A Novel Approach to Increase Human Islet Cell Mass While Preserving β-Cell Function
Bibliographic record
Abstract
Human islet expansion in monolayer culture leads to loss of function and senescence. By maintaining the 3-D configuration of islets in fibrin gels, it is feasible to expand beta-cells in response to hepatocyte growth factor (HGF) while preserving physiologic glucose responsiveness both in vitro and in vivo after transplantation into nude mice. Islets were cultured free floating with or without growth factors and nicotinamide and in fibrin gels with the same conditions. Proliferation was observed only in islets cultured in fibrin gels and the cocktail; total insulin increased by threefold, with a concomitant increase in beta-cell mass by morphometry. Insulin release after glucose challenge was also preserved. Islets in fibrin gels gave rise in vivo to large grafts rich in insulin and glucagon, and grafts from free-floating islets were smaller with fewer endocrine cells. Circulating human C-peptide levels were higher than in the mice receiving free-floating islets. In summary, fibrin allows for HGF-mediated cell proliferation while preserving glucose responsiveness in an environment that preserves cell-cell contacts. Limited islet ex vivo expansion under these conditions may improve recipient-donor tissue ratios to equal the functional results of whole-organ transplants.
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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) | 0.001 | 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".