Interleukin‐6 and its mRNA responses in exercise and recovery: relationship to muscle glycogen
Bibliographic record
Abstract
Increases in circulating interleukin-6 (IL-6) during exhaustive exercise have been suggested to be related to declining muscle glycogen. We addressed two hypotheses: (a) exhaustive exercise on two occasions will result in similar decreases in glycogen and increases in circulating IL-6 and its muscle mRNA; (b) increasing the rate of glycogen restoration via high-carbohydrate feeding in recovery will be associated with more rapid declines in muscle mRNA and circulating IL-6. Ten male subjects (22.6+/-0.8 year) cycled to exhaustion (65% VO(2 max)) on two occasions (117.8+/-2.9 min). Carbohydrate (1 g/kg bw) or water was ingested at exhaustion, 60, 120, 180, and 240 min post-exercise. Muscle biopsies were taken at rest, exhaustion, 30, 60, 120 and 300 min of recovery. Exercise resulted in a 14.5-fold increase (P<0.05) in IL-6 mRNA, 14.4-fold increase (P<0.05) in circulating IL-6, and a 80% decrease (P<0.05) in muscle glycogen from rest. The decline in glycogen was not correlated with the increase in IL-6 or IL-6 mRNA. During recovery, circulating IL-6 and its muscle mRNA decreased similarly in both trials; however, glycogen increased 150% (P<0.05) and 40% in the carbohydrate and water trials, respectively. Therefore, the declining IL-6 mRNA and IL-6 plasma concentrations during recovery were not related to carbohydrate availability or changes in glycogen.
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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".