Theory Of Space Radiation Risk Assessment And Its Applications

Space radiation is generally considered to be one of the most important risk factors to cause biological damage and even to threaten the health of the astronauts in space environment. Therefore, space radiation risk assessment is one of the critical issues in the radiation risk warning and protection of the astronauts in manned space flight project. Currently, the simulation methods are firstly applied to calculate the physical qualities of space radiation received by the astronaut. And then, the tissue-specific risk coefficients of cancer incidence and/or mortality, which come from Japanese atomic bomb survivors under the acute dose of low linear energy transfer (LET) radiation, are used to estimate space radiation risks by the linear-no-threshold (LNT) hypothesis. However, there exist high uncertainties in estimating space radiation risks for manned interplanetary missions. For example, for Mars mission, the risk assessment uncertainties by the conventional method ranged from 400%～600% at the 95% confidence interval. This is primarily due to the fact that we lack the necessary data of the biological effects of heavy-ion radiations, the appropriate models in the low-dose ranges for space radiation risk assessment, and the sensitive biomarkers to space environment, etc. In order to reduce the uncertainties in space radiation risk assessment, the critical scientific problems that need to be solved involves at least three aspects, the determination of radiation quality factors (Q), the estimation of low-dose radiation risk, and the mining of sensitive biomarkers to space environment, etc. In this paper, we mainly focused on the theoretical and applied studies on these key scientific issues.Firstly, we investigated the initial interaction process between space radiation and biological organism from the microscopic point of view. Based on the idea of the two-step stochastic process of radiation damage, we proposed a new target theory based on the Gaussian distribution. Theoretical analysis and numerical simulation indicate that the present theory is suitable for describing the biological effects of the sparsely ionizing radiation. Fitting results to the experimental data of typical radiation biology show that the present theory is superior to the traditional target theory and linear quadratic (LQ) model. In addition, theoretical analysis and experimental data fitting also indicate that the mean number of potentially lethal damage (PLD) per gray per target based on the present theory can be used to estimate the radiation damage degree, which is mainly dependent on the radiation quality.In order to further evaluate the Q for the continuous LET spectrum in sapce radiation, we proposed a generalized target effects model based on Yager’s negative operator by considering the non-target effects of radiation and the repair function of organism. Analysis shows that this model can be reduced to the LQ and multitarget models in the low-dose and high-dose regions, respectively, which has the characteristic of linear-quadratic-linear. The fitting results show that this model agrees well with the usual experimental observations induced by radiations with different LETs. In addition, the present model can be used to effectively predict cellular repair capacity, radiosensitivity, and RBE, etc. Moreover, the generalized target effects model was used to calculate the RBE of the proton radiations with various LETs in ten kinds of cell lines. And then, the dose-dependent Q can be obtained based on the LET and cell line dependent RBE. By integrating the adsorbed doses in different organs of the astronauts, the acute and chronic space radiation risks were determined for the condition of solar proton event. The calculation results show that the relationship between dose and space radiation risk does not follow the LNT hypothesis in the low-dose ranges, which is lower than the expected by LNT hypothesis. This possibly indicates that the biological repair capacity is one of important factors in space radiation risk assessment.Finally, taking into account the data characteristics of space radiation biology, i.e., the small samples with big data, a combined algorithm, which integrates the feature selection techniques, was used to deal with the microarray datasets of Caenorhabditis elegans obtained in the Shenzhou-8 mission. Compared with the traditional method, this algorithm can select more functional genes in responses to space synthetic environment or space radiation environment. A total of 17 potential biomarkers were selected to reflect the space environment stresses. And the synergetic biological effects are likely to exist between space radiation and microgravity.The proposed biophysical models and algorithms for estimating the radiation quality factors and selecting the biomarkers can provide a theoretical foundation to space radiation risk warning and the corresponding countermeasures for the astronauts in manned spaceflights.