A century-old vaccine against tuberculosis helps to regulate blood sugar in people with diabetes go.nature.com/4xlRHTb

Only a few 100s of people are working on aging - we need an Apollo scale effort

Let’s discuss the scaling law of virtual cells. A Nature Methods paper (nature.com/articles/s41592-0…) published yesterday is being interpreted by some as evidence that scaling laws do not hold for virtual cells. I read it in detail, and here are my 2 cents: It is a useful benchmark, but not a direct test of scaling laws in causal single-cell foundation models or perturbation-native virtual cells. The paper mainly studies PCA, scVI, Geneformer, and SCimilarity (which are relative small models) on observational atlas-style pretraining, with perturbation evaluation limited to a narrow Tahoe small-molecule/cancer-cell-line setting. These are important baselines on scFMs that focuses on learning cell embeddings, but they are not large-scale causal perturbation models (e.g, diffusion-based virtual cells, or other modern architectures designed natively for causal perturbation biology). The metrics also matter. Cell-type F1 and batch-integration AvgBIO are reasonable atlas/embedding metrics, but they are also tasks that can saturate quickly. They are not direct measures of causal perturbation prediction, target ranking, rare differential-expression tails, OOD genetic perturbations, or generalization across biological contexts. The “learning saturation point” in the paper is useful, but it is not really a scaling law. It asks: what is the smallest pretraining size that reaches within 95% of the best observed score on this benchmark? That is a helpful diagnostic, but it can be overinterpreted when the downstream task itself is saturated. The perturbation result is, IMO, limited: a few selected Tahoe-100M small molecules across several cancer cell lines, evaluated with genewise R²/MSE. The paper itself reports that a “no-change” baseline beats fine-tuned models for most drugs, which says as much about the evaluation regime as about model scaling. In fact, our scGPT work already showed three years ago that simply scaling the number of observational cells saturates quickly after a few million cells. So I agree with the warning: naive “more atlas cells = better virtual cell” is not enough. But that is not the real scaling question. In X-Cell, we study scaling across multiple axes: number of perturbation cells, number of biological contexts, perturbation diversity, and model parameters. On our Perturb-seq-scale data, we observe clear and encouraging scaling behavior. Similar trends are emerging from other perturbation-native virtual cell efforts as well. The important question is not: can more atlas cells improve cell-type F1? It is: with larger Perturb-seq datasets, larger models, better architectures, and harder OOD splits, can we predict causal cellular responses across genes, combinations, doses, cell states, and contexts? For X-Cell and the next generation of virtual-cell models, the goal is not just better embeddings. It is target ranking, rare DE tails, counterfactual biology, and prospective perturbation prediction. So my reading is: this paper is a useful caution against naive scaling, not evidence that scaling laws do not apply to virtual cells. The exciting regime is still open: scaling the right data, the right models, and the right objectives for causal cellular biology.

Hibernation is linked to unusually long lifespans, but natural torpor bundles lower metabolism, lower body temperature, reduced food intake, and inactivity. Causality has been hard to isolate. We funded Sinisa Hrvatin's @hrvatin_sinisa lab through @ImpetusGrants to make progress on that front. In their paper, they induced a torpor-like state in mice by stimulating preoptic-area neurons, then repeated torpor-wake cycles for months. Blood epigenetic aging slowed by 36.9% over 9 months, and the mice showed improved frailty measures. But when the authors separated lower metabolism from lower body temperature, only the low-temperature condition reproduced the aging effect. That points to hypothermia, not hypometabolism alone, as the key driver of slowed blood epigenetic aging in this model. On our map of the field’s bottlenecks, this sits at mechanism discovery,: a way to move from “hibernation is linked to longevity” to controlled tests of which torpor features causally affect aging. Read the paper here: doi.org/10.1038/s43587-025-0…

BIG anti aging news China just launched its first large scale stem cell anti aging trial, enrolling 2,000 adults aged 50 to test whether stem cells can help preserve strength, physical function, resilience, and healthier aging. The therapy uses umbilical cord derived mesenchymal stem cells, which may help regulate inflammation, support tissue repair, and improve age related decline. This is a large, multicenter randomized controlled trial, exactly the kind of serious human evidence longevity medicine needs. That's a pretty big deal. anti aging progress is accelerating enormously in the last few weeks. LEV is near

TechBio Companies per category Mapping. Source: MMC

pumped to announce the first AI Scientist hack in LDN! we're teaming up w @AnthropicAI to help you build out next gen. scientific discovery provided: > mansion in central London > all the tokens and pizza you can eat through > great vibes insanely cracked people link below

Unlimited Bio won the startup pitch competition without even mentioning the Kardashian injections. We also have winning preclinics and human gene therapy combo trial coming up, not only hype! 2-minute video pitch in the comments.

The Greenland shark genome: Insights into lifespan extremes and population dynamics pnas.org/doi/abs/10.1073/pna…

I’m excited to launch the Longevity Biotech Atlas, a personal project mapping 700 organizations across the aging and longevity ecosystem. Longevity biotech is moving fast. A lot of the public narrative has centered on GLP-1s, peptides, biohacking, and consumer health optimization. But beneath that, a much deeper ecosystem is forming: companies, labs, investors, and non-profits working on core technologies that will enable fundamentally new forms of measurement and rejuvenation. The field is growing quickly, but it is still hard to answer basic questions: Who is building what? Which areas are crowded? Which technologies are underexplored? Where should founders, scientists, investors, and operators be paying attention? To help make the space easier to navigate, I synthesized public information on 700 organizations—ranging from therapeutics developers, diagnostics, investors, and more— into a single database. You can explore the atlas at the link in the comments. This is still an early version, and I’ll be adding more over time: new organizations, personal accounts, bookmarks, and better ways to track how the field is evolving. My hope is to build this out into a core tool for people trying to understand, build, fund, or work in longevity. If you’re interested in aging biology, biotech, investing, company formation, or the future of medicine, follow along. I’ll be sharing updates, maps of different subfields, and analyses of where the biggest opportunities and gaps may be. Let me know what you find useful!