Researchers at the Yong Loo Lin School of Medicine, National University of Singapore (NUS Medicine), have demonstrated that doxycycline, a commonly available and inexpensive antibiotic, can improve survival rates and neurological outcomes in central nervous system tuberculosis (CNS-TB) in a preclinical non-human study.
Researchers at the University of Southampton have developed an artificial intelligence (AI) tool that can spot hard-to-see objects lodged in patients’ airways better than expert radiologists.
Go-Pen announced today on social media that it received CE mark approval for its user-fillable insulin pen.
University of Mississippi researchers have developed a new tool to help scientists study how environmental and genetic factors interact to influence autism spectrum disorder.
Rice University bioengineers have demonstrated a nonsurgical way to quiet a seizure-relevant brain circuit in an animal model. The team used low-intensity focused ultrasound to briefly open the blood-brain barrier (BBB) in the hippocampus, delivered an engineered gene therapy only to that region and later flipped an on-demand “dimmer switch” with an oral drug.
Tandem Diabetes Care (Nasdaq:TNDM) announced today that it received FDA approval for the Android version of its Mobi mobile app
Stanford Health Policy researchers built a model to test whether AI could effectively manage disease spread between prisons and surrounding communities. The results were decisive.
A low-intensity ultrasound treatment clears neurotoxic debris from the brains of mice with induced bleeding that resembles a hemorrhagic stroke, according to a study published in Nature Biotechnology.
Researchers at Wayne State University and the Barbara Ann Karmanos Cancer Institute have developed a chemical imaging probe that resists the breakdown of certain compounds in the body, giving doctors a more dependable way to track and potentially treat prostate cancer.
Researchers at the University of California San Diego School of Medicine have developed a new approach for identifying individuals with skin cancer that combines genetic ancestry, lifestyle and social determinants of health using a machine learning model. Their model, more accurate than existing approaches, also helped the researchers better characterize disparities in skin cancer risk and outcomes.