Evaluation of proniosomes as an alternative strategy to optimize piroxicam transdermal delivery
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 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 it