Nuclear Receptors in Leydig Cell Gene Expression and Function1
Bibliographic record
Abstract
Several signals, such as hormones and signaling molecules, have been identified as important regulators of Leydig cell differentiation and function. Conveying these signals and translating them into a genomic response to ensure an accurate physiological output requires the action of a network of transcription factors, including those belonging to the nuclear receptor superfamily. Nuclear receptors regulate expression of genes important for growth, differentiation, development, and homeostasis. Several nuclear receptors, such as steroid hormone receptors (NR3A and NR3C families), are activated upon ligand binding, whereas others, including members of the NR2C, NR2F, and NR4A families, either do not require a ligand or ligands have yet to be identified. Several nuclear receptors (e.g., NR2F2 and NR5A1) have been shown to play essential roles in Leydig cells, whereas for others (e.g., NR2B1 and NR4A1), the assessment of their function has been precluded by the early embryonic lethality associated with null mice or by redundancy mechanisms by other family members. This is now being overcome with the generation of novel approaches, including Leydig cell-specific knockout models. This review provides an overview of the nuclear receptor family of transcription factors as they relate to Leydig cell gene expression and function.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".