Lifeboat Foundation: Safeguarding Humanity
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Blog – Page 1917

Contrary to popular perception, traumatic brain injury (TBI) is not the reserve of car accidents and punishing contact sports; it’s surprisingly common. Up to 50 million new cases of traumatic brain injury are registered each year worldwide. Notably, 80% of TBI occurs in low-to middle-income countries, and it is also the leading cause of death and disability in young adults. Overall, the global economic burden of TBI is estimated at 400 billion USD.

Minimising the devastating effects of TBI doesn’t rely solely on reducing the risk of an injury; it’s also essential to improve treatment after one has happened. For that, physiological real-time monitoring of vital signals is critical. One inventor has made it his mission to create devices that can do this accurately, easily, anywhere, and what’s more, they are also non-invasive.

Professor Arminas Ragauskas is a founder and director of the Health Telematics Science Institute at Kaunas University of Technology in Lithuania, which develops innovative industrial and physiological measurement and process monitoring technologies. He is particularly known for his work on non-invasive intracranial pressure and cerebral blood flow autoregulation measurement devices. He was also the national coordinator of the CENTER-TBI project, funded by the European Commission and the EU industry, with a budget of 40 million EUR, and focused European efforts to advance the care of patients with traumatic brain injury.

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While current diagnostic definitions of attention-deficit hyperactivity disorder (ADHD) are relatively new, the general condition has been identified by clinicians under a variety of names for centuries. Recent genetic studies have revealed the condition to be highly heritable, meaning the majority of those with the condition have genetically inherited it from their parents.

Depending on diagnostic criteria, anywhere from two to 16% of children can be classified as having ADHD. In fact, increasing rates of diagnosis over recent years have led to some clinicians arguing the condition is overdiagnosed.

What is relatively clear, however, is that the behavioural characteristics that underpin ADHD have been genetically present in human populations for potentially quite a long time. And that has led some researchers to wonder what the condition’s evolutionary benefits could be.

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Dr. Jean Ristaino: “We searched those descriptions by keywords, and by doing that we were able to recreate the original outbreak maps using location coordinates mentioned in the documents. We were also trying to learn what people were thinking about the disease at the time and where it came from.”

Can plant diseases be tracked through analyzing past reports? This is what a recent study published in Scientific Reports hopes to address as a team of researchers at North Carolina State University (NCSU) attempted to ascertain the causes behind blight disease on plants, known as Phytophthora infestans, that resulted in the Irish potato famine during the 1840s. This study holds the potential to help scientists and farmers not only better understand the causes of blight disease in plants, but also how they might be able to predict them in the future.

Image of a blight lesion on a potato leaf. (Credit: Jean Ristaino, NC State University)

For the study, the researchers analyzed United States farm reports from 1,843 to 1,845 by searching for keywords, including “evil”, “murrain”, “rot”, “black spots”, and “decay”, just to name a few, within the scanned documents using the computer programming language, Python. In the end, the researchers discovered a notable increase in the usage of the keywords, “disease”, “blight”, and “rot” within the reports between 1,843 and 1,845, with the researchers noting the usage of these keywords began occurring in 1,844, indicating the disease began in 1843.

Continue reading “Tracking the Trajectory of Late Blight Disease: A Text Mining Study from 1840s to Modern Times” | >

Summary: Researchers developed an artificial intelligence model that accurately determines the sex of individuals based on brain scans, with over 90% success. This breakthrough supports the theory that significant sex differences in brain organization exist, challenging long-standing controversies.

The AI model focused on dynamic MRI scans, identifying specific brain networks—such as the default mode, striatum, and limbic networks—as critical in distinguishing male from female brains.

This research not only deepens our understanding of brain development and aging but also opens new avenues for addressing sex-specific vulnerabilities in psychiatric and neurological disorders.

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