Reanimating Pancreatic Grafts Subjected to Prolonged Cold Ischemic Injury Using Normothermic Ex Vivo Perfusion
Bibliographic record
Abstract
Background: Pancreas transplant volumes are limited because of poor utilization of "extended criteria grafts." Prolonged cold ischemia is a risk factor associated with poor allograft survival. We aimed to establish the feasibility of transplantation using grafts subjected to prolonged cold ischemia and determine whether these grafts could be optimized using normothermic ex vivo perfusion (NEVP) in a porcine model. Methods: The study population consisted of 35 to 40 kg male Yorkshire pigs in an allotransplantation model with a 3-d survival plan for recipients. Control grafts were subjected to cold storage (CS) in a University of Wisconsin solution for 21 to 24 h (n = 6), whereas the test group received an additional 3 h NEVP after CS of 21 h (n = 5). Results: = 0.008; log rank). Graft parenchyma was 60% to 70% preserved in the NEVP arm at necropsy on gross appearance. In addition, the islet function was well preserved, and both the pancreas (including the islets) and the duodenal morphology were maintained histologically. The intravenous glucose tolerance test on the day of euthanasia was in the normoglycemic range for 80% of cases in the NEVP arm. Conclusions: Optimization of pancreas grafts exposed to extended CS with NEVP seems promising at rescuing and reanimating these grafts for transplantation, resulting in significantly improved survival in a porcine pancreas transplant model.
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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 | 0.000 | 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 | 0.001 |
| 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".