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Record W1986836517 · doi:10.1210/en.2008-1519

Absence of Nuclear Receptors for Oxysterols Liver X Receptor Induces Ovarian Hyperstimulation Syndrome in Mice

2009· article· en· W1986836517 on OpenAlexaff
Kévin Mouzat, Fanny Volat, Silvère Baron, Georges Alves, Aurélien Pommier, David H. Volle, Geoffroy Marceau, Angélique DeHaze, Pierre Déchelotte, Raj Duggavathi, Françoise Caira, Jean‐Marc A. Lobaccaro

Bibliographic record

VenueEndocrinology · 2009
Typearticle
Languageen
FieldMedicine
TopicCholesterol and Lipid Metabolism
Canadian institutionsMcGill University
Fundersnot available
KeywordsEndocrinologyInternal medicineReceptorOvarian hyperstimulation syndromeNuclear receptorOvaryBiologyMedicineChemistryEmbryoCell biologyIn vitro fertilisationBiochemistry

Abstract

fetched live from OpenAlex

Ovarian hyperstimulation syndrome is a frequent complication occurring during in vitro fertilization cycles. It is characterized by a massive ovarian enlargement associated with an accumulation of extra vascular fluid. Here we show that liver X receptor (LXR)-alpha and LXR-beta deficient mice present many clinical and biological signs of ovarian hyperstimulation syndrome: ovarian enlargement, hemorrhagic corpora lutea, increased ovarian vascular permeability, and elevated estradiol. Ovulation stimulation resulted in excessive ovarian response to exogenous gonadotropins because follicle number and estradiol production were higher in transgenic mice. LXR deficiency also leads to perturbations in general inflammatory status, associated with ovarian il-6 deregulation. Upon treatment with the synthetic LXR agonist T09101317, serum estradiol and expression of star and cyp11a1 genes were markedly increased in wild-type mice, showing that LXRs are key regulators of ovarian steroidogenesis. These results suggest that LXRs control the ovulation by regulating endocrine and vascular processes.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.008
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0080.002

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 head0.025
GPT teacher head0.273
Teacher spread0.247 · 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 source (direct Gemma or distilled Codex), 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

Citations39
Published2009
Admission routes1
Has abstractyes

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