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Aging will be cured within 20 years

Lately, there’s been growing pushback against the idea that AI will transform geroscience in the short term.
When Nobel laureate Demis Hassabis told 60 Minutes that AI could help cure every disease within 5–10 years, many in the longevity and biotech communities scoffed. Leading aging biologists called it wishful thinking — or outright fantasy.
They argue that we still lack crucial biological data to train AI models, and that experiments and clinical trials move too slowly to change the timeline.

Our guest in this episode, Professor Derya Unutmaz, knows these objections well. But he’s firmly on Team Hassabis.
In fact, Unutmaz goes even further. He says we won’t just cure diseases — we’ll solve aging itself within the next 20 years.

And best of all, he offers a surprisingly detailed, concrete explanation of how it will happen:
building virtual cells, modeling entire biological systems in silico, and dramatically accelerating drug discovery — powered by next-generation AI reasoning engines.

🧬 In this wide-ranging conversation, we also cover:

✅ Why biological complexity is no longer an unsolvable barrier.
✅ How digital twins could revolutionize diagnosis and treatment.
✅ Why clinical trials as we know them may soon collapse.
✅ The accelerating timeline toward longevity escape velocity.
✅ How reasoning AIs (like GPT-4o, o1, DeepSeek) are changing scientific research.
✅ Whether AI creativity challenges the idea that only biological minds can create.
✅ Why AI will force a new culture of leisure, curiosity, and human flourishing.
✅ The existential stress that will come as AI outperforms human expertise.
✅ Why “Don’t die” is no longer a joke — it’s real advice.

🎙️ Hosted — as always — by Peter Ottsjö (tech journalist and author of Evigt Ung) and Dr. Patrick Linden (philosopher and author of The Case Against Death).

‘Magic mushrooms’ show promise for improving motor function and mood in Parkinson’s patients

Psilocybin, a natural compound found in certain mushrooms, has shown promise in treating depression and anxiety. UC San Francisco researchers wanted to know if it could be used to help Parkinson’s patients who often experience debilitating mood dysfunction in addition to their motor symptoms and don’t respond well to antidepressants or other medications.

The results were surprising.

Not only did participants tolerate the drug without or worsening symptoms, which is what the was designed to test, they also experienced clinically significant improvements in mood, cognition, and that lasted for weeks after the drug was out of their systems.

Engineering Cup-Shaped Nanomotors for Promoting Cell Internalization and Synergistic Tumor Therapy

Self-propulsion enzymatic nanomotors have shown tremendous potential in the field of diagnostics. In a study led by Wang and coworkers, nanoenzyme-driven cup-shaped nanomotors were designed for enhanced cell penetration and synergistic photodynamic/thermal treatments under single near-infrared laser irradiation. By combining the concepts of self-propulsion enzymatic nanomotors and synergistic dual-modal therapy, this work provides a new idea and tool for the application of nanomotors in the biomedical field.

Enzymes critical for astrocytic GABA production in Alzheimer’s disease identified!

A research team has identified a previously unknown enzyme, SIRT2, that plays a key role in memory loss associated with Alzheimer’s disease (AD). The study provides critical insights into how astrocytes contribute to cognitive decline by producing excessive amounts of the inhibitory neurotransmitter GABA.

Astrocytes, once thought to only support neurons, are now known to actively influence brain function. In Alzheimer’s disease, astrocytes become reactive, meaning they change their behavior in response to the presence of amyloid-beta (Aβ) plaques, a hallmark of the disease. While astrocytes attempt to clear these plaques, this process triggers a harmful chain reaction. First, they uptake them via autophagy and degrade them by the urea cycle, as discovered in previous research. However, this breakdown results in the overproduction of GABA, which dampens brain activity and leads to memory impairment. Additionally, this pathway generates hydrogen peroxide (H2O2), a toxic byproduct that causes further neuronal death and neurodegeneration.

The research team set out to uncover which enzymes were responsible for excessive GABA production, hoping to find a way to selectively block its harmful effects without interfering with other brain functions. Using molecular analysis, microscopic imaging, and electrophysiology, the researchers identified SIRT2 and ALDH1A1 as critical enzymes involved in GABA overproduction in Alzheimer’s-affected astrocytes.

Ultrasound-activated cilia can clean implanted stents and catheters

Urinary stents and catheters are implanted medical tubes that are widely used in human and veterinary medicine to drain urine to/from the bladder. Ureteral stents are used when the ureter, the duct between the kidney and bladder, is blocked by tumors, pregnancy, stones or anatomical narrowing.

Biofilm, produced by bacteria, and crystalline deposits, called encrustation, grow on the inner and outer walls of such stents and catheters soon after implantation and are among the main causes of failure of these devices because they lead to painful blockages and urinary infections.

To mitigate these issues, urinary stents and catheters therefore must be replaced every two to six months, which not only considerably restricts the quality of life of those affected but also leads to high hospital load and costs.

Computational analysis clarifies cancer risk for families with genetic variants

QIMR Berghofer-led research has shown that new advanced computational prediction tools can improve the accuracy of genetic testing for families affected by an inherited condition that significantly increases their risk of developing cancer, paving the way to better targeted care.

The findings have been published in the American Journal of Human Genetics alongside complementary studies by international collaborators, which together show how incorporating the new computational biology tools with existing modeling methods improved the predictive power of genetic test results.

Computational tools are used to predict if and how a genetic is likely to impact the function of the protein encoded by the gene.

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