Genomic assessment of follicular marker genes as pregnancy predictors for human IVF
Bibliographic record
Abstract
Embryo selection efficiency in human IVF procedure is still suboptimal as shown by low pregnancy rates with single embryo transfer (SET). Bidirectional communication between the oocyte and follicular cells (FC) is essential to achieve developmental competence of the oocyte. Differences in the gene expression profile of FCs from follicles leading to pregnancy could provide useful markers of oocyte developmental competence. FCs were recovered by individual follicle puncture. FC expression levels of potential markers were assessed by Q-PCR with an intra-patient and an inter-patient analysis approach. Using gene expression, a predictive model of ongoing pregnancy was investigated. Using intra-patient analysis, four candidate genes, phosphoglycerate kinase 1 (PGK1), regulator of G-protein signalling 2 (RGS2), regulator of G-protein signalling 3 (RGS3) and cell division cycle 42 (CDC42) showed a difference between FCs from follicles leading to a pregnancy or developmental failure. The best predictors for ongoing pregnancy were PGK1 and RGS2. Additionally, inter-patient analysis revealed differences in FC expression for PGK1 and CDC42 between follicles leading to a transferred embryo with positive pregnancy results and those with negative results. Both inter-patient and intra-patient approaches must be taken into consideration to delineate gene expression variations in the context of follicular competence. A predictor model using biomarkers could improve the efficiency of predicting developmental competence of oocytes. These new approaches provide useful tools in the context of embryo selection and in the improvement of pregnancy rates with SET.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".