Preparation and characterization of alginate microspheres for sustained protein delivery within tissue scaffolds
Bibliographic record
Abstract
Tissue engineering scaffolds are designed not only to provide structural support for the repair of damaged tissue, but can also serve the function of bioactive protein delivery. Here we present a study on the preparation and characterization of protein-loaded microspheres, either alone or incorporated into mock tissue scaffolds, for sustained protein delivery. Alginate microspheres were prepared by a novel, small-scale water-in-oil emulsion technique and loaded with fluorescently labeled immunoglobulin G (IgG). Microsphere size appears to be influenced by the magnitude and distribution of force generated by mechanical stirring during emulsion. Protein release studies show that sustained IgG release from microspheres could be achieved and that application of a secondary coating of chitosan could further slow the rate of protein release. Preservation of bioactivity of released IgG protein was confirmed using an immunohistochemical assay. When IgG-loaded microspheres were incorporated into mock scaffolds, initial protein release was diminished and the overall time course of release was extended. The present study demonstrates that protein-loaded microspheres can be prepared with a controlled release profile and preserved biological activity, and can be incorporated into scaffolds to achieve sustained and prolonged protein delivery in a tissue engineering application.
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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 | 0.001 | 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) | 0.000 | 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".