Experience of islet isolation without neutral protease supplementation
Bibliographic record
Abstract
BACKGROUND: We have reported improved islet isolation outcomes using a new digestion protocol where the pancreas is perfused only with collagenase, and neutral protease (NP) is administered during the digestion phase. Since the inception of this protocol, we have had some cases where administration of NP was not required. Our new protocol was utilized in 94 islet isolations. The timing of adding NP was dependent on the progression of digestion but in 10 cases the progression was rapid and most islets in the assessment samples were free from the exocrine tissue. As a result NP was not added at all for these isolations (no-NP group). In the remaining 84 isolations, NP was added during digestion phase (control group). RESULTS: Pancreata in the each group were digested with a similar collagenase dose. Digestion time was shorter in the no-NP (15.0±1.8 vs 19.5±0.6 min, P=0.004). At post-digestion, the no-NP had fewer trapped islets (10.9±2.8 vs 28.1±2.4%, P=0.009). Post-purification islet yield was similar (355±45 x10 ( 3) vs 318±17 x10 ( 3) IE, P=0.29). Five preparations in the no-NP were used for clinical transplantation, leading to a 64.3±15.2% reduction in insulin usage. Interestingly, cold ischemia time was longer in the no-NP (10.3±0.9 vs 7.9±0.4 h, P=0.04). One particular collagenase lot having the highest NP activity contamination was used in 7 isolations in the no-NP. Our experience indicates that supplementation of collagenase with NP is not always necessary for effective isolation. Cold ischemia time and NP contamination should be evaluated for optimal NP dosage.
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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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".