How stressful life events are associated with depression: the mediating pathway of security in a clinical adolescent sample
Medical evaluation of first presentation of psychotic symptoms in children and adolescents
Self images: an empirical enquiry into Rembrandt’s self-portraits
Rapid temporal processing in the olfactory bulb underlies concentration-invariant odor identification and signal decorrelation
Nature Neuroscience, Published online: 14 April 2026; doi:10.1038/s41593-026-02250-y
This study shows that the brain’s smell center uses precise timing and inhibition to read out early odor signals, enabling reliable odor identity across concentrations while rapidly separating (decorrelating) similar smell patterns for better discrimination.
Coming soon: 10 Things That Matter in AI Right Now
Each year we compile our 10 Breakthrough Technologies list, featuring our educated predictions for which technologies will have the biggest impact on how we live and work.
This year, however, we had a dilemma. While our final picks encompass all our core coverage areas (energy, AI, and biotech, plus a few more), our 2026 list was harder to wrangle than normal. Why? We had so many worthy AI candidates we couldn’t fit them all in! (The ones that made it were AI companions, generative coding, and hyperscale data centers.) Many great ideas fell by the wayside to keep the list as wide-ranging as possible.
Well, that got us thinking: What if we made an entirely new list that was all about AI? We got excited about that idea—and before we knew it we had the beginnings of what we’re calling 10 Things That Matter in AI Right Now. It’s an entirely new annual list that we’re proud to be publishing for the first time on April 21, 2026. We’ll unveil it on stage for attendees at our signature AI conference, EmTech AI, held on MIT’s campus (it’s not too late to get tickets), and then publish the list online later that day.
The process for coming up with the list was similar to the way we pick our 10 Breakthrough Technologies. We petitioned our AI team of reporters and editors to propose ideas, put them all in a document, and engaged in some robust discussion. Eventually, we voted for our favorites and whittled the long list down to a final 10.
But there’s a slight difference between this list and our 10 Breakthrough Technologies. AI is already such a big part of our lives that we didn’t want to restrict ourselves to nominating only technologies. Instead, we wanted to put together a definitive annual list that highlights what we believe are the biggest ideas, topics, and research directions in AI right now. So yes, it will include cutting-edge AI technologies, but it will also feature other trends and developments in AI that we want to bring to our subscribers’ attention.
Think of it as a sneak peek inside the collective brain of our crack AI reporting team: These are the things that our reporters will be watching this year. We intend to follow the items on this list really closely, and you will see it reflected in the news and feature stories we publish in 2026.
For us, 10 Things That Matter in AI Right Now is a guide to how we view the current AI landscape. It will be a source of discussion, debate, and maybe some arguments! We are so excited to share it with you on April 21. If you want to be among the first to see it—join us at EmTech AI or become a subscriber to livestream the announcement.
Blood Biomarker Can Predict Signs of Alzheimer’s Before PET Scans
A blood test that measures plasma phosphorylated tau 217 is capable of predicting future Alzheimer’s disease onset in cognitively normal older adults even when positron emission tomography scans do not show amyloid or tau build up in the brain.
“We used to think that positron emission tomography (PET) scan detection was the earliest sign of Alzheimer’s disease progression, revealing amyloid accumulation in the brain 10 to 20 years before symptoms appear,” said lead author Hyun-Sik Yang, MD, a neurologist with Mass General Brigham Neuroscience Institute, in a press statement.
“But now we are seeing that phosphorylated tau 217 (pTau217) can be detected years earlier, well before clear abnormalities appear on amyloid PET scans.”
In 2025, the FDA approved two blood tests for Alzheimer’s disease, one that compares the ratio of pTau217 to beta amyloid developed by Fujirebio and a pTau181 plasma test developed by Roche. However, both of these tests are only indicated for people who already have some symptoms of the condition.
The current study, published in Nature Communications, aimed to test whether pTau217 in the blood can forecast beta‑amyloid and tau build‑up in the brain before individuals become amyloid‑positive on PET scans.
Overall, 317 older adults, aged 72 years on average, from the Harvard Aging Brain Study were included in the study. About 60% were women. There were no signs of cognitive decline on enrollment, and the group had a high education level. The researchers followed up 245 people in the group with repeat amyloid scans for about six years on average.
