Evidence that protease activated receptor 2 expression is enhanced in human coronary atherosclerotic lesions
Bibliographic record
Abstract
AIM: To investigate protease activated receptor 2 (PAR-2) expression in human coronary atherosclerotic lesions because PAR-2 is involved in the modulation of inflammatory events and vascular function. METHODS: An immunohistochemical analysis was performed on serial arterial sections, using the following antibodies: MDA2, a murine monoclonal antibody against malondialdehyde lysine epitopes of oxidised low density lipoprotein (oxLDL); HAM-56, a monoclonal antibody against human macrophages/foam cells; B5, a rabbit polyclonal antibody against PAR-2; and SAM11, a mouse monoclonal antibody against human PAR-2. Sections containing at least one lesion showing substantial immunostaining were counted as positive, and results were expressed as per cent of all sections of the same artery. RESULTS: PAR-2 expression was enhanced in human coronary atherosclerotic lesions. This phenomenon correlated with an increase in oxLDL epitopes in the coronary artery. CONCLUSION: This study shows for the first time that PAR-2 expression is enhanced in human coronary atherosclerotic lesions, and suggests that PAR-2 dependent cellular trafficking may be one of the regulatory signalling responses to vascular injury. Further pharmacological studies will establish whether modulation (and in which direction) of PAR-2 represents a possible therapeutic target for controlling the vascular response to injury.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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 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".