PAR2 (Protease-Activated Receptor 2) Deficiency Attenuates Atherosclerosis in Mice
Bibliographic record
Abstract
Objective— PAR2 (protease-activated receptor 2)-dependent signaling results in augmented inflammation and has been implicated in the pathogenesis of several autoimmune conditions. The objective of this study was to determine the effect of PAR2 deficiency on the development of atherosclerosis. Approach and Results— PAR2 mRNA and protein expression is increased in human carotid artery and mouse aortic arch atheroma versus control carotid and aortic arch arteries, respectively. To determine the effect of PAR2 deficiency on atherosclerosis, male and female low-density lipoprotein receptor–deficient ( Ldlr −/− ) mice (8–12 weeks old) that were Par2 +/+ or Par2 −/− were fed a fat- and cholesterol-enriched diet for 12 or 24 weeks. PAR2 deficiency attenuated atherosclerosis in the aortic sinus and aortic root after 12 and 24 weeks. PAR2 deficiency did not alter total plasma cholesterol concentrations or lipoprotein distributions. Bone marrow transplantation showed that PAR2 on nonhematopoietic cells contributed to atherosclerosis. PAR2 deficiency significantly attenuated levels of the chemokines Ccl2 and Cxcl1 in the circulation and macrophage content in atherosclerotic lesions. Mechanistic studies using isolated primary vascular smooth muscle cells showed that PAR2 deficiency is associated with reduced Ccl2 and Cxcl1 mRNA expression and protein release into the supernatant resulting in less monocyte migration. Conclusions— Our results indicate that PAR2 deficiency is associated with attenuation of atherosclerosis and may reduce lesion progression by blunting Ccl2 - and Cxcl1 -induced monocyte infiltration.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".