Network Pharmacology and Integrated Molecular Docking Study on the Mechanism of the Protective Effect of Litchi Seed in Skin Photoaging
Bibliographic record
Abstract
Background: Ultraviolet radiation (UVR) causes premature skin aging.Litchi seed (LS) is considered a natural plant extract with potential antioxidant, anti-aging and anti-inflammatory properties.However, the mechanisms of LS's protective effects on skin photoaging remain unclear.Objective: This study aims to perform a rapid and efficient virtual screening of the main targets and possible mechanisms of the protective effect of LS on skin photoaging through network pharmacology, bioinformatics and molecular docking.Methods: The primary active compounds and their corresponding targets of LS were obtained from the TCMSP, STP, and UniProt databases.Concurrently, photoaging-related targets were mined from the GEO, GeneCards, and OMIM databases."LS-photoaging" targets were identified using Venn diagrams created with R software.Protein-protein interaction (PPI) networks and "compound-target-disease" networks were constructed and analyzed using Cytoscape.GO and KEGG pathway enrichment analyses were then performed to predict the protective mechanisms of LS against skin photoaging.Finally, key targets and active compounds were validated through molecular docking using AutoDock Vina.Results: The screening identified 368 targets of LS active compounds and 872 photoaging-related targets.Network topology analysis revealed 87 common targets, with AKT1, IL6, TP53, and CASP3 as core targets.Enrichment analysis reveals that LS can modulate the ROS/MAPK/AP-1 pathway, thereby inhibiting inflammatory responses and reducing oxidative stress, which leads to a decrease in pro-inflammatory factors.Additionally, it promotes collagen restoration by suppressing the expression of MMPs.Molecular docking validation demonstrated a strong binding affinity between the core targets and the key compounds.Conclusion: LS shows potential for treating photoaging by counteracting inflammation and oxidative stress, regulating collagen and lipid metabolism, and inhibiting apoptosis.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".