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Record W4390203062 · doi:10.1177/01466453231173632

Estimating the impact of indirect action in neutron-induced DNA damage clusters and neutron RBE

2023· article· en· W4390203062 on OpenAlexaff
James Manalad, Logan Montgomery, J. Kildea

Bibliographic record

VenueAnnals of the ICRP · 2023
Typearticle
Languageen
FieldMedicine
TopicRadiation Therapy and Dosimetry
Canadian institutionsKingston Health Sciences CentreMcGill University
Fundersnot available
KeywordsNeutronIonizing radiationPhysicsMonte Carlo methodRelative biological effectivenessDNA damageRadiationNuclear physicsPhotonIrradiationDNABiologyOpticsGeneticsStatisticsMathematics

Abstract

fetched live from OpenAlex

Background: Neutron radiation protection factors indicate that the risk of neutron-induced stochastic biological effects is higher compared with other types of ionising radiation, and has strong energy dependence. Recent Monte Carlo studies simulated neutron radiation on a nuclear DNA model and demonstrated that the energy-dependent radiobiological risk of neutrons can be correlated with the induction of DNA damage clusters, particularly those that contain difficult-to-repair double-strand breaks. The main limitation of these studies is that only direct radiation action was investigated. Thus, for a more comprehensive understanding of pre-repair neutron-induced DNA damage, indirect action must be modelled and its impact must be quantified. Methods: An open-source algorithm for indirect action available in the TOPAS-nBio track structure Monte Carlo toolkit was adapted into our group’s existing TOPAS and TOPAS-nBio simulation pipeline for neutron and photon radiation. We performed 100 independent simulated irradiations of monoenergetic neutrons from 1 eV to 10 MeV on our existing custom-built nuclear DNA model, and scored various types of DNA damage due to direct and indirect action. The yields of DNA damage were stratified according to the damage-inducing action. For the resulting clusters of DNA damage, we determined the average cluster length and lesion count per cluster. This procedure was also performed for 250-keV x-ray photons that served as our reference radiation. We estimated neutron relative biological effectiveness (RBE) by dividing the neutron-inflicted yield of DNA damage clusters by photon-inflicted counterparts. Finally, we compared our RBE results with established radiation protection factors and previous studies that modelled direct action alone. Results: The inclusion of indirect action increased the yield of DNA damage significantly (the increase varies with damage type). We found that the majority of neutron-induced DNA damage events were isolated simple lesions due to indirect action, while most clustered lesions were hybrid in nature (i.e. contain lesions due to direct and indirect action). As for cluster properties, the inclusion of indirect action increased the average length of clusters by approximately 50% and the number of lesions per cluster by approximately 25%. Our estimated energy-dependent neutron RBE for inducing DNA damage clusters follows similar trends as the established radiation protection factors and the previous direct-action-only estimates, but is lower in magnitude. We found that this lower magnitude is due to the greater impact of indirect action in the yield of photon-induced clustered DNA damage compared with neutron-induced clustered lesions. Conclusion: Indirect action has significant effects on radiation-induced DNA damage clusters in terms of yield, length, and lesion count per cluster, and serves to amplify the effects of direct action. The energy-dependent risk of neutron-induced stochastic effects is likely related to, but not completely explained by, the induction of DNA damage clusters. For a more complete model of neutron RBE, the investigation of factors such as DNA damage repair and non-targeted radiation effects is recommended.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.787
Threshold uncertainty score0.180

Codex and Gemma teacher scores by category

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

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 head0.088
GPT teacher head0.390
Teacher spread0.302 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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".

Quick stats

Citations2
Published2023
Admission routes1
Has abstractyes

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