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Record W2121258776 · doi:10.1111/jne.12208

Molecular Analysis of the Koala Reproductive Hormones and Their Receptors: Gonadotrophin‐Releasing Hormone (GnRH),<scp> Follicle‐Stimulating Hormone </scp>β and <scp>Luteinising Hormone </scp>β with Localisation of <scp>G</scp>n<scp>RH</scp>

2014· article· en· W2121258776 on OpenAlexaff
Ellen R. Busby, Satoshi Soeta, Nancy M. Sherwood, Stephen D. Johnston

Bibliographic record

VenueJournal of Neuroendocrinology · 2014
Typearticle
Languageen
FieldMedicine
TopicHypothalamic control of reproductive hormones
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsBiologyHormoneInternal medicineEndocrinologyReceptorFollicle-stimulating hormoneHormone receptorLuteinizing hormoneGonadotropic cellGeneticsMedicine

Abstract

fetched live from OpenAlex

During evolution, reproductive hormones and their receptors in the brain-pituitary-gonadal axis have been altered by genetic mechanisms. To understand how the neuroendocrine control of reproduction evolved in mammals, it is important to examine marsupials, the closest group to placental mammals. We hypothesised that at least some of the hormones and receptors found in placental mammals would be present in koala, a marsupial. We examined the expression of koala mRNA for the reproductive molecules. Koala cDNAs were cloned from brain for gonadotrophin-releasing hormones (GnRH1 and GnRH2) or from pituitary for GnRH receptors, types I and II, follicle-stimulating hormone (FSH)β and luteinising hormone (LH)β, and from gonads for FSH and LH receptors. Deduced proteins were compared by sequence alignment and phylogenetic analysis with those of other vertebrates. In conclusion, the koala expressed mRNA for these eight putative reproductive molecules, whereas at least one of these molecules is missing in some species in the amniote lineage, including humans. In addition, GnRH1 and 2 are shown by immunohistochemistry to be expressed as proteins in the brain.

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)0.020
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.832
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.020
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0020.002
Science and technology studies0.0000.002
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.002
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 head0.012
GPT teacher head0.233
Teacher spread0.221 · 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.

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

Citations7
Published2014
Admission routes1
Has abstractyes

Explore more

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