Ginsenoside Rg2 Inhibits Lipopolysaccharide-Induced Adhesion Molecule Expression in Human Umbilical Vein Endothelial Cell
Bibliographic record
Abstract
Vascular cell adhesion molecule 1 (VCAM-1), intercellular adhesion molecule 1 (ICAM-1), P- and E-selectin play a pivotal role for initiation of atherosclerosis. Ginsenoside, a class of steroid glycosides, is abundant in Panax ginseng root, which has been used for prevention of illness in Korea. In this study, we investigated the mechanism(s) by which ginsenoside Rg2 may inhibit VCAM-1 and ICAM-1 expressions stimulated with lipopolysaccharide (LPS) in human umbilical vein endothelial cell (HUVEC). LPS increased VCAM-1 and ICAM-1 expression. Ginsenoside Rg2 prevented LPS-mediated increase of VCAM-1 and ICAM-1 expression. On the other hand, JSH, a nuclear factor kappa B (NF-κB) inhibitor, reduced both VCAM-1 and ICAM-1 expression stimulated with LPS. SB202190, inhibitor of p38 mitogen-activated protein kinase (p38 MAPK), and wortmannin, phosphatidylinositol 3-kinase (PI3-kinase) inhibitor, reduced LPS-mediated VCAM-1 but not ICAM-1 expression. PD98059, inhibitor of mitogen-activated protein kinase kinase/extracellular signal-regulated kinase (MEK/ERK) did not affect VCAM-1 and ICAM-1 expression stimulated with LPS. SP600125, inhibitor of c-Jun N-terminal kinase (JNK), reduced LPS-mediated ICAM-1 but not VCAM-1 expression. LPS reduced IkappaBα (IκBα) expression, in a time-dependent manner within 1 hr. Ginsenoside Rg2 prevented the decrease of IκBα expression stimulated with LPS. Moreover, ginsenoside Rg2 reduced LPS-mediated THP-1 monocyte adhesion to HUVEC, in a concentration-dependent manner. These data provide a novel mechanism where the ginsenoside Rg2 may provide direct vascular benefits with inhibition of leukocyte adhesion into vascular wall thereby providing protection against vascular inflammatory disease.
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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 | 0.000 | 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".