@MarkFaberDO Not always concordant. treating someone with hydrea (which technically reduces counts) doesnt help from the very limited experience (vs HMA). It's not simply about "count reduction" in the PB
@msnnaydn Send for lysozyme/muramidase in the blood. if really high, consider kidney biopsy. Obv don't send this on everyone. Look at CBC (absolute monos) trends over time and track with Scr for clues
S2213 asks whether SCT adds value after modern ind in good-risk AL. At best, SCT may modestly improve PFS or MRD depth not OS. But tx interruption & toxi remain concerns, parti with cardiac AL.
In AL, rapid and sustained LC suppression may matter more than maximal cytoreduction
OpenAI has acquired 🔥@TorchHealth
The Torch team and I are joining OAI to help build ChatGPT Health into the best AI tool in the world for health and wellness.
How does an embryo reliably "compute" its form - "cell by cell" - using only local interactions and mechanics, yet produce a precise global body plan? I’m excited to share our Nature Methods paper "MultiCell: geometric learning in multicellular development", presenting #AIxBiology research led by @HaiqianYang and the result of a great collaboration with Ming Guo, George Roy, Tomer Stern, Anh Nguyen and Dapeng Bi.
A long-standing challenge in developmental biology is to predict how thousands of cells collectively self-organize as tissues fold, divide, and rearrange. In MultiCell, we represent a developing embryo as a dual graph that unifies two complementary views of tissue mechanics with single-cell resolution: cells as moving points (granular) and cells as a connected foam (junction network). This lets the model learn dynamics from both geometry and cell–cell connectivity.
On whole-embryo 4D light-sheet movies of Drosophila gastrulation (~5,000 cells), our model predicts key cell behaviors and the timing of events, including junction loss, rearrangements, and divisions with high accuracy, at single-cell resolution. Beyond prediction, the same representation supports robust time alignment across embryos and offers interpretable activation maps that highlight the morphogenetic "drivers" of development. The broader goal is a foundation for cell-by-cell forecasting in more complex tissues, and eventually for detecting subtle dynamical signatures of disease.
Kudos to the team for this inspiring collaboration with brilliant researchers to push the boundary of AI for biology!
Citation: Yang, H., Roy, G., Nguyen, A.Q., Buehler, M.J., et al. MultiCell: geometric learning in multicellular development. Nature Methods (2025), DOI: 10.1038/s41592-025-02983-x
Code/data links are in the manuscript.