Early Loss of Renal Transcripts in Kidney Allografts: Relationship to the Development of Histologic Lesions and Alloimmune Effector Mechanisms
Bibliographic record
Abstract
We sought to understand the epithelial response to the T-cell mediated inflammatory process in kidney allograft rejection. Using microarrays, we studied transcriptome changes of kidney parenchymal cells and their relationship to the development of pathologic lesions such as tubulitis in mouse kidney allografts and isografts. Inflammatory infiltrate in allografts developed by day 5, but tubulitis first appeared at day 7 and was severe by day 21. Using microarrays, we selected 70 solute carrier transcripts with high renal parenchymal expression and known epithelial function. Transcript expression was reduced early in isografts and allografts, followed by progressive loss in allografts and recovery in isografts. The expression pattern of day 21 allografts developed progressively from the time of engraftment and was established before histologic lesions. These changes are probably functionally significant: selected proteins showed decreased immunostaining at days 7 and 21. Allospecific loss of transcripts was dependent on T cells but independent of perforin, granzymes A/B, CD103, or B cells. Weighted sum decomposition revealed multiple components of the epithelial response with allospecific changes from day 1. We conclude that loss of renal transcripts indicates an early stage of T-cell mediated alloimmune injury that later progresses to pathologic lesions such as tubulitis.
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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".