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Background: COVID-19 symptoms vary widely among pregnant women. We aimed to assess the most frequent symptoms amongst pregnant women with SARS-CoV-2 infection in a tertiary hospital in Mexico City. Methods: A cross-sectional study of pregnant women attending the National Institute of Perinatology in Mexico City was performed. All women who attended the hospital, despite their symptoms, were tested for SARS-CoV-2. A multivariate-age-adjusted logistic regression was used to assess the association between the main outcome and each characteristic of the clinical history. Results: A total of 1880 women were included in the data analysis. Among all women, 30.74% (n = 578) had a positive PCR for SARS-CoV-2 from which 2.7 (n = 50) were symptomatic. Symptoms associated with a positive PCR result were headache (p=.01), dyspnea (p=.043), and myalgia (p=.043). Conclusions: At universal screening for SARS-CoV-2, one-third of the population had a positive result, while those symptoms associated with a positive PCR were headache, dyspnea, and myalgia. [ABSTRACT FROM AUTHOR]

Health systems and society are facing the growing problem of obesity and its accompanying comorbidities. New approaches to reduce these problems must be oriented to population groups in which long-lasting effects of interventions may occur. Biological processes occurring during the first 1000 days of life, which may be modulated by environmental modifications and result in phenotypes with differential risk for noncommunicable chronic disease, constitute an opportunity for interventions. The nutritional and general health conditions of pregnant women and the fetus, as well as toddlers, can be improved with interventions during the first 1000 days, offering pregnancy care, promoting breastfeeding, instructing on the use of complementary foods, and educating on the adequacy of the family dietary patterns for children. Evidence that interventions during this period result in promotion of children's growth and development, influencing the risk for development of obesity in infancy, is available. In this article, an ongoing program in Mexico City directed to offer continuum of care during the first 1000 days is described. [ABSTRACT FROM AUTHOR]

Maternal obesity has been related to adverse neonatal outcomes and fetal programming. Oxidative stress and adipokines are potential biomarkers in such pregnancies; thus, the measurement of these molecules has been considered critical. Therefore, we developed artificial neural network (ANN) models based on maternal weight status and clinical data to predict reliable maternal blood concentrations of these biomarkers at the end of pregnancy. Adipokines (adiponectin, leptin, and resistin), and DNA, lipid and protein oxidative markers (8-oxo-2'-deoxyguanosine, malondialdehyde and carbonylated proteins, respectively) were assessed in blood of normal weight, overweight and obese women in the third trimester of pregnancy. A Back-propagation algorithm was used to train ANN models with four input variables (age, pre-gestational body mass index (p-BMI), weight status and gestational age). ANN models were able to accurately predict all biomarkers with regression coefficients greater than R² = 0.945. P-BMI was the most significant variable for estimating adiponectin and carbonylated proteins concentrations (37%), while gestational age was the most relevant variable to predict resistin and malondialdehyde (34%). Age, gestational age and p-BMI had the same significance for leptin values. Finally, for 8-oxo-2'-deoxyguanosine prediction, the most significant variable was age (37%). These models become relevant to improve clinical and nutrition interventions in prenatal care. [ABSTRACT FROM AUTHOR]