Coconut oil supplementation and physical exercise improves baroreflex sensitivity and oxidative stress in hypertensive rats
Bibliographic record
Abstract
The hypothesis that oral supplementation with virgin coconut oil (Cocos nucifera L.) and exercise training would improve impaired baroreflex sensitivity (BRS) and reduce oxidative stress in spontaneously hypertensive rats (SHR) was tested. Adult male SHR and Wistar Kyoto rats (WKY) were divided into 5 groups: WKY + saline (n = 8); SHR + saline (n = 8); SHR + coconut oil (2 mL·day(-1), n = 8); SHR + trained (n = 8); and SHR + trained + coconut oil (n = 8). Mean arterial pressure (MAP) was recorded and BRS was tested using phenylephrine (8 μg/kg, intravenous) and sodium nitroprusside (25 μg·kg(-1), intravenous). Oxidative stress was measured using dihydroethidium in heart and aorta. SHR + saline, SHR + coconut oil, and SHR + trained group showed higher MAP compared with WKY + saline (175 ± 6, 148 ± 6, 147 ± 7 vs. 113 ± 2 mm Hg; p < 0.05). SHR + coconut oil, SHR + trained group, and SHR + trained + coconut oil groups presented lower MAP compared with SHR + saline group (148 ± 6, 147 ± 7, 134 ± 8 vs. 175 ± 6 mm Hg; p < 0.05). Coconut oil combined with exercise training improved BRS in SHR compared with SHR + saline group (-2.47 ± 0.3 vs. -1.39 ± 0.09 beats·min(-1)·mm Hg(-1); p < 0.05). SHR + saline group showed higher superoxide levels when compared with WKY + saline (774 ± 31 vs. 634 ± 19 arbitrary units (AU), respectively; p < 0.05). SHR + trained + coconut oil group presented reduced oxidative stress compared with SHR + saline in heart (622 ± 16 vs. 774 ± 31 AU, p < 0.05). In aorta, coconut oil reduced oxidative stress in SHR compared with SHR + saline group (454 ± 33 vs. 689 ± 29 AU, p < 0.05). Oral supplementation with coconut oil combined with exercise training improved impaired BRS and reduced oxidative stress in SHR.
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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) | 0.001 | 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".