Scientists found a way to flush Alzheimer’s plaque out of the brain in minutes. Without any surgery and drugs. Your brain has a waste-clearance network called the glymphatic system. It flushes out toxic proteins and dead cells. But it only really activates during deep, slow-wave sleep. If you don't sleep, the trash piles up. Over time, that buildup leads to cognitive decline and Alzheimer's. Until today. Researchers just published a paper proving they can manually trigger this cleaning cycle. Without you ever going to sleep. They used 40.68 MHz ultrashort waves (USW) beamed non-invasively into the brain. Within 10 minutes, the brain's fluid flow rapidly accelerated. The waves artificially synchronized the neurons into a Delta state, the exact frequency of deep sleep. This forced the brain's water channels to open and start flushing out the system. They tested it on mice with traumatic brain injuries and Alzheimer's pathology. The brain swelling dropped. The toxic proteins were cleared out. Cognitive deficits actually improved. No surgery. No massive chemical interventions. Just a frequency.

There’s a really pronounced divide between people who are on the AI side of things and people who are on the biology side of things. Even among people working in AI, the divide persists based on where they’re focused. The AI side, adherents of scaling laws and the bitter lesson, believe that AI getting better means AI being more capable of novel discoveries in medicine (that go beyond discovering new drugs in well understood targets or speeding up dry lab work etc). The biology people think the idea of a bitter lesson in biology is kind of ridiculous and that regardless of AI’s capabilities it’s going to be at least 5-10 years until you could hope to reliably simulate human toxicology in silico (right now AI/ML can’t even reliably simulate in vivo studies like binding affinities or functional assays). Even the most AI pilled of them, who will list all the areas that AI can reduce the time spent on various areas, don’t think the concept of AI discovering novel targets or generating new understanding of causal biology is realistic in the near term. Most, in fact, will readily characterize the assumption that even an “ASI” level model could find cures hidden in our current understanding (without expanding that understanding with significantly more biological data) as patently false. This was a big part of why we wanted to experience it ourselves by using AI for a (very) small portion of what it can do in biology. It was both mind blowing and revealed some poignant insights into how different the problem of making AI better at something like coding is to the problems of biology. Even if it unlocked the ability to generate new molecules at a stunning pace, its simulation (in silico) of binding only had 49% accuracy. And if we wanted to improve that, we’d need to feed it back the data from the lab - data that took about 30x as long to get as it took to generate the molecules. And these were tests in a test tube (turn of phrase), imagine how long it would take if we wanted to improve the model with data that required recruiting humans and monitoring their reactions. Human biology is very complex, and adding a drug - especially a novel drug and doubly so for a novel drug acting on a novel mechanism - means mapping interactions that have nth-order-effects that we don’t have the data to predict. Virtual cells have made advancements enabled by AI, but the perturbations equate to a small fraction of the possible. This is before you even get into curing disease, especially uniquely human diseases. Brain function involves cellular identities that are unique to humans, meaning animal studies are of little use. How long does it take to understand what the side effects of chronic administration of a drug in humans are? AI can speed up analyzing the data, or designing the trial or even getting it approved…but it can’t make a 6 month trial complete in 3 months. I think its important to point out that significant advancements can be made in AI that contribute to biology, while also being realistic about how much it speeds up something as complex as curing a specific cancer or diseases of the brain.

Metabolic surgery vs GLP-1 medication is a tradeoff: about 0.5% early mortality, but 32% lower long term mortality, around 12 life years gained per 100 patients, and fewer CV and renal events. onlinelibrary.wiley.com/doi/…

GPT-Rosalind surfaced a new lead for drug discovery: could restoring cholesterol transport protect neurons from degeneration? DCTN1 is a gene linked to Perry syndrome, a rare neurodegenerative disorder. In mouse-brain data, disrupting it increased expression of genes involved in making cholesterol. GPT-Rosalind suggested a possible explanation: cholesterol may not be reaching where neurons need it. That gives scientists a specific mechanism to test and, if confirmed, a direction for developing new treatments for neurodegenerative disorders. With @perturbai_tx and @scrippsresearch, a team of thousands of GPT-Rosalind agents worked in parallel, connecting gene-expression results from millions of mouse brain cells with scientific literature in just 32 minutes. We’re now expanding this collaboration to identify new drug targets. openai.com/index/scaling-ai-…

Design of synthetic activators for programmable gene expression doi.org/10.64898/2026.10.19.…

Ever wondered what a cell has experienced in the past? This is in general hard to measure. Here we introduce the recordanelle, to equip cells with a tool that lets them record their own history. nature.com/articles/s41587-0… @NatureBiotech @ETH @randall_platt @ETH_BSSE

Delighted to share a landmark preprint from our lab, using genome language models and AI agents for biological discovery and genome mining! The stretches of DNA between protein-coding genes are often called the genome’s “dark matter,” but they are far from empty. They encode RNA molecules that sense metabolites, regulate genes, and control some of molecular biology's most important tools (such as programmable genome editors e.g. CRISPR/bridge) Much of this biology is unexplored. One reason is that RNAs can retain similar structures and functions even as their DNA sequences change dramatically, making their relationships difficult to recognize with conventional computational tools