Immunophenotypic characteristics of mast cells in non-metastatic seminoma
Bibliographic record
Abstract
Germ cell tumors are rare testicular neoplasms that occur in young men, and their proportion is 2%. Pathological changes in the immune landscape of seminoma - the interaction of mast cells, T-, B-lymphocytes and macrophages with atypical spermatogenic cells, possibly impart a certain uniqueness to seminoma, and their number determines the stage of tumor growth. At the same time, the question of the participation of mast cells in the progression of seminoma remains debatable. OBJECTIVE: Immunophenotypic analysis of mast cells in non-metastatic seminoma. MATERIAL AND METHODS: =21, age 20-53 years) - intact testicles. Histochemical (Toluidine blue) and immunohistochemical (antibodies to Tryptase, Chymase and CPA3) research methods were used. RESULTS: Based on the conducted histochemical reactions, it was found that mature mast cells predominate in seminoma, and their number is directly proportional to the pTNM stage. In immunohistochemical analysis of mast cells, we also observed a quantitative change in specific proteases, especially Tryptase, depending on the pTNM stage of seminoma, towards their decrease. CONCLUSION: Immunophenotypic distribution of secretome granules indicates a decrease in the number of Tryptase-, Chymase- and CPA3-mast cells, which is inversely proportional to the pTNM stage of non-metastatic seminoma.
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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) | 0.000 | 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) | 0.002 | 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".