Naringenin in Melanoma: Mechanistic Insights and Nanodelivery Strategies for Clinical Translation
Bibliographic record
Abstract
Skin cancer, particularly melanoma, represents a major global health challenge primarily driven by ultraviolet (UV) radiation. Recent research highlights naringenin, a citrus-derived flavonoid, as a promising therapeutic candidate due to its ability to generate reactive oxygen species (ROS), induce cell cycle arrest, activate caspase-3, inhibit ERK1/2 and JNK MAPK signaling, and suppress angiogenesis. It also promotes melanogenesis by modulating Wnt/β-catenin and PI3K pathways and enhancing tyrosinase activity. Beyond its anticancer actions, naringenin exerts antioxidant, anti-inflammatory, and antiphotoaging effects, making it a versatile agent for dermatological applications. However, rapid metabolism, poor solubility, and limited bioavailability hinder its clinical translation. To overcome these challenges, advanced nanocarrier systems, including lipid nanoparticles, polymeric nanoparticles, liposomes, ethosomes, and nanostructured lipid carriers, have been engineered to enhance stability, skin penetration, and tumor-targeted delivery. These innovations highlight the potential of integrating mechanistic insights with delivery strategies to maximize therapeutic efficacy. Nonetheless, challenges such as high production costs, variability in phytochemical composition, and the need for harmonized manufacturing protocols remain. Addressing these barriers through multidisciplinary research and international collaboration is essential to translate phytochemical-based nanotherapies into safe and effective clinical options for melanoma management.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".