Gold nanoparticle sensitize radiotherapy of prostate cancer cells by regulation of the cell cycle
Bibliographic record
Abstract
Glucose-capped gold nanoparticles (Glu-GNPs) have been used to improve cellular targeting and radio-sensitization. In this study, we explored the mechanism of Glu-GNP enhanced radiation sensitivity in radiation-resistant human prostate cancer cells. Cell survival and proliferation were measured using MTT and clonogenic assay. Flow cytometry with staining by propidium iodide (PI) was performed to study the cell cycle changes induced by Glu-GNPs, and western blotting was used to determine the expression of p53 and cyclin proteins that correlated to cell cycle regulation. With 2 Gy of ortho-voltage irradiation, Glu-GNP showed a 1.5-2.0 fold enhancement in growth inhibition when compared to x-rays alone. Comparing the cell cycle change, Glu-GNPs induced acceleration in the G0/G1 phase and accumulation of cells in the G2/M phase at 29.8% versus 18.4% for controls at 24 h. G2/M arrest was accompanied by decreased expression of p53 and cyclin A, and increased expression of cyclin B1 and cyclin E. In conclusion, Glu-GNPs trigger activation of the CDK kinases leading to cell cycle acceleration in the G0/G1 phase and accumulation in the G2/M phase. This activation is accompanied by a striking sensitization to ionizing radiation, which may have clinical implications.
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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".