Combined Effects of Rosuvastatin and Exercise on Gene Expression of Key Molecules Involved in Cholesterol Metabolism in Ovariectomized Rats
Bibliographic record
Abstract
The purpose of this study was to investigate the effects of three weeks of rosuvastatin (Ros) treatment alone and in combination with voluntary training (Tr) on expression of genes involved in cholesterol metabolism (LDLR, PCSK9, LRP-1, SREBP-2, IDOL, ACAT-2 and HMGCR) in the liver of eight week-old ovariectomized (Ovx) rats. Sprague Dawley rats were Ovx or sham-operated (Sham) and kept sedentary for 8 weeks under a standard diet. Thereafter, rats were transferred for three weeks in running wheel cages for Tr or kept sedentary (Sed) with or without Ros treatment (5mg/kg/day). Six groups were formed: Sham-Sed treated with saline (Sal) or Ros (Sham-Sed-Sal; Sham-Sed-Ros), Ovx-Sed treated with Sal or Ros (Ovx-Sed-Sal; Ovx-Sed-Ros), Ovx trained treated with Sal or Ros (Ovx-Tr-Sal; Ovx-Tr-Ros). Ovx-Sed-Sal rats depicted higher (P < 0.05) body weight, plasma total cholesterol (TC) and LDL-C, and liver TC content compared to Sham-Sed-Sal rats. In contrast, mRNA levels of liver PCSK9, LDLR, LRP-1 as well as plasma PCSK9 concentrations and protein levels of LRP-1 were reduced (P < 0.01) in Ovx-Sed-Sal compared to Sham-Sed-Sal rats. However, protein levels of LDLR increased (P < 0.05) in Ovx-Sed-Sal compared to Sham-Sed-Sal rats. Treatment of Ovx rats with Ros increased (P < 0.05) mRNA and protein levels of LRP-1 and PCSK9 but not mRNA levels of LDLR, while its protein abundance was reduced at the level of Sham rats. As a result, plasma LDL-C was not reduced. Exercise alone did not affect the expression of any of these markers in Ovx rats. Overall, Ros treatment corrected Ovx-induced decrease in gene expression of markers of cholesterol metabolism in liver of Ovx rats, but without reducing plasma LDL-C concentrations. Increased plasma PCSK9 levels could be responsible for the reduction of liver LDLR protein abundance and the absence of reduction of plasma LDL-C after Ros treatment.
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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 | 0.000 | 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".