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Record W80553248 · doi:10.1096/fasebj.21.5.a180-b

Signalling pathways that regulate human eosinophil migration

2007· article· en· W80553248 on OpenAlexaff
Anick Langlois, Claudine Ferland, Nathalie Pagé, Michel Laviolette

Bibliographic record

VenueThe FASEB Journal · 2007
Typearticle
Languageen
FieldMedicine
TopicEosinophilic Disorders and Syndromes
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsMAPK/ERK pathwayProtein kinase CEosinophilCell biologyMatrigelKinaseChemistrySecretionp38 mitogen-activated protein kinasesProtein kinase AMolecular biologyBiologyBiochemistryImmunologyIn vitro

Abstract

fetched live from OpenAlex

Rationale: To initiate eosinophil migration in vitro, 5‐oxo‐6,8,11,14‐eicosatetraenoic acid (5‐oxo‐ETE), a potent eosinophil chemotactic factor, activates proteolysis, notably by promoting matrix metalloproteinase (MMP)‐9 secretion. Hypothesis : We postulated that protein kinase C (PKC) and mitogen‐activated protein kinase/extracellular signal‐regulated kinase (MAPK/ERK) are involved in 5‐oxo‐ETE‐induced eosinophil migration through extracellular matrix by increasing MMP‐9 secretion. Methods : Purified peripheral blood eosinophils were pre‐incubated with or without different selective PKC inhibitors (isoforms α β: Ro‐31‐8425, isoform δ: Rottelin or PKC ζ blocking peptide), ERK inhibitor (PD98054) or p38 MAPK inhibitor (SB203580) for 30 min at 37°C, followed by addition of 5‐oxo‐ETE. Migration assays using Matrigel, a reconstituted basement membrane, was assessed, and rapid MMP‐9 release (within an hour) was evaluated by zymography. Results and Conclusion : All of these inhibitors decreased by 30 to 90% 5‐oxo‐ETE‐induced eosinophil migration through Matrigel. However, only PKC δ and ERK signalling pathway inhibitors reduced rapid release of MMP‐9, suggesting that the other kinases studied could act on MMP‐9 synthesis, generation of other proteases, adherence or cell motility. More experiments are needed to clarify the mechanisms that regulate eosinophil migration.

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 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)0.001
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.416
Threshold uncertainty score0.397

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.041
GPT teacher head0.271
Teacher spread0.230 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2007
Admission routes1
Has abstractyes

Explore more

Same venueThe FASEB JournalSame topicEosinophilic Disorders and SyndromesFrench-language works237,207