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This article discusses a study that aims to demonstrate the scientific validity of a novel cell-based non-invasive prenatal testing (cbNIPT) method for detecting fetal copy number variants (CNVs) using single cell sequencing of circulating extravillous trophoblasts (cEVTs). The study enrolled 1388 high-risk pregnancies and found a screen positive rate of 18.9% for aneuploidies and 11.2% for pCNVs. The results showed that cbNIPT was able to detect pCNVs <1 Mb in size, which is below the resolution of cell-free DNA screening. The authors conclude that analyzing cEVTs from maternal blood could significantly reduce the residual risk for pCNVs in early gestational weeks. [Extracted from the article]

Since identification of women at risk of NIPT failure is crucial in counseling, we evaluated the feasibility of using a machine learning (ML) model based on maternal and fetal characteristics to predict low FF. Although non-invasive prenatal testing (NIPT) based on cell-free fetal DNA fragments in maternal blood samples is a well-established method for aneuploidy screening, in approximately 4% of women NIPT fails to return a result due to an insufficient fetal fraction (FF) cell-free DNA in maternal plasma. VP17.03: Prediction of low fetal fraction in maternal blood cell-free DNA using a machine learning model. [Extracted from the article]