Kidney Transplants With Progressing Chronic Diseases Express High Levels of Acute Kidney Injury Transcripts
Bibliographic record
Abstract
We previously reported that kidney transplants with early acute injury express transcripts indicating injury repair--the acute kidney injury signal. This study investigated the significance of this signal in transplants with other conditions, including rejection and recurrent disease. The injury signal was elevated in biopsies in many different conditions, including T cell-mediated rejection and potentially progressive diseases such as antibody-mediated rejection and glomerulonephritis. A high injury signal correlated with poor function and with inflammation in areas of fibrosis, but not with fibrosis without inflammation. In multivariate survival analysis, the injury signal in late kidney transplant biopsies strongly predicted future graft loss, similar to a published molecular risk score derived in late kidneys. Indeed, the injury signal shared many individual transcripts with the risk score, e.g. ITGB6, VCAN, NNMT. The injury signal was a better predictor of future graft loss than fibrosis, inflammation or expression of collagen genes. Thus the acute injury signal, first defined in early reversible injury, is present in many diseases as a reflection of parenchymal distress, where its significance is dictated by the inducing insult, i.e. treatable/self-limited versus untreatable and sustained. Progression in troubled transplants is primarily a function of ongoing parenchymal injury by disease, not fibrogenesis.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".