Proteinase-Activated Receptor-2-Induced Colonic Inflammation in Mice: Possible Involvement of Afferent Neurons, Nitric Oxide, and Paracellular Permeability
Bibliographic record
Abstract
Activation of colonic proteinase-activated receptor-2 (PAR-2) provokes colonic inflammation and increases mucosal permeability in mice. The mechanism of inflammation is under debate and could be neurogenic and/or the consequence of tight-junction opening with passage of exogenous pathogens into the lamina propria. The present study aimed to further characterize the inflammatory effect of PAR-2 activation by investigating: 1) the role of NO, 2) the role of afferent neurons, and 3) a possible cause and effect relationship between colonic paracellular permeability changes and mucosal inflammation. Thus, intracolonic infusion to mice of the PAR-2-activating peptide, SLIGRL, increased both myeloperoxidase (MPO) activity and damage scores indicating colonic inflammation, and enhanced colonic permeability to (51)Cr-EDTA from 2 to 4 h after its infusion. NO synthase inhibitors, L-NAME and aminoguanidine, as well as the neurotoxin capsaicin and NK1, calcitonin gene-related peptide (CGRP) receptor antagonists, SR140333 and CGRP(8-37), prevented SLIGRL-induced MPO and damage score increases and permeability. In contrast, although the tight-junction blocker, 2,4,6-triaminopyrimidine, and the myosin L chain kinase inhibitor, ML-7, prevented SLIGRL-induced increase in permeability, they did not prevent MPO and damage score increases. Taken together our data show that both NO and capsaicin-sensitive afferent neurons are involved in PAR-2-mediated colonic inflammation and paracellular permeability increase. Nevertheless, the inflammation process is not a consequence of increased permeability which results at least in part from the activation of myosin L chain kinase.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".