Hepatocyte growth factor (HGF) and the satellite cell response following muscle lengthening contractions in humans
Bibliographic record
Abstract
The time-courses of satellite cell (SC) activation and protein expression of hepatocyte growth factor (HGF), HGF activator (HGFA), HGFA inhibitor-1 (HAI-1), and HGFA inhibitor-2 (HAI-2) in human skeletal muscle, as well as serum HGF following a single bout of muscle lengthening contractions, were determined. Eight recreationally active participants were recruited for the study. Subjects performed 300 lengthening contractions involving the quadriceps femoris muscles of a single leg at a fixed velocity of 180 degrees/s. Percutaneous muscle biopsies were taken before (PRE) and at 4 h (T4), 24 h (T24), 72 h (T72), and 120 h (T120) following the exercise. The protocol resulted in an increase in the number of SCs [neural cell adhesion molecule (NCAM)-labeled cells] expressed relative to total myonuclei, at T24, compared with both PRE and T4 (P<0.05), and peaked at T72 (approximately 80% increase vs. PRE, P<0.05). HGF protein increased significantly in serum from baseline (PRE) to T4 (P<0.05). Active HGF protein was detected in skeletal muscle at rest [14.4+/-1.3 average integrated density value (IDV)/actin average IDV] and tended to increase at early time-points (P=0.12). HGFA protein increased significantly from PRE to T24 (P<0.05). HAI-1 protein increased significantly from PRE to T24 (P<0.05). HAI-2 (32 kDa) increased significantly from baseline (PRE) by T24 (P<0.05), and also by T72 and T120 (P<0.05). We conclude that a single bout of lengthening muscle contractions is sufficient to activate SCs, which may involve both a local and systemic HGF response to contraction-induced injury.
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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".