Novel method of real-time PCR-based screening for common fetal trisomies

Background The non-invasive prenatal test (NIPT) is based on next generation sequencing (NGS) and is used for screening for fetal trisomy. However, it is time-consuming and technically difficult. Recently, peptide nucleic acid (PNA) probe-based real-time polymerase chain reaction (RT-PCR) was developed. This study aimed to examine the performance of the RT-PCR-based NIPT for screening of common fetal trisomies Methods From stored maternal plasma, RT-PCR was performed using Patio™ NIPT Detection Kit. In melting curve analysis, the height of melting peaks of target chromosome and reference chromosome was calculated as a peak ratio. The adjusted peak ratio of 8 markers with correction factors in each target chromosome was summated and calculated to z-score. The cut-off value for each target chromosome was established for classification (low risk vs. high risk for trisomy) whose performance was obtained in the validation phase. Results 330 plasma samples from pregnant women with normal fetus and 22 trisomy cell-line samples were used to establish the optimal cut-off values for z-score of each target chromosome. In the validation phase, 1023 samples from pregnant women including 22 cases with fetal trisomy and 1001 cases of normal control were used. The RT-PCR-based NIPT showed 95.45% sensitivity [95% confidence interval (CI) 77.16–99.88%], 98.60% specificity (95% CI 97.66–99.23%), and 98.53% accuracy (95% CI 97.59–99.18%) for the identification of trisomy 21, 18, or 13. Of 1023 samples, fifteen cases were mismatched for classification [one case as a false negative (false negative rate: 4.5%) and 14 cases as false positives (false positive rate: 1.4%)]. Conclusion The RT-PCR-based NIPT showed high sensitivity and specificity for the detection of common fetal trisomies and it could be a feasible alternative to NGS-based NIPT.This study was supported by the Technology Innovation Program (or Industrial Strategic Technology Development Program) (N0002392) funded by the Ministry of Trade, Industry & Energy (MOTIE, Korea). And this work was funded by grants (HI16C0628) from the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health and Welfare, Republic of Korea. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

Risk of vertical transmission of human papillomavirus throughout pregnancy: a prospective study.

OBJECTIVE: Much controversy still exists about maternal-to-infant transmission of human papillomavirus (HPV) infection, specifically about the magnitude of the risk and the route and timing of such vertical transmission. This prospective cohort study examines the risk of vertical transmission of maternal HPV in each trimester of pregnancy. STUDY DESIGN: One hundred fifty three healthy pregnant women were followed longitudinally throughout pregnancy and cervical swabs obtained in each trimester and postpartum for HPV detection. Cord blood, neonatal nasopharyngeal aspirates, and placental biopsies were collected at delivery. DNA isolation, polymerase chain reaction, and hybridization were performed using the GG HPV Genotyping Chip Kit (Goodgene Inc., Seoul, Korea). Detection of HPV in neonates was defined as the presence of HPV DNA in either cord blood or neonatal nasopharyngeal aspirate. RESULTS: HPV DNA was detected in 14%(22/153) of healthy women in the first trimester, 18%(22/124) in the second trimester, and 10%(15/153) in the third trimester; 24%(37/153) were positive for HPV DNA on at least one occasion in pregnancy. At birth, 5.2%(8/153) of neonates were HPV DNA positive. Seven of these eight infants were born to HPV-positive mothers. Placental HPV DNA was positive in 3.3%(5/152) of cases, and all five cases were from mothers with at least one HPV-positive test. Detection of HPV DNA in neonates was associated with detection of HPV in mothers during any of the three trimesters of pregnancy. CONCLUSION: HPV DNA was detected at birth in 5.2%(8/153) of neonates born to healthy women, and was associated with the detection of HPV in mothers during any of the three trimesters of pregnancy

HPV DNA detection in neonates according to the presence or absence of HPV DNA in mothers during the antenatal period.

<p>HPV DNA (-): Mothers with no detectable HPV DNA at any point during pregnancy. 1^st/2^nd trimester (+)→3^rd trimester (−): Mothers with detectable HPV DNA during either the 1^st or 2^nd trimester, which cleared in the 3^rd trimester. 3^rd trimester (+): Mothers with detectable HPV DNA during the 3^rd trimester. * p<0.05 compared with mothers without detectable HPV DNA during pregnancy. HPV: Human Papillomavirus.</p

Characteristics of the study population.

