Evaluation of the radioprotective ability of cystamine for 150 keV – 500 MeV proton irradiation: a Monte Carlo track chemistry simulation study
Bibliographic record
Abstract
Cystamine, an organic diamino-disulfide, is among the best of the known radiation-protective compounds, although the underlying molecular mechanisms by which it operates remain poorly understood. This study aims to use the aqueous ferrous sulfate (Fricke) dosimeter to evaluate the protective properties of this compound when present during irradiation by fast incident protons in the energy range of 150 keV – 500 MeV, that is, for “linear energy transfer” (LET) values ranging from ∼72.3 to 0.23 keV/μm. The presence of cystamine in irradiated Fricke solutions prevents the oxidation of Fe2+ ions by the oxidizing species produced in the radiolysis of acidic water, resulting in reduced Fe3+ ion yields. A Monte Carlo computer code is used to simulate the radiation-induced chemistry of the studied Fricke–cystamine solutions under aerated conditions while covering a wide range of cystamine concentrations from 5 × 10−7 to 1 mol/L. Results indicate that the protective activity of cystamine is due to its radical-capturing ability, a clear signature of the strong antioxidant profile of this compound. In addition, our simulations show that at low and intermediate concentrations of cystamine, its protective efficiency decreases with increasing LET, which is consistent with previous work. This finding stems from differences in the geometry of the track structures that change from low-LET isolated spherical “spurs” to high-LET dense continuous cylindrical tracks as LET increases. This study concludes that Monte Carlo simulations represent a powerful method for understanding, at the molecular level, indirect radiation damage to complex molecules such as cystamine.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".