Development of Hydrophilic Drug Encapsulation and Controlled Release Using a Modified Nanoprecipitation Method
Bibliographic record
Abstract
The improvement of the loading content of hydrophilic drugs by polymer nanoparticles (NPs) recently has received increased attention from the field of controlled release. We developed a novel, simply modified, drop-wise nanoprecipitation method which separated hydrophilic drugs and polymers into aqueous phase (continuous phase) and organic phase (dispersed phase), both individually and involving a mixing process. Using this method, we produced ciprofloxacin-loaded NPs by Poly (d,l-lactic acid)-Dextran (PLA-DEX) and Poly lactic acid-co-glycolic acid-Polyethylene glycol (PLGA-PEG) successfully, with a considerable drug-loading ability up to 27.2 wt% and an in vitro sustained release for up to six days. Drug content with NPs can be precisely tuned by changing the initial drug feed concentration of ciprofloxacin. These studies suggest that this modified nanoprecipitation method is a rapid, facile, and reproducible technique for making nano-scale drug delivery carriers with high drug-loading abilities
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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) | 0.001 | 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 | 0.001 | 0.001 |
| 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".