Assessing the skin-whitening property of plant extracts from taiwanese species using zebrafish as a rapid screening platform
Bibliographic record
Abstract
Zebrafish can be used as a phenotype-based fast-screening platform for the discovery of skin-whitening products by observing the formation of melanin in vivo through a dissecting microscope. In this study, we first evaluated commercial whitening ingredients to confirm the potency of the zebrafish model. The zebrafish screening system verified that verbascoside and 5 other commonly used commercial skin-whitening compounds– including sodium ascorbate, azelaic acid, transamin, kojic acid, and PTU- significantly reduce melanin content in a dose-dependent manner. We subsequently conducted preliminary screening of 123 natural sources to identify those with whitening property. Through a two-step screening process, anti-melanogenesis effects of 4 and 9 candidates at low and high concentration were verified respectively. The toxicity, mortality, and malformation of zebrafish embryo were also evaluated. We further investigated the components of C. manghas L. and found 6 major compounds, one of which was manghaslin, a compound known for its melanin-suppressing effect on zebrafish without cytotoxicity. With these properties, manghaslin is a promising candidate as a skin lightening agent or a treatment for melanoma.
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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".