Improved Islet Yields From Macaca Nemestrina and Marginal Human Pancreata After Two‐Layer Method Preservation and Endogenous Trypsin Inhibition
Bibliographic record
Abstract
We tested whether two-layer method (TLM) pancreas preservation and trypsin inhibition (Pefabloc) during processing allows longer preservation while retaining or improving viable islet recovery. Non-marginal primate (Macaca nemestrina) and marginal human (ischemic or preservation-injured) pancreata were processed with a research-oriented pan technique (Seattle method). Organs were processed upon arrival (+/- Pefabloc), or after TLM or University of Wisconsin solution (UW) preservation (+ Pefabloc). Islet yield, viability, and function were assessed. Pefabloc increased M. nemestrina islet yields from 9696 +/- 1749 IE/g to 15 822 +/- 1332 IE/g (p < 0.01). Two-layer method preservation (< 6 h) further increased yields, to 23 769 +/- 2773 IE/g (vs. + Pefabloc; p < 0.01). Similarly, Pefabloc increased marginal human islet yields from 2473 +/- 472 IE/g to 4723 +/- 1006 IE/g (p < 0.04). This increase was maintained after lengthy TLM preservation (> 30 h; 4801 +/- 1066 IE/g). We also tested the applicability of TLM preservation (23.5 +/- 3.2 h) to the processing of marginal human pancreata by the Edmonton/Immune Tolerance Network clinical protocol. Islet yield and function approached published results of pancreata processed 4.8 +/- 0.8 h after organ recovery (p = 0.06). Pefabloc, and TLM vs. UW preservation, prolonged the tolerable interval between organ recovery and islet isolation. Islet yield, viability, and functionality improved from both marginal and nonmarginal pancreata.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".