Immunohistochemical expression of estrogen receptor α and β in pituitary adenomas
Bibliographic record
Abstract
Estrogen is involved in pituitary tumorigenesis, acting through estrogen receptors alpha (ERα) and beta (ERβ). Our study investigated the prognostic value of ERα/ERβ as markers in pituitary adenomas. Tissue samples and clinico‐pathological data (patient age and sex, tumor size, invasiveness and recurrence) were obtained from 78 patients with pituitary adenomas. Immunohistochemistry was performed using the streptavidin‐biotin‐peroxidase complex method. A monoclonal and polyclonal antibody were used for ERα and ERβ expression respectively. Intensity of ERα/ERβ expression was evaluated using a scale of 0 to 3. Percentage of positive cells was evaluated on a scale of 0 to 4. ERα immunopositivity was evident in 40 of 78 cases (51%), while ERβ stained positive in 49 of 78 cases (63%). No correlation was found between ERβ expression and pituitary tumor cell types or clinico‐pathological factors. A significant difference was present in ERα expression between different tumor types. ERα positivity was present to a greater extent in macroadenomas. This finding supports the role of ERα in tumor growth and progression. Elevated ERα positivity and intensity were also found in non‐invasive adenomas. It appears that ERα may be a valuable biological marker for tumor size and invasiveness, while ERβ is a poor marker. The role of ERα expression in pituitary tumor initiation and progression warrants further investigation.
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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) | 0.002 | 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".