estimate the biological age of an individual person is more controversial. They’re just not good enough for that yet.

Part of the problem is that we don’t really understand what each clock is capturing. Glorioso hopes that data collected through the Younger contest might help answer that question. She’s hoping to generate “the world’s most comprehensive clock dataset.”

Some longevity clinics try to get around the limits of biological age testing by using multiple clocks. That’s this competition’s approach too. As a standard competitor, I’ll be sending a blood spot sample to TruDiagnostic, a company that measures epigenetic chemical groups on DNA and offers to reveal an overall biological age as well as a person’s pace of aging and the biological ages of each of 11 organ systems, including the heart and brain.

I’ll get a brain age score from NeuroAge once I complete the cognitive tasks on offer. I’ll also submit some physical measures like grip strength. And I’ll upload a series of selfies to an app developed by a team at Harvard that promises to tell me my face age. That’s exactly what it sounds like—an estimate of how old my face looks.

I’m intrigued to see my scores, even though I know I shouldn’t put too much stock in them. The first time I took a test, four years ago, the company I used gave me a biological age that matched my chronological age, which was a little disappointing and, if I’m honest, a bit underwhelming. A year later, the same company told me I had a young heart but a relatively old brain, liver, and hormonal system. My lifestyle hasn’t changed much since then. Will the years have made much difference?

(Ultra competitors—who pay $4,499 rather than the $999 cost of regular entry—are also offered a range of other blood tests and clocks, as well as MRI and DEXA scans. I’ve got complimentary press access to the standard package.)

Glorioso is also hoping to gather information about the effects of some purported longevity approaches. During the competition, some participants will be offered red-light masks, access to a sauna, or other low-risk offerings that partner companies who are sponsoring the event to varying degrees might want to collect data on. “A lot of these companies just can’t afford to run [clinical] trials,” says Glorioso.

She also sees the event as a public health initiative. A couple of years ago, David Sinclair, who studies aging at Harvard Medical School, and his colleagues estimated that if we could slow down aging in the general population and increase life expectancy by one year, it would save the US economy $38 trillion.

The event officially kicks off in January, but around 120 people have already signed up, says Glorioso. Ultimately, she expects 500 participants. Each person’s six-month run starts as soon as they take their baseline measurements, whenever that may be. A handful of people have already started. “I think the majority of people are going to want to sign up after the holidays,” says Glorioso. “They’re not going to want to be trying to win an aging contest over Christmas and Hanukkah.”

Glorioso took her own baseline measurements back in August. Those initial tests suggested that her biological age was around 10 years below her chronological age. That sounded pretty impressive to me until I looked at the leaderboard and saw that competitor @Fred had a biological age 28.8 years below his chronological age.

The leaderboard is pretty sparse at the moment, as only seven people have logged their baseline measurements so far. Six of them have biological ages tracking low, but one 47-year-old has been given a biological age of 68.1. She’s competing publicly, so I can see the breakdown of her scores. The worst is for the speed at which she can stand from sitting in a chair—by that measure, she’s been given a biological age of 100.

Hillary Lin, a longevity-focused physician in New York, has just started logging her baseline measurements for the competition. She tells me she’s taken biological age tests in the past and that they’ve tended to give scores below her chronological age, which is currently 37. Last year, the TruDiagnostic test put her pace of aging at 0.75—suggesting she’s only doing nine months’ worth of aging in a typical year. “That’s supposed to be quite good,” she says.

If it had been up to her, Lin says, she would have incorporated more blood tests into the competition, to build a fuller picture of a person’s health. And she’s worried that a six-month period might not be long enough to see changes in biological measures of aging. Still, she’s excited, and is already making plans to improve her sleep, diet, and exercise.

But she already follows a high-protein, low-carb diet and works out at the gym every day. She also visits a sauna every few days (sauna use has been linked to improved heart health). Glorioso’s efforts are impressive too. She tells me she’s started drinking a smoothie made from a pond plant that has been linked to health improvements, and that she’s cut added sugar and deep-fried foods from her diet. She’s also bought “a bunch of fancy Korean sunscreens” in an attempt to lower her face age.

