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Epithelial effects of proteinase-activated receptors in the gastrointestinal tract

2005· review· en· W2099979653 on OpenAlexaff
Wallace K. MacNaughton

Bibliographic record

VenueMemórias do Instituto Oswaldo Cruz · 2005
Typereview
Languageen
FieldMedicine
TopicBlood Coagulation and Thrombosis Mechanisms
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsEpitheliumBiologySecretionBarrier functionReceptorGastrointestinal tractIntestinal epitheliumPathologyInflammationCell biologyImmunologyEndocrinologyMedicine

Abstract

fetched live from OpenAlex

The intestinal epithelium plays a crucial role in providing a barrier between the external environment and the internal milieu of the body. A compromised mucosal barrier is characteristic of mucosal inflammation and is a key determinant of the development of intestinal diseases such as Crohn's disease and ulcerative colitis. The intestinal epithelium is regularly exposed to serine proteinases and this exposure is enhanced in numerous disease states. Thus, it is important to understand how proteinase-activated receptors (PARs), which are activated by serine proteinases, can affect intestinal epithelial function. This review surveys the data which demonstrate the wide distribution of PARs, particularly PAR-1 and PAR-2, in the gastrointestinal tract and accessory organs, focusing on the epithelium and those cells which communicate with the epithelium to affect its function. PARs have a role in regulating secretion by epithelia of the salivary glands, stomach, pancreas and intestine. In addition, PARs located on subepithelial nerves, fibroblasts and mast cells have important implications for epithelial function. Recent data outline the importance of the cellular site of PAR expression, as PARs expressed on epithelia may have effects that are countered by PARs expressed on other cell types. Finally, PARs and their ability to promote epithelial cell proliferation are discussed in terms of colon cancer.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.978
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.051
GPT teacher head0.333
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.

Study designOther design
Domainnot available
GenreReview

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

Citations25
Published2005
Admission routes1
Has abstractyes

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