Publications by authors named "Yejin Park"

15 Publications

  • Page 1 of 1

The Feasibility of Cervical Elastography in Predicting Preterm Delivery in Singleton Pregnancy with Short Cervix Following Progesterone Treatment.

Int J Environ Res Public Health 2021 02 19;18(4). Epub 2021 Feb 19.

Division of Maternal-Fetal Medicine, Department of Obstetrics and Gynecology, Institute of Women's Medical Life Science, Yonsei University College of Medicine, Yonsei University Health System, Seoul 03722, Korea.

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 <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 <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 <32 weeks, respectively (adjusted for cervical length (CL) at diagnosis; = 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 ( = 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 <32 weeks of gestation.
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http://dx.doi.org/10.3390/ijerph18042026DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7922916PMC
February 2021

Automated ultrasound assessment of amniotic fluid index using deep learning.

Med Image Anal 2021 04 7;69:101951. Epub 2021 Jan 7.

School of Mathematics and Computing (Computational Science and Engineering), Yonsei University, Seoul 03722, Republic of Korea.

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.
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http://dx.doi.org/10.1016/j.media.2020.101951DOI Listing
April 2021

Applications of Converged Various Forces for Detection of Biomolecules and Novelty of Dielectrophoretic Force in the Applications.

Sensors (Basel) 2020 Jun 7;20(11). Epub 2020 Jun 7.

Department of Medical Biotechnology, Dongguk University, Seoul 04620, Korea.

Since separation of target biomolecules is a crucial step for highly sensitive and selective detection of biomolecules, hence, various technologies have been applied to separate biomolecules, such as deoxyribonucleic acid (DNA), protein, exosome, virus, etc. Among the various technologies, dielectrophoresis (DEP) has the significant advantage that the force can provide two different types of forces, attractive and repulsive DEP force, through simple adjustment in frequency or structure of microfluidic chips. Therefore, in this review, we focused on separation technologies based on DEP force and classified various separation technologies. First, the importance of biomolecules, general separation methods and various forces including DEP, electrophoresis (EP), electrothermal flow (ETF), electroosmosis (EO), magnetophoresis, acoustophoresis (ACP), hydrodynamic, etc., was described. Then, separating technologies applying only a single DEP force and dual force, moreover, applying other forces simultaneously with DEP force were categorized. In addition, advanced technologies applying more than two different kinds of forces, namely complex force, were introduced. Overall, we critically reviewed the state-of-the-art of converged various forces for detection of biomolecules with novelty of DEP.
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http://dx.doi.org/10.3390/s20113242DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7309140PMC
June 2020

Recapitulation of the accessible interface of biopsy-derived canine intestinal organoids to study epithelial-luminal interactions.

PLoS One 2020 17;15(4):e0231423. Epub 2020 Apr 17.

Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX, United States of America.

Recent advances in canine intestinal organoids have expanded the option for building a better in vitro model to investigate translational science of intestinal physiology and pathology between humans and animals. However, the three-dimensional geometry and the enclosed lumen of canine intestinal organoids considerably hinder the access to the apical side of epithelium for investigating the nutrient and drug absorption, host-microbiome crosstalk, and pharmaceutical toxicity testing. Thus, the creation of a polarized epithelial interface accessible from apical or basolateral side is critical. Here, we demonstrated the generation of an intestinal epithelial monolayer using canine biopsy-derived colonic organoids (colonoids). We optimized the culture condition to form an intact monolayer of the canine colonic epithelium on a nanoporous membrane insert using the canine colonoids over 14 days. Transmission and scanning electron microscopy revealed a physiological brush border interface covered by the microvilli with glycocalyx, as well as the presence of mucin granules, tight junctions, and desmosomes. The population of stem cells as well as differentiated lineage-dependent epithelial cells were verified by immunofluorescence staining and RNA in situ hybridization. The polarized expression of P-glycoprotein efflux pump was confirmed at the apical membrane. Also, the epithelial monolayer formed tight- and adherence-junctional barrier within 4 days, where the transepithelial electrical resistance and apparent permeability were inversely correlated. Hence, we verified the stable creation, maintenance, differentiation, and physiological function of a canine intestinal epithelial barrier, which can be useful for pharmaceutical and biomedical researches.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0231423PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7164685PMC
July 2020

Prediction model development of late-onset preeclampsia using machine learning-based methods.

PLoS One 2019 23;14(8):e0221202. Epub 2019 Aug 23.

Department of Internal Medicine, College of Medicine, Institute of Kidney Disease Research, Yonsei University, Seoul, Korea.

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, naïve 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, naïve 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.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0221202PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6707607PMC
March 2020

Direct Patterning of a Carbon Nanotube Thin Layer on a Stretchable Substrate.

Micromachines (Basel) 2019 Aug 11;10(8). Epub 2019 Aug 11.

