CRISPR-GPT, a large language model developed at Stanford Medicine, is accelerating gene-editing processes and increasing accessibility to CRISPR.
Category: robotics/AI – Page 55
The hidden meanings of texts and tweets
A ‘spherical’ AI model finds hidden themes in large collections of headlines and other short texts.
From tweets and chat messages to headlines and status updates, short bursts of text are everywhere. These snippets may be brief, but they are packed with the potential to reveal anything from emerging trends to business decisions or circulating misinformation.
While AI is becoming increasingly adept at extracting meaning from long-form text, it still struggles with these bite-sized ones.
AI may help researchers with medical chart review, Stanford Medicine study finds
Stanford Medicine researchers trained a large language model to read medical charts, looking for signs that kids with ADHD received the right follow-up care when using new medications.
AI-Validated Brain Targeted mRNA Lipid Nanoparticles with Neuronal TropismClick to copy article linkArticle link copied!
Targeting therapeutic nanoparticles to the brain poses a challenge due to the restrictive nature of the blood–brain barrier (BBB). Here we report the development of mRNA-loaded lipid nanoparticles (LNPs) functionalized with BBB-interacting small molecules, thereby enhancing brain delivery and gene expression. Screening brain-targeted mRNA-LNPs in central nervous system (CNS) in vitro models and through intravenous administration in mice demonstrated that acetylcholine-conjugated LNPs achieved superior brain tropism and gene expression, outperforming LNP modifications with nicotine, glucose, memantine, cocaine, tryptophan, and other small molecules. An artificial intelligence (AI)-based model designed to predict the BBB permeability of small-molecule ligands showed strong alignment with our experimental results, providing in vivo validation of its predictive capacity. Cell-specific biodistribution analysis in Cre-reporter Ai9 mice showed that acetylcholine-functionalized LNPs preferentially transfected neurons and astrocytes following either intravenous or intracerebral administration. Mechanistic studies suggest that acetylcholine-LNP uptake is mediated by the functional engagement of acetylcholine receptors (AchRs) followed by endocytosis, which synergistically enhances intracellular mRNA delivery. Moreover, acetylcholine-LNPs successfully crossed a human BBB-on-a-chip model, enabling transgene expression in human iPSC-derived neurons. Their effective penetration and transfection in human brain organoids further support their potential activity in human-based systems. These findings establish a predictive and modular framework for engineering CNS-targeted LNPs, advancing precision gene delivery for brain disorders.
New approach improves accuracy of quantum chemistry simulations using machine learning
A new trick for modeling molecules with quantum accuracy takes a step toward revealing the equation at the center of a popular simulation approach, which is used in fundamental chemistry and materials science studies.
The effort to understand materials and chemical reactions eats up roughly a third of national lab supercomputer time in the U.S. The gold standard for accuracy is the quantum many-body problem, which can tell you what’s happening at the level of individual electrons. This is the key to chemical and material behaviors as electrons are responsible for chemical reactivity and bonds, electrical properties and more. However, quantum many-body calculations are so difficult that scientists can only use them to calculate atoms and molecules with a handful of electrons at a time.
Density functional theory, or DFT, is easier—the computing resources needed for its calculations scale with the number of electrons cubed, rather than rising exponentially with each new electron. Instead of following each individual electron, this theory calculates electron densities—where the electrons are most likely to be located in space. In this way, it can be used to simulate the behavior of many hundreds of atoms.
