Preeclampsia is characterized by hypertension and proteinuria in pregnant women. Its exact cause is unknown. Preeclampsia increases the risk of maternal and fetal morbidity and mortality. Although delivery, often premature, is the only known cure, early targeted interventions may improve maternal and fetal outcomes. Successful intervention requires a better understanding of the molecular etiology of preeclampsia and the development of accurate methods to predict women at risk. To this end, we tested the role of miR-210, a miRNA up-regulated in preeclamptic placentas, in first-trimester extravillous trophoblasts. miR-210 overexpression reduced trophoblast invasion, a process necessary for uteroplacental perfusion, in an extracellular signal-regulated kinase/mitogen-activated protein kinase-dependent manner. Conversely, miR-210 inhibition promoted invasion. Furthermore, given that the placenta secretes miRNAs into the maternal circulation, we tested if serum expression of miR-210 was associated with the disease. We measured miR-210 expression in two clinical studies: a case-control study and a prospective cohort study. Serum miR-210 expression was significantly associated with a diagnosis of preeclampsia (P = 0.007, area under the receiver operator curves = 0.81) and was predictive of the disease, even months before clinical diagnosis (P < 0.0001, area under the receiver operator curve = 0.89). Hence, we conclude that aberrant expression of miR-210 may contribute to trophoblast function and that miR-210 is a novel predictive serum biomarker for preeclampsia that can help in identifying at-risk women for monitoring and treatment.

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ITK-SNAP is an interactive software tool for manual and semi-automatic segmentation of 3D medical images. This paper summarizes major new features added to ITK-SNAP over the last decade. The main focus of the paper is on new features that support semi-automatic segmentation of multi-modality imaging datasets, such as MRI scans acquired using different contrast mechanisms (e.g., T1, T2, FLAIR). The new functionality uses decision forest classifiers trained interactively by the user to transform multiple input image volumes into a foreground/background probability map; this map is then input as the data term to the active contour evolution algorithm, which yields regularized surface representations of the segmented objects of interest. The new functionality is evaluated in the context of high-grade and low-grade glioma segmentation by three expert neuroradiogists and a non-expert on a reference dataset from the MICCAI 2013 Multi-Modal Brain Tumor Segmentation Challenge (BRATS). The accuracy of semi-automatic segmentation is competitive with the top specialized brain tumor segmentation methods evaluated in the BRATS challenge, with most results obtained in ITK-SNAP being more accurate, relative to the BRATS reference manual segmentation, than the second-best performer in the BRATS challenge; and all results being more accurate than the fourth-best performer. Segmentation time is reduced over manual segmentation by 2.5 and 5 times, depending on the rater. Additional experiments in interactive placenta segmentation in 3D fetal ultrasound illustrate the generalizability of the new functionality to a different problem domain.

Objective To combine early, direct assessment of the placenta with indirect markers of placental development to identify pregnancies at greatest risk of delivering small-for-gestational age infants (SGA10). Methods We prospectively collected 3D-ultrasound volume sets, uterine artery pulsatility index (UtAPI) and maternal serum of singleton pregnancies at 11–14 weeks. Placental volume (PV), quotient (PQ=PV/gestational age), mean placental and chorionic diameters (MPD and MCD, respectively), and the placental morphology index (PMI=MPD/PQ and adjusts the lateral placental dimensions for quotient) were measured offline. Maternal serum was assayed for placental growth factor (PlGF) and placental protein-13 (PP13). These variables were evaluated as predictors of SGA10. Results Of the 578 pregnancies included in the study, 56 (9.7%) delivered SGA10. SGA10 pregnancies had a significantly smaller PV, PQ, MPD and MCD and higher PMI compared to normal pregnancies (P<0.001 for each). Each placental measure remained significantly associated with SGA10 after adjusting for confounders and significantly improved the performance of the model using clinical variables alone (P<0.04 for each) with adjusted AUCs ranging from 0.71 to 0.74. UtAPI did not remain significantly associated with SGA10 after adjusting for confounders (P=0.06). PlGF was significantly lower in SGA10 pregnancies (P=0.02) and remained significant in adjusted models, but failed to significantly improve the predictive performance of the models as measured by AUC (P>0.3). PP13 was not associated with SGA10 (P=0.99). Conclusions Direct assessment of placental size and shape with 3-dimensional ultrasound can serve as the foundation upon which to build a multivariable model for the early prediction of SGA.

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Elevated fetal fraction levels at 14.1-20.0 weeks' gestation were significantly associated with an increased incidence of preterm birth. Our findings warrant future exploration including validation in a larger, general population and investigation of the potential mechanisms that may be responsible for the initiation of preterm labor associated with increased fetal cell-free DNA.

With the increased use and quality of ultrasound in pregnancy, adenxal masses are being encountered with greater frequency. Fortunately, most of such masses are benign and resolve on their own. However, complications such as ovarian torsion can occur. In addition, a malignancy can be present in a small minority of cases. In this article, we review the available literature on this subject to help guide the clinician in the diagnosis and management of adnexal masses in pregnancy.

BACKGROUND: Access to prenatal care can be challenging due to physician shortages and rural geography. The multiple prenatal visits performed to collect basic fetal measurements lead to significant patient burden as well. The standard of care tools for fetal monitoring, external fetal heart rate monitoring with cardiotocography, as used today, must be applied by a medical professional in a healthcare setting. Novel tools to enable a remote and self-administered fetal monitoring solution would significantly alleviate some of the current barriers to care. OBJECTIVE: To compare maternal and fetal heart rate monitoring data obtained by 'Invu system' (a wireless, wearable, self-administered, fixedlocation device containing passive electrical and acoustic sensors) to cardiotocography, toward a true remote fetal monitoring solution. MATERIALS AND METHODS:A prospective, open-label, multicenter study evaluated concurrent use of Invu and cardiotocography in pregnant women, aged 18 to 50 years, with singleton pregnancies !32þ0 weeks' gestation (NCT03504189). Simultaneous recording sessions from Invu and cardiotocography lasted for !30 minutes. Data from the 8 electrical sensors and 4 acoustic sensors in the Invu belt were acquired, digitized, and sent wirelessly for analysis by an algorithm on cloud-based servers. The algorithm validates the data, preprocesses the data to remove noise, detects heartbeats independently from the two data sources (electrical and acoustic), and fuses the detected heartbeat arrays to calculate fetal heart rate (FHR) and maternal heart rate (MHR). The primary performance endpoint was Invu FHR limit of agreement within AE 10 beats per minute (bpm) of FHR measured with cardiotocography.RESULTS: A total of 147 women were included in the study analysis. The mean (SD) maternal age was 31.8 AE6.9 years, and the mean gestational age was 37.7 AE2.3 weeks. There was a highly significant correlation between FHR measurements from Invu and cardiotocography (r ¼ 0.92; P<0.0001). The 95% limits of agreement for the difference, the range within which most differences between the two measurements will lie, were -8.84 bpm to 8.24 bpm. Invu measurements of MHR were also very similar to cardiotocography and were highly significantly correlated (r ¼ 0.97; P<0.0001). No adverse events were reported during the study. CONCLUSION: Although captured by very different methods, the FHR and MHR outputs wirelessly obtained by the Invu system through passive methods were very similar to those obtained by the current standard of care. The limits of agreement for FHR measured by Invu were within a clinically acceptable AE 8 bpm of cardiotocography FHR. The Invu device uses passive technology to allow for safe, non-invasive and convenient monitoring of patients in the clinic and remotely. Further work should investigate how remote perinatal monitoring could best address some of the recent challenges seen with prenatal care and maternal and fetal outcomes.