Department of Medical Biotechnology, Dongguk University, Seoul 04620, Korea.

Solution-based direct patterning on an elastomer substrate with meniscus-dragging deposition (MDD) enables fabrication of very thin carbon nanotube (CNT) layers in the nanometer scale (80-330 nm). To fabricate the CNT pattern with CNT solution, contact angle, electrical variation, mechanical stress, and surface cracks of elastomer substrate were analyzed to identify the optimal conditions of O treatment (treatment for 30 s with RF power of 50 W in O atmosphere of 50 sccm) and mixture ratio between Ecoflex and polydimethylsiloxane (PDMS) (Ecoflex:PDMS = 5:1). The type of mask for patterning of the CNT layer was determined through quantitative analysis for sharpness and uniformity of the fabricated CNT pattern. Through these optimization processes, the CNT pattern was produced on the elastomer substrate with selected mask (30 μm thick oriented polypropylene). The thickness of CNT pattern was also controlled to have hundreds nanometer and 500 μm wide rectangular and circular shapes were demonstrated. Furthermore, the change in the current and resistance of the CNT layer according to the applied strain on the elastomer substrate was analyzed. Our results demonstrated the potential of the MDD method for direct CNT patterning with high uniformity and the possibility to fabricate a stretchable sensor.
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http://dx.doi.org/10.3390/mi10080530DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6722655PMC
August 2019

Automatic evaluation of fetal head biometry from ultrasound images using machine learning.

Physiol Meas 2019 07 1;40(6):065009. Epub 2019 Jul 1.

Deepnoid Inc., Seoul 03722, Republic of Korea.

Objective: Ultrasound-based fetal biometric measurements, such as head circumference (HC) and biparietal diameter (BPD), are frequently used to evaluate gestational age and diagnose fetal central nervous system pathology. Because manual measurements are operator-dependent and time-consuming, much research is being actively conducted on automated methods. However, the existing automated methods are still not satisfactory in terms of accuracy and reliability, owing to difficulties dealing with various artefacts in ultrasound images.

Approach: Using the proposed method, a labeled dataset containing 102 ultrasound images was used for training, and validation was performed with 70 ultrasound images.

Main Results: A success rate of 91.43% and 100% for HC and BPD estimations, respectively, and an accuracy of 87.14% for the plane acceptance check.

Significance: This paper focuses on fetal head biometry and proposes a deep-learning-based method for estimating HC and BPD with a high degree of accuracy and reliability.
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http://dx.doi.org/10.1088/1361-6579/ab21acDOI Listing
July 2019

CD133+/C-kit+Lin endothelial progenitor cells in fetal circulation demonstrate impaired differentiation potency in severe preeclampsia.

Pregnancy Hypertens 2019 Jan 31;15:146-153. Epub 2018 Dec 31.

Division of Maternal-Fetal Medicine, Department of Obstetrics and Gynecology, Institute of Women's Medical Life Science, Placenta-derived Stem Cell Genomic Research Lab, Yonsei University College of Medicine, Seoul, Republic of Korea. Electronic address:

Objectives: 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.

Study Design: The capacity of CD133/C-kit/Lin (CKL) human umbilical cord blood endothelial progenitor cells (EPCs) derived from gestationally matched normal and preeclamptic (n = 10 each) pregnancies to differentiate to form outgrowth endothelial cells (OECs) was assessed by observing both their morphology, and the number and size of generated OECs colonies. Likewise, OECs angiogenic function was evaluated via migration, adhesion, and tube-formation assays. EPCs from preeclampsia were cultured in normal-, and preeclampsia-derived serum-conditioned media to assess the effects of environmental factors on EPC differentiation potency and OEC angiogenic function, and finally, EPCs H3K4, H3K9, and H3K27 trimethylation levels were assayed.

Results: The preeclampsia-derived CKL EPCs exhibited decreased H3K4 and H3K9 trimethylation levels, significantly delayed differentiation times, and a significant reduction in both their number of generated OECs colonies, and exhibited reduced OECs migration, adhesion, and tube formation activities compared to those achieved by the normal-derived EPCs. Interestingly, the reduced differentiation potency of the preeclampsia-derived EPCs was not rescued via exposure to normal serum.

Conclusions: Exposure to preeclampsia significantly and irreversibly reduced CKL EPC differentiation potency and OEC angiogenic function, likely reflecting incurred irreversible epigenetic changes.
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http://dx.doi.org/10.1016/j.preghy.2018.12.005DOI Listing
January 2019

Cesarean section does not increase the prevalence of allergic disease within 3 years of age in the offsprings.

Obstet Gynecol Sci 2019 Jan 27;62(1):11-18. Epub 2018 Nov 27.

Department of Obstetrics and Gynecology, Institute of Women's Life Medical Science, Seoul, Korea.

