Metallothionein Expression in Horses With Chronic Liver Disease and Its Correlation With Ki-67 Immunoreactivity
Bibliographic record
Abstract
Chronic liver disease is an important cause of illness in horses, and treatment is mainly supportive. Research into new treatment modalities for humans has shown promising data regarding metallothionein (MT), which has been shown to possess regenerative, antifibrotic, and anti-inflammatory properties. This study aimed to examine the relationship between hepatic MT expression and the histopathologic markers of hepatic inflammation, fibrosis and bile duct proliferation, as well as cellular regeneration in 77 selected cases of chronic liver disease in horses. We hypothesized that higher MT expression would be associated with increased heptocellular proliferation and decreased fibrosis, inflammation, and bile duct proliferation. Hepatocellular MT expression was evaluated with immunohistochemistry. Additionally, cellular regeneration was evaluated with immunohistochemistry for Ki-67, a protein expressed during all active stages of the cell cycle. The severity of inflammation and fibrosis was scored, and bile duct proliferation was assessed by counting bile duct profiles. MT expression was observed in 73 of 77 (94.8%) cases of chronically diseased livers. Ki-67 expression was seen in resident Kupffer cells ( n = 42, 54.6%), lymphocytes ( n = 39, 50.7%), bile duct epithelium ( n = 10, 13.0%), and hepatocytes ( n = 8, 10.4%). MT expression was significantly associated with Ki-67 staining in bile duct epithelium and Kupffer cells. Additionally, median MT expression was higher in cases containing lymphocytic infiltrates as compared with cases with no lymphocytic infiltrate ( P < .05). These findings are the first known report of MT expression within chronic equine hepatic disease.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".