Monoammonium glycyrrhizate suppresses tumor necrosis factor-α induced chemokine production in HMEC-1 cells, possibly by blocking the translocation of nuclear factor-κB into the nucleus
Bibliographic record
Abstract
Monoammonim glycyrrhizate (MAG) derived from licorice has been shown to have anti-inflammatory properties. Chemokines are vital inflammatory mediators that are involved with endothelial damage from leukocyte infiltrates in various inflammatory skin diseases. In this study, we investigated the anti-inflammatory effects and mechanisms of MAG on tumor necrosis factor-α (TNF-α) induced chemokine production in a human dermal microvascular endothelial cell line (HMEC-1). HMEC-1 cells were treated with TNF-α, with or without MAG. The results showed that MAG suppressed TNF-α-induced chemokine (including CXCL8, CX3CL1, and CXCL16) mRNA expression in HMEC-1 cells, in a dose-dependent manner, and reduced the secretion of these chemokines in culture supernatant. Moreover, endothelial activation in the presence of MAG blocked the chemotactic activities of TNF-α-stimulated HMEC-1 cell supernatant on the migration of primary neutrophils and primary monocytes. In addition, Western blot and immunofluorescence data revealed that MAG inhibited nuclear translocation of nuclear factor-κB p65 (NF-κB p65). It is the first report to demonstrate that MAG suppresses TNF-α-induced chemokine production in HMEC-1 cells, and that the mechanism may be inhibiting the translocation of NF-κB p65 into the nucleus to prevent the starting of inflammatory signaling pathway. Our results revealed that MAG is a potential anti-inflammatory agent capable of improving inflammatory skin 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".