Glucagon‐like peptide‐1 receptor expression on human eosinophils and its regulation of eosinophil activation
Bibliographic record
Abstract
BACKGROUND: Glucagon-like peptide-1 (GLP-1) and its receptor are part of the incretin family of hormones that regulate glucose metabolism. GLP-1 also has immune modulatory roles. OBJECTIVES: To measure the expression of the GLP-1 receptor (GLP-1R) on eosinophils and neutrophils in normal and asthmatic subjects and evaluate effects of a GLP-1 analog on eosinophil function. METHODS: Peripheral blood samples were taken from 10 normal and 10 allergic asthmatic subjects. GLP-1R expression was measured on eosinophils and neutrophils. Subsequently, the asthmatic subjects underwent allergen and diluent inhalation challenges, and GLP-1R expression was measured. Purified eosinophils, collected from mild asthmatic subjects, were stimulated with lipopolysaccharide (LPS) and a GLP-1 analog to evaluate eosinophil cell activation markers CD11b and CD69 and cytokine (IL-4, IL-5, IL-8 and IL-13) production. RESULTS: Glucagon-like peptide-1 receptor is expressed on human eosinophils and neutrophils. Eosinophil, but not neutrophil, expression of GLP-1R is significantly higher in normal controls compared to allergic asthmatics. The expression of GLP-1R did not change on either eosinophils or neutrophils following allergen challenge. A GLP-1 analog significantly decreased the expression of eosinophil-surface activation markers following LPS stimulation and decreased eosinophil production of IL-4, IL-8 and IL-13, but not IL-5. CONCLUSION AND CLINICAL RELEVANCE: Glucagon-like peptide-1 receptor is expressed on human eosinophils and neutrophils. A GLP-1 analog attenuates LPS-stimulated eosinophil activation. GLP-1 agonists may have additional adjunctive indications in treating persons with concomitant type 2 diabetes mellitus and asthma.
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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) | 0.002 | 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".