Lifeboat Foundation

Human decision-making has been the focus of countless neuroscience studies, which try to identify the neural circuits and brain regions that support different types of decisions. Some of these research efforts focus on the choices humans make while gambling and taking risks, yet the neural underpinnings of these choices have not yet been fully elucidated.

Researchers at University of Louisville carried out a study aimed at better understanding the patterns in neural network communication associated with ‘bad’ decisions made while gambling. Their paper, published in Frontiers in Neuroscience, shows that different types of ‘bad’ decisions made while gambling, namely avoidant and approach decisions, are associated with distinct neural communication patterns.

“Our recent work follows a line of research that examines how humans approach rewarding and punishing situations in the environment,” Brendan Depue and Siraj Lyons, the researchers who carried out the study, told Medical Xpress.

“The disease starts to develop 20 years before the onset of symptoms, so it’s important to detect it early,” Per Nilsson said.

Helen Waters, chief operating officer at the EHR giant, describes the company’s artificial intelligence strategy and shows how provider organizations can benefit today from NLP, LLMs, generative AI and more.

They also recognised that AI itself may exhibit certain biases, and different settings it was deployed with were able to dramatically change output, in extreme cases rendering it unusable. In other words, setting the bots up correctly is a prerequisite to success. At least today.

So, for the time being, I think we’re going to see a rapid rise in human-AI cooperation rather than outright replacement.

However, it’s also difficult to escape the impression that through it we will be raising our successors and, in not so distant future, humans will be limited to only setting goals for AI to accomplish, while mastering programming languages will be akin to learning Latin.

In a study of more than 2,000 chest X-rays, radiologists outperformed AI in accurately identifying the presence and absence of three common lung diseases, according to a study published in Radiology, a journal of the Radiological Society of North America (RSNA).

“Chest radiography is a common diagnostic tool, but significant training and experience is required to interpret exams correctly,” said lead researcher Louis L. Plesner, M.D., resident radiologist and Ph.D. fellow in the Department of Radiology at Herlev and Gentofte Hospital in Copenhagen, Denmark.

While commercially available and FDA-approved AI tools are available to assist radiologists, Dr. Plesner said the clinical use of deep-learning-based AI tools for radiological diagnosis is in its infancy.

The thymus, a small and relatively unknown organ, may play a bigger role in the immune system of adults than was previously believed. With age, the glandular tissue in the thymus is replaced by fat, but, according to a new study from Linköping University, the rate at which this happens is linked to sex, age and lifestyle factors. These findings also indicate that the appearance of the thymus reflects the aging of the immune system.

“We doctors can assess the appearance of the thymus from largely all chest CT scans, but we tend to not see this as very important. But now it turns out that the appearance of the thymus can actually provide a lot of valuable information that we could benefit from and learn more about,” says Mårten Sandstedt, MD, Ph.D., at the Department of Radiology in Linköping and Department of Health, Medicine and Caring Sciences, Faculty of Medicine and Health Sciences, Linköping University.

The thymus is a gland located in the upper part of the chest. It has been long known that this small organ is important for immune defense development in children. After puberty, the thymus decreases in size and is eventually replaced by fat, in a process known as fatty degeneration. This has been taken to mean that it loses its function, which is why the thymus has for a long time been considered as being not important in adult life.

Machine learning is essential to designing the polymers, Murthy emphasizes, because they must be tailored to the specific gene therapy.

“There’s a tight interplay between the payload and in vivo mechanism of action, and the delivery vehicle needed to bring [the therapy] to that location,” he says. “You can’t have one without the other, so they have to be integrated at an early stage.”

The company hopes to use machine learning to explore the polymer design space, giving them a starting point to design a polymer. Subsequently, as the gene therapy moves from the preclinical to clinical stage, they can use artificial intelligence to tweak the polymer to make the therapy work better.

A new study looks at decades of gain-of-function pathogen studies and concludes that the controversial research field will be difficult to regulate. It comes as activists and politicians are seeking to ban the practice and as the federal government is considering new restrictions.

Researchers at the University of Wisconsin–Madison and Academia Sinica of Taiwan have harnessed a combination of lab-grown cells to regenerate damaged heart muscle.

The study is published in Circulation. It addresses major challenges of using heart muscle cells, called cardiomyocytes, grown from stem cells, and takes a crucial step toward future clinical applications.

Previous research has shown that transplanting cardiomyocytes made from induced pluripotent stem cells (iPSC) can replace muscle in the hearts of mammals. Researchers have struggled to bring the treatment to the clinic, in part because the implanted cells haven’t developed enough life-sustaining blood vessels to survive very long.