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Record W2091209445 · doi:10.4161/gmic.29100

Polarizing the T helper 17 response in<i>Citrobacter rodentium</i>infection via expression of resistin-like molecule α

2014· article· en· W2091209445 on OpenAlexfundno aff
Gang Chen, Alexander J. Chan, Josiah I. Chung, Jessica Jang, Lisa C. Osborne, Meera G. Nair

Bibliographic record

VenueGut Microbes · 2014
Typearticle
Languageen
FieldImmunology and Microbiology
TopicImmune Response and Inflammation
Canadian institutionsnot available
FundersNational Institute of Allergy and Infectious DiseasesAplastic Anemia and Myelodysplasia Association of Canada
KeywordsCitrobacter rodentiumBiologyCitrobacterEffectorMicrobiologyImmunologyCytokineInflammationMacrophageIntestinal mucosaPathogenImmunityImmune systemEscherichia coliEnterobacteriaceaeMedicineGeneIn vitro

Abstract

fetched live from OpenAlex

Citrobacter rodentium infection is a murine model of pathogenic Escherichia coli infection that allows investigation of the cellular and molecular mechanisms involved in host-protective immunity and bacterial-induced intestinal inflammation. We recently demonstrated that following C. rodentium infection, the absence of Resistin-Like Molecule (RELM) α resulted in attenuated Th17 cell responses and reduced intestinal inflammation with minimal effects on bacterial clearance. In this addendum, we investigated the cytokine modulatory effects of RELMα and RELMα expression in the intestinal mucosa following C. rodentium infection. We show that in addition to promoting Th17 cytokine responses, RELMα inhibits Th2 cytokine expression and Th2-cytokine effector macrophage responses in the C. rodentium-infected colons. Second, utilizing reporter C. rodentium, we examined RELMα expression and macrophage recruitment at the host pathogen interface. We observed infection-induced macrophage infiltration and RELMα expression by intestinal epithelial cells. The influence of infection-induced RELMα on macrophage recruitment in the intestine is discussed.

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 score0.100
Threshold uncertainty score0.506

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 studies0.0000.000
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 head0.006
GPT teacher head0.212
Teacher spread0.206 · 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

Citations8
Published2014
Admission routes1
Has abstractyes

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