Reactive Oxygen Species Yield near Gold Nanoparticles Under Ultrahigh-Dose-Rate Electron Beams: A Monte Carlo Study
Bibliographic record
Abstract
Ultrahigh dose rate (UHDR) radiotherapy, also known as FLASH radiotherapy (FLASH-RT), has shown potential for increasing tumor control while sparing normal tissue. In parallel, gold nanoparticles (GNPs) have been extensively explored as radiosensitizers due to their high atomic number and ability to enhance the generation of reactive oxygen species (ROS) through water radiolysis. In this study, we investigate the synergistic effects of UHDR electron beams and GNP-mediated radiosensitization using Monte Carlo (MC) simulations based on the Geant4-DNA code. A spherical water phantom with embedded GNPs of varying sizes (5-100 nm) was irradiated using pulsed electron beams (100 keV and 1 MeV) at dose rates of 60, 100, and 150 Gy/s. The chemical yield of ROS near the GNPs was quantified and compared to an equivalent water nanoparticle model, and the yield enhancement factor (YEF) was used to evaluate radiosensitization. Results demonstrated that YEF increased with smaller GNP sizes and at lower UHDR, particularly for 1 MeV electrons. A maximum YEF of 1.25 was observed at 30 nm from the GNP surface for 5 nm particles at 60 Gy/s. The elevated ROS concentration near GNPs under FLASH conditions is expected to intensify DNA damage, especially double-strand breaks, due to increased hydroxyl radical interactions within nanometric distances of critical biomolecular targets. These findings highlight the significance of nanoparticle size and beam parameters in optimizing ROS production for FLASH-RT. The results provide a computational basis for future experimental investigations into the combined use of GNPs and UHDR beams in nanoparticle-enhanced radiotherapy.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".