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Record W2754574717 · doi:10.1111/all.13313

Proteinase‐activated receptor‐2 blockade inhibits changes seen in a chronic murine asthma model

2017· article· en· W2754574717 on OpenAlexafffund
Muhammad Asaduzzaman, C. Davidson, Drew Nahirney, Yahya Fiteih, Lakshmi Puttagunta, Harissios Vliagoftis

Bibliographic record

VenueAllergy · 2017
Typearticle
Languageen
FieldMedicine
TopicBlood Coagulation and Thrombosis Mechanisms
Canadian institutionsUniversity of Alberta
FundersCanadian Institutes of Health ResearchAlberta Innovates - Health Solutions
KeywordsInflammationBronchoalveolar lavageImmunologyMedicineAllergic inflammationAsthmaLungReceptorAirwayInternal medicineAnesthesia

Abstract

fetched live from OpenAlex

Abstract Background Proteinase‐Activated Receptor‐2 ( PAR 2 ) is a G protein‐coupled receptor activated by serine proteinases. We have shown that PAR 2 activation in the airways is involved in the development of allergic inflammation and airway hyperresponsiveness ( AHR ) in acute murine models. We hypothesized that functional inhibition of PAR 2 prevents allergic inflammation, AHR and airway remodeling in chronic allergic airway inflammation models. Material and Methods We developed and used a 12 week model of cockroach extract ( CE )‐mediated AHR , airway inflammation and remodeling in BALB /c mice. Results Mice sensitized and challenged with CE for 12 weeks exhibit AHR , increased numbers of eosinophils in bronchoalveolar lavage ( BAL ) and increased collagen content in the lung tissue compared to saline controls. Administration of an anti‐ PAR 2 antibody, SAM ‐11, after the initial development of airway inflammation significantly inhibited all these parameters. Conclusions Our data demonstrate that PAR 2 signaling plays a key role in CE ‐induced AHR and airway inflammation/remodeling in long term models of allergic airway inflammation. Targeting PAR 2 activation may be a successful therapeutic strategy for allergic asthma.

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)0.000
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.050
Threshold uncertainty score0.772

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.037
GPT teacher head0.292
Teacher spread0.255 · 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

Citations22
Published2017
Admission routes2
Has abstractyes

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