RETRACTED: Estrogen receptor-interacting protein that modulates its nongenomic activity-crosstalk with Src/Erk phosphorylation cascade
Post-publication record
Source: Retraction Watch, joined by DOI. OpenAlex records retraction as is_retracted, a boolean over a state space with at least four values, so it cannot express an expression of concern, a correction or a reinstatement; it reports them as false, which reads as “fine”.
Bibliographic record
Abstract
Numerous studies have demonstrated that estrogens induce rapid and transient activation of the Src/Erk phosphorylation cascade. Activation of this cascade triggers vital cellular functions including cell proliferation and differentiation. However, the details of the molecular mechanism of this process remain to be elucidated. We have identified a previously uncharacterized nuclear receptor-interacting protein designated as modulator of nongenomic activity of estrogen receptor (MNAR). Here we show that MNAR modulates estrogen-receptor (ER) interaction with members of the Src family of tyrosine kinases, which leads to a stimulation of Src enzymatic activity and activation of Erk1 and Erk2 kinases. We also show that MNAR, through activation of the Src/Erk phosphorylation cascade, affects ER transcriptional activity and ultimately ER-mediated gene expression. These data reveal that MNAR mediates the crosstalk between two important classes of signal transducing molecules and suggest that ER "genomic" and "nongenomic" activities are interrelated.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".