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Record W2049555445 · doi:10.1210/me.2007-0295

Estrogen Suppresses Uterine Epithelial Apoptosis by Inducing Birc1 Expression

2007· article· en· W2049555445 on OpenAlexaff
Yan Yin, Weiwei Huang, Congxing Lin, Hong Chen, Alex MacKenzie, Liang Liang

Bibliographic record

VenueMolecular Endocrinology · 2007
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCell death mechanisms and regulation
Canadian institutionsChildren's Hospital of Eastern Ontario
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Institute of Environmental Health Sciences
KeywordsDiethylstilbestrolEstrogenApoptosisBiologyEstrogen receptorCell biologyEndocrinologyEstrogen receptor alphaProgrammed cell deathInternal medicineCancer researchBiochemistryMedicineCancer

Abstract

fetched live from OpenAlex

The decision whether or not a cell undergoes apoptosis is determined by the opposing forces of pro- and antiapoptotic effectors. Here we demonstrate genetically that estrogen can tip this balance toward cell survival in uterine epithelial cells by inducing the expression of baculoviral inhibitors of apoptosis repeat-containing 1 (Birc1), a family of antiapoptotic proteins. In neonatal mice, both 17beta-estradiol and the potent synthetic estrogen diethylstilbestrol strongly suppress uterine epithelial apoptosis while markedly elevating Birc1 transcript level in an estrogen receptor-alpha-dependent manner. The induction of Birc1 before any effect on apoptosis suppression and failure of diethylstilbestrol to completely inhibit apoptosis in Birc1a-deficient uterine epithelium indicate a functional role for Birc1a in estrogen-mediated apoptosis suppression. In ovariectomized adult mice, expression of Birc1 is also induced by ovarian hormones, suggesting a role for these proteins in normal uterine physiology. We propose that by binding to active caspases, Birc1 proteins can eliminate them through proteasome degradation. These results for the first time establish Birc1 proteins as functional targets of estrogen in suppressing apoptosis in the uterus.

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 score0.108
Threshold uncertainty score0.888

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 head0.006
GPT teacher head0.252
Teacher spread0.246 · 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

Citations27
Published2007
Admission routes1
Has abstractyes

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