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Record W2089457788 · doi:10.1155/2012/717294

Expression of Nuclear Receptor Coactivators in the Human Fetal Membranes at Term before and after Labor

2012· article· en· W2089457788 on OpenAlexafffund
Che‐Wei Ou, Meihua Sun, Weronika Sadej, William Gibb

Bibliographic record

VenueObstetrics and Gynecology International · 2012
Typearticle
Languageen
FieldImmunology and Microbiology
TopicReproductive System and Pregnancy
Canadian institutionsOttawa HospitalUniversity of Ottawa
FundersCanadian Institutes of Health Research
KeywordsAmnionDeciduaPCAFNuclear receptorFetal membraneNuclear receptor coactivator 1Nuclear receptor coactivator 2ReceptorFetusImmunohistochemistryMedicineAndrologyInternal medicineEndocrinologyCell biologyBiologyPregnancyGeneTranscription factorPlacentaGenetics

Abstract

fetched live from OpenAlex

Human fetal membranes play an important role in term and preterm labor and are responsive to steroids. We examined the expression of steroid receptor coactivators in fetal membranes obtained prior to and following labor at term. Proteins were localized by immunohistochemistry, Western analysis was carried out in nuclear extracts, and mRNA levels were determined by real-time RT-PCR. SRC-1, SRC-2, p300, and PCAF proteins were present in all nuclear extracts. The amnion nuclei expressed higher levels of SRC-1, p300, and PCAF than nuclei from the chorion-decidua, whereas the reverse was true for SRC-2. Chorion-decidua from patients before labor expressed higher levels of SRC-1 than those from patients after labor. Also, the PCAF level was higher in the amnion obtained before labor than the same tissue obtained after labor. In contrast to the protein expression, mRNA levels of SRC-1 and p300 were higher in the chorion-decidua compared to the amnion, whereas there was no difference in levels of SRC-2 and PCAF mRNAs between these two tissues. These data underline that the regulation of the expression of the coactivators in these tissues occurs during labor and is complex and tissue specific.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.089
Threshold uncertainty score0.633

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)0.0010.000

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 head0.009
GPT teacher head0.239
Teacher spread0.230 · 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 designObservational
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

Citations1
Published2012
Admission routes2
Has abstractyes

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