Clinical Characteristics and Molecular Basis of Hereditary Skin Diseases in Dogs
Bibliographic record
Abstract
Genetic skin diseases in dogs are conditions that are inherited, begin during their life (e.g. pruritus), affect the health and welfare of the dog, and recently have become an area of clinical research . The objective of this study was to provide a comprehensive review of canine genetic skin disease at the clinical, molecular, inheritance patterns, and research methodologies levels. The data suggest that dog breeds have distinct phenotypic characteristics, for example, Labrador Retrievers with pathogenic mutations in the PNPLA1 gene have ichthyosis-like scales versus Goldens that have mutations in the ABHD5 gene and have more hyperatic symptoms. Collie Shepherds with a pathogenic mutation in the STS gene, had some similarities and differences (gender differences) but all have X-linked ichthyosis. There are core pathogenic genes including PNPLA1, ABHD5, STS and KRT10 that disrupt the skin functions affecting lipid metabolism (dyslipidemia), keratinocyte abnormal differentiation and barrier difficulties. Modes of inheritance included autosomal recessive, autosomal dominant and x-linked. Useful molecular techniques suggested include whole genome sequencing, exome sequencing, and/or gene chips, combined with cosegregation and or genome wide association studies (GWAS) to identify disease loci. The study presents and supports an evidence-based approach for breed based diagnosis, genetic screening, and selective breeding improvement of canine genetic skin 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 | 0.000 |
| 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".