A new analysis identified several ways that we can reduce the odds of cancer occurring.
It’s easy to feel powerless against cancer, but a new study has identified several ways that we can reduce the odds of it occurring.
According to new analysis from the World Health Organization (WHO), more than a third of all cancer cases globally are preventable.
Lung, stomach, and cervical cancers make up nearly half of those cases.
Gu et al. present NEAT, a nanoengineered extrusion-aligned tract bioprinting strategy that fabricates aligned, human neural stem cell-laden collagen hydrogel constructs through shear-induced fibrillar organization. In a rat model of complete spinal cord transection, NEAT enables axonal reconnection and functional locomotor recovery, demonstrating its translational potential for spinal cord repair and neural tissue engineering.
Chronic sleep disruption doesn’t just leave people tired and irritable. It may quietly undermine the gut’s ability to repair itself, increasing vulnerability to serious digestive diseases. A new study from the University of California, Irvine, the University of Chinese Academy of Sciences and the China Agricultural University reveals, step by step, how disturbed sleep causes the brain to send harmful signals to the intestines, ultimately damaging the stem cells responsible for maintaining a healthy gut lining.
The research uncovers a previously unknown biological chain reaction linking the brain’s sleep center to intestinal health. The findings are published in Cell Stem Cell and offer new insight into why people with chronic sleep problems are more likely to develop gastrointestinal disorders such as inflammatory bowel disease, diabetes-related gut complications and chronic inflammation.
Physicians have long known that irregular or insufficient sleep is associated with a wide range of health problems, from mood disorders to high blood pressure. Yet how changes in sleep can directly harm organs that do not sleep themselves, such as the intestines, has remained largely elusive. This study answers that question by tracing the damage from its neurological origins all the way to the gut’s regenerative machinery.
All life on Earth shares a common ancestor that lived roughly four billion years ago. This so-called “last universal common ancestor” (LUCA) represents the most ancient organism that researchers can study. Previous research on the last universal common ancestor has found that all the characteristics we see in organisms today, like having a cell membrane and a DNA genome, were already present by the time of this ancestor. So, if we want to understand how these foundational characteristics of life first emerged, then we need to be able to study evolutionary history prior to the last universal common ancestor.
In an article published in the journal Cell Genomics, scientists Aaron Goldman (Oberlin College), Greg Fournier (MIT), and Betül Kaçar (University of Wisconsin‑Madison) describe a method to do just that.
“While the last universal common ancestor is the most ancient organism we can study with evolutionary methods,” said Goldman, “some of the genes in its genome were much older.” The authors describe a type of gene family known as a “universal paralog,” which provides evidence of evolutionary events that occurred before the last universal common ancestor.
The trajectory of a storm, the evolution of stock prices, the spread of disease — mathematicians can describe any phenomenon that changes in time or space using what are known as partial differential equations. But there’s a problem: These “PDEs” are often so complicated that it’s impossible to solve them directly.
Mathematicians instead rely on a clever workaround. They might not know how to compute the exact solution to a given equation, but they can try to show that this solution must be “regular,” or well-behaved in a certain sense — that its values won’t suddenly jump in a physically impossible way, for instance. If a solution is regular, mathematicians can use a variety of tools to approximate it, gaining a better understanding of the phenomenon they want to study.
But many of the PDEs that describe realistic situations have remained out of reach. Mathematicians haven’t been able to show that their solutions are regular. In particular, some of these out-of-reach equations belong to a special class of PDEs that researchers spent a century developing a theory of — a theory that no one could get to work for this one subclass. They’d hit a wall.
Matthijs Luxen et al. comment on Neda Vishlaghi et al.: https://doi.org/10.1172/JCI201199
Address correspondence to: Benjamin Levi, Department of Surgery, University of Texas Southwestern, 6,000 Harry Hines Blvd., Dallas, Texas 75,235, USA. Phone: 214.648.9017; Email: Benjamin. [email protected].
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1Department of Surgery, University of Texas Southwestern Medical Center, Dallas, Texas, USA.
An AI-powered model developed at the University of Michigan can read a brain MRI and diagnose a person in seconds, a study suggests. The model detected neurological conditions with up to 97.5% accuracy and predicted how urgently a patient required treatment.
Researchers say the first-of-its-kind technology could transform neuroimaging at health systems across the United States. The results are published in Nature Biomedical Engineering.
“As the global demand for MRI rises and places significant strain on our physicians and health systems, our AI model has the potential to reduce burden by improving diagnosis and treatment with fast, accurate information,” said senior author Todd Hollon, M.D., a neurosurgeon at University of Michigan Health and assistant professor of neurosurgery at U-M Medical School.
Retinal vein occlusion (RVO) is a severe disease that occurs when a vein in the light-sensitive layer at the back of the eye (i.e., the retina) becomes blocked, which results in a loss of vision. There are currently a few medical interventions that address RVO, including the periodic injection of medications that block the abnormal growth of blood vessels or of steroids, which reduce swelling and inflammation.
A promising procedure for the treatment of RVO is retinal vein cannulation (RVC). This is a very delicate surgical intervention that requires surgeons to insert a tiny needle into the blocked vein with high precision, delivering clot-dissolving drugs or medications that control the abnormal growth of blood vessels.
Given that retinal veins targeted for cannulation are similar in thickness to a human hair, manually inserting a needle inside them with high precision is very challenging. Robots could potentially assist surgeons in performing RVO procedures, ensuring that needles are inserted correctly and without damaging the patients’ retina.