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Record W2471171793 · doi:10.3389/fendo.2016.00071

The Role of RFamide-Related Peptide-3 in Age-Related Reproductive Decline in Female Rats

2016· article· en· W2471171793 on OpenAlexaff
Anna C. Geraghty, Sandra E. Muroy, Lance J. Kriegsfeld, George E. Bentley, Daniela Kaufer

Bibliographic record

VenueFrontiers in Endocrinology · 2016
Typearticle
Languageen
FieldMedicine
TopicHypothalamic control of reproductive hormones
Canadian institutionsCanadian Institute for Advanced Research
FundersNational Institute of Mental Health
KeywordsBiologyInternal medicineEndocrinologyPhysiologyMedicineAndrology

Abstract

fetched live from OpenAlex

Reproductive senescence, the point in time when females cease to show estrous cyclicity, is associated with endocrine changes in the hypothalamus, pituitary, and gonads. However, the mechanisms triggering this transition are not well understood. To gain a better understanding of the top-down control of the transition from reproductive competence to a state of reproductive senescence, we investigated middle-aged female rats exhibiting varying degrees of reproductive decline, including individuals with normal cycles, irregular cycles, and complete cessation of cycles. We identified hormonal changes in the brain that manifest before ovarian cycles exhibit any deterioration. We found that females exhibit an increase in RFamide-related peptide-3 (RFRP3) mRNA expression in the hypothalamus in middle age prior to changes in estrous cycle length. This increase is transient and followed by subsequent decreases in kisspeptin (KiSS1) and gonadotropin-releasing hormone (GnRH) mRNA expression. Expression of RFRP3 and its receptor also increased locally in the ovaries with advancing age. While it is well known that aging is associated with decreased GnRH release and downstream disruption of the hypothalamic-pituitary-gonadal (HPG) axis, herein, we provide evidence that reproductive senescence is likely triggered by alterations in a network of regulatory neuropeptides upstream of the GnRH system.

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)0.002
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 score0.143
Threshold uncertainty score0.734

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.009
GPT teacher head0.244
Teacher spread0.236 · 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

Citations7
Published2016
Admission routes1
Has abstractyes

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