Triggering method in assisted reproduction alters the cumulus cell transcriptome
Bibliographic record
Abstract
RESEARCH QUESTION: How does the choice of triggering final oocyte maturation affect the cumulus cell transcriptome? DESIGN: Sixty patients undergoing gonadotrophin-releasing hormone antagonist (GnRH-ant) IVF cycles were recruited for this nested case-control study. Patients were stratified into three subgroups based on their ovarian reserve (high, normal and low). Triggering final oocyte maturation was accomplished by either single trigger (with human chorionic gonadotrophin [HCG] only or gonadotrophin-releasing hormone agonist [GnRH-ag] only) or dual trigger combining HCG and GnRH-ag. The choice of trigger was at the discretion of the treating physician. Within each group patients receiving a dual trigger were matched by demographic and pre-stimulation parameters with patients receiving a single trigger. The matching was performed to minimize the biological variability within each subgroup. Thirty patients were included in the final analysis. Cumulus cells were stripped away from the retrieved oocytes. Cumulus cells from three sibling oocytes were pooled, the RNA extracted and libraries prepared. Next-generation sequencing was performed on all samples. RESULTS: Dual triggering supports key ovarian pathways of oocyte maturation and extracellular matrix remodelling, while attenuating vasculo-endothelial growth and providing antioxidant protection to the growing follicles. CONCLUSIONS: This is the first study to delineate key transcriptomic changes under dual triggering of final oocyte maturation, across different patient populations. The findings underline the need for larger-scale studies validating transcriptomic effects of methods for triggering final oocyte maturation. Furthermore, there is a need for large-scale clinical randomized controlled studies to relate the findings of this study with clinical outcomes.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".