Evaluation of cytotoxicity and radiation enhancement using 1.9 nm gold particles: potential application for cancer therapy
Bibliographic record
Abstract
High atomic number (Z) materials such as gold preferentially absorb kilovoltage x-rays compared to soft tissue and may be used to achieve local dose enhancement in tumours during treatment with ionizing radiation. Gold nanoparticles have been demonstrated as radiation dose enhancing agents in vivo and in vitro. In the present study, we used multiple endpoints to characterize the cellular cytotoxic response of a range of cell lines to 1.9 nm gold particles and measured dose modifying effects following transient exposure at low concentrations. Gold nanoparticles caused significant levels of cell type specific cytotoxicity, apoptosis and increased oxidative stress. When used as dose modifying agents, dose enhancement factors varied between the cell lines investigated with the highest enhancement being 1.9 in AGO-1522B cells at a nanoparticle concentration of 100 microg ml(-1). This study shows exposure to 1.9 nm gold particles to induce a range of cell line specific responses including decreased clonogenic survival, increased apoptosis and induction of DNA damage which may be mediated through the production of reactive oxygen species. This is the first study involving 1.9 nm nanometre sized particles to report multiple cellular responses which impact on the radiation dose modifying effect. The findings highlight the need for extensive characterization of responses to gold nanoparticles when assessing dose enhancing potential in cancer therapy.
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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 | 0.001 | 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".