Predominance des fibres musculaires lentes chez le chien cnm, analyse en situation de regeneration provoquee
Bibliographic record
Abstract
La myopathie centronucleaire (CNM) du Labrador Retriever, est caracterisee principalement par une faiblesse musculaire, une amyotrophie marquee et une areflexie tendineuse. L’histologie musculaire revele une centralisation nucleaire et une predominance des fibres musculaires lentes. La premiere partie de notre travail s’interesse donc aux differents facteurs pouvant influencer le phenotype des fibres musculaires. L’influx nerveux et un mediateur cellulaire, la calcineurine, interviennent dans la determination et le maintien de l’expression du phenotype lent des fibres musculaires. Nous avons egalement etudie dans cette partie, les differentes methodes d’induction de necrose musculaire et les mecanismes cellulaires de la regeneration, en vue de notre etude experimentale. La seconde partie, experimentale, decrit une etude conduite pour evaluer le pouvoir de regeneration des fibres chez les Labradors atteints. La regeneration a ete observee apres une necrose localisee du biceps femoral provoquee par l’injection de notexine. L’expression des isoformes de chaine lourde de la myosine et la centralisation nucleaire ont ete utilisees comme criteres d’evolution de la regeneration. Chez le chien CNM, nous avons constate que le potentiel de regeneration musculaire, bien que conserve, est differe dans le temps. De plus, on a observe que le tableau histologique anormal du muscle atteint etait totalement restaure des 30 jours. En conclusion, les muscles atteints regenerent, moins vite et selon un patron phenotypique coherent avec le genotype des animaux.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".