Chelerythrine alleviates inflammation and angiogenesis in a mouse rosacea model via suppressing the NF-κB/p38 MAPK/STAT3 pathways
Bibliographic record
Abstract
Rosacea is a chronic inflammatory skin condition marked by excessive M1 macrophage polarization and angiogenesis, resulting in erythema and tissue inflammation. Despite available treatments, many patients experience recurrent flare-ups. This study explores chelerythrine, a bioactive component of Phellodendri Chinensis Cortex, for its therapeutic potential in rosacea through modulation of NF-κB, p38 MAPK, and STAT3 signaling, inflammation, and vascular regulation. Using an LL-37-induced rosacea-like mouse model, THP-1-derived M1 macrophages and human umbilical vein endothelial cells (HUVECs), chelerythrine's effects on macrophage polarization, cytokine expression, angiogenesis, and pathway activation of NF-κB, p38 MAPK, and STAT3 were evaluated. Chelerythrine significantly reduced epidermal thickness, inflammatory cell infiltration, and pro-inflammatory markers (TNF-α and IL-1β). It inhibited NF-κB, p38 MAPK, and STAT3 activation and decreased M1 polarization markers, shifting towards an anti-inflammatory profile. Furthermore, chelerythrine reduced vascular density and VEGF expression, impairing angiogenesis-related behaviors in HUVECs. These findings suggest that chelerythrine holds promise as a treatment for rosacea by mitigating inflammation and angiogenesis through targeted multiple pathways and macrophage modulation.
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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 | 0.001 |
| 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".