The Dog Model in the Spotlight: Legacy of a Trustful Cooperation
Bibliographic record
Abstract
Dogs have long been used as a biomedical model system and in particular as a preclinical proof of concept for innovative therapies before translation to humans. A recent example of the utility of this animal model is the promising myotubularin gene delivery in boys affected by X-linked centronuclear myopathy after successful systemic, long-term efficient gene therapy in Labrador retrievers. Mostly, this is due to unique features that make dogs an optimal system. The continuous emergence of spontaneous inherited disorders enables the identification of reliable complementary molecular models for human neuromuscular disorders (NMDs). Dogs' characteristics including size, lifespan and unprecedented medical care level allow a comprehensive longitudinal description of diseases. Moreover, the highly similar pathogenic mechanisms with human patients yield to translational robustness. Finally, interindividual phenotypic heterogeneity between dogs helps identifying modifiers and anticipates precision medicine issues.This review article summarizes the present list of molecularly characterized dog models for NMDs and provides an exhaustive list of the clinical and paraclinical assays that have been developed. This toolbox offers scientists a sensitive and reliable system to thoroughly evaluate neuromuscular function, as well as efficiency and safety of innovative therapies targeting these NMDs. This review also contextualizes the model by highlighting its unique genetic value, shaped by the long-term coevolution of humans and domesticated dogs. Because the dog is one of the most protected research animal models, there is considerable opposition to include it in preclinical projects, posing a threat to the use of this model. We thus discuss ethical issues, emphasizing that unlike many other models, the dog also benefits from its contribution to comparative biomedical research with a drastic reduction in the prevalence of morbid alleles in the breeding stock and an improvement in medical care.
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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 | 0.023 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".