Dosimetric effects of polyethylene glycol surface coatings on gold nanoparticle radiosensitization
Bibliographic record
Abstract
One of the main appeals of using gold nanoparticles (GNPs) as radiosensitizers is that their surface coatings can be altered to manipulate their pharmacokinetic properties. However, Monte Carlo studies of GNP dosimetry tend to neglect these coatings, potentially changing the dosimetric results. This study quantifies the dosimetric effects of including a polyethylene glycol (PEG) surface coating on GNPs over both nanoscopic and microscopic ranges. Two dosimetric scales were explored using PENELOPE Monte Carlo simulations. In microscopic simulations, 500-1000 GNPs, with and without coatings, were placed in cavities of side lengths 0.8-4 µm, and the reduction of dose deposited to surrounding medium within these volumes due to the coating was quantified. Including PEG surface coatings of up to 20 nm thickness resulted in reductions of up to 7.5%, 4.0%, and 2.0% for GNP diameters of 10, 20, and 50 nm, respectively. Nanoscopic simulations observed the dose falloff in the first 500 nm surrounding a single GNP both with and without surface coatings of various thicknesses. Over the first 500 nm surrounding a single GNP, the presence of a PEG surface coating reduced dose by 5-26%, 8-28%, 8-30%, and 8-34% for 2, 10, 20, and 50 nm diameter GNPs, respectively, for various energies and coating thicknesses. Reductions in dose enhancement due to the inclusion of a GNP surface coating are non-negligible and should be taken into consideration when investigating GNP dose enhancement. Further studies should be carried out to investigate the biological effects of these coatings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".