A web-based tool for designing experimental studies to detect hormesis and estimate the threshold dose

Optimal designs for health risk assessments using fractional polynomial models

Fractional polynomials (FP) have been shown to be more flexible than polynomial models for fitting data from an univariate regression model with a continuous outcome but design issues for FP models have lagged. We focus on FPs with a single variable and construct D-optimal designs for estimating model parameters and I-optimal designs for prediction over a user-specified region of the design space. Some analytic results are given, along with a discussion on model uncertainty. In addition, we provide an applet to facilitate users find tailor made optimal designs for their problems. As applications, we construct optimal designs for three studies that used FPs to model risk assessments of (a) testosterone levels from magnesium accumulation in certain areas of the brains in songbirds, (b) rats subject to exposure of different chemicals, and (c) hormetic effects due to small toxic exposure. In each case, we elaborate the benefits of having an optimal design in terms of cost and quality of the statistical inference.

A global environmental health perspective and optimisation of stress

The phrase "what doesn't kill us makes us stronger" suggests the possibility that living systems have evolved a spectrum of adaptive mechanisms resulting in a biological stress response strategy that enhances resilience in a targeted quantifiable manner for amplitude and duration. If so, what are its evolutionary foundations and impact on biological diversity? Substantial research demonstrates that numerous agents enhance biological performance and resilience at low doses in a manner described by the hormetic dose response, being inhibitory and/or harmful at higher doses. This Review assesses how environmental changes impact the spectrum and intensity of biological stresses, how they affect health, and how such knowledge may improve strategies in confronting global environmental change.