Molecular Correlates of Renal Function in Kidney Transplant Biopsies
Bibliographic record
Abstract
The molecular changes in the parenchyma that reflect disturbances in the function of kidney transplants are unknown. We studied the relationships among histopathology, gene expression, and renal function in 146 human kidney transplant biopsies performed for clinical indications. Impaired function (estimated GFR) correlated with tubular atrophy and fibrosis but not with inflammation or rejection. Functional deterioration before biopsy correlated with inflammation and tubulitis and was greater in cases of rejection. Microarray analysis revealed a correlation between impaired renal function and altered expression of sets of transcripts consistent with tissue injury but not with those consistent with cytotoxic T cell infiltration or IFN-gamma effects. Multivariate analysis of clinical variables, histologic lesions, and transcript sets confirmed that expression of injury-related transcript sets independently correlated with renal function. Analysis of individual genes confirmed that the transcripts with the greatest positive or negative correlations with renal function were those suggestive of response to injury and parenchymal dedifferentiation not inflammation. We defined new sets of genes based on individual transcripts that correlated with renal function, and these highly correlated with the previously developed injury sets and with atrophy and fibrosis. Thus, in biopsies performed for clinical reasons, functional disturbances are reflected in transcriptome changes representing tissue injury and dedifferentiation but not the inflammatory burden.
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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 | 0.001 |
| 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".