Deadly decisions: the role of genes regulating programmed cell death in human preimplantation embryo development
Bibliographic record
Abstract
Human preimplantation embryo development is prone to high rates of early embryo wastage, particularly under current in vitro culture conditions. There are many possible underlying causes for embryo demise, including DNA damage, poor embryo metabolism and the effect of suboptimal culture media, all of which could result in an imbalance in gene expression and the failed execution of basic embryonic decisions. In view of the complex interactions involved in embryo development, a thorough understanding of these parameters is essential to improving embryo quality. An increasing body of evidence indicates that cell fate (i.e. survival/differentiation or death) is determined by the outcome of specific intracellular interactions between pro- and anti-apoptotic proteins, many of which are expressed during oocyte and preimplantation embryo development. The recent availability of mutant mice lacking expression of various genes involved in the regulation of cell survival has enabled rapid progress towards identifying those molecules that are functionally important for normal oocyte and preimplantation embryo development. In this review we will discuss the current understanding of the regulation of cell death gene expression during preimplantation embryo development, with a focus on human embryology and a discussion of animal models where appropriate.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".