Evaluation of the impact of density gradient centrifugation on fetal cell loss during enrichment from maternal peripheral blood
Bibliographic record
Abstract
OBJECTIVE: Physical separation by density gradient centrifugation (DGC) is usually used as an initial step of multistep enrichment protocols for purification of fetal cells (FCs) from maternal blood. Many protocols were designed but no single approach was efficient enough to provide noninvasive prenatal diagnosis. Procedures and methods were difficult to compare because of the nonuniformity of protocols among different groups. Recovery of FCs is jeopardized by their loss during the process of enrichment. Any loss of FCs must be minimized because of the multiplicative effect of each step of the enrichment process. The main objective of this study was to evaluate FC loss caused by DGC. METHODS: Fetal cells were quantified in peripheral blood samples obtained from both euploid and aneuploid pregnancies before and after enrichment by buoyant DGC using Histopaque 1.119 g/mL. RESULTS: Density gradient centrifugation results in major loss of 60% to 80% of rare FCs, which may further complicate subsequent enrichment procedures. Eliminating aggressive manipulations can significantly minimize FC loss. CONCLUSION: Data obtained raise questions about the appropriateness of the DGC step for the enrichment of rare FCs and argues for the use of the alternative nonaggressive version of the procedure presented here or prioritizing other methods of enrichments.
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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 | 0.000 | 0.000 |
| 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".