Transcriptome changes in renal allograft protocol biopsies at 3 months precede the onset of interstitial fibrosis/tubular atrophy (IF/TA) at 6 months
Bibliographic record
Abstract
BACKGROUND: Interstitial fibrosis and tubular atrophy (IF/TA) in renal transplants are the major morphological correlates of progressive graft deterioration. Early diagnosis of IF/TA is a pre-requisite for a timely therapeutic intervention in patients at risk. To evaluate events occurring before the overt onset of IF/TA, gene expression profiling of 3-month protocol biopsies from patients with IF/TA was performed in a patient group (n = 8) who developed mild IF/TA [chronic allograft nephropathy (CAN) grade I, by the Banff scoring system] in the subsequent 6-month protocol biopsy ('progressors'), and in 12 patients without IF/TA at 6 months ('non-progressors'). METHODS: RNA was extracted, labelled and hybridized to human specific genome wide DNA microarrays. Normalized data were subjected to gene-centric and pathway-centric statistical methods. RESULTS: Compared to the non-progressors, the 3-month biopsies of the progressor group showed overexpression of several genes that are important in the T- and B-cell activation and immune response. Genes involved in pro-fibrotic processes were identified in the biopsies of the progressors that preceded the observed IF/TA at 6 months. Furthermore, several genes with transporter and metabolic functions were underrepresented in the progressors in the 3-month biopsies. CONCLUSION: Gene expression profiling of early protocol biopsies identified changes in the transcriptome of grafts, which may be important for the development of IF/TA. Such early detection of transcriptome changes can facilitate the identification of patients at risk shifting the intervention time point well before the histological diagnosis of irreversible IF/TA.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".