I haven’t received my kit or logged my baseline measurements yet, and I’ll wait until then to start setting health goals. I don’t fancy my chances against these two competitors. But at the very least, I hope I can stand up faster than a 100-year-old.

This article first appeared in The Checkup, MIT Technology Review’s weekly biotech newsletter. To receive it in your inbox every Thursday, and read articles like this first, sign up here.

A new AI tool can guess what you’re looking at just by analyzing your brain scans—and re-create that image with remarkable precision. It can go the other way, too, and predict a person’s brain activity based on what they’re looking at. 

In the image above, for example, the left-hand image of each pair is what the user actually saw—and its right-hand counterpart is what the model reconstructed from the brain scan.  

Michal Irani, who developed the tool with her colleagues at the Weizmann Institute of Science in Rehovot, Israel, hopes her “mind-reading” tool will ultimately reveal more about how the brain works, and could perhaps be used to help locked-in people communicate, or allow scientists to re-create the content of dreams. 

Judy Illes, a neuroethicist and professor of neurology at the University of British Columbia in Canada, who was not involved in the research, describes the work as “magnificent.” “The idea [of using this approach] to help people with neurologic conditions … therapeutically is tremendously exciting,” she says.

But other scientists warn that a similar approach could be used to reveal people’s inner thoughts and mental imagery, potentially without their consent. “The results seem very impressive,” says Tommy Sprague, a neuroscientist at the University of California, Santa Barbara. “But if there’s a way to surreptitiously extract information about what you’re thinking about, then …150 years of sci-fi can come true anytime, and that’s worrisome in a lot of ways.”

Peeking into the brain

Neuroscientists have been working for years on ways to use functional magnetic resonance imaging (fMRI) to reconstruct what people see and what’s going on in their minds. The first attempts produced images that were blurry and hard to make sense of. Advances in technology—both in the fMRI scans themselves and in the tools used to make sense of the results—have led to improvements over the years.

Irani and her colleagues started by analyzing publicly available brain-scan data. Other researchers had already collected scans from volunteers who were shown hundreds of images while they lay in fMRI scanners.

fMRI uses a giant magnet to track the flow of oxygenated blood through the brain. Brain areas that “light up” on the scans are thought to be those that are particularly active at any given moment. The results are not especially specific—in typical fMRI scanners, a highlighted “voxel” of activity covers around three cubic millimeters. Each cubic millimeter contains around 16,000 neurons.

But Irani and her colleagues used newer datasets collected using scanners with a higher resolution—each voxel covered around one cubic millimeter of neurons, she says. Those datasets showed what the brain activity of volunteers looked like when they viewed various images.

Other teams have done this too, and several other tools have been used to re-create images from brain-scan data. But they’re not good enough, says Irani. Say a person saw a banana. These models can generate an image of a banana, but it would look different, she says. “It wouldn’t have the same structure, the same position.”

A better decoder

The researchers wanted to more closely re-create the images that had been seen. The first step was to train an AI model on already available data from eight people who each had been shown around 9,000 images while in a high-resolution fMRI scanner.

Crucially, their “brain decoder” has two branches—one to predict the structure of an image (where the colors are, for instance) and a second to predict its content (for example, a bunch of bananas on a plate). The predictions allow a diffusion model, a type of AI best known for creating video and images by gradually cleaning up a noisy mess of pixels, to produce a much more accurate representation of what the person saw.

But to improve the models they needed more data—far more than was actually available.  

To get around this problem, Irani and her colleagues trained another model—an encoder that can predict brain activity from an image. The team then used the encoder and decoder together to improve both tools.

It works like this: Start with a new image of, say, a leopard. Then use the encoder to predict what someone’s fMRI brain scan would look like when the person saw that picture. The decoder is then used to reconstruct the image. At first, the reconstruction probably won’t look much like a leopard, says Irani. But repeatedly training the models this way eventually leads to dramatic improvements.

This approach also allows the team to train their models on as many images as they want, even images that have never been shown to a person in an fMRI scanner. Irani says that around 70% of the training data is from images that were not originally paired with fMRI scans.

