Exercise training protects against an acute inflammatory insult in mouse epididymal adipose tissue
Bibliographic record
Abstract
Exercise training reduces systemic and adipose tissue inflammation. However, these beneficial effects seem to be largely tied to reductions in adipose tissue mass. The purpose of the present study was to determine if exercise training confers a protective effect against an acute inflammatory challenge. We hypothesized that the induction of inflammatory markers, such as interleukin 6 (IL-6), suppressor of cytokine signaling 3 (SOCS3), and TNF-α by the beta-3 adrenergic agonist CL 316,243 would be reduced in adipose tissue from trained mice and this would be associated with reductions in transient receptor potential cation channel 4 (TRPV4), a protein recently shown to regulate the expression of proinflammatory cytokines. Exercise training (4 wk of treadmill running, 1 h/day, 5 days/wk) increased markers of skeletal muscle mitochondrial content and the induction of PPAR-gamma coactivator 1 alpha in epididymal adipose tissue. The mRNA expression of IL-6, SOCS3, and TNFα were not different in subcutaneous and epididymal adipose tissue from sedentary and trained mice; however, the CL 316,243-mediated induction of these genes was attenuated ∼50% in epididymal adipose tissue from trained mice as were increases in plasma IL-6. The effects of training were not explained by reductions in lipolytic responsiveness, but were associated with decreases in TRPV4 protein content. These results highlight a previously unappreciated anti-inflammatory effect of exercise training on adipose tissue immunometabolism and underscores the value of assessing adipose tissue inflammation in the presence of an inflammatory insult.
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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) | 0.001 | 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".