Radiosensitization of DNA by Gold Nanoparticles Irradiated with High-Energy Electrons
Bibliographic record
Abstract
Zheng, Y., Hunting, D. J., Ayotte, P. and Sanche, L. Radiosensitization of DNA by Gold Nanoparticles Irradiated with High-Energy Electrons. Radiat. Res. 168, 19-27 (2008). Thin films of pGEM-3Zf(-) plasmid DNA were bombarded by 60 keV electrons with and without gold nanoparticles. DNA single- and double-strand breaks (SSBs and DSBs) were measured by agarose gel electrophoresis. From transmission electron micrographs, the gold nanoparticles were found to be closely linked to DNA scaffolds, probably as a result of electrostatic binding. The probabilities for formation of SSBs and DSBs from exposure of 1:1 and 2:1 gold nanoparticle:plasmid mixtures to fast electrons increase by a factor of about 2.5 compared to neat DNA samples. For monolayer DNA adsorbed on a thick gold substrate, the damage increases by an order of magnitude. The results suggest that the enhancement of radiosensitivity is due to the production of additional low-energy secondary electrons caused by the increased absorption of ionizing radiation energy by the metal, in the form of gold nanoparticles or of a thick gold substrate. Since short-range low-energy secondary electrons are produced in large amounts by any type of ionizing radiation, and since on average only one gold nanoparticle per DNA molecule is needed to increase damage considerably, targeting the DNA of cancer cells with gold nanoparticles may offer a novel approach that is generally applicable to radiotherapy treatments.
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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 | 0.000 |
| 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) | 0.001 | 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".