The Molecular Phenotype of 6-Week Protocol Biopsies from Human Renal Allografts: Reflections of Prior Injury but Not Future Course
Bibliographic record
Abstract
We assessed the molecular phenotype of 107 6-week protocol biopsies from human renal allografts, using Affymetrix microarrays. Transcript changes were summarized as nonoverlapping pathogenesis-based transcript sets (PBTs) reflecting inflammation (T cells, macrophages, IFNG effects) and the injury-repair response of the parenchyma, stroma and microcirculation-increased ('injury-up') and decreased ('injury-down') transcripts. The molecular changes were highly correlated with each other, even when all rejection and borderline cases were excluded. Inflammation and injury-down PBTs correlated with histologic inflammation and tubulitis, and the inflammation transcripts were greater in kidneys diagnosed as T cell-mediated or borderline rejection. Injury-up PBTs did not correlate with histopathology but did correlate with kidney function: thus functional disturbances are represented in transcript changes but not in histopathology. PBT changes correlated with prior delayed graft function. However, there was little difference between live donor kidneys and deceased donor kidneys that had not shown delayed graft function. Molecular changes did not predict future biopsies for clinical indications, rejection episodes, functional deterioration or allograft loss. Thus while detecting T cell-mediated inflammation, the molecular phenotype of early protocol biopsies mostly reflects the injury-repair response to implantation stresses, and has little relationship to future events and 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".