Stable reference genes in granulosa cells of bovine dominant follicles during follicular growth, FSH stimulation and maternal aging
Bibliographic record
Abstract
The aim of the present study was to determine a set of reference genes in granulosa cells of dominant follicles that are suitable for relative gene expression analyses during maternal and follicular aging. Granulosa cells of growing and preovulatory dominant follicles were collected from aged and young cows (maternal aging study) and from FSH-stimulated follicles developing under different durations of FSH treatment (follicular aging study). The mRNA levels of the two commonly used reference genes (GAPDH, ACTB) and four novel genes (UBE2D2, EIF2B2, SF3A1, RNF20) were analysed using cycle threshold values. Results revealed that mRNA levels of GAPDH, ACTB, EIF2B2, RNF20, SF3A1 and UBE2D2 were similar (P>0.05) between dominant follicle type, age and among follicles obtained after FSH-stimulation, but differed (P=0.005) due to mRNA processing (i.e. with versus without amplification). The stability of reference genes was analysed using GeNorm, DeltaCT and NormFinder programs and comprehensive ranking order was determined using RefFinder. The mRNA levels of GAPDH and ACTB were less stable than those of UBE2D2 and EIF2B2. The geometric mean of multiple genes (UBE2D2, EIF2B2, GAPDH and SF3A1) is a more appropriate reference control than the use of a single reference gene to compare relative gene expression among dominant and FSH-stimulated follicles during maternal and/or follicular aging studies.
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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) | 0.001 | 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 | 0.000 |
| 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".