Lifeboat Foundation: Safeguarding Humanity
Toggle light / dark theme

Blog

Get the latest international news and world events from around the world.

Log in for authorized contributors

Anthropic is “destructively scanning” millions of books. Should you care?

An informative data-driven perspective which explores why the alarming-sounding headlines about Anthropic’s destructive book scanning efforts may be misleading (and even manipulative). To be clear, I’m not saying that all critiques of tech companies are invalid. It’s just that the evidence for the validity of this specific moral panic is thinner than it might appear.

I’m a proponent of looking deeply into the underlying context and data of complex issues like this one. Many news outlets do report in a negativity-biased fashion since they can stir up outrage and get more attention. So, I feel it is important to be media literate and pay attention to the nuances of issues.

I should also note that this is just a Substack post and thus does not constitute a verified primary source like a scientific paper. From what I can tell, it makes legitimate arguments and presents legitimate data, but this is not a 100% certain conclusion.

On the curious debate over AI companies buying, digitizing, and discarding “rare” books.

A protein that fuels cancer could also be the key to fighting it

The findings, published in Nature Immunology, could be especially important for colorectal cancer, which has become the leading cause of cancer-related death among people under 50. Many patients with difficult-to-treat forms of the disease have limited options, particularly when chemotherapy and newer immunotherapy treatments fail. The research suggests that a protein normally associated with cancer growth could instead become a powerful cancer-fighting “force” when activated in the right cells.

Turning a Cancer-Promoting Protein Into an Immune System Weapon

Scientists have long understood that a patient’s physical fitness can influence their ability to withstand illness. But CSHL Assistant Professor Semir Beyaz is exploring a different kind of fitness: the metabolic health and performance of individual cells.

Application of Artificial Intelligence in the Prediction and Management of Stroke Rehabilitation

Stroke prediction research includes acute diagnosis, acute prognostication, rehabilitation-outcome forecasting, and long-term risk management. Each addresses a different clinical question and requires different supporting evidence. Even technically sophisticated models may be difficult to use across heterogeneous settings because performance in a development dataset may not persist in other populations or fit routine workflows.

Acute imaging and early prognostic models provide baseline information that may inform subsequent rehabilitation planning. Neuroimaging can characterize lesion location, volume, and tissue viability; early clinical severity scores capture initial impairment; and acute-treatment data document reperfusion status and complication risk. These variables may be incorporated as covariates in rehabilitation prediction models, while timely acute diagnosis and treatment may influence the functional baseline from which rehabilitation begins.

Measures of acute diagnostic accuracy or early prognostic discrimination answer an early-care question; they do not show that a rehabilitation intervention is effective or that algorithm-guided adjustment improves long-term recovery. Rehabilitation claims therefore require models developed and validated for outcomes such as motor function, ADLs, participation, or quality of life. Whenever possible, studies should also test whether using the model changes clinical decisions or patient outcomes compared with standard care.

Identifying Genomic Biomarkers of Rare Disease Subtypes Using Minority-Weighted Feature Selection

Introduces a novel minority-aware and robust feature selection framework, GenMWS, for high-dimensional class-imbalanced gene expression data.

Constructs class-specific core intervals and weighted own-class/other-class membership scores to capture gene-wise class structure.

Develops a weighted nonoverlap mask and class-specific support summaries to emphasize minority-class informative patterns while controlling majority-supported structure.

Top Longevity Experts Debate Each Other About Extending Human Lifespan

What if the fastest way to a longer life isn’t a miracle drug, but a wartime mentality against aging?

This episode is brought to you by AgelessRx, where aging becomes a puzzle that can be solved.

Curious about quantum energy wellness? Check out Leela Quantum Tech. Check out https://leelaq.com/

On this episode of Beyond Tomorrow, four heavyweights of the longevity field take a rapid-fire statement format and turn it into a real debate. Aubrey de Grey, Alex Zhavoronkov (Insilico Medicine), George Church (Harvard Medical School), Michael Ringel (Life Biosciences) argue over whether hype helps or hurts the field, and when longevity escape velocity, the point where damage is repaired faster than it accumulates, might actually arrive. Aubrey puts it at a 50/50 chance within 12 to 15 years. Alex says 20 to 50 years and doubts drugs alone can get there. Michael pushes back on the idea of infinite lifespans entirely, while George suggests we may already be gaining years without the evidence to prove it.

From there the panel digs into the FDA’s new interest in aging, why the TAME metformin trial never opened the floodgates, and whether AI will really shrink clinical timelines. George cites a gene therapy that reached a patient in seven months, while Alex, running many programs, argues that biology still moves at the speed of traffic. The back half covers biohacking and early adopters, the gap between everyday \.