Automating dicentric chromosome detection from cytogenetic biodosimetry data

Development of high-throughput systems for biodosimetry

Biomarkers for ionising radiation exposure have great utility in scenarios where there has been a potential exposure and physical dosimetry is missing or in dispute, such as for occupational and accidental exposures. Biomarkers that respond as a function of dose are particularly useful as biodosemeters to determine the dose of radiation to which an individual has been exposed. These dose measurements can also be used in medical scenarios to track doses from medical exposures and even have the potential to identify an individual's response to radiation exposure that could help tailor treatments. The measurement of biomarkers of exposure in medicine and for accidents, where a larger number of samples would be required, is limited by the throughput of analysis (i.e. the number of samples that could be processed and analysed), particularly for microscope-based methods, which tend to be labour-intensive. Rapid analysis in an emergency scenario, such as a large-scale accident, would provide dose estimates to medical practitioners, allowing timely administration of the appropriate medical countermeasures to help mitigate the effects of radiation exposure. In order to improve sample throughput for biomarker analysis, much effort has been devoted to automating the process from sample preparation through automated image analysis. This paper will focus mainly on biological endpoints traditionally analysed by microscopy, specifically dicentric chromosomes, micronuclei and gamma-H2AX. These endpoints provide examples where sample throughput has been improved through automated image acquisition, analysis of images acquired by microscopy, as well as methods that have been developed for analysis using imaging flow cytometry.

Radiation exposure determination in a secure, cloud-based online environment

Rapid sample processing and interpretation of estimated exposures will be critical for triaging exposed individuals after a major radiation incident. The dicentric chromosome (DC) assay assesses absorbed radiation using metaphase cells from blood. The Automated Dicentric Chromosome Identifier and Dose Estimator System (ADCI) identifies DCs and determines radiation doses. This study aimed to broaden accessibility and speed of this system, while protecting data and software integrity. ADCI Online is a secure web-streaming platform accessible worldwide from local servers. Cloud-based systems containing data and software are separated until they are linked for radiation exposure estimation. Dose estimates are identical to ADCI on dedicated computer hardware. Image processing and selection, calibration curve generation, and dose estimation of 9 test samples completed in < 2 days. ADCI Online has the capacity to alleviate analytic bottlenecks in intermediate-to-large radiation incidents. Multiple cloned software instances configured on different cloud environments accelerated dose estimation to within clinically relevant time frames.

A DAPI-Based Modified C-banding Technique for a Rapid Achieving High Photographic Contrast of Centromeres on Chromosomes

Many chromosome assays rely on the quantification of chromosome abnormalities in cells, and one important abnormality is the existence of more than one centromere for each chromosome. The quantification of such abnormalities has been studied before. However, this process is labor-intensive and time consuming. Thus, this assay is challenging for ex-laboratory applications, where speed is required. We present a visualization method that uses a cheap stain-DAPI, long (e.g., high-resolution) chromosomes and our modified C-banding method for labeling chromosomes. The labeled chromosomes can then be easily seen with a conventional and readily available fluorescence microscopy system. This method achieves an acceleration of the detection of the presence of constitutive heterochromatin in chromosomal centromeres by more than 10 times, to ~2 h, in Human lymphocyte cells and in cells of the human Jurkat line. This new procedure will ultimately provide an easier and cheaper alternative to FISH/PNA probes, or the classic Giemsa staining method. Simplification and reduction in time of the overall procedure will enable the utilization of centromere-counting assays in laboratory and ex-laboratory applications, including in emergency response.

Influence of sample preparation optimization on the accuracy of dose assessment of an automatic non-fluorescent MN scoring system

PURPOSE

Automatizing the scoring of the cytokinesis-blocked micronucleus assay spares a lot of valuable time. The dose-effect relationship can be applied reliably for dose estimation if the quality of the slides is the same from the perspective of the used image processing algorithm. This aspect brings in additional requirements against the quality of the slides compared to the conventional visual scoring.

MATERIALS AND METHODS

An add-in software was created to the non-fluorescent RS-MN automatic MN scoring system which is capable of measuring quantitatively the degree of typical anomalies. The image processing is less reliable when the presence of these anomalies is more frequent. The behavior of the designed sample quality parameters (SQPs) was tested on in vitro irradiated peripheral blood samples (0, 1, and 2 Gy) obtained from a healthy donor and also on samples from patients undergoing low dose-rate brachytherapy.

