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There’s no doubt that Dr. Ilia Stambler’s Longevity promotion: multidisciplinary perspective is a great book for the advocate and keen supporter of healthy life extension. Check out our review by Nicola Bagalà.


There’s no doubt that Dr. Ilia Stambler’s Longevity promotion: multidisciplinary perspective is a thorough book that all kinds of advocates of healthy longevity may find very useful. The book reads pretty much like a collection of academics papers, each dealing with a different aspect of the matter, including science, history, social and moral implications, legislation, and advocacy. Just like you would expect from an academic work, each section of this book is complete with exhaustive sources that will indubitably prove helpful should you wish to dig deeper into the topic being discussed.

The first section of the book focuses on advocacy, discussing typical concerns raised in the context of life extension, outreach material, and initiatives, and it offers suggestions for effective policies to promote aging and longevity research. The latter part of this section was one of the hardest for me to read since policies and legislation are not at all my strongest suit, but I do believe that professional lobbyists and advocates who have legal and regulatory backgrounds and wish to take action will find numerous ideas in it.

The longevity history section discusses the progression of longevity science during the last century. It was surprising to learn that quite a few well-established scientific disciplines of today, such as endocrinology, owe their existence to early efforts to create rejuvenation treatments. This section discusses other aspects as well, such as the holism vs reductionism controversy in the history of longevity research and the legacy of Elie Metchnikoff, a pioneering immunologist and microbiologist who can safely be regarded as the father of gerontology and made no mystery of his conviction that aging should be considered a disease and treated as such.

Summary: Funding bias is junk science used by industry to hoodwink consumers. This report shows you how to protect yourself against the problem.

The funding bias scandal made headlines recently when the Journal of the American Medical Association (JAMA) broke the news that the sugar industry had paid-off Harvard scientists to down-play sugars role in heart disease. JAMA reported that the sugar industry trade group called the Sugar Research Foundation instructed Harvard researchers to publish reports that down-played sugar’s connection to heart disease, and instead cast doubts on saturated fat.

And in another study, after examining over 200 research studies paid for by a food or beverage organization, researchers from Children’s Hospital Boston found that industry-funded studies were four to eight times more likely to report positive health benefits from consuming those products.

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A growing body of evidence shows that coffee prevents type 2 diabetes.

Type 2 diabetes is a massive health problem that’s about to get worse. A recent study concluded that 40% of Americans alive today are expected to get the disease. Left untreated, the soaring blood sugar caused by type 2 diabetes creates serious health problems throughout the body, including heart disease, stroke, loss of limbs, kidney failure, blindness, and double the risk of death.

Research has shown that drinking coffee is a way to prevent type 2 diabetes.

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We are using A.I. and Computer Vision Techniques to Determine Age and Assess the Effect of Therapies Against Aging in Mice, Increasing the Pace of Life Extension Research. Please subscribe, share, and fund our campaign today! ►Campaign Link: https://www.lifespan.io/mouseage ►Subscribe: https://www.youtube.com/user/LifespanIO?sub_confirmation=1


MouseAGE is working to develop the first photographic biomarker of aging in mice to help validate potential anti-aging interventions, save animal lives, and greatly speed up the pace of longevity research.

To create it we will harness the power of an area of artificial intelligence called Machine Learning, and in particular Deep Learning.

Machine Learning, where a computer system can train itself to become better at a task without explicit programming, has already showed great performance in areas such as human facial recognition, autonomous driving, medical image processing, recommendation engines and many others. While these results are powerful, building up the necessary Neural Networks, or algorithms inspired by the human brain, requires a large dataset of images to use for training: thousands of them.

Due to this, the first stage of the project will be to build a simple instrument for data collection, implemented in a mobile application. This will be distributed among numerous mouse breeding facilities and research universities all over the world to rapidly collect and properly annotate image data for analysis.

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