Bibliographic record
Abstract
Objective To explore the cellular location of ER subtypes (ERα and ERβ) in normal human mammary gland and provid the foundation for exploring the relationship between ERα,ERβ and development of normal mammary gland.Methods Using immunohistochemistry,the expressions and cellular distribution of ERα and ERβ in the normal tissues of 16 patients who had accepted reduction mammoplasty specimens in the Affiliated Hospital of University Laval were detected.Results ERα immunoreactivity was detected in the nuclei of epithelial cells lining lobules and ducts.The positive cells located in the inner layer of the two epithelial layers in the lobules and intralobular ducts,but in the interlobular ducts the positive cells located in the outer layer.Although ERβ was also seen in these cells,there was irregularity of its distribution.Weak to moderate cytoplastic staining of ERβ in epithelial cells of lobules and ducts were fonud.Occasional nuclear staining was seen in the stromal cells,endothelial cells and lymphocytes.The percentage of ERβ positive expression in the epithelial cells was much higher than that of ERα,there was markedly difference between them (t=29.789,P0.01 ).The positive expression rate of ERβ in the mammary gland was much higher than that of ERα,there was markedly difference between them (χ2=8.127,P0.05).Conclusion ER subtypes have distinct distribution patterns in the normal mammary gland.The widespread distribution of ERβ suggests that it may be the dominant ER in the mammary gland.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".