The test was able to pick up cases where beta amyloid was visible on brain scans with a high level of accuracy. The study also showed that higher starting pTau217 levels predicted faster amyloid build‑up over time, even after accounting for age, sex, and APOE status
Centiloid units are the standard scale for amyloid on brain scans with 100 centiloids typical of full-blown disease. The team found that the test could also predict future amyloid buildup even if no signs could be seen on initial PET scans. Each one‑percentage‑point increase in pTau217 at baseline was linked to an extra 0.35 centiloid units of amyloid buildup per year. Notably, those with low pTau217 levels on enrollment were still amyloid negative on scans years later.
“What stood out in our study is that even when amyloid scans appear normal in the clinic, the pTau217 biomarker can identify individuals who later become amyloid-positive,” said Yang. “It also shows that those with low pTau217 levels are likely to stay amyloid-negative for several years.”
The post Blood Biomarker Can Predict Signs of Alzheimer’s Before PET Scans appeared first on Inside Precision Medicine.
The problem with thinking you’re part Neanderthal
You’ve probably heard some version of this idea before: that many of us have an “inner Neanderthal.” That is to say, around 45,000 years ago, when Homo sapiens first arrived in Europe, they met members of a cousin species—the broad-browed, heavier-set Neanderthals—and, well, one thing led to another, which is why some people now carry a small amount of Neanderthal DNA.
This DNA is arguably the 21st century’s most celebrated discovery in human evolution. It has been connected to all kinds of traits and health conditions, and it helped win the Swedish geneticist Svante Pääbo a Nobel Prize.
But in 2024, a pair of French population geneticists called into question the foundation of the popular and pervasive theory.
Lounès Chikhi and Rémi Tournebize, then colleagues at the Université de Toulouse, proposed an alternative explanation for the very same genomic patterns. The problem, they said, was that the original evidence for the inner Neanderthal was based on a statistical assumption: that humans, Neanderthals, and their ancestors all mated randomly in huge, continent-size populations. That meant a person in South Africa was just as likely to reproduce with a person in West Africa or East Africa as with someone from their own community.
Archaeological, genetic, and fossil evidence all shows, though, that Homo sapiens evolved in Africa in smaller groups, cut off from one another by deserts, mountains, and cultural divides. People sometimes crossed those barriers, but more often they partnered up within them.
In the terminology of the field, this dynamic is called population structure. Because of structure, genes do not spread evenly through a population but can concentrate in some places and be totally absent from others. The human gene pool is not so much an Olympic-size swimming pool as a complex network of tidal pools whose connectivity ebbs and flows over time.
This dynamic greatly complicates the math at the heart of evolutionary biology, which long relied on assumptions like randomly mating populations to extract general principles from limited data. If you take structure into account, Chikhi told me recently, then there are other ways to explain the DNA that some living people share with Neanderthals—ways that don’t require any interspecies sex at all.
“I believe most species are spatially organized and structured in different, complex ways,” says Chikhi, who has researched population structure for more than two decades and has also studied lemurs, orangutans, and island birds. “It’s a general failure of our field that we do not compare our results in a clear way with alternative scenarios.” (Pääbo did not respond to multiple requests for comment.)
The inner Neanderthal became a story we could tell ourselves about our flaws and genetic destiny: Don’t blame me; blame the prognathic caveman hiding in my cells.
Chikhi and Tournebize’s argument is about population structure, yes, but at heart, it is actually one about methods—how modern evolutionary science deploys computer models and statistical techniques to make sense of mountains upon mountains of genetic data.
They’re not the only scientists who are worried. “People think we really understand how genomes evolve and can write sophisticated algorithms for saying what happened,” says William Amos, a University of Cambridge population geneticist who has been critical of the “inner Neanderthal” theory. But, he adds, those models are “based on simple assumptions that are often wrong.”
And if they’re wrong, what’s at stake is far more than a single evolutionary mystery.
A captivating story of interspecies passion
Back in 2010, Pääbo’s lab pulled off something of a miracle. The researchers were able to extract DNA from nuclei in the cells of 40,000-year-old Neanderthal bones. DNA breaks down quickly after death, but the group got enough of it from three different individuals to produce a draft sequence of the entire Neanderthal genome, with 4 billion base pairs.
