School of Medicine

A team of researchers from the McKelvey School of Engineering and the School of Medicine are using Fitbit data and deep learning to detect depression and anxiety.

Diabetes researchers at the School of Medicine have learned why islet beta cells produced from stem cells may not be as good at making insulin in response to blood sugar. The findings could improve treatment for those with insulin-dependent diabetes.

Researchers at the McKelvey School of Engineering discovered how to use focused ultrasound with microbubbles to examine the glymphatic system. Impairments in this system may contribute to brain diseases, such as neurodegenerative diseases and stroke.

A team led by Washington University in St. Louis has been awarded $1 million over two years from the National Science Foundation’s Regional Innovation Engines program to help advance neuroscience technologies.

Martha Bagnall, an assistant professor of neuroscience at the School of Medicine, received a $1.9 million R01 grant from the National Institutes of Health (NIH) to map neuronal connections.

The School of Medicine has received two grants totaling $22.5 million to help lead national efforts to understand how DNA changes create differences in genomes across tissues within the same person.

Researchers have published a new set of reference human genome sequences that reveals far more genomic diversity from different populations of people than was available previously. The School of Medicine serves as the national coordinating center for the program, called the Human Pangenome Reference Consortium.

Paul Taghert, a professor of neuroscience at the School of Medicine, received an Outstanding Investigator Award of nearly $2 million from the National Institutes of General Medical Sciences to study how the circadian clock orchestrates multiple biological cycles that operate at different phases.

Abhinav Jha, an assistant professor at the McKelvey School of Engineering and the School of Medicine, received a National Science Foundation CAREER award to develop approaches for list-mode imaging that improve information collection.

Repeated mammograms contain data on changes in breast density over time that could help identify women at high risk of breast cancer and even reveal which breast is likely to be affected, according to a study by researchers at the School of Medicine.