Bibliographic record
Abstract
This review emphasizes the effects of tocotrienols on the risk factors for atherosclerosis, plaque instability and thrombogenesis, and compares these effects with tocopherol. Tocotrienols reduce serum lipids and raise serum HDL-C. Alpha-tocopherol, on the other hand, has no effect on serum lipids. Tocotrienols have greater antioxidant activity than tocopherols. Both reduce the serum levels of C-reactive protein (CRP) and advanced glycation end products, and expression of cell adhesion molecules. The CRP-lowering effects of tocotrienols are greater than tocopherol. Tocotrienols reduce inflammatory mediators, δ-tocotrienol being more potent, followed by γ- and α-tocotrienol. Tocotrienols are antithrombotic and suppress the expression of matrix metalloproteinases. They suppress, regress and slow the progression of atherosclerosis, while tocopherol only suppresses, and has no effect on regression and slowing of progression of atherosclerosis. Tocotrienol reduces risk factors for destabilization of atherosclerotic plaques. There are no firm data to suggest that tocotrienols are effective in reducing the risk of cardiac events in established ischemic heart disease. Alpha-tocopherol is effective in primary prevention of coronary artery disease (CAD), but has no conclusive evidence that it has beneficial effects in patients with established ischemic heart disease. Tocotrienols are effective in reducing ischemia-reperfusion cardiac injury in experimental animals and has the potential to be used in patients undergoing angioplasty, stent implantation and aorto-coronary bypass surgery. In conclusion, experimental data suggest that tocotrienols have a potential for cardiovascular health, but long-term randomized clinical trials are needed to establish their efficacy in primary and secondary prevention of CAD.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".