Self‐Assembled Nanoparticles from Phenolic Derivatives for Cancer Therapy
Bibliographic record
Abstract
Therapeutic nanoparticles hold clinical promise for cancer treatment by avoiding limitations of conventional pharmaceuticals. Herein, a facile and rapid method is introduced to assemble poly(ethylene glycol) (PEG)-modified Pt prodrug nanocomplexes through metal-polyphenol complexation and combined with emulsification, which results in ≈100 nm diameter nanoparticles (PtP NPs) that exhibit high drug loading (0.15 fg Pt per nanoparticle) and low fouling properties. The PtP NPs are characterized for potential use as cancer therapeutics. Mass cytometry is used to quantify uptake of the nanoparticles and the drug concentration in individual cells in vitro. The PtP NPs have long circulation times, with an elimination half-life of ≈18 h in healthy mice. The in vivo antitumor activity of the PtP NPs is systematically investigated in a human prostate cancer xenograft mouse model. Mice treated with the PtP NPs demonstrate four times better inhibition of tumor growth than either free prodrug or cisplatin. This study presents a promising strategy to prepare therapeutic nanoparticles for biomedical applications.
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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) | 0.001 | 0.001 |
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".