By combining data from multiple studies, they were also able to identify brain regions that seem to share functions across all individuals. One region seemed to respond to images of food, for example, while another responded to images of sports. Irani, a computer scientist, says she is now working with neuroscientists “to see if we can actually use these tools that we’ve developed to really find out new things about the brain.”

The resulting “universal brain encoder” can work on a scan from a new person with minimal calibration. In other attempts, a tool has typically required about 40 hours of fMRI data on anyone new before it can be used to predict what that person is seeing. Irani’s decoder only needs one hour of data, she says. The finding was presented at the Cognitive Computational Neuroscience conference in New York last month.

That could make it valuable for neuroscientists studying the brain, says Sprague. “None of us can afford 40 hours of imaging for a new subject,” he says. “It’s something like $600 to $1,000 an hour.” Tools like this one could speed up research, he says.

State of the art

The encoder and decoder aren’t perfect. “Of course we have failures,” says Irani. Over a Zoom call, she pointed out an image of a cake that her tool reconstructed as a pile of three sandwiches, and another of a dog in a bathtub that was reconstructed as a similarly colored goat in a bathtub.

But they represent the state of the art. In a comparison test, the tool was found to be much better than previously described ones. “All in all, really we outperformed the others by a significant margin,” Irani says. “Mind reading” is a “cute, jazzy name” for what they’re doing, she adds.

Irani is now planning to move beyond images to video and audio. She wants to be able to reconstruct what people are thinking about or imagining, and the contents of their dreams. “That’s something we don’t have yet,” she says. “But we’re striving to achieve it.”

Such a tool might enable people who are “locked in” and completely paralyzed to communicate using their brain activity alone, she says. It could also help scientists unpick some enduring mysteries surrounding the inner workings of our minds, such as what PTSD flashbacks look like.

Advances like this inevitably raise questions about mental privacy. What if some bad actor could reconstruct someone’s thoughts or memories?

“If you’d asked me that 10 years ago, I’d have laughed a lot,” says Sprague. Getting a willing person to lie still in a scanner and actively engage with a research question is hard enough; imagine making someone do it involuntarily. But Irani and other scientists are working on similar approaches to decode brain activity from EEG—electrical brain activity measures collected via a cap of electrodes or even through headphones. 

And as models improve, it will become even easier to analyze the brain activity collected this way. “We have to be a little more serious about the ethical considerations,” says Sprague. He thinks Irani’s approach would probably “work quite well” in predicting images that a person is thinking about but not looking at.

The move to EEG would be a “game changer,” says Marcello Ienca, a neuroscientist and philosopher at the Technical University of Munich, Germany. Once an EEG device has been calibrated to a user’s own brain, it could be relatively easy for companies to extract additional information from that person’s brain—potentially without consent. Ienca can also imagine some courts allowing mental image reconstructions as legal evidence.

“I have no doubt that this is, you know, well-intentioned research, but I think it’s also pretty obvious that it could be co-opted for … ethically and societally problematic commercial uses,” he says.

Irani acknowledges the potential for misuse with the use of EEG. But she’s not concerned for now. “I’m trying to think only of good things,” she says.

Around this time last year I was attending an aging conference in Manchester, listening to a talk about fly aging, when my phone started pinging. News outlets were reporting that a hot mic had caught Russia’s and China’s leaders discussing the possibility of living forever.

“With the developments of biotechnology, human organs can be continuously transplanted, and people can live younger and younger, and even achieve immortality,” Russia’s Vladimir Putin reportedly told China’s Xi Jinping.

He seems to have been referring to the “replacement” theory of longevity, which has been supported by multiple experiments that involved physically stitching young mice to old ones. Something about the young blood rejuvenated the old mice. Perhaps young organs could rejuvenate world leaders in their 70s, too.

Unfortunately for Putin, new research pours a little cold water on this idea. Studies on transplanted hearts in both mice and humans suggest that new hearts soon adopt the biological age of the recipient, no matter how young they were to begin with. The finding could be important for transplantation, but it also highlights just how complex aging—and rejuvenation—are.

Jesse Poganik at Brigham and Women’s Hospital in Boston is one of the many scientists trying to understand exactly what it is about the bodies of young mice that rejuvenates old ones. Plenty of research has focused on seeking the secrets of youth in young blood. But what if it’s something about young organs instead?

