Bibliographic record
Abstract
Eosinophil granules store a vast array of cytokines and chemokines, many of which possess opposing activities. Specific stimuli can induce the release of entire granules or selective mediators through a process termed piecemeal degranulation. Until recently, the mechanisms that governed the decision to opt for either of these processes were unknown. Recent research has identified a mechanism through which differential secretion occurs during piecemeal degranulation. Eotaxin stimulation of eosinophils induces the selective mobilization of the granule-stored alpha chain of the interleukin-4 (IL-4) receptor into secretory vesicles. This process selectively recruits IL-4 to these vesicles and facilitates its differential secretion. There is also recent evidence for a mechanism of differential mobilization and membrane fusion of secretory vesicles versus granules. These two compartments possess a different set of SNARE and Rab molecules as vesicle fusion and transport-docking proteins, respectively. This presumably allows differential regulation of the processes of mobilization and plasma membrane fusion. These findings provide a model to explain the mechanism by which eosinophils, and likely many other cell types, differentially secrete cytokines and chemokines.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".