Modulation of immune response gene expression by echinacea extracts: results of a gene array analysisThis article is one of a selection of papers published in this special issue (part 2 of 2) on the Safety and Efficacy of Natural Health Products.
Bibliographic record
Abstract
Echinacea extracts have traditionally been used in the treatment of many infectious and other diseases (such as rhinovirus colds), and research has revealed the presence of various bioactivities in these extracts, particularly those connected with immune responses. We examined the effects of Echinacea by using gene expression analysis in a line of human bronchial epithelial cells, with or without rhinovirus infection. More than 13 000 human genes were evaluated. From these analyses we focused primarily on immune response genes and found that both Echinacea extracts, one predominantly rich in polysaccharides and the other rich in alkylamides and caffeic acid derivatives, stimulated the expression of numerous genes. These included a number of cytokines and chemokines, although the pattern of stimulation was different. In addition, Echinacea extracts tended to neutralize the effects of the rhinovirus. When the immune response gene pathways were analyzed with the Ingenuity Pathway program, it became apparent that many of them were interconnected through a major node, the transcription factor C/EBPbeta (CAAT/enhancer-binding protein beta) and its related C/EBP proteins. This suggests that Echinacea can bring about important biological responses in cells by virtue of interactions between components of the extract and a small number of intracellular factors involved in multiple signaling pathways.
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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) | 0.001 | 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".