†<p>values are given as median (range).</p><p>HPV: Human Papillomavirus.</p

Association between HPV status of the neonates and the detection of HPV in the mothers.

*<p>One placental sample was not sent for HPV DNA testing.</p>†<p>Adjusted for maternal age, smoking, alcohol use, and mode of delivery.</p><p>HPV: Human Papillomavirus.</p

Cases in which HPV DNA was detected in neonates.

<p>High-risk HPV are in bold.</p><p>NA: not available, CD: cesarean delivery, VD: vaginal delivery.</p><p>HPV: Human Papillomavirus.</p

Nonalcoholic fatty liver disease is a risk factor for large-for-gestational-age birthweight.

ObjectiveNonalcoholic fatty liver disease (NAFLD) is a well-recognized hepatic manifestation of metabolic disease in adults and has been associated with the development of gestational diabetes (GDM). Hepatic insulin resistance can result in increased release of glucose (from gluconeogenesis) and free fatty acids (due to enhanced lipolysis), which can lead in turn to fetal overgrowth. However, the relationship between maternal metabolic factors (such as circulating levels of triglycerides, free fatty acids [FFA], or adipokines) and excessive fetal birthweight in NAFLD has not been carefully examined. In this study, we evaluated the relationship between NAFLD and the subsequent risk of large-for-gestational-age (LGA) birthweight.MethodSingleton nondiabetic pregnant women were evaluated for the presence of fatty liver at 10-14 weeks of gestation by abdominal ultrasound. The degree of fatty liver was classified as Grade 0-3 steatosis. At the time of liver ultrasound, maternal blood was taken after fasting and measured for adiponectin and FFA. LGA was defined as birthweight >90th percentile for gestational age.ResultsA total of 623 women were included in the analysis. The frequency of LGA was 10.9% (68/623), and the frequency of NAFLD was 18.9%. The risk of LGA increased significantly in patients with Grade 2-3 steatosis in the first trimester. The relationship between Grade 2-3 steatosis and LGA remained significant after adjustment for maternal age, pre-pregnancy BMI, GDM, and maternal serum triglyceride levels. The concentration of maternal blood adiponectin at 10-14 weeks was significantly lower in cases with LGA than non-LGA, but the maternal blood FFA concentrations were not different between the groups.ConclusionThe presence of Grade 2-3 steatosis on ultrasound in early pregnancy was associated with the increased risk of delivering an LGA infant, even after adjustment for multiple confounding factors including GDM. Adiponectin may be the linking biomarker between NAFLD and LGA

Correction: Nonalcoholic fatty liver disease is a risk factor for large-for-gestational-age birthweight.

[This corrects the article DOI: 10.1371/journal.pone.0221400.]

Nonalcoholic fatty liver disease and early prediction of gestational diabetes mellitus using machine learning methods

Background/Aims: To develop an early prediction model for gestational diabetes mellitus (GDM) using machine learning and to evaluate whether the inclusion of nonalcoholic fatty liver disease (NAFLD)-associated variables increases the performance of model. Methods: This prospective cohort study evaluated pregnant women for NAFLD using ultrasound at 10-14 weeks and screened them for GDM at 24-28 weeks of gestation. The clinical variables before 14 weeks were used to develop prediction models for GDM (setting 1, conventional risk factors; setting 2, addition of new risk factors in recent guidelines; setting 3, addition of routine clinical variables; setting 4, addition of NALFD-associated variables, including the presence of NAFLD and laboratory results; and setting 5, top 11 variables identified from a stepwise variable selection method). The predictive models were constructed using machine learning methods, including logistic regression, random forest, support vector machine, and deep neural networks. Results: Among 1,443 women, 86 (6.0%) were diagnosed with GDM. The highest performing prediction model among settings 1-4 was setting 4, which included both clinical and NAFLD-associated variables (area under the receiver operating characteristic curve [AUC] 0.563-0.697 in settings 1-3 vs. 0.740-0.781 in setting 4). Setting 5, with top 11 variables (which included NAFLD and hepatic steatosis index), showed similar predictive power to setting 4 (AUC 0.719-0.819 in setting 5, P=not significant between settings 4 and 5). Conclusions: We developed an early prediction model for GDM using machine learning. The inclusion of NAFLD-associated variables significantly improved the performance of GDM prediction.Y