Lifeboat Foundation

ABOVE: After years of research, brain organoids now come close to mimicking endogenous brain cells. © iStock, StockSnap.

As a developmental neurobiologist at Harvard University, Paola Arlotta spends most of her time thinking about how the brain develops, how it functions, and what goes wrong in the context of neurological disease. Using human brain organoids as a model for brain development and disease research has been a game changer, providing Arlotta a novel view into brain pathologies that form in utero.

Microplastics found in blood vessels are linked to a greater risk of heart problems, a new study reports.

The new hardware reimagines AI chips for modern workloads and can run powerful AI systems using much less energy than today’s most advanced semiconductors, according to Naveen Verma, professor of electrical and computer engineering. Verma, who will lead the project, said the advances break through key barriers that have stymied chips for AI, including size, efficiency and scalability.

Chips that require less energy can be deployed to run AI in more dynamic environments, from laptops and phones to hospitals and highways to low-Earth orbit and beyond. The kinds of chips that power today’s most advanced models are too bulky and inefficient to run on small devices, and are primarily constrained to server racks and large data centers.

Now, the Defense Advanced Research Projects Agency, or DARPA, has announced it will support Verma’s work, based on a suite of key inventions from his lab, with an $18.6 million grant. The DARPA funding will drive an exploration into how fast, compact and power-efficient the new chip can get.

Harvard longevity researcher David Sinclair recently made an astonishing assertion: Scientists had developed the first pill “proven to reverse aging” in dogs.

The University of Chicago Medicine is among the first 30 institutions in the country to offer tumor-infiltrating lymphocyte (TIL) therapy for advanced melanoma, immediately activating as an authorized treatment center after federal regulators approved the treatment on February 16, 2024.

Effortless, enjoyable productivity is a state of consciousness prized and sought after by people in business, the arts, research, education and anyone else who wants to produce a stream of creative ideas and products. That’s the flow, or the sense of being “in the zone.” A new neuroimaging study from Drexel University’s Creativity Research Lab is the first to reveal how the brain gets to the creative flow state.

The study is published in the journal Neuropsychologia.

Continue reading “Your brain in the zone: A new neuroimaging study reveals how the brain achieves a creative flow state” »

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.

We may be on the cusp of an era of astonishing innovation — the limits of which aren’t even clear yet.

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

The amount of digital data available is greater than ever before, including in health care, where doctors’ notes are routinely entered into electronic health record systems. Manually reviewing, analyzing, and sorting all these notes requires a vast amount of time and effort, which is exactly why computer scientists have developed artificial intelligence and machine learning techniques to infer medical conditions, demographic traits, and other key information from this written text.

However, safety concerns limit the deployment of such models in practice. One key challenge is that the medical notes used to train and validate these models may differ greatly across hospitals, providers, and time. As a result, models trained at one hospital may not perform reliably when they’re deployed elsewhere.

Previous seminal works by Johns Hopkins University’s Suchi Saria—an associate professor of computer science at the Whiting School of Engineering—and researchers from other top institutions recognize these “dataset shifts” as a major concern in the safety of AI deployment.