IFN‐γ is an Absolute Requirement for Spontaneous Acceptance of Liver Allografts
Bibliographic record
Abstract
Experimental liver allografts undergo spontaneous acceptance despite undergoing rejection during the first few weeks post transplant. We explored the role of interferon-gamma (IFN-gamma) in the spontaneous acceptance of mouse liver allografts. Strain of mouse (CBA) liver allografts transplanted into normal BALB/c mice developed histologic changes typical of rejection that spontaneously regressed, permitting long-term survival of these allografts similar to that of syngeneic grafts. In contrast, CBA liver allografts in IFN-gamma-deficient hosts manifested not only infiltration but also hemorrhage and necrosis, with no survival beyond 14 days. Despite differences in survival, local expression of cytotoxic T-cell genes in the transplant was not increased in IFN-gamma-deficient hosts, but livers in interferon-gamma-deficient mice (GKO) hosts displayed much less induction of major histocompatibility complex (MHC) class I and II expression. To determine whether the difference in survival was secondary to the direct effects of IFN-gamma on the liver, we transplanted livers from IFN-gamma-receptor-deficient mice into normal hosts. Liver allografts lacking IFN-gamma receptors also developed hemorrhage and necrosis with minimal induction of MHC expression. Thus IFN-gamma mediates a direct effect on rejecting liver allografts that reduces hemorrhage and necrosis, induces MHC expression, and is absolutely required for spontaneous acceptance.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".