<b><i>In vivo</i></b>and<b><i>in vitro</i></b>characteristics for insulin-loaded PLA microparticles prepared by w/o/w solvent evaporation method with electrolytes in the continuous phase
Bibliographic record
Abstract
Insulin-loaded poly(lactide) (PLA) microparticles were successfully prepared by 6% w/v PLA in the organic phase, 10% w/v PVP and varied types of 5%w/v electrolytes in the continuous phase, by using a water-in-oil-in-water emulsion/ solvent extraction technique. Addition of electrolytes such as NaCl, CaCl2 into the external phase significantly improved insulin entrapment efficiency compared to the case of no additives. NaCl was the most effective for obtaining high entrapment efficiency, with microparticle yield 81.2%, trapping efficiencies 49%, insulin-loading level 5.5% w/w and mean particle size 14.8 microm. The distribution (%) of insulin on the PLA microparticles surface, outer layer and core were 8, 37 and 43%, respectively. The cumulative release of insulin had an upper limit of approximately 24% of the insulin load at 24 days. A steady release rate was 0.5 microg insulin/mg microparticles/day of insulin release maintained for 24 days. Total protein-leaking amount was reduced after addition of electrolytes in the continuous aqueous phase. Rabbit glucose levels were evaluated after subcutaneous 20 mg insulin-loaded PLA microparticles or PLA blank microparticles. Study results show that the insulin-loaded PLA microparticles significantly reduced the glucose level than PLA blank microparticles. The insulin-loaded PLA microparticles, physicochemical characterization data and the animal result obtained in this study may be relevant in optimizing the PLA microparticle formulation incorporation and delivery insulin carriers.
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 | 0.000 | 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".