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Record W2346951180 · doi:10.1152/ajpgi.00435.2015

Mechanisms of innate immune activation by gluten peptide p31-43 in mice

2016· article· en· W2346951180 on OpenAlexafffund
Romina E. Araya, María Florencia Gómez Castro, Paula Carasi, Justin L. McCarville, Jennifer Jury, Allan McI. Mowat, Elena F. Verdú, Fernando G. Chirdo

Bibliographic record

VenueAmerican Journal of Physiology-Gastrointestinal and Liver Physiology · 2016
Typearticle
Languageen
FieldMedicine
TopicCeliac Disease Research and Management
Canadian institutionsMcMaster UniversityPopulation Health Research Institute
FundersCanadian Institutes of Health ResearchConsejo Nacional de Investigaciones Científicas y Técnicas
KeywordsInnate immune systemTLR3TLR4BiologyEnteropathyTLR7Immune systemImmunologyPathogenesisInflammationImmunityInnate lymphoid cellToll-like receptorCell biologyDiseaseMedicine

Abstract

fetched live from OpenAlex

Celiac disease (CD) is an immune-mediated enteropathy triggered by gluten in genetically susceptible individuals. Innate immunity contributes to the pathogenesis of CD, but the mechanisms remain poorly understood. Although previous in vitro work suggests that gliadin peptide p31-43 acts as an innate immune trigger, the underlying pathways are unclear and have not been explored in vivo. Here we show that intraluminal delivery of p31-43 induces morphological changes in the small intestinal mucosa of normal mice consistent with those seen in CD, including increased cell death and expression of inflammatory mediators. The effects of p31-43 were dependent on MyD88 and type I IFNs, but not Toll-like receptor 4 (TLR4), and were enhanced by coadministration of the TLR3 agonist polyinosinic:polycytidylic acid. Together, these results indicate that gliadin peptide p31-43 activates the innate immune pathways in vivo, such as IFN-dependent inflammation, relevant to CD. Our findings also suggest a common mechanism for the potential interaction between dietary gluten and viral infections in the pathogenesis of CD.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.919
Threshold uncertainty score0.466

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 studies0.0000.001
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 head0.007
GPT teacher head0.242
Teacher spread0.235 · 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

Citations50
Published2016
Admission routes2
Has abstractyes

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