Sci‐Sat AM: Radiation Dosimetry and Practical Therapy Solutions ‐ 02: Dosimetric effects of gold nanoparticle surface coatings
Bibliographic record
Abstract
Introduction: Gold nanoparticles (GNPs) can enhance radiation therapy within a tumour, increasing local energy deposition under irradiation, but experimental evidence suggests the enhancement is not as large as predicted by dose enhancement alone. Many studies neglect to account for surface coatings that are frequently used to optimize GNP uptake and biological distribution. This study uses Monte Carlo methods to investigate the consequences on local dose enhancement due to including these surface coatings. Methods: Using the PENELOPE Monte Carlo code system, GNP irradiation was simulated both with and without surface coatings of polyethylene glycol (PEG) of various molecular weights. Dose was scored to the gold, coating, and surrounding water, and the dosimetric differences between these scenarios were examined. Results: The simulated PEG coating absorbs a large portion of the energy that would otherwise be deposited in the medium. The mean dose to water was reduced by up to 2.5, 3.5, and 4.5% for GNPs of diameters 50, 20, and 10 nm, respectively. This effect was more pronounced for smaller GNPs, thicker coatings, and low photon source energies where the enhancement due to GNPs is the greatest. The molecular weight of the coating material did not have a significant impact on the dose. Conclusions: The inclusion of a coating material in GNP enhanced radiation may reduce the dose enhancement due to the nanoparticles. Both the composition and size of the coating play a role in the level of this reduction and should be considered carefully.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.006 |
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".