SU‐G‐TeP3‐04: Evaluation of the Dose Enhancement with Gold Nanoparticle in Microdosimetry Level Using the Geant4‐DNA Toolkit
Bibliographic record
Abstract
Purpose: This study investigated the dose enhancement effect of using gold nanoparticles (GNP) as radiation sensitizers radiated by different photon beam energies. Microdosimetry of photon‐irradiated GNP was determined by the Geant4‐DNA process in the DNA scale. Methods: Monte Carlo simulation was conducted using the Geant4 toolkit (ver. 10.2). A GNP with different sizes (30, 50, and 100nm diameter sphere) and a DNA were placed in a water cube (1µm3). The GNP was irradiated by photon beams with different energies (50, 100, and 150keV) and produced secondary electrons to increase the dose to the DNA. Energy depositions were calculated for both with and without GNP and to investigate the dose enhancement effect at the DNA. The distance between the GNP and DNA was varied to optimize the best GNP position to the DNA. The photon beam source was set to 200nm from the GNP in each simulation. Results: It is found that GNP had a dose enhancement effect on kV photon radiations. For Monte Carlo results on different GNP sizes, distances between the GNP and DNA, and photon beam energies, enhancement ratio was found increasing as GNP size increased. The distance between the GNP and DNA affected the result that as distance increased while the dose enhancement ratio decreased. However, the effect of changing distance was not as significant as varying the GNP size. In addition, increasing the photon beam energy also increased the dose enhancement ratio. The largest dose enhancement ratio was found to be 3.5, when the GNP (100nm diameter) irradiated by the 150keV photon beam was set to 80nm from the DNA. Conclusion: Dose enhancement was determined in the DNA with GNP in the microdosimetry scale. It is concluded that the dose enhancement varied with the photon beam energy, GNP size and distance between the GNP and DNA.
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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 | 0.001 |
| 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 | 0.001 | 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".