Pancreatic cell immobilization in alginate beads produced by emulsion and internal gelation
Bibliographic record
Abstract
Alginate has been used to protect transplanted pancreatic islets from immune rejection and as a matrix to increase the insulin content of islet progenitor cells. The throughput of alginate bead generation by the standard extrusion and external gelation method is limited by the rate of droplet formation from nozzles. Alginate bead generation by emulsion and internal gelation is a scaleable alternative that has been used with biological molecules and microbial cells, but not mammalian cells. We describe the novel adaptation of this process to mammalian cell immobilization. After optimization, the emulsion process yielded 90 ± 2% mouse insulinoma 6 (MIN6) cell survival, similar to the extrusion process. The MIN6 cells expanded at the same rate in both bead types to form pseudo-islets with increased glucose stimulation index compared to cells in suspension. The emulsion process was suitable for primary pancreatic exocrine cell immobilization, leading to 67 ± 32 fold increased insulin expression after 10 days of immobilized culture. Due to the scaleability and broad availability of stirred mixers, the emulsion process represents an attractive option for laboratories that are not equipped with extrusion-based cell encapsulators, as well as for the production of immobilized or encapsulated cellular therapeutics on a clinical scale.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".