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Fast-tracking genetic leads to reverse cellular aging

Google DeepMind Blog · deepmind.google · May 18, 2026

Biologists use Co-Scientist to find novel factors that successfully rejuvenate human cells.

Read on Google DeepMind Blog → Opens the original article in a new tab.

More from the blog

MIT News · Artificial Intelligence Aug 20, 2026

Paving the way for greener ammonia production

New MIT research could lead to better materials for a fossil-fuel-free process for making the chemical that's essential to fertilizer and other products.

MIT News · Artificial Intelligence Jul 17, 2026

Following the questions where they lead

Assistant Professor Bailey Flanigan has arrived at complex computational methods for helping democracy thrive.

Related papers

#artificial intelligence Review Dec 2025

Professional Software Developers Don't Vibe, They Control: AI Agent Use for Coding in 2025

Investigating how experienced developers use agents in building software, including their motivations, strategies, task suitability, and sentiments finds that while experienced developers value agents as a productivity boost, they retain their agency in software design and implementation out of insistence on fundamental software quality attributes.

Ruanqianqian Huang, Avery Reyna, Sorin Lerner et al. · 19 citations · ⚡1
#gene editing Review Sep 2026

Unlocking non-model organisms with CRISPR-Cas: A roadmap for sustainable biotechnology.

It is concluded that bridging the gap between foundational CRISPR research and its real-world applications is imperative and future efforts should focus on democratizing tools via open-source platforms, advancing delivery systems, and fostering sustainable innovation through synthetic biology integration to fully realize the transformative potential of genome editing in organisms beyond model organisms.

S. Sarsaiya, Archana Jain, Jishuang Chen et al. · 2 citations
#artificial intelligence Preprint Jun 2026

Same Facts, Different Updates: Inference Setup Shapes LLM Behavior in Medical Allocation

This work studies a medical example in which a model is asked to assign resource-allocation probabilities to two people given brief clinical context, and then sees the same scenario with a single extra sentence containing contrasting patient information, showing the context-dependent effect of patient information in a sensitive medical use case.

Spencer J. Gibson, Tyler Crosse, Magnus Saebo et al. · 0 citations