Immunohistochemical Expression of Pituitary Tumor Transforming Gene (PTTG) in Pituitary Adenomas: A Correlative Study of Tumor Subtypes
Bibliographic record
Abstract
OBJECTIVE: We investigated the correlation between immunohistochemical expression of the pituitary tumor transforming gene (PTTG) and pituitary adenoma subtype. METHODS: Pituitary adenomas (n = 89) were stained for PTTG using the streptavidin-biotin-peroxidase complex method and a monoclonal PTTG antibody. RESULTS: PTTG staining was found to be cytoplasmic with a pronounced paranuclear expression pattern. Reactivity was highest in growth hormone (GH) adenomas as compared with other tumors, including prolactin (PRL), follicle-stimulating hormone/luteinizing hormone/alpha subunit, as well as adrenocorticotrophic hormone-secreting adenomas. PRL adenomas exhibited the lowest expression levels. Among GH adenomas, untreated tumors demonstrated significantly higher PTTG levels than octreotide-treated examples. Although dopamine agonist-treated PRL adenomas tended to show lower expression levels, statistical significance was not reached. CONCLUSIONS: Our finding that PTTG was differentially expressed in pituitary adenoma subtypes suggests a cell-specific function for PTTG. Moreover, treatment of GH adenomas with somatostatin analogues lowered PTTG expression. Further investigation into mechanisms mediating cell-specific expression of PTTG is warranted.
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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 | 0.000 |
| 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".