Electrospun nanofibers of poly (lactic acid)/poly (<i>ε</i>‐caprolactone) blend for the controlled release of levetiracetam
Bibliographic record
Abstract
Abstract This study deals with poly(lactic acid) (PLA), poly(‐caprolactone) (PCL), and their blend electrospun nanofibers (ESNF) as novel systems for the release of levetiracetam (LEV). Scanning electron microscopy demonstrated that the morphology of all samples is smooth and beads‐free. In addition, with increasing LEV content, the average diameters of PLA, PCL, and PCL/PLA ESNF enhanced by almost 69%, 41%, and 14%, respectively. FTIR spectroscopy was utilized to confirm the structure of polymer and drug, polymer‐drug interaction, and the observation of functional groups. The pore percentage was also diminished by adding LEV. The results of x‐ray diffraction revealed that the crystallinity decreased from 18.2%, 40.1%, and 21.5% for PLA, PCL, and PLA/PCL ESNF, respectively, to 15%, 31.2%, and 13.6% for the samples containing 18 wt% LEV. In addition, PLA ESNF containing 10 and 18 wt% LEV demonstrated a steady uptrend for drug release, while PCL and PLA/PCL ESNF containing 10 and 18 wt% LEV initially indicated an abrupt increase in drug release and then became steady. Moreover, drug release kinetics were evaluated using different models such as zero order, first order, Higuchi, and Korsmeyer‐Peppas models and the best model for predicting the drug release behavior was selected.
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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".