<i>Echinacea</i>and anti-inflammatory cytokine responses: Results of a gene and protein array analysis
Bibliographic record
Abstract
Preparations of Echinacea (Asteraceae) are frequently consumed for the control and prevention of rhinovirus-induced colds and other respiratory disorders. Since it is now generally believed that the symptoms of rhinovirus colds are due to the enhanced secretion of inflammatory chemokines and cytokines, we decided to analyze the effects of rhinovirus infection and Echinacea treatment [defined extracts of E. purpurea (L.) Moench] on cytokine/chemokine gene expression and protein secretion in a line of human tracheo- bronchial epithelial cells. Among the collection of more than 50 cytokines and chemokines present in the gene arrays, 12 showed significant induction of expression by the virus (> 2-fold), some of them by more than 5-fold. However, not all of these resulted in similar changes in the corresponding proteins, presumably as a consequence of post-transcriptional changes. A total of 16 cytokines, mostly chemokines, showed substantial protein increases, including several, such as the well known pro-inflammatory cytokines IL-6 and IL-8 (CXCL8), which were induced in the absence of additional transcription. These results support the concept that virus-induced multiple inflammatory cytokines are responsible for the cold symptoms. In most cases, one or both Echinacea preparations reversed the viral stimulation, thus providing a basis for the anti-inflammatory properties attributed to Echinacea.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
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".