Gene response of human monocytic cells for the detection of antimigraine activity of feverfew extractsThis 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
The herb feverfew is a folk remedy for various conditions, including inflammation, fever, psoriasis, rheumatism, and asthma. Like many herbal medicines, feverfew's mechanisms of action in the human body are largely unknown and its active ingredients remain elusive. Very often, different extraction methods of herb material produce different physical and biochemical properties and variation in clinical efficacy. We identified 3 major methods of extraction for feverfew aerial parts and used microarray technology to test the hypothesis that extracts produced by different methods elicit different gene expression profiles. We have identified approximately 200 genes that are consistently regulated by the 2 presumptive active antimigraine feverfew extracts but not associated with the inactive extract. Our results suggest that the presumptive active feverfew extracts potently stimulate more genes in human cells than the inactive extracts. We also identified several genes as unique signatures for these active extracts. All 3 feverfew extracts exhibited similar blockades on lipopolysaccharide-mediated TNF-alpha (tumor necrosis factor alpha) release, implicating that TNF-alpha is not responsible for the differences in the effects of the 3 feverfew extracts in human cells. In contrast, the active extracts more effectively suppressed CCL2 (also known as monocyte chemoattractant protein 1, MCP-1) than the inactive extracts, suggesting that CCL2 is a potential cellular target for feverfew's antimigraine effects.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".