Ameliorative effects of aqueous extract from rosemary on oxidative stress and inflammation pathways caused by a high-fat diet in C57BL/6 mice
Bibliographic record
Abstract
Rosemary is an herb exhibits biological properties, attenuates inflammation, oxidative stress, and improves lipid profile. Here, we evaluated the effects of rosemary aqueous extract (RE) on mice fed with a high-fat diet (HFD). Male C57BL/6 mice were administered a control diet or HFD for 10 weeks. The treated groups received RE in the diet at different concentrations: 25, 250, and 500 mg/100 g. After 10 weeks, serum concentrations of glucose, lipid, insulin, leptin, adiponectin, and cytokines were evaluated and the oxygen radical absorbance capacity was determined. Histological analysis was performed to determine the concentrations of triacylglycerides (TG), total cholesterol, cytokines, and antioxidant enzymes as well as the expression of genes involved in lipid metabolism, oxidative stress, and inflammation. The dietary RE ameliorated HFD-induced weight gain, adipose tissue weight, glucose intolerance, and insulin, leptin, and free fatty acid levels. Reduction in hepatic TG deposition was observed. The levels of inflammatory cytokines decreased, and the expression of genes involved in lipid metabolism increased. RE mitigated oxidative stress and reduced the production of reactive oxygen species in HepG2 and 3T3-L1 cells. Therefore, RE is a potential therapeutic agent for the prevention of inflammation and oxidative stress outcomes associated with obesity.
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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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".