Choosing a culture medium for SCNT and iSCNT reconstructed embryos: from domestic to wildlife species
Bibliographic record
Abstract
Over the past decades, in vitro culture media have been developed to successfully support IVF embryo growth in a variety of species. Advanced reproductive technologies, such as somatic cell nuclear transfer (SCNT), challenge us with a new type of embryo, with special nutritional requirements and altered physiology under in vitro conditions. Numerous studies have successfully reconstructed cloned embryos of domestic animals for biomedical research and livestock production. However, studies evaluating suitable culture conditions for SCNT embryos in wildlife species are scarce (for both intra- and interspecies SCNT). Most of the existing studies derive from previous IVF work done in conventional domestic species. Extrapolation to non-domestic species presents significant challenges since we lack information on reproductive processes and embryo development in most wildlife species. Given the challenges in adapting culture media and conditions from IVF to SCNT embryos, developmental competence of SCNT embryos remains low. This review summarizes research efforts to tailor culture media to SCNT embryos and explore the different outcomes in diverse species. It will also consider how these culture media protocols have been extrapolated to wildlife species, most particularly using SCNT as a cutting-edge technical resource to assist in the preservation of endangered 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".