In case of a large-scale radiological incident, the pooling of ressources by networks can enhance the rapid classification of individuals in medically relevant treatment groups based on the DCA. The performance of the RENEB network as a whole has clearly benefited from harmonization processes and specific training activities for the network partners.
The dose response for dicentrics plus centric rings and total unstable chromosome-type aberrations was studied in the first mitoses of cultured human peripheral blood lymphocytes irradiated in vitro to doses of ∼2, 4, 6, 8, 10, 16 and 20 Gy of acute (60)Со gamma-rays. A dose-dependent increase of aberration yield was accompanied by a tendency to the underdispersion of dicentrics and centric rings among cells distributions compared with Poisson statistics at doses ≥6 Gy. The formal fitting of the data to a linear-quadratic model resulted in an equation with the linear and quadratic coefficients ranged 0.098-0.129×cell(-1)×Gy(-1) and 0.039-0.034×cell(-1)×Gy(-2), respectively, depending on the fitting method. The actual radiation-induced aberration yield was markedly lower than expected from a calibration curve, generated earlier within a lower dose range. Interlaboratory variations in reported dicentric yields induced by medium-to-high radiation doses in vitro are discussed.
The scientific literature concerning cytogenetic biodosimetry has been reviewed to identify the range of scenarios of radiation exposure where biodosimetry has been carried out. Limitations in the existing standardized statistical methodology have been identified and categorized, and the reasons for these limitations have been explored. Statistical problems generally occur due to either low numbers of aberrations leading to large uncertainties or deviations in aberration-per-cell distributions leading to over- or under-dispersion with respect to the Poisson model. A number of difficulties also stem from limitations of the classical statistical methodology, which requires that chromosome aberration yields be considered as something "fixed" and thus provides a deterministic estimate of radiation dose and associated confidence limits (because an assignment of a probability to an event is based solely on the observed frequency of occurrence of the event). Therefore, it is suggested that solutions to the listed problems should be based in the Bayesian framework. This will allow the investigator to take a probabilistic approach to analysis of cytogenetic data, which can be considered highly appropriate for biological dose estimation.
Classical methods of assessing the uncertainty associated with radiation doses estimated using cytogenetic techniques are now extremely well defined. However, several authors have suggested that a Bayesian approach to uncertainty estimation may be more suitable for cytogenetic data, which are inherently stochastic in nature. The Bayesian analysis framework focuses on identification of probability distributions (for yield of aberrations or estimated dose), which also means that uncertainty is an intrinsic part of the analysis, rather than an 'afterthought'. In this paper Bayesian, as well as some more advanced classical, data analysis methods for radiation cytogenetics are reviewed that have been proposed in the literature. A practical overview of Bayesian cytogenetic dose estimation is also presented, with worked examples from the literature.
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