There are many places on the earth, where natural background radiation exposures are elevated significantly above about 2.5 mSv/year. The studies of health effects on populations living in such places are crucially important for understanding the impact of low doses of ionizing radiation. This article critically reviews some recent representative literature that addresses the likelihood of radiation-induced cancer and early childhood death in regions with high natural background radiation. The comparative and Bayesian analysis of the published data shows that the linear no-threshold hypothesis does not likely explain the results of these recent studies, whereas they favor the model of threshold or hormesis. Neither cancers nor early childhood deaths positively correlate with dose rates in regions with elevated natural background radiation.

A re-analysis has been carried out of thirty-two case–control and two ecological studies concerning the influence of radon, a radioactive gas, on the risk of lung cancer. Three mathematically simplest dose–response relationships (models) were tested: constant (zero health effect), linear, and parabolic (linear–quadratic). Health effect end-points reported in the analysed studies are odds ratios or relative risk ratios, related either to morbidity or mortality. In our preliminary analysis, we show that the results of dose–response fitting are qualitatively (within uncertainties, given as error bars) the same, whichever of these health effect end-points are applied. Therefore, we deemed it reasonable to aggregate all response data into the so-called Relative Health Factor and jointly analysed such mixed data, to obtain better statistical power. In the second part of our analysis, robust Bayesian and classical methods of analysis were applied to this combined dataset. In this part of our analysis, we selected different subranges of radon concentrations. In view of substantial differences between the methodology used by the authors of case–control and ecological studies, the mathematical relationships (models) were applied mainly to the thirty-two case–control studies. The degree to which the two ecological studies, analysed separately, affect the overall results when combined with the thirty-two case–control studies, has also been evaluated. In all, as a result of our meta-analysis of the combined cohort, we conclude that the analysed data concerning radon concentrations below ~1000 Bq/m³ (~20 mSv/year of effective dose to the whole body) do not support the thesis that radon may be a cause of any statistically significant increase in lung cancer incidence.

We report here on various biophysical aspects of irradiated cells, beginning with a phenomenological description of radiation-induced cancer cells. This description includes detrimental factors such as chromosomal aberrations, as well as beneficial factors, such as adaptive response. Also discussed here is the dose- and time-dependent evolution of cancer cells using a purely mathematical approach. The general dose-response shape, which is sigmoidal, is shown to be modified by such mechanisms as adaptive response or bystander effect. The many aspects of the sigmoid function, which most appropriately demonstrates the relationships among irradiated organisms, are discussed here as well. Finally, the balance equation is presented as the most general relationship for irradiated cell behavior.

This article focuses on the analytic modeling of responses of cells in the body to ionizing radiation. The related mechanisms are consecutively taken into account and discussed. A model of the dose- and time-dependent adaptive response is considered for 2 exposure categories: acute and protracted. In case of the latter exposure, we demonstrate that the response plateaus are expected under the modelling assumptions made. The expected total number of cancer cells as a function of time turns out to be perfectly described by the Gompertz function. The transition from a collection of cancer cells into a tumor is discussed at length. Special emphasis is put on the fact that characterizing the growth of a tumor (ie, the increasing mass and volume), the use of differential equations cannot properly capture the key dynamics—formation of the tumor must exhibit properties of the phase transition, including self-organization and even self-organized criticality. As an example, a manageable percolation-type phase transition approach is used to address this problem. Nevertheless, general theory of tumor emergence is difficult to work out mathematically because experimental observations are limited to the relatively large tumors. Hence, determination of the conditions around the critical point is uncertain.

“At the early stages of evolution, increasingly complex organisms developed powerful defense mechanisms against such adverse radiation effects as mutation and malignant change. These effects originate in the cell nucleus, where the DNA is their primary target. That evolution has apparently proceeded for so long is proof, in part, of the effectiveness of living things’ defenses against radiation.” Zbigniew Jaworowski 1999 “Chernobyl was indeed an historic event; it is the only nuclear power station disaster that ever resulted in an occupational death toll, albeit a comparatively small one. A vast environmental dispersion of radioactivity occurred that did not cause any scientifically confirmed fatalities in the general population. The worst harm to the population was caused not by radiation, and not to flesh, but to minds.” Zbigniew Jaworowski 2010

There is evidence from experimental, ecological, and epidemiological studies that low doses of ionizing radiation may be beneficial to human health by causing an adaptive response. A quantitative phenomenological model has been reported earlier, where the time-evolution response to gamma radiation was modeled like the time-evolution of a damped oscillator. The model predicted that an organism’s resistance to radiation stress can be considerably improved by “radiation training” -pre-irradiation. Here, we report an improved more realistic model, taking into account not only the radiation dose rate but also the total radiation dose. The model, if verified by future experiments, might be used to considerably improve efficacy of radiation therapy by enabling increased therapeutic doses.