Schöllnberger H, Scott B R, Hanson T E
Lovelace Respiratory Research Institute, Inhalation Toxicology Laboratory, P.O. Box 5890, Albuquerque, New Mexico 87185-5890, USA.
Bull Math Biol. 2001 Sep;63(5):865-83. doi: 10.1006/bulm.2001.0243.
Improved risk characterization for stochastic biological effects of low doses of low-LET radiation is important for protecting nuclear workers and the public from harm from radiation exposure. Here we present a Bayesian approach to characterize risks of stochastic effects from low doses of low-LET radiation. The stochastic effect considered is neoplastic transformation of cells because it relates closely to cancer induction. We have used a published model of neoplastic transformation called NEOTRANS1. It is based on two different classes of cellular sensitivity for asynchronous, exponentially growing populations (in vitro). One sensitivity class is the hypersensitive cell; the other is the resistant cell. NEOTRANS1 includes the effects of genomic damage accumulation, DNA repair during cell cycle arrest, and DNA misrepair (non-lethal repair errors). The model-associated differential equations are solved for conditions of in vitro irradiation at a fixed rate. Previously published solutions apply only to high dose rates and were incorrectly assumed to apply to only high-LET radiation. Solutions provided here apply to any fixed dose rate and to both high- and low-LET radiations. Markov chain Monte Carlo methods are used to carry out the Bayesian inference of the low-dose risk for neoplastic transformation of aneuploid C3H 10T1/2 cells for X-ray doses from 0 to 1000 mGy. We have assumed that for this low-dose range only the hypersensitive fraction of the cells are affected. Our results indicate that the initial slope of the risk vs dose relationship for neoplastic transformation is as follows: (1) directly proportional to the fraction, f1, of hypersensitive cells; (2) directly proportional to the radiosensitivity of the genomic target; and (3) inversely proportional to the rate at which hypersensitive cells with radiation-induced damage are committed to undergo correct repair of genomic damage. Further, our results indicate that very fast molecular events are associated with the commitment of cells to the correct repair pathway. Results also indicate a relatively large probability for misrepair that leads to genomic instability. Our results are consistent with the view that for very low doses, dose rate is not an important variable for characterizing low-LET radiation risks so long as age-related changes in sensitivity do not occur during irradiation.
改善低剂量低传能线密度辐射随机生物效应的风险特征描述,对于保护核工业工人和公众免受辐射暴露危害至关重要。在此,我们提出一种贝叶斯方法来描述低剂量低传能线密度辐射随机效应的风险。所考虑的随机效应是细胞的肿瘤转化,因为它与癌症诱发密切相关。我们使用了一个已发表的名为NEOTRANS1的肿瘤转化模型。它基于异步指数生长群体(体外)的两种不同细胞敏感性类别。一种敏感性类别是超敏细胞;另一种是抗性细胞。NEOTRANS1包括基因组损伤积累、细胞周期停滞期间的DNA修复以及DNA错配修复(非致死性修复错误)的影响。针对固定速率的体外照射条件求解与模型相关的微分方程。先前发表的解仅适用于高剂量率,并且被错误地假定仅适用于高传能线密度辐射。这里提供的解适用于任何固定剂量率以及高传能线密度和低传能线密度辐射。使用马尔可夫链蒙特卡罗方法对非整倍体C3H 10T1/2细胞在0至1000 mGy X射线剂量下肿瘤转化的低剂量风险进行贝叶斯推断。我们假定在这个低剂量范围内仅超敏细胞部分受到影响。我们的结果表明,肿瘤转化风险与剂量关系的初始斜率如下:(1)与超敏细胞的比例f1成正比;(2)与基因组靶点的放射敏感性成正比;(3)与具有辐射诱导损伤的超敏细胞进行基因组损伤正确修复的速率成反比。此外,我们的结果表明非常快速的分子事件与细胞进入正确修复途径有关。结果还表明导致基因组不稳定的错配修复概率相对较大。我们的结果与以下观点一致,即对于非常低的剂量,只要在照射期间不发生与年龄相关的敏感性变化,剂量率就不是描述低传能线密度辐射风险的重要变量。