Preclinical models of Wilson’s disease, why dogs are catchy alternatives
Bibliographic record
Abstract
Copper toxicosis is frequently encountered in various dog breeds. A number of differences and similarities occur between Wilson disease and copper toxicosis in Bedlington terriers, caused by a mutation in the COMMD1 gene, and copper toxicosis in Labrador retrievers, caused by mutations in both ATP7A and ATP7B gene. First the specific population structure of dog breeds is explained with reference to its applicability for genetic investigations. The relatively large body size (variable from less than 1 kg to over 50 kg) and life-span (over 10 years) of dogs facilitates preclinical studies on safety on long-term effects of novel procedures. Then copper toxicosis in the two dog breeds is described in detail with an emphasis on the functions of the causative proteins. Some of the advantages of this species for preclinical studies are described with an example of liver stem cell transplantations in COMMD1 deficient dogs. Since the genetic background of copper toxicosis in other dogs’ breeds has not yet been elucidated, it is conceivable that novel copper-related gene products or modifier genes will be discovered. About a century after the Novel prize was awarded to the research on dogs (Pavlov), dogs are in spotlight again as important preclinical model animals.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".