Functional Maturation and In Vitro Differentiation of Neonatal Porcine Islet Grafts
Bibliographic record
Abstract
BACKGROUND: There is a strong rationale to pursue the use of neonatal porcine islets (NPIs) as an unlimited source of islets for clinical xenotransplantation. Because NPIs are composed of immature insulin producing beta (ß) cells and ductal precursor cells, they provide an ideal model to examine culture conditions to enhance ß cell proliferation and/or ß cell neoformation from ductal cells. In an attempt to optimize the potential of NPIs as a source of ß cell grafts, we used an in vitro differentiation protocol and measured its effect on the functional maturation and differentiation of NPIs. METHODS: Pancreata from 1- to 3-day-old neonatal pigs were digested and cultured in standard Ham's F10 media for 5 days. Each independent preparation was then further cultured in Dulbecco's modified Eagle medium nutrient mixture-F12 differentiation media containing growth factors added in a stepwise fashion, or cultured in control Ham's F10 media. After 20 days in culture, islets were assessed for insulin secretory capacity, cellular composition, gene expression, and metabolic activity after transplantation in immunodeficient mice with diabetes. RESULTS: Compared with control islets, differentiated islets exhibited a significantly higher proportion of endocrine cells, proliferating cell nuclear antigen double positive ß cells, and an enhanced glucose-stimulated insulin secretory activity. Mice transplanted with differentiated islets had significantly lower blood glucose values at weeks 18 and 20 compared with nondifferentiated controls and were shown to be more glucose tolerant. CONCLUSIONS: Culturing NPIs in a 20-day stepwise differentiation media increases the proportion of endocrine cells and augments both in vitro and in vivo function of the islets.
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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 | 0.000 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".