Enhancing the Success of Human Islet Isolation Through Optimization and Characterization of Pancreas Dissociation Enzyme
Bibliographic record
Abstract
A major obstacle to successful human islet isolation has been the variability of the enzymatic digestion phase. The aim of this study was to define optimal enzyme activity ranges normalized by the pancreas weight and to identify valid parameters for the optimal selection of successful lots of collagenase enzyme blends. Our results from 251 islet isolations showed that optimization of thermolysin dosage based on Caseinase unit/g pancreas contributed considerably to islet isolation outcome but that collagenase dosage measured by the manufacturer (Wünsch unit/g pancreas) was not a major determinant of islet isolation outcome. We also found that lot-to-lot inconsistency of enzyme performance was not explained by the activity values provided by the manufacturer, but rather by an in-house assay of class I collagenase (CI) and class II collagenase (CII); using a lot with a lower CII/CI resulted in a higher success rate. The odds of successful isolation was 8.67 times higher when a vial with CII/CI ratio <0.204 was used than when a vial with CII/CI ≥0.204 was used, suggesting that CII/CI ratio may be a strong predictor to distinguish potential lot success. This study provides a framework for improved enzymatic digestion in human islet isolation. A major obstacle to successful human islet isolation has been the variability of the enzymatic digestion phase. The aim of this study was to define optimal enzyme activity ranges normalized by the pancreas weight and to identify valid parameters for the optimal selection of successful lots of collagenase enzyme blends. Our results from 251 islet isolations showed that optimization of thermolysin dosage based on Caseinase unit/g pancreas contributed considerably to islet isolation outcome but that collagenase dosage measured by the manufacturer (Wünsch unit/g pancreas) was not a major determinant of islet isolation outcome. We also found that lot-to-lot inconsistency of enzyme performance was not explained by the activity values provided by the manufacturer, but rather by an in-house assay of class I collagenase (CI) and class II collagenase (CII); using a lot with a lower CII/CI resulted in a higher success rate. The odds of successful isolation was 8.67 times higher when a vial with CII/CI ratio <0.204 was used than when a vial with CII/CI ≥0.204 was used, suggesting that CII/CI ratio may be a strong predictor to distinguish potential lot success. This study provides a framework for improved enzymatic digestion in human islet isolation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 teacher head, 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".