Proton and light ion RBE for the induction of direct DNA double strand breaks
Bibliographic record
Abstract
PURPOSE: To present and characterize a Monte Carlo (MC) tool for the simulation of the relative biological effectiveness for the induction of direct DNA double strand breaks (RBEDSB (direct)) for protons and light ions. METHODS: The MC tool uses a pregenerated event-by-event tracks library of protons and light ions that are overlaid on a cell nucleus model. The cell nucleus model is a cylindrical arrangement of nucleosome structures consisting of 198 DNA base pairs. An algorithm relying on k-dimensional trees and cylindrical symmetries is used to search coincidences of energy deposition sites with volumes corresponding to the sugar-phosphate backbone of the DNA molecule. Strand breaks (SBs) are scored when energy higher than a threshold is reached in these volumes. Based on the number of affected strands, they are categorized into either single strand break (SSB) or double strand break (DSB) lesions. The number of SBs composing each lesion (i.e., its size) is also recorded. RBEDSB (direct) is obtained by taking the ratio of DSB yields of a given radiation field to a (60)Co field. The MC tool was used to obtain SSB yields, DSB yields, and RBEDSB (direct) as a function of linear energy transfer (LET) for protons ((1)H(+)), (4)He(2+), (7)Li(3+), and (12)C(6+) ions. RESULTS: For protons, the SSB yields decreased and the DSB yields increased with LET. At ≈24.5 keV μm(-1), protons generated 15% more DSBs than (12)C(6+) ions. The RBEDSB (direct) varied between 1.24 and 1.77 for proton fields between 8.5 and 30.2 keV μm(-1), and it was higher for iso-LET ions with lowest atomic number. The SSB and DSB lesion sizes showed significant differences for all radiation fields. Generally, the yields of SSB lesions of sizes ≥2 and the yields of DSB lesions of sizes ≥3 increased with LET and increased for iso-LET ions of lower atomic number. On the other hand, the ratios of SSB to DSB lesions of sizes 2-4 did not show variability with LET nor projectile atomic number, suggesting that these metrics are independent of the radiation quality. Finally, a variance of up to 8% in the DSB yields was observed as a function of the particle incidence angle on the cell nucleus. This simulation effect is due to the preferential alignment of ion tracks with the DNA nucleosomes at specific angles. CONCLUSIONS: The MC tool can predict SSB and DSB yields for light ions of various LET and estimate RBEDSB (direct). In addition, it can calculate the frequencies of different DNA lesion sizes, which is of interest in the context of biologically relevant absolute dosimetry of particle beams.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".