Researchers in the Lyda Hill Department of Bioinformatics have estimated that the amount of COVID-19 cases is nearly triple those confirmed in the U.S.
While it has long been speculated that the number of COVID-19 cases is significantly higher than those reported, a new machine learning algorithm created by UT Southwestern Medical Center researchers from the Lyda Hill Department of Bioinformatics confirms this theory.
According to the algorithm, over 71 million people in the U.S. have contracted the virus. This number is almost three times as much as the 26.7 million publicly-reported number of confirmed cases, according to Jungsik Noh, Ph.D., a UT Southwestern assistant professor in the Lyda Hill Department of Bioinformatics… Continue reading.
Using human lung cancer cells, UT Southwestern researchers have uncovered how cells in general modulate their energy consumption based on their surroundings and, furthermore, how cancer cells override those cues to maximize energy use. The findings, published this week in Nature, extend a report from last year in which the same group discovered that the cell’s skeleton can promote cancer cell growth in metastasis or when under chemotherapy assault.
“Cancer cells experience variable mechanical conditions during tumor growth and spread, so we wondered whether the mechanical conditions also affect glycolysis — the cell’s energy use. Enhanced glycolysis is a hallmark of cancer,” says Gaudenz Danuser, Ph.D., a professor of cell biology and chair of the Lyda Hill Department of Bioinformatics… Continue reading.
WASHINGTON, D.C.—The American Institute for Medical and Biological Engineering (AIMBE) has announced the induction of Gaudenz Danuser, Ph.D., Patrick E. Haggerty Distinguished Chair in Basic Biomedical Science CPRIT Scholar of Cancer Research, Departments of Bioinformatics and Cell Biology, University of Texas Southwestern Medical Center, to its College of Fellows. Dr. Danuser was nominated, reviewed, and elected by peers and members of the College of Fellows for pioneering development of computer vision methods to analyze biomedical images to answer fundamental questions in cell biology.