Quantification of all fetal nucleated cells in maternal blood in different cases of aneuploidies
Bibliographic record
Abstract
We quantified all fetal nucleated cells (FNCs) per unit volume of maternal blood in different aneuploid pregnancies using molecular cytogenetic techniques. Seven cases of male trisomy 18, two triploidies (69,XXX), two 47,XXX, one 47,XXY, one 47,XYY, one male trisomy 13, and one case of 47,XY,r(22),+r(22) were analyzed. Whole blood samples were obtained from 15 women between 17 and 29 gestational weeks and harvested without using fetal cell enrichment procedures. Fluorescence in situ hybridization and primed in situ labeling were performed to identify the FNCs. All slides were manually scanned to quantify those cells. We have identified 4-20 FNCs/ml of maternal blood in the cases of trisomy 18; 10 and 25 FNCs/ml in the two cases of triploidy; 16 and 14 FNCs/ml, respectively, in the two X trisomies; 19 FNCs/ml in the 47,XXY; 26 FNCs/ml in the 47,XYY; nine FNCs/ml in the trisomy 13; and 10 FNCs/ml in the case of r(22). To detect all FNCs in all aneuploid pregnancies, we have used a very simple method that minimizes the manipulation steps to avoid losing fetal cells. The number of FNCs identified in aneuploid pregnancies was 2-5 times higher than in normal pregnancies. This higher number of FNCs will favor the design of a non-invasive pre-natal test.
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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 | 0.001 |
| 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".