Prostaglandin E1 dose-dependently promotes stability of atherosclerotic plaque in a rabbit model
Bibliographic record
Abstract
This study evaluated the effect of prostaglandin E1 (PGE1) on the stability of atherosclerotic plaque. A vulnerable plaque model was established in rabbits, using balloon injury combined with a high-cholesterol diet. The rabbits were distributed into a control group, a low-dose PGE1 treatment group, a moderate-dose PGE1 treatment group, a high-dose PGE1 treatment group, and a simvastatin treatment group, with treatments lasting for 4 weeks. At week 13 (at the end of the experiments), atherosclerotic plaque was triggered by injection of Russell's viper venom (Chinese) and histamine. Serological, pathological, immunohistochemical, and gene-expression studies were subsequently performed. PGE1 treatment did not alter serum lipid levels; however, PGE1 dose-dependently increased the thickness of the fibrous caps, and decreased the plaque vulnerability index. The plaque contents of macrophage- and the mRNA levels of monocyte-chemotactic protein-1, matrix metalloproteinase-1, and matrix metalloproteinase-9 were markedly reduced in all of the PGE1 treatment groups, with the high-dose of PGE1 being more effective than the simvastatin treatment. These findings suggest that PGE1 dose-dependently enhances the stability of atherosclerotic plaque. The high-dose of PGE1 presented more protection in terms of inhibiting macrophage accumulation and inflammatory expression in plaque. Our findings suggest a novel drug for the treatment of atherosclerosis.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".