Changes in Inflammatory Cytokines and Irisin in Response to High Intensity Swimming in Adolescent versus Adult Male Swimmers
Bibliographic record
Abstract
Swimming is a popular youth sport that is considered beneficial for cardiovascular fitness. However, the potential inflammatory outcomes of high intensity swimming in younger swimmers are unclear, as is the response of irisin, a myokine released during exercise with anti-inflammatory properties. This study compared the plasma concentrations of interleukins 1-beta (IL-1β), 6 (IL-6), 10 (IL-10), tumor necrosis factor alpha (TNF-α) and irisin in response to intense swimming between adolescent and adult male swimmers. Thirty-two swimmers (16 adolescents, 14 ± 1 years; 16 adults, 21.5 ± 3.1 years) completed a high intensity interval swimming trial. At rest, only TNF-α was higher (33%, p < 0.05) in adolescents compared with adults. There was an overall significant increase in IL-1β from pre- to post-swimming (3% in adolescents, 24% in adults), but no significant interaction. IL-10 significantly increased in both groups (+34% in adolescents, +56% in adults). IL-6 and TNF-α increased significantly (+32% and +26%, respectively) in adults, but not in adolescents (+2% and −9%, respectively). Adults showed a small, but significant decrease in irisin (−5%), with no change in adolescents. The lack of an IL-6, TNF-α and irisin response to intense swimming in adolescent swimmers may suggest a blunted inflammatory and myokine response following high intensity exercise in trained youth.
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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 | 0.001 |
| 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".