Proteinase-activated Receptor-1 is an Anti-Inflammatory Signal for Colitis Mediated by a Type 2 Immune Response
Bibliographic record
Abstract
BACKGROUND: Activation of colonic proteinase activated receptor-1 (PAR1) provokes colonic inflammation and increases mucosal permeability in mice. The mechanism of inflammation is not neurogenic like in the paw of rats but depends on PAR1-mediated activation monocytic cells. PAR1 activation in the colon increases the release of lymphocyte T helper-1 (TH1) cytokines. Moreover, PAR1 expression is increased in biopsies from patients with inflammatory bowel disease, and its activation during TH1-mediated colitis in mice increases all of the hallmarks of inflammation. METHODS: This study aimed to characterize the effects of PAR1 activation in oxazolone-mediated colitis, involving a TH2 cytokine profile. RESULTS: Intracolonic administration of oxazolone increased myeloperoxidase activity, damage score, and interleukin (IL)-4, IL-10, tumor necrosis factor alpha, and IL-1beta mRNA expression but lowered interferon-gamma mRNA expression, indicating colonic inflammation of a TH2 profile. The concurrent intracolonic administration of a PAR1 agonist in oxazolone-treated mice inhibited colitis, resulting in a reduction of myeloperoxidase activity, damage score, and inflammatory cytokine mRNA expression. Using PAR1-deficient mice, we confirmed that the anti-inflammatory effects of PAR1 agonists were mediated by PAR1. Moreover, in PAR1-deficient mice or in mice treated with a PAR1 antagonist, oxazolone-induced colitis was exacerbated, showing an endogenous modulatory role for PAR1 in this TH2 cytokine profile of colitis. CONCLUSIONS: Thus, as opposed to a previously shown proinflammatory role for PAR1 in a TH1 cytokine-mediated colitis, our new data show anti-inflammatory role for PAR1 activation in the setting of TH2 cytokine colitis model.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".