Sex differences in IL-10’s anti-inflammatory function: greater STAT3 activation and stronger inhibition of TNF-α production in male blood leukocytes ex vivo
Bibliographic record
Abstract
Interleukin-10 (IL-10) inhibits proinflammatory cytokine production in blood leukocytes—an effect mediated by signal transducer and activator of transcription 3 (STAT3) activation. To examine potential sex-based differences in IL-10’s anti-inflammatory function, we treated whole blood from healthy males and females ( n = 16 participants of each sex; age: 28 ± 6 yr; body mass index: 23.5 ± 2.3 kg/m2) with increasing concentrations of IL-10 (1–100 ng/mL) and quantified changes in phosphorylated STAT3 (pSTAT3) in CD14+ monocytes and CD4+ lymphocytes via flow cytometry. In parallel, liposaccharide (LPS)-stimulated whole blood cultures were used to assess sex-based differences in IL-10’s ability to inhibit tumor necrosis factor (TNF)-α production. IL-10 concentration dependently increased pSTAT3 median fluorescent intensity (MFI) in CD14+ and CD4+ cells (main effects of concentration, P < 0.01) with males exhibiting larger changes in pSTAT3 MFI in both cell types (main effects of sex, P < 0.01). Accordingly, IL-10-mediated inhibition of TNF-α production was more pronounced in males (main effect of sex, P < 0.01) with changes in other monocyte-derived cytokines (IL-1β, IL-1RA, and IL-15) also supporting a sexual dimorphism in IL-10 action ( P < 0.05). These sex-based differences were not explained by differences in circulating plasma IL-10 concentrations, basal IL-10 receptor expression in unstimulated CD14+ and CD4+ cells, nor the basal expression of IL-10 signaling proteins (STAT3, SHIP1, and p38 MAPK) in unstimulated peripheral blood mononuclear cells. We conclude that IL-10’s anti-inflammatory function differs between male and female blood leukocytes ex vivo. This sexual dimorphism should be considered in future work investigating IL-10’s anti-inflammatory action in humans as it may represent a mechanism contributing to sex differences in overall immune function.
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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) | 0.004 | 0.001 |
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".