Immunological Determinants of Liver Transplant Outcomes Uncovered by the Rat Model
Bibliographic record
Abstract
For many individuals with end-stage liver disease, the only treatment option is liver transplantation. However, liver transplant rejection is observed in 24%-80% of transplant patients and lifelong drug regimens that follow the transplant procedure lead to serious side effects. Furthermore, the pool of donor livers available for transplantation is far less than the demand. Well-characterized and physiologically relevant models of liver transplantation are crucial to a deeper understanding of the cellular processes governing the outcomes of liver transplantation and serve as a platform for testing new therapeutic strategies to enhance graft acceptance. Such a model has been found in the rat transplant model, which has an advantageous size for surgical procedures, similar postoperative immunological progression, and high genome match to the human liver. From rat liver transplant studies published in the last 5 years, it is clear that the rat model serves as a strong platform to elucidate transplant immunological mechanisms. Using the model, we have begun to uncover potential players and possible therapeutic targets to restore liver tolerance and preserve host immunocompetence. Here, we present an overview of recent literature for rat liver transplant models, with an aim to highlight the value of the models and to provide future perspectives on how these models could be further characterized to enhance the overall value of rat models to the field of liver transplantation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".