One way cancer specialists detect the disease is by examining cells and bodily fluids under a microscope, a time-consuming and labor-intensive process called cytology. It involves visually inspecting tens of thousands to one million cells per slide for subtle 3D morphological changes that might signal the onset of cancer. But AI offers an approach that is potentially faster and more accurate.

Researchers from several partner institutions of the German Center for Diabetes Research (DZD) have collaborated with international colleagues to develop a new approach for visualizing subtle tissue changes in the pancreas in type 2 diabetes. The results provide new insights into the development of type 2 diabetes. The study has now been published in Nature Communications.

Using machine learning, an electronic nose can “smell” early signs of ovarian cancer in the blood. The method is precise and, according to the LiU researchers behind the study, it could eventually be used to find many different cancers. The study is published in Advanced Intelligent Systems.

A protein lurking around in the blood can help with the accurate diagnosis of Alzheimer’s disease. In a recent study, researchers from Spain investigated how blood-based biomarkers, such as a protein called p-tau217, affect both the clinical diagnosis of Alzheimer’s and neurologists’ confidence in their diagnosis.

As aging populations and rising diabetes rates drive an increase in chronic wounds, more patients face the risk of amputations. UC Riverside researchers have developed an oxygen-delivering gel capable of healing injuries that might otherwise progress to limb lo