Optimization and Scale-up Isolation and Culture of Neonatal Porcine Islets: Potential for Clinical Application
Bibliographic record
Abstract
One challenge that must be overcome to allow transplantation of neonatal porcine islets (NPIs) to become a clinical reality is defining a reproducible and scalable protocol for the efficient preparation of therapeutic quantities of clinical grade NPIs. In our standard protocol, we routinely isolate NPIs from a maximum of four pancreases, requiring tissue culture in 16 Petri dishes (four per pancreas) in Ham's F10 and bovine serum albumin (BSA). We have now developed a scalable and technically simpler protocol that allows us to isolate NPIs from a minimum of 12 pancreases at a time by employing automated tissue chopping, collagenase digestion in a single vessel, and tissue culture/media changes in 75% fewer Petri dishes. For culture, BSA is replaced with human serum albumin and supplemented with Z-VAD-FMK general caspase inhibitor and a protease inhibitor cocktail. The caspase inhibitor was added to the media for only the first 90 min of culture. NPIs isolated using the scalable protocol had significantly more cellular insulin recovered (56.9 ± 1.4 µg) when compared to the standard protocol (15.0 ± 0.5 µg; p < 0.05). Compared to our standard protocol, recovery of β-cells (6.0 × 10(6) ± 0.2 vs. 10.0 × 10(6) ± 0.4; p < 0.05) and islet equivalents (35,135 ± 186 vs. 41,810 ± 226; p < 0.05) was significantly higher using the scalable protocol. During a static glucose stimulation assay, the SI of islets isolated by the standard protocol were significantly lower than the scale-up protocol (4.3 ± 0.2 vs. 5.5 ± 0.1; p < 0.05). Mice transplanted with NPIs using the scalable protocol had significantly lower blood glucose levels than the mice that receiving NPIs from the standard protocol (p < 0.01) and responded significantly better to a glucose tolerance test. Based on the above findings, this improved simpler scalable protocol is a significantly more efficient means for preparing therapeutic quantities of clinical grade NPIs.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".