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Previous studies demonstrated an association between cervical strain and risk of spontaneous preterm delivery (sPTD). The present study aimed to assess the efficacy of elastography in predicting sPTD at &lt, 32 weeks of gestation in women with singleton pregnancies receiving progesterone for short cervix (≤2.5 cm) diagnosed between 16 and 28 weeks of gestation Among 115 participants eligible for analysis, nine had sPTD at &lt, 32 weeks. Preprogesterone (PP0) mean internal os strain (IOS), elasticity contrast index (ECI), hardness ratio (HR), one-week postprogesterone (PP1) IOS, mean external os strain (EOS), ECI, and HR were significantly different between groups. Higher PP0 IOS, PP1 IOS, and PP1 EOS were associated with a 2.92, 4.39 and 3.65-fold increase in the risk of sPTD at &lt, 32 weeks, respectively (adjusted for cervical length (CL) at diagnosis, p = 0.04, 0.012 and 0.026, respectively). A combination of CL at diagnosis, PP0 IOS and PP1 EOS showed a significantly higher area under the receiver operating characteristic curve (0.858) than that of CL alone (p = 0.041). In women with singleton pregnancies receiving progesterone for short cervix, cervical elastography performed before and one week after progesterone treatment may be useful in predicting sPTD at &lt, 32 weeks of gestation.

Inflammation is an important part of human response to pathogen to recruit immune cells and remove infected or damaged cells from the system. However, immune response should be fine tuned as uncontrolled proinflammatory response leads to extensive tissue damage and organ failure. Immune homeostasis at the maternal-fetal interface is crucial in the maintenance of healthy pregnancy. TAM receptor tyrosine kinase is known to be present in immune, nervous, and reproductive systems and has been reported to have biological function in the immunological aspect including cell debris clearance, cytokine secretion modulation, and regulation of innate immunity. The presence of TAM receptors at the maternal-fetal interface has been reported and its role in pregnancy has been in question. In the present review, functional aspects of TAM receptors, clinical implications in other diseases in an immunological context, and what is known about association of TAM receptors in complicated pregnancy are discussed with suggestion of potential area of future research.

Preeclampsia is one of the leading causes of maternal and fetal morbidity and mortality. Due to the lack of effective preventive measures, its prediction is essential to its prompt management. This study aimed to develop models using machine learning to predict late-onset preeclampsia using hospital electronic medical record data. The performance of the machine learning based models and models using conventional statistical methods were also compared. A total of 11,006 pregnant women who received antenatal care at Yonsei University Hospital were included. Maternal data were retrieved from electronic medical records during the early second trimester to 34 weeks. The prediction outcome was late-onset preeclampsia occurrence after 34 weeks' gestation. Pattern recognition and cluster analysis were used to select the parameters included in the prediction models. Logistic regression, decision tree model, naive Bayes classification, support vector machine, random forest algorithm, and stochastic gradient boosting method were used to construct the prediction models. C-statistics was used to assess the performance of each model. The overall preeclampsia development rate was 4.7% (474 patients). Systolic blood pressure, serum blood urea nitrogen and creatinine levels, platelet counts, serum potassium level, white blood cell count, serum calcium level, and urinary protein were the most influential variables included in the prediction models. C-statistics for the decision tree model, naive Bayes classification, support vector machine, random forest algorithm, stochastic gradient boosting method, and logistic regression models were 0.857, 0.776, 0.573, 0.894, 0.924, and 0.806, respectively. The stochastic gradient boosting model had the best prediction performance with an accuracy and false positive rate of 0.973 and 0.009, respectively. The combined use of maternal factors and common antenatal laboratory data of the early second trimester through early third trimester could effectively predict late-onset preeclampsia using machine learning algorithms. Future prospective studies are needed to verify the clinical applicability algorithms.

The estimation of antenatal amniotic fluid (AF) volume (AFV) is important as it offers crucial information about fetal development, fetal well-being, and perinatal prognosis. However, AFV measurement is cumbersome and patient specific. Moreover, it is heavily sonographer-dependent, with measurement accuracy varying greatly depending on the sonographer's experience. Therefore, the development of accurate, robust, and adoptable methods to evaluate AFV is highly desirable. In this regard, automation is expected to reduce user-based variability and workload of sonographers. However, automating AFV measurement is very challenging, because accurate detection of AF pockets is difficult owing to various confusing factors, such as reverberation artifact, AF mimicking region and floating matter. Furthermore, AF pocket exhibits an unspecified variety of shapes and sizes, and ultrasound images often show missing or incomplete structural boundaries. To overcome the abovementioned difficulties, we develop a hierarchical deep-learning-based method, which consider clinicians' anatomical-knowledge-based approaches. The key step is the segmentation of the AF pocket using our proposed deep learning network, AF-net. AF-net is a variation of U-net combined with three complementary concepts - atrous convolution, multi-scale side-input layer, and side-output layer. The experimental results demonstrate that the proposed method provides a measurement of the amniotic fluid index (AFI) that is as robust and precise as the results from clinicians. The proposed method achieved a Dice similarity of 0.877±0.086 for AF segmentation and achieved a mean absolute error of 2.666±2.986 and mean relative error of 0.018±0.023 for AFI value. To the best of our knowledge, our method, for the first time, provides an automated measurement of AFI.

