Energy Dependence of Gold Nanoparticle Radiosensitization in Plasmid DNA
Bibliographic record
Abstract
Gold nanoparticles (GNPs) are of considerable interest for use as a radiosensitizer, because of their biocompatibility and their ability to increase dose deposited because of their high mass energy absorption coefficient. Their sensitizing properties have been verified experimentally, but a discrepancy between the experimental results and theoretical predictions suggests that the sensitizing effect does not depend solely on gold’s superior absorption of energetic photons. This work presents the results of three sets of experiments that independently mapped out the energy dependence of the radiosensitizing effects of GNPs on plasmid DNA suspended in water. Incident photon energy was varied from 11.8 to 80 keV through the use of monochromatic synchrotron and broadband X-rays. These results depart significantly from the theoretical predictions in two ways: First, the sensitization is significantly larger than would be predicted; second, it does not vary with energy as would be predicted from energy absorption coefficients. These results clearly demonstrate that the effects of GNP-enhanced therapies cannot be predicted by considering additional dose alone and that a greater understanding of the processes involved is necessary for the development of future therapeutics.
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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 | 0.000 |
| 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) | 0.001 | 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".