MicroRNAomic Analysis of Spent Media From Slow and Fast-Growing Bovine Embryos Reveal Distinct Differences
Bibliographic record
Abstract
In bovine embryos, microRNA (miRNA) expression has been profiled at each stage of early development in-vitro. 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 aim of the study was to profile miRNAs in the spent media of slow and fast-growing bovine embryos throughout the pre-implantation period. Bovine cumulus-oocyte-complexes were aspirated from ovaries, fertilized, and cultured to blastocyst stage of development. At 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. Following hybridization on a GeneChip miRNA 4.0 array, comparative analysis was conducted between spent media of slow and fast-growing embryos. In total, 34 differentially expressed miRNAs were identified between the comparison groups, with 14 of the miRNAs detected in the 2-cell samples, 7 miRNAs detected in the 8-cell samples, and 12 miRNAs detected in the blastocyst samples. The results demonstrate distinct miRNAs populations can be identified between slow and fast-growing embryos, highlighting 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 | 0.001 | 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".