Proteinase‐activated receptor‐2 (PAR<sub>2</sub>) and mouse osteoblasts: Regulation of cell function and lack of specificity of PAR<sub>2</sub>‐activating peptides
Bibliographic record
Abstract
1. Using synthetic proteinase-activated receptor-2 (PAR(2))-activating peptides (PAR(2)APs) corresponding to the tethered ligand domain of the extracellular N-terminus of PAR(2) to mimic the actions of activating proteinases and using primary cultures of calvarial osteoblasts derived from both wild-type (WT) and PAR(2)-null (KO) mice, we investigated the potential role of PAR(2) in regulating osteoblast function. 2. Primary calvarial osteoblasts from WT and KO mice were evaluated for their growth kinetics and mineralization in the absence of PAR(2) agonists and for their responses in a variety of functional assays to the PAR(2)APs Ser-Leu-Ile-Gly-Arg-Leu-amide (SLIGRL-NH(2)) and 2-furoyl-Leu-Ile-Gly-Arg-Leu-Orn-amide (2-fLIGRLO-NH(2)), as well as to trypsin. 3. In contrast with WT cells, PAR(2)-KO osteoblasts did not exhibit increased collagen Type I mRNA expression in response to SLIGRL-NH(2). When grown in serum-containing medium, KO cells increased in number more rapidly than WT cells, an effect that could be attributed to decreased apoptosis rather than increased proliferation. Surprisingly, in both WT and KO osteoblasts, the two PAR(2)APs induced mobilization of intracellular calcium stores. Similarly, the PAR(2)APs inhibited serum deprivation-induced apoptosis and parathyroid hormone-, 1,25-dihydroxyvitamin D(3)- or interleukin-11-induced mineralization in WT and KO cells. 4. We conclude that PAR(2) plays a role in osteoblast survival and collagen Type I mRNA induction and that osteoblasts can respond to the PAR(2)APs via both PAR(2)-dependent and -independent mechanisms.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".