Lifeboat Foundation

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.

A UK-led team of researchers restrained mice for 6 hours to induce a stress response and then analyzed the rodents’ brains on a molecular level.⁠

This led to the discovery of increased levels of five microRNAs (miRNAs) — small molecules that help determine which genes in a cell are expressed and which aren’t — in the amygdala, the brain region implicated in anxiety. When the researchers took a closer look at the miRNA that reached the highest levels, miR-483-5p, they saw that it suppressed the expression of the Pgap2 gene — and that this suppression appeared to provide stress relief and reduce anxiety-related behavior.⁠

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Continue reading “Scientists discover ‘anxiety gene’ in the brain — and a natural way to turn it off” »

Progress update: Our latest AlphaFold model shows significantly improved accuracy and expands coverage beyond proteins to other biological molecules, including ligands.

Since its release in 2020, AlphaFold has revolutionized how proteins and their interactions are understood. Google DeepMind and Isomorphic Labs have been working together to build the foundations of a more powerful AI model that expands coverage beyond just proteins to the full range of biologically-relevant molecules.

Today we’re sharing an update on progress towards the next generation of AlphaFold. Our latest model can now generate predictions for nearly all molecules in the Protein Data Bank (PDB), frequently reaching atomic accuracy.

While a federal judge advanced an infringement claim against Stability AI, he dismissed the rest of the lawsuit.

In a new study, Deepmind and colleagues at Isomorphic Labs show early results from a new version of AlphaFold that brings fully automated structure prediction of biological molecules closer to reality.

The Google Deepmind AlphaFold and Isomorphic Labs team today unveiled the latest AlphaFold model. According to the companies, the updated model can now predict the structure of almost any molecule in the Protein Data Bank (PDB), often with atomic accuracy. This development, they say, is an important step towards a better understanding of the complex biological mechanisms within cells.

Since its launch in 2020, AlphaFold has influenced protein structure prediction worldwide. The latest version of the model goes beyond proteins to include a wide range of biologically relevant molecules such as ligands, nucleic acids and post-translational modifications. These structures are critical to understanding biological mechanisms in cells and have been difficult to predict with high accuracy, according to Deepmind.