Herbal Extracts as Potential Antioxidant, Anti‐Aging, Anti‐Inflammatory, and Whitening Cosmeceutical Ingredients
Bibliographic record
Abstract
The aim of this research was to investigate and compare the antioxidant, anti-tyrosinase, anti-aging, and anti-inflammatory activities of 16 herbal extracts for topical application in cosmetic/cosmeceutical products. Herbal plant materials were extracted by infusion in boiled water for 15 min. The total phenolic content and total flavonoid content of each extract were investigated by the Folin-Ciocalteu and aluminum chloride methods, respectively. Antioxidant activities were investigated using 2,2'-diphenyl-1-picrylhydrazyl and a ferric reducing antioxidant power assay. Anti-tyrosinase and anti-aging activities were investigated using an in vitro enzymatic-spectrophotometric method. Anti-inflammatory activities were investigated using an enzyme-linked immunosorbent assay. The findings show that the Stevia rebaudiana extract has the most significant levels of both phenols and flavonoids (p<0.05). The S. rebaudiana, Rosa damascene, and Phyllanthus emblica extracts possessed the most significant antioxidant activities (p<0.05) and a promising whitening effect with moderate anti-tyrosinase activities. Furthermore, the Echinacea purpurea extract possessed the most significant anti-collagenase (78.5±0.0 %), anti-elastase (69.0±1.4 %), and anti-hyaluronidase activity (64.2±0.3 %). The Morus alba extract possessed the most significant anti-inflammatory activity since it could inhibit the secretion of interleukin-6 and tumor necrosis factor-α (p<0.05). Therefore, these herbal extracts have promising skin benefits and have potential for use as active ingredients in cosmetic/cosmeceutical products.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".