Argan Oil as an Effective Nutri-Therapeutic Agent in Metabolic Syndrome: A Preclinical Study
Bibliographic record
Abstract
The present study aims at examining the effects of argan oil on the three main cardiovascular risk factors associated with metabolic syndrome (hypertension, insulin resistance and obesity) and on one of its main complications, neuropathic pain. Male Sprague-Dawley rats had free access to a drinking solution containing 10% d-glucose or tap water for 12 weeks. The effect of argan oil was compared to that of corn oil given daily by gavage during 12 weeks in glucose-fed rats. Glucose-fed rats showed increases in systolic blood pressure, epididymal fat, plasma levels of triglycerides, leptin, glucose and insulin, insulin resistance, tactile and cold allodynia in association with a rise in superoxide anion production and NADPH oxidase activity in the thoracic aorta, epididymal fat and gastrocnemius muscle. Glucose-fed rats also showed rises in B₁ receptor protein expression in aorta and gastrocnemius muscle. Argan oil prevented or significantly reduced all those anomalies with an induction in plasma adiponectin levels. In contrast, the same treatment with corn oil had a positive impact only on triglycerides, leptin, adiponectin and insulin resistance. These data are the first to suggest that argan oil is an effective nutri-therapeutic agent to prevent the cardiovascular risk factors and complications associated with metabolic syndrome.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".