On the role of low-energy electrons in the radiosensitization of DNA by gold nanoparticles
Bibliographic record
Abstract
Four different gold nanoparticle (GNP) preparations, including naked GNPs and GNPs coated either with thiolated undecane (S-C(11)H(23)), or with dithiolated diethylenetriaminepentaacetic (DTDTPA) or gadolinium (Gd) DTDTPA chelating agents, were synthesized. The average diameters, for each type of nanoparticle, are 5 nm, 10 and 13 nm, respectively. Dry films of plasmid DNA pGEM-3Zf(-), DNA with bound GNPs and DNA with coated GNPs were bombarded with 60 keV electrons. The yields of single and double strand breaks were measured as a function of exposure by electrophoresis. The binding of just one GNP without coating to DNA containing 3197 base pairs increases single and double strand breaks by a factor of 2.3 while for GNPs coated with S-C(11)H(23) this factor is reduced to 1.6. The GNPs coated with DTDTPA and DTDTPA:Gd in the same ratio with the DNA, produce essentially no increment in damage. These results could be explained by the attenuation by the coatings of the intensity of the low-energy photoelectrons emitted from the GNPs. Thus, coatings of GNPs may considerably attenuate the short-range low-energy electrons emitted from gold, leading to a considerable decrease of radiosensitization. According to our results, the highest radiosensitization should be obtained with GNPs having the shortest possible ligand, directed to the DNA of cancer cells.
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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".