Serine Protease Inhibitor Serp-1 Strongly Impairs Atherosclerotic Lesion Formation and Induces a Stable Plaque Phenotype in ApoE <sup>−/−</sup> Mice
Bibliographic record
Abstract
The myxoma virus protein Serp-1 is a member of the serine protease inhibitor superfamily. Serp-1 potently inhibits human serum proteases including plasmin, urokinase-type plasminogen activator (uPA), and tissue-type plasminogen activator (tPA). Serp-1 also displays a high antiinflammatory activity, rendering it a promising candidate for antiatherosclerotic therapy. In this study, we have thus examined the effect of Serp-1 on de novo atherosclerotic plaque formation and on advanced lesions. Perivascular collars were placed around carotid arteries of ApoE-/- mice to induce atherosclerotic plaques and Serp-1 treatment started at week 1 and week 5 after collar placement. Effects of Serp-1 on de novo atherogenesis were characterized by a significantly lower plaque size than that of control mice (18+/-5x10(3) versus 57+/-12x10(3) microm2, respectively; P=0.007). Immunostaining showed a 50% (P=0.004) decrease in the MOMA-2-stained lesion area of Serp-1-treated mice. Treatment of advanced lesions with Serp-1 resulted in a decrease in plaque size and lumen stenosis (P=0.028). Alpha-actin staining of these lesions was significantly increased compared with the control (P=0.017). In both studies, a higher cellularity of the plaque and increased collagen content was observed in Serp-1-treated mice. In vitro studies showed that Serp-1 induces proliferation and migration of vascular smooth muscle cells. In conclusion, Serp-1 inhibits carotid artery plaque growth and progression in ApoE-/- mice. Equally relevant, it enhances cellularity of the plaque core potentially leading to improved plaque stability. The above results indicate that Serp-1 constitutes a promising lead in antiatherosclerotic therapy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".