DNA Damage of Iron-Gold Nanoparticle Heterojunction Irradiated by kV Photon Beams: A Monte Carlo Study
Bibliographic record
Abstract
This study aims to evaluate the dependence of DNA damage on the proportion of iron and gold in iron-gold nanoparticle heterojunctions using Monte Carlo simulations. The simulation setup included a spherical nanoparticle with varying percentages of iron and gold, irradiated by photon beams of different energies (50–150 keV). The Geant4-DNA Monte Carlo code was utilized for the accurate tracking of radiation transport. The results reveal that DNA damage increases with a higher percentage of gold volume in the heterojunction, primarily due to photoelectric enhancement. Furthermore, a lower photon beam energy of 50 keV induces greater DNA damage compared to energies of 100 keV and 150 keV. The findings suggest that for effective cancer cell eradication through DNA damage, the gold volume should be equal to or greater than 50% in the iron-gold nanoparticle heterojunction. In conclusion, the findings from this study will shed light on the potential of iron-gold nanoparticle heterojunctions in enhancing radiotherapy outcomes. The investigation of DNA damage resulting from the combination of contrast agents and radiosensitizers is crucial for advancing cancer research and treatment. The knowledge gained from this research will aid in the development of personalized and effective radiotherapy approaches, ultimately improving patient outcomes in cancer treatment.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 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".