Roles of neutrophil granule proteins in orchestrating inflammation and immunity
Bibliographic record
Abstract
Neutrophil granulocytes form the first line of host defense against invading pathogens and tissue injury. They are rapidly recruited from the blood to the affected sites, where they deploy an impressive arsenal of effectors to eliminate invading microbes and damaged cells. This capacity is endowed in part by readily mobilizable proteins acquired during granulopoiesis and stored in multiple types of cytosolic granules with each granule type containing a unique cargo. Once released, granule proteins contribute to killing bacteria within the phagosome or the extracellular milieu, but are also capable of inflicting collateral tissue damage. Neutrophil-driven inflammation underlies many common diseases. Research over the last decade has documented neutrophil heterogeneity and functional versatility far beyond their antimicrobial function. Emerging evidence indicates that neutrophils utilize granule proteins to interact with innate and adaptive immune cells and orchestrate the inflammatory response. Granule proteins have been identified as important modulators of neutrophil trafficking, reverse transendothelial migration, phagocytosis, neutrophil life span, neutrophil extracellular trap formation, efferocytosis, cytokine activity, and autoimmunity. Hence, defining their roles within the inflammatory locus is critical for minimizing damage to the neighboring tissue and return to homeostasis. Here, we provide an overview of recent advances in the regulation of degranulation, granule protein functions, and signaling in modulating neutrophil-mediated immunity. We also discuss how targeting granule proteins and/or signaling could be harnessed for therapeutic benefits.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".