A Monte Carlo-based model of gold nanoparticle radiosensitization accounting for increased radiobiological effectiveness
Bibliographic record
Abstract
Radiosensitization using gold nanoparticles (AuNPs) has been shown to vary widely with cell line, irradiation energy, AuNP size, concentration and intracellular localization. We developed a Monte Carlo-based AuNP radiosensitization predictive model (ARP), which takes into account the detailed energy deposition at the nano-scale. This model was compared to experimental cell survival and macroscopic dose enhancement predictions. PC-3 prostate cancer cell survival was characterized after irradiation using a 300 kVp photon source with and without AuNPs present in the cell culture media. Detailed Monte Carlo simulations were conducted, producing individual tracks of photoelectric products escaping AuNPs and energy deposition was scored in nano-scale voxels in a model cell nucleus. Cell survival in our predictive model was calculated by integrating the radiation induced lethal event density over the nucleus volume. Experimental AuNP radiosensitization was observed with a sensitizer enhancement ratio (SER) of 1.21 ± 0.13. SERs estimated using the ARP model and the macroscopic enhancement model were 1.20 ± 0.12 and 1.07 ± 0.10 respectively. In the hypothetical case of AuNPs localized within the nucleus, the ARP model predicted a SER of 1.29 ± 0.13, demonstrating the influence of AuNP intracellular localization on radiosensitization.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| 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".