Characterization of terpene extract from Cannabis sativa flower and evaluation of its anti-melanogenetic effect in melan-a cells
Bibliographic record
Abstract
Inhibition of melanogenesis is of major interest in the cosmetic industry. The mechanism underlying melanogenesis includes regulating tyrosinase by activating various signaling pathways, such as protein kinase A and C pathways. Natural material-based agents have recently gained attention as alternative medicines to suppress melanogenic pathways. Cannabis sativa contains many bioactive compounds that are widely used in traditional herbal medicines. The aim of this study was to identify and assess the anti-melanogenic effect of terpene extract from Cannabis sativa flower (TCF) to explore novel depigmenting agents. Gas chromatography-mass spectrometry revealed that TCF contains five monoterpenes, eleven sesquiterpenes, and a diterpene. MTT and melanin content assays demonstrated that TCF significantly decreased the melanin content per live cell in a dose-dependent manner. Tyrosinase inhibition assay showed that TCF exhibited little inhibitory effect on tyrosinase at an equal dose, which reduced the melanin content. However, western blot analysis revealed that TCF downregulated microphthalmia-associated transcription factor (MITF), and tyrosinase-related proteins TRP1, and TRP2 expression by suppressing PKC, p38, and ERK/STAT3, while upregulated Akt pathway in melan-a. These results highlight the potential application of TCF as a natural whitening agent in the cosmetics industry.
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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) | 0.001 | 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".