Effect of docosahexaenoic acid monoacylglyceride on systemic hypertension and cardiovascular dysfunction
Bibliographic record
Abstract
ω-3 Fatty acid supplementation has been associated with lower blood pressure. Cardiovascular diseases are also known to be linked directly to an increase in ω-6 and a reduction in ω-3 fatty acid levels in blood circulation and tissues. To determine the effect of docosahexaenoic acid monoglycerides (MAG-DHA) on blood pressure, lipid profiles, and vascular remodeling in rats fed a high-fat/high-carbohydrate (HFHC) diet. Studies were performed in male rats subjected to 8 wk of HFHC diet supplemented or not with 3 g/day MAG-DHA. After 8 wk of daily MAG-DHA treatment, rats in the HFHC + MAG-DHA group had lower arterial blood pressure and heart rate compared with the HFHC group. Moreover, MAG-DHA prevented the increase aortic wall thickness, whereas lipid analysis of aortic tissues revealed an increase in DHA/AA ratio correlated with the production of resolvin D2 and D3 metabolites. Histological analysis revealed that MAG-DHA prevented the development of LVH in the HFHC group. Serum lipid profile analysis further showed a decrease in total cholesterol (TC) and LDL, including very low-density lipoprotein (VLDL) and triglyceride (TG) levels, together with an increase in HDL levels after 8 wk of MAG-DHA treatment compared with the HFHC group. Furthermore, daily MAG-DHA treatment resulted in reduced proinflammatory marker levels such as CRP, IL-6, TNFα, and IL-1β. Altogether, these findings revealed that per os administration of MAG-DHA prevents HFHC-diet induced hypertension and LVH in rats.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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) | 0.002 | 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".