As part of their study, they performed a statistical test comparing their Neanderthal genome with the genomes of five present-day people from different parts of the world. That’s how they discovered that modern humans of non-African ancestry had a small amount of DNA in common with Neanderthals, a species that diverged from the Homo sapiens line more than 400,000 years ago, that they did not share with either modern humans of African ancestry or our closest living relative, the chimpanzee.

Pääbo’s team interpreted this as evidence of sexual reproduction between ancient Homo sapiens and the Neanderthals they encountered after they expanded out of Africa. “Neanderthals are not totally extinct,” Pääbo said to the BBC in 2010. “In some of us, they live on a little bit.”
The discovery was monumental on its own—but even more so because it reversed a previous consensus. More than a decade earlier, in 1997, Pääbo had sequenced a much smaller amount of Neanderthal DNA, in that case from a cell structure called a mitochondrion. It was different enough from Homo sapiens mitochondrial DNA for his team to cautiously conclude there had been “little or no interbreeding” between the two species.
After 2010, though, the idea of hybridization, also called admixture, effectively became canon. Top journals like Science and Nature published study after study on the inner Neanderthal. Some scientists have argued that Homo sapiens would never have adapted to colder habitats in Europe and Asia without an infusion of Neanderthal DNA. Other research teams used Pääbo’s techniques to find genetic traces of interbreeding with an extinct group of hominins in Asia, called the Denisovans, and a mysterious “ghost lineage” in Africa. Biologists used similar tests to find evidence of interbreeding between chimpanzees and bonobos, polar and brown bears, and all kinds of other animals.
The inner-Neanderthal hypothesis also took a turn for the personal. Various studies linked Neanderthal DNA to a head-spinning range of conditions: alcoholism, asthma, autism, ADHD, depression, diabetes, heart disease, skin cancer, and severe covid-19. Some researchers suggested that Neanderthal DNA had an impact on hair and skin color, while others assigned individuals a “NeanderScore” that was correlated with skull shape and prevalence of schizophrenia markers. Commercial genetic testing companies like 23andMe started offering customers Neanderthal ancestry reports.
The inner Neanderthal became a story we could tell ourselves about our flaws and genetic destiny: Don’t blame me; blame the prognathic caveman hiding in my cells. Or as Latif Nasser, a host of the popular-science program Radiolab, put it when he was hospitalized with Crohn’s disease, another Neanderthal-associated condition: “I just keep imagining these tiny Neanderthals … just, like, stabbing me and drawing these little droplets of blood out of me.”
“These things become meaningful to people,” Chikhi says. “What we say will be important to how people view themselves.”
The pitfalls of simplistic solutions
When population geneticists built the theoretical framework for evolutionary biology in the early 20th century, genes were only abstract units of heredity inferred from experiments with peas and fruit flies. Population genetics developed theory far more quickly than it accumulated data. As a result, many data-driven scientists dismissed the study of evolution as a form of storytelling based on unexamined assumptions and preconceived ideas.
By the ’90s, though, genes were no longer abstractions but sequenced segments of DNA. Genomic sequencing grounded evolutionary studies in the kind of hard data that a chemist or physicist could respect.
Yet biologists could not simply read evolutionary history from genomes as though they were books. They were trying to determine which of a nearly infinite number of plausible histories was the most likely to have created the patterns they observed in a small sample of genomes. For that, they needed simplified, algorithmic models of evolution. The study of evolution shifted from storytelling to statistics, and from biology to computer science.
That suited Chikhi, who as a child was drawn to the predictable laws and numerical precision of math and science. He entered the field in the mid-’90s just as the first big studies of human DNA were settling old debates about human origins. DNA showed that Africa harbored far more genetic diversity than the entire rest of the planet. The new evidence supported the idea that modern humans evolved for hundreds of thousands of years in Africa and expanded to the other continents only in the last 100,000 years. For Chikhi, whose parents were Algerian immigrants, this discovery was a powerful challenge to the way some archaeologists and biologists talked about race. DNA could be used to deconstruct rather than encourage the pernicious idea that human races had deep-seated evolutionary differences based on their places of origin.
