Exploring the molecular mechanisms of huperzine a in the treatment of rosacea through network pharmacology, machine learning, and molecular dynamics simulations
Bibliographic record
Abstract
Introduction: Rosacea is a common chronic inflammatory skin disorder and dysregulation of neuroimmune functions and neurovascular loops play critical roles in the development of rosacea. Huperzine A (Hup A) has several bioactive properties, including anti-inflammatory, antioxidant, and neuroprotective effects. However, the potential roles of Hup A in treating rosacea is unknown. Methods: Network pharmacology, molecular docking, and molecular dynamics simulation techniques has been used to investigate the anti-rosacea mechanisms of Hup A in rosacea. Results: Our results predicted 21 potential anti-rosacea targets of Hup A through public databases. KEGG pathway enrichment analysis revealed that these key targets participated in the regulation of MAPK signaling, NF-kappa B signaling, and PI3KAKT signaling pathways. Further machine learning analysis identified six core targets (BCL2, RXRA, PKN2, XDH, PRKCA, and FAP). Analysis of the GSE65914 dataset showed that XDH was upregulated in rosacea lesions, while BCL2 and RXRA were downregulated, with no significant expression changes of the other genes. Molecular docking results indicated that Hup A could bind to key targets (XDH, BCL2, and RXRA), which were further confirmed by molecular dynamics simulations. Discussion: This study systematically elucidates the potential mechanisms of Hup A in the treatment of rosacea and provides a theoretical basis for its application in rosacea therapy.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".