Role and Modulation of CD16 Expression on Eosinophils by Cytokines and Immune Complexes
Bibliographic record
Abstract
BACKGROUND: Blood eosinophils express CD16 on their surface when stimulated in vitro with platelet-activating factor or IFNgamma. Transient expression of CD16 is also observed in vivo following aeroallergen challenge of asthmatic subjects. The present work is aimed at evaluating the possible mechanisms modulating eosinophil expression of CD16 and the biological functions of this receptor. METHODS: First, purified blood eosinophils were incubated with IL-1beta, IL-2, IL-4, IL-5, IL-9 or IL-16, GM-CSF, IFNgamma, eotaxin or 5-oxo-ETE and CD16 expression was measured. Second, the capacity of CD16 to mediate degranulation induced by IgG immune complexes (IC) was evaluated in eosinophils with low and high CD16 expression. Finally, serum allergen-specific IgE and IgG, and total IgE levels were measured at baseline in allergic asthmatics and correlated with changes observed in blood eosinophil CD16 expression (DeltaCD16) following allergen challenge. RESULTS: Only IFNgamma and IL-2 significantly increased the number of CD16+ eosinophils, respectively, 37 +/- 10% (p = 0.0038) and 38 +/- 8% (p = 0.0006), compared to control, 7 +/- 2%. IgG IC induced degranulation in eosinophils with low and high CD16 expression and monoclonal anti-CD16 and anti-CD32 antibodies inhibited this. IgG IC increased eosinophil CD16 expression (14 +/- 6%, p = 0.0008) and this effect was blocked by pretreatment with anti-CD32 antibodies. DeltaCD16 following allergen challenge correlated with the specific IgG/total IgE ratio (r(2) = 0.41, p = 0.036). CONCLUSION: These data suggest that formation of IgG IC is associated with surface eosinophil CD16 expression in asthma and that CD16 in cooperation with CD32 mediates IC-induced degranulation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".