The brain‐derived neurotrophic factor (BDNF) regulates skeletal muscle regeneration and is mis‐regulated in dystrophic muscle
Bibliographic record
Abstract
Our recent studies have shown that BDNF is highly expressed in skeletal muscle satellite cells (J. Neurosci., 26: , 2006). Additionally, BDNF depletion by siRNA results in precocious differentiation of myoblasts. In order to determine whether BDNF plays similar roles in vivo, we designed several complementary experiments. First, we examined mouse muscle induced to degenerate/regenerate following cardiotoxin injection. We show that BDNF expression is significantly increased during early phases of regeneration but that its expression decreases (to control levels) within 5 days post‐injection. Moreover, BDNF down‐regulation coincides with increased expression of several markers of differentiation. Constitutive in vivo expression of siRNAs to knockdown BDNF in regenerating muscles results in enhanced differentiation as demonstrated by the pattern of myosin heavy chain isoforms expressed. Given the therapeutic importance of regeneration for treating various muscle diseases, we also examined the expression profile of BDNF in dystrophic muscles from mdx mice. In comparison to muscles from wildtype animals, mdx mouse muscles consistently display greater levels of BDNF. Finally, genetic inactivation of BDNF in developing skeletal muscle using Cre‐lox technology will also allow us to determine the impact of BDNF expression during embryonic and neonatal muscle development. Funded by MDA and CIHR.
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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 | 0.000 | 0.000 |
| 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 | 0.001 |
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".