Non-invasive Biomarkers in Non-Alcoholic Steatohepatitisinduced Hepatocellular Carcinoma
Bibliographic record
Abstract
Non-alcoholic fatty liver disease (NAFLD) is by far the most common form of chronic liver disease worldwide, affecting adults as well as children. Under the term of NAFLD there is a wide spectrum of diseases ranging from simple steatosis to the non-alcoholic steatohepatitis (NASH), which can progress to cirrhosis and hepatocellular carcinoma (HCC). Several mechanisms have been described to influence the progression of the disease from the benign NAFL to the aggressive NASH. The imbalance between pro- and anti-oxidant mechanisms and between pro- and anti-inflammatory cytokines is thought to play a pivotal role in the pathogenesis of NAFLD and disease progression toward NASH and fibrosis. The present review intends to look at some of the mechanistic biomarkers to be employed in establishing an early diagnosis in HCC derived from NASH.Abbreviations: ANGPT: angiopoietin-2; AFP: α-fetoprotein; ALT: alanine aminotransferase; AST: aspartate aminotransferase; CI: confidence interval; COL: collagen; DCP: des-carboxyprothrombin; γGT: gamma glutamyl transpeptidase; HBV: hepatitis B virus; HCC: hepatocellular carcinoma; HCV: hepatitis C virus; HR: hazard ratio; ITG: integrin; LAM: laminin collagen genes; MMP: matrix metalloproteinase; MS: metabolic syndrome; NAFLD: non-alcoholic fatty liver disease; NASH: non-alcoholic steatohepatitis; PDGFRA: platelet derived growth factor receptor-α
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".