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Stressors, glucocorticoids and ovarian function in teleosts

2010· review· en· W2043766468 on OpenAlexaff
J. F. Leatherland, M. Li, Sangjucta Barkataki

Bibliographic record

VenueJournal of Fish Biology · 2010
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicReproductive Physiology in Livestock
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsBiologyInternal medicineGlucocorticoidEndocrinologyHormoneHypothalamusEndocrine systemVitellogeninPituitary glandOvaryFish <Actinopterygii>

Abstract

fetched live from OpenAlex

The purpose of this overview is to re-examine the postulated direct and indirect actions of glucocorticoids on ovarian function in teleosts. The re-examination is undertaken in light of recent advances in the understanding of the stress response itself, the mode of action of the hypothalamus-pituitary gland-ovarian (HPO) axis, the mechanisms of control of oestrogen-dependent hepatic vitellogenin (VtG) secretion and the apparent roles of corticotrophin-releasing hormone (CRH) and CRH-related factors in the regulation of feeding activity. Many of the results of different studies, particularly whole-animal studies, are conflicting, and little is known as to whether the hormone acts directly on various components of the HPO axis or indirectly by virtue of redirection of energy resources away from ovarian growth to provide a source of metabolic resources for other organ systems involved in the physiological stress response. In vitro studies provide some new insights into the direct actions of glucocorticoid on hepatic VtG synthesis and ovarian follicle steroidogenesis, but even here, in some studies the cellular sites of action of these hormones is not altogether clear. The overview emphasizes the complexity of the stress response, the complexity of the regulation of glucocorticoid-dependent gene expression and the extensive interactive nature of the HPO with other hypothalamus-pituitary gland-peripheral endocrine gland axes, such as the thyroid (HPT), 'somatic' (GH-IGF) and interrenal tissue (HPI) axes.

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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.996
Threshold uncertainty score0.501

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.035
GPT teacher head0.290
Teacher spread0.255 · 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 designOther design
Domainnot available
GenreReview

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

Citations74
Published2010
Admission routes1
Has abstractyes

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