The genomic response of human granulosa cells (KGN) to melatonin and specific agonists/antagonists to the melatonin receptors
Bibliographic record
Abstract
Melatonin is a known modulator of follicle development; it acts through several molecular cascades via binding to its two specific receptors MT1 and MT2. Even though it is believed that melatonin can modulate granulosa cell (GC) functions, there is still limited knowledge of how it can act in human GC through MT1 and MT2 and which one is more implicated in the effects of melatonin on the metabolic processes in the dominant follicle. To better characterize the roles of these receptors on the effects of melatonin on follicular development, human granulosa-like tumor cells (KGN) were treated with specific melatonin receptor agonists and antagonists, and gene expression was analyzed with RNA-seq technology. Following appropriate normalization and the application of a fold change cut-off of 1.5 (FC 1.5, p ≤ 0.05) for each treatment, lists of the principal differentially expressed genes (DEGs) are generated. Analysis of major upstream regulators suggested that the MT1 receptor may be involved in the melatonin antiproliferative effect by reprogramming the metabolism of human GC by activating the PKB signaling pathway. Our data suggest that melatonin may act complementary through both MT1 and MT2 receptors to modulate human GC steroidogenesis, proliferation, and differentiation. However, MT2 receptors may be the ones implicated in transducing the effects of melatonin on the prevention of GC luteinization and follicle atresia at the antral follicular stage through stimulating the PKA pathway.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".