Immunohistochemical expression of melan-A and tyrosinase in uveal melanoma
Bibliographic record
Abstract
BACKGROUND: Melan-A and tyrosinase are new immunohistochemical markers that can be used in the diagnosis of melanocytic lesions. The aim of this study was to investigate the correlation between radiotherapy or clinicohistopathological parameters and the expression of melan-A and tyrosinase in uveal melanoma. METHODS: Thirty-six enucleated cases of uveal melanoma were studied. The formalin-fixed, paraffin-embedded specimens were immunostained with monoclonal antibodies against melan-A and tyrosinase. The samples were classified as either positive or negative. The chi-square or the Student-t tests were used to test for the correlation of the expression rates of melan-A and tyrosinase with clinico-pathological parameters. RESULTS: Melan-A and tyrosinase were positive in 33 (91.7%) and 35 (97.2%) of the specimens, respectively. There was no significant association between the expression of melan-A or tyrosinase and radiotherapy or any clinico-pathological parameter. All specimens were positive for at least one of the immunohistochemical markers. CONCLUSION: To the best of our knowledge this is the first study concluding that the expression of melanocytic markers such as melan-A and tyrosinase is not influenced by radiotherapy or any clinico-pathological parameter. Moreover, when tyrosinase and melan-A are used together, 100% of the formalin-fixed, paraffin-embedded uveal melanoma samples tested positive for one of those markers.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".