CONTROLLED-RELEASE PREDNISOLONE POLY (DL-LACTIDE) MICROSPHERES: IMPACT OF FORMULATION PARAMETERS, CHARACTERIZATION AND RELEASE MECHANISM
Bibliographic record
Abstract
The steroidal drug prednisolone was encapsulated into microspheres using the biodegradable polymer poly (DL-lactide) using emulsion-solvent evaporation method. The produced microspheres were characterized using scanning electron microscopy, X-ray diffractometery, FT-IR spectroscopy, DSC, and laser light diffraction. The morphology, particle size distribution, encapsulation efficiency (EE%), and drug release showed marked dependence upon formulation parameters viz. initial polymer concentration, surfactant concentration, drug-to-polymer ratio, and volume of the external aqueous phase. The effect of the addition of hydrophilic additives such as PVP or PEG 8000 was also investigated. The encapsulation efficiency percent and the mean particle size were increased by increasing the initial polymer concentration and drug polymer ratio. On the other hand, increasing the surfactant concentration resulted in decreasing the mean particle size and increasing the drug release from the microspheres. The probable mechanism of drug release was estimated and found to be via diffusion through channels and/or pores present within the polymeric matrix. Release data of almost all formulae fitted Higuchi's planar model better than spherical model. This finding could be due to the small extent of drug release (~ 40%), or the presence of a large fraction of the encapsulated drug nearby the surface of the microspheres.
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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 | 0.001 |
| 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".