Folic acid attenuates homocysteine and enhances antioxidative capacity in atherosclerotic rats
Bibliographic record
Abstract
Atherosclerosis is a chronic disease that can seriously endanger human life. Folic acid supplementation modulates several disorders, including atherosclerosis, via its antiapoptotic and antioxidative properties. This study investigated whether folic acid alleviates atherogenesis by restoring homocysteine levels and antioxidative capacity in atherosclerosis Wistar rats. To this end, 28 Wistar rats were randomly divided into 4 groups (7 rats/group) as follows: (i) wild-type group, fed only the AIN-93 semi-purified rodent diet (folic acid: 2.1 mg/kg); (ii) high-fat + folic acid-deficient group (HF+DEF) (folic acid: 0.2 mg/kg); (iii) high-fat + normal folic acid group (folic acid: 2.1 mg/kg); and (iv) high-fat + folic acid-supplemented group (folic acid: 4.2 mg/kg). After 12 weeks, histopathological changes in the atherosclerotic lesions of the aortic arch were determined. In addition, serum folate levels, plasma homocysteine levels, plasma S-adenosyl-homocysteine levels, antioxidant status, oxidant status, and lipid profiles were evaluated. The results show aggravated atherosclerotic lesions in the HF+DEF group. Folic acid supplementation increased concentrations of serum folate. Further, folic acid supplementation increased high-density lipoprotein-cholesterol, decreased plasma homocysteine levels, and improved antioxidant capacity in atherogenic rats. These findings are consistent with the hypothesis that folic acid alleviates atherogenesis by reducing plasma homocysteine levels and improving antioxidant capacity in rats fed a high-fat diet.
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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 | 0.000 |
| 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 | 0.001 |
| 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".