Effect of a Single Dose of Caffeine Supplementation and Intermittent-interval Exercise on Muscle Damage Markers in Soccer Players
Bibliographic record
Abstract
This study examined the effect of caffeine supplementation on the white cell count and muscle damage marker responses to intermittent-interval exercise as performed by soccer players. Subjects (n = 20) completed a placebo-controlled double-blind test protocol. Forty-five minutes before exercise, participants ingested 4.5 mg·kg−1 body mass of caffeine (EXP) or placebo (CONT). Blood samples were collected before and after exercise to measure hematological parameters, serum creatine kinase (CK), lactate dehydrogenase (LDH), alanine aminotransferase (ALT), aspartate aminotransferase (AST), alkaline phosphatase (AP) and γ-glutamyl transferase (γ-GT) activity. To compare differences among all variables, 2 (time) × 2 (group) repeated measures ANOVA (with Tukey's post hoc tests) was conducted. Exercise caused leukocytosis (38.5% and 36.1%in EXP and CONT, respectively), lymphocytosis (42.1%and 44.9%; p < 0.05) and neutrophilia (38.2% and 31.5%; p < 0.05) without an additional effect due to caffeine (p > 0.05). Also, serum CK and LDH activity were enhanced by exercise in both groups (p < 0.05), without a synergistic effect of caffeine. ALT, AST, AP and γ-GT serum activity was not modulated by exercise or caffeine. The findings demonstrate that white cells and muscle damage markers increase after intense intermittent exercise, but acute caffeine supplementation has no influence on immune responses or muscle cellular integrity.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".