Acceleration of Functional Maturation and Differentiation of Neonatal Porcine Islet Cell Monolayers Shortly In Vitro Cocultured with Microencapsulated Sertoli Cells
Bibliographic record
Abstract
The limited availability of cadaveric human donor pancreata as well as the incomplete success of the Edmonton protocol for human islet allografts fasten search for new sources of insulin the producing cells for substitution cell therapy of insulin-dependent diabetes mellitus (T1DM). Starting from isolated neonatal porcine pancreatic islets (NPIs), we have obtained cell monolayers that were exposed to microencapsulated monolayered Sertoli cells (ESCs) for different time periods (7, 14, 21 days). To assess the development of the cocultured cell monolayers, we have studied either endocrine cell phenotype differentiation markers or c-kit, a hematopoietic stem cell marker, has recently been involved with growth and differentiation of β-cell subpopulations in human as well as rodent animal models. ESC which were found to either accelerate maturation and differentiation of the NPIs β-cell phenotype or identify an islet cell subpopulation that was marked positively for c-kit. The insulin/c-kit positive cells might represent a new, still unknown functionally immature β-cell like element in the porcine pancreas. Acceleration of maturation and differentiation of our NPI cell monolayers might generate a potential new opportunity to develop insulin-producing cells that may suite experimental trials for cell therapy of T1DM.
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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 | 0.001 |
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".