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Fetal sex alters maternal anti-Mullerian hormone during pregnancy in cattle

2017· article· en· W2756576412 on OpenAlexaff
Anja Stojsin-Carter, Nathália Nogueira da Costa, Rodrigo de Morais, Tiago Henrique Camara De, Mayra Pauline Ribeiro Costa, Timothy F. Carter, Daniel Gillis, Michael S. Neal, O. M. Ohashi, Moysés dos Santos Miranda, Flávio Vieira Meirelles, Laura A. Favetta, W.A. King

Bibliographic record

VenueAnimal Reproduction Science · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicReproductive Physiology in Livestock
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsFetusAnti-Müllerian hormoneGestationPlacentaHormonePregnancyEndocrinologyBiologyInternal medicineAndrologyMedicine

Abstract

fetched live from OpenAlex

Anti-Mullerian hormone (AMH) is expressed by both male and female fetuses during mammalian development, with males expressing AMH earlier and at significantly higher concentration. The aim of the current study was to explore the potential impact of pregnancy and fetal sex on maternal AMH and to determine if plasma (Pl) AMH or placenta intercotyledonary membrane and cotyledonary AMH receptor 2 (AMHR2) mRNA expression differ in pregnant cows carrying male vs. female fetuses. AMH levels in blood were measured using a bovine optimized ELISA kit. Cows pregnant with a male fetus were observed to have a significantly greater difference in Pl AMH between day 35 and 135 of gestation. Average fetal AMH level between 54 and 220days of gestation was also observed to be significantly higher in male vs. female fetuses. Intercotyledonary membranes and cotyledons were found to express AMHR2 between days 38 and 80 of gestation at similar levels in both fetal sexes. These findings support the hypothesis that fetal sex alters maternal Pl AMH during pregnancy in cattle.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
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 head0.033
GPT teacher head0.276
Teacher spread0.243 · 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 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

Citations17
Published2017
Admission routes1
Has abstractno

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