Preparation and preclinical evaluation of aceclofenac loaded pectinate mucoadhesive microspheres
Bibliographic record
Abstract
The aim of the present research work was to fabricate aceclofenac loaded pectinate microspheres by ionic gelation method and evaluate the effect of different cross-linking agents and polymer concentration on particle size, encapsulation efficacy and drug release behavior. It was also investigated that whether this pectinate dosage form was able to target the drug release in intestinal region and prevent the different side effect associated with the drug in stomach or not. It was observed that particle size, encapsulation efficacy and in vitro drug release were largely depended on polymer concentration and cross-linking agents. It was also observed that pectinate microspheres showed excellent pH depended mucoadhesive properties and they were able to restrict the drug release in stomach. In vitro drug release study showed that alminium-pectinate microspheres have more sustaining property as compared to barium-pectinate microspheres. Holm-Sidak multiple comparison analysis suggested a significant difference in measured t50% values among all the formulations with same cross-linking agent. In vivo studies revealed that the anti inflammatory and analgesic effects induced by pectinate microspheres were significantly high and prolonged as compared to pure drug. So, pectinate microspheres can be an excellent carrier for targeting the delivery of aceclofenac as well as help in improving the patient compliance by prolonging the systemic absorption.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".