Insights into the mechanism of Piper betle leaf-induced contact leukomelanosis using C57BL/6 mice as the animal model and tyrosinase assays
Bibliographic record
Abstract
BACKGROUND/OBJECTIVES: Steamed piper betle leaves (PBL) were once used by many Taiwanese women to treat pigment disorders on the face. Most women claimed a quick, favourable response at first, only to be overcome with facial leukomelanosis later. METHODS: C57BL/6 mice were randomly assigned to different groups to study if PBL could cause the following effects: contact dermatitis, leukomelanosis, or hair bleaching. Intracellular melanin content was measured by tyrosinase assays. RESULTS: Most steamed PBL-treated mice developed contact dermatitis and postinflammatory hyperpigmentation (PIH) on their shaved backs. About half developed bleached hair to varying extents. The steamed PBL did not only bleach the hairs, but also, unexpectedly, stimulated melanocyte replication, indicated by the fact that the number of functional melanocytes in the tail epidermis increased significantly after treatment (P = 0.007). Using tyrosinase assays PBL extract at the undiluted concentration showed limited inhibition of melanogenesis, probably via melanocytotoxicity. CONCLUSIONS: The leukomelanosis observed in patients might be the consequence of PIH combined with a mixed reaction (hyper- and hypopigmentation), probably due to the different volatile chemicals that surface after steaming the PBL. This conflicting mixed reaction suggests that counteractive ingredients might exist in PBL. PBL, if purified, might be a promising source of a novel bleaching agent.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".