Characterization of biologically active insulin-loaded alginate microparticles prepared by spray drying
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Bibliographic record
Abstract
BACKGROUND: Spray drying has been used as a means to encapsulate therapeutics in polymeric matrices to improve stability and alter pharmacokinetics. This research aims to characterize alginate microparticles formed by spray drying to encapsulate insulin for therapeutic delivery applications. METHODS: Particle size was characterized by laser diffraction spectroscopy, morphology by scanning electron microscopy, and protein and polymer distribution by confocal laser scanning microscopy. In addition, particle fines collected from the spray-dryer exhaust unit were characterized for size and morphology. The insulin encapsulation efficiency (EE) was determined after particle dissolution through quantification by spectrophotometric analysis. An in-vitro bioassay involving stimulation of rat L6 myoblasts was developed to confirm the bioactivity of released insulin. RESULTS: Mean diameter of the product was 2.1 ± 0.3 μm. Larger particles appeared spherical, with some smaller particles presenting surface topography variability and divoting. Protein EE was 38.2% ± 9.5%, with confocal microscopy showing the protein and polymer concentrated at the surface of larger particles, but more evenly distributed throughout smaller particles. A bioassay for the in-vitro quantification of insulin bioactivity was developed by calibrating the ratio of phosphorylated to total cellular protein kinase B (PKB; also known as AKT). in insulin-stimulated rat L6 myoblasts. Insulin released from the particles was 88% ± 15% bioactive, showing that spray drying had minimal impact on protein structure. CONCLUSION: Spray drying was effective in producing microparticles containing bioactive insulin. Future studies will focus on the improvement of the EE and particle uniformity with the aim of developing this technology further for the encapsulation and delivery of peptide or protein-based therapeutics.
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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 | 0.000 | 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) | 0.000 | 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 it