Assessing the skin-whitening property of plant extracts from taiwanese species using zebrafish as a rapid screening platform
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 it