In Vitro and In Vivo Synergetic Radiotherapy with Gold Nanoparticles and Docetaxel for Pancreatic Cancer
Bibliographic record
Abstract
This research underscores the potential of combining nanotechnology with conventional therapies in cancer treatment, particularly for challenging cases like pancreatic cancer. We aimed to enhance pancreatic cancer treatment by investigating the synergistic effects of gold nanoparticles (GNPs) and docetaxel (DTX) as potential radiosensitizers in radiotherapy (RT) both in vitro and in vivo, utilizing a MIA PaCa-2 monoculture spheroid model and NRG mice subcutaneously implanted with MIA PaCa-2 cells, respectively. Spheroids were treated with GNPs (7.5 μg/mL), DTX (100 nM), and 2 Gy of RT using a 6 MV linear accelerator. In parallel, mice received treatments of GNPs (2 mg/kg), DTX (6 mg/kg), and 5 Gy of RT (6 MV linear accelerator). In vitro results showed that though RT and DTX reduced spheroid size and increased DNA DSBs, the triple combination of DTX/RT/GNPs led to a significant 48% (p = 0.05) decrease in spheroid size and a 45% (p = 0.05) increase in DNA DSBs. In vivo results showed a 20% (p = 0.05) reduction in tumor growth 20 days post-treatment with (GNPs/RT/DTX) and an increase in mice median survival. The triple combination exhibited a synergistic effect, enhancing anticancer efficacy beyond individual treatments, and thus could be employed to improve radiotherapy and potentially reduce adverse effects.
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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".