Syncytiotrophoblast stress in early onset preeclampsia: The issues perpetuating the syndrome

Carrasco-Wong, Ivo; Aguilera-Olguín, Macarena; Escalona-Rivano, Rodrigo; Chiarello, Delia I.; Barragán-Zúñiga, L. Jazel; Sosa-Macías, Martha; Galavíz-Hernández, Carlos; Martín, Sebastián San; Gutiérrez, Jaime

doi:10.1016/j.placenta.2021.05.002

Cited by 23 publications

(15 citation statements)

References 133 publications

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“…According to the literature, the high rate of preterm birth is a public health problem, since these newborns suffer substantial morbidity and mortality in the neonatal period, which translates to high medical costs ( McCormick et al, 2011 ). Pre-eclampsia is a pregnancy disorder characterized by the new onset of hypertension after 20 weeks gestation and organ damage with underlying causes being endothelial dysfunction ( ACOG (American College of Obstetricians and Gynecologists), 2020 ; Carrasco-Wong et al, 2021 ; Roberts, 1998 ). It is the leading cause of maternal and neonatal mortality and morbidity ( Salsoso et al, 2017 ; Fondjo et al, 2019 ).…”

Section: Discussionmentioning

confidence: 99%

Using Machine Learning to Predict Complications in Pregnancy: A Systematic Review

Bertini

Salas

Chabert

et al. 2022

Front. Bioeng. Biotechnol.

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Introduction: Artificial intelligence is widely used in medical field, and machine learning has been increasingly used in health care, prediction, and diagnosis and as a method of determining priority. Machine learning methods have been features of several tools in the fields of obstetrics and childcare. This present review aims to summarize the machine learning techniques to predict perinatal complications.Objective: To identify the applicability and performance of machine learning methods used to identify pregnancy complications.Methods: A total of 98 articles were obtained with the keywords “machine learning,” “deep learning,” “artificial intelligence,” and accordingly as they related to perinatal complications (“complications in pregnancy,” “pregnancy complications”) from three scientific databases: PubMed, Scopus, and Web of Science. These were managed on the Mendeley platform and classified using the PRISMA method.Results: A total of 31 articles were selected after elimination according to inclusion and exclusion criteria. The features used to predict perinatal complications were primarily electronic medical records (48%), medical images (29%), and biological markers (19%), while 4% were based on other types of features, such as sensors and fetal heart rate. The main perinatal complications considered in the application of machine learning thus far are pre-eclampsia and prematurity. In the 31 studies, a total of sixteen complications were predicted. The main precision metric used is the AUC. The machine learning methods with the best results were the prediction of prematurity from medical images using the support vector machine technique, with an accuracy of 95.7%, and the prediction of neonatal mortality with the XGBoost technique, with 99.7% accuracy.Conclusion: It is important to continue promoting this area of research and promote solutions with multicenter clinical applicability through machine learning to reduce perinatal complications. This systematic review contributes significantly to the specialized literature on artificial intelligence and women’s health.

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Section: Discussionmentioning

confidence: 99%

Using Machine Learning to Predict Complications in Pregnancy: A Systematic Review

Bertini

Salas

Chabert

et al. 2022

Front. Bioeng. Biotechnol.

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“…STBs are located in the outer layer of the placental villi and are in direct contact with maternal blood, forming a barrier between the mother and the fetus [14]. Besides, STBs have endocrine functions, secreting human chorionic gonadotropin, placental prolactin, and pregnancy-specific glycoproteins, and can transport nutrients and oxygen to the developing fetus [15,16].…”

Section: Trophoblastsmentioning

confidence: 99%

Human leukocyte antigens: the unique expression in trophoblasts and their crosstalk with local immune cells

Lin¹,

Xie²,

Zhao³

et al. 2022

Int. J. Biol. Sci.

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Trophoblasts differentiate and form the placenta during pregnancy in a complex and finely orchestrated process, which is dependent on the establishment of maternal-fetal immune tolerance and the proper function of trophoblasts. Trophoblasts express HLA-C and non-classical HLA-Ib molecules (HLA-E, HLA-F, and HLA-G). Numerous studies have shown that the unique expression pattern of the HLA molecules is closely linked to the successful acceptance of allogeneic fetus by the mother during pregnancy. However, some controversies still exist concerning the exact expression and recognition patterns of HLA molecules in different trophoblast subpopulations and cell lines. Thus, we summarize three types of trophoblast subpopulations as well as the common trophoblast lineages. Then, the classification and structural characteristics of HLA molecules were elucidated. Finally, the presence of HLA-C and non-classical HLA-Ib molecules (HLA-E, HLA-F, and HLA-G) in various trophoblasts and cell lines, as well as their potential role in establishing and maintaining normal pregnancy were also discussed. Together, this review will help people comprehensively understand the complex immune interactions between maternal and fetal crosstalk during pregnancy and ultimately better understand the physiological and pathological etiologies of pregnancy.

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“…The trafficking of cells between the mother and the fetus is a crucial component of the interaction between maternal and fetal immune cells (13,(23)(24)(25), highlighting the importance of maternal cells in "educating" the fetal immune system for the antigenic environment it will face in postnatal life [ (26), reviewed in (27,28)]. The presence of maternal immune cells at the maternal-fetal interface is accepted as a critical component of the physiology of gestation (29) and in pathological conditions such as hypertensive disorders of pregnancy, including preeclampsia (30)(31)(32).…”

Section: From Medawar's Paradox To Neuroimmunoendocrine Metabolic and Environmental Integrationmentioning

confidence: 99%

Editorial: Veterinary Reproductive Immunology

2022

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Syncytiotrophoblast stress in early onset preeclampsia: The issues perpetuating the syndrome

Cited by 23 publications

References 133 publications

Using Machine Learning to Predict Complications in Pregnancy: A Systematic Review

Using Machine Learning to Predict Complications in Pregnancy: A Systematic Review

Human leukocyte antigens: the unique expression in trophoblasts and their crosstalk with local immune cells

Editorial: Veterinary Reproductive Immunology

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