Active Loading and Tunable Release of Doxorubicin from Block Copolymer Vesicles
Bibliographic record
Abstract
Vesicles are spherical bilayers that offer a hydrophilic reservoir, suitable for the incorporation of water-soluble molecules, as well as a hydrophobic wall that protects the loaded molecules from the external solution. The permeability of a vesicle wall made from polystyrene can be enhanced by adding a plasticizer such as dioxane. Tuning the wall permeability allows loading and release of molecules from vesicles to be controlled. In this study, vesicles are prepared from polystyrene(310)-b-poly(acrylic acid)(36) and used as model carriers for doxorubicin (DXR), a weak amine and a widely used anticancer drug. To increase the wall permeability, different amounts of dioxane are added to the vesicle solution. A pH gradient is created across the vesicle wall (inside acidic) and used as an active loading method to concentrate the drug inside the vesicles. The results show that a pH gradient of ca. 3.8 units can enhance the loading level up to 10-fold relative to loading in the absence of the gradient. After loading, the release of DXR from vesicles is followed as a function of the wall permeability. The diffusion coefficient of doxorubicin through polystyrene (D) is evaluated from the initial slope of the release curves; the value of D ranges from 8 x 10(-17) to 6 x 10(-16) cm(2)/s, depending on the degree of plasticization of the vesicle wall.
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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".