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Record W1898642356 · doi:10.1139/er-2013-0047

Known and emerging factors modulating estrogenic effects of endocrine-disrupting chemicals

2013· article· en· W1898642356 on OpenAlexvenueno aff
Wanglong Zhang, Yu Luo, Li Zhang, Qian Cai, Xuejun Pan

Bibliographic record

VenueEnvironmental Reviews · 2013
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEstrogen and related hormone effects
Canadian institutionsnot available
FundersSpecialized Research Fund for the Doctoral Program of Higher Education of ChinaNational Natural Science Foundation of China
KeywordsGPERSignal transductionBiologyEstrogen receptorReceptorG protein-coupled receptorBiological pathwayEstrogenCell biologyGeneBiochemistryGeneticsGene expression

Abstract

fetched live from OpenAlex

A variety of endocrine-disrupting chemicals (EDCs) have estrogenic effects and are termed xenoestrogens (XEs). The genomic pathway mediated by estrogen receptors (ERs) has been considered the major explanation for the estrogenic effects elicited by XEs. Presently, nongenomic pathways have achieved considerably more attention because the genomic pathways cannot fully elucidate many biological and physiological responses. Genomic and nongenomic pathways act either separately or cooperatively. XEs activate a variety of signaling pathways and downstream kinases, which in turn alter the posttranslational modification and activation of ERs. Classical ERs and their splice variants reside at the membrane and mediate rapid effects cooperatively with G protein-coupled estrogen receptor 1 (GPER), G protein, and many other signaling molecules. GPER seems more effective in mediating the synthetic chemical-induced effects. There are also feedback and feedforward mechanisms between both pathways. Numerous factors may affect both pathways. Recently, some new layers of regulation on the estrogenic effects were identified. In this review, we summarize these multiple regulation layers and discuss the newly identified factors.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.041
Threshold uncertainty score0.600

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.004
GPT teacher head0.224
Teacher spread0.220 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations19
Published2013
Admission routes1
Has abstractyes

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