Echinacea extracts modulate the pattern of chemokine and cytokine secretion in rhinovirus‐infected and uninfected epithelial cells
Bibliographic record
Abstract
Extracts of Echinacea purpurea are among the most widely used herbal medicines throughout Europe and North America for the prevention or treatment of common cold, coughs, bronchitis and other upper respiratory infections. Popular preparations include expressed juice from the aerial parts of the plant (which contain polysaccharides) and alcoholic tinctures from roots (containing caffeic acid derivatives and alkylamides). Since immune modulation has been reported for similar extracts, cytokine antibody arrays were used to investigate the changes in the pro-inflammatory cytokines and chemokines released from a cultured line of human bronchial epithelial cells exposed to Rhinovirus 14 and two different chemically characterized Echinacea extracts. Virus infection stimulated the release of at least 31 cytokine-related molecules, including several important chemokines known to attract inflammatory cells. Most of these effects were reversed by simultaneous exposure to either of the two Echinacea extracts, although the patterns of response were different for the two extracts. These results could explain the antiinflammatory properties of Echinacea extracts. Furthermore, a number of these cytokines were stimulated by the same Echinacea preparations in uninfected cells. These observations therefore provide support for the alleged beneficial uses of Echinacea extracts.
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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".