RESULTS

We examined 20 different SQPs and identified two that are independent and correlate significantly with the error of the fully automatic MN frequency. One is related to the size of the cells and the other reflects the homogeneity of the environment. An equation was established which presents a connection between the error of the auto MN frequency and the SQPs. By adding a fourth cleaning step to the conventional sample preparation and changing the pre-dripping temperature of the slide, the SQP can be modified, and consequently, the sample quality can be improved. The gain in accuracy is 54 ± 10 MN per 1000 binucleated cells, which corresponds to the effects of 0.5 Gy. Around the lowest limit of detection (<0.5 Gy), it means a 50-100% drop in the error of dose, which is significant. With sample quality harmonization, the positive predictive value was raised to 80-93% depending on the dose.

CONCLUSIONS

With the technique described in this paper, the suitability for automated scoring of a micronucleus slide can be tested quantitatively and objectively. A method is presented with which in some cases the uncertainty of the assessed doses due to variance in sample quality can be decreased or if it is not possible its bias can be predicted. The proposed protocol leads to more reliable estimation of dose. The SQPs are designed in a way that they have the potential to be adapted to similar systems.

Automated Cytogenetic Biodosimetry at Population-Scale

The dicentric chromosome (DC) assay accurately quantifies exposure to radiation; however, manual and semi-automated assignment of DCs has limited its use for a potential large-scale radiation incident. The Automated Dicentric Chromosome Identifier and Dose Estimator (ADCI) software automates unattended DC detection and determines radiation exposures, fulfilling IAEA criteria for triage biodosimetry. This study evaluates the throughput of high-performance ADCI (ADCI-HT) to stratify exposures of populations in 15 simulated population scale radiation exposures. ADCI-HT streamlines dose estimation using a supercomputer by optimal hierarchical scheduling of DC detection for varying numbers of samples and metaphase cell images in parallel on multiple processors. We evaluated processing times and accuracy of estimated exposures across census-defined populations. Image processing of 1744 samples on 16,384 CPUs required 1 h 11 min 23 s and radiation dose estimation based on DC frequencies required 32 sec. Processing of 40,000 samples at 10 exposures from five laboratories required 25 h and met IAEA criteria (dose estimates were within 0.5 Gy; median = 0.07). Geostatistically interpolated radiation exposure contours of simulated nuclear incidents were defined by samples exposed to clinically relevant exposure levels (1 and 2 Gy). Analysis of all exposed individuals with ADCI-HT required 0.6–7.4 days, depending on the population density of the simulation.

Meeting radiation dosimetry capacity requirements of population-scale exposures by geostatistical sampling

BACKGROUND

Accurate radiation dose estimates are critical for determining eligibility for therapies by timely triaging of exposed individuals after large-scale radiation events. However, the universal assessment of a large population subjected to a nuclear spill incident or detonation is not feasible. Even with high-throughput dosimetry analysis, test volumes far exceed the capacities of first responders to measure radiation exposures directly, or to acquire and process samples for follow-on biodosimetry testing.

AIM

To significantly reduce data acquisition and processing requirements for triaging of treatment-eligible exposures in population-scale radiation incidents.

METHODS

Physical radiation plumes modelled nuclear detonation scenarios of simulated exposures at 22 US locations. Models assumed only location of the epicenter and historical, prevailing wind directions/speeds. The spatial boundaries of graduated radiation exposures were determined by targeted, multistep geostatistical analysis of small population samples. Initially, locations proximate to these sites were randomly sampled (generally 0.1% of population). Empirical Bayesian kriging established radiation dose contour levels circumscribing these sites. Densification of each plume identified critical locations for additional sampling. After repeated kriging and densification, overlapping grids between each pair of contours of successive plumes were compared based on their diagonal Bray-Curtis distances and root-mean-square deviations, which provided criteria (<10% difference) to discontinue sampling.

RESULTS/CONCLUSIONS

We modeled 30 scenarios, including 22 urban/high-density and 2 rural/low-density scenarios under various weather conditions. Multiple (3-10) rounds of sampling and kriging were required for the dosimetry maps to converge, requiring between 58 and 347 samples for different scenarios. On average, 70±10% of locations where populations are expected to receive an exposure ≥2Gy were identified. Under sub-optimal sampling conditions, the number of iterations and samples were increased, and accuracy was reduced. Geostatistical mapping limits the number of required dose assessments, the time required, and radiation exposure to first responders. Geostatistical analysis will expedite triaging of acute radiation exposure in population-scale nuclear events.

