THE EFFECTS OF ROSUVASTATIN EFFECT ON THE KIDNEY IN MALE ALBINO RATS
Bibliographic record
Abstract
Background and Aim: Rosuvastatin is known to competitively inhibit HMG-CoA reductase, selectively and reversibly, this enzyme converts HMG-CoA to mevalonic acid in the cholesterol biosynthesis pathway, which is the rate-limiting step in cholesterol synthesis. Rosuvastatin is one of the most important cholesterol-lowering stanins that has been on the market by AstraZeneca since 2003, with unique pharmacokinetic and pharmacodynamic properties. There are some studies that dealt with the effect of rosuvastatin on the kidneys and were contradictory in their results. Therefore, the aim of the current study was to evaluate the effect of rosuvastatin (pharma science Incorporated company - Montreal/ Canada) on the physiological and histopathological parameters in the kidneys of male albino rats. Material and Methods: Forty adult of albino rats were used in the study. After acclimation, the rats were randomly divided into four groups (8 rats in each group) as follows: The first control group: was given normal saline solution (NS), the second group 10 mg/kg rosuvastatin, the third group was given 20 mg/kg rosuvastatin, while the fourth group was given 40 mg/kg rosuvastatin. The dose was continued for 60 days in a once-daily dosing regimen. After the end of the experiment, chemical analyzes were performed for kidney function, including cystatin C and vitamin D3. Then the rats were dissected, and kidney tissues were taken for histological study
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".