Elevated Plasma IL-6 Coincides with Activation of STAT3 in PBMC After Acute Resistance Exercise
Bibliographic record
Abstract
INTRODUCTION: Changes in plasma concentrations of anti-inflammatory cytokines, such as interleukin-6 (IL-6) and IL-10, after acute resistance exercise (RE) have been widely explored. Whether observed changes in plasma cytokine concentration correspond to the activation of anti-inflammatory signaling pathways in immune cells after acute RE is unknown. This study aimed to determine if changes in plasma cytokines after acute RE resulted in the activation of anti-inflammatory signaling pathways in peripheral blood mononuclear cells (PBMC). METHODS: Healthy young males (N = 16; age = 23.5 ± 2.7 yr; BMI = 22.4 ± 1.7 kg·m-2) participated in a single session of whole-body RE (4 sets of 4 different exercises at 70% 1-repetition maximum with the last set to failure) and a sedentary control (CON) condition in a randomized crossover design. Blood samples were collected at several time points before and after the exercise bout. RESULTS: Higher plasma IL-6, IL-10, and IL-1 RA concentrations were observed after RE compared with CON. Phosphorylation of STAT3 and protein expression of SOCS3 in PBMC were increased in RE compared with CON. The elevation of plasma IL-6, but not IL-10, coincided with the activation of STAT3 signaling in PBMC. CONCLUSIONS: These results highlight a potential mechanism by which RE may exert anti-inflammatory actions in circulating immune cells.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".