To find out, he and his colleagues performed a set of heart transplants in mice. In humans, heart transplants typically involve removing a damaged or injured heart and replacing it with another from a donor who is usually much younger than the recipient. (When Poganik assessed hospital records, he found that most recipients were about 20 years older than their donors.)

The mouse transplants were different: Mice received a second heart, implanted in the neck—a procedure that’s slightly simpler and allows scientists to compare the new hearts with the existing ones. In some cases, young adult mice were given a heart from a middle-aged donor. In others, middle-aged mice got young hearts.

The team used a trio of “aging clocks”—molecular tools used to estimate the biological ages of tissues and whole organisms—to assess whether the additional hearts affected the mice in any way. These clocks were good at predicting the chronological age of mice that didn’t get new hearts.

Poganik says he was expecting to see a reciprocal effect, and that young hearts might benefit older animals, for example. In previous work by other members of his team, young mice that got old hearts experienced a buildup of senescent cells in their other organs. These cells are thought to contribute to the aging process, suggesting that receiving an old organ might prematurely age an animal.

But that’s not what he found. When he and his colleagues analyzed the transplanted hearts between four and six months after surgery, they found that the hearts seemed to have adopted the biological age of the recipients. Young hearts got older, and old hearts got younger. “The environment of the transplanted organ really dictates how it seems to behave biologically,” he says. The findings were published online at bioRxiv last week.

The team also looked at each mouse’s blood and other organs—including its original heart—and were surprised to find that they seemed to be unaffected by the presence of the new heart, despite the age of its donor.

The finding was backed up by data from human heart transplants. People who receive a donor heart must typically undergo a series of heart biopsies after surgery. The tiny pieces of heart tissue collected from people who had heart transplants at Brigham and Women’s have been stored for decades. And when Poganik and his colleagues tested their biological ages with the aging clocks, they found a similar pattern: No matter the age of the donor, a new heart quickly adopts the biological age of the person who received it.

It’s not clear why this is, but João Pedro de Magalhães, who studies aging at the University of Birmingham in the UK and was not involved in the study, thinks it might have something to do with the recipient’s immune system. Perhaps immune cells circulating in the blood might affect markers of aging in the new organ, he says.

Perhaps the potential rejuvenating effects of a young heart end up being diluted by all the other aged components of an older body, Poganik suggests. Or maybe a single organ just isn’t enough to see an effect.

Poganik hopes his finding will encourage surgeons to consider using hearts from older donors, many of which are discarded on the assumption they won’t function as well. (Machines used to preserve organs before transplantation are changing that already—and Poganik has worked on another study showing that these devices seem to rejuvenate donor livers to some extent.)

But the study also highlights just how complicated aging is. If organs seem to be getting older by one measure but not by another, how can we get a full picture of the biological age of an organ, or a person?

This complexity means that scientists are not likely to discover a true way to completely reverse aging, says Poganik. “That would mean that every aspect of aging has to go back in time,” he says. Reversing DNA damage, structural damage, and all the other degradations that are part of the aging package, across all our various cells and tissues, presents an enormous challenge.

“There are aspects of biological age that are probably reversible, and there are aspects that are probably not,” he says. Sorry, Putin.

This article first appeared in The Checkup, MIT Technology Review’s weekly biotech newsletter. To receive it in your inbox every Thursday, and read articles like this first, sign up here.

In recent weeks, leaders of some of the biggest AI companies have warned that the very tech they are developing is dangerous. Last weekend, Anthropic CEO Dario Amodei argued that AI carries serious risk and that progress should be slowed. OpenAI CEO Sam Altman responded on X: “I agree with Dario that we need to pace the frontier.”

Those posts came a few days after the AI researcher Jacob Coxon announced that he was leaving a role at Anthropic, charging that neither it nor OpenAI (where he had also worked) was acting responsibly. “The people building AI earnestly believe that it could kill us all by the end of the decade,” he posted on X. Another Anthropic employee, Evan Hubinger, publicly agreed with him. “We really do earnestly believe AI could kill all humans!” he responded on X. “I personally think it is >10% within the next decade.”

