Alternative Macrophage Activation‐Associated Transcripts in T‐Cell‐Mediated Rejection of Mouse Kidney Allografts
Bibliographic record
Abstract
Macrophages display two activation states that are considered mutually exclusive: classical macrophage activation (CMA), inducible by IFNG, and alternative macrophage activation (AMA), inducible by IL4 and IL13. CMA is prominent in allograft rejection and AMA is associated with tissue remodeling after injury. We studied expression of AMA markers in mouse kidney allografts and in kidneys with acute tubular necrosis (ATN). In rejecting allografts, unlike interferon gamma (IFNG) effects and T-cell infiltration that developed rapidly and plateaued by day 7, AMA transcripts (Arg1, Mrc1, Mmp12 and Ear1) rose progressively as tubulitis and parenchymal deterioration developed at days 21 and 42, despite persistent IFNG effects. AMA in allografts was associated with transcripts for AMA inducers IL4, IL13 and inhibin A, but also occurred when hosts lacked IL4/IL13 receptors, suggesting a role for inhibin A. Kidneys with ATN injured by ischemia/reperfusion also had increased expression of AMA markers and inhibin A. Thus kidneys undergoing T-cell-mediated rejection progressively acquire macrophages with alternative activation phenotype despite strong local IFNG effects, independent of IL4 and IL13. Although the mechanisms and causal relationships remain to be determined, high AMA transcript levels in rejecting allografts are strongly associated with and may be a consequence of parenchymal deterioration similar to ATN.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".