Follicular fluid cytokine concentration impact on oocyte maturity, embryo quality and pregnancy outcomes during ovarian stimulation cycles in infertile women
Bibliographic record
Abstract
Introduction: Since cytokines in follicular fluid play an important role in ovulation and oocyte maturation, they also have an essential role in reproductive processes such as fertilization, early embryonic development, and implantation potential. The present study was performed aimed to evaluate the effect of these cytokines on oocyte maturation and embryo quality during assisted reproductive therapy (ART) and pregnancy outcomes of primary infertile women.Methods: This case-control study was performed in 2020 on 44 infertile women undergoing ART in Yazd Reproductive Sciences Institute. The relationship between the concentrations of interleukin (IL)-6, IL-12, Transforming growth factor-beta (TGF-ß) and tumor necrosis factor alpha (TNF-α) in follicular fluid of each patient was assessed by Enzyme linked immune sorbent assay. The results were statistically analyzed by demographic and embryological variables of patients. Data were analyzed by SPSS software (version 21) and non-parametric statistical tests. P <0.05 was considered statistically significant.Results: The total fertilization rate in patients was 62.7%, the total oocyte maturation rate was 78.6% and the total pregnancy rate was 22%.There was a positive significant correlation between IL-6 concentration and fertilization rate (P = 0.04), but no significant correlation was found between the concentration of other studied cytokines with patients' age and BMI, oocyte maturation and fertilization rate and embryo quality (P≥0.05).Conclusion: In the present study, there was no relationship between the concentrations of cytokines IL-6, IL-12, TGF-ß and TNF-α in the follicular fluid of infertile women with oocyte maturation, embryo quality and pregnancy outcomes. More extensive studies with more samples are suggested to validate the results.
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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 | 0.001 |
| 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".