Flexible Management of Enzymatic Digestion Improves Human Islet Isolation Outcome from Sub‐Optimal Donor Pancreata
Bibliographic record
Abstract
Worldwide growing interest in reproducing the result of the Edmonton protocol in islet transplantation trials poses the problem of paucity of donors to supply sufficient amount of islets for clinical use. Improved outcomes include finding better ways to obtain higher yields from every donor organ processed and the possibility of extending islet isolation processing to glands of suboptimal quality. In order to optimize enzymatic digestion of marginal donor organs, we have modified the technique of tissue collection following enzymatic digestion of human pancreatic organs, allowing for reduced time of exposure of free islets to warm Liberase trade mark solution. Our results indicate that better controlled exposure to enzyme yields: (i) higher islet numbers; (ii) complete dissociation of all parts of pancreatic tissue; (iii) successful islet harvest from organs otherwise excluded. We also show that by limiting the exposure of free islets to enzyme solution, islet fragmentation and loss of insulin content are reduced. We further support evidence that enzymatic digestion may contribute to impairment of insulin secretory capacity of the islets in vitro during culture.
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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 | 0.001 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".