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Record W1999517476 · doi:10.1186/1710-1492-2-3-98

Mechanisms of Degranulation in Neutrophils

2006· article· en· W1999517476 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueAllergy Asthma and Clinical Immunology · 2006
Typearticle
Languageen
FieldImmunology and Microbiology
TopicNeutrophil, Myeloperoxidase and Oxidative Mechanisms
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsDegranulationChemotaxisInflammationCell biologyImmunologyProtein tyrosine phosphataseBiologyReceptorSignal transductionBiochemistry

Abstract

fetched live from OpenAlex

Neutrophils are critical inflammatory cells that cause tissue damage in a range of diseases and disorders. Being bone marrow-derived white blood cells, they migrate from the bloodstream to sites of tissue inflammation in response to chemotactic signals and induce inflammation by undergoing receptor-mediated respiratory burst and degranulation. Degranulation from neutrophils has been implicated as a major causative factor in pulmonary disorders, including severe asphyxic episodes of asthma. However, the mechanisms that control neutrophil degranulation are not well understood. Recent observations indicate that granule release from neutrophils depends on activation of intracellular signalling pathways, including beta-arrestins, the Rho guanosine triphosphatase Rac2, soluble NSF attachment protein (SNAP) receptors, the src family of tyrosine kinases, and the tyrosine phosphatase MEG2. Some of these observations suggest that degranulation from neutrophils is selective and depends on nonredundant signalling pathways. This review focuses on new findings from the literature on the mechanisms that control the release of granule-derived mediators 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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.681
Threshold uncertainty score0.806

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.013
GPT teacher head0.260
Teacher spread0.247 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it