History, as is often the case in science, sounded very attractive. Cells have a molecular clock that determines how long they live. If you can just stop the clock, cells can live indefinitely. And the same should happen to people who, after all, are made up of cells. Stop the cellular clock and you can stay young.

Clocks are shaped like caps at the ends of chromosomes, long twisted strands of DNA that carry cellular genes. The caps on chromosomes, called telomeres, are chains of short, repetitive segments of DNA. Every time a cell divides, its telomeres get a little shorter, until finally they get so short that the cell dies.

“Short telomeres were thought to be bad—people with premature aging syndromes had short telomeres—so by analogy, long telomeres were considered good,” the doctor said. Mary Armanios is professor of oncology at the Johns Hopkins University School of Medicine and director of the Telomere Center at the Sidney Kimmel Comprehensive Cancer Center at the medical school. “And the longer the better.”

But, of course, in biology everything is not so simple. And paper published Thursday in the New England Journal of Medicine with the results of a study by Dr. Armanios shows that the telomere story is no exception. While short telomeres do lead to health problems, long telomeres themselves lead to health problems. Far from prolonging life, long telomeres seem to cause cancer and a blood disorder known as CHIP, a condition that increases the risk of blood cancer and heart disease.

Dr. Elizabeth Blackburn, professor emeritus at the University of California, San Francisco, who received the Nobel Prize for discovering telomeres and was not involved in the study, said it was a “great article” that goes beyond correlations and shows a direct link between them. link between long telomeres and disease. She added that the study “sheds light on this whole compromise.”

For Dr. Armanios, this is the culmination of a work she began 20 years ago.

When scientists started studying telomeres, they noticed that younger people had longer telomeres than older people. When cells are grown in the lab, their telomeres act like a ticking clock that determines how long they have left to live.

Telomeres were soon hailed as the secret to aging—companies advertised that they could determine your biological age by measuring your telomere length. Others have said that you can prolong your life by preserving your telomeres with nutritional supplements.

But dr. Armanios and other explorers noticed that telomere length appears to be limited to a narrow range, indicating that there is a price to pay for very long or very short telomeres.

Population research several groups seem to have supported the idea. They found a correlation—rather than cause and effect—with increased disease risk at both ends of the normal telomere spectrum.

Those with shorter telomeres than average were Increased Risk problems with the immune system and various degenerative diseases, as well as pulmonary fibrosis, lung disease. Those with longer than average telomeres have a slightly increased risk of developing cancer.

There were, however, some misunderstandings.

“Some organisms have insanely long telomeres, like mice,” the doctor said. Benjamin Ebert, Chief of Medical Oncology, Dana-Farber Cancer Institute. And mice don’t live that long.

Dr. Armanios, as a human geneticist, was thinking how to get answers, what to study people. “There are things you just can’t deduce from studying cells,” she said.

She suspected, she said, that “you just can’t lengthen telomeres without a price,” and began looking for people with very long telomeres to see what the cost might be.

She decided to look for people with a common POT1 genetic mutation that can lead to long telomeres. It was known to increase the risk of cancer, but most researchers thought it was for reasons other than telomere lengthening.

As a result, she had 17 people from five families. Their ages ranged from 7 to 83 and they had unusually long telomeres.

They also had tumors ranging from benign ones like goiter and uterine fibroids to malignant ones like melanoma and blood cancer. During the two-year study, four patients died from various types of cancer.

Harriet Brown, 73, of Frederick, Maryland, is one of the study participants with very long telomeres. She had benign tumors called paragangliomas on her neck and throat, thyroid cancer, and two melanomas. She also has CHIP, a blood disorder associated with heart disease and blood cancer.

She often gets scans and checkups, but, she says, “There’s really not much I can do at the moment” because there’s no way to prevent new tumors from developing.

The effect of long telomeres on people like Ms. Brown is understandable, the doctor said. Norman Sharpless is professor of cancer policy and innovation at the University of North Carolina School of Medicine and former director of the National Cancer Institute.

“Not so long telomeres make cells grow,” he said. “The thing is, they don’t have the brakes to stop them from growing.” And because the telomeres of people with POT1 mutations don’t shorten with every cell division, the cells hang in place, dividing regularly. The longer they divide in the body, the more time they have for the accumulation of random mutations, some of which cause tumor growth.

This is especially true in the blood, where cells are constantly being produced. POT1 mutations in some of these blood cells may give them time to accumulate other mutations that give them a selective growth advantage. Soon, some of these mutated blood cells will take over most of the human bone marrow. The result is CHIP.

