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Record W2979014919 · doi:10.1530/joe-19-0013

Impaired LH surge amplitude in gonadotrope-specific progesterone receptor knockout mice

2019· article· en· W2979014919 on OpenAlexafffund
Chirine Toufaily, Gauthier Schang, Xiang Zhou, Philipp Wartenberg, Ulrich Boehm, John P. Lydon, Ferdinand Roelfsema, Daniel J. Bernard

Bibliographic record

VenueJournal of Endocrinology · 2019
Typearticle
Languageen
FieldMedicine
TopicHypothalamic control of reproductive hormones
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health Research
KeywordsEndocrinologyInternal medicineGonadotropic cellBiologyKnockout mouseConditional gene knockoutUterusEstrous cycleOvaryLuteinizing hormoneReceptorHormonePhenotypeMedicineGeneticsGene

Abstract

fetched live from OpenAlex

The progesterone receptor (PR, encoded by Pgr) plays essential roles in reproduction. Female mice lacking the PR are infertile, due to the loss of the protein's functions in the brain, ovary, and uterus. PR is also expressed in pituitary gonadotrope cells, but its specific role therein has not been assessed in vivo. We therefore generated gonadotrope-specific Pgr conditional knockout mice (cKO) using the Cre-LoxP system. Overall, both female and male cKO mice appeared phenotypically normal. cKO females displayed regular estrous cycles (vaginal cytology) and normal fertility (litter size and frequency). Reproductive organ weights were comparable between wild-type and cKO mice of both sexes, as were production and secretion of the gonadotropins, LH and FSH, with one exception. On the afternoon of proestrus, the amplitude of the LH surge was blunted in cKO females relative to controls. Contrary to predictions of earlier models, this did not appear to derive from impaired GnRH self-priming. Collectively, these data indicate that PR function in gonadotropes may be limited to regulation of LH surge amplitude in female mice via a currently unknown mechanism.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.654
Threshold uncertainty score0.768

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.021
GPT teacher head0.266
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 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

Citations18
Published2019
Admission routes2
Has abstractyes

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