Factors Affecting the Success of Ovum Pick-Up, In Vitro Production and Cryopreservation of Embryos in Cattle
Bibliographic record
Abstract
Increasing global demand for animal proteins warrants improved productivity by genetic selection of superior cattle and faster dissemination of genetics. Availability of more progeny for genomic selection should maximize chances of identifying animals with desirable traits and increase selection pressure. OPU and IVP of embryos using these oocytes will substantially increase calves produced compared to conventional embryo transfer (ET). The OPU-IVP technology not only supports genetic improvement but also contributes to reducing environmental impacts of livestock production systems by improving efficiency and optimizing resources, aligning with the Sustainable Development Goals of the United Nations. However, there are several factors influencing the success of OPU-IVP. This review is focused on these factors and the impacts of in vitro culture conditions on the lipid content of embryos and potential role of L-carnitine, a lipolytic agent, on developmental competence of IVP embryos. The documented effects of L-carnitine and current knowledge regarding regulation of the Hippo signaling pathway suggest that supplementation of embryo culture media with L-carnitine will increase post-thaw survival of IVP embryos and their subsequent developmental competence by regulating lipid metabolism, production of reactive oxygen species, and Hippo signaling. Therefore, this review highlights current advancements in the field of OPU-IVP and potential areas for refining culture conditions to yield developmentally competent embryos that survive cryopreservation procedures.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".