One of the ways they fear AI might end us all is by somehow aiding the design, creation, and release of some kind of bioweapon. Let’s take a closer look at why.

A bioweapon might be a highly lethal virus that targets people according to their genes. It could be a fungus that wipes out a crop and causes food insecurity. Perhaps it would be a tasteless, odorless toxin that could be slipped into a region’s water supply, undetected.

The concern is that AI tools can be used to help generate agents like these. In 2022, researchers at Collaborations Pharmaceuticals found that it was remarkably easy to do so using an AI “molecule generator” they’d developed to find potential drugs for human disease. In less than six hours, the model generated 40,000 molecules with the potential to serve as chemical warfare agents. Some of them were designed to be even more toxic than known nerve agents. “Without being overly alarmist, this should serve as a wake-up call for our colleagues in the ‘AI in drug discovery’ community,” the authors wrote at the time.

It was a wake-up call for David Magnus, a professor of medicine and biomedical ethics at Stanford University, even though he had been assessing the risks associated with the misuse of medical science and biotechnology since the late 1990s. “That was very scary to me,” he says. “Of course, everything since then has just sort of blown up.”

Today, AI bots can answer questions on topics spanning all realms of science. Anyone can use large language models trained on the knowledge and experience of “almost every scientist who ever lived on this planet,” says Dunja Sabra, a biosecurity researcher at the University of Hamburg in Germany. Those models can provide instructions and video training on how to conduct experiments.

Combine that with advances in biotech that have made gene editing and synthetic biology tools much more accessible (the “DIY biology” movement has already enabled many people to set up labs at home), and you’ve got a potentially very dangerous situation. “The chances are that someone determined would succeed eventually,” Sabra says.

There are safeguards in place. People who want to build new genomes must typically order the pieces of DNA from companies that screen for suspicious requests. Responsible researchers put potentially risky research through rounds of analysis called “red-teaming,” in which independent scientists look for ways the work might be misused, and “blue-teaming,” where others come up with potential mitigations. And AI companies have tweaked their tools in attempts to prevent them from offering up scientific information that could be misused. But none of these protections are ironclad.

In a report published last week, Anthropic acknowledged that people had attempted to use its models to explore ways to make the chikungunya virus more transmissible, create a form of bird flu that is more dangerous to humans, and build an “atlas of venom toxin peptides,” among other things.

“We’ve got a constant back and forth,” says Magnus. “We have to build better surveillance and screening tools, [but] AI is really good at figuring out ways around them.” We’ll probably need to use AI to find ways to restrict the use of AI, he says.

I should add here that not all scientists agree on the level of risk. At a recent media briefing, some biologists at Imperial College London argued that AI tools just aren’t good enough to fully develop bioweapons, and that testing new pathogens requires difficult, time-consuming, human work. Some think the guardrails we have in place are sufficient.

And Wendy Barclay, a professor of infectious disease at Imperial, pointed out that, as things stand, the greatest risk of a pandemic isn’t from a bioweapon, but from pathogens that are already circulating. Take H5N1, the bird flu virus that has already killed millions of birds and spread widely through US dairy cattle; last month it was also detected in captive mink at a farm in Utah.

Sabra, on the other hand, likes to think five to 10 years ahead. Countries should be strengthening their health-care systems, preparing antidotes to known toxins, and stockpiling medicines, she says: “We need to be prepared.”

Kevin Esvelt, an MIT biologist who invented both technology to fast-track the propagation of a genetic feature through an entire population and ways to limit that technology, echoed these concerns in an X post on Wednesday, stating that a large language model had “disclosed a novel form of bioweapon that I hadn’t realized was possible.”

He added, “Please, for the love of God, children, the future of humanity, or whatever you consider holy, let’s err on the side of caution here.”

This article first appeared in The Checkup, MIT Technology Review’s weekly biotech newsletter. To receive it in your inbox every Thursday, and read articles like this first, sign up here.

Once an organ is removed from a donor’s body, the clock starts ticking. Surgeons usually flush the organ with a preservative solution, bag it, and put it on ice—where it immediately starts to degrade. The team has a matter of hours to get it into a recipient’s body.