At the same time, though, he was wary of the tendency to treat DNA as the final verdict on open questions in evolution. Chikhi had been surprised when, back in 1997, Pääbo and his team used that small amount of mitochondrial DNA to rule out hybridization between Homo sapiens and Neanderthals. He didn’t think that the absence of Neanderthal DNA there necessarily meant it wouldn’t be found elsewhere in the Homo sapiens genome.
Chikhi’s own research in the aughts opened his eyes to the gaps between historical reality and models of evolution. For one, despite the assumption of random mating, none of the animals Chikhi studied actually mated randomly. Orangutans lived in highly fragmented habitats, which restricted their pool of potential mates, and female birds were often extremely picky about their male partners.
These factors could confound an evolutionary biologist’s traditional statistical tool kit. Scientists were starting to apply a mathematical technique to estimate historical population sizes for a species from the genome of just a single individual. This method showed sharp population declines in the histories of many different species. Chikhi realized, though, that the apparent declines could be an artifact of treating a structured population as one that evolved with random mating; in that case, the technique could indicate a bottleneck even if all the subgroups were actually growing in size. “This is completely counterintuitive,” he says.
That’s at least partly why, when Pääbo’s 2010 Neanderthal genome came out, Chikhi was impressed with the sheer technical accomplishment but also leery of the findings about hybridization. “It was the type of thing we conclude too quickly based on genetic data,” he says. Pääbo’s work mentioned population structure as a possible alternative explanation—but didn’t follow up.
Just a couple of years later, a pair of independent scientists named Anders Eriksson and Andrea Manica picked up the idea, building a model with simple population structure that explicitly excluded admixture. They simulated human evolution starting from 500,000 years ago and found that their model produced the same genomic patterns Pääbo’s group had interpreted as evidence of hybridization.
“Working with structured models is really out of the comfort zone of a lot of population geneticists,” says Eriksson, now a professor at the University of Tartu in Estonia.
Their research impressed Chikhi. “At the time, I thought people would focus on population structure in the evolution of humans,” he says. Instead, he watched as the inner-Neanderthal hypothesis took on a life of its own. Scientists produced new methods to quantify hybridization but rarely examined whether population structure would yield the same results. To Chikhi, this wasn’t science; it was storytelling, like some of the old narratives about the evolution of racial differences.
Chikhi and Tournebize decided to take a crack at the problem themselves. “I’ve always been very skeptical about science, and population genetics in particular,” says Tournebize, now a researcher at the French National Research Institute for Sustainable Development. “We make a lot of assumptions, and the models we use are very simplistic.” As detailed in a 2024 paper published in Nature Ecology & Evolution, they built a model of human evolution that replaced randomly mating continent-wide populations with many smaller populations linked by occasional migration. Then they let it run—a million times.
At the end of the simulation, they kept the 20 scenarios that produced genomes most similar to the ones in a sample of actual Homo sapiens and Neanderthals. Many of these scenarios produced long segments of DNA like the ones their peers argued could only have been inherited from Neanderthals. They showed that several statistics, which other scientists had proposed as measurements of Neanderthal DNA, couldn’t actually distinguish between hybridization and population structure. What’s more, they showed that many of the models that supported hybridization failed to accurately predict other known features of human evolution.
“A model will say there was admixture but then predict diversity that is totally incompatible with what we actually know of human diversity,” Chikhi says. “Nobody seems to care.”
So how did Neanderthal DNA wind up in living people if not via interspecies passion? Chikhi and Tournebize think it’s more likely that it was inherited by both Neanderthals and some sapiens groups in Africa from a common ancestor living at least half a million years ago. If the sapiens groups carrying those genetic variants included the people who migrated out of Africa, then the two human species would have already had the DNA in common when they came into contact in Europe and Asia—no sex required.
“The interpretation of genetic data is not straightforward,” Chikhi says. “We always have to make assumptions. Nobody takes data and magically comes up with a solution.”
Embracing the uncertainty
Most of the half-dozen population geneticists I spoke with praised Chikhi and Tournebize’s ingenuity and appreciated the spirit of their critique. “Their paper forces us to think more critically about the model we use for inference and consider alternatives,” says Aaron Ragsdale, a population geneticist at the University of Wisconsin–Madison. His own work likewise suggests that the earliest Homo sapiens populations in Africa were probably structured—and that this is the likely reason for genomic patterns that other research groups had attributed to hybridization with a mysterious “ghost lineage” of hominins in Africa.
