Interleukin‐4 is a Potential Regulator of Satellite Cell Function in Response to Acute Myotrauma in Humans
Bibliographic record
Abstract
Interleukin‐4 (IL‐4) has been implicated in myoblast fusion and growth; however, the localization of IL‐4 to satellite cells (SC) in humans, has not been demonstrated. Furthermore, the role of IL‐4 in SC in response to acute injury in humans has not been investigated. Eight subjects (age 22 ± 1 y; 79 ± 8 kg) performed 300 maximal unilateral leg lengthening contractions (3.14 rad.s −1 ) and muscle samples were collected at PRE, 4 (T4), 24 (T24), 72 (T72), and 120 hr (T120) post intervention. Immunohistochemical analysis revealed that IL‐4 protein was localized with Pax7 + SC at T4, T24 and at T72, with no visible IL‐4 detected at PRE. IL‐4 and IL‐4R mRNA demonstrated significant (p < 0.05) 4.4 fold upregulations at T4 while IL‐4R maintained this high expression at T24 post exercise (p < 0.05). Similarly, a downstream target of IL‐4, SOCS1, demonstrated a 2.9 fold increase (p < 0.05) at T4. Furthermore, IL‐13 mRNA levels remained low until T120 where it became significantly increased compared with all other time points (p < 0.05). Pearson product correlation analysis demonstrated positive associations between IL‐4, IL‐4R (p < 0.001, R 2 = 0.67) and SOCS1 (p < 0.001, R 2 = 0.69) mRNA expression levels. Furthermore, IL‐4R mRNA correlated with SOCS1 expression (p < 0.001, R 2 = 0.57). These data suggest a role for IL‐4 in the SC response to acute myotrauma in humans, possibly signalling via the STAT6 pathway.
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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".