There’s another option—one that has been growing in popularity in recent years, especially for donated organs that aren’t in the healthiest state. Some hospitals opt to put them on machines that pump them with nutrients and remove waste products, usually for around six to 12 hours. It’s a bit like being back in a body.

This allows doctors to assess the organs, and some recent studies suggest that time spent on these perfusion machines helps them do better once they’re transplanted. Now, scientists have found that perfused organs seem to get younger, at least at a molecular level.

The research, shared with MIT Technology Review, provides molecular clues as to why organs from younger donors are known to have a higher success rate. It might also help explain why perfused organs are less likely to fail once they make it into a recipient. 

The researchers behind the study hope to find new ways to test the health of donated organs and potentially develop additional tools to repair organs that might otherwise be discarded. “If [we] can improve the utilization of organs beyond what the current systems can do, then that’s a win in my book,” says Jesse Poganik, who studies aging at Brigham and Women’s Hospital in Boston and coauthored the study.

Clocking organs

Poganik—along with colleagues including Heidi Yeh and Alban Longchamp, transplant surgeons at Mass General Brigham—used “aging clocks” to assess donated livers. These are scientific tools designed to measure biological age—a result that is meant to convey more about the health status of an organ (or person) than chronological age.

In an initial experiment, the team used a clock to look at the patterns of chemical marks on DNA in 37 samples taken from 19 donated livers. Such epigenetic patterns are known to change as we age. But when the team compared samples from livers kept on ice and those that were perfused, the team found a “striking” pattern in the latter.

“Machine-perfused livers, in spite of being older or having other disadvantageous characteristics, had a biological age that was lower than [non-perfused] livers that were chronologically younger,” says Yeh, who led the work.

To investigate further, Yeh and her colleagues analyzed another 208 samples from 103 donated livers. This time, they used different aging clocks—ones that essentially measure how genes are working. They studied samples biopsied from the livers after they had been stored for up to around six hours either in cold storage or on machine perfusion.

In most cases, they also assessed a second sample taken around an hour after the livers had been transplanted into a recipient. Once the organ’s blood supply is reestablished in the body, “you have a few other things to do,” says Longchamp. “Then you just do a quick biopsy before you close.”

According to the clocks, which were developed to measure age and risk of death, the machine-perfused livers were biologically younger, the team found. “Pumping them at 34 degrees with oxygen and nutrients actually reversed the biological age,” says Longchamp. The results have been been shared with colleagues at an industry conference, he says. 

“If you adjust out chronological age … to have a fair head-to-head comparison, the difference between the two is on the order of 30%,” says Poganik. “It’s logical to say that perfusion drives this effect.”

The biological ages of all the livers tended to increase as soon as they were put into a recipient’s body, probably as a result of stresses on the organs. But still, the effect endured—the perfused organs remained biologically younger. 

Nathanael Raschzok, a transplant surgeon at Charité Universitätsmedizin Berlin in Germany who was not involved in the research, says the work is impressive. But it’s not yet clear what these changes might mean for the recipients of these organs, he says. The organs in the study were donated by people in their 30s, 40s, and 50s. Raschzok wants to know the effect of perfusion on the liver of an 80-year-old. “Every so often, we use organs from 70-, 80-, 85-year-old donors,” he says.

A better understanding of why the organs appear to be getting biologically younger might lead to therapies that achieve the same effect with a drug that could potentially be used to treat a donated organ for a fraction of the price, he adds. That’s important because perfusion is expensive—Raschzok says it costs around €10,000 in Germany (a quarter of the budget for a transplant), while the cost in the US comes to around $80,000 to $100,000 per organ, says Yeh.

Molecular repair

Yeh and her colleagues weren’t able to study most of the livers before perfusion. That’s because donated organs are generally not considered to be under the purview of the hospital until they’ve been placed on perfusion machines, she says. (Organ procurement procedures vary, but for the team as Mass General Brigham, donated organs are put on perfusion devices at the donor’s hospital. “There’s this sort of nebulous period where it’s not clear who the organ belongs to,” says Yeh.)

