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Record W3082000960 · doi:10.1101/2020.09.01.278200

Estimating partial body ionizing radiation exposure by automated cytogenetic biodosimetry

2020· preprint· en· W3082000960 on OpenAlexaffabout
Ben C. Shirley, Joan H.M. Knoll, Jayne Moquet, Elizabeth A. Ainsbury, Pham Ngoc Duy, Farrah Norton, Ruth C. Wilkins, Peter K. Rogan

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2020
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCarcinogens and Genotoxicity Assessment
Canadian institutionsWestern UniversityHealth CanadaCanadian Nuclear LaboratoriesCytodiagnostics (Canada)
Fundersnot available
KeywordsDicentric chromosomeBiodosimetryIonizing radiationNuclear medicineIrradiationDosimetryRadiationMetaphaseBiologyMedicineChromosomePhysicsOpticsKaryotypeGenetics

Abstract

fetched live from OpenAlex

Abstract Purpose Inhomogeneous exposures to ionizing radiation can be detected and quantified with the Dicentric Chromosome Assay (DCA) of metaphase cells. Complete automation of interpretation of the DCA for whole body irradiation has significantly improved throughput without compromising accuracy, however low levels of residual false positive dicentric chromosomes (DCs) have confounded its application for partial body exposure determination. Materials and Methods We describe a method of estimating and correcting for false positive DCs in digitally processed images of metaphase cells. Nearly all DCs detected in unirradiated calibration samples are introduced by digital image processing. DC frequencies of irradiated calibration samples and those exposed to unknown radiation levels are corrected subtracting this false positive fraction from each. In partial body exposures, the fraction of cells exposed, and radiation dose can be quantified after applying this modification of the contaminated Poisson method. Results Dose estimates of three partially irradiated samples diverged 0.2 to 2.5 Gy from physical doses and irradiated cell fractions deviated by 2.3-15.8% from the known levels. Synthetic partial body samples comprised of unirradiated and 3 Gy samples from 4 laboratories were correctly discriminated as inhomogeneous by multiple criteria. Root mean squared errors of these dose estimates ranged from 0.52 to 1.14 Gy 2 and from 8.1 to 33.3% 2 for the fraction of cells irradiated. Conclusions Automated DCA can differentiate whole-from partial-body radiation exposures and provides timely quantification of estimated whole-body equivalent dose. Biographical Note Ben Shirley M.Sc. is Chief Software Architect, CytoGnomix Inc. Canada; Joan Knoll Ph.D. Dipl.ABMGG, FCCMG is Professor in Pathology and Laboratory Medicine, Schulich School of Medicine and Dentistry, University of Western Ontario, Canada and cofounder, CytoGnomix Inc.; Jayne Moquet Ph.D. is Principal Radiation Protection Scientist in the Cytogenetics Group, Public Health England; Elizabeth Ainsbury Ph.D. is Head, Cytogenetics Group and the Chromosome Dosimetry Service, Public Health England; Pham Ngoc Duy M.Sc. is deputy director of Biotechnology Center, Dalat Nuclear Research Institute, Vietnam; Farrah Norton M.Sc.is Research Scientist and Lead of the Biodosimetry emergency response and research capability at Canadian Nuclear Laboratories; Ruth Wilkins, Ph.D. is Research Scientist and Chief of the Ionizing Radiation Health Sciences Division at Health Canada, Ontario, Canada; and Peter K. Rogan Ph.D. is Professor of Biochemistry and Oncology, Schulich School of Medicine and Dentistry, University of Western Ontario, Canada, and President, CytoGnomix Inc.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.011
GPT teacher head0.240
Teacher spread0.229 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2020
Admission routes2
Has abstractyes

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