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Abstract The knowledge on responses of human lens epithelial cells (HLECs) to ionizing radiation exposure is important to understand mechanisms of radiation cataracts that are of concern in the field of radiation protection and radiation therapy. However, biological effects in HLECs following protracted exposure have not yet fully been explored. Here, we investigated the temporal kinetics of γ-H2AX foci as a marker for DNA double-strand breaks (DSBs) and cell survival in HLECs after exposure to photon beams at various dose rates (i.e., 150 kVp X-rays at 1.82, 0.1, and 0.033 Gy/min, and 137Cs γ-rays at 0.00461 Gy/min (27.7 cGy/h) and 0.00081 Gy/min (4.9 cGy/h)), compared to those in human lung fibroblasts (WI-38). In parallel, we quantified the recovery for DSBs and cell survival using a biophysical model. The study revealed that HLECs have a lower DSB repair rate than WI-38 cells. There is no significant impact of dose rate on cell survival in both cell lines in the dose-rate range of 0.033–1.82 Gy/min. In contrast, the experimental residual γ-H2AX foci showed inverse dose rate effects (IDREs) compared to the model prediction, highlighting the importance of the IDREs in evaluating radiation effects on the ocular lens.

Abstract For many cancer sites low-dose risks are not known and must be extrapolated from those observed in groups exposed at much higher levels of dose. Measurement error can substantially alter the dose–response shape and hence the extrapolated risk. Even in studies with direct measurement of low-dose exposures measurement error could be substantial in relation to the size of the dose estimates and thereby distort population risk estimates. Recently, there has been considerable attention paid to methods of dealing with shared errors, which are common in many datasets, and particularly important in occupational and environmental settings. In this paper we test Bayesian model averaging (BMA) and frequentist model averaging (FMA) methods, the first of these similar to the so-called Bayesian two-dimensional Monte Carlo (2DMC) method, and both fairly recently proposed, against a very newly proposed modification of the regression calibration method, the extended regression calibration (ERC) method, which is particularly suited to studies in which there is a substantial amount of shared error, and in which there may also be curvature in the true dose response. The quasi-2DMC with BMA method performs well when a linear model is assumed, but very poorly when a linear-quadratic model is assumed, with coverage probabilities both for the linear and quadratic dose coefficients that are under 5% when the magnitude of shared Berkson error is large (50%). For the linear model the bias is generally under 10%. However, using a linear-quadratic model it produces substantially biased (by a factor of 10) estimates of both the linear and quadratic coefficients, with the linear coefficient overestimated and the quadratic coefficient underestimated. FMA performs as well as quasi-2DMC with BMA when a linear model is assumed, and generally much better with a linear-quadratic model, although the coverage probability for the quadratic coefficient is uniformly too high. However both linear and quadratic coefficients have pronounced upward bias, particularly when Berkson error is large. By comparison ERC yields coverage probabilities that are too low when shared and unshared Berkson errors are both large (50%), although otherwise it performs well, and coverage is generally better than the quasi-2DMC with BMA or FMA methods, particularly for the linear-quadratic model. The bias of the predicted relative risk at a variety of doses is generally smallest for ERC, and largest for the quasi-2DMC with BMA and FMA methods (apart from unadjusted regression), with standard regression calibration and Monte Carlo maximum likelihood exhibiting bias in predicted relative risk generally somewhat intermediate between ERC and the other two methods. In general ERC performs best in the scenarios presented, and should be the method of choice in situations where there may be substantial shared error, or suspected curvature in the dose response.

