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Record W4226389230 · doi:10.3389/fendo.2022.881309

Transcription Factors in the Regulation of Leydig Cell Gene Expression and Function

2022· review· en· W4226389230 on OpenAlexafffund
Karine de Mattos, Robert S. Viger, Jacques Tremblay

Bibliographic record

VenueFrontiers in Endocrinology · 2022
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic and Clinical Aspects of Sex Determination and Chromosomal Abnormalities
Canadian institutionsUniversité LavalCentre hospitalier universitaire de Québec
FundersCanadian Institutes of Health Research
KeywordsTranscription factorBiologyLeydig cellCellular differentiationGeneCell biologyRegulation of gene expressionTranscription (linguistics)Embryonic stem cellCell fate determinationGene expressionTCF4Cell typePsychological repressionCellGeneticsEndocrinologyLuteinizing hormoneEnhancerHormone

Abstract

fetched live from OpenAlex

Cell differentiation and acquisition of specialized functions are inherent steps in events that lead to normal tissue development and function. These processes require accurate temporal, tissue, and cell-specific activation or repression of gene transcription. This is achieved by complex interactions between transcription factors that form a unique combinatorial code in each specialized cell type and in response to different physiological signals. Transcription factors typically act by binding to short, nucleotide-specific DNA sequences located in the promoter region of target genes. In males, Leydig cells play a crucial role in sex differentiation, health, and reproductive function from embryonic life to adulthood. To better understand the molecular mechanisms regulating Leydig cell differentiation and function, several transcription factors important to Leydig cells have been identified, including some previously unknown to this specialized cell type. This mini review summarizes the current knowledge on transcription factors in fetal and adult Leydig cells, describing their roles and mechanisms of action.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.991
Threshold uncertainty score0.538

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.026
GPT teacher head0.267
Teacher spread0.241 · 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 designNot applicable
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

Citations44
Published2022
Admission routes2
Has abstractyes

Explore more

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