Chemical Marker Profile and Biological Effects of Natural Products Containing Echinacea
Bibliographic record
Abstract
Natural health products containing Echinacea have been used by many patient populations and although there are reports of adverse effects with products containing Echinacea, few clearly characterized the nature of the product with respect to constituent content, the nature of the products and the mechanism underlying the interaction. The objective of this study was to examine blended and single-entity Echinacea products containing ground plant material or extracts in commercial capsules, herbal teas, tablets, tinctures and soft gel liquid-filled capsule formulations in an attempt to correlate biomarker constituent content and effects on cellular and subcellular parameters of interest. HPLC analysis indicated significant variability in the major biomarker constituent content in extracts from these Echinacea products. These extracts were also examined for their potential to affect cytochrome P450 CYP1A1/2, 2C9*1, 2C9*2, 2C19, 2D6, 3A4, 3A5, 3A7, and flavin-containing monooxygenase 3 (FMO3); CYP3A5-mediated metabolism, and expression of CYP3A4 and ABCB1. The extracts of some products were also examined for their effect cellular processes such as cell proliferation, nitric oxide formation as a marker of immunostimulatory capacity, and lactate dehydrogenase release as a marker for cell toxicity. The present study indicated that key Echinacea constituents varied widely within and between the products tested and that these levels did not correlate with the ability of these products to markedly affect the cellular processes studied.
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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".