Interplay between the gold nanoparticle sub-cellular localization, size, and the photon energy for radiosensitization
Bibliographic record
Abstract
There are large variations in the reported efficiency of gold nanoparticle (GNP) radiosensitization. We have previously reported on a predictive model, which accounts for the detailed Auger and photoelectron tracks to calculate the cell survival probability. After validating our model using PC-3 cells incubated with 2 mg/ml of 30 nm GNPs and irradiated with 100 kVp or 300 kVp beams, we evaluated the interplay between photon energy, GNP size (1.9 and 100 nm) and sub-cellular localization. Experiments were in excellent agreement with the model. In predictive modeling, using a 100 kVp source and 1.9 nm nanoparticles, GNP localization had a significant impact on cell survival. A sensitizer enhancement ratio of 1.34 was achieved when GNPs were localized outside the cells, increasing to 2.56 when GNPs were also distributed in the cytoplasm and nucleus. Using a 300 kVp source, which emits photons mainly above the gold K-edge, the dependence on GNP localization and size was barely detectable, since long ranged electrons dominate the energy deposition. In summary, achieving intracellular uptake with targeted-GNPs can significantly enhance radiosensitization for photon energies below the gold K-edge, where Auger electrons contribute significantly to the local energy deposition. For higher energies, this is much less important.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".