International Dose-Response Society | Focusing on the Dose-Response in the Low-Dose Zone |

In The News...

SEMINARS ON “HORMESIS” – LOW DOSE EXPOSURES
2009

Microbiology and Molecular Genetics, University of Texas Medical School in Houston
October 1, 2009, Ransome van der Hoeven
"Hormesis and aging in C. elegans"

University of Kansas Medical Center
October 2, 2009, Karl Rozman, Ph.D
"Hormesis or the Effects of Low Doses of Toxic Agents"

Metagenics International Congress of Nutritional Medicine 2009
Jun 6-8, 2009, Dr Jeffrey Bland PhD

Dr Jeffrey Bland's 30 year career in nutritional biochemistry has spanned roles as a research professor, thought leader and internationally recognised expert in human nutrition. More than 60,000 Health Professionals have attended Dr Bland's seminars and educational programs during the last 20 years. He is regarded worldwide as the principal educator in nutritional and functional medicine. Dr Bland will present the latest research in the following areas:

Nutrigenomics and tissue specific inflammation.
Hormones and hormesis - Who's pulling the strings?
Hormesis and epigenetics in clinical practice.

Bloomsburg University, Department of Mathematics, Computer Science and Statistics
November 18, 2009, Jason Elsinger
Use of Double Quadratic Polynomials for Nonlinear Dose-Response Modeling in Biological Experiments

The study of dose-response modeling, and in particular, the favorable biological responses to low exposures to chemicals known as hormesis, can be used to determine the best dose level for maximal benefit. The study of hormesis has become very popular over the past decade. Gaylor (2004) used a single quadratic to model the hormesis curve. Here, we argue that using a double quadratic (one for the down trend and one for the up trend), will result in a better description of the process. The maximum likelihood method is used to estimate model parameters. In order to solve the nonlinear likelihood equations, the EM (Expectation-Maximazation) algorithm is utilized to convert the nonlinearity to a linear problem. We then use an example with real data to illustrate our methodology.