The Effects of Pretreatment with Atorvastatin, Fenofibrate, or Both Drugs in a Mouse Model of Acute Lipemia Induced by the General Lipase Inhibitor Poloxamer 407
Bibliographic record
Abstract
Abstract Dyslipidemia is a well-known risk factor for the development of cardiovascular diseases and atherosclerosis. The effects of combined pretreatment with atorvastatin and fenofibrate (Tricor) were studied in a mouse model of acute lipemia induced by a general lipase inhibitor, poloxamer 407 (P-407, 250 mg/kg). This lipemia is characterized by significantly increased serum levels of triglycerides (TG), low-density lipoprotein (LDL) cholesterol, together with decreased concentration of high-density lipoprotein (HDL) cholesterol. Atorvastatin pretreatment had a hypolipidemic effect, decreasing concentrations of LDL cholesterol and increasing HDL cholesterol. Pretreatment of mice with fenofibrate decreased TG level, increasing HDL cholesterol. Combined pretreatment with atorvastatin and fenofibrate decreased TG and total cholesterol. Elevation of the serum cystatin C level was found in control and lipemic mice pretreated with atorvastatin, fenofibrate, or both. Liver expression of lysosomal acid lipase increased in atorvastatin- or/and fenofibrate-pretreated groups of lipemic mice. It was concluded that increased expression of lysosomal acid lipase is related to the removal of lipid droplets from hepatocytes, thus preventing acute lipemia. Lastly, cystatin C may be a “theranostic” biomarker for hypolipidemic drugs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".