IFN‐γ Prevents Early Perforin‐Granzyme‐Mediated Destruction of Kidney Allografts by Inducing Donor Class I Products in the Kidney
Bibliographic record
Abstract
Interferon-gamma (Ifng) protects organ allografts: mouse kidney allografts lacking Ifng receptors rapidly fail with massive ischemic necrosis around days 5 to 7, reflecting microcirculation failure. We hypothesized that Ifng protects the graft by preventing perforin-granzyme-mediated cytotoxic damage to the microcirculation by inducing class Ia and/or Ib products. We transplanted kidney allografts lacking Ifng receptors into various knockout hosts. The necrosis/congestion phenotype did not require host B cells or IL-4 and IL-13 receptors, but required the T-cell alloresponse: it did not occur if the hosts were syngeneic or T-cell deficient. However, host perforin-granzyme mechanisms were required: no necrosis developed if hosts lacked either perforin or granzymes A and B. The ability of Ifng to protect the allograft required donor class I products: allografts lacking class I products due to Tap1 or beta2 microglobulin deficiency developed a similar necrosis-congestion phenotype at day 7 despite Ifng receptors being present. Thus when host cytotoxic T cells infiltrate organ allografts, Ifng prevents their perforin-granzyme mechanism from compromising the microcirculation by a mechanism requiring donor class Ia or Ib products. We propose that donor class Ia or Ib products are needed to trigger inhibitory receptors on effector T cells.
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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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".