Organized at the Faculty Meeting Room, Administration Building, 2nd floor, Prof. Dr. Prasit Wattanapa, Dean of Faculty of Medicine together with Mr. Jason Anderson, Chief Executive Officer of Liberty Biosecurity, LLC and Mr. Marek Stepniak, President of CP Medical Center Co., Ltd., signed the Memorandum of Understanding on behalf of their respective cooperates on November 13th, 2018. The MOU contains a detailed framework for cooperation in research and the establishment of Joint Research Program on Cancer Precision Medicine.
In the 15 years since the Human Genome Project first exposed our DNA blueprint, vast amounts of genetic data have been collected from millions of people in many different parts of the world. Carlos D. Bustamante’s job is to search that genetic data for clues to everything from ancient history and human migration patterns to the reasons people with different ancestries are so varied in their response to common diseases.
Noninvasive test may screen for disease before symptoms appear
By Jim Dryden
It may be possible in the future to screen patients for Alzheimer’s disease using an eye exam.
Using technology similar to what is found in many eye doctors’ offices, researchers at Washington University School of Medicine in St. Louis have detected evidence suggesting Alzheimer’s in older patients who had no symptoms of the disease.
Their study, involving 30 patients, is published Aug. 23 in the journal JAMA Ophthalmology.
Dr. Kyle Landry, Chief Scientist at Liberty Biosecurity, presented at the U.S. Environmental Protection Agency’s (EPA’s) Office of Research and Development’s National Homeland Security Research Center (ORD/NHSRC) regarding the use of novel extremophilic enzymes for the removal of biofilms relevant to space flight.
“Finding the Code,” the first in a three-part series with STAT, tells the story of one of biology’s most spectacular achievements. The race to sequence the human genome was billed as a way to end disease. Here’s where it led.
Authors: Paras Lakhani, MD, Adam B. Prater, MPH, MD, R. Kent Hutson, MD, Kathy P. Andriole, PhD, Keith J. Dreyer, DO, PhD, Jose Morey, MD, Luciano M. Prevedello, MD, MPH, Toshi J. Clark, MD, J. Raymond Geis, MD, Jason N. Itri, MD, PhD, C. Matthew Hawkins, MD Much attention has been given to machine learning and its perceived impact in radiology, particularly in light of recent success with image classification in international competitions. However, machine learning is likely to impact radiology outside of image interpretation long before a fully functional “machine radiologist” is implemented in practice. Here, we describe an overview of machine learning, its application to radiology and other domains, and many cases of use that do not involve image interpretation. We hope that better understanding of these potential applications will help radiology practices prepare for the future and realize performance improvement and efficiency gains.