Yet most researchers still believe that modern humans and Neanderthals did probably have children with each other tens of thousands of years ago. Several pointed to the fact that fossil DNA of Homo sapiens who died thousands of years ago had longer chunks of apparent Neanderthal DNA than living people, which is exactly what you would expect if they had a more recent Neanderthal ancestor. (To address this possibility, Chikhi and Tournebize included DNA from 10 ancient humans in their study and found that most of them fit the structured model.) And while the Harvard population geneticist David Reich, who helped design the statistical test from Pääbo’s 2010 study, declined an interview, he did say he thought Chikhi and Tournebize’s model was “weak” and “very contrived,” adding that “there are multiple lines of evidence for Neanderthal admixture into modern humans that make the evidence for this overwhelming.” (Two other authors of that study, Richard Green and Nick Patterson, did not respond to requests for comment.)
Nevertheless, most scientists these days welcome the development of structured, or “spatially explicit,” models that account for the fact that any given member of a population is usually more closely related to individuals living nearby than to those living far away.
Loosening our attachment to certain narratives of evolution can create space for wonder at the sheer complexity of life’s history.
Other scientists also say that random mating isn’t the only assumption in population genetics that merits scrutiny. Models rarely factor in natural selection, which can also create genetic patterns that look like hybridization. Another common assumption is that everyone’s DNA mutates at the same, constant rate. “All the theory says the mutation rate is fixed,” says Amos, the Cambridge population geneticist. But he thinks that rate would have slowed drastically in the group of Homo sapiens that expanded to Europe around 45,000 years ago. This, too, could have created genomic patterns that other scientists interpret as evidence of interbreeding with Neanderthals.

The point here isn’t that a complex model of evolution with many moving pieces is necessarily better than a simple one. Scientists need to reduce complexity in order to see the underlying processes more clearly. But simple models require assumptions, and scientists need to reevaluate those assumptions in light of what they learn. “As you get more data, you can justify more complex models of the world,” says Mark Thomas, a population geneticist at University College London, who wrote a history of random mating in population genetics that highlighted how the field was starting to see it as “a limiting assumption as opposed to a simplifying one.”
It can feel discouraging to couch conversations about the past in confusing terms like “population structure” and “mutation rates.” It seems almost antithetical to the spirit of science to talk more about uncertainty at the same time we are developing powerful technologies and enormous data sets for analyzing evolution. These tools often yield novel answers, but they can also limit the questions we ask. The French archaeologist Ludovic Slimak, for example, has complained that the idea of the inner Neanderthal has domesticated our image of Neanderthals and made it difficult to imagine their humanity as distinct from our own. Investigating Neanderthal DNA is sexier to many young researchers than searching for archaeological and fossil evidence of how Neanderthals actually lived.
Loosening our attachment to certain narratives of evolution can create space for wonder at the sheer complexity of life’s history. Ultimately, that’s what Chikhi and Tournebize hope to do. After all, they don’t believe the question of population structure versus hybridization is either-or. It’s possible, and even likely, that both played a role in human evolution. “Our structured model does not necessarily mean that no admixture ever took place,” Chikhi and Tournebize wrote in their study. “What our results suggest is that, if admixture ever occurred, it is currently hard to identify using existing methods.”
Future methods might disentangle the different factors, but it’s just as important, Chikhi says, for scientists to be up-front about their assumptions and test alternatives. “There’s still so much uncertainty on so many aspects of the demographic history of Neanderthals and Homo sapiens,” he notes.
Keep that in mind the next time you read about your inner Neanderthal. The association between this DNA and some diseases may be real, of course—but would journals publish these studies without the additional claim that the DNA is from Neanderthals? Any good storyteller knows that sex sells, even in science.
Ben Crair is a science and travel writer based in Berlin.
