Rapid Aneuploidy Detection of Chromosomes 13, 18, 21, X and Y Using Quantitative Fluorescent Polymerase Chain Reaction with Few Microdissected Fetal Cells
Bibliographic record
Abstract
OBJECTIVES: Analysis of DNA from small numbers of cells, such as fetal cells in maternal blood, is a major limiting factor for their use in clinical applications. Traditional methods of single-cells whole genome amplification (SCs-WGA) and accurate analysis have been challenging to date. Our purpose was to assess the feasibility of using a few fetal cells to determine fetal sex and major chromosomal abnormalities by quantitative fluorescent polymerase chain reaction (QF-PCR). METHODS: Cultured cells from 26 amniotic fluid samples were used for standard DNA extraction and recovery of 5 fetal cells by laser-capture microdissection. SCs-WGA was performed using the DNA from the microdissected cells. PCR amplification of short tandem repeats specific for chromosomes 13, 18, 21, X and Y was performed on extracted and amplified DNA. Allele dosage and sexing were quantitatively analyzed following separation by capillary electrophoresis. RESULTS: Microsatellite QF-PCR analysis showed high concordance in chromosomal copy number between extracted and amplified DNA when 5 or more cells were used. Results were in concordance with that of conventional cytogenetic analysis. CONCLUSION: Satisfactory genomic coverage can be obtained from SCs-WGA. Clinically, SCs-WGA coupled with QF-PCR can provide a reliable, accurate, rapid and cost-effective method for detection of major fetal chromosome abnormalities.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".