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Record W2036893053 · doi:10.1073/pnas.0409535102

A major role for proteolytic activity and proteinase-activated receptor-2 in the pathogenesis of infectious colitis

2005· article· en· W2036893053 on OpenAlexaff
Kristina K. Hansen, Philip M. Sherman, Laurie Cellars, Patricia Andrade‐Gordon, Zhengying Pan, Amos Baruch, John L. Wallace, Morley D. Hollenberg, Nathalie Vergnolle

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2005
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEscherichia coli research studies
Canadian institutionsHospital for Sick ChildrenUniversity of TorontoUniversity of Calgary
Fundersnot available
KeywordsCitrobacter rodentiumMicrobiologyBiologyProteasesColitisInflammationPathogenesisPathogenReceptorWestern blotBandicootImmunologyMarsupialEnzymeBiochemistry

Abstract

fetched live from OpenAlex

Citrobacter rodentium is a bacterial pathogen that causes a murine infectious colitis equivalent to enterohemorrhagic Escherichia coli infection in humans. Colonic luminal fluid from C. rodentium-infected mice, but not from sham-infected mice, contains active serine proteinases that can activate proteinase-activated receptor-2 (PAR2). We have identified granzyme A and murine trypsins to be present in C. rodentium-infected luminal fluid, as determined by mass spectrometry and Western blot analysis. Inflammatory indices (colonic mucosa macroscopic damage score, increased intestinal wall thickness, granulocyte infiltration, and bacterial translocation from the colonic lumen to peritoneal organs) were all increased in C. rodentium-infected mice, compared with sham-infected mice. Soybean trypsin inhibitor-treated wild-type mice and untreated PAR2-deficient (PAR2-/-) mice (compared with their wild-type littermates) both had substantially reduced levels of C. rodentium-induced inflammation. These data point to an important role for both pathogen-induced host serine proteinases and PAR2 in the setting of infectious colitis.

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)0.001
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 score0.121
Threshold uncertainty score0.188

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.023
GPT teacher head0.304
Teacher spread0.282 · 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

Citations177
Published2005
Admission routes1
Has abstractyes

Explore more

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