Alginate microparticles as novel carrier for oral insulin delivery
Bibliographic record
Abstract
Alginate microparticles produced by emulsification/internal gelation were investigated as a promising carrier for insulin delivery. The procedure involves the dispersion of alginate solution containing insulin protein, into a water immiscible phase. Gelation is triggered in situ by instantaneous release of ionic calcium from carbonate complex via gentle pH adjustment. Particle size is controlled through the emulsification parameters, yielding insulin-loaded microparticles. Particle recovery was compared using several washing protocols. Recovery strategies are proposed and the influence on particle mean size, morphology, recovery yield (RY), encapsulation efficiency, insulin release profile, and structural integrity of released insulin were evaluated. Spherical micron-sized particles loaded with insulin were produced. The recovery process was optimized, improving yield, and ensuring removal of residual oil from the particle surface. The optimum recovery strategy consisted in successive washing with a mixture of acetone/hexane/isopropanol coupled with centrifugation. This strategy led to small spherical particles with an encapsulation efficiency of 80% and a RY around 70%. In vitro release studies showed that alginate itself was not able to suppress insulin release in acidic media; however, this strategy preserves the secondary structure of insulin. Particles had a mean size lower than the critical diameter necessary to be orally absorbed through the intestinal mucosa followed by their passage to systemic circulation and thus can be considered as a promising technology for insulin delivery.
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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".