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.
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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