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.
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.
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.
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.
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.
For families living with neurodegenerative disease, the hardest part is not always the diagnosis. It is the slow erosion that follows: memory fading, personality shifting, independence shrinking. It unfolds quietly. First, forgotten appointments. Then repeated questions. Then moments when a familiar face no longer feels familiar. The illness does not isolate itself to one body. It rearranges the lives around it.
Calling nanoscientists: your field needs you to try to replicate a landmark finding that quantum dots can act as biosensors inside living cells. As part of the first large-scale effort in the physical sciences to tackle the reproducibility crisis, researchers in France and the Netherlands are offering funds and resources in exchange for a few months of work. "We are trying to use
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.
We knew that if you inject these nanoparticles into an animal model, the nanoparticles get taken up by antigen presenting cells and this resulted in increased regulatory T-cells and decreased inflammatory disease. However, we did not know how this happens,
Martschenko's argument is largely that genetic research and data have almost always been used thus far as a justification to further entrench extant social inequalities. But we know the solutions to many of the injustices in our world-trying to lift people out of poverty, for example-and we certainly don't need more genetic research to implement them. Trejo's point is largely that more information is generally better than less.
