Myricetin Attenuates IMQ-Induced Psoriatic Inflammation Through Multi-Target Modulation: Evidence from Network Pharmacology and Experimental Validation
Bibliographic record
Abstract
Background: Psoriasis is a chronic inflammatory skin disease driven by keratinocyte hyperproliferation and immune dysregulation. Despite the availability of biologics and immunosuppressants, recurrence and adverse effects remain major limitations. Myricetin (Myr), a natural flavonoid with well-documented anti-inflammatory and immunomodulatory properties, has shown promise in inflammatory disorders; however, its efficacy and mechanisms in psoriasis have not been fully elucidated. Methods: The therapeutic effects of topical Myr (0.5–2%) were evaluated in an imiquimod (IMQ)-induced psoriatic mouse model. Network pharmacology and molecular docking were employed to predict potential targets, followed by validation using histological analysis, cytokine profiling, qPCR, and Western blotting. Results: Network analysis identified 52 overlapping targets between Myr and psoriasis, including TNF, PTGS2, MMP9, and EGFR, with enrichment in TNF, IL-17, and PI3K/AKT signaling pathways. Myr treatment significantly alleviated IMQ-induced erythema, scaling, and epidermal thickening, improved skin-barrier function, and reduced the expression of IL-6, IL-17A, and TNF-α. Molecular docking showed strong binding affinities of Myr with TNF, PTGS2, MMP9, and EGFR. Western blotting confirmed that Myr suppressed EGFR and AKT phosphorylation and downregulated Mmp9, Ptgs2, and Tnf expression. Conclusions: Myr exerts multi-target anti-psoriatic effects by inhibiting the EGFR/AKT axis and inflammatory mediators, highlighting its potential as a safe and effective natural therapeutic agent for psoriasis.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".