Cross-sectional analysis of blood leukocyte responsiveness to interleukin-10 and interleukin-6 across age and physical activity level
Bibliographic record
Abstract
Objectives: To determine how the anti-inflammatory actions of interleukin-10 (IL-10) and IL-6 differ across age and physical activity levels. Methods: , >300 min of weekly MVPA) individuals and analyzed for leukocyte counts, IL-10 and IL-6-related signaling, and cytokine secretion Results: Total white blood cells and monocytes were similar between groups (p=0.8) but YA and OA had lower lymphocyte counts than YI (p<0.01). The ability of IL-10 (1 ng/mL) to phosphorylate signal transducer and activator of transcription 3 (STAT3) in CD14 monocytes was greater in YA vs. YI (p<0.03) despite YA having lower IL-10 receptor expression (p<0.01). IL-6 (10 ng/mL) mediated STAT3 phosphorylation in CD4 lymphocytes was higher in OA compared YI (p<0.01), with a similar tendency observed for YA vs. YI (p=0.08). Despite enhanced responsiveness of STAT3 to IL-10/6 in active individuals, the ability of IL-10/6 to inhibit tumor necrosis factor-alpha (TNF-⍺) secretion from lipopolysaccharide-stimulated whole-blood was similar between groups. Conclusions: Highly active younger and older individuals demonstrate enhanced IL-10- and IL-6-mediated activation of immune cell STAT3. Although the ability of IL-10/6 to inhibit TNF-⍺ secretion appeared unimpacted by activity level, anti-inflammatory cytokine actions were preserved in older active individuals.
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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 | 0.001 |
| 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".