Investigation of the effects of cell model and subcellular location of gold nanoparticles on nuclear dose enhancement factors using Monte Carlo simulation
Bibliographic record
Abstract
PURPOSE: The authors' aims were to model how various factors influence radiation dose enhancement by gold nanoparticles (AuNPs) and to propose a new modeling approach to the dose enhancement factor (DEF). METHODS: The authors used Monte Carlo N-particle (MCNP 5) computer code to simulate photon and electron transport in cells. The authors modeled human breast cancer cells as a single cell, a monolayer, or a cluster of cells. Different numbers of 5, 30, or 50 nm AuNPs were placed in the extracellular space, on the cell surface, in the cytoplasm, or in the nucleus. Photon sources examined in the simulation included nine monoenergetic x-rays (10-100 keV), an x-ray beam (100 kVp), and (125)I and (103)Pd brachytherapy seeds. Both nuclear and cellular dose enhancement factors (NDEFs, CDEFs) were calculated. The ability of these metrics to predict the experimental DEF based on the clonogenic survival of MDA-MB-361 human breast cancer cells exposed to AuNPs and x-rays were compared. RESULTS: NDEFs show a strong dependence on photon energies with peaks at 15, 30/40, and 90 keV. Cell model and subcellular location of AuNPs influence the peak position and value of NDEF. NDEFs decrease in the order of AuNPs in the nucleus, cytoplasm, cell membrane, and extracellular space. NDEFs also decrease in the order of AuNPs in a cell cluster, monolayer, and single cell if the photon energy is larger than 20 keV. NDEFs depend linearly on the number of AuNPs per cell. Similar trends were observed for CDEFs. NDEFs using the monolayer cell model were more predictive than either single cell or cluster cell models of the DEFs experimentally derived from the clonogenic survival of cells cultured as a monolayer. The amount of AuNPs required to double the prescribed dose in terms of mg Au/g tissue decreases as the size of AuNPs increases, especially when AuNPs are in the nucleus and the cytoplasm. For 40 keV x-rays and a cluster of cells, to double the prescribed x-ray dose (NDEF = 2) using 30 nm AuNPs, would require 5.1 ± 0.2, 9 ± 1, 10 ± 1, 10 ± 1 mg Au/g tissue in the nucleus, in the cytoplasm, on the cell surface, or in the extracellular space, respectively. Using 50 nm AuNPs, the required amount decreases to 3.1 ± 0.3, 8 ± 1, 9 ± 1, 9 ± 1 mg Au/g tissue, respectively. CONCLUSIONS: NDEF is a new metric that can predict the radiation enhancement of AuNPs for various experimental conditions. Cell model, the subcellular location and size of AuNPs, and the number of AuNPs per cell, as well as the x-ray photon energy all have effects on NDEFs. Larger AuNPs in the nucleus of cluster cells exposed to x-rays of 15 or 40 keV maximize NDEFs.
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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 | 0.000 |
| 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".