The Early Course of Kidney Allograft Rejection: Defining the Time When Rejection Begins
Bibliographic record
Abstract
We studied the early events in mouse kidney allografts and isografts to define when allorecognition begins and when alloimmune tissue injury begins. Allografts but not isografts showed T-cell infiltration in perivascular areas from day 1, but tubulitis and arteritis did not develop until day 7. Flow cytometry confirmed the early allospecific CD3(+)CD8(+) T-cell infiltrate. At day 1, both allografts and isografts showed extensive transcriptome changes, reflecting the response to surgery, but only allografts showed expression of interferon-gamma (IFN-gamma)-inducible transcripts and T-cell-associated transcripts. Although the number of CD68(+) myeloid cell numbers did not increase in day 1 isografts or allografts, mRNA expression for myeloid markers was increased in isografts and allografts, suggesting activation of resident cells of the macrophage-dendritic cell series (MMDCs) in response to injury, followed by increased CD68(+) cell numbers from day 2. By day 3, an interstitial T-cell and MMDC infiltrate was established in allografts, corresponding with the emergence of allospecific tissue injury, as reflected by decreased parenchymal transcripts. Thus, in renal allografts, allorecognition by T cells occurs in perivascular sites by day 1, but alloimmune parenchymal damage begins at day 3, coinciding with the emergence of the interstitial T-cell-MMDC infiltrate.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".