Hepatocyte Growth Factor Signaling in Mediating Human Muscle Satellite Cell Activation and Proliferation Following Eccentric Exercise
Bibliographic record
Abstract
Eight subjects (20.6 ± 2.1 yr; 81.4 ± 9.8 kg) performed 300 isolateral eccentric contractions (knee extensions at 180 °/s) to evaluate the skeletal muscle satellite cell (SC) response and the potential of hepatocyte growth factor (HGF) signaling in mediating SC activation and proliferation following acute damaging exercise. Muscle biopsies were obtained from the vastus lateralis of the non‐exercised leg prior to exercise (PRE) and the exercised leg at 4 (T4), 24 (T24), 72 (T72), and 120 h (T120) post‐exercise. The number of SC (N‐CAM + ) increased (p < 0.001) by T4 and remained elevated at T120 (p = 0.002). Serum HGF increased at T4 (p < 0.05) with a trend for increased muscle HGF and HGF activator (HGFA) protein expression at T24 with active HGF detected in all PRE samples. HGFA inhibitors (HAI‐1 and HAI‐2) protein content increased at T72 and remained elevated at T120 (p < 0.05). Myogenic regulatory factors (MyoD and Myf5) mRNA expression increased at T4 and T24 (p < 0.05) respectively, with increased myogenin mRNA expression at T24 (p < 0.05). Furthermore, plasma interleukin‐6 concentration increased ∼200% (p < 0.05) by T4, illustrating a roll of both local and systemic signaling in the activation and proliferation of SC; with HGF signaling playing an important regulatory role in the SC response to exercise‐induced muscle damage. Supported by NSERC
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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 | 0.000 |
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".