NSE S100B protein blood level assessment during a long-distance trail race
Bibliographic record
Abstract
The acute and chronic consequences of long-distance running on brain function have received little attention. The impact of such a hard-physical burden associated with sleep privation during such events such has never been explored in terms of neuropsychological function and brain damage. METHODS: Blood samples were collected from 4 athletes before, during and at the end of one of two races: Grand Raid de la Réunion 2017 (GRR: 165 km, elevation gain: 9529 m, 2 runners) and Trail de la Bourbon 2017 (TB: 111 km, elevation gain: 6433 m, 2 runners). Serum S100B and NSE levels were measured for each runner before, during and after the race. RESULTS: Serum S100B levels (normal range: < 0.15 μg/L) increased early during the race and remained high up to the end of the race in all 4 runners (range: 0.17-0.59 μg/L). NSE level (normal range: < 15 μg/L) increased in 3 of the 4 runners (range: 16.8-39.2 μg/L). CONCLUSIONS: This preliminary study shows the potential interest of S100B and NSE serum assessment during long-distance races. Further studies are needed to confirm these results and to investigate the origins and significance of this increase in brain injury markers.
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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 | 0.001 | 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".