In silico studies of neutron-induced DNA damage and misrepair at the single-cell and cell population levels
Bibliographic record
Abstract
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
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".