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Record W2291857833 · doi:10.1515/hmbci-2015-0046

Roles of leptin, adiponectin and resistin in the transcriptional regulation of steroidogenic genes contributing to decreased Leydig cells function in obesity

2015· review· en· W2291857833 on OpenAlexaff
Pauline Roumaud, Luc J. Martin

Bibliographic record

VenueHormone Molecular Biology and Clinical Investigation · 2015
Typereview
Languageen
FieldMedicine
TopicAdipokines, Inflammation, and Metabolic Diseases
Canadian institutionsUniversité de Moncton
Fundersnot available
KeywordsResistinLeptinAdiponectinInternal medicineEndocrinologyAdipose tissueHormoneSteroidogenic acute regulatory proteinTestosterone (patch)BiologyAdipokineAndrogenObesityMedicineGene expressionInsulin resistanceGene

Abstract

fetched live from OpenAlex

The increase in obesity rate is a major public health issue associated with increased pathological conditions such as type 2 diabetes or cardiovascular diseases. Obesity also contributes to decreased testosterone levels in men. Indeed, the adipose tissue is an endocrine organ which produces hormones such as leptin, adiponectin and resistin. Obesity results in pathological accumulations of leptin and resistin, whereas adiponectin plasma levels are markedly reduced, all having a negative impact on testosterone synthesis. This review focuses on current knowledge related to transcriptional regulation of Leydig cells' steroidogenesis by leptin, adiponectin and resistin. We show that there are crosstalks between the regulatory mechanisms of these hormones and androgen production which may result in a dramatic negative influence on testosterone plasma levels. Indeed leptin, adiponectin and resistin can impact expression of different steroidogenic genes such as Star, Cyp11a1 or Sf1. Further investigations will be required to better define the implications of adipose derived hormones on regulation of steroidogenic genes expression within Leydig cells under physiological as well as pathological conditions.

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)0.003
metaresearch head score (Gemma)0.001
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: Review · Consensus signal: Review
Teacher disagreement score0.864
Threshold uncertainty score0.621

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
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 integrity0.0000.000
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 head0.057
GPT teacher head0.355
Teacher spread0.298 · 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
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

Citations68
Published2015
Admission routes1
Has abstractyes

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