Molecular markers of fertility in cattle oocytes and embryos progress and challenges
Bibliographic record
Abstract
In order for assisted reproduction technologies to improve, better methods to discern eggs and embryos according to their level of developmental competence are urgently required to substitute or complement the subjective morphological selection criteria still broadly in use. Objective and reliable molecular markers of viability have been studied during the last decades as robust options to select the best oocytes and embryos for embryo transfer programs. These molecular methodologies rely mostly on the novel “OMICS” technologies. Among these, transcriptomicsis the primary platform applied so far in animal breeding research mainly due to the possibility to amplify small samples. In addition, biomarkers of competence have not only been instrumental to select the best oocytes and embryos for reproductive technologies, but have also shed light on the intricate molecular physiology leading to the acquisition of developmental capacity within the ovary and on how the embryo manifests this potential during culture. Nevertheless, such molecular profiling usually implies the destruction of the oocyte/embryo hereby preventing the practical use of biomarkers in in vitro embryo production and transfer systems. Alternatively, encouraging results have been lately obtained from non-invasive technologies based on biopsies of follicular somatic cells surrounding the developing egg, as well as from metabolic analysis of follicular fluid or spent culture media. This work summarizes the achievements of recent years in the field of biomarkers of competent bovine embryos. The main challenges will be exposed, while the future guidelines will help to comprehend why biomarkers of developmental competence appear promising to take us steps forward in the amelioration of reproductive technologies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".