Automated Cytogenetic Biodosimetry at Population-Scale

Rogan, Peter K.; Lu, Ruipeng; Ali, Shahzad; Shirley, Ben C.; Li, Y; Wilkins, Ruth; Norton, Farrah; Sevriukova, Olga; Pham, Duy; Ainsbury, Elizabeth A.; Moquat, J; Cooke, R; Peerlaproulx, T; Waller, Edward; Knoll, Jhm

doi:10.1101/718973

Cited by 4 publications

(16 citation statements)

References 27 publications

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“…Processing and analysis of samples from all known or suspected irradiated individuals for biodosimetry is too labor intensive, a significant bottleneck in identifying treatment-eligible exposures [32]. While rapid interpretation of cytogenetic biodosimetry data is feasible [36], sample acquisition and data generation exceed the capacities of small teams of first responders and individual testing laboratories [37]. The proposed approach may partially overcome capacity resource limitations of first responders and biodosimetry laboratories to provide data for triage assessment of entire populations.…”

Section: Discussionmentioning

confidence: 99%

“…Field sampling, laboratory and computational resources could be amplified through simultaneous deployment of multiple dedicated teams. With high performance computing and parallel processing [36], it should be possible to model multiple sample data sources concurrently, and then combine these into more robust geostatistical models. It may also be possible to independently verify exposures by multi-parameter co-kriging [17], for example, with laboratory measurements of white blood cell counts in the same samples-such measurements would be expected to be inversely related to radiation dose.…”

Section: Discussionmentioning

confidence: 99%

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

Rogan

Mucaki

et al. 2020

PLoS ONE

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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.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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

Rogan

Mucaki

et al. 2020

PLoS ONE

Self Cite

View full text Add to dashboard Cite

show abstract

“…Processing and analysis of samples from all known or suspected irradiated individuals for biodosimetry is too labor intensive, a significant bottleneck in identifying treatment-eligible exposures (32). While rapid interpretation of cytogenetic biodosimetry data is feasible (36), sample acquisition and data generation exceeds the capacities of small teams of first responders and individual testing laboratories (38). The proposed approach may partially overcome capacity resource limitations of first responders and biodosimetry laboratories to provide data for triage assessment of entire populations.…”

Section: Discussionmentioning

confidence: 99%

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

Rogan

Mucaki

et al. 2020

Preprint

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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 rootmean-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.

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“…The realization of the first two parts has rich literature since besides DC assay they are also used for karyotyping. However, for the localization of the centromeres only a few methods have been developed in the last decade [23,[125][126][127][128][129]. The greatest challenge is to make a difference between the intersection of overlapping chromosomes and real centromeres.…”

Section: Automatization Of Dicentric Chromosome Assaymentioning

confidence: 99%

“…Biological dosimetry relies on quantifying the amount of damage induced by radiation at a cellular level, for example counting dicentrics chromosome (DCs), centric rings, or micronuclei [18,19]. The frequency of these chromosome aberrations is an established biological indicator of radiation dose received [20][21][22][23]. Quantifying the absorbed radiation dose is essential for predicting health consequences in irradiated patients in the short, medium, and long term.…”

Section: Introductionmentioning

confidence: 99%

Cytogenetic bio-dosimetry techniques in the detection of dicentric chromosomes induced by ionizing radiation: A review

et al. 2021

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Ionizing radiation is ubiquitous in the environment. Its source can be natural, such as radioactive materials present in soil and cosmic rays, or artificial, such as the fuel for nuclear power plants. Overexposure to ionizing radiation may damage living tissue and could cause severe health problems (i.e., mutations, radiation sickness, cancer, and death). Cytogenetic bio-dosimetry has the great advantage to take into account the inter-individual variation, and it is informative even when physical dosimetry is not applicable; moreover, it is the definitive method to assess exposure to ionizing radiation recommended by the World Health Organization (WHO). Such a procedure involves counting the frequency of dicentric chromosomes (DCs), which are the most studied chromosomal aberrations used as absorbed radiation biomarkers, during the metaphase of cells. A set of algorithms, tested on different programming languages to automatically identify DCs, is analyzed by the authors together with an Automated Dicentric Chromosome Identifying software (ADCI) mostly based on OpenCV programming libraries. The purpose of this work is to review the main results regarding the correlation between ionizing radiation and dicentric chromosomes in cytogenetic bio-dosimetry.

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Automated Cytogenetic Biodosimetry at Population-Scale

Cited by 4 publications

References 27 publications

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

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

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

Cytogenetic bio-dosimetry techniques in the detection of dicentric chromosomes induced by ionizing radiation: A review

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