Extract from<i>Acanthopanax senticosus</i>prevents LPS-induced monocytic cell adhesion via suppression of LFA-1 and Mac-1
Bibliographic record
Abstract
A crude extract from Acanthopanax senticosus (AS) has drawn increased attention because of its potentially beneficial activities, including anti-fatigue, anti-stress, anti-gastric-ulcer, and immunoenhancing effects. We previously reported that AS crude extract exerts anti-inflammatory activity through blockade of monocytic adhesion to endothelial cells. However, the underlying mechanisms remained unknown, and so this study was designed to investigate the pathways involved. It was confirmed that AS extract inhibited lipopolysaccharide (LPS)-induced adhesion of monocytes to endothelial cells, and we found that whole extract was superior to eleutheroside E, a principal functional component of AS. A series of PCR experiments revealed that AS extract inhibited LPS-induced expression of genes encoding lymphocyte function-associated antigen-1 (LFA-1) and macrophage-1 antigen (Mac-1) in THP-1 cells. Consistently, protein levels and cell surface expression of LFA-1 and Mac-1 were noticeably reduced upon treatment with AS extract. This inhibitory effect was mediated by the suppression of LPS-induced degradation of IκB-α, a known inhibitor of nuclear factor-κB (NF-κB). In conclusion, AS extract exerts anti-inflammatory activity via the suppression of LFA-1 and Mac-1, lending itself as a potential therapeutic galenical for the prevention and treatment of various inflammatory diseases.
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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".