Dimensional phenotype measurement in children with rare genetic conditions: new insights into the aetiology of neurodevelopmental and psychiatric disorders
Why Leukemia Cells Escape Immune Attack—and How to Stop Them
Immunotherapy strategies that harness the body’s innate immune system have long focused on a central concept: cancer cells evade destruction by displaying “don’t eat me” signals that inhibit macrophages. Blocking these signals, most notably CD47, has been a major therapeutic goal. Yet in acute myeloid leukemia (AML), clinical responses to CD47-targeting therapies have been inconsistent, raising questions about whether additional immune evasion mechanisms are at play.
A new study published in Science by researchers at Mass General Brigham, Dana-Farber Cancer Institute, and the Broad Institute suggests that the field may have been overlooking a more dominant signal. The team identifies CD43, a heavily glycosylated surface protein, as a key regulator of macrophage evasion in AML.
Revisiting macrophage immune evasion
Macrophages are critical components of the innate immune system, capable of recognizing and engulfing tumor cells through phagocytosis. This process is regulated by a balance between pro-phagocytic “eat me” signals and inhibitory “don’t eat me” signals expressed on the surface of cancer cells.
Therapeutic efforts have largely focused on CD47, a well-characterized inhibitory signal that binds to SIRPα on macrophages to suppress phagocytosis. However, the limited success of CD47 inhibitors in AML has suggested that this pathway may not fully account for immune evasion in these cancers.
To systematically explore alternative mechanisms, the researchers performed a genome-scale loss-of-function screen in AML cell lines, turning off genes one by one and assessing their impact on macrophage recognition.
CD43 emerges as a dominant signal
The results were unexpected. While CD47 had only a modest effect, CD43 stood out as a major determinant of whether leukemia cells were engulfed by macrophages.
The study reveals that CD43 functions not simply as a surface marker, but as part of a broader protective structure. Specifically, its sialylated form creates a dense, glycosylation-based barrier that interferes with immune recognition.
As described by the authors, “Sialylated CD43 forms a glyco-immune barrier that restrains anti-leukemic immunity.”
This finding introduces a new conceptual framework for immune evasion in AML—one that emphasizes the role of glycosylation and surface architecture, rather than individual receptor–ligand interactions alone.
Explaining limits of current therapies
The identification of CD43 helps clarify why targeting CD47 alone has not produced the expected therapeutic outcomes in AML. If CD43-mediated shielding plays a dominant role, then blocking CD47 may be insufficient to restore effective macrophage activity.
The study suggests that immune evasion in AML is more complex than previously appreciated, involving multiple overlapping mechanisms that together suppress phagocytosis.
By uncovering this additional layer, the work highlights the need for combination strategies or alternative targets in macrophage-based immunotherapy.
A new therapeutic opportunity
From a translational perspective, CD43 represents a promising new target. Interventions that disrupt its glycosylation or block its function could weaken the protective barrier surrounding leukemia cells, making them more susceptible to immune clearance.
Because CD43 operates through a distinct mechanism, targeting it could complement existing therapies rather than replace them. Combining CD43 inhibition with CD47 blockade or other immunotherapies may enhance overall efficacy.
The findings also point to the broader relevance of glyco-immune interactions in cancer. Similar glycosylation-dependent barriers may exist in other tumor types, suggesting that the implications of this work could extend beyond AML.
A shift toward glyco-immunology
The study reflects a growing recognition of the role of glycobiology in cancer and immunology. While protein-based signaling pathways have dominated the field, complex carbohydrate structures on the cell surface are increasingly understood to play critical roles in immune recognition.
By identifying CD43 as a key mediator of immune evasion, the research highlights how these glycosylated molecules can shape interactions between cancer cells and the immune system.
Looking ahead
Although the findings are based on preclinical models, the researchers believe that they provide a strong rationale for further investigation in patient samples and clinical settings. Future studies will be needed to determine how CD43 expression varies across AML subtypes and whether it correlates with treatment response.
If validated, targeting CD43 could represent a new direction for immunotherapy in AML—one that addresses a fundamental mechanism of immune escape.
More broadly, the work highlights the importance of revisiting established paradigms in cancer biology. By moving beyond well-studied targets like CD47 and systematically exploring the full landscape of immune interactions, researchers are uncovering new vulnerabilities that could be exploited therapeutically.
The post Why Leukemia Cells Escape Immune Attack—and How to Stop Them appeared first on Inside Precision Medicine.

