Aqueous extracts of<i>Cimicifuga racemosa</i>and phenolcarboxylic constituents inhibit production of proinflammatory cytokines in LPS-stimulated human whole blood
Bibliographic record
Abstract
Cimicifuga racemosa (black cohosh) is commonly used in traditional medicines as treatment for menopausal symptoms and as an antiinflammatory remedy. To clarify the mechanism of action and active principle for the antiinflammatory action, the effects of aqueous C. racemosa root extracts (CRE) and its major constituents on the release of the proinflammatory cytokines IL-6, TNF-alpha, IFN-gamma, and the chemokine IL-8 were investigated in lipopolysaccharide (LPS)-stimulated whole blood of healthy volunteers. CRE (3 microg/microL and 6 microg/microL) reduced LPS-induced release of IL-6 and TNF-alpha in a concentration- and time-dependent manner and almost completely blocked release of IFN-gamma into the plasma supernatant. Except for IFN-gamma, these effects were attenuated at longer incubation periods. IL-8 secretion was stimulated by CRE. As shown by quantitative real-time RT-PCR, effects on cytokines were based on preceding changes in mRNA levels except for IL-8. According to their content in CRE, the phenolcarboxylic compounds caffeic acid, ferulic acid, and isoferulic acid, as well as the triterpene glycosides 23-epi-26-deoxyactein and cimigenol-3-O-xyloside, were tested at representative concentrations. Among these, isoferulic acid was the prominent active principle in CRE, responsible for the observed inhibition of IL-6, TNF-alpha, and IFN-gamma, but not for IL-8 stimulation. The effect of this compound may explain the antiinflammatory activities of CRE and its beneficial actions in rheumatism and other inflammatory diseases.
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) | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".