Anti-inflammatory activities of <i>Echinacea</i> extracts do not correlate with traditional marker components
Bibliographic record
Abstract
Numerous preparations of Echinacea (Asteraceae), mainly derived from three species, Echinacea purpurea ( L.) Moench, Echinacea angustifolia (D.C.) and Echinacea pallida (Nutt.) Nutt., are available for the treatment of symptoms of colds and influenza which, along with a number of other respiratory conditions, have been ascribed to the induction of pro-inflammatory cytokines. We evaluated various chemically characterized extracts and fractions, derived from the three species of Echinacea, for their possible inhibitory effects on the secretion of pro-inflammatory cytokines IL-6 and IL-8 (CXCL-8) by human bronchial epithelial cells infected with rhinovirus type 14. All of the E. purpurea fractions, comprising aqueous or ethanol extracts of roots, leaves and stems, but to a lesser degree flowers, strongly inhibited the secretion of both cytokines. In contrast, corresponding fractions derived from E. angustifolia and E. pallida showed relatively weak cytokine-inhibitory activity, whereas their aqueous fractions significantly enhanced cytokine secretion, both in virus-infected and in uninfected cells. These properties did not correlate with the presence or absence of alkylamides or specific caffeic acid derivatives, although the alkylamide rich fraction of E. angustifolia showed a significant inhibitory effect. However, there was some correlation between anti-cytokine effects and our previously reported anti-viral activities. Thus, none of the groups of compounds traditionally used as “markers” for Echinacea are responsible for anti-inflammatory activity. Consequently, we believe that other constituents of Echinacea should be evaluated.
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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) | 0.000 | 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".