Ketotifen Induces Primary Necrosis of Human Eosinophils
Bibliographic record
Abstract
Eosinophils are considered essential in the pathogenesis of allergy. Reduced eosinophil apoptosis is considered to be a key element in the formation of eosinophilia in allergic conditions. Antihistamines are widely used in the treatment of allergic disorders, but their effects on eosinophil apoptosis are poorly understood. The histamine H1-receptor antagonist, ketotifen, is available orally and as eye drops for the treatment of allergic symptoms. The aim of our study was to investigate the possible effect of ketotifen on constitutive eosinophil apoptosis and on interleukin (IL)-5-mediated eosinophil survival. Isolated peripheral blood eosinophils were cultured with or without the survival-prolonging cytokine IL-5 and ketotifen. Apoptosis was assessed by measuring the relative DNA content and by morphological analysis. Ketotifen was found to reverse eosinophil survival induced by interleukin-5. However, the flow cytometry histogram of DNA in propidium iodide-stained cells was not typical to apoptosis. Morphological analysis of the eosinophils by bright-field microscopy suggested that the effect of ketotifen was due to the induction of primary necrosis rather than apoptosis. Histological assessment of eosinophil ultrastructure by transmission electron microscopy confirmed signs of advanced necrosis. In summary, our results suggest that at clinically relevant drug concentrations, ketotifen induces primary necrosis in IL-5-treated human eosinophils.
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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) | 0.003 | 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".