While some other creatures, most notably salamanders and starfish, can regenerate entire limbs, mammals don't have this evolutionary superpower. The big question is: Why are mammals limited?
Small organic molecules underpin modern life, from medicines and flavours to advanced materials. Much of this functional diversity comes from shape: modest changes in a molecule's 3D structure can completely change its properties.
Using CRISPR-Cas9 and adeno-associated virus (AAV)-mediated homology-directed repair, we targeted CAR integration into the endogenous human TCR alpha locus (TRAC). TRAC-CAR T cells display dynamic CAR expression that delays exhaustion and improves tumour control in xenograft and immunocompetent models. This work has been critical for the development of allogeneic CAR T cell therapy, as it disrupts the TCR after transgene insertion—a necessary step to limit graft-versus-host disease.
When mitochondria are exposed to tissue or blood, they lose the electrical gradient across their outer membrane. Mitochondria that lack such a gradient are recognized by a cell's internal machinery as damaged and quickly destroyed. The vast majority of previous studies involved injecting 'naked' mitochondria directly into the bloodstream or tissue sites, but the approach isn't very efficient, so researchers often have to use 'ridiculous' doses of mitochondria.
Since its release in 2021, this repository has become a bedrock in discovery and a first port of call for research projects that try to understand life at the molecular level. But previous iterations of the database lacked predictions of how proteins form complexes, which can be indispensable for their function.
The researchers at Eon Systems have taken several pre-existing components: a fruit fly brain scan, a tool for modelling neurons, a model of some of the fly's muscles and body, and a very simple virtual environment, connected them together and ran it. The team claims that the result displays some of the behavior of the real insect.
Before treatment began, participants underwent neuroimaging. Instead of relying on a single modality, the researchers fused structural connectivity (how regions are physically wired) with functional connectivity (how regions co-activate at rest). The goal was not to throw every possible feature at a black box, but to learn a constrained pattern-what the authors call structure-function "covariation"-that carries the most predictive signal for outcome. In other words, the model tries to find the smallest set of connections that meaningfully forecasts symptom change.
Scientists in the laboratory of Rendong Yang, PhD, associate professor of Urology, have developed a new large language model that can interpret transcriptomic data in cancer cell lines more accurately than conventional approaches, as detailed in a recent study published in Nature Communications. Long-read RNA sequencing technologies have transformed transcriptomics research by detecting complex RNA splicing and gene fusion events that have often been missed by conventional short-read RNA-sequencing methods.
Now, researchers have created an artificial-intelligence system that vastly simplifies and accelerates the process of chemical synthesis. The system, which is called MOSAIC and is described in a study published in Nature on 19 January, recommended conditions that researchers were able to use to generate 35 compounds with the potential to become products like pharmaceuticals, agrochemicals or cosmetics without needing to do any further trawling or tweaking.
Although specific bacterial taxa have been associated with favourable clinical responses to immune checkpoint blockade (ICB) in cancer patients12,13,18,19,20,21,22, the mechanisms by which the intestinal microbiota influences anti-tumour immune responses remain poorly defined. Products of the microbiota, including metabolites23,24,25 and innate receptor ligands26, may reprogramme myeloid cells27, lowering the activation threshold for antigen presentation and thereby facilitating priming and activation of tumour-reactive T cells.
The exponential growth of scientific literature presents an increasingly acute challenge across disciplines. Hundreds of thousands of new chemical reactions are reported annually, yet translating them into actionable experiments becomes an obstacle1,2. Recent applications of large language models (LLMs) have shown promise3,4,5,6, but systems that reliably work for diverse transformations across de novo compounds have remained elusive. Here we introduce MOSAIC (Multiple Optimized Specialists for AI-assisted Chemical Prediction), a computational framework that enables chemists to harness the collective knowledge of millions of reaction protocols.
GEMINI leverages a computationally designed protein assembly as an intracellular memory device to record the history of individual cells. GEMINI grows predictably within live cells, capturing cellular events as tree-ring-like fluorescent patterns for imaging-based retrospective readout. Absolute chronological information of activity histories is attainable with hour-level accuracy.
The human brain is complex. Artificial intelligence (AI) machine learning and medical imaging data are accelerating breakthroughs in brain health, especially in medical diagnostics. A peer-reviewed study published today in Nature Neuroscience unveils an AI foundation model called BrainIAC (Brain Imaging Adaptive Core) that is capable of predicting brain age, dementia, time-to-stroke, and brain cancer from brain magnetic resonance imaging (MRI).
Biology is undergoing a transformation. After centuries of studying life as it evolves naturally, researchers are now using a combination of computation and genome engineering to intervene, generating new proteins and even whole bacteria from scratch. The use of artificial-intelligence tools to design biological components, an approach known as generative biology, is set to turbocharge this area of research. Just last year, scientists used AI-assisted design to produce artificial genes that can be expressed in mammalian cells.
Called AlphaGenome, the model could help scientists discover why subtle differences in our DNA put us at risk of conditions such as high blood pressure, dementia and obesity. It could also dramatically accelerate our understanding of genetic diseases and cancer. The developers of the model acknowledge it's not perfect, but experts have described it as "an incredible feat" and "a major milestone".
Generalist models "fail miserably" at the benchmarks used to measure how AI performs scientific tasks, Alex Zhavoronkov, Insilico's founder and CEO, told Fortune. " You test it five times at the same task, and you can see that it's so far from state of the art...It's basically worse than random. It's complete garbage." Far better are specialist AI models that are trained directly on chemistry or biology data.
The team, which is being led by Jülich neurophysics professor Markus Diesmann, will leverage the Joint Undertaking Pioneer for Innovative and Transformative Exascale Research (JUPITER) supercomputer for their simulation. JUPITER is currently the fourth most powerful supercomputer in the world according to the TOP500 list, and features thousands of graphical processing units. The team demonstrated last month that a " spiking neural network " could be scaled up and run on JUPITER, effectively matching the cerebral cortex's 20 billion neurons and 100 trillion connections.
In the originally published version of this article, Extended Data Fig. 4 contained inadvertent duplications introduced during figure assembly: panel 4c (the bottom of the second column) erroneously reused images from panel 4a (the bottom of the third column); panel 4c (the upper panel of the third column) erroneously reused images from panel 4a (the upper panel of the rightmost column);
