Gonadotropin-Releasing Hormones I and II Induce Apoptosis in Human Granulosa Cells
Bibliographic record
Abstract
BACKGROUND: The direct effects of GnRH-I or GnRH-II on apoptosis in human granulosa cells are unknown and, if present, can be influenced by FSH. Apoptosis involves activation of the intracellular proteolytic cascade of caspases. We therefore evaluated the roles of GnRH-I and -II, and the effects of FSH, on apoptosis in human granulosa cells and on caspases. METHODS: Human immortalized granulosa cells treated with GnRH-I or GnRH-II or nothing were cultured with and without antide (a GnRH-I antagonist), a broad-spectrum caspase inhibitor or selective caspase-8, -3, or -7 inhibitor, or FSH in replicates for 72 h. Apoptotic changes were evaluated by terminal deoxynucleotidyl-transferase-mediated biotin-dUTP nick-end labeling (TUNEL) assays, immunoblotting, and expression levels of caspases and compared by ANOVA. RESULTS: GnRH-I and -II induced TUNEL-positive apoptotic cells and increased cleavage activities of caspase-8, -3, and -7 by 48 h and peaked at 72 h, changes that were blocked by FSH cotreatment. Antide also effectively blocked these TUNEL-positive changes and expression levels of caspase-3 induced by GnRH-I or -II. Activation of caspase-8, -3, and -7 was inhibited by the corresponding caspase inhibitor. Caspase-8 inhibitor also abolished cleavages of caspase-3 and -7 induced by GnRH-I and -II. CONCLUSION: GnRH-I and -II induce apoptosis in human granulosa cells through GnRH-I receptors, which mediate the proteolytic caspase cascade involving caspase-8 (the initiator) and caspase-3 and -7 (the effectors). FSH protects human granulosa cells from apoptosis induced by GnRH-I or -II. This raises potentially important roles of GnRH-I and GnRH-II in regulating follicle development and atresia together with FSH.
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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) | 0.002 | 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".