Pathogenetic mechanisms of vitiligo in a patient with Sezary syndrome
Bibliographic record
Abstract
Patients exhibiting association between vitiligo and cutaneous T-cell lymphoma (CTCL) remain rare and it is not known whether some T-cell subpopulations of CTCL in the skin are able to recognize specific melanocytic epitopes and thus induce vitiligo. The aim of our study was to determine whether T cells specific to melanocyte differentiation antigens were detectable among tumour-infiltrating lymphocytes (TIL) in the hypopigmented skin of a patient with Sézary syndrome (SS). A 71-year-old patient presented with SS and developed vitiligo during the course of her disease. Immunohistochemical studies showed staining with HMB45 and MelanA antibodies in the pigmented skin biopsy, whereas no staining was observed in the hypopigmented skin biopsy. To analyse responses to melanocyte differentiation antigens, we used a transient COS transfection assay that permits an estimation of CD8 T-cell responses against a large number of HLA/antigen combinations. This technique allowed the detection of melanocyte differentiation antigen-specific T lymphocytes, directed mainly against Melan-A/MART1 antigen in the HLA-A*23 context. Our study supports the concept that vitiligo that has developed during the evolution of a CTCL is related to the presence of a T-lymphocyte subpopulation reactive against melanocyte differentiation antigens (mainly Melan-A/MART1) present in skin lesions. The role of interferon in the induction of this T-lymphocyte subpopulation is discussed.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".