Quantification of all fetal nucleated cells in maternal blood between the 18th and 22nd weeks of pregnancy using molecular cytogenetic techniques
Bibliographic record
Abstract
Different types of nucleated fetal cells (trophoblasts, erythroblasts, lymphocytes, and granulocytes) have been recovered in maternal peripheral blood. In spite of many attempts to estimate the number of fetal cells in maternal circulation, there is still much controversy concerning this aspect. The numbers obtained vary widely, ranging from 1 nucleated cell per 104 to 1 per 109 nucleated maternal cells. The purpose of our project was to determine the absolute number of all different types of male fetal nucleated cells per unit volume of peripheral maternal blood. Peripheral blood samples were obtained from 12 normal pregnant women known to carry a male fetus between 18 and 22 weeks of pregnancy. Three milliliters (3 ml) of maternal blood has been processed without any enrichment procedures. Fluorescence in situ hybridization (FISH) and primed in situ labeling (PRINS) were performed, and fetal XY cells were identified (among maternal XX cells) and scored by fluorescent microscopy screening. The total number of male fetal nucleated cells per milliliter of maternal blood was consistent in each woman studied and varied from 2 to 6 cells per milliliter within the group of normal pregnancies. The number of fetal cells in maternal blood, at a given period, is reproducible and can therefore be assessed by cytogenetic methods. This confirms the possibility of developing a non-invasive prenatal diagnosis test for aneuploidies. Furthermore, we demonstrate that it is possible to repeatedly identify an extremely small number of fetal cells among millions of maternal cells.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".