Investigators have long been interested in the natural phenomenon of fetal and placental cell trafficking into the maternal circulation. The scarcity of these circulating cells makes their detection and isolation technically challenging. However, as a DNA source of fetal origin not mixed with maternal DNA, they have the potential of considerable benefit over circulating cell-free DNA-based noninvasive prenatal genetic testing (NIPT). Endocervical trophoblasts, which are less rare but more challenging to recover are also being investigated as an approach for cell-based NIPT. We review published studies from around the world describing both forms of cell-based NIPT and highlight the different approaches' advantages and drawbacks. We also offer guidance for developing a sound cell-based NIPT protocol.

Overview and recent developments in cell-based noninvasive prenatal testing.

An association between the vaginal microbiome and preterm birth has been reported. However, in practice, it is difficult to predict premature birth using the microbiome because the vaginal microbial community varies highly among samples depending on the individual, and the prediction rate is very low. The purpose of this study was to select markers that improve predictive power through machine learning among various vaginal microbiota and develop a prediction algorithm with better predictive power that combines clinical information. As a multicenter case–control study with 150 Korean pregnant women with 54 preterm delivery group and 96 full-term delivery group, cervicovaginal fluid was collected from pregnant women during mid-pregnancy. Their demographic profiles (age, BMI, education level, and PTB history), white blood cell count, and cervical length were recorded, and the microbiome profiles of the cervicovaginal fluid were analyzed. The subjects were randomly divided into a training (n = 101) and a test set (n = 49) in a two-to-one ratio. When training ML models using selected markers, five-fold cross-validation was performed on the training set. A univariate analysis was performed to select markers using seven statistical tests, including the Wilcoxon rank-sum test. Using the selected markers, including Lactobacillus spp., Gardnerella vaginalis, Ureaplasma parvum, Atopobium vaginae, Prevotella timonensis, and Peptoniphilus grossensis, machine learning models (logistic regression, random forest, extreme gradient boosting, support vector machine, and GUIDE) were used to build prediction models. The test area under the curve of the logistic regression model was 0.72 when it was trained with the 17 selected markers. When analyzed by combining white blood cell count and cervical length with the seven vaginal microbiome markers, the random forest model showed the highest test area under the curve of 0.84. The GUIDE, the single tree model, provided a more reasonable biological interpretation, using the 10 selected markers (A. vaginae, G. vaginalis, Lactobacillus crispatus, Lactobacillus fornicalis, Lactobacillus gasseri, Lactobacillus iners, Lactobacillus jensenii, Peptoniphilus grossensis, P. timonensis, and U. parvum), and the covariates produced a tree with a test area under the curve of 0.77. It was confirmed that the association with preterm birth increased when P. timonensis and U. parvum increased (AUC = 0.77), which could also be explained by the fact that as the number of Peptoniphilus lacrimalis increased, the association with preterm birth was high (AUC = 0.77). Our study demonstrates that several candidate bacteria could be used as potential predictors for preterm birth, and that the predictive rate can be increased through a machine learning model employing a combination of cervical length and white blood cell count information.

/pdf/predicting-preterm-birth-through-vaginal-microbiota-cervical-29p60lfj.pdf

Predicting preterm birth through vaginal microbiota, cervical length, and WBC using a machine learning model

Intra-amniotic infection and inflammation (IAI) affect fetal development and are highly associated with preterm labor and premature rupture of membranes, which often lead to adverse neonatal outcomes. Human amniotic membrane (hAM), the inner part of the amnio-chorionic membrane, protects the embryo/fetus from environmental dangers, including microbial infection. However, weakened amnio-chorionic membrane may be breached or pathogens may enter through a different route, leading to IAI. The hAM and human amniotic fluid (hAF) respond by activation of all components of the innate immune system. This includes changes in 1) hAM structure, 2) presence of immune cells, 3) pattern recognition receptors, 4) cytokines, 5) antimicrobial peptides, 6) lipid derivatives, and 7) complement system. Herein we provide a comprehensive and integrative review of the current understanding of the innate immune response in the hAM and hAF, which will aid in design of novel studies that may lead to breakthroughs in how we perceive the IAI.

/pdf/the-role-of-innate-immune-system-in-the-human-amniotic-5bdkxu0r1h.pdf

The Role of Innate Immune System in the Human Amniotic Membrane and Human Amniotic Fluid in Protection Against Intra-Amniotic Infections and Inflammation

Prevention of preterm birth: Proactive and reactive clinical practice-are we on the right track?

Cervicovaginal Cytokines to Predict the Onset of Normal and Preterm Labor: a Pseudo-longitudinal Study

Commentary on a combined approach to the problem of developing biomarkers for the prediction of spontaneous preterm labor that leads to preterm birth.

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Commentary on a combined approach to the problem of developing biomarkers for the prediction of spontaneous preterm labor that leads to preterm birth.