Angiotensin-converting enzyme for noninvasive assessment of liver fibrosis in autoimmune hepatitis
Bibliographic record
Abstract
BACKGROUND AND AIM: There are no validated noninvasive markers of liver fibrosis in autoimmune hepatitis (AIH). An activated renin-angiotensin system (RAS) and its key element angiotensin-converting enzyme (ACE) have been implicated in the pathogenesis of hepatic fibrogenesis. We aimed to study the assumed role of activated RAS in the fibrogenic process and whether the serum concentration of ACE can predict different fibrosis stages in AIH. PATIENTS AND METHODS: Serum samples of 73 consecutive patients who were diagnosed with AIH were analysed for ACE concentration. All patients underwent a liver biopsy. RESULTS: Serum ACE levels increased significantly for each fibrosis score. The median ACE was 45 U/l in patients with fibrosis score I, 54 U/l in patients with fibrosis score II, 68 U/l in patients with fibrosis score III and 87 U/l in patients with fibrosis score IV. For significant fibrosis (≤F2), a 56 U/l cut-off value of ACE had 95.5% sensitivity and 74.5% specificity, and receiver-operating characteristic curves showed an area under the curve (AUC) of 0.89. For advanced fibrosis (≤F3), a 64 U/l cut-off level of ACE had 85.2% sensitivity and 94.8% specificity, and AUC was 0.91. For cirrhosis, a 68 U/l cut-off level of ACE had 100% sensitivity and 84.4% specificity, and AUC was 0.95. CONCLUSION: Our results suggest that activated RAS may sustain hepatic fibrogenesis in AIH. Measurement of serum ACE offers an easy, accurate and inexpensive noninvasive method that differentiates significant from nonsignificant liver fibrosis in AIH. Blockade of RAS may exert beneficial effects on fibrosis progression in AIH.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".