Serum S100B level increases after running but not cycling exercise
Bibliographic record
Abstract
The objective of this study was to investigate the effect of running versus cycling exercises upon serum S100B levels and typical markers of skeletal muscle damage such as creatine kinase (CK), aspartate aminotransferase (AST) and myoglobin (Mb). Although recent work demonstrates that S100B is highly expressed and exerts functional properties in skeletal muscle, there is no previous study that tries to establish a relationship between muscle damage and serum S100B levels after exercise. We conducted a cross-sectional study on 13 male triathletes. They completed 2 submaximal exercise protocols at anaerobic threshold intensity. Running was performed on a treadmill with no inclination (RUN) and cycling (CYC) using a cycle-simulator. Three blood samples were taken before (PRE), immediately after (POST) and 1 h after exercise for CK, AST, Mb and S100B assessments. We found a significant increase in serum S100B levels and muscle damage markers in RUN POST compared with RUN PRE. Comparing groups, POST S100B, CK, AST and Mb serum levels were higher in RUN than CYC. Only in RUN, the area under the curve (AUC) of serum S100B is positively correlated with AUC of CK and Mb. Therefore, immediately after an intense exercise such as running, but not cycling, serum levels of S100B protein increase in parallel with levels of CK, AST and Mb. Additionally, the positive correlation between S100B and CK and Mb points to S100B as an acute biomarker of muscle damage after running exercise.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".