Endogenous Pancreatic Enzyme Activity Levels Show no Significant Effect on Human Islet Isolation Yield
Bibliographic record
Abstract
Despite advances in human islet isolation, islet yield remains inconsistent and unreliable. In recent studies, it has been suggested that serine proteases, in particular trypsin, have been shown to have a damaging effect on islet yield. This study evaluated enzyme activity levels throughout 42 human islet isolation procedures. Trypsin, chymotrypsin, and elastase activity was determined spectrophotometrically using suitable chromophoric substrates. The results of the islet isolations were rated as successful (n = 19) or unsuccessful (n = 23) based on the islet yield and functionality. The enzyme activity profiles of the isolations were compared. No significant differences in donor-related variables were found in this study. However, in the successful isolations, a significantly greater amount (85.6 +/- 1.9%; p = 0.0017) of the pancreas was digested in a significantly shorter digestion time (19.7 +/- 0.6 min; p = 0.0054) compared with 74.8 +/- 2.5% of digested tissue in 22.6 +/- 0.7 min in the poor isolations. This study showed no significant effect of serine protease levels on the outcome of islet isolations, regardless of enzyme inhibitor supplementation. These data suggest that serine protease activity does not sufficiently affect islet yield. However, the data show that the most successful human islet isolations are achieved when the maximum amount of tissue is digested in the shortest amount of time. This suggests that further understanding of the isolation process should focus on the role of the collagenase digestion solution in the dissociation of the endocrine-exocrine tissue connection.
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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 | 0.000 |
| 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".