Recurrent triploid digynic conceptions and mature ovarian teratomas: Are they different manifestations of the same genetic defect?
Bibliographic record
Abstract
Miscarriages affect 15% of clinically recognized pregnancies. Recurrent miscarriage (RM) is defined by the occurrence of at least two consecutive pregnancy losses and affects 1%-5% of couples trying to conceive. In an attempt to categorize patients with RM and identify the mechanisms leading to their miscarriages, we first used flow cytometry to assess the ploidy of 93 products of conception (POCs) from 53 patients with RM (≥3 miscarriages). We identified a single patient with four triploid POCs. We then used fluorescent in situ hybridization to confirm the triploidies and fluorescent microsatellite genotyping with distal and pericentromeric markers to determine their parental origin and the mechanisms leading to their formation. We found that all four triploidies were digynic and due to a failure in meiosis II (MII), suggesting a genetic predisposition. Upon further investigation into the family, we found a remarkable history of ovarian cysts and dysfunctions on the maternal side. Notably, one maternal cousin had a mature ovarian teratoma that we analyzed and found an identical mechanism at its origin, a failure in MII. The identification of two patients in the same family with two different manifestations-digynic triploid conceptions and mature ovarian teratomas, both resulting from the failure of MII-suggests an inherited genetic susceptibility toward an error in MII segregating in the family that may manifest in the form of a triploid digynic miscarriage or a mature ovarian teratoma. Our findings may facilitate the future identification of causative mutations for MII defects.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".