Individuals delivered from preeclamptic pregnancies exhibit a long-term increased risk of developing cardiovascular and metabolic diseases, likely caused by aberrant fetal cell reprogramming incurred in utero. The present study investigated the functional impairment and epigenetic changes exhibited by endothelial progenitor cells derived from offspring born to preeclamptic pregnancies.The capacity of CD133The preeclampsia-derived CKLExposure to preeclampsia significantly and irreversibly reduced CKL

Problem The lymphatic vasculature controls leukocytes trafficking and limits the adaptive immune response. In previous models of preeclampsia (PE), defective immune function caused by disruption of lymphangiogenesis was shown to be involved in the disease pathophysiology. Especially, the dysfunction of regulatory T cells (Treg) at the maternal-fetal interface may be one of the causes of severe PE. In particular, activation of Tregs to obtain immune tolerance requires adequate antigen presentation through the lymphatic system. We hypothesized that impaired lymphangiogenesis and imbalanced Tregs at the maternal-fetal interface are associated with the pathophysiology of severe PE. However, the current research addressing this hypothesis is limited. Therefore, to compare differences in lymphangiogenesis in severe PE and normal conditions, we aimed to examine the location of lymphatics at the maternal-fetal interface and to investigate the association between lymphangiogenesis and Tregs in severe PE. Method of study We obtained entire uterus from normal pregnant mice. Placental and fetal membranes, including decidua, were obtained from 10 pregnant women with severe PE and 10 gestational age-matched controls. Immunohistochemistry for LYVE1 was used to localize the distribution of lymphatic vessels and CD4, CD25, and FOXP3 for Treg. Results LYVE1-positive vessels were present in the uterine wall of mice. LYVE1-positive lymphatic vessels were localized on the human decidua. Tubular lymphatics were abundant in the control decidua, but significantly reduced in severe PE. Furthermore, lymphatic vessel density correlated with the number of decidual Tregs. Conclusion Abnormal decidual lymphangiogenesis is associated with reduced numbers of decidual Tregs in severe PE.

Background Multiple studies have been reported regarding preeclampsia as a possible risk factor of cerebrovascular disease (CVD). However, the correlation of preeclampsia and CVD, whether it is a cause-effect relationship or they are sharing common predisposing condition, is not well understood. Therefore, the aim of this study was to investigate the association between the preeclampsia during pregnancy and development of postpartum CVD. Methods A total of 1,384,550 Korean women who had a delivery between January 1, 2010 and December 31, 2012, were enrolled. Women with the risk of CVD within 1 year prior to pregnancy were excluded based on the Charlson comorbidity index. Primary endpoint was the event of CVD within a year from delivery. After exclusion, 1,075,061 women were analyzed. Results During the follow-up of 1 year postpartum, there were 25,577 preeclampsia out of 1,072,041 women without postpartum CVD (2.39%), and 121 of 3,020 women with postpartum CVD had preeclampsia before delivery (4.01%). In multivariate logistic regression analysis, women who had preeclampsia during pregnancy showed a higher risk for postpartum CVD (odds ratio, 1.64; 95% confidence interval, 1.37–1.98). Conclusion The incidence of CVD after delivery was higher in women who had preeclampsia during pregnancy., Graphical Abstract

Ultrasound diagnosis is routinely used in obstetrics and gynecology for fetal biometry, and owing to its time-consuming process, there has been a great demand for automatic estimation. However, the automated analysis of ultrasound images is complicated because they are patient specific, operator dependent, and machine specific. Among various types of fetal biometry, the accurate estimation of abdominal circumference (AC) is especially difficult to perform automatically because the abdomen has low contrast against surroundings, nonuniform contrast, and irregular shape compared to other parameters. We propose a method for the automatic estimation of the fetal AC from two-dimensional ultrasound data through a specially designed convolutional neural network (CNN), which takes account of doctors' decision process, anatomical structure, and the characteristics of the ultrasound image. The proposed method uses CNN to classify ultrasound images (stomach bubble, amniotic fluid, and umbilical vein) and Hough transformation for measuring AC. We test the proposed method using clinical ultrasound data acquired from 56 pregnant women. Experimental results show that, with relatively small training samples, the proposed CNN provides sufficient classification results for AC estimation through the Hough transformation. The proposed method automatically estimates AC from ultrasound images. The method is quantitatively evaluated and shows stable performance in most cases and even for ultrasound images deteriorated by shadowing artifacts. As a result of experiments for our acceptance check, the accuracies are 0.809 and 0.771 with expert 1 and expert 2, respectively, whereas the accuracy between the two experts is 0.905. However, for cases of oversized fetus, when the amniotic fluid is not observed or the abdominal area is distorted, it could not correctly estimate AC.

Objective: Obstetricians mainly use ultrasound imaging for fetal biometric measurements. However, such measurements are cumbersome. Hence, there is urgent need for automatic biometric estimation. Automated analysis of ultrasound images is complicated owing to the patient-specific, operator-dependent, and machine-specific characteristics of such images. Approach: This paper proposes a method for the automatic fetal biometry estimation from 2D ultrasound data through several processes consisting of a specially designed convolutional neural network (CNN) and U-Net for each process. These machine learning techniques take clinicians' decisions, anatomical structures, and the characteristics of ultrasound images into account. The proposed method is divided into three steps: initial abdominal circumference (AC) estimation, AC measurement, and plane acceptance checking. Main results: A CNN is used to classify ultrasound images (stomach bubble, amniotic fluid, and umbilical vein), and a Hough transform is used to obtain an initial estimate of the AC. These data are applied to other CNNs to estimate the spine position and bone regions. Then, the obtained information is used to determine the final AC. After determining the AC, a U-Net and a classification CNN are used to check whether the image is suitable for AC measurement. Finally, the efficacy of the proposed method is validated by clinical data. Significance: Our method achieved a Dice similarity metric of for AC measurement and an accuracy of 87.10% for our acceptance check of the fetal abdominal standard plane.