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Record W7115036918

In silico studies of neutron-induced DNA damage and misrepair at the single-cell and cell population levels

2025· dissertation· en· W7115036918 on OpenAlexafffund

Bibliographic record

VenueeScholarship@McGill (McGill) · 2025
Typedissertation
Languageen
FieldMedicine
TopicEffects of Radiation Exposure
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Space Agency
KeywordsIn silicoDNA damagePopulationCellDNA
DOInot available

Abstract

fetched live from OpenAlex

Neutron radiation poses a notable concern in high-energy photon radiotherapy (above 8 MeV), particle therapy, and in occupational environments such as aviation and space travel. To support the development of effective radioprotection frameworks, it is essential to understand and quantify the energy-dependent stochastic risks associated with neutron exposure. However, progress in this area is limited by the lack of experimental data.Track-structure Monte Carlo (TSMC) simulations incorporating detailed DNA models offer a promising tool for advancing our mechanistic understanding of neutron-induced biological effects. To date, TSMC studies on neutron relative biological effectiveness (RBE) that include both direct and indirect action of radiation have been limited to pre-repair endpoints such as different types of DNA damage clusters and have not included DNA repair mechanisms. Furthermore, all these studies have been conducted on a single cell for a set dose, which is not adequately representative of the heterogeneous conditions in the real world.DNA repair mechanisms are an important factor in the steps that lead to carcinogenesis, and therefore, the objective of the first study presented in this thesis was to make the neutron-induced DNA damage simulation pipeline that was previously developed by our team compatible with the DNA Mechanistic Repair Simulator (DaMaRiS), to obtain neutron RBE estimates for neutron energies of 1 eV to 10 MeV. Additionally, we developed a DNA damage clustering algorithm that reproduces various pre-repair endpoints found in the literature, as well as damage clusters based on Euclidean distances, in order to compare them with the RBE results obtained for DNA misrepairs. In vitro experiments are typically conducted at the cell population level. Because radiation energy depositions are inherently stochastic, the dose distribution across a population of cells is heterogeneous, complicating direct comparisons between single cell simulations and in vitro observations. The objective of the second study presented in this thesis was thus to develop a multi-cellular methodology that couples condensed history Monte Carlo (CHMC) methods with TSMC methods using phase space files to enable cell population studies of ionizing radiation-induced DNA damage while requiring reasonable computational resources for basic research.The RBE obtained in the first study was found to exhibit a qualitatively similar trend to the values reported for pre-repair damage endpoints, but reaches a higher peak, with a maximum of 23(1) at a neutron energy of 0.5 MeV. Notably, the RBE associated with nearby double-strand break (DSB) pairs separated by a Eucledian distance of 11 nm or less showed a closer match to the RBE for DNA misrepairs than did the RBE for prerepair endpoints based on base pair distances. This suggests that certain aspects of the spatial distribution of neutron-induced DNA damage can be better reflected through explicit repair modeling or through cluster analysis based on Eucledian distances.In the second study, we used our multi-cellular methodology to simulate the gamma-H2AX foci yield per cell of a cell population irradiated with 72.09 mGy of 2.5 MeV monoenergetic neutrons. The resulting per-cell gamma-H2AX foci distributions reproduced key features observed in experimental data from the literature, providing strong evidence of the validity of the presented approach. Simulating a population of 2,600 cells required approximately 4E3 CPU-hours. Since step-by-step modeling of the chemical stage accounted for most of the computational cost, the total runtime could be significantly reduced by employing more efficient chemistry simulation approaches

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.245
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.027
GPT teacher head0.281
Teacher spread0.254 · 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.

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".

Quick stats

Citations0
Published2025
Admission routes2
Has abstractyes

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