Identification of follicular marker genes as pregnancy predictors for human IVF: new evidence for the involvement of luteinization process
Bibliographic record
Abstract
Multiple pregnancy represents an important health risk to both mother and child in fertility treatment. To reduce a high twin rate, restriction to one embryo transfer is needed. Morphological evaluation methods for predicting embryo viability has significant limitations. Tight communication exists between the follicular cells (FCs) and the oocyte; therefore, developmental competence may be determined by markers expressed in the surrounding FCs. In this study, cells were recovered on a per-follicle basis by individual follicle puncture. Hybridization analysis using a custom-made complementary DNA microarray containing FC transcripts was performed. Genes expressed in FCs associated with good morphological transferred embryos were identified from follicles associated with a pregnancy outcome (pregnancy group) or no pregnancy (non-pregnancy group). Ten candidates from the Pregnancy group and three from the Non-pregnancy group were validated by quantitative RT-PCR. The best predictors associated with pregnancy were UDP-glucose pyrophosphorylase-2 and pleckstrin homology-like domain, family A, member 1. Genes assessment showed no significant candidate genes associated with non-pregnancy outcome, but GA-binding protein transcription factor beta1 showed a tendency to be potentially more expressed in the non-pregnancy group. These markers could be related to granulosa luteinization process and could be used to improve embryo selection for successful single embryo transfer.
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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".