<i>Echinacea</i> as an antiinflammatory agent: the influence of physiologically relevant parameters
Bibliographic record
Abstract
Numerous Echinacea preparations are available on the market for the prevention and treatment of cold and 'flu symptoms and inflammatory conditions associated with infections. Most of these preparations are consumed orally in the form of aqueous or ethanol extracts and tinctures. Since the recommended consumption normally involves a brief local exposure to the diluted preparation at an unspecified time in relation to the actual infection, then it is important that experimental models for the evaluation of Echinacea reflect these limitations. A line of human bronchial epithelial cells, in which rhinoviruses stimulate the production of pro-inflammatory cytokines, was used to evaluate several relevant parameters. The chemically characterized Echinacea preparation (Echinaforce) was capable of inhibiting completely the rhinovirus induced secretion of IL-6 (interleukin-6) and IL-8 (chemokine CXCL-8) in these cells, regardless of whether the Echinacea was added before or after virus infection, and in response to a range of virus doses. This inhibitory effect was also manifest under conditions resembling normal consumption with respect to the duration of exposure to Echinacea and the Echinacea dilution. It is concluded that under real life conditions of Echinacea consumption, the virus-induced stimulation of pro-inflammatory cytokines can be effectively reversed or alleviated.
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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) | 0.002 | 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".