Cytokine Diversity in Human Peripheral Blood Eosinophils: Profound Variability of IL-16
Bibliographic record
Abstract
Abstract Eosinophilic leukocytes develop in the bone marrow and migrate from peripheral blood to tissues, where they maintain homeostasis and promote dysfunction via release of preformed immunomodulatory mediators. In this study, we explore human eosinophil heterogeneity with a specific focus on naturally occurring variations in cytokine content. We found that human eosinophil-associated cytokines varied on a continuum from minimally (coefficient of variation [CV] ≤ 50%) to moderately variable (50% < CV ≤ 90%). Within the moderately variable group, we detected immunoreactive IL-27 (953 ± 504 pg/mg lysate), a mediator not previously associated with human eosinophils. However, our major finding was the distinct and profound variability of eosinophil-associated IL-16 (CV = 103%). Interestingly, eosinophil IL-16 content correlated directly with body mass index (R2 = 0.60, ***p < 0.0001) in one donor subset. We found no direct correlation between eosinophil IL-16 content and donor age, sex, total leukocytes, lymphocytes, or eosinophils (cells per microliter), nor was there any relationship between IL-16 content and the characterized −295T/C IL-16 promoter polymorphism. Likewise, although eosinophil IL-1β, IL-1α, and IL-6 levels correlated with one another, there was no direct association between any of these cytokines and eosinophil IL-16 content. Finally, a moderate increase in total dietary fat resulted in a 2.7-fold reduction in eosinophil IL-16 content among C57BL/6-IL5tg mice. Overall, these results suggest that relationships between energy metabolism, eosinophils, and IL-16 content are not direct or straightforward. Nonetheless, given our current understanding of the connections between asthma and obesity, these findings suggest important eosinophil-focused directions for further exploration.
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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".