Far from being a blueprint for an organism, genes are mere tools used by life’s true expert builders: cells.

The root cause is frustratingly simple: one gene mutation, which affects a critical protein that helps support skin integrity. The single genetic error makes the illness a perfect candidate for gene therapy. Yet with the skin already fragile, injections—a current standard for gene therapy—are hard to tolerate.
What about a genetic moisturizer instead?
This month, the FDA approved the first rub-on gene therapy. Similar to aloe vera for treating sunburns, the therapy comes in a gel that’s gently massaged onto blisters and wounds to help with healing. Dubbed Vyjuvek, it directly delivers healthy copies of the mutated gene onto damaged skin. An alternative version is configured into eye drops to reconstruct the eye’s delicate architecture to better support sight.
Don’t try finding Zuzalu on a map; it doesn’t exist anymore. It was a “pop-up city” conceived by the tech entrepreneur Vitalik Buterin, creator of Ethereum, and a group of like-minded people to facilitate co-living and collaboration in fields like crypto, network states, AI, and longevity. It was also, in substantial part, funded by Vitalik.
Zuzalu, located on the Adriatic coast of Montenegro, began its short history on March 25 and wound down on May 25. It was a complex and memorable phenomenon, and I’m wrapping my mind around a larger article in the works.
Usually, I don’t eat breakfast due to my intermittent fasting regimen, but in Zuzalu, breakfast, served at a particular local restaurant, was the healthiest meal of the day. Also, it was free (kudos to Vitalik, and more on that later). Most importantly, it was the place to meet new people.
A groundbreaking manifesto on living better and longer that challenges the conventional medical thinking on aging and reveals a new approach to preventing chronic disease and extending long-term health, from a visionary physician and leading longevity expert.
“One of the most important books you’ll ever read.”—Steven D. Levitt, New York Times bestselling author of Freakonomics
Wouldn’t you like to live longer? And better? In this operating manual for longevity, Dr. Peter Attia draws on the latest science to deliver innovative nutritional interventions, techniques for optimizing exercise and sleep, and tools for addressing emotional and mental health.
😗😁
In the pursuit of extending healthy human lifespans, scientists have achieved a remarkable breakthrough that marks a significant milestone in the field. Researchers from Taipei Medical University in Taiwan have uncovered a genetic modification in mice that can empower cancer-killing cells, increasing their effectiveness by two to seven times while extending their lifespan by up to 20 percent.
Building upon last year’s groundbreaking study, the scientists have now successfully replicated these extraordinary outcomes in ordinary mice through a single transplant of blood stem cells. The findings, published in the scientific journal Cold Spring Harbor Protocols, hold immense importance, according to Che-Kun James Shen, the lead researcher of the study. He believes that these findings could have profound implications for human health and anticipates that clinical trials could commence as early as the end of this year or next year.
The initial discovery involved identifying an amino acid, known as KLF1, that, when altered, preserves the youthful characteristics of the mice. This includes improved motor function, enhanced learning and memory, as well as more effective anti-cancer cells. Additionally, the mice exhibited darker and shinier hair, and a significant reduction in fibrosis—a process associated with impaired organ functioning that occurs during aging.
Fiber-optic imaging methods enable in vivo imaging deep inside hollow organs or tissues that are otherwise inaccessible to free-space optical techniques, playing a vital role in clinical practice and fundamental research, such as endoscopic diagnosis and deep-brain imaging.
Recently, supervised learning-based fiber-optic imaging methods have gained popularity due to their superior performance in recovering high-fidelity images from fiber-delivered degraded images or even scrambled speckle patterns. Despite their success, these methods are fundamentally limited by their requirements for strictly-paired labeling and large training datasets.
The demanding training data requirements result in time-consuming data acquisition, complicated experimental design, and tedious system calibration processes, making it challenging to satisfy practical application needs.