This is a new look at the CHIP. The thought was that since people with CHIP are at an increased risk of blood cancer, this CHIP itself causes cancer.

instead, Dr. Armanios said long telomeres both create the CHIP and independently give cells time to develop cancer-causing mutations.

“The biology of aging is much more complex than we hoped,” says the doctor. Sharpless said.

Or like Dr. Blackburn observed: Long telomeres are not the secret to eternal youth.

“There are no free meals,” she said.

Think of the words swirling around in your head: that tasteless joke you wisely kept quiet at dinner; your unspoken impression of your best friend’s new partner. Now imagine that someone can overhear you.

On Monday, scientists from the University of Texas at Austin took another step in that direction. In a study published in the journal Nature NeuroscienceThe researchers described an AI that could translate people’s private thoughts by analyzing fMRI scans that measure blood flow to different areas of the brain.

Researchers have already developed language decoding methods for pick up speech attempt people who have lost the ability to speak, and to allow paralyzed people to write while thinking about writing. But the new language decoder is one of the first to not rely on implants. In the study, he was able to turn a person’s imaginary speech into real speech, and when subjects were shown silent films, he was able to generate relatively accurate descriptions of what was happening on the screen.

“It’s not just a language stimulus,” said Alexander Hut, a neuroscientist at the university who helped lead the study. “We are approaching the meaning, the idea of ​​what is happening. And the fact that it’s possible is very exciting.”

The study focused on three participants who contacted Dr. Huth Labs for 16 hours over several days to listen to The Moth and other narrative podcasts. As they listened, the fMRI scanner recorded blood oxygenation levels in parts of their brains. The researchers then used a large language model to match the patterns of brain activity to the words and phrases the participants heard.

Large language models such as OpenAI’s GPT-4 and Google’s Bard are trained on a large number of texts to predict the next word in a sentence or phrase. In the process, the models create maps showing how words relate to each other. Several years ago Dr. Hut noticed that individual pieces of these maps—so-called context embeddings that capture semantic features or phrase meanings—can be used to predict how the brain will fire in response to language.

According to Shinji Nishimoto, a neuroscientist at Osaka University who was not involved in the study, “Brain activity is a kind of coded signal, and language models provide ways to decipher it.”

In his study, dr. Huth and colleagues effectively changed the process by using a different AI to translate a participant’s fMRI images into words and phrases. The researchers tested the decoder by having participants listen to new recordings and then see how closely the translation matched the actual transcript.

Almost every word in the transcribed letter was out of place, but the meaning of the passage was regularly preserved. Essentially, the decoders have been rehashed.

Original transcript: “I got up from the air mattress and pressed my face against the glass of the bedroom window, expecting to see eyes staring at me, but instead found only darkness.”

Deciphered by brain activity: “I just kept going to the window and opening the glass, I stood on tiptoe and looked out, I didn’t see anything, and looked up again, I didn’t see anything.”

During the fMRI scan, participants were also asked to silently imagine that they were telling a story; after that, they repeated the story aloud for reference. And here the decoding model has captured the essence of the unspoken version.

Member Version: “Look for a message from my wife that she has changed her mind and that she will be back.”

decoded version: “To see her, for some reason I thought that she would come up to me and say that she misses me.”

Finally, the subjects watched a short silent animated film while undergoing the fMRI scan again. By analyzing their brain activity, the language model could decipher a rough synopsis of what they were viewing—perhaps their internal description of what they were viewing.

The result suggests that the AI ​​decoder captured not only the words, but also the meaning. “Linguistic perception is an externally controlled process, while imagination is an active internal process,” says the doctor. Nishimoto said. “And the authors showed that the brain uses shared representations for these processes.”

Greta Takut, a neuroscientist at MIT who was not involved in the study, said it was a “high-level question.”

“Can we decode meaning from the brain?” she continued. “In a way, they show that yes, we can.”

This method of language decoding had limitations. Huth and his colleagues noted. First, fMRI scanners are bulky and expensive. Moreover, model training is a long and tedious process, and in order to be effective, it must be performed on individuals. When the researchers tried to use a decoder trained on one person to read another’s brain activity, they failed, assuming each brain has unique ways of representing meaning.

The members were also able to screen their internal monologues by resetting the decoder while thinking about other things. AI can read our minds, but for now it will have to read them one at a time and with our permission.