Still, by looking at the genes and molecular pathways that seem to be altered in perfused organs, she and her colleagues can garner some clues. At a molecular level, the team saw changes in cell pathways linked to inflammation and the structure of tissues, for example. They also saw more activity in a pathway that allows cells to remove and recycle damaged cell parts, says Yeh.

Poganik hopes to develop some kind of test that would determine which organs, on the basis of their biological age, are suitable for transplantation. He and his colleagues are also experimenting with potential drug treatments that might push the biological age of an organ even lower.

In the meantime, any liver that is not from a “perfect, young, brain-dead donor” could probably benefit from perfusion, says Yeh. The devices are already transforming transplant surgery. Just a few years ago, she says, she and her colleagues would avoid using livers from people who’d suffered a circulatory death (when the heart stops beating and there’s a damaging lack of blood flow to organs) and were over 40. Today, they use livers from such donors over the age of 70. “Perfusion has completely changed the landscape of transplantation in the last three years,” she says.

Every year, MIT Technology Review puts together a list of some of the brightest and best young minds working across science and technology. Our 35 Innovators Under 35 are the ones to watch—people whose research and technical work stands to shape the future of their fields.

This year, the list includes nine people who are transforming biotech. And this week, I’m going to give you a taste of some of the very cool stuff five of them are working on, which includes lifesaving innovations and groundbreaking “age reversal” tech.  

1. Preventing maternal deaths

Let’s start with Paschal Kija, a 28-year-old who has developed a device to treat postpartum hemorrhage—a dangerous birth complication that contributes to around 29% of maternal deaths in his home country, Tanzania. The Mkanda Salama (“Safe Wrap” in Swahili) is easy to use and costs just $70. A study found that it stopped postpartum bleeding in 73% of women within 20 minutes.

2. Making brain electrodes inspired by Japanese art

For decades, scientists have been developing, testing, and implanting brain electrodes. These devices are literally inserted into people’s brains, so while they can help us understand brain activity and treat various neurological disorders, it’s not totally surprising that they can also cause a bit of damage. Xiao Yang, 34, is working on ultra-small electrodes, which she hopes will have less of an impact on surrounding brain tissue. Her electrodes are flexible, too—in fact, they look a lot like actual neurons.

Yang is also creating sheets of electrodes to study brain cells in the lab. Inspired by kirigami—the traditional Japanese art of cutting paper to form three-dimensional shapes—she’s created a sheet of electrodes with a honeycombed structure shaped like a spiral basket. And she’s already using it to study brain cells.

3. Developing an all-new treatment for baby KJ

In 2024, Kyle “KJ” Muldoon Jr. was born with a rare and potentially fatal genetic disorder. Sarah Grandinette was a member of a team that developed an entirely new, personalized treatment for him—a gene-editing therapy essentially designed to correct a genetic misspelling.

Grandinette, who is now 26, created cells with KJ’s genetic variant and used them to screen gene-editing approaches; then she tested potential medicines in mice and monkeys. KJ ultimately got his first dose of the resulting treatment when he was about seven months old. He responded well and was eventually discharged from hospital. He’s “doing pretty great,” she says.

4. Reversing the aging process to treat eye disease

The buzziest tech in longevity right now centers on reprogramming—attempts to rewind the age of cells by resetting them to a more embryonic-like state. In a study published in 2020, Yuancheng (Ryan) Lu (now 34) and his colleagues showed that a reprogramming therapy reversed vision loss in aged, blind mice. Now an almost identical version of that therapy is being tested in people with eye disease. Life Biosciences, the company developing the drug, dosed its first volunteer in June.

5. Using AI to design new viruses

Last year, Samuel King used a generative AI model to come up with new genetic blueprints for bacteriophages—teeny viruses that can infect bacteria. Once he had those blueprints, he printed them out as strands of DNA. In experiments, he found that those AI-designed viruses could create new copies of themselves, burst out of bacterial cells, and infect other nearby bacteria. Viruses aren’t alive, but King, 27, hopes that AI-designed life forms might one day be used to make drugs or soak up pollution.

You can read more about these innovators, and the others on the biotech list, here.

This article first appeared in The Checkup, MIT Technology Review’s weekly biotech newsletter. To receive it in your inbox every Thursday, and read articles like this first, sign up here.