Modulation of nanoparticle uptake, intracellular distribution, and retention with docetaxel to enhance radiotherapy
Bibliographic record
Abstract
OBJECTIVE: One of the major issues in current radiotherapy (RT) is the normal tissue toxicity. A smart combination of agents within the tumor would allow lowering the RT dose required while minimizing the damage to healthy tissue surrounding the tumor. We chose gold nanoparticles (GNPs) and docetaxel (DTX) as our choice of two radiosensitizing agents. They have a different mechanism of action which could lead to a synergistic effect. Our first goal was to assess the variation in GNP uptake, distribution, and retention in the presence of DTX. Our second goal was to assess the therapeutic results of the triple combination, RT/GNPs/DTX. METHODS: We used HeLa and MDA-MB-231 cells for our study. Cells were incubated with GNPs (0.2 nM) in the absence and presence of DTX (50 nM) for 24 h to determine uptake, distribution, and retention of NPs. For RT experiments, treated cells were given a 2 Gy dose of 6 MV photons using a linear accelerator. RESULTS: RT/DTX. Our experimental therapeutic results were supported by Monte Carlo simulations. CONCLUSION: The ability to not only trap GNPs at clinically feasible doses but also to retain them within the cells could lead to meaningful fractionated treatments in future combined cancer therapy. Furthermore, the suggested triple combination of RT/GNPs/DTX may allow lowering the RT dose to spare surrounding healthy tissue. ADVANCES IN KNOWLEDGE: This is the first study to show intracellular GNP transport disruption by DTX, and its advantage in radiosensitization.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".