Clinical islet isolation outcomes with a highly purified neutral protease for pancreas dissociation
Bibliographic record
Abstract
BACKGROUND: Pancreas dissociation is a critical initial component of the islet isolation procedure and introduces high variability based on factors including the enzyme type, specificity and potency. Product refinement and alterations to the application strategies have improved isolation outcomes over time; however, islet utilization from donor organs remains low. In this study we evaluate a low endotoxin-high activity grade neutral protease in clinical islet isolation. MATERIALS AND METHODS: The use of a non-collagenolytic enzyme, either thermolysin or high active neutral protease, was randomized in clinical islet isolations to evaluate efficacy. Additionally a retrospective comparison to neutral protease NB was conducted. RESULTS: The thermolysin group had lower trapped islet population and increased purity and post-culture islet mass in comparison to high active grade neutral protease. Comparison of neutral protease NB GMP grade to high active neutral protease displayed no measurable difference in islet mass or viability and transplantation outcomes at 1 mo post-transplant were favorable for both groups. CONCLUSIONS: High activity neutral protease can generate clinical grade islets and may prove beneficial to islet function and viability based on a reduced endotoxin load but dosing of neutral protease requires ongoing optimization.
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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 | 0.002 | 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 | 0.001 |
| 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".