Comparison of Human Islet Isolation Outcomes Using a New Mammalian Tissue-Free Enzyme Versus Collagenase NB-1
Bibliographic record
Abstract
BACKGROUND: After the discontinuation of the manufacturing Liberase HI because of a small potential for prion disease transmission, Roche Diagnostics (Indianapolis, IN) developed a new enzyme product (Liberase MTF [mammalian tissue free]), which is similar to Liberase HI with the exception that no mammalian tissue is used in the manufacture of the collagenase component. We report our experience using the MTF enzyme in clinical islet isolations compared with Serva NB-1 with modified enzyme delivery method. METHODS: Islets were isolated from 41 pancreata using MTF enzyme (n=17) or NB-1 enzyme (n=24). NB-1 enzymes were delivered using a modified (nonsimultaneous) enzyme delivery method whereas isolations using MTF used the standard method of simultaneous collagenase and thermolysin perfusion. Islets were purified on a COBE 2991 Cell Blood Processor and subsequently cultured. RESULTS: The average islet mass after purification was 392+/-36 x 10 islet equivalent (IE) for MTF versus 371+/-40 x 10 IE for Serva NB-1 (P=0.63). Post-IE/cm of tissue was 110+/-9 x 10 IE/cm and 91+/-11 x 10 IE/cm for MTF and NB-1, respectively (P=0.07). The isolation success rate (>400,000 IE) for MTF was 53% compared with 33% for Serva (P=0.33). CONCLUSION: We conclude that MTF may be successfully used for high-yield human islet isolation and clinical transplantation and provides similar quality islets to those derived using NB-1.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.002 | 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".