Abstract There is direct evidence of risks at moderate and high levels of radiation dose for highly radiogenic cancers such as leukaemia and thyroid cancer. For many cancer sites, however, it is necessary to assess risks via extrapolation from groups exposed at moderate and high levels of dose, about which there are substantial uncertainties. Crucial to the resolution of this area of uncertainty is the modelling of the dose–response relationship and the importance of both systematic and random dosimetric errors for analyses in the various exposed groups. It is well recognised that measurement error can alter substantially the shape of this relationship and hence the derived population risk estimates. Particular attention has been devoted to the issue of shared errors, common in many datasets, and particularly important in occupational settings. We propose a modification of the regression calibration method which is particularly suited to studies in which there is a substantial amount of shared error, and in which there may also be curvature in the true dose response. This method can be used in settings where there is a mixture of Berkson and classical error. In fits to synthetic datasets in which there is substantial upward curvature in the true dose response, and varying (and sometimes substantial) amounts of classical and Berkson error, we show that the coverage probabilities of all methods for the linear coefficient $$\alpha$$ α are near the desired level, irrespective of the magnitudes of assumed Berkson and classical error, whether shared or unshared. However, the coverage probabilities for the quadratic coefficient $$\beta$$ β are generally too low for the unadjusted and regression calibration methods, particularly for larger magnitudes of the Berkson error, whether this is shared or unshared. In contrast Monte Carlo maximum likelihood yields coverage probabilities for $$\beta$$ β that are uniformly too high. The extended regression calibration method yields coverage probabilities that are too low when shared and unshared Berkson errors are both large, although otherwise it performs well, and coverage is generally better than these other three methods. A notable feature is that for all methods apart from extended regression calibration the estimates of the quadratic coefficient $$\beta$$ β are substantially upwardly biased.

Abstract For improvement of the radiation protection system it is crucial to know the factors that modify the radiation dose–response relationship. One of such key factors is the ionizing radiation dose rate. There are, however, very few studies that examine the impact of the dose rate on radiogenic risks observed in human cohorts exposed to radiation at various dose rates. Here we investigated the impact of the dose rate (in terms of the recorded annual dose) on ischemic heart disease (IHD) mortality among Russian nuclear workers chronically exposed to radiation. We observed significantly increased excess relative risks (ERR) of IHD mortality per unit of external gamma-ray absorbed dose accumulated at higher dose rates (0.005–0.050 Gy/year). The present findings provide evidence for the association between radiation dose rate and ERRs of IHD mortality in occupationally chronically exposed workers per unit total dose. IHD mortality risk estimates considerably increased with increasing duration of uninterrupted radiation exposure at high rates. The present findings are consistent with other studies and can contribute to the scientific basis for recommendations on the radiation protection system.

Abstract Insoluble radioactive microparticles emitted by the incident at the Fukushima nuclear power plant have drawn keen interests from the viewpoint of radiation protection. Cs-bearing particles have been assumed to adhere in the long term to trachea after aspirated into respiratory system, leading to heterogeneous dose distribution within healthy tissue around the particles. However, the biological effects posed by an insoluble radioactive particle remain unclear. Here, we show cumulative DNA damage in normal human lung cells proximal and distal to the particle (β-ray and γ-ray-dominant areas, respectively) under localized chronic exposure in comparison with uniform exposure. We put a Cs-bearing particle into a microcapillary tip and placed it onto a glass-base dish containing fibroblast or epithelial cells cultured in vitro. A Monte Carlo simulation with PHITS code provides the radial distribution of absorbed dose-rate around the particle, and subsequently we observed a significant change in nuclear γ-H2AX foci after 24 h or 48 h exposure to the particle. The nuclear foci in the cells distal to the particle increased even under low-dose-rate exposure compared with uniform exposure to 137Cs γ-rays, which was suppressed by a treatment with a scavenger of reactive oxygen species. In contrast, such focus formation was less manifested in the exposed cells proximal to the particle compared with uniform exposure. These data suggest that the localized exposure to a Cs-bearing particle leads to not only disadvantage to distal cells but also advantage to proximal cells. This study is the first to provide quantitative evaluation for the spatial distribution of DNA double strand breaks after the heterogeneous chronic exposure to a Cs-bearing particle in comparison with uniform Cs exposure.