Utilizing Gold Nanoparticles as Prospective Radiosensitizers in 3D Radioresistant Pancreatic Co-Culture Model
Bibliographic record
Abstract
Pancreatic cancer stands among the deadliest forms of cancer, and the existing treatments fall short of providing adequate efficacy. Novel and more effective treatment approaches are urgently required to address this critical medical challenge. In this study, we aimed to evaluate the anti-cancer efficacy of gold nanoparticles (GNPs) in combination with radiotherapy (RT). A 3D pancreatic cancer co-culture spheroid model of MIA PaCa-2 cancer cells and patient-derived cancer-associated fibroblasts (CAF-98) was used. The spheroids were treated with GNPs (7.5 μg/mL) and 2 Gy of RT. The spheroids’ cell viability was assessed through the CellTiter-Glo 3D assay, and an immunofluorescence assay was used to assess the DNA DSBs via the expression of the DNA damage marker 53BP1. Co-culture samples showed a 10.8% (p < 0.05) increase in proliferation and a 13.0% (p < 0.05) decrease in DNA DSB when compared to monoculture samples, However, they displayed a 175% (p < 0.001) increase in GNPs uptake when compared to monoculture spheroids. Using GNPs/RT, we were able to show a significant reduction of 6.2% (p < 0.05) in spheroid size and an increase of 14.3% (p < 0.05) in DNA DSB damage in co-culture samples. The combination of GNPs with RT demonstrated remarkable radiosensitization effects, representing a promising approach to enhance cancer treatment efficacy. These effects were particularly noteworthy in the more treatment-resistant co-culture spheroid model.
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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) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".