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 distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".