Continuous Glucose Monitoring for Diabetes

Biomedical Engineering Distinguished Lecturer Series sponsored by Stradling Yocca Carlson & Rauth

Speaker: Professor David A. Gough, Ph.D.

UC San Diego

Host: Assistant Professor Elliot L. Botvinick, Ph.D.



Abstract:

Diabetes is a major health care problem that has reached crisis proportions, and new therapies are urgently needed. Although glucose monitoring is central to all therapies, the present methods of glucose monitoring are not reliable for hypoglycemia detection, not continuous in some cases, and are generally not acceptable to users. This seminar will describe the development of a unique, fully implanted long-term glucose sensor with wireless telemetry for use in subcutaneous tissues.  The system has functioned for over 500 days implanted in pigs with only occasional recalibration, and incorporates an innovative design that overcomes previous limitations, such as implant encapsulation by the body, the oxygen deficit, and  lags between blood and tissue glucose.  This is an example of systematic engineering design leading to a biomedical application.



Bio:



David Gough received his Ph.D. from the University of Utah in 1974, and was a post-doctoral fellow at the Joslin Clinic of the Harvard Medical School. He is a Founding Fellow of the American Institute of Medical and Biological Engineering, and a recipient of the M. J. Kugel Award presented by the Juvenile Diabetes Foundation, and the Jacobs School's Teacher of the Year Award in 1996. He is a former chair of the bioengineering department.



Professor Gough is working to create state-of-the-art implantable glucose sensors. To achieve his objectives, Gough conducts research on glucose and oxygen transport through tissues, sensor biocompatibility, and glucose gradients in the bloodstream. In addition, his research interests include control theory applied to metabolic regulation and dynamic models of the natural pancreas, insulin islet, and beta cell. He is also interested in machine learning applications to predict protein-protein interactions and dynamic physiologic processes.