Changes in serum cytokines after repeated bouts of downhill running
Bibliographic record
Abstract
The purpose of this study was to examine changes in serum cytokines after repeated bouts of aerobically biased eccentric exercise. Six untrained males ran down a –13.5% treadmill grade for 60 min on two occasions (RUN1 and RUN2) at a speed equal to 75% of their VO2 peakon a level grade; runs were spaced 14 d apart. Serum was collected before, after, and every hour for 12 h, and every 24 h for 6 d. Cytokines were assessed using 17 multiplex bead technology (Bio-Rad). Creatine kinase (CK) and delayed-onset muscle soreness (DOMS) were assessed before and 24–120 h after. Results were analyzed using a repeated measures analysis of variance (p ≤ 0.05). All comparisons were between RUN1 and RUN2. CK and DOMS were significantly elevated after RUN1 compared with RUN2, indicative of a repeated bout effect. Regarding cytokines, during the initial 12 h period after RUN2, there was a 50% decrease in pro-inflammatory interleukin-6 (IL-6), a 10% decrease in pro-inflammatory macrophage chemotactic protein-1, and a 95% elevation in anti-inflammatory interleukin-10 (IL-10). Regarding 24 h periods, after RUN2 there was an 8% reduction in pro-inflammatory interleukin-8 (IL-8). However, pro-inflammatory macrophage inflammatory factor-1β (MIF-1β) was 18% higher during the 12 h after RUN2. The overall cytokine profile suggests a slight reduction in systemic inflammation after RUN2.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 | 0.000 | 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".