Microfluidic Processing Approach to Controlling Drug Delivery Properties of Curcumin-Loaded Block Copolymer Nanoparticles
Bibliographic record
Abstract
We apply gas–liquid microfluidic reactors containing flow-variable, high-shear “hot spots” to produce curcumin-loaded polymer nanoparticles (CUR-PNPs) comprised of poly(caprolactone)- block -poly(ethylene oxide) (PCL- b -PEO) block copolymers at various flow rates and CUR loading ratios. CUR-PNPs prepared using the conventional nanoprecipitation method (bulk method) showed decreased encapsulation efficiency and increased drug precipitation as the loading ratio increased. However, CUR-PNPs prepared by microfluidic manufacturing showed both increased encapsulation efficiency and increased drug loading as either the flow rate or the loading ratio increased. This enabled microfluidic CUR loading percentages of up to 30% to be achieved in this study, which to our knowledge is a record for block copolymer PNPs. As well, it is shown that increased flow rate of microfluidic manufacturing leads to decreased mean CUR-PNP sizes (down to ∼50 nm) and narrower size distributions, along with significantly different CUR release kinetics compared to CUR-PNPs prepared at slower flow rates. In vitro antiproliferation experiments against MDA-MB-231 cells give an average IC 50 value of 24 μM for CUR-PNPs compared to 13 μM for free CUR at the same incubation time of 72 h. Compared to conventional bulk and single-phase microfluidic strategies, this unique two-phase reactor represents an exciting manufacturing platform for optimizing polymeric CUR nanomedicines though flow-directed shear processing.
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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".