microRNAs for qPCR Normalization Under Morphofunctional Conditions in Bovine Sperm (<i>Bos taurus</i>)
Bibliographic record
Abstract
Cattle represents one of the most common and widely distributed categories of large ruminants, with well-established production practices. Fertility is a key factor that significantly influences the success of this production. Studies have shown that microRNAs (miRNAs) present in sperm cells play a crucial role as regulators of processes related to sperm functionality. miRNAs quantification by qPCR is one of the most accurate and straightforward methods, but this technique requires data normalization, and there is no universal consensus on which miRNAs should be used. The present study aimed to identify suitable miRNAs normalizers for qPCR analysis of Bos taurus semen. To achieve this, normalization candidates were assessed under different semen quality conditions, considering sperm morphology and motility. A small nuclear RNA (U6) and six miRNA candidates (Let-7c-5p, miR-100-5p, miR-25-3p, miR-26a-5p, miR-204-5p, miR-92a-3p) were selected. The expression stability of each candidate was analyzed using four independent methods (delta Ct, geNorm, NormFinder, and BestKeeper), under the semen quality conditions. Additionally, a comprehensive stability analysis was conducted using RefFinder, for each condition individually and for the combined conditions. The results indicated that miR-92a-3p was the most stable reference miRNA for motility-related analyses, while Let-7c-5p emerged as the best candidate for morphology-focused analyses. As a normalizer to analyze samples concomitantly, Let-7c-5p was identified as the optimal normalizer, while miR-26a-5p was the least stable candidate. This study provides the first identification of miRNA normalizers for qPCR analysis of Bos taurus semen, enabling more accurate miRNA quantification in this biological matrix and species.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".