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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".