RADIATION DOSE ESTIMATION BY COMPLETELY AUTOMATED INTERPRETATION OF THE DICENTRIC CHROMOSOME ASSAY

Accuracy of the automated dicentric chromosome (DC) assay relies on metaphase image selection. This study validates a software framework to find the best image selection models that mitigate inter-sample variability. Evaluation methods to determine model quality include the Poisson goodness-of-fit of DC distributions for each sample, residuals after calibration curve fitting and leave-one-out dose estimation errors. The process iteratively searches a pool of selection model candidates by modifying statistical and filter cut-offs to rank the best candidates according to their respective evaluation scores. Evaluation scores minimize the sum of squared errors relative to the actual radiation dose of the calibration samples. For one laboratory, the minimum score for the curve fit residual method was 0.0475 Gy2, compared to 1.1975 Gy2 without image selection. Application of optimal selection models using samples of unknown exposure produced estimated doses within 0.5 Gy of physical dose. Model optimization standardizes image selection among samples and provides relief from manual DC scoring, improving accuracy and consistency of dose estimation.

Development of electronic training and telescoring tools to increase the surge capacity of dicentric chromosome scorers for radiological/nuclear mass casualty incidents

Dicentric chromosome assay (DCA) is most frequently used for estimating the absorbed radiation dose in the peripheral blood lymphocytes of humans after occupational or incidental radiation exposure. DCA is considered to be the "gold standard" for estimating the absorbed radiation dose because the dicentric chromosome formation is fairly specific to ionizing radiation exposure and its baseline frequency is extremely low in non-exposed humans. However, performance of DCA for biodosimetry is labor intensive and time-consuming making its application impractical for radiological/nuclear mass casualty incidents. Realizing the critical need for rapid dose estimation particularly after radiological/nuclear disaster events, several laboratories have initiated efforts to automate some of the procedural steps involved in DCA. Although metaphase image capture and dicentric chromosome analysis have been automated using commercially available platforms, lack or an insufficient number of these platforms may pose a serious bottleneck when hundreds and thousands of samples need to be analyzed for rapid dose estimation. To circumvent this problem, a web-based approach for telescoring was initiated by our laboratory, which enabled the cytogeneticists around the globe to analyze and score digital images. To further increase the surge capacity of dicentric scorers, we recently initiated a dicentric training and scoring exercise involving a total of 50 volunteers at all academic levels without any prerequisite for experience in radiation cytogenetics. Out of the 50 volunteers enrolled thus far, only one outlier was found who overestimated the absorbed radiation dose. Our approach of training the civilians in dicentric chromosome analysis holds great promise for increasing the surge capacity of dicentric chromosome scorers for a rapid biodosimetry in the case of mass casualty scenarios.

DEVELOPMENT OF A MINIATURIZED VERSION OF DICENTRIC CHROMOSOME ASSAY TOOL FOR RADIOLOGICAL TRIAGE

Use of ionizing radiation (IR) in various industrial, medical and other applications can potentially increase the risk of medical, occupational or accidental human exposure. Additionally, in the event of a radiological or nuclear (R/N) incident, several tens of hundreds and thousands of people are likely to be exposed to IR. IR causes serious health effects including mortality from acute radiation syndrome and therefore it is imperative to determine the absorbed radiation dose, which will enable physicians in making an appropriate clinical 'life-saving' decision. The 'Dicentric Chromosome Assay (DCA)' is the gold standard for estimating the absorbed radiation dose but its performance is time consuming and laborious. Further, timely evaluation of dicentric chromosomes (DCs) for dose estimation in a large number of samples provides a bottleneck because of a limited number of trained personnel and a prolonged time for manual analysis. To circumvent some of these technical issues, we developed and optimized a miniaturized high throughput version of DCA (mini-DCA) in a 96-microtube matrix with bar-coded 1.4 ml tubes to enable the processing of a large number of samples. To increase the speed of DC analysis for radiation dose estimation, a semi-automated scoring was optimized using the Metafer DCScore algorithm. The accuracy of mini-DCA in dose estimation was verified and validated though comparison with conventional DCA performed in 15 ml conical tubes. The mini-DCA considerably reduced the sample processing time by a factor of 4 when compared to the conventional DCA. Further, the radiation doses estimated by mini-DCA using the triage mode of scoring (50 cells or 30 DCs) were similar to that of conventional DCA using 300-500 cells. The mini-DCA coupled with semi-automated DC scoring not only reduced the sample processing and analysis times by a factor of 4 but also enabled the processing of a large number of samples at once. Our mini-DCA method, once automated for high throughput robotic platforms, will be an effective radiological triage tool for mass casualty incidents.

