Labs that can’t afford expensive super-resolution microscopes could use a new expansion technique to image nanoscale structures inside cells. Source: MIT News

As strains of pathogens resistant to frontline antibiotics become more common worldwide, clinicians are more often turning to combination treatments that degrade this resistance as a first treatment option.

Researchers from Duke University have discovered the mechanism behind why some antibiotic-resistant pathogens haven't adapted to the combination treatments—the bacteria’s level of “selfishness.” The insight provides guidance to clinicians on how to best tailor these combination treatments to different pathogens, minimize the selection for resistance and formulate new antibiotic resistance inhibitors.  Source: Duke University Pratt School of Engineering

Using smartly trained neural networks, researchers at the University of Technology Graz funded in part by NIBIB have succeeded in generating precise real-time images of the beating heart from just a few MRI measurement data. Other MRI applications can also be accelerated using this procedure. Source: TU Graz News

Research into harnessing the immune system to encourage injured tissue to regenerate has landed a Maryland researcher on a TIME magazine list of 2024 innovators. During a WTOP visit to the lab she leads at the National Institute of Biomedical Imaging and Bioengineering, researcher Kaitlyn Sadtler, explained its goal is to understand the immune system’s role in wound healing and how it could be leveraged by medical technology to regenerate tissue.

As AI is deployed in clinical centers across the U.S., one important consideration is to assure that models are fair and perform equally across patient groups and populations. To better understand the fairness of medical imaging AI, researchers trained over 3,000 models spanning multiple model configurations, algorithms, and clinical tasks. Their analysis of these models reinforced some previous findings about bias in AI algorithms and uncovered new insights about deployment of models in diverse settings.

UCLA researchers have developed a deep-learning framework that teaches itself quickly to automatically analyze and diagnose MRIs and other 3D medical images – with accuracy matching that of medical specialists in a fraction of the time.  Source: UCLA Computational Medicine News

Cancer cells frequently overhaul their surroundings, making tumors stiffer than nearby healthy tissue. While tumor stiffening makes some cancers easier to detect, it can also ramp up drug resistance. New research suggests that these detrimental changes are not set in stone, however.

The dynamics of blood nutrient and lipid levels after consuming a high-fat meal are crucial indicators of both current and future cardiovascular health. A recent NIBIB-funded study from Boston University, Harvard Medical School, and Brigham and Women's Hospital investigated how meal composition affects skin tissue properties shortly after eating. Source: The International Society for Optics and Photonics News 

Within bacterial cells, specialized immune systems known as retrons fend off viral attacks. They can also perform precise DNA editing. 

In a new study published in Nature Biotechnology, Shipman and his team greatly expand the universe of retron knowledge. They carried out a “census” of 163 never-before-tested retrons and identified many that can edit DNA more quickly and efficiently than those currently used in research. This research may contribute to genome engineering and future gene therapies.