Melatonin Signaling Pathways Implicated in Metabolic Processes in Human Granulosa Cells (KGN)
Bibliographic record
Abstract
Female reproduction depends on the metabolic status, especially during the period of folliculogenesis. Even though it is believed that melatonin can improve oocyte competence, there is still limited knowledge of how it can modulate metabolic processes during folliculogenesis and which signaling pathways are involved in regulating gene expression. To investigate the effects of melatonin on metabolic signals during the antral stage of follicular development, human granulosa-like tumor cells (KGN) were treated with melatonin or forskolin, 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), 1009 and 922 genes were identified as differentially expressed in response to melatonin and forskolin, respectively. Analysis of major upstream regulators suggested that melatonin may activate PKB/mTOR signaling pathways to program the metabolism of KGN cells to support slower growth and differentiation and to prevent follicular atresia. Similarly, PKA activation through stimulation of cAMP synthesis with FSK seemed to exert the same effects as melatonin in reducing follicular growth and regulating differentiation. This study suggests that melatonin may act through PKA and PKB simultaneously in human granulosa cells to prevent follicular atresia and early luteinization at the antral stage.
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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 | 0.001 |
| 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".