Objective: In this study, we evaluated the prevalence of allergic disease in offsprings delivered via the delivery modes of vaginal delivery vs. planned Cesarean section vs. Cesarean section with labor.

Methods: This study included 175 mother-neonate pairs from Severance Hospital who were enrolled in the Cohort for Childhood Origin of Asthma and allergic diseases study. Information regarding prenatal environmental factors, delivery, and diagnosis of allergic diseases was obtained from a questionnaire and medical record review. Patients with at least 3 years of follow-up data were included in this study. Results were adjusted for sex, birth weight, gestational age at birth, season of birth, neonatal intensive care unit admission, parity, breastfeeding, and maternal factors.

Results: A total of 175 offsprings were eligible for analysis. Among the subjects, 52.0% were delivered by vaginal delivery, 34.3% by planned Cesarean section, and 16.6% by Cesarean section with labor. Fifty-nine offsprings (33.7%) were diagnosed with allergic disease at a median age of 1 year (range 0.5-3 years). The prevalence of allergic disease was not associated with delivery mode after adjusting for confounding variables. Time period from membrane rupture to delivery, duration of the active phase, and the beginning of the pelvic division prior to Cesarean section were not associated with allergic disease development in offsprings.

Conclusion: Cesarean section, irrespective of the occurrence of labor before surgery, did not increase the prevalence of allergic disease in infants up to 3 years of age.
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http://dx.doi.org/10.5468/ogs.2019.62.1.11DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6333761PMC
January 2019

Machine-learning-based automatic identification of fetal abdominal circumference from ultrasound images.

Physiol Meas 2018 10 22;39(10):105007. Epub 2018 Oct 22.

Department of Computational Science and Engineering, Yonsei University, Seoul 03722, Republic of Korea.

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 [Formula: see text] for AC measurement and an accuracy of 87.10% for our acceptance check of the fetal abdominal standard plane.
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http://dx.doi.org/10.1088/1361-6579/aae255DOI Listing
October 2018

Automatic Estimation of Fetal Abdominal Circumference From Ultrasound Images.

IEEE J Biomed Health Inform 2018 09 21;22(5):1512-1520. Epub 2017 Nov 21.

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.
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http://dx.doi.org/10.1109/JBHI.2017.2776116DOI Listing
September 2018

Abnormal lymphatic vessel development is associated with decreased decidual regulatory T cells in severe preeclampsia.

Am J Reprod Immunol 2018 07 14;80(1):e12970. Epub 2018 May 14.

Department of Obstetrics and Gynecology, Institute of Women's Life Medical Science, Yonsei University College of Medicine, Yonsei University Health System, Seoul, Korea.

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.
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http://dx.doi.org/10.1111/aji.12970DOI Listing
July 2018

Prenatally detected thoracic neuroblastoma.

Obstet Gynecol Sci 2018 Mar 30;61(2):278-281. Epub 2018 Jan 30.

Department of Obstetrics and Gynecology, Institute of Women's Life Medical Science, Yonsei University College of Medicine, Seoul, Korea.

Neuroblastoma is the most common pediatric extracranial solid tumor derived from primitive neural crest cells of the sympathetic nervous system. Although one-fifths of all neuroblastomas occurs within the thorax, thoracic neuroblastomas detected in fetus have been rarely reported. We report a case of fetal thoracic neuroblastoma with massive pleural effusion detected with prenatal ultrasonography. A 34-year-old Korean second-gravida was referred to our hospital at 30 weeks of gestation for evaluation, after the right lung mass found in the fetus. Approximately 3 cm, well-defined, hyperechoic mass was found in the right thorax with right pleural effusion, with the initial suspicion of teratoma. However, as mass continued to grow with deteriorating pleural effusion and fetal hydrops, the mass was considered malignant after 3 weeks. After a cesarean delivery, an approximately 4 cm mass with peripheral calcification and hemothorax was found on neonatal ultrasonography. Neuroblastoma was diagnosed on excision biopsy.
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http://dx.doi.org/10.5468/ogs.2018.61.2.278DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5854910PMC
March 2018

Preeclampsia Increases the Incidence of Postpartum Cerebrovascular Disease in Korean Population.

J Korean Med Sci 2018 Feb 5;33(6):e35. Epub 2018 Feb 5.

Department of Obstetrics and Gynecology, Yonsei University College of Medicine, Seoul, Korea.

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.
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http://dx.doi.org/10.3346/jkms.2018.33.e35DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5777915PMC
February 2018

Current status of infrastructures of obstetrics and gynecology in South Korea.

Obstet Gynecol Sci 2015 Sep 22;58(5):435-7. Epub 2015 Sep 22.

Department of Obstetrics and Gynecology, Institute of Women's Life Medical Science, Yonsei University College of Medicine, Seoul, Korea.

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http://dx.doi.org/10.5468/ogs.2015.58.5.435DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4588854PMC
September 2015
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