Development of a microchannel emulsification process for pancreatic beta cell encapsulation
Bibliographic record
Abstract
Abstract In this study, we developed a high‐throughput microchannel emulsification process to encapsulate pancreatic beta cells in monodisperse alginate beads. The process builds on a stirred emulsification and internal gelation method previously adapted to pancreatic cell encapsulation. Alginate bead production was achieved by flowing a 0.5–2.5% alginate solution with cells and CaCO 3 across a 1‐mm thick polytetrafluoroethylene plate with 700 × 200 μm rectangular straight‐through channels. Alginate beads ranging from 1.5–3 mm in diameter were obtained at production rates exceeding 140 mL/hr per microchannel. Compared to the stirred emulsification process, the microchannel emulsification beads had a narrower size distribution and demonstrated enhanced compressive burst strength. Both microchannel and stirred emulsification beads exhibited homogeneous profiles of 0.7% alginate concentration using an initial alginate solution concentration of 1.5%. Encapsulated beta cell viability of 89 ± 2% based on live/dead staining was achieved by minimizing the bead residence time in the acidified organic phase fluid. Microchannel emulsification is a promising method for clinical‐scale pancreatic beta cell encapsulation as well as other applications in the pharmaceutical, food, and cosmetic industries.
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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 | 0.000 | 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".