Antioxidant activity of Tibetan plant remedies used for cardiovascular disease
Bibliographic record
Abstract
Antioxidant activity was measured in 14 plant species incorporated in more than 30% of Tibetan medicines used for cardiovascular disease and related symptoms according to indigenous pharmacopoeias. The study was undertaken in order to explore possible dietary/medicinal elements which may contribute to the reportedly low incidence of cardiovascular disease among Tibetan highlanders despite high hematocrit levels and a high saturated fat/low fruit and vegetable diet. Extracts of Terminalia chebula, Syzygium aromaticum, Aquilaria agallocha, Santalum album, Amomum subulatum, Justicia adhatoda and Myristica fragrans were strong scavengers of the 1,1 diphenyl-2-picryl-hydrazyl (DPPH) radical (P < 0.05). Cu2+-catalyzed low-density lipoprotein (LDL) oxidation was measured in vitro using thiobarbituric acid reactive substances (TBARS) formation and monitoring change in absorbency at 234 rim from conjugated dienes. The hexane fraction of S. aromaticum significantly reduced LDL susceptibility to oxidation (1339.96 +/- 7.01 min. lag time, P < 0.05), more than three times longer than TroloxRTM (431.02 +/- 21.19 min). Results of TBARS (90 min.: r = 0.71, P < 0.005; 180 min.: r = 0.74, P < 0.005) and DPPH (r = 0.69, P < 0.05) assays positively correlated to conjugated dienes formation. Our results suggest that these plants are likely to contribute to the therapeutic effects of traditional drugs used to treat cardiovascular disease.
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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) | 0.000 | 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 | 0.000 | 0.000 |
| 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".