Predicting ionizing radiation exposure using biochemically-inspired genomic machine learning

Background: Gene signatures derived from transcriptomic data using machine learning methods have shown promise for biodosimetry testing. These signatures may not be sufficiently robust for large scale testing, as their performance has not been adequately validated on external, independent datasets. The present study develops human and murine signatures with biochemically-inspired machine learning that are strictly validated using k-fold and traditional approaches. Methods: Gene Expression Omnibus (GEO) datasets of exposed human and murine lymphocytes were preprocessed via nearest neighbor imputation and expression of genes implicated in the literature to be responsive to radiation exposure (n=998) were then ranked by Minimum Redundancy Maximum Relevance (mRMR). Optimal signatures were derived by backward, complete, and forward sequential feature selection using Support Vector Machines (SVM), and validated using k-fold or traditional validation on independent datasets. Results: The best human signatures we derived exhibit k-fold validation accuracies of up to 98% ( DDB2,  PRKDC, TPP2, PTPRE, and GADD45A) when validated over 209 samples and traditional validation accuracies of up to 92% ( DDB2,  CD8A,  TALDO1,  PCNA,  EIF4G2,  LCN2,  CDKN1A,  PRKCH,  ENO1,  and PPM1D) when validated over 85 samples. Some human signatures are specific enough to differentiate between chemotherapy and radiotherapy. Certain multi-class murine signatures have sufficient granularity in dose estimation to inform eligibility for cytokine therapy (assuming these signatures could be translated to humans). We compiled a list of the most frequently appearing genes in the top 20 human and mouse signatures. More frequently appearing genes among an ensemble of signatures may indicate greater impact of these genes on the performance of individual signatures. Several genes in the signatures we derived are present in previously proposed signatures. Conclusions: Gene signatures for ionizing radiation exposure derived by machine learning have low error rates in externally validated, independent datasets, and exhibit high specificity and granularity for dose estimation.

Predicting ionizing radiation exposure using biochemically-inspired genomic machine learning

Background: Gene signatures derived from transcriptomic data using machine learning methods have shown promise for biodosimetry testing. These signatures may not be sufficiently robust for large scale testing, as their performance has not been adequately validated on external, independent datasets. The present study develops human and murine signatures with biochemically-inspired machine learning that are strictly validated using k-fold and traditional approaches. Methods: Gene Expression Omnibus (GEO) datasets of exposed human and murine lymphocytes were preprocessed via nearest neighbor imputation and expression of genes implicated in the literature to be responsive to radiation exposure (n=998) were then ranked by Minimum Redundancy Maximum Relevance (mRMR). Optimal signatures were derived by backward, complete, and forward sequential feature selection using Support Vector Machines (SVM), and validated using k-fold or traditional validation on independent datasets. Results: The best human signatures we derived exhibit k-fold validation accuracies of up to 98% ( DDB2,  PRKDC, TPP2, PTPRE, and GADD45A) when validated over 209 samples and traditional validation accuracies of up to 92% ( DDB2,  CD8A,  TALDO1,  PCNA,  EIF4G2,  LCN2,  CDKN1A,  PRKCH,  ENO1,  and PPM1D) when validated over 85 samples. Some human signatures are specific enough to differentiate between chemotherapy and radiotherapy. Certain multi-class murine signatures have sufficient granularity in dose estimation to inform eligibility for cytokine therapy (assuming these signatures could be translated to humans). We compiled a list of the most frequently appearing genes in the top 20 human and mouse signatures. More frequently appearing genes among an ensemble of signatures may indicate greater impact of these genes on the performance of individual signatures. Several genes in the signatures we derived are present in previously proposed signatures. Conclusions: Gene signatures for ionizing radiation exposure derived by machine learning have low error rates in externally validated, independent datasets, and exhibit high specificity and granularity for dose estimation.

