Hepatocyte Growth Factor in Patients with Three Different Stages of Chronic Liver Disease including Hepatocellular Carcinoma, Cirrhosis and Chronic Hepatitis: An Immunohistochemical Study
Bibliographic record
Abstract
BACKGROUND AND AIMS: The specific role of hepatocyte growth factor in liver disease is unknown. The presence and density of this factor in patients with three different stages of liver disease were investigated, with the aim of assessing its prognostic significance. PATIENTS AND METHODS: Liver specimens from patients with chronic hepatitis (n=20), cirrhosis (n=20), hepatocellular carcinoma (n=30) and normal livers (n=20) were immunohistochemically stained to determine the presence and density of hepatocyte growth factor. RESULTS: There were significantly more hepatocyte growth factor-positive Kupffer and Ito cells in all three diseased groups than in the control group. Also, there was significantly more positive staining in chronic hepatitis specimens than in specimens from the cirrhosis, hepatocellular carcinoma and control groups (P<0.05). The hepatoma cells in 10 of the hepatocellular carcinoma cases stained positive, but none of the hepatocytes in the chronic hepatitis, cirrhosis and normal liver specimens stained. It was only possible to assess nonmalignant hepatocytes adjacent to the hepatocellular carcinoma in the four resection specimens, and no staining for hepatocyte growth factor was observed in these areas. There was no statistical association between density of hepatocyte growth factor and histological activity index in chronic hepatitis, or between density of hepatocyte growth factor and grade of hepatocellular carcinoma. CONCLUSIONS: Similar to some previous reports, this study revealed that hepatoma cells can also express this growth factor. Immunohistochemical detection of hepatocyte growth factor may prove to be a useful method of diagnosing hepatocellular carcinoma in challenging cases.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".