Melanocyte-specific immune response in a patient with multiple regressing nevi and a history of melanoma.
Bibliographic record
Abstract
BACKGROUND/AIM: Regressing nevi are considered an example of an efficient early antitumoral response preventing the development of neoplasia. The underlying mechanism has not been elucidated, although an immune-based destruction of melanocytes is supposed. The aim of this study was to provide evidence of an effective immunosurveillance of pigment lesions in a patient at high risk of melanoma. CASE REPORT: A patient with the dysplastic nevus syndrome and a history of melanoma was included in this study. Since 2003, a marked regression of almost all nevi was observed. Immunohistochemistry was performed and the antigen specificity of T-cells was analyzed on T-cells isolated from a regressing nevus by flow cytometry using HLA-A2-peptide tetramers containing Mart-1(26-35), gp100(280-288), gp100(209-217) and tyrosinase(369-377). Immunohistochemistry of the regressing nevi showed a strong infiltrate of CD4 + and CD8 + T-cells. Flow cytometric analyses demonstrated the presence of a CD8 + T-cell response against gp100(280-288) and Mart-1(26-35) both in peripheral blood and in a regressing nevus. CONCLUSION: These findings indicate that an immune reaction against melanocyte differentiation antigens can target specifically nevi without signs of vitiligo and suggests that boosting the anti-melanocyte immune response in patients at high risk for melanoma may prevent tumor development at an early stage.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| 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".