Cytokine concentrations in saliva vs. plasma at rest and in response to intense exercise in adolescent athletes
Bibliographic record
Abstract
Background Salivary measures are advantageous in conducting large paediatric studies involving repeated measures. However, research measuring salivary cytokines in youth is limited.Aim Compare salivary with plasma concentrations of inflammatory cytokines at rest and following exercise in adolescent swimmers (21 male, 22 female).Methods Following collection of resting saliva and blood samples, participants performed a bout of high-intensity interval swimming, with samples taken again ∼15 min post-swimming and analysed for interleukin-6 (IL-6), interleukin 10 (IL-10), and tumour necrosis factor-alpha (TNF-α).Results Resting IL-10 was significantly lower, while IL-6 and TNF-α were significantly higher in saliva compared with plasma. IL-10 increased from pre- to post-swimming in plasma, but less so in saliva (51% vs. 29%; p = 0.02). TNF-α decreased post-swimming in saliva, but not in plasma (–27% vs −1%; p = 0.01). IL-6 decreased post-swimming in saliva compared with plasma (–21% vs. −3%; p = 0.06). Intraclass correlation coefficients (ICC) revealed no association between salivary and plasma IL-6 and TNF-α, while IL-10 showed a weak correlation only at rest (ICC = 0.39; p = 0.05).Conclusions Differences in concentrations and exercise responses, along with weak correlations, suggest that salivary cytokine levels are not an accurate representation of blood cytokine levels, and should not be used as a surrogate measure in paediatric studies.
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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 | 0.001 | 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".