Normothermic Acellular Ex Vivo Liver Perfusion Reduces Liver and Bile Duct Injury of Pig Livers Retrieved After Cardiac Death
Bibliographic record
Abstract
We compared cold static with acellular normothermic ex vivo liver perfusion (NEVLP) as a novel preservation technique in a pig model of DCD liver injury. DCD livers (60 min warm ischemia) were cold stored for 4 h, or treated with 4 h cold storage plus 8 h NEVLP. First, the livers were reperfused with diluted blood as a model of transplantation. Liver injury was determined by ALT, oxygen extraction, histology, bile content analysis and hepatic artery (HA) angiography. Second, AST levels and bile production were assessed after DCD liver transplantation. Cold stored versus NEVLP grafts had higher ALT levels (350 ± 125 vs. 55 ± 35 U/L; p < 0.0001), decreased oxygen extraction (250 ± 65 mmHg vs. 410 ± 58 mmHg, p < 0.01) and increased hepatocyte necrosis (45% vs. 10%, p = 0.01). Levels of bilirubin, phospholipids and bile salts were fivefold decreased, while LDH was sixfold higher in cold stored versus NEVLP grafts. HA perfusion was decreased (twofold), and bile duct necrosis was increased (100% vs. 5%, p < 0.0001) in cold stored versus NEVLP livers. Following transplantation, mean serum AST level was higher in the cold stored versus NEVLP group (1809 ± 205 U/L vs. 524 ± 187 U/L, p < 0.05), with similar bile production (2.5 ± 1.2 cc/h vs. 2.8 ± 1.4 cc/h; p = 0.2). NEVLP improved HA perfusion and decreased markers of liver duct injury in DCD grafts.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".