Improving Fetal Fraction of Noninvasive Prenatal Screening Samples Collected in EDTA-Gel Tubes Using Gel Size Selection
Bibliographic record
Abstract
The aim of this study was to compare the use of EDTA-gel blood collection tubes with and without size selection to cell-stabilizing collection tubes for remote blood sampling for noninvasive prenatal screening (NIPS). Sixty-one pregnant women at 10 to 14 weeks' gestation undergoing NIPS were recruited. Participants were phlebotomized with Streck and EDTA-gel tubes. EDTA-gel tubes were centrifuged before shipping. Libraries prepared from cell-free DNA (cfDNA) extracted from both types of tubes were sequenced on Illumina NextSeq 500, and fetal fraction was estimated using SeqFF. EDTA-gel tube libraries were size selected on agarose gel to eliminate cfDNA fragments >160 bp and resequenced. The main outcome measure was fetal fraction expressed as percentage of total cfDNA sequenced, calculated from sequence read counts (SeqFF). Streck tube samples showed an average 1% higher fetal fraction than centrifuged EDTA-gel tubes without size selection. This difference increased with temperature. When EDTA-gel samples' libraries were size selected, the mean fetal fraction increased from 7% to 13%, with no sample having fetal fraction <4%. Using EDTA-gel tubes reduces NIPS sampling cost and tube processing time in the laboratory. Also, using EDTA-gel tubes does not lead to cfDNA degradation. Size selection increases fetal fraction, reduces the number of test failures, increases NIPS clinical performance, and may be helpful in situations asking for a higher fetal fraction, such as twin pregnancies or screening for sub-chromosomal imbalances.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".