Echinacea extracts modulate the production of multiple transcription factors in uninfected cells and rhinovirus-infected cells
Why this work is in the frame
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Bibliographic record
Abstract
Extracts of Echinacea purpurea are widely used for the prevention and treatment of common colds, coughs, bronchitis and other upper respiratory infections, many of which are caused by rhinoviruses (RVs). Recent reports have indicated that rhinoviruses can stimulate the release of various pro-inflammatory cytokines and chemokines from cultured nasal and bronchial human epithelial cells, and several transcription factors (TFs) have been implicated in this process. The effects of Echinacea treatment and rhinovirus infection on the activation of a range of transcription factors were evaluated by means of a protein/DNA array analysis. The BEAS-2B cell line was used as the model, and nuclear extracts of uninfected cells and rhinovirus-14 infected cells were examined with and without treatment with one of two chemically different Echinacea extracts. It was found that both Echinacea extracts increased the nuclear content of more than 30 transcription factors, including the 12 pro-inflammatory factors examined, such as NFkB, AP-1, AP-2 and STATs 1-6. Virus infection resulted in a more dramatic increase in these same TFs. However, when RV-infected cells were treated with either of the two Echinacea extracts, TF levels were reduced to low levels, although the pattern of the reductions was different for the two extracts. These results indicate that rhinovirus infection of epithelial cells, and treatment with Echinacea extracts, led to profound effects on numerous transcription factors, which could explain the previously observed modulation of secreted cytokines and chemokines, as well as other signaling pathways. In addition, the results could help to explain the beneficial effects of Echinacea consumption.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it