The Molecular Phenotype of Kidney Transplants
Bibliographic record
Abstract
Microarray studies of kidney transplant biopsies provide an opportunity to define the molecular phenotype. To facilitate this process, we used experimental systems to annotate transcripts as members of pathogenesis-based transcript sets (PBTs) representing biological processes in injured or diseased tissue. Applying this annotation to microarray results revealed that changes in single molecules and PBTs reflected a large-scale coordinate disturbance, stereotyped across various diseases and injuries, without absolute specificity of individual molecules or PBTs for rejection. Nevertheless, expression of molecules and PBTs was quantitatively specific: IFNG effects for rejection; T cell and macrophage transcripts for T cell-mediated rejection; endothelial and NK transcripts for antibody-mediated rejection. Various diseases and injuries induced the same injury-repair response, undetectable by histopathology, involving epithelium, stroma and endothelium, with increased expression of developmental, cell cycle and apoptosis genes and decreased expression of differentiated epithelial features. Transcripts reflecting this injury-repair response were the best correlates of functional disturbance and risk of future graft loss. Late biopsies with atrophy-fibrosis, reflecting their cumulative burden of injury, displayed more transcripts for B cells, plasma cells and mast cells. Thus the molecular phenotype is best described in terms of three elements: specific diseases, including rejection; the injury-repair response and the cumulative burden of injury.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".