<i>Ex Vivo</i> Liver Machine Perfusion: Comprehensive Review of Common Animal Models
Bibliographic record
Abstract
The most common preservation technique for liver grafts is static cold storage. Due to the organ shortage for liver transplantation (LT), extended criteria donor (ECD) allografts are increasingly used—despite the higher risk of inferior outcome after transplantation. Ex vivo liver machine perfusion (MP) has been developed to improve the outcome of transplantation, especially with ECD grafts, and is currently under evaluation in clinical trials. We performed a literature search on PubMed and ISI Web of Science to assemble an overview of rodent and porcine animal models of ex vivo liver MP for transplantation, which is essential for the present and future development of clinical liver MP. Hypothermic, subnormothermic, and normothermic MP systems have been successfully used for rat and pig LT. In comparison with hypothermic systems, normothermic perfusion often incorporates a dialysis unit. Moreover, it enables metabolic assessment of liver grafts. Allografts experiencing warm ischemic time have a superior survival rate after MP compared with cold storage alone, irrespective of the temperature used for perfusion. Furthermore, ex vivo MP improves the outcome of regular and ECD liver grafts in animal models. Small and large animal models of ex vivo liver MP are available to foster the further development of this new technology. Impact Statement Ex vivo machine perfusion is an important part of current research in the field of liver transplantation. While evidence for improve storage is constantly rising, the development of future applications such as quality assessment and therapeutic interventions necessitates robust animal models. This review is intended to provide an overview of this technology in common large and small animal models and to give an outlook on future applications. Moreover, we describe developmental steps that can be followed by others, and which can help to decrease the number of animals used for experiments based on the replace, reduce, refine concept.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".