Amelioration of atherosclerosis in apolipoprotein E-deficient mice by inhibition of lipoprotein-associated phospholipase A2
Bibliographic record
Abstract
PURPOSE: Lipoprotein-associated phospholipase A(2) (Lp-PLA(2)) is involved in the pathogenesis of atherosclerosis, especially in advanced plaques. In the present study, the abilities of darapladib, a selective Lp-PLA(2) inhibitor, and lentivirus-mediated Lp-PLA(2) silencing on inflammation and atherosclerosis in apolipoprotein E-deficient mice were compared. METHODS: Apolipoprotein E-deficient mice were fed on a high-fat diet and a constrictive collar was placed around the left carotid artery to induce plaque formation. The mice were randomly divided into control, negative control (NC), darapladib and RNA interference (RNAi) groups. Eight weeks after surgery, lentivirus-mediated RNAi construct or darapladib were used to decrease the expression of Lp-PLA(2). Plaques were collected five weeks later for histological analysis. Inflammatory gene expression in the atherosclerotic lesions were then determined at the mRNA and protein level. RESULTS: The expression of pro-inflammatory cytokines was significantly reduced in the treatment group, compared to nontreatment group, whereas the plasma concentration of anti-inflammatory cytokines increased markedly. Moreover, our results demonstrated a significant reduction in plaque lipid content, as well as a rise in collagen content following Lp-PLA(2) inhibition. Interestingly, when comparing the two methods of Lp-PLA(2) inhibition, animals treated with Lp-PLA(2) RNAi were found to exhibit lower plaque areas and enhanced improvement of plaque stability as compared with animals treated with darapladib. Darapladib had no attenuating effect on atherosclerotic plaque area. These therapeutic effects were independent of plasma lipoprotein levels. CONCLUSIONS: Lp-PLA(2) inhibition by darapladib or lentivirus-mediated RNAi ameliorated inflammation and atherosclerosis in apolipoprotein E-deficient mice. The effect was more prominent in the RNAi group.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 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".