Mechanism of Action of Flavonoids in Prevention of Inflammation- Associated Skin Cancer
Bibliographic record
Abstract
Skin cancer in humans represents about 30% of all new cancers and is by far the most common malignancy in the Caucasian population. Exposure to radiations especially ultraviolet-B (UV-B) radiation is the major cause for development of skin cancers along with other chemical or biological factors. The growing incidence rates of skin cancer around the world, demand the need for new treatment options. Understanding the etiology and pathogenesis of skin cancer is therefore crucial for developing an effective drug against this prevailing disease. Medicinal plants are rich with numerous secondary metabolites such as flavonoids, which are now known to treat various chronic diseases, including inflammations and cancers. Flavonoids are sub-classified in to flavones, flavonols, iosflavones, flavanones, flavanols and anthocyanidins. They act on different targets including scavenging reactive oxygen species (ROS), regulation of the cell cycle, and initiation of DNA repair mechanisms, apoptotic induction and inhibition of metastasis. Innumerable evidence suggested that an increased consumption of flavonoid-rich fruits and vegetables rendered DNA protection to normal skin exposed to carcinogens such as UV-B radiation. Flavonoids also showed the potential to induce cell death mechanisms in melanoma, the most dreadful form of skin cancer. This comprehensive review presents flavonoids and their mechanism of action in relation to inflammation and skin cancer management.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".