Analysis of correlations between zona pellucida birefringence and molecular markers of oocyte developmental competence
Bibliographic record
Abstract
Background Human infertility is the incapacity of a couple to conceive after one year of unprotected sexual intercourse. Selection of the best gametes for subsequent steps of fertilization and embryo transfer was shown to be the crucial step in infertility treatment procedure [1,2]. Oocyte selection using morphological criteria has been the gold standard method in assisted reproductive technologies (ART) clinics. Zona Pellucida (ZP) , a filamentous matrix of glycosylated glycoproteins surrounding the oocyte, is one of these morphological criteria of oocyte selection. In fact, ZP thickness and birefringence was reported to be positively correlated with higher ability of the oocyte to achieve successful pregnancy, but this selection approach has limitations in terms of accuracy, objectivity and constancy. Recent studies using OMICs approaches have identified key molecular markers in somatic cells (cumulus and/or granulosa cells) and follicular fluid that quantitatively and non-invasively predict the oocyte quality for better selection, higher pregnancy rates and efficient infertility treatment. These biomarkers could be a valuable reinforcement of the morphological selection criteria widely used in IVF clinics. In this context, this study was designed to study the relationship between some molecular predictors of oocyte quality found by our group and the conventional morphological parameters of oocyte quality. We expect to find a positive correlation between the ZP birefringence and molecular markers of oocyte competence. Such integrative strategy should lead to a powerful combined approach that will precisely predict the oocyte developmental potential, allowing therefore efficient infertility treatment and elective single embryo transfer (eSET).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".