Intratumoral Injection of Low-Energy Photon-Emitting Gold Nanoparticles: A Microdosimetric Monte Carlo-Based Model
Bibliographic record
Abstract
Gold nanoparticles (Au NPs) distributed in the vicinity of low-dose rate (LDR) brachytherapy seeds could multiply their efficacy thanks to the secondary emissions induced by the photoelectric effect. Injections of radioactive LDR gold nanoparticles (LDR Au NPs), instead of conventional millimeter-size radioactive seeds surrounded by Au NPs, could further enhance the dose by distributing the radioactivity more precisely and homogeneously in tumors. However, the potential of LDR Au NPs as an emerging strategy to treat cancer is strongly dependent on the macroscopic diffusion of the NPs in tumors, as well as on their microscopic internalization within the cells. Understanding the relationship between interstitial and intracellular distribution of NPs, and the outcomes of dose deposition in the cancer tissue is essential for considering future applications of radioactive Au NPs in oncology. Here, LDR Au NPs (103Pd:Pd@Au-PEG NPs) were injected in prostate cancer tumors. The particles were visualized at time-points by computed tomography imaging (in vivo), transmission electron microscopy (ex vivo), and optical microscopy (ex vivo). These data were used in a Monte Carlo-based dosimetric model to reveal the dose deposition produced by LDR Au NPs both at tumoral and cellular scales. 103Pd:Pd@Au-PEG NPs injected in tumors produce a strong dose enhancement at the intracellular level. However, energy deposition is mainly confined around vesicles filled with NPs, and not necessarily close to the nuclei. This suggests that indirect damage caused by the production of reactive oxygen species might be the leading therapeutic mechanism of tumor growth control, over direct damage to the DNA.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".