Rapid MCNP simulation of DNA double strand break (DSB) relative biological effectiveness (RBE) for photons, neutrons, and light ions
Bibliographic record
Abstract
To account for particle interactions in the extracellular (physical) environment, information from the cell-level Monte Carlo damage simulation (MCDS) for DNA double strand break (DSB) induction has been integrated into the general purpose Monte Carlo N-particle (MCNP) radiation transport code system. The effort to integrate these models is motivated by the need for a computationally efficient model to accurately predict particle relative biological effectiveness (RBE) in cell cultures and in vivo. To illustrate the approach and highlight the impact of the larger scale physical environment (e.g. establishing charged particle equilibrium), we examined the RBE for DSB induction (RBEDSB) of x-rays, (137)Cs γ-rays, neutrons and light ions relative to γ-rays from (60)Co in monolayer cell cultures at various depths in water. Under normoxic conditions, we found that (137)Cs γ-rays are about 1.7% more effective at creating DSB than γ-rays from (60)Co (RBEDSB = 1.017) whereas 60-250 kV x-rays are 1.1 to 1.25 times more efficient at creating DSB than (60)Co. Under anoxic conditions, kV x-rays may have an RBEDSB up to 1.51 times as large as (60)Co γ-rays. Fission neutrons passing through monolayer cell cultures have an RBEDSB that ranges from 2.6 to 3.0 in normoxic cells, but may be as large as 9.93 for anoxic cells. For proton pencil beams, Monte Carlo simulations suggest an RBEDSB of about 1.2 at the tip of the Bragg peak and up to 1.6 a few mm beyond the Bragg peak. Bragg peak RBEDSB increases with decreasing oxygen concentration, which may create opportunities to apply proton dose painting to help address tumor hypoxia. Modeling of the particle RBE for DSB induction across multiple physical and biological scales has the potential to aid in the interpretation of laboratory experiments and provide useful information to advance the safety and effectiveness of hadron therapy in the treatment of cancer.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".