Enhanced radiation sensitivity in prostate cancer by gold-nanoparticles
Bibliographic record
Abstract
PURPOSE: Nanotechnology is an emerging field with significant translational potential in medicine. In this study, we applied gold nanoparticles (GNP) to enhance radiation sensitivity and growth inhibition in radiation-resistant human prostate cancer cells. METHODS: Gold nanoparticles (GNPs) were synthesized using HAuCl4 as the gold particle source and NaBH4 as the reductant. Either thio-glucose or sodium citrate was then added to the solution separately to bind the GNPs to form thio-glucose-capped gold nanoparticles (Glu-GNP) and neutral gold nanoparticles (TGS-GNPs). Human prostate carcinoma DU-145 cells were exposed to vehicle, irradiation, 15nM TGS-GNPs, or 15nM Glu-GNPs, or GNPs plus irradiation. The uptake assays of GNP were performed using hemocytometer to count cells and the mass spectrometry was applied to calculate gold mass. The cytotoxicity induced by GNPs, irradiation, or GNPs plus irradiation was measured using a standard colorimetric MTT assay. RESULTS: Exposure to Glu-GNPs resulted in a three times increase of nanoparticle uptake compared to that of TGS-GNPs in each target cell (p < 0.005). Cytoplasmic intracellular uptake of both TGS-GNPs and Glu-GNPs resulted in a growth inhibition by 30.57% and 45.97% respectively, comparing to 15.88% induced by irradiation alone, in prostate cancer cells after exposure to the irradiation. Glu-GNPs showed a greater enhancement, 1.5 to 2 fold increases within 72 hours, on irradiation cytotoxicity compared to TGS-GNPs. Tumour killing, however, did not appear to correlate linearly with nanoparticle uptake concentrations. CONCLUSION: These results showed that functional glucose-bound gold nanoparticles enhanced radiation sensitivity and toxicity in prostate cancer cells. In vivo studies will be followed to verify our research findings.
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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".