Enhancement of Radiation Cytotoxicity in Breast‐Cancer Cells by Localized Attachment of Gold Nanoparticles
Bibliographic record
Abstract
Gold nanoparticles (GNPs) and modified GNPs having two kinds of functional molecules, cysteamine (AET) and thioglucose (Glu), are synthesized. Cell uptake and radiation cytotoxicity enhancement in a breast-cancer cell line (MCF-7) versus a nonmalignant breast-cell line (MCF-10A) are studied. Transmission electron microscopy (TEM) results show that cancer cells take up functional Glu-GNPs significantly more than naked GNPs. The TEM results also indicate that AET-capped GNPs are mostly bound to the MCF-7 cell membrane, while Glu-GNPs enter the cells and are distributed in the cytoplasm. After MCF-7 cell uptake of Glu-GNPs, or binding of AET-GNPs, the in vitro cytotoxicity effects are observed at 24, 48, and 72 hours. The results show that these functional GNPs have little or no toxicity to these cells. To validate the enhanced killing effect on cancer cells, various forms of radiation are applied such as 200 kVp X-rays and gamma-rays, to the cells, both with and without functional GNPs. By comparison with irradiation alone, the results show that GNPs significantly enhance cancer killing.
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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) | 0.000 | 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".