Association of the canine ATP7A and ATP7B with hepatic copper accumulation in Dobermann dogs
Bibliographic record
Abstract
BACKGROUND: Hepatic copper accumulation causes chronic hepatitis in dogs. Mutations in the copper transporters ATP7A and ATP7B were, respectively, associated with attenuation and enhancement of hepatic copper concentrations in Labrador Retrievers. There is a predisposition of Dobermanns to hepatitis with increased hepatic copper concentrations. OBJECTIVES: To investigate whether the ATP7A:c.980C>T and ATP7B:c.4358G>A mutations identified in Labrador Retrievers were associated with hepatic copper concentrations in Dobermanns. ANIMALS: Dobermanns from the Netherlands (n = 122) and the United States (n = 78). METHODS: In this retrospective study, mutations in ATP7A and ATP7B were investigated as risk factors for hepatic copper accumulation in Dobermanns. Liver biopsies of 200 Dobermanns were evaluated by histochemical copper staining, quantitative copper measurement, or both modalities. ATP7A and ATP7B genotypes were obtained by Kompetitive Allele Specific PCR. A linear regression model was used to investigate an association between genotype and hepatic copper concentrations. RESULTS: The ATP7A:c.980C>T was identified in both Dutch (2 heterozygous individuals) and American Dobermanns. In the American cohort, the minor allele frequency of the mutation was low (.081) and a possible effect on hepatic copper concentrations could not be established from this data set. A significant association of the ATP7B:c.4358G>A variant with increased hepatic copper concentrations in Dobermanns was observed. CONCLUSIONS AND CLINICAL IMPORTANCE: The ATP7B:c.4358G>A variant could be a contributor to hepatic copper accumulation underlying the risk of development of copper-associated hepatitis in breeds other than the Labrador Retriever.
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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".