Causes and consequences of chromosome segregation error in preimplantation embryos
Bibliographic record
Abstract
Errors in chromosome segregation are common during the mitotic divisions of preimplantation development in mammalian embryos, giving rise to so-called 'mosaic' embryos possessing a mixture of euploid and aneuploid cells. Mosaicism is widely considered to be detrimental to embryo quality and is frequently used as criteria to select embryos for transfer in human fertility clinics. However, despite the clear clinical importance, the underlying defects in cell division that result in mosaic aneuploidy remain elusive. In this review, we summarise recent findings from clinical and animal model studies that provide new insights into the fundamental mechanisms of chromosome segregation in the highly unusual cellular environment of early preimplantation development and consider recent clues as to why errors should commonly occur in this setting. We furthermore discuss recent evidence suggesting that mosaicism is not an irrevocable barrier to a healthy pregnancy. Understanding the causes and biological impacts of mosaic aneuploidy will be pivotal in the development and fine-tuning of clinical embryo selection methods.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".