Enhancing nanoparticle accumulation in two dimensional, three dimensional, and xenograft mouse cancer cell models in the presence of docetaxel
Bibliographic record
Abstract
Recent clinical trials show docetaxel (DTX), given in conjunction with radiation therapy (RT) and androgen suppression, improves survival in high-risk prostate cancer. Addition of gold nanoparticles (GNPs) to this current DTX/RT protocol is expected to further improve therapeutic benefits remarkably. However, the foundation for the triple combination of RT, DTX, and GNPs must be elucidated to ensure quicker facilitation to the clinic. In this study, we explored the use of low concentrations of DTX combined with GNPs in two prostate cancer cell lines in a two-dimensional monolayer, a three-dimensional spheroid, and a mouse xenograft model. When used together, DTX and GNPs induced a nearly identical relative increase in uptake of gold in both the spheroid model and the mouse xenograft, which saw a 130% and 126% increase respectively after 24 h, showcasing the benefit of using spheroids as an in vitro model to better optimize in vivo experiments. Further, the benefits of using low concentrations of DTX combined with GNPs extended for over 72 h, allowing for less frequency in dosing when translating to the clinic. Overall, these results highlight the benefits of using DTX combined with GNPs and lays the groundwork for the translation of the triple combination of RT, GNPs, and DTX to the clinic.
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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 | 0.001 |
| 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".