Synthesis, Self-Assembly, and Drug Delivery Characteristics of Poly(methyl caprolactone-<i>co</i>-caprolactone)-<i>b</i>-poly(ethylene oxide) Copolymers with Variable Compositions of Hydrophobic Blocks: Combining Chemistry and Microfluidic Processing for Polymeric Nanomedicines
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide The synthesis, characterization, and self-assembly of a series of biocompatible poly(methyl caprolactone- co -caprolactone)- b -poly(ethylene oxide) amphiphilic block copolymers with variable MCL contents in the hydrophobic block are described. Self-assembly gives rise to polymeric nanoparticles (PNPs) with hydrophobic cores that decrease in crystallinity as the MCL content increases, and their morphologies and sizes show nonmonotonic trends with MCL content. PNPs loaded with the anticancer drug paclitaxel (PAX) give rise to in vitro PAX release rates and MCF-7 GI 50 (50% growth inhibition concentration) values that decrease as the MCL content increases. We also show for selected copolymers that microfluidic manufacturing at a variable flow rate enables further control of PAX release rates and enhances MCF-7 antiproliferation potency. These results indicate that more effective and specific drug delivery PNPs are possible through tangential efforts combining polymer synthesis and microfluidic manufacturing.
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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 | 0.000 |
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".