Lifeboat Foundation: Safeguarding Humanity
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Category: biotech/medical – Page 272

There used to be the concept of a “singularity”. The idea was that computers would become smarter than humans and start to replace them. Even the idea that humanity would be substituted by silicon-based computing machines (robots) was suggested. Against that two years ago we set the concept of “The convergence”. This assumes that biological and silicon-based computation would merge in the sense of better control over biological processes like diseases and longevity. It is essentially a deeply humanistic perspective, in spite of being futuristic, and not taking misuses actively into account.

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In a paper titled, “Multimodal MRI reveals brainstem connections that sustain wakefulness in human consciousness,” published today in Science Translational Medicine, a group of researchers at Massachusetts General Hospital, a founding member of the Mass General Brigham healthcare system, and Boston Children’s Hospital, created a connectivity map of a brain network that they propose is critical to human consciousness.

The study involved high-resolution scans that enabled the researchers to visualize brain connections at submillimeter spatial resolution. This technical advance allowed them to identify previously unseen pathways connecting the brainstem, thalamus, hypothalamus, basal forebrain, and cerebral cortex.

Together, these pathways form a “default ascending arousal network” that sustains wakefulness in the resting, conscious human brain. The concept of a “default” network is based on the idea that specific networks within the brain are most functionally active when the brain is in a resting state of consciousness. In contrast, other networks are more active when the brain is performing goal-directed tasks.

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Summary: Researchers developed a groundbreaking model called Brain Language Model (BrainLM) using generative artificial intelligence to map brain activity and its implications for behavior and disease. BrainLM leverages 80,000 scans from 40,000 subjects to create a foundational model that captures the dynamics of brain activity without the need for specific disease-related data.

This model significantly reduces the cost and scale of data required for traditional brain studies, offering a robust framework that can predict conditions like depression, anxiety, and PTSD more effectively than other tools. The BrainLM demonstrates a potent application in clinical trials, potentially halving the costs by identifying patients most likely to benefit from new treatments.

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Depression and cardiovascular disease (CVD) are serious concerns for public health. Approximately 280 million people worldwide have depression, while 620 million people have CVD.

It has been known since the 1990s that the two diseases are somehow related. For example, people with depression run a greater risk of CVD, while effective early treatment for depression cuts the risk of subsequently developing CVD by half. Conversely, people with CVD tend to have depression as well. For these reasons, the American Heart Association (AHA) advises to monitor teenagers with depression for CVD.

What wasn’t yet known is what causes this apparent relatedness between the two diseases. Part of the answer probably lies in lifestyle factors common in patients with depression and which increase the risk of CVD, such as smoking, alcohol abuse, lack of exercise, and a poor diet. But it’s also possible that both diseases might be related at a deeper level, through shared developmental pathways.

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