Pressurized water versus ethanol as a Silybum marianum extraction solvent for inhibition of low-density lipoprotein oxidation mediated by copper and J774 macrophage cellsThis article is one of a selection of papers published in this special issue (part 1 of 2) on the Safety and Efficacy of Natural Health Products.
Bibliographic record
Abstract
Silybum marianum contains flavonolignans, termed silymarin (SM), that are therapeutic agents for many inflammation-based diseases including atherosclerosis. Oxidation of human low-density lipoprotein was induced by CuSO4 or J774 macrophage cells and measured by the formation of thiobarbituric acid reactive substances (TBARS). SM was extracted by pressurized hot water (PHWE) or ethanol, and the effects of these extracts on TBARS formation were evaluated in comparison with those of SM preparations made from blending masses of individual flavonolignan standards in ratios identical to those of the water and ethanol extracts. Ethanol-extracted SM and its blended counterpart inhibited the generation of TBARS by 82% and 43%, respectively, at 150 mumol/L doses. TBARS levels in the presence of 150 micromol/L of the PHWE and its blended SM counterpart were reduced by 84% and 38%, respectively. Extracts from milk thistle fruit displayed higher protective effects than blended SM solutions of the same concentration with an identical compositional makeup. The appearance of degradation peaks in the water extract did not create any cytotoxic effects. Results of this study confirm that PHWE can be used to extract flavonolignans from milk thistle and that these extracts may possess therapeutic potential different from or beyond that of traditional organic solvent preparations.
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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 | 0.000 | 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 | 0.000 |
| 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".