Docetaxel-Mediated Uptake and Retention of Gold Nanoparticles in Tumor Cells and in Cancer-Associated Fibroblasts
Bibliographic record
Abstract
Due to recent advances in nanotechnology, the application of nanoparticles (NPs) in cancer therapy has become a leading area in cancer research. Despite the importance of cancer-associated fibroblasts (CAFs) in creating an optimal niche for tumor cells to grow extensively, most of the work has been focused on tumor cells. Therefore, to effectively use NPs for therapeutic purposes, it is important to elucidate the extent of NP uptake and retention in tumor cells and CAFs. Three tumor cell lines and three CAF cell lines were studied using gold NPs (GNPs) as a model NP system. We found a seven-fold increase in NP uptake in CAFs compared to tumor cells. The retention percentage of NPs was three-fold higher in tumor cells as compared to CAFs. Furthermore, NP uptake and retention were significantly enhanced using a 50 nM concentration of docetaxel (DTX). NP uptake was improved by a factor of three in tumor cells and a factor of two in CAFs, while the retention of NPs was two-fold higher in tumor cells compared to CAFs, 72 h post-treatment with DTX. However, the quantity of NPs in CAFs was still three-fold higher compared to tumor cells. Our quantitative data were supported by qualitative imaging data. We believe that targeting of NPs in the presence of DTX is a very promising approach to accumulate a higher percentage of NPs and maintain a longer retention in both tumor cells and CAFs for achieving the full therapeutic potential of cancer nanotechnology.
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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) | 0.000 | 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".