title: Methodologies for predicting the expected combined stochastic radiobiological effects of different ionizing radiations and some applications.
abstract: A methodology for predicting the expected combined stochastic radiobiological effects of sequential exposure to different ionizing radiations is used to arrive at a methodology for predicting the radiobiological effects of simultaneous exposure. Both methodologies require developing additive-damage dose-effect models. Additive-damage dose-effect models are derived assuming (a) each radiation comprised by the combined exposure produces initial damage called critical damage that could lead to the radiobiological effect of interest; (b) doses of different radiations that lead to the same level of radiobiological effect (or risk) can be viewed as producing the same amount of critical damage and being indistinguishable as far as the effects of subsequently administered radiation. Derived dose-effect functions that describe the risk per individual, conditional on radiation dose, are called risk functions. The methodologies allow the use of known radiation-specific risk functions to derive risk functions for combined effects of different radiations. The risk functions for combined exposure to different radiations are called global risk functions. For sequential exposures to different ionizing radiations, the global risk functions derived depend on how individual radiation doses are ordered. Global risk functions can also differ for sequential and simultaneous exposure. The methodologies are used to account for some previously unexplained radiobiological effects of combined exposure to high and low linear-energy-transfer radiations.
mesh_terms: D000512:Alpha Particles; D002470:Cell Survival / Q000528:radiation effects; D004307:Dose-Response Relationship, Radiation; D005720:Gamma Rays; D006801:Humans; D007953:Leukemia, Radiation-Induced; D008433:Mathematics; D008954:Models, Biological; D009154:Mutation; D009502:Neutrons; D011830:Radiation Effects; D012306:Risk; D013269:Stochastic Processes