Lifeboat Foundation

NOTE FROM TED: Research around aging discussed in this talk remains an ongoing field of study. Please do not look to this talk for health advice. TEDx events are independently organized by volunteers. The guidelines we give TEDx organizers are described in more detail here: http://storage.ted.com/tedx/manuals/tedx_content_guidelines.pdf.

Have you ever wondered how long you will live? And if so, how could you change that number to live drastically longer? The science might be in your favor: follow David Sinclair, Australian biologist and professor of genetics at Harvard University, as he shares his research on slowing and reversing the process of aging in mice, and how the same technology may someday be transferable to humans. David Sinclair, Australian biologist and professor of genetics at Harvard Universityhis insightful research into the science of age reversal and anti-aging medicine.

Continue reading “Is Aging Reversible? A Scientific Look with David Sinclair | David Sinclair | TEDxBoston” »

Scientists have managed to safely reverse the signs of aging in mice by genetically reprogramming some of their cells.

Speculate to accumulate lifespan – how crypto-conglomerate Moon Rabbit is going bigger and better for everyone.

Scientists said this full picture of the genome will give humanity a greater understanding of our evolution and biology while also opening the door to medical discoveries in areas like aging, neurodegenerative conditions, cancer and heart disease.

“We’re just broadening our opportunities to understand human disease,” said Karen Miga, an author of one of the six studies published Thursday.

The research caps off decades of work. The first draft of the human genome was announced in a White House ceremony in 2000 by leaders of two competing entities: an international publicly funded project led by an agency of the U.S. National Institutes of Health and a private company, Maryland-based Celera Genomics.

How the Japanese manufacturer YKK has stayed at the top of its industry.

Reward maximisation is one strategy that works for reinforcement learning to achieve general artificial intelligence. However, deep reinforcement learning algorithms shouldn’t depend on reward maximisation alone.

Identifying dual-purpose therapeutic targets implicated in aging and disease will extend healthspan and delay age-related health issues.

AI is all that matters now, and reaching Agi before 2030 is all that matters for this decade.

A substantial percentage of the human clinical trials, including those evaluating investigational anti-aging drugs, fail in Phase II, a phase where the efficacy of the drug is tested. This poor success is in part due to inadequate target choice and the inability to identify a group of patients who will most likely respond to specific agents. This challenge is further complicated by the differences in the biological age of the patients, as the importance of therapeutic targets varies between the age groups. Unfortunately, most targets are discovered without considering patients’ age and being tested in a relatively younger population (average age in phase I is 24). Hence, identifying potential targets that are implicated in multiple age-associated diseases, and also play a role in the basic biology of aging, may have substantial benefits.

Identifying dual-purpose targets that are implicated in aging and disease at the same time will extend healthspan and delay age-related health issues – even if the target is not the most important in a specific patient, the drug would still benefit that patient.

Continue reading “Insilico identifies therapeutic targets implicated in aging using AI and hallmarks of aging framework” »

Great article by the great Steve Hill. One little thing:

“The life extension community unfortunately does have a reputation for being long on promises and short on delivery. With what is now decades of research, there are still no effective therapies against aging.”

Continue reading “Life Extension and Anti-Aging Have a Branding Problem” »

Mayo Clinic researchers have proposed a new model for mapping the symptoms of Alzheimer’s disease to brain anatomy. This model was developed by applying machine learning to patient brain imaging data. It uses the entire function of the brain rather than specific brain regions or networks to explain the relationship between brain anatomy and mental processing. The findings are reported in Nature Communications.

“This new model can advance our understanding of how the brain works and breaks down during aging and Alzheimer’s disease, providing new ways to monitor, prevent and treat disorders of the mind,” says David T. Jones, M.D., a Mayo Clinic neurologist and lead author of the study.

Alzheimer’s disease typically has been described as a protein-processing problem. The toxic proteins amyloid and tau deposit in areas of the brain, causing neuron failure that results in clinical symptoms such as memory loss, difficulty communicating and confusion.