Relationships among injury, fibrosis, and time in human kidney transplants
Bibliographic record
Abstract
BACKGROUND: Kidney transplant biopsies offer an opportunity to understand the pathogenesis of organ fibrosis. We studied the relationships between the time of biopsy after transplant (TxBx), histologic fibrosis, diseases, and transcript expression. METHODS: = 399, >1 year) after transplant were used to analyze the molecular landscape of fibrosis in relationship to histologic fibrosis and diseases. RESULTS: Fibrosis was absent at transplantation but was present in some early biopsies by 4 months after transplant, apparently as a self-limited response to donation implantation injury not associated with progression to failure. The molecular phenotype of early biopsies represented the time sequence of the response to wounding: immediate expression of acute kidney injury transcripts, followed by fibrillar collagen transcripts after several weeks, then by the appearance of immunoglobulin and mast cell transcripts after several months as fibrosis appeared. Fibrosis in late biopsies correlated with injury, fibrillar collagen, immunoglobulin, and mast cell transcripts, but these were independent of time. Pathway analysis revealed epithelial response-to-wounding pathways such as Wnt/β-catenin. CONCLUSION: Fibrosis in late biopsies had different associations because many kidneys had potentially progressive diseases and subsequently failed. Molecular correlations with fibrosis in late biopsies were independent of time, probably because ongoing injury obscured the response-to-wounding time sequence. The results indicate that fibrosis in kidney transplants is driven by nephron injury and that progression to failure reflects continuing injury, not autonomous fibrogenesis. TRIAL REGISTRATION: INTERCOM study (www.clinicalTrials.gov; NCT01299168). FUNDING: Canada Foundation for Innovation and Genome Canada.
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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 | 0.002 | 0.002 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".