Pathogenesis, Non‐Invasive Assessments and Treatment of Hepatic Fibrosis in Autoimmune Liver Diseases
Bibliographic record
Abstract
BACKGROUND AND AIMS: Autoimmune liver diseases (AILD), including autoimmune hepatitis (AIH), primary biliary cholangitis (PBC) and primary sclerosing cholangitis (PSC), can lead to progressive liver fibrosis, development of cirrhosis, decompensation, hepatocellular carcinoma (HCC), and need for liver transplantation (LT). METHODS: This review aims to provide a comprehensive overview of the mechanisms of liver fibrogenesis, non-invasive methods to assess hepatic fibrosis and potential anti-fibrotic interventions in AILD. RESULTS AND CONCLUSIONS: Current management for AILD should incorporate non-invasive methods to evaluate changes in hepatic fibrosis and consider potential interventions aiming at controlling the progression of the disease, interruption and, potentially, reversal of liver fibrosis. Several laboratory tests can help distinguish patients with advanced fibrosis or cirrhosis but their utility in discriminating earlier histological stages of fibrosis is unclear. A current shift toward non-invasive radiological methods, such as vibration-controlled transient elastography, shear wave elastography, acoustic radiation force impulse imaging and magnetic resonance elastography, opens promising avenues for their wide application; however, their performances may be compromised by hepatic inflammation, ascites, biliary obstruction, or concomitant obesity and metabolic dysfunction-associated steatotic liver disease. Corticosteroids and immunomodulators have been shown to regress fibrosis in AIH patients. In PBC, treatment with either synthetic bile acids, farnesoid X receptor agonists or peroxisome proliferator-activated receptor agonist leads to the improvement or stabilization in the fibrosis stage. There is an urgent need for effective medical treatment in PSC, and available evidence of antifibrotic treatment is particularly limited. Promising anti-fibrotic interventions in AILD encompass conventional pharmacological agents as well as potential new treatments, such as fibrates, monoclonal antibodies, and site- and organelle-specific agents.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".