Eosinophil granules function extracellularly as receptor-mediated secretory organelles
Bibliographic record
Abstract
Intracellular granules in several types of leukocytes contain preformed proteins whose secretions contribute to immune and inflammatory functions of leukocytes, including eosinophils, cells notably associated with asthma, allergic inflammation, and helminthic infections. Cytokines and chemokines typically elicit extracellular secretion of granule proteins by engaging receptors expressed externally on the plasma membranes of cells, including eosinophils. Eosinophil granules, in addition to being intracellular organelles, are found as intact membrane-bound structures extracellularly in tissue sites of eosinophil-associated diseases. Neither the secretory capacities of cell-free eosinophil granules nor the presence of functional cytokine and chemokine receptors on membranes of leukocyte granules have been recognized. Here, we show that granules of human eosinophils express membrane receptors for a cytokine, IFN-gamma, and G protein-coupled membrane receptors for a chemokine, eotaxin, and that these receptors function by activating signal-transducing pathways within granules to elicit secretion from within granules. Capacities of intracellular granule organelles to function autonomously outside of eosinophils as independent, ligand-responsive, secretion-competent structures constitute a novel postcytolytic mechanism for regulated secretion of eosinophil granule proteins that may contribute to eosinophil-mediated inflammation and immunomodulation.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".