ATP7A, ATP7B, and RETN genotypes in Labrador Retrievers with and without copper-associated hepatopathy
Bibliographic record
Abstract
OBJECTIVE: To determine the frequency of previously reported coding variants in the ATP7A, ATP7B, and RETN genes in a US population of Labrador Retrievers and to explore potential associations of these genotypes with pathologic hepatic copper accumulation. SAMPLE: Archived hepatic specimens from 90 Labrador Retrievers collected between 2013 and 2021. PROCEDURES: The Michigan State University Veterinary Diagnostic Laboratory database was searched to identify archived tissues from Labrador Retrievers that had undergone hepatic histopathologic assessment. Cases were classified into control, copper-associated hepatopathy (CAH), and intermediate populations on the basis of histopathologic features and hepatic copper accumulation. The DNA was extracted from archived tissues and genotyped for reported variants in the 3 genes. Allele frequencies were determined, and associations of genotypes with CAH status were evaluated. RESULTS: 29 control dogs, 45 CAH dogs, and 16 intermediate dogs were included in the study. The overall ATP7A and RETN variant allele frequencies were 30% and 13%, respectively, and were not significantly different among control, CAH, and intermediate populations. The ATP7B variant allele frequency was significantly higher in the CAH population (30%) as compared to the control population (13%). However, 21 of 45 (47%) CAH dogs did not have an ATP7B variant allele whereas 7 of 28 (25%) control dogs did have an ATP7B variant allele. CLINICAL RELEVANCE: Study results supported a contributory role for the ATP7B variant in CAH pathogenesis in Labrador Retrievers. However, the application of genetic testing in a clinical setting is complicated by genotypic variability within healthy and diseased dogs.
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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 | 0.001 |
| 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".