Expedited Radiation Biodosimetry by Automated Dicentric Chromosome Identification (ADCI) and Dose Estimation

Biological radiation dose can be estimated from dicentric chromosome frequencies in metaphase cells. Performing these cytogenetic dicentric chromosome assays is traditionally a manual, labor-intensive process not well suited to handle the volume of samples which may require examination in the wake of a mass casualty event. Automated Dicentric Chromosome Identifier and Dose Estimator (ADCI) software automates this process by examining sets of metaphase images using machine learning-based image processing techniques. The software selects appropriate images for analysis by removing unsuitable images, classifies each object as either a centromere-containing chromosome or non-chromosome, further distinguishes chromosomes as monocentric chromosomes (MCs) or dicentric chromosomes (DCs), determines DC frequency within a sample, and estimates biological radiation dose by comparing sample DC frequency with calibration curves computed using calibration samples. This protocol describes the usage of ADCI software. Typically, both calibration (known dose) and test (unknown dose) sets of metaphase images are imported to perform accurate dose estimation. Optimal images for analysis can be found automatically using preset image filters or can also be filtered through manual inspection. The software processes images within each sample and DC frequencies are computed at different levels of stringency for calling DCs, using a machine learning approach. Linear-quadratic calibration curves are generated based on DC frequencies in calibration samples exposed to known physical doses. Doses of test samples exposed to uncertain radiation levels are estimated from their DC frequencies using these calibration curves. Reports can be generated upon request and provide summary of results of one or more samples, of one or more calibration curves, or of dose estimation.

Expedited Radiation Biodosimetry by Automated Dicentric Chromosome Identification (ADCI) and Dose Estimation

Biological radiation dose can be estimated from dicentric chromosome frequencies in metaphase cells. Performing these cytogenetic dicentric chromosome assays is traditionally a manual, labor-intensive process not well suited to handle the volume of samples which may require examination in the wake of a mass casualty event. Automated Dicentric Chromosome Identifier and Dose Estimator (ADCI) software automates this process by examining sets of metaphase images using machine learning-based image processing techniques. The software selects appropriate images for analysis by removing unsuitable images, classifies each object as either a centromere-containing chromosome or non-chromosome, further distinguishes chromosomes as monocentric chromosomes (MCs) or dicentric chromosomes (DCs), determines DC frequency within a sample, and estimates biological radiation dose by comparing sample DC frequency with calibration curves computed using calibration samples. This protocol describes the usage of ADCI software. Typically, both calibration (known dose) and test (unknown dose) sets of metaphase images are imported to perform accurate dose estimation. Optimal images for analysis can be found automatically using preset image filters or can also be filtered through manual inspection. The software processes images within each sample and DC frequencies are computed at different levels of stringency for calling DCs, using a machine learning approach. Linear-quadratic calibration curves are generated based on DC frequencies in calibration samples exposed to known physical doses. Doses of test samples exposed to uncertain radiation levels are estimated from their DC frequencies using these calibration curves. Reports can be generated upon request and provide summary of results of one or more samples, of one or more calibration curves, or of dose estimation.

Radiation Dose Estimation by Automated Cytogenetic Biodosimetry

The dose from ionizing radiation exposure can be interpolated from a calibration curve fit to the frequency of dicentric chromosomes (DCs) at multiple doses. As DC counts are manually determined, there is an acute need for accurate, fully automated biodosimetry calibration curve generation and analysis of exposed samples. Software, the Automated Dicentric Chromosome Identifier (ADCI), is presented which detects and discriminates DCs from monocentric chromosomes, computes biodosimetry calibration curves and estimates radiation dose. Images of metaphase cells from samples, exposed at 1.4-3.4 Gy, that had been manually scored by two reference laboratories were reanalyzed with ADCI. This resulted in estimated exposures within 0.4-1.1 Gy of the physical dose. Therefore, ADCI can determine radiation dose with accuracies comparable to standard triage biodosimetry. Calibration curves were generated from metaphase images in ~10 h, and dose estimations required ~0.8 h per 500 image sample. Running multiple instances of ADCI may be an effective response to a mass casualty radiation event.

Foundations of identifying individual chromosomes by imaging flow cytometry with applications in radiation biodosimetry

Biodosimetry is an important tool for triage in the case of large-scale radiological or nuclear emergencies, but traditional microscope-based methods can be tedious and prone to scorer fatigue. While the dicentric chromosome assay (DCA) has been adapted for use in triage situations, it is still time-consuming to create and score slides. Recent adaptations of traditional biodosimetry assays to imaging flow cytometry (IFC) methods have dramatically increased throughput. Additionally, recent improvements in image analysis algorithms in the IFC software have resulted in improved specificity for spot counting of small events. In the IFC method for the dicentric chromosome analysis (FDCA), lymphocytes isolated from whole blood samples are cultured with PHA and Colcemid. After incubation, lymphocytes are treated with a hypotonic solution and chromosomes are isolated in suspension, labelled with a centromere marker and stained for DNA content with DRAQ5. Stained individual chromosomes are analyzed on the ImageStream®^X (EMD-Millipore, Billerica, MA) and mono- and dicentric chromosome populations are identified and enumerated using advanced image processing techniques. Both the preparation of the isolated chromosome suspensions as well as the image analysis methods were fine-tuned in order to optimize the FDCA. In this paper we describe the method to identify and score centromeres in individual chromosomes by IFC and show that the FDCA method may further improve throughput for triage biodosimetry in the case of large-scale radiological or nuclear emergencies.

