Modulation of human immune responses by ginseng extracts (89.56)
Bibliographic record
Abstract
Abstract Ginseng (GS) has been used as an herbal remedy for centuries but its immunomodulatory effects remain unclear. Here we report the effects of standardized North-American ginseng (Panax quinquefollius) extracts (ethanol, aqueous and polysaccharide extracts) on innate and adaptive immune responses of human peripheral blood mononuclear cells (PBMCs) of healthy volunteers. We found that endotoxin (LPS)-free GS by itself induced the production of pro-inflammatory cytokines (IL-1β, IL-6, TNFα) and of IL-10 by PBMCs in a dose-dependent manner. Of the three extracts of GS tested, the aqueous extract was the most potent. GS extracts did not inhibit but rather enhanced the pro-inflammatory response induced by LPS. However, the T cell IL-2 response to bacterial superantigens was down-regulated in the presence of GS. Next, we examined the signalling pathways involved in the immune response to GS. We found that the GS aqueous extract activated the MAPK (ERK1/2, p38), the PI3K/Akt, and the NFκB pathways. Inhibition of src kinases and PKC decreased GS-induced cytokine production whereas inhibition of PI3K selectively blocked IL-10 production. Based on these results, we conclude that GS, and in particular its aqueous extract, has modulatory properties on innate and adaptive immunity. This work should help to focus the search for compounds in these extracts with specific immunomodulatory activities.
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 | 0.000 |
| 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".