The perinatal consequences of SARS-CoV-2 infection are still largely unknown. This study aimed to describe the features and outcomes of pregnant women with or without SARS-CoV-2 infection after the universal screening was established in a large tertiary care center admitting only obstetric related conditions without severe COVID-19 in Mexico City. This retrospective case-control study integrates data between April 22 and May 25, 2020, during active community transmission in Mexico, with one of the highest COVID-19 test positivity percentages worldwide. Only pregnant women and neonates with a SARS-CoV-2 result by quantitative RT-PCR were included in this study. Among 240 pregnant women, the prevalence of COVID-19 was 29% (95% CI, 24% to 35%); 86% of the patients were asymptomatic (95% CI, 76%-92%), nine women presented mild symptoms, and one patient moderate disease. No pregnancy baseline features or risk factors associated with severity of infection, including maternal age > 35 years, Body Mass Index >30 kg/m2, and pre-existing diseases, differed between positive and negative women. The median gestational age at admission for both groups was 38 weeks. All women were discharged at home without complications, and no maternal death was reported. The proportion of preeclampsia was higher in positive women than negative women (18%, 95% CI, 10%-29% vs. 9%, 95% CI, 5%-14%, P<0.05). No differences were found for other perinatal outcomes. SARS-CoV-2 test result was positive for nine infants of positive mothers detected within 24h of birth. An increased number of infected neonates were admitted to the NICU, compared to negative neonates (44% vs. 22%, P<0.05) and had a longer length of hospitalization (2 [2–18] days vs. 2 [2–3] days, P<0.001); these are potential proxies for illness severity. This report highlights the importance of COVID-19 detection at delivery in pregnant women living in high transmission areas.

(1) Background: The relationship between enteral nutrition and neonatal necrotizing enterocolitis (NEC) among premature neonates is still unclear. The present work was designed to assess the relationship between NEC and feeding strategies compared to control infants. (2) Methods: A retrospective case-control study of premature infants (<35 weeks’ gestation) with or without NEC that examined feeding practices and clinical characteristics at birth and 3, 7, and 14-day hospitalization, with a longitudinal and cross-sectional analysis. (3) Results: A total of 100 newborns with NEC diagnosis and 92 neonates without the disease with similar demographic and clinical characteristics were included. The median day of NEC diagnosis was 15 days (Interquartile Range (IQR) 5–25 days). A significantly higher number of neonates that were fasting on days 7 and 14 developed NEC (p < 0.05). In the longitudinal analysis, generalized linear and mixed models were fit to evaluate NEC association with feeding strategies and showed that exclusive mother’s own milk (MM) and fortified human milk (FHM) across time were significantly less likely associated with NEC (p < 0.001) and that enteral fasting was positively related with NEC. In the cross-sectional analysis, a binary logistic regression model was fit and predicted 80.7% of NEC cases. MM was also found to correlate with a reduced risk for NEC (OR 0.148, 95% CI 0.044–0.05, p = 0.02), and in particular, on day 14, several factors were related to a decreased odd for NEC, including birth weight, antenatal steroids, and the use of FHM (p < 0.001). (4) Conclusions: MM and FHM were associated with less NEC compared to fasting on days 7 and 14. Feeding practices in Neonatal Intensive Care Units (NICUs) should promote exclusive MM across the two-week critical period as a potential guideline to improve NEC outcome.

(1) Background: Size at birth is an important early determinant of health later in life. The prevalence of small for gestational age (SGA) newborns is high worldwide and may be associated with maternal nutritional and metabolic factors. Thus, estimation of fetal growth is warranted. (2) Methods: In this work, we developed an artificial neural network (ANN) model based on first-trimester maternal body fat composition, biochemical and oxidative stress biomarkers, and gestational weight gain (GWG) to predict an SGA newborn in pregnancies with or without obesity. A sensibility analysis to classify maternal features was conducted, and a simulator based on the ANN algorithm was constructed to predict the SGA outcome. Several predictions were performed by varying the most critical maternal features attained by the model to obtain different scenarios leading to SGA. (3) Results: The ANN model showed good performance between the actual and simulated data (R2 = 0.938) and an AUROC of 0.8 on an independent dataset. The top-five maternal predictors in the first trimester were protein and lipid oxidation biomarkers (carbonylated proteins and malondialdehyde), GWG, vitamin D, and total antioxidant capacity. Finally, excessive GWG and redox imbalance predicted SGA newborns in the implemented simulator. Significantly, vitamin D deficiency also predicted simulated SGA independently of GWG or redox status. (4) Conclusions: The study provided a computational model for the early prediction of SGA, in addition to a promising simulator that facilitates hypothesis-driven constructions, to be further validated as an application.