Normothermic ex situ pancreas perfusion for the preservation of porcine pancreas grafts
Bibliographic record
Abstract
Pancreas transplantation improves and extends the life of patients with insulin-dependent diabetes. Pancreata from extended criteria donors have been increasingly used due to the scarcity of available grafts. Normothermic ex situ pancreas perfusion (NESPP) can keep grafts metabolically active, potentially allowing for assessment and organ repair, and could improve outcomes of marginal grafts. A novel NESPP technique was developed and tested. Porcine pancreata were removed after a short period of warm ischemia and subjected to 6 h of NESPP. Perfusion parameters, potential graft assessment markers and graft injury were measured. Next, pancreata subjected to 3 h of NESPP were transplanted and animals were followed for up to 3 days. Graft function and injury post-transplantation were evaluated. Using this novel system of perfusion, pancreata were perfused for an extended period of time with minimal edema. Histology at the end of perfusion showed intact islet cells with only mild signs of tissue injury. NESPP transplanted grafts showed immediate function after transplantation, with glucose levels in normal range. NESPP maintains a physiologic environment and excellent graft function without causing significant graft injury. Porcine pancreas transplantation is feasible and allows for in vivo graft assessment of pancreas function and injury after NESPP.
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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 | 0.001 | 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".