The Effects of Atorvastatin on Protein Kinase C and C-reactive Protein in Experimental Atherosclerosis
Bibliographic record
Abstract
Objective To reveal the protective effects of atorvastatin against atherosclerosis independent of cholesterol-lowering effect, we investigated the effects of atorvastation on the expression of protein kinase C (PKC) and C-reactive protein in experimental atherosclerosis of rats. Method Fifty female Sprague-Dawley rats were randomly divided into normal diet group (n=10, control group), vitamin D3 injection and high cholesterol diet group (n=40). After 8 weeks, vitamin D3 injection and high cholesterol diet rats were randomized to receive either atorvastatin (5 mg•kg^(-1)•d^(-1)) (n=20, atorvastatin group) or normal diet (n=20, model group). Another eight weeks later, all rats were killed and part of their aortas were examined by light and electron microscope and the left were removed for western blot analysis to measure PKC; At the begin and end of experiment, serumcollected for lipid and C-reactive protein determining determination. Results Cholesterol, low-density lipoprotein, triglyceride levels in atorvastatin group were significantly lower than those in model group but higher than control group. The pathologic changes in atorvastatin group were less severe than those in model group, there showed no any pathological changes in control group. The levels of C-reactive protein in model group [(18.64±0.94) mg/L] were higher than those in control group [(9.21±0.21) mg/L] (P＜0.05). C-reactive protein levels also differed significantly between control and atorvastatin group (12.52±0.65 mg/L) (P＜0.05). PKC levels were significantly higher in model group (7786.12 ± 264.75) and atorvastatin group (4267.57 ± 233.94) than in control group (2468.75 ± 145.53)(all P＜0.05). But compared with model group, PKC levels were markedly lower in the atorvastatin group (P＜0.01). Conclusions Atorvastatin may be useful not only as a cholesterol-lowering agents but also as anti-arteriosclerotic agent that provide vascular protection by inhibition PKC expression and inflammatory reaction.
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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 | 0.000 | 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 | 0.001 |
| 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".