Evaluation of proniosomes as an alternative strategy to optimize piroxicam transdermal delivery
Bibliographic record
Abstract
The current investigation aims to evaluate the transdermal potential of niosomes bearing a potent non-steroidal anti-inflammatory, piroxicam. Piroxicam-loaded niosomes were prepared and characterized for surface morphology, entrapment efficiency and in vitro permeation across excised rat skin from various proniosome gel formulations using Franz diffusion cells. Various non-ionic surfactants were used to achieve optimum encapsulation efficiency. The prepared proniosomes significantly improved drug permeation and reduced the lag time (p < 0.05). Proniosomes prepared with Span 60 provided a higher piroxicam flux across the skin than did those prepared with Tween 80. Niosomes prepared using Span 60 showed a higher release rate than those prepared using non-ionic surfactants, Span 20 and Span 80, while those prepared from Tween showed higher release rate than formula prepared with Span. This indicates that lipophilicity and hydrophilicity of surfactant has a main role in release rates of piroxicam. Particle size of piroxicam niosomal vesicles formed by proniosome was determined by scanning electron microscopy. The encapsulation efficiency was evaluated by a specific high performance liquid chromatography method. Niosomes formed from using Spans and Tweens exhibited very high encapsulation efficiency. The results are very encouraging and suggest that niosomes can act as promising carriers offering an alternative approach for transdermal delivery of piroxicam.
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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 | 0.001 |
| 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".