A drug widely used to treat asthma and allergies may also help fight aggressive cancers, reports a new Northwestern Medicine study that uncovered how tumors hijack common white blood cells to evade immunotherapy.
The findings in mice and human tissues point to a practical, new way to improve treatment for tough tumors, such as triple-negative breast cancer, where immunotherapy often fails.
The study is published in Nature Cancer.
For decades, the relationship between fat and cancer has been treated as a question of quantity: Eat less fat, reduce your risk of developing cancer. But new research published April 29 in Cancer Discovery shows that for pancreatic cancer, the type of fat you consume matters more than the amount.
“It’s really the type of fat that you’re consuming, not just total fat content,” says Christian Felipe Ruiz, Ph.D., an associate research scientist in YSM’s Department of Genetics and lead author of the study. “Depending on the type of fat that you consume, it can go completely different ways. We found that some fats promote cancer, as we would expect, while other fats are really good at suppressing cancer.”
One fat in particular—oleic acid, the primary fatty acid in olive oil—may be accelerating tumor growth in ways scientists never anticipated. The result was surprising given oleic acid’s reputation in medicine. “It’s traditionally been considered a healthy type of fat for cardiovascular health,” Ruiz says.
Underlying cardiovascular risk, rather than older age, drives complications such as venous thromboembolism, cardiomyopathy and heart failure during pregnancy, according to new Weill Cornell Medicine research. The findings may encourage doctors to more actively address cardiovascular health in patients before they become pregnant.
The study, published in Nature Communications, suggests that instead of pregnancy becoming inherently riskier as people get older, it amplifies a person’s baseline cardiovascular risk, regardless of age.
“Pregnancy seems to be a uniform stress test, so to speak,” said the study’s lead author, Dr. Hooman Kamel, vice chair of clinical research and chief of neurocritical care in the Department of Neurology and the Helen and Albert Moon Professor of Neurology at Weill Cornell Medicine.
Losing muscle strength is a natural part of aging. At the core of this decline is a drop in the number of muscle stem cells (MuSCs), the specialized cells responsible for maintaining and regenerating muscle tissue throughout our lives. Loss of muscle strength can severely affect mobility, increasing the risk of falls, fractures and, most importantly, the loss of independence.
Published in Nature Aging, a recent study took a crucial first step toward restoring stem cell function in aging muscles—gaining a clearer understanding of how metabolism changes when stem cells are activated and how these critical processes weaken with age.
The researchers’ investigation led them to glutamine metabolism, the process by which cells use the amino acid glutamine to support essential functions. They found that for MuSCs, glutamine is more than just a nutrient. It provides the raw material needed to produce fatty acids that help cells grow, divide, and repair damaged muscles.
Proteins are critical to life—and to industry. There are countless proteins that could be engineered to treat and even cure serious diseases and cellular dysfunctions. Industrial applications are similarly promising, with proteins increasingly used as enzymes in food manufacturing and in consumer detergents.
While AI can help suggest improvements, each novel protein must still be created in the real world and tested for performance. It is a labor-intensive process that involves constructing the DNA instructions for each protein in yeast or bacteria and growing individual clones for protein production and testing. This can take many days for a single protein of interest and even longer if the protein needs to be tested in mammalian cells, a process that requires retrieving DNA from microbes for transfer to the mammalian cells.
In a new paper, Michael Z. Lin, a professor of neurobiology and of bioengineering in the schools of Engineering and Medicine, and graduate students, Yan Wu in bioengineering and Pengli Wang in chemical engineering, say they have condensed the time-intensive protein building and testing process to just 24 hours.
Age-related macular degeneration (AMD) is the leading cause of irreversible blindness among aging people globally. Around one in seven Australians over the age of 50 have some signs of AMD.
The disease results in blurred and distorted vision, and often loss of function at the center of the eye’s visual field.
The best current treatment involves a series of injections to slow the progression of the disease, but this process can be expensive and difficult with potentially negative long-term effects.
Computer Vision has become ubiquitous in our society, with applications in search, image understanding, apps, mapping, medicine, drones, and self-driving cars. Core to many of these applications are visual recognition tasks such as image classification, localization and detection. Recent developments in neural network (aka “deep learning”) approaches have greatly advanced the performance of these state-of-the-art visual recognition systems. This course is a deep dive into deep learning methods with a focus on end-to-end models for core vision tasks, alongside modern approaches such as transformers, diffusion models, and visual-language models that power today’s AI systems. During the 10-week course, students will learn to implement and train their own neural networks and gain a detailed understanding of cutting-edge research in computer vision. Additionally, the final assignment will give them the opportunity to train and apply multi-million parameter networks on real-world vision problems of their choice. Through multiple hands-on assignments and the final course project, students will acquire the toolset for setting up deep learning tasks and practical engineering tricks for training and fine-tuning deep neural networks. https://online.stanford.edu/courses/cs231n-deep-learning-computer-vision