Lifeboat Foundation: Safeguarding Humanity
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Idiopathic intracranial hypertension (IIH) is a rare condition characterized by increased intracranial pressure, with an unknown cause. However, the pathophysiology of antibiotic-induced IIH remains unclear. The clinical symptoms include headache, visual disturbances, and vomiting. The diagnosis is confirmed by an elevated intracranial pressure (ICP) with normal CSF study and cerebral imaging. Management includes discontinuing the offending antibiotic and reducing ICP with medications such as acetazolamide or diuretics. Therefore, surgical intervention may be necessary in severe cases.

In this article, we report the case of a 19-year-old patient, admitted with symptoms of intracranial hypertension syndrome, occurring three days after receiving antibiotics (gentamicin, penicillin). Physical examination revealed bilateral optic disc edema.

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MIT and Dana-Farber Cancer Institute have teamed up to create an AI model that CRACKS the code of mysterious cancer origins! No more guesswork-this model predicts where tumors come from with up to 95% accuracy. For more insight, visit https://www.channelchek.com #Cancer #CancerBreakthrough #AIinMedicine #MedicalScience #BioTech #FutureOfHealthcare #FightCancer #HealthTech #CancerResearch #PrecisionMedicine

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Researchers at CHU Sainte-Justine and Université de Montréal have discovered a new mechanism involved in the expression of Down syndrome, one of the main causes of intellectual disability and congenital heart defects in children. The study’s findings were published today in Current Biology.

Down (SD), also called trisomy 21 syndrome, is a genetic condition that affects approximately one in every 800 children born in Canada. In these individuals, many genes are expressed abnormally at the same time, making it difficult to determine which contribute to which differences.

Professor Jannic Boehm’s research team focused on RCAN1, a gene that is overexpressed in the brains of fetuses with Down syndrome. The team’s work provides insights into how the gene influences the way the condition manifests itself.

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AI systems, such as GPT-4, can now learn and use human language, but they learn from astronomical amounts of language input—much more than children receive when learning how to understand and speak a language. The best AI systems train on text with a word count in the trillions, whereas children receive just millions per year.

Due to this enormous data gap, researchers have been skeptical that recent AI advances can tell us much about human learning and development. An ideal test for demonstrating a connection would involve training an AI model, not on massive data from the web, but on only the input that a single child receives. What would the model be able to learn then?

A team of New York University researchers ran this exact experiment. They trained a multimodal AI system through the eyes and ears of a single child, using headcam video recordings from when the child was 6 months and through their second birthday. They examined if the AI model could learn words and concepts present in a child’s everyday experience.

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