Intracellular monocyte and serum cytokine expression is modulated by exhausting exercise and cold exposure
Bibliographic record
Abstract
This study tested the hypothesis that exercise elicits monocytic cytokine expression and that prolonged cold exposure modulates such responses. Nine men (age, 24.6 +/- 3.8 y; VO(2 peak), 56.8 +/- 5.6 ml. kg(-1). min(-1)) completed 7 days of exhausting exercise (aerobic, anaerobic, resistive) and underwent three cold, wet exposures (CW). CW trials comprised </=6 h (six 1-h rest-work cycles) exposure to cold (5 degrees C, 20 km/h wind) and wet (5 cm/h rain) conditions. Blood samples for the determination of intracellular and serum cytokine levels and circulating hormone concentrations were drawn at rest (0700), after exercise (approximately 1130), and after CW (~2000). Whole blood was incubated with (stimulated) or without (spontaneous) lipopolysaccharide (LPS; 1 microgram/ml) and stained for CD14 monocyte surface antigens. Cell suspensions were stained for intracellular cytokine expression and analyzed by flow cytometry. The proportion of CD14(+) monocytes exhibiting spontaneous and stimulated intracellular expression of interleukin (IL)-1beta, IL-6, and tumor necrosis factor (TNF)-alpha increased after exercise, but these cells produced less IL-1beta and TNF-alpha after CW when CW was preceded by exhausting exercise. Serum cytokine concentrations followed a parallel trend. These findings suggest that blood monocytes contribute to exercise-induced cytokinemia and that cold exposure can differentially modulate cytokine production, upregulating expression of IL-6 and IL-1 receptor antagonist but downregulating IL-1beta and TNF-alpha. The cold-induced changes in cytokine expression appear to be linked to enhanced catecholamine secretion associated with cold exposure.
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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) | 0.001 | 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".