Echinacea extracts modulate the production of multiple transcription factors in uninfected cells and rhinovirus-infected cells
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".