Nonsimultaneous Administration of Pancreas Dissociation Enzymes During Islet Isolation
Bibliographic record
Abstract
BACKGROUND: Successful islet isolation relies heavily on enzyme products. Among them, Liberase was used in islet transplantation programs until the islet community was notified of the use of a bovine brain component during the manufacturing process. To minimize potential risk of prion disease transmission, many islet isolation facilities switched to Serva enzyme, which is considered to pose less risk. However, this conversion significantly affected the field in transplant activity. Here, we report our successful conversion from Liberase to Serva collagenase with the use of a modified digestion protocol. METHODS: We compared the quality of Serva versus Liberase enzyme using chromatography and collagenase activity assay. On the basis of the findings, we developed a pancreas digestion protocol optimized for Serva enzyme, where only collagenase was injected into the pancreas through the duct, and then neutral protease was added to the circulating system during the digestion phase. RESULTS: Class I collagenase activity of Serva was remarkably reduced compared with Liberase. Chromatography of Serva demonstrated suspected degradation of class I collagenase. When the modified protocol was applied to donor pancreata more than 35 years of age, we recovered 3119+/-147 islet equivalent/g pancreas with a success rate of 51% (35/68), whereas the standard method yielded only 1809+/-266 islet equivalent/g pancreas (P=0.02) with a success rate of 11% (1/9). This beneficial effect of the modified method was, however, diminished when applied to younger donor pancreata. CONCLUSIONS: Our study brings new insight into the role of collagenase and noncollagenolytic protease on pancreas dissociation.
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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) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".