Caractérisation génétique et fonctionnelle d'affections canines, modèles de maladies neuromusculaires
Bibliographic record
Abstract
Myotubular/centronuclear myopathies form a group of rare congenital diseases in man. Their molecular etiology has been widely investigated over the last years and proteins involved in those diseases all contribute to formation, maintenance or function of T-tubules in muscle cells triads. In Labradors, spontaneous myopathies with similar features have been described worldwide. Thanks to a French pedigree, a causal mutation in the PTPLA gene has been linked with the disease. It consists in the insertion of a retrotransposon in exon 2 of the gene. Our work has shown that these various myopathies in Labradors are indeed caused by a single mutation resulting from a founder effect and thus constitute a single disease entity for which we suggest the name of « centronuclear myopathy of the Labrador Retriever ». In order to better characterize the pathophysiological mechanisms of this disease model, we analyzed the expression pattern of PTPLA in dogs and mice. Two major splice transcripts have been described which are expressed differently in a space and time-dependent manner. In addition, the mutation induced defects in the splicing in every organ sampled in affected dogs. Pan-specific antibodies have been characterised in order to identify and precisely localize protein isoforms of PTPLA. Finally, anomalies in the T-tubules network have been described in myofibers from affected dogs, confirming the relevance of this spontaneous model for comparative pathology and ultimately, for preclinical trials. .
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".