Identification of differentially expressed markers in human follicular cells associated with competent oocytes
Bibliographic record
Abstract
BACKGROUND: The development of an accurate method for selection of high-quality embryos is essential to achieve high pregnancy rates with single embryo transfer in human IVF. The developmental competence of the oocyte is acquired during follicle maturation and strong communication also exists between the follicular cells (FCs) and the oocytes; thus oocyte developmental competence may be determined by markers expressed in the surrounding FCs. METHODS: From consenting patients (n = 40), FCs were recovered on a per follicle basis by individual follicle puncture. Hybridization analyses using a custom-made complementary DNA microarray containing granulosa/cumulus expressed sequence tags (ESTs) from subtracted libraries and an Affymetrix GeneChip were performed to identify specific genes expressed in follicles leading to a pregnancy. The selected candidate genes were validated by quantitative-PCR (Q-PCR). RESULTS: Subtractive libraries prepared from pooled samples representing pregnant versus non-pregnant patients produced 1694 ESTs. Hybridization data analysis discriminated 115 genes associated with competent follicles. Selected candidates were confirmed by Q-PCR: 3-beta-hydroxysteroid dehydrogenase 1 (P = 0.0078), Ferredoxin 1 (P = 0.0203), Serine (or cysteine) proteinase inhibitor clade E member 2 (P = 0.0499), Cytochrome P450 aromatase (P = 0.0359) and Cell division cycle 42 (P = 0.0396). CONCLUSIONS: Microarray technologies are useful to mine the transcriptome of FCs expressed in follicles associated with competent oocytes and could be used to improve embryo selection with the objective of 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 | 0.000 |
| 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".