MicroRNAomic Analysis of Spent Media from Slow- and Fast-Growing Bovine Embryos Reveal Distinct Differences
Bibliographic record
Abstract
In bovine embryos, the microRNA (miRNA) expression has been profiled at each stage of early development in vitro. The miRNAomic analysis of spent media has the potential to reveal characteristics of embryo health; however, applications are limited without categorizing miRNA profiles by embryo quality. Time-lapse imaging has shown the timing of embryo development in vitro may be indicative of their developmental potential. The study aimed to characterize miRNAs in the spent media of bovine embryos with different growth rates during the pre-implantation phase. Bovine cumulus-oocyte complexes were aspirated from ovaries, fertilized, and cultured to blastocyst stage of development. At the 2-cell, 8-cell, and blastocyst stage, each microdrop of 30 presumptive zygotes were classified as slow- or fast-growing based on the percentage of embryos that had reached the desired morphological stage. A comparative analysis was performed on the spent media of slow- and fast-growing embryos using the results of a GeneChip miRNA 4.0 array hybridization. In total, 34 differentially expressed miRNAs were identified between the comparison groups: 14 miRNAs were found in the 2-cell samples, 7 in the 8-cell samples, and 12 in the blastocyst samples. The results demonstrate distinct miRNAs populations can be identified between slow- and fast-growing embryos, highlighting the novel biomarkers of developmental potential at each stage of pre-implantation development.
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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".