Encapsulation of protein microfiber networks supporting pancreatic islets
Bibliographic record
Abstract
Networks of discrete, genipin-crosslinked gelatin microfibers enveloping pancreatic islets were incorporated within barium alginate microcapsules. This novel technique enabled encapsulation of cellular aggregates in a spherical fibrous matrix <300 μm in diameter. Microfibers were produced by vortex-drawn extrusion within an alginate support matrix. Optimization culminated in a hydrated fiber diameter of 22.3 ± 0.4 μm, a significant reduction relative to that available through current gelatin microfiber spinning techniques, while making the process more reliable and less labor intensive. Microfibers were encapsulated at 40 vol % within 294 ± 4 μm 1.6% barium alginate microparticles by electrostatic-mediated dropwise extrusion. Pancreatic islets extracted from Sprague Dawley rats were encapsulated within the microparticles and analyzed over 21 days. Acridine orange and propidium iodide fluorescent viability staining and light microscopy indicated a significant increase in viability for islets within the fiber-embedded particles relative to fiber-free controls at days 7, 14, and 21. The fiber-embedded system also promoted cellular aggregate cohesion, reducing the incidence of dispersed islet morphologies within the capsules from 31 to 8% at day 21. Further enquiry into benefits of islet encapsulation within a protein fiber network will be the subject of future investigation.
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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".