Donor and Isolation Variables Predicting Human Islet Isolation Success
Bibliographic record
Abstract
BACKGROUND: Recent advances in the fields of islet transplantation and in vitro islet cell expansion place a renewed emphasis on the optimization of islet isolation from cadaveric human donor organs. We retrospectively analyzed 171 islet isolations to identify variables that predict islet yield and isolation success. METHODS: Cadaveric human donor pancreata were procured and processed according to established protocols. Donor-, procurement-, and isolation-related variables were analyzed for correlation with islet yield and isolation success (> or =250,000 islet equivalents). RESULTS: Univariate analysis suggested correlations between islet yield and donor age (P<0.005), body surface area (P<0.005), duration of enzymatic digestion (P<0.001), and pancreatic beta-cell volume (P<0.05). Donor sex (P<0.01), procurement team (P<0.05), and peridigestion serine protease inhibition (P<0.05) affected islet yield, whereas enzyme lot (P<0.01) and pancreatic fatty infiltration (P<0.05) influenced isolation success. By logistic regression, donor sex and age, and duration of enzymatic digestion could predict a successful isolation with 72% accuracy. The use of Liberase CI improved islet yield (P<0.05) in young donors (< or =25 years). CONCLUSIONS: While donor-related variables are useful in predicting islet yield, these are likely surrogates for pancreatic beta-cell volume. Enzyme lot, and the associated duration of enzymatic digestion (P<0.05), appears to be key determinants of isolation success.
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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 | 0.004 |
| 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) | 0.003 | 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".