Estrogen receptor subtypes dictate the proliferative nature of the mammary gland
Bibliographic record
Abstract
Estrogen induces proliferation of breast epithelial cells and is responsible for breast development at puberty. This tightly regulated control is lost in estrogen-receptor-positive (ER+) breast cancers, which comprise over 70% of all breast cancers. Currently, breast cancer diagnosis and treatment considers only the α isoform of ER; however, there is a second ER, ERβ. Whilst ERα mediates estrogen-driven proliferation of the normal breast in puberty and breast cancers, ERβ has been shown to exert an anti-proliferative effect on the normal breast. It is not known how the expression of each ER (alone or in combination) correlates with the ability of estrogen to induce proliferation in the breast. We assessed the levels of each ER in normal mouse mammary glands subdivided into proliferative and non-proliferative regions. ERα was most abundant in the proliferative regions of younger mice, with ERβ expressed most abundantly in old mice. We correlated this expression profile with function by showing that the ability of estrogen to induce proliferation was reduced in older mice. To show that the ER profile associated with breast cancer risk, we assessed ER expression in parous mice which are known to have a reduced risk of developing ERα breast cancer. ERα expression was significantly decreased yet co-localization analysis revealed ERβ expression increased with parity. Parous mice had less unopposed nuclear ERα expression and increased levels of ERβ. These changes suggest that the nuclear expression of ERs dictates the proliferative nature of the breast and may explain the decreased breast cancer risk with parity.
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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 | 0.000 | 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 | 0.001 |
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".