Identification of copy number variants in miscarriages from couples with idiopathic recurrent pregnancy loss
Bibliographic record
Abstract
BACKGROUND: Recurrent pregnancy loss (RPL), defined as two or more miscarriages, affects 3-5% of couples trying to establish a family. Despite extensive evaluation, no factor is identified in ∼40% of cases. In this study, we investigated the possibility that submicroscopic chromosomal changes, not detectable by conventional cytogenetic analysis, exist in miscarriages with normal karyotypes (46,XY or 46,XX) from couples with idiopathic RPL. METHODS: Array comparative genomic hybridization (array-CGH) was used to assess for DNA copy number variants (CNVs) in 26 miscarriages with normal karyotypes. Parental array-CGH analysis was performed to determine if miscarriage CNVs were de novo or inherited. RESULTS: There were 11 unique (previously not described) CNVs, all inherited, identified in 13 miscarriages from 8 couples. The maternal origin of two CNVs was of interest as they involved the imprinted genes TIMP2 and CTNNA3, which are only normally expressed from the maternal copy in the placenta. Two additional cohorts, consisting of 282 women with recurrent miscarriage (RM) and 61 fertile women, were screened for these two CNVs using a Quantitative Multiplex Fluorescent PCR of Short Fragments assay. One woman with RM, but none of the fertile women, carried the CTNNA3-associated CNV. CONCLUSIONS: This preliminary study shows that array-CGH is useful for detecting CNVs in cases of RPL. Further investigations of CNVs, particularly those involving genes that are imprinted in placenta, in women with RPL could be worthwhile.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".