Metallothionein Expression in Dogs With Chronic Hepatitis and Its Correlation With Hepatic Fibrosis, Inflammation, and Ki-67 Expression
Bibliographic record
Abstract
The chronic form of primary hepatitis occurs commonly in dogs, and the etiology is rarely found. Metallothionein (MT) is a heavy metal-binding protein found in many organs, including the liver. MT was recently shown to enhance liver regeneration and decrease hepatic fibrosis in human beings. This study examined the expression of MT in 24 cases of chronic hepatitis in dogs using immunohistochemistry. To understand the role of MT as a determinant of hepatic inflammation, fibrosis, bile duct proliferation, and regeneration, we correlated its expression with histologic lesions of chronic hepatitis, such as hepatic inflammation, fibrosis, and bile duct proliferation, as well as hepatocellular growth fraction as measured by Ki67 immunolabeling. Hepatocellular growth fraction was used as a measure of hepatic regeneration. Regression analysis revealed a significant positive correlation between MT labeling intensity and growth fraction (r(2) = 0.29, P < .05). The percentage of MT-positive cells and the overall MT expression were both positively correlated with growth fraction (r(2) = 0.25 and 0.26, respectively; P < .05). A negative correlation was found between the overall MT labeling and fibrosis (r(2) = 0.18, P < .05). A similar trend of negative correlation was also found between the percentage of MT-positive cells and fibrosis, but the P value was not statistically significant (r(2) = 0.14, P = .0684). These findings suggest a protective role of MT in dogs affected by chronic hepatitis, similar to its role in human beings. These dogs may respond to treatment modules focusing on enhancing the expression of MT.
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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 | 0.001 |
| 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".