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Domain of hCLCA1 that is responsible for macrophage activation

2016· article· en· W4389027340 on OpenAlexafffund
John C.H. Ching, Matthew E. Loewen

Bibliographic record

VenueThe FASEB Journal · 2016
Typearticle
Languageen
FieldMedicine
TopicAsthma and respiratory diseases
Canadian institutionsUniversity of Saskatchewan
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsInflammationCell biologyMacrophageImmune systemImmunologyBiologyChemistryIn vitroBiochemistry

Abstract

fetched live from OpenAlex

The CLCA gene family produces both secreted and membrane‐associated proteins that modulate ion‐channel function, drive mucus production and have a poorly understood pleiotropic effect on airway inflammation. The mechanism of how hCLCA1 regulates airway inflammation remains unclear. However, hCLCA1 is highly expressed in airway diseases in which alveolar macrophages’ are central to disease progression. Thus, it is possible that hCLCA1's role in airway inflammation is to regulate the immune response of macrophages. Previously, we showed that secreted hCLCA1 is able to activate macrophages, inducing them to express cytokines and to undertake a pivotal role in airway inflammation. In this study, we have purified the functional domains of hCLCA1, and we activated macrophages with these domains. There are three functional domains in hCLCA1 (hydrolase domain, von Willebrand type A domain, and fibronectin type III domain), and we found that one of these hCLCA1's domains significantly induced the expression of pro‐inflammatory cytokines in macrophages. To further investigate this activation, we have also found the cell signaling pathways that are involved in this activation Support or Funding Information Natural Sciences and Engineering Research Council (NSERC) 371364/2010 to MEL

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.006
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

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)0.0060.001

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 head0.026
GPT teacher head0.289
Teacher spread0.263 · 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 source (direct Gemma or distilled Codex), 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
Published2016
Admission routes2
Has abstractyes

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