Dosimetric consequences of gold nanoparticle clustering during photon irradiation
Bibliographic record
Abstract
PURPOSE: The radiation dose enhancement caused by introducing gold nanoparticles (GNP) into cells can increase the dose locally absorbed. A disconnect between experimentally determined survival and dose enhancements predicted by Monte Carlo simulations on macroscopic scales, suggests small-scale energy deposition patterns play an important role in GNP dose enhancement. Clustering of the GNPs could potentially alter small-scale energy deposition patterns. Here we use Monte Carlo simulations to quantify energy deposition patterns in the presence of clustered GNPs and address the question of whether clustering of the nanoparticles affects the energy deposition patterns and ultimately cellular response. METHODS: Using the PENELOPE Monte Carlo code, we examine the absorption of energy in the environment of a single irradiated GNP following its interaction with a set of primary monoenergetic photon beams. We introduce successive GNPs to form a cluster about the particle in which the primary photon interactions occur and report on the energy deposited locally (within a 500 nm radius) and nonlocally (beyond 500 nm) in the surrounding water-equivalent medium as a function of the number of additional GNPs and the packing geometry they assume. RESULTS: When additional GNPs cluster in tightly packed formations about a GNP in which an incident photon interacts, both the energy deposited locally and released nonlocally are reduced relative to the case where other GNPs are not present. The degree of the reduction depends on incident photon energy, the number of GNPs added to the cluster, and the packing geometry. With 90 additional GNPs in a hexagonal close packing (HCP) cluster about a directly irradiated test particle, the local energy deposition was reduced to 29% (of the value in the absence of neighbors) in the most extreme monoenergetic case. Energy released into the nonlocal volume was most affected by the cluster for low-incident photon energies (< 40 keV), where reductions to 26% of the value in the absence of a cluster were shown. The packing geometry mitigated these results. When the irradiated GNP was on the periphery of the HCP cluster, or when the cluster was confined to a plane, the observed effects were weaker and when an equal number of GNPs were uniformly distributed in the local volume, the changes were trivial (less than 2%). CONCLUSIONS: The findings provide grounds for reconciling the observations of cell survival with Monte Carlo predictions of GNP dose enhancement. This work is significant because it demonstrates that GNP clustering needs to be understood and accounted to optimize local dose enhancement.
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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 | 0.001 |
| 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 | 0.000 | 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".