Genomic RNA profiling and the programme controlling preimplantation mammalian development
Bibliographic record
Abstract
Preimplantation development shifts from a maternal to embryonic programme rapidly after fertilization. Although the majority of oogenetic products are lost during the maternal to embryonic transition (MET), several do survive this interval to contribute directly to supporting preimplantation development. Embryonic genome activation (EGA) is characterized by the transient expression of several genes that are necessary for MET, and while EGA represents the first major wave of gene expression, a second mid-preimplantation wave of transcription that supports development to the blastocyst stage has been discovered. The application of genomic approaches has greatly assisted in the discovery of stage specific gene expression patterns and the challenge now is to largely define gene function and regulation during preimplantation development. The basic mechanisms controlling compaction, lineage specification and blastocyst formation are defined. The requirement for embryo culture has revealed plasticity in the developmental programme that may exceed the adaptive capacity of the embryo and has fostered important research directions aimed at alleviating culture-induced changes in embryonic programming. New levels of regulation are emerging and greater insight into the roles played by RNA-binding proteins and miRNAs is required. All of this research is relevant due to the necessity to produce healthy preimplantation embryos for embryo transfer, to ensure that assisted reproductive technologies are applied in the most efficient and safest way possible.
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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 | 0.001 | 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 | 0.001 |
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".