The follicular fluid adipocytokine milieu could serve as a prediction tool for fertility treatment outcomes
Bibliographic record
Abstract
RESEARCH QUESTION: Can the adipocytokine milieu of the follicular niche improve the ability to predict treatment outcomes in infertile patients? DESIGN: Follicular fluid samples from overweight patients were analysed and compared with samples from matched normal-weight patients. Concentrations of adiponectin, chemerin, C-reactive protein, interleukin-6 (IL-6), IL-10, IL-18, insulin, leptin, prolactin, resistin, tumour necrosis factor alpha (TNF-α) and bone morphogenetic protein-15 (BMP-15) were assessed by multiple magnetic bead immunoassay (MMBI) and enzyme-linked immunosorbent assay and correlated with fertility treatment outcomes. RESULTS: Analysis of samples from 22 overweight and 22 normal-weight patients demonstrated that TNF-α can predict oocyte maturation rate. When stratified by body mass index (BMI), IL-10 emerges as a better predictor of oocyte maturation in normal-weight patients. Prolactin was a negative predictor for fertilization rate in the full cohort, and this prediction power was lost upon stratification. No adipocytokines were predictive of blastulation rate, and only age remained predictive. BMP-15 was a strong predictor of high-quality blastulation in the full cohort, more so in the normal-weight population. CONCLUSIONS: The adipocytokine milieu of the follicular fluid provides a snapshot of the growing oocyte's environment and can help predict fertility treatment outcomes, fine-tuning understanding of the dysregulation caused by increasing BMI. Inflammatory cytokines can predict oocyte maturation; prolactin, oocyte competence; and BMP-15, high-quality blastulation. Further analysis of these findings with a larger sample size and assessing individual oocytes will help shed more light on the clinical significance of these findings.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".