Micelles of poly(ethylene oxide)‐<i>b</i>‐poly(ε‐caprolactone) as vehicles for the solubilization, stabilization, and controlled delivery of curcumin
Bibliographic record
Abstract
Curcumin is recognized as a potential chemotherapeutic agent against a variety of tumors. However, the clinical application of curcumin is hindered due to its poor water solubility and fast degradation. The objective of this study was to investigate amphiphilic block copolymer micelles of poly(ethylene oxide)-b-poly(epsilon-caprolactone) (PEO-PCL) as vehicles for the solubilization, stabilization, and controlled delivery of curcumin. Curcumin-loaded PEO-PCL micelles were prepared by a cosolvent evaporation technique. PEO-PCL micelles were able to solubilize curcumin effectively, protect the encapsulated curcumin from hydrolytical degradation in physiological matrix, and control the release of curcumin over a few days. The characteristics of resultant micelles were found to depend on the polymerization degrees of epsilon-caprolactone. Among different PEO-PCL micelles, PEO(5000)-PCL(24500) was the most efficient in solubilizing curcumin while PEO(5000)-PCL(13000) was the best carrier in reducing its release rate. PEO-PCL micelle-encapsulated curcumin retained its cytotoxicity in B16-F10, a mouse melanoma cell line, and SP-53, Mino, and JeKo-1 human mantle cell lymphoma cell lines. These results demonstrated the potential of PEO-PCL micelles as an injectable formulation for efficient solubilization, stabilization, and controlled delivery of curcumin.
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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 | 0.001 |
| 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".