Bibliographic record
Abstract
Neutrophils are granulocytes derived from bone marrow that circulate through the blood and become recruited to tissues during infection or inflammation. They are the most abundant white blood cell and comprise the first line of defence in the innate immune system. However, they are also capable of causing tissue damage in a wide range of diseases. Release of chemotactic signals from inflamed or infected tissues trigger neutrophil migration from the bloodstream to inflammatory foci, where they contribute to inflammation by undergoing receptor-mediated respiratory burst and degranulation. Degranulation from neutrophils has been implicated as a major causative factor in numerous inflammatory diseases. However, the mechanisms that control neutrophil degranulation are not well understood. Recent observations indicate that receptor-mediated granule release from neutrophils depends on activation of distal signaling pathways that include the src family of tyrosine kinases, beta-arrestins, the tyrosine phosphatase MEG2, the kinase MARCK, Rabs and SNAREs, and the Rho GTPase, Rac2. Some of these pathways are specifically required for membrane fusion between the granule and plasma membrane, leading to exocytosis. This review focuses on the understanding of distal molecular mechanisms controlling exocytosis from neutrophils.
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 | 0.000 | 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 | 0.000 | 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".