Decellularized bladder as scaffold to support proliferation and functionality of insulin‐secreting pancreatic cells
Bibliographic record
Abstract
Loss in the number or function of insulin-producing β-cells in pancreatic islets has been associated with diabetes mellitus. Although islet transplantation can be an alternative treatment, complications such as apoptosis, ischaemia and loss of viability have been reported. The use of decellularized organs as scaffolds in tissue engineering is of interest owing to the unique ultrastructure and composition of the extracellular matrix (ECM) believed to act on tissue regeneration. In this study, a cell culture system has been designed to study the effect of decellularized porcine bladder pieces on INS-1 cells, a cell line secreting insulin in response to glucose stimulation. Porcine bladders were decellularized using two techniques: a detergent-containing and a detergent-free methods. The resulting ECMs were characterized for the removal of both cells and dsDNA. INS-1 cells were not viable on ECM produced using detergent (i.e., sodium dodecyl sulfate). INS-1 cells were visualized following 7 days of culture on detergent-free decellularized bladders using a cell viability and metabolism assay (MTT) and cell proliferation quantified (CyQUANT™ NF Cell Proliferation Assay). Further, glucose-stimulated insulin secretion and immunostaining confirmed that cells were functional in response to glucose stimulation, as well as they expressed insulin and interacted with the detergent-free produced ECM, respectively.
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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".