Molecular Correlates of Scarring in Kidney Transplants: The Emergence of Mast Cell Transcripts
Bibliographic record
Abstract
In the Banff consensus, infiltrates in areas of scarring are ignored. This study aimed to characterize the molecular correlates and clinical significance of scarring and inflammation in scarred areas. We assessed the extent of interstitial infiltrates, tubulitis and scarring in 129 clinically indicated renal allograft biopsies, and correlated the results with microarray expression data and allograft survival. Findings were validated in 50 additional biopsies. Transplants with scarring had a worse prognosis if the scarred area showed infiltrates. Infiltration in unscarred and scarred areas was associated with reduced death censored graft survival. In microarray analysis, infiltration in unscarred areas strongly (>r +/- 0.4) correlated with 484 transcripts associated with cytotoxic T cells, interferon-gamma, macrophages and injury. Scarring correlated with a distinct set of 172 transcripts associated with B cells, plasma cells, and others of unknown significance. The strongest correlation was with four mast cell transcripts. In biopsies with scarring, high expression of mast cell transcripts was associated with reduced graft survival and poor functional recovery. In renal allograft biopsies, infiltrates in scarred areas have implications for poor outcomes. Scarring is associated with a distinct pattern of inflammatory molecules, including B cell/immunoglobulin but particularly mast cell-associated transcripts, which correlated with poor outcomes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".