MétaCan
Menu
Back to cohort
Record W2057355956 · doi:10.1038/sj.bjp.0705750

Modulation of visceral pain and inflammation by protease‐activated receptors

2004· review· en· W2057355956 on OpenAlexaff
Nathalie Vergnolle

Bibliographic record

VenueBritish Journal of Pharmacology · 2004
Typereview
Languageen
FieldMedicine
TopicBlood Coagulation and Thrombosis Mechanisms
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsProteasesInflammationIrritable bowel syndromeReceptorProtease-activated receptorVisceral painPathophysiologyMediatorNociceptionMedicineBiologyImmunologyInternal medicineBiochemistry

Abstract

fetched live from OpenAlex

The gastrointestinal (GI) tract is exposed to a large array of proteases, under both physiological and pathophysiological conditions. The discovery of G protein-coupled receptors activated by proteases, the protease-activated receptors (PARs), has highlighted new signaling functions for proteases in the GI tract, particularly in the domains of inflammation and pain mechanisms. Activation of PARs by selective peptidic agonists in the intestine or the pancreas leads to inflammatory events and changes in visceral nociception, suggesting that PARs could be involved in the modulation of visceral pain and inflammation. PARs are present in most of the cells that are potentially actors in the generation of irritable bowel syndrome (IBS) symptoms. Activation of PARs interferes with several pathophysiological factors that are involved in the generation of IBS symptoms, such as altered motility patterns, inflammatory mediator release, altered epithelial functions (immune, permeability and secretory) and altered visceral nociceptive functions. Although definitive studies using genetically modified animals, and, when available, pharmacological tools, in different IBS and inflammatory models have not yet confirmed a role for PARs in those pathologies, PARs appear as promising targets for therapeutic intervention in visceral pain and inflammation processes.

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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.975
Threshold uncertainty score0.796

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.027
GPT teacher head0.332
Teacher spread0.306 · 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 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

Citations97
Published2004
Admission routes1
Has abstractyes

Explore more

Same venueBritish Journal of PharmacologySame topicBlood Coagulation and Thrombosis MechanismsFrench-language works237,207