From https://news.ycombinator.com/item?id=44693991 :

> EPS3.9 also had significant anti-tumor effects in the mice with liver cancer and activated anti-tumor immune responses

“A Novel Exopolysaccharide, Highly Prevalent in Marine Spongiibacter, Triggers Pyroptosis to Exhibit Potent Anticancer Effects” (2025) DOI: 10.1096/fj.202500412R https://faseb.onlinelibrary.wiley.com/doi/10.1096/fj.2025004...

"A Gemma model helped discover a new potential cancer therapy pathway" https://news.ycombinator.com/item?id=45604231 :

> eCPMV VNPs + EPS3.9 + [...]

"Scientists are discovering a powerful new way to prevent cancer" https://news.ycombinator.com/item?id=45474404

Notes re: Kidneys not Livers: https://news.ycombinator.com/item?id=47460486 ; gh/topic/healthcare-ai

It is 2026. How are we still publishing articles on medical diagnostics data science and using area under the ROC curve as the primary metric of success. ROC-AUC of 0.9 under severe class imbalance (almost always the case in diagnostics) could still mean something like 4/5 predicted diagnoses are wrong (false positives). Precision-Recall curve + mAP or GTFO.

Science article in question: https://www.science.org/doi/abs/10.1126/science.aec6129

Also, the most interesting result here is that the CNN-based feature encoder significantly outperformed a vision transformer encoder backbone…

ScholarlyArticle: "Large-scale AI-guided liver malignancy diagnosis: multicenter study and a single-arm trial" (2026) Nature Medicine https://www.nature.com/articles/s41591-026-04589-y

Source: https://github.com/alibaba-damo-academy/damo-radar

Model: https://huggingface.co/radar-generalist

I don't agree with the flak Chinese labs get. If it's really that easy to distill and compete with frontier models, why aren't other countries anywhere near this AI race?

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