Acute long-distance trail running increases serum IL-6, IL-15, and Hsp72 levels
Bibliographic record
Abstract
Interleukin-6 (IL-6), IL-15, and heat shock protein 72 (Hsp72) are molecules that have significant metabolic effects on glucose and fat metabolism and a cell’s stress response. The aim of this study is to determine serum levels of these molecules in runners after a long-distance trail run. Serum IL-15 levels after such endurance events have not been investigated yet. Blood samples were collected from 37 athletes (11 female, 26 male) before and after a 35-km trail run, with a total climb of 940 m. Serum was obtained from the samples, and IL-6, IL-15, and Hsp72 levels were measured from using the sandwich ELISA method. The athletes completed the race in 308.3 ± 37.4 min on average. After the race, the mean serum IL-6, IL-15, and Hsp72 concentrations increased 13.2-fold, 2.22-fold, and 1.6-fold, respectively (p < 0.001, p < 0.001, and p = 0.039, respectively). This is the first study to demonstrate the increase in serum IL-15 levels following an acute endurance exercise. In addition to IL-15, we report that IL-6 and soluble Hsp72 levels also increased significantly following a 35-km trail run. Since these molecules are involved in regulating glucose and fat metabolism, significant increases of IL-6, IL-15, and soluble Hsp72 may have health benefits that may be associated with long-distance trail runs, which are becoming more popular worldwide.
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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 | 0.000 | 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 | 0.001 |
| 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".