Epithelial effects of proteinase-activated receptors in the gastrointestinal tract
Bibliographic record
Abstract
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.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".