Examinando por Autor "Pardo, Fabián"

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    Gestational diabesity and foetoplacental vascular dysfunction
    (Wiley, 2021) Cornejo, Marcelo; Fuentes, Gonzalo; Valero, Paola; Vega, Sofía; Grismaldo, Adriana; Toledo, Fernando; Pardo, Fabián; Moore-Carrasco, Rodrigo; Subiabre, Mario; Casanello, Paola; Faas, Marijke M.; Van Goor, Harry; Sobrevia, Luis
    Gestational diabetes mellitus (GDM) shows a deficiency in the metabolism of D-glucose and other nutrients, thereby negatively affecting the foetoplacental vascular endothelium. Maternal hyperglycaemia and hyperinsulinemia play an important role in the aetiology of GDM. A combination of these and other factors predisposes women to developing GDM with pre-pregnancy normal weight, viz. classic GDM. However, women with GDM and prepregnancy obesity (gestational diabesity, GDty) or overweight (GDMow) show a different metabolic status than women with classic GDM. GDty and GDMow are associated with altered l-arginine/nitric oxide and insulin/adenosine axis signalling in the human foetoplacental microvascular and macrovascular endothelium. These alterations differ from those observed in classic GDM. Here, we have reviewed the consequences of GDty and GDMow in the modulation of foetoplacental endothelial cell function, highlighting studies describing the modulation of intracellular pH homeostasis and the potential implications of NO generation and adenosine signalling in GDty-associated foetal vascular insulin resistance. Moreover, with an increase in the rate of obesity in women of childbearing age worldwide, the prevalence of GDty is expected to increase in the next decades. Therefore, we emphasize that women with GDty and GDMow should be characterized with a different metabolic state from that of women with classic GDM to develop a more specific therapeutic approach for protecting the mother and foetus.
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    Using Machine Learning to Predict Complications in Pregnancy: A Systematic Review
    (Frontiers, 2022) Bertini, Ayleen; Salas, Rodrigo; Chabert, Steren; Sobrevia, Luis; Pardo, Fabián
    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.