Dominant expression and distribution of oestrogen receptor beta over oestrogen receptor alpha in the human corpus luteum
Bibliographic record
Abstract
To investigate the potential importance of oestrogen as a local regulator of human corpus luteum function, the mRNA expression pattern and cellular localization of oestrogen receptors (ERs), ER-alpha and ER-beta, were studied in corpora lutea grouped according to age, where days 2-5 post-LH rise were designated as the early luteal phase, days 6-10 as mid-luteal and days 11-14 as the late luteal phase respectively. Northern blot analysis using an ER-beta probe in samples from whole ovarian tissue and isolated corpora lutea, revealed a major band at 7.5 kb and several minor bands between 4-10 kb, while no signals for ER-alpha mRNA were obtained. However, using a semi-quantitative reverse transcription-polymerase chain reaction followed by Southern blotting, ER-beta mRNA levels were found to be 63% lower (P: < 0.05, n = 39) in the mid-luteal phase compared with the early luteal phase, while ER-alpha mRNA expression showed no statistical differences between the different age groups. Using in-situ hybridization, ER-beta mRNA expression was localized to the steroidogenic luteal cells as well as perivascular cells and fibroblasts in the corpus luteum. Immunohistochemistry confirmed the localization of ER-beta protein, but no clear staining of luteal cells was found using antibodies against ER-alpha. Collectively, the findings of low to moderate expression of ER-beta mRNA and protein in the steroidogenic cells, and also in vascular endothelial cells of the corpus luteum, as opposed to diminutive amounts of ER-alpha mRNA, suggest that oestrogen activity is primarily transduced via ER-beta in the human corpus luteum.
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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) | 0.001 | 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".