Evaluating the Special Needs of The Military for Radiation Biodosimetry for Tactical Warfare Against Deployed Troops: Comparing Military to Civilian Needs for Biodosimetry Methods

The aim of this paper is to delineate characteristics of biodosimetry most suitable for assessing individuals who have potentially been exposed to significant radiation from a nuclear device explosion when the primary population targeted by the explosion and needing rapid assessment for triage is civilians vs. deployed military personnel. The authors first carry out a systematic analysis of the requirements for biodosimetry to meet the military's needs to assess deployed troops in a warfare situation, which include accomplishing the military mission. Then the military's special capabilities to respond and carry out biodosimetry for deployed troops in warfare are compared and contrasted systematically, in contrast to those available to respond and conduct biodosimetry for civilians who have been targeted by terrorists, for example. Then the effectiveness of different biodosimetry methods to address military vs. civilian needs and capabilities in these scenarios was compared and, using five representative types of biodosimetry with sufficient published data to be useful for the simulations, the number of individuals are estimated who could be assessed by military vs. civilian responders within the timeframe needed for triage decisions. Analyses based on these scenarios indicate that, in comparison to responses for a civilian population, a wartime military response for deployed troops has both more complex requirements for and greater capabilities to use different types of biodosimetry to evaluate radiation exposure in a very short timeframe after the exposure occurs. Greater complexity for the deployed military is based on factors such as a greater likelihood of partial or whole body exposure, conditions that include exposure to neutrons, and a greater likelihood of combined injury. These simulations showed, for both the military and civilian response, that a very fast rate of initiating the processing (24,000 d) is needed to have at least some methods capable of completing the assessment of 50,000 people within a 2- or 6-d timeframe following exposure. This in turn suggests a very high capacity (i.e., laboratories, devices, supplies and expertise) would be necessary to achieve these rates. These simulations also demonstrated the practical importance of the military's superior capacity to minimize time to transport samples to offsite facilities and use the results to carry out triage quickly. Assuming sufficient resources and the fastest daily rate to initiate processing victims, the military scenario revealed that two biodosimetry methods could achieve the necessary throughput to triage 50,000 victims in 2 d (i.e., the timeframe needed for injured victims), and all five achieved the targeted throughput within 6 d. In contrast, simulations based on the civilian scenario revealed that no method could process 50,000 people in 2 d and only two could succeed within 6 d.

An automated imaging system for radiation biodosimetry

We describe here an automated imaging system developed at the Center for High Throughput Minimally Invasive Radiation Biodosimetry. The imaging system is built around a fast, sensitive sCMOS camera and rapid switchable LED light source. It features complete automation of all the steps of the imaging process and contains built-in feedback loops to ensure proper operation. The imaging system is intended as a back end to the RABiT-a robotic platform for radiation biodosimetry. It is intended to automate image acquisition and analysis for four biodosimetry assays for which we have developed automated protocols: The Cytokinesis Blocked Micronucleus assay, the γ-H2AX assay, the Dicentric assay (using PNA or FISH probes) and the RABiT-BAND assay.

Advances in a framework to compare bio-dosimetry methods for triage in large-scale radiation events

Planning and preparation for a large-scale nuclear event would be advanced by assessing the applicability of potentially available bio-dosimetry methods. Using an updated comparative framework the performance of six bio-dosimetry methods was compared for five different population sizes (100-1,000,000) and two rates for initiating processing of the marker (15 or 15,000 people per hour) with four additional time windows. These updated factors are extrinsic to the bio-dosimetry methods themselves but have direct effects on each method's ability to begin processing individuals and the size of the population that can be accommodated. The results indicate that increased population size, along with severely compromised infrastructure, increases the time needed to triage, which decreases the usefulness of many time intensive dosimetry methods. This framework and model for evaluating bio-dosimetry provides important information for policy-makers and response planners to facilitate evaluation of each method and should advance coordination of these methods into effective triage plans.