Brain‐derived neurotrophic factor drives muscle adaptation similar to aerobic training in mice
Bibliographic record
Abstract
This study, in vivo and in vitro, investigated the role of brain-derived neurotrophic factor (BDNF) in skeletal muscle adaptations to aerobic exercise. BDNF is a contraction-induced protein that may play a role in muscle adaptations to aerobic exercise. BDNF is involved in muscle repair, increased fat oxidation, and mitochondrial biogenesis, all of which are adaptations observed with aerobic training. The purpose of this study was two-pronged and investigated the skeletal muscle BDNF response to (1) acute and (2) chronic exercise in male C57BL/6J mice. It also examined if chronic BDNF treatment resulted in similar adaptations to chronic exercise. In aim 1, mice underwent a 2 hr. treadmill exercise bout. In aim 2, mice were assigned to one of four groups: (1) control (CON); (2) endurance training (ET; treadmill running 1 h/day, 5 days/wk); (3) BDNF (BDNF; 0.5 mg/kg·bw, 5 days/wk); (4) endurance training and BDNF (ET + BDNF) for 8 weeks. Results demonstrated that the soleus (SOL) had higher BDNF content compared with the extensor digitorum longus (EDL) and that SOL BDNF increased with acute exercise. After chronic exercise and BDNF treatment, treadmill testing to exhaustion demonstrated a main effect of BDNF and ET on increasing exercise capacity. In vitro contractile assessment of the EDL revealed BDNF treatment resulted in similar increases in the max rate of relaxation as ET. EDL force-frequency analysis showed ET + BDNF produced higher force than CON and BDNF, indicating an additive effect. BDNF increased EDL mitochondrial proteins, COXIV, and CS. These results demonstrate that BDNF contributes to muscle adaptations observed with ET.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".