Signaling by estrogens
Bibliographic record
Abstract
By regulating activities and expression levels of key signaling molecules, estrogens control mechanisms that are responsible for crucial cellular functions. Ligand binding to estrogen receptor (ER) leads to conformational changes that regulate the receptor activity, its interaction with other proteins and DNA. In the cytoplasm, receptor interactions with kinases and scaffolding molecules regulate cell signaling cascades (extranuclear/nongenomic action). In the nucleus, estrogens control a repertoire of coregulators and other auxiliary proteins that are associated with ER, which in turn determines the nature of regulated genes and level of their expression (genomic action). The combination of genomic and nongenomic actions of estrogens ultimately confers the cell-type and tissue-type selectivity. Recent studies have revealed some important new insights into the molecular mechanisms underlying ER action, which may help to explain the functional basis of existing selective ER modulators (SERMs) and provide evidence into how ER might be selectively targeted to achieve specific therapeutic goals. In this review, we will summarize some new molecular details that relate to estrogen signaling. We will also discuss some new strategies that may potentially lead to the development of functionally selective ER modulators that can separate between the beneficial, prodifferentiative effects in bone, the cardiovascular system and the CNS as well as the "detrimental," proliferative effects in reproductive tissues and organs.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".