Recent progress in Monte Carlo simulation on gold nanoparticle radiosensitization
Bibliographic record
Abstract
Gold nanoparticles (GNPs) are proven effective heavy-atom radiosensitizers to produce imaging contrast and dose enhancement in radiotherapy. To understand the physical and biological effect of adding GNPs to the tumour cells, Monte Carlo simulation based on particle tracking and transport, is employed to predict the dosimetry in the cellular and DNA scale. In this review, we first explore the recent advances in Monte Carlo simulation on GNP radiosensitisation. The development of particle tracking algorithm for very low energy electron in the simulation is discussed, followed by some results regarding the prediction of dose enhancement (microscopic and macroscopic). We then review different Monte Carlo cell models with GNPs in the simulation, the biological effect resulting from DNA damage, and the effects of increasing imaging contrast in the tumour cell due to photoelectric enhancement. Moreover, we explain and look at different studies and results on GNP-enhanced radiotherapy using gamma rays (brachytherapy), megavoltage photon, kilovoltage photon, electron and proton beams.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".