Quantification of extracellular matrix remodeling for the non-invasive identification of graft fibrosis after liver transplantation
Bibliographic record
Abstract
Detecting patients with early post-transplant fibrosis after liver transplantation (LT) is very important. Non-invasive tests are needed to avoid liver biopsies. We aimed to detect fibrosis in liver transplant recipients (LTR) using extracellular matrix (ECM) remodeling biomarkers. ECM biomarkers for type III (PRO-C3), IV (PRO-C4), VI (PRO-C6) and XVIII (PRO-C18L) collagen formation and type IV collagen degradation (C4M) were measured by ELISA in prospectively collected, cryopreserved plasma samples (n = 100) of LTR with paired liver biopsies from a protocol biopsy program. Fibrosis ≥ F2 was present in 29% of patients (median 44 months post-LT). APRI and FIB-4 neither identified significant fibrosis nor were correlated with histopathological fibrosis scores, while ECM biomarkers (AUCs 0.67-0.74) did. The median levels of PRO-C3 (15.7 vs. 11.6 ng/ml; p = 0.002) and C4M (22.9 vs. 11.6 ng/ml; p = 0.006) levels were elevated in T-cell-mediated rejection compared to normal graft function. The median levels of PRO-C4 (178.9 vs. 151.8 ng/ml; p = 0.009) and C4M (18.9 vs. 16.8 ng/ml; p = 0.004) levels were increased if donor-specific antibodies were present. PRO-C6 had the highest sensitivity (100%), NPV (100%) and negative likelihood-ratio (0) for graft fibrosis. To conclude, ECM biomarkers are helpful in identifying patients at risk of relevant graft fibrosis.
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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 | 0.001 |
| 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 | 0.000 |
| 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".