IFN-γ Alters the Pathology of Graft Rejection: Protection from Early Necrosis
Bibliographic record
Abstract
We studied the effect of host IFN-gamma on the pathology of acute rejection of vascularized mouse heart and kidney allografts. Organs from CBA donors (H-2k) were transplanted into BALB/c (H-2d) hosts with wild-type (WT) or disrupted (GKO, BALB/c mice with disrupted IFN-gamma genes) IFN-gamma genes. In WT hosts, rejecting hearts and kidneys showed mononuclear cell infiltration, intense induction of donor MHC products, but little parenchymal necrosis at day 7. Rejecting allografts in GKO recipients showed infiltrate but little or no induction of donor MHC and developed extensive necrosis despite patent large vessels. The necrosis was immunologically mediated, since it developed during rejection, was absent in isografts, and was prevented by immunosuppressing the recipient with cyclosporine or mycophenolate mofetil. Rejecting kidneys in GKO hosts showed increased mRNA for heme oxygenase 1, and decreased mRNA for NO synthase 2 and monokine inducible by IFN-gamma (MIG). The mRNA levels for CTL genes (perforin, granzyme B, and Fas ligand) were similar in rejecting kidneys in WT and GKO hosts, and the host Ab responses were similar. The administration of recombinant IFN-gamma to GKO hosts reduced but did not fully prevent the effects of IFN-gamma deficiency: MHC was induced, but the prevention of necrosis and induction of MIG were incomplete compared with WT hosts. Thus, IFN-gamma has unique effects in vascularized allografts, including induction of MHC and MIG, and protection against parenchymal necrosis, probably at the level of the microcirculation. This is probably a local action of IFN-gamma produced in large quantities in the allograft.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".