Co‐expression of IGF‐1 family members with myogenic regulatory factors following acute damaging muscle‐lengthening contractions in humans
Bibliographic record
Abstract
Muscle regeneration following injury is dependent on the ability of muscle satellite cells to activate, proliferate and fuse with damaged fibres. This process is controlled by the myogenic regulatory factors (MRF). Little is known about the temporal relation of the MRF with the expression of known myogenic growth factors (i.e. IGF-1) in humans following muscle damage. Eight subjects (20.6 +/- 2.1 years; 81.4 +/- 9.8 kg) performed 300 lengthening contractions (180 deg s(-1)) of their knee extensors in one leg on a dynamometer. Blood and muscle samples were collected before and at 4 (T4), 24 (T24), 72 (T72) and 120 h (T120) post-exercise. Mechano growth factor (MGF), IGF-1Ea and IGF-1Eb mRNA were quantified. Serum IGF-1 did not change over the post-exercise time course. IGF-1Ea and IGF-1Eb mRNA increased approximately 4- to 6-fold by T72 (P < 0.01) and MGF mRNA expression peaked at T24 (P = 0.005). MyoD mRNA expression increased approximately 2-fold at T4 (P < 0.05). Myf5 expression peaked at T24 (P < 0.05), while MRF4 and myogenin mRNA expression peaked at T72 (P < 0.05). Myf5 expression strongly correlated with the increase in MGF mRNA (r(2) = 0.83; P = 0.03), while MRF4 was correlated with both IGF-1Ea and -Eb (r(2) = 0.90; r(2) = 0.81, respectively; P < 0.05). Immunofluorescence analysis showed IGF-1 protein expression localized to satellite cells at T24, and to satellite cells and the myofibre at T72 and T120; IGF-1 was not detected at T0 or T4. These results suggest that the temporal response of MGF is probably related to the activation/proliferation phase of the myogenic programme as marked by an increase in both Myf5 and MyoD, while IGF-1Ea and -Eb may be temporally related to differentiation as marked by an increase in MRF4 and myogenin expression following acute muscle damage.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".