Gold nanoparticle DNA damage in radiotherapy: A Monte Carlo study
Bibliographic record
Abstract
This study investigated the DNA damage due to the dose enhancement of using gold nanoparticles (GNPs) as a radiation sensitizer in radiotherapy. Nanodosimetry of a photon irradiated GNP was performed with Monte Carlo simulations using Geant4-DNA (ver. 10.2) in the nanometer scale. In the simulation model, GNP spheres (with diameters of 30, 50, and 100 nm) and a DNA model were placed in a water cube (1 µm3). The GNPs were irradiated by photon beams with varying energies (50, 100, and 150 keV), which produced secondary electrons, enhancing the dose to the DNA. To investigate the dose enhancement effect at the DNA level, energy deposition to the DNA with and without the GNP were determined in simulations for calculation of the dose enhancement ratio (DER). The distance between the GNP and the DNA molecule was varied to determine its effect on the DER. Monte Carlo results were collected for three variables; GNP size, distances between the GNP and DNA molecule, and the photon beam energy. The DER was found to increase with the size of GNP and decrease with the distance between the GNP and DNA molecule. The largest DER was found to be 3.7 when a GNP (100 nm diameter) was irradiated by a 150 keV photon beam set at 30 nm from the DNA molecule. We conclude that there is significant dependency of the DER on GNP size, distance to the DNA and photon energy and have simulated those relationships.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".