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Record W2160151670 · doi:10.1095/biolreprod67.2.361

Haploinsufficiency of the Follicle-Stimulating Hormone Receptor Accelerates Oocyte Loss Inducing Early Reproductive Senescence and Biological Aging in Mice1

2002· article· en· W2160151670 on OpenAlexafffund
Natalia Danilovich, M.R. Sairam

Bibliographic record

VenueBiology of Reproduction · 2002
Typearticle
Languageen
FieldMedicine
TopicReproductive Biology and Fertility
Canadian institutionsMcGill UniversityUniversité de MontréalMontreal Clinical Research Institute
FundersCanadian Institutes of Health Research
KeywordsBiologyHaploinsufficiencyOvarySenescenceOffspringEndocrinologyInternal medicineOocyteEstrogenEstrogen receptorFolliculogenesisAndrologyEmbryoGeneGeneticsPhenotypeEmbryogenesisPregnancyCancer

Abstract

fetched live from OpenAlex

Female mice that are null for the FSH-receptor (FSH-R) gene are estrogen deficient, acyclic, and sterile. However, the heterozygous (+/-) mice initially have reduced fertility and stop breeding by 7-9 mo. The purpose of this study was to understand the basis of reduced fertility in mice with haploinsufficiency of the FSH-R. Heterozygous females were compared to +/+ females at 3, 7, and 12 mo of age. By 7 mo most of the +/- females were acyclic and <50% delivered pups. The wild-type females were normal in these respects. None of the 1-yr-old +/- females gave viable offspring (73% in +/+). Many degenerative changes, including atresia and apoptosis, and profound loss of oocytes, were apparent in +/- mice by 7 mo. The 1-yr-old +/- ovary had very few follicles and consisted mostly of fibroid tissue and cysts. Our data support the hypothesis that reproductive deficits in +/- FSH-R mice occur because of accelerated oocyte loss due to increased cell death in the ovary. These events contribute to early reproductive senescence and biological aging in mice. Thus FSH-R status is an important determinant of ovarian aging and all phenomena that arise from subsequent estrogen deficiency and other aberrations.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.315
Threshold uncertainty score0.494

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.055
GPT teacher head0.286
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

Citations48
Published2002
Admission routes2
Has abstractyes

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