GDNF-Induced Downregulation of miR-145-5p Enhances Human Oocyte Maturation and Cumulus Cell Viability
Bibliographic record
Abstract
Context: Although glial cell line-derived neurotrophic factor (GDNF) and microRNAs (miRNAs) have been shown to regulate mammalian oocyte maturation, little is known about their effects on human oocyte maturation and the underlying molecular mechanisms. Objectives: To examine the effects of GDNF on both nuclear and cytoplasmic maturation in cultured immature human oocytes and to investigate the involvement of miRNAs in GDNF-induced oocyte maturation. Design: A total of 200 human immature oocytes were used to evaluate the effects of GDNF on oocyte maturation. The involvement of miRNAs in GDNF-induced oocyte maturation was identified by comparing the miRNA expression profiles of cumulus cells (CCs) either with or without GDNF stimulation. Setting: An in vitro fertilization center at the Women's Hospital, Zhejiang University School of Medicine. Methods: Agilent human miRNA (8*60K) arrays were used to examine the miRNA expression patterns of human CCs either with or without GDNF stimulation. miR-145-5p inhibitor and mimic transfections were performed to study downstream gene expression in human CCs. Results: During the in vitro maturation process, GDNF significantly increased the percentage of metaphase II-stage oocytes and downregulated the expression of miR-145-5p in cultured human CCs. Expression of miR-145-5p in CCs is negatively correlated with oocyte maturation. miR-145-5p mimic significantly decreased the expression of GDNF family receptor-α1, ret proto-oncogene, and epidermal growth factor receptor, whereas miR-145-5p inhibitor reversed these effects. GDNF treatment inhibited cell apoptosis in cultured CCs, and this suppressive effect was reversed by transfection with the miR-145-5p mimic. Conclusion: Downregulation of miR-145-5p may contribute to GDNF-induced enhancement of oocyte maturation and of cell viability against cell apoptosis.
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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".