Molecular and morphological characterization of piecemeal degranulation in human neutrophil azurophilic granules
Bibliographic record
Abstract
Mediators pre-stored in neutrophil azurophilic granules are central to the acute inflammatory response and tissue degradation and damage through their proteolytic activity. Different granule populations mobilize and release their content via distinct and hierarchical molecular mechanisms. The molecular mechanisms by which mediators pre-stored in azurophilic granules are mobilized and released to the extracellular space remain largely unknown. We used a number of complementary techniques including; confocal laser scanning microscopy, subcellular fractionation, flow cytometric analyses, Western blot analyses and electron microscopy to examine the ultrastructural and molecular nature of mediator release in neutrophil azurophilic granules. We found that following IL-8 activation, neutrophil azurophilic granules undergo piecemeal degranulation (selective mediator release) leading to altered granule content. Piecemeal degranulation of azurophilic granules is characterized by budding of small secretory vesicles and consequent reduction in granule density. Furthermore, budding of small secretory vesicles and selective mediator mobilization and release from azurophilic granules is associated with reduced localization of CD63, Hck and b-arrestin-1 to granule membranes and also cell surface upregulation of these molecules. Our study is first to identify piecemeal degranulation as a potential underlying mechanism of mediator release from neutrophil azurophilic granules and supports the involvement of CD63, Hck, and b-arrestin-1 in this process.
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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) | 0.001 | 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".