Repigmentation of vitiligo‐associated leukotrichia after autologous, non‐cultured melanocyte‐keratinocyte transplantation
Bibliographic record
Abstract
BACKGROUND: Many cases of leukotrichia are associated with vitiligo. Hair follicles are believed to be the source of melanocytes for skin repigmentation using standard medical therapy for vitiligo. Vitiliginous areas with overlying leukotrichia usually fail to achieve repigmentation by conventional medical treatments as a result of a deficient melanocyte reservoir. Even after the successful repigmentation of vitiliginous skin by medical therapies, leukotrichia hairs may remain depigmented, causing a major psychological impact to the patient. In such cases, surgical treatments may help to achieve the repigmentation of leukotrichia hairs. Reports of successful repigmentation in leukotrichia using different surgical treatments for vitiligo are few. OBJECTIVE: This study reports the benefit of autologous non-cultured melanocyte-keratinocyte transplantation (MKT) in patients with vitiligo-associated leukotrichia. METHODS: We report four cases of vitiligo-associated leukotrichia treated with MKT. RESULTS: All four patients showed significant repigmentation in vitiligo-associated leukotrichia after MKT. CONCLUSIONS: Melanocyte-keratinocyte transplantation may represent a good therapeutic option for the repigmentation of vitiligo-associated leukotrichia. This series includes only four responsive cases. Larger prospective studies are needed to determine the true response rate and mechanism of repigmentation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".