Heterogenous expression of endocrine and progenitor cells within the neonatal porcine pancreatic lobes–Implications for neonatal porcine islet xenotransplantation
Bibliographic record
Abstract
Neonatal porcine islets (NPIs) are a source of islets for xenotransplantation. In the pig, the pancreatic lobes remain separate, thus, when optimizing NPI isolation, the pancreatic lobes included in the pancreatic digest should be specified. These lobes are the duodenal (DL), splenic (SL) and connecting (CL) lobe that correspond to the head, body-tail, and uncinate process of the human pancreas. In this study we are the first to evaluate all three neonatal porcine pancreatic lobes and NPIs isolated from these lobes. We report, a significant difference in endocrine and progenitor cell composition between lobes, and observed pancreatic duct glands (PDG) within the mesenchyme surrounding exocrine ducts in the DL and CL. Following in vitro differentiation, NPIs isolated from each lobe differed significantly in the percent increase of endocrine cells and final cell composition. Compared to other recipients, diabetic immunodeficient mice transplanted with NPIs isolated from the SL demonstrated euglycemic control as early as 4 weeks (p < 0.05) and achieved normoglycemia by 6 weeks post-transplant (p < 0.01). For the first time we report significant differences between the neonatal porcine pancreatic lobes and demonstrate that NPIs from these lobes differ in xenograft function.
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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".