Modulation of the Microtubule Network for Optimization of Nanoparticle Dynamics for the Advancement of Cancer Nanomedicine
Bibliographic record
Abstract
Nanoparticles (NPs) have shown promise in both radiotherapy and chemotherapy. NPs are mainly transported along cellular microtubules (MTs). Docetaxel (DTX) is a commonly used chemotherapeutic drug that can manipulate the cellular MT network to maximize its clinical benefit. However, the effect of DTX on NP behaviour has not yet been fully elucidated. We used gold NPs of diameters 15 and 50 nm at a concentration of 0.2 nM to investigate the size dependence of NP behaviour. Meanwhile, DTX concentrations of 0, 10 and 50 nM were used to uphold clinical relevance. Our study reveals that a concentration of 50 nM DTX increased NP uptake by ~50% and their retention by ~90% compared to cells treated with 0 and 10 nM DTX. Smaller NPs had a 20-fold higher uptake in cells treated with 50 nM DTX vs. 0 and 10 nM DTX. With the treatment of 50 nm DTX, the cells became more spherical in shape, and NPs were redistributed closer to the nucleus. A significant increase in NP uptake and retention along with their intracellular distribution closer to the nucleus with 50 nM DTX could be exploited to target a higher dose to the most important target, the nucleus in both radiotherapy and chemotherapy.
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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) | 0.002 | 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".