🧬 Your body's fat cells could be the newest weapon against cancer. Scientists have engineered adipose tissue to consume more nutrients than tumors, effectively starving cancer cells. A radical rethinking of how we target cancer, with results that demand attention... The numbers tell the story: 50% tumor reduction across multiple cancer types - breast, pancreatic, prostate - achieved not by attacking cancer cells, but by simply outcompeting them for resources. Here's why this matters... 🔍 For decades, we've fought cancer by trying to poison it. But cancer cells adapt and survive. Current treatments often fail because: - Severe side effects limit dosing - Tumors develop resistance - Healthy tissues get damaged - Costs reach $200k-500k annually 📊 NEW APPROACH: Scientists used CRISPR to modify fat cells to consume more glucose & fatty acids - the very fuels cancer needs. In controlled studies: - Tumors shrank by up to 50% - Cancer cell metabolism dropped by 80% - Effects seen in 4 different cancer types - Worked both near & far from tumors 🧪 The science is elegant: Modified fat cells showed: - 3x higher glucose uptake - 2x increased fatty acid consumption - 62% reduction in tumor blood vessel formation - Significant decrease in hypoxia markers All without directly attacking cancer cells Key advantage: This uses your own fat cells. Doctors already routinely transfer fat tissue in reconstructive surgery. This approach could integrate with existing medical procedures, potentially simplifying the path to clinical use. ⚡️ Most intriguing finding: The modified fat cells worked even when placed away from tumors. This suggests potential for systemic effects - a critical advantage since cancer often spreads throughout the body. 🔬 The team validated their approach using: - Human cell cultures - Patient-derived tumors - Multiple cancer types - Genetic mouse models This thorough validation strengthens the findings' reliability. ⚖️ Important context: While impressive, hurdles remain: - Human trials still years away - Manufacturing challenges exist - Delivery methods need optimization - Long-term effects unknown Reality check: Most promising lab results take 5-8 years to reach patients. 💡 Why this excites cancer researchers: - Works differently than existing drugs - Uses natural biological competition - Could complement current treatments - Potentially fewer side effects - Adaptable to different cancer types 🏥 The path forward includes: - Safety studies in larger animals - Optimization of cell production - Development of delivery systems - Clinical trial design Each step carefully evaluated for safety & efficacy 📈 Beyond cancer: This work demonstrates how engineered cells could become "living medicines" - the beginning of a new therapeutic approach where we program cells to perform specific functions in treating disease. 🔍 Technical achievement: The team used: - Precise gene activation (CRISPRa) - Controlled delivery systems - Real-time monitoring - Multiple validation methods Showing feasibility at each step ⚖️ Balanced perspective: This adds an exciting new approach to cancer treatment, but it's one piece of a complex puzzle. Most effective cancer therapy will likely combine multiple strategies. Read the complete study in @NatureBiotech Cancer treatment advances when we think differently. This approach shows promise not through brute force, but through clever metabolic competition. Time will tell if it translates to humans. #CancerResearch #MedicalInnovation Sources: - @NCICancerStats - @FDA_Drug_Info - @NLM_NIH

RT @BoWang87: 🚀 Introducing scGPT-spatial! 🧬🌍 A game-changing spatial-omic foundation model, built on the powerful scGPT framework with MoE…

RT @AdamRodmanMD: There is a lot of buzz about our new paper in Nature Medicine on the effects of LLMs (GPT-4) on physician management reas…

3. MedRAX: Integrating Specialized Medical AI Tools - Traditional medical AI tools are like individual specialists - one for reading X-rays, another for generating reports, and another for checking drug interactions. MedRAX represents a new approach: coordinating these specialized tools under a unified reasoning system. Concrete Example: When examining a chest X-ray, MedRAX can: 1. Use specialized vision AI to detect abnormalities 2. Cross-reference findings with the patient's history 3. Generate a structured report 4. Suggest follow-up tests if needed, all while explaining its reasoning process. Validation Approach: The team created ChestAgentBench - 675 expert-curated cases with 2,500 complex queries. This provides transparent testing of the system's capabilities and limitations. What's Novel: Previous attempts at integration often led to decreased performance as systems became more complex. MedRAX maintains or improves accuracy while handling more sophisticated tasks. @BoWang87's detailed post: - Paper: - Code:

RT @BoWang87: Agentic AI Meets Medicine!!! 🔬 Excited to announce MedRAX: a groundbreaking Medical Reasoning Agent for Chest X-ray interpre…

RT @DeryaTR_: I’m now sharing an extremely detailed colon cancer treatment report from OpenAI Deep Research, posted with my patient friend’…

RT @emollick: This paper is wild - a Stanford team shows the simplest way to make an open LLM into a reasoning model. They used just 1,000…

RT @MitoPsychoBio: If you want to explore your favorite mitochondrial gene, function, or pathway in different human tissues and organs, hav…

RT @Aiims1742: New @Nature paper from @DrChoueiri @DanaFarber on neoantigen vaccines in surgically resected renal cell carcinoma. With a ca…

RT @EricTopol: Taking non-invasive glucose tracking to the next level, using Raman spectroscopy, without any needles

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RT @drkeithsiau: Incredible scenes of a killer T-cell destroying an ovarian cancer cell many times its size 👾💥

RT @NikoMcCarty: This video shows a subset of ribosomes inside a cancer cell actively translating mRNA molecules. Each point of light is o…

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RT @EricTopol: The largest medical #AI randomized controlled trial yet performed, enrolling >100,000 women undergoing mammography screening…

RT @EricTopol: An update with latest data available for graph of life expectancy vs health care expenditures per capita for 20 countries an…