Skeletal myocytes are a source of interleukin‐6 mRNA expression and protein release during contraction: evidence of fiber type specificity
Bibliographic record
Abstract
In this study, we aimed to determine whether skeletal muscle cells per se are a source of interleukin (IL)-6 during contraction and whether IL-6 production is fiber type specific. Muscle biopsy samples were collected from seven males before (PRE) and after (POST) completing 120 min of continuous bicycle ergometry. Biopsies were sectioned and analyzed for the following: IL-6 protein detected by immunohistochemistry (IHC), IL-6 mRNA content detected by in situ hybridization, fiber type measured by either IHC or myofibrillar ATPase activity stain, and glycogen content measured by periodic acid schiff (PAS) assay. Fibers were qualitatively categorized according to glycogen content to one of five groups (1-5), with 1 being very low (LOW) and 5 being very high (HIGH) glycogen. Total fluorescence (PRE vs. POST) and glycogen-dependent fluorescence (LOW vs. HIGH) of IL-6 protein were quantitated using Metamorph software. Total IL-6 protein was elevated from PRE to POST exercise (P<0.05). At PRE, IL-6 protein was evenly distributed across all fibers at low levels, consistent with glycogen distribution. At POST, IL-6 protein was greater (P<0.05) in HIGH compared with LOW glycogen fibers, which coincided with type 2 fibers. IL-6 mRNA was distributed peripherally in all fibers at PRE. At POST, however, IL-6 mRNA appeared predominantly in type 2 fibers, which also had higher glycogen content (P<0.05). These data demonstrate that myocytes per se are a source of IL-6 produced during contraction. Our data also suggest that type 2 fibers predominantly produce IL-6 during muscle contractile activity.
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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".