Infection of Fetal Membranes with Mycobacterium tuberculosis and Tissue Processing to Isolate RNA for Expression Analysis.

This chapter outlines the methodology employed to infect the chorionic and amniotic membranes with Mycobacterium tuberculosis during pregnancy. Particularly, congenital tuberculosis, a rare and serious condition associated with cases in neonates and reactivation of latent tuberculosis in pregnant mothers, is interesting to study. Understanding the mechanisms of infection and the response of fetal membranes is crucial for developing effective treatments in these cases, which will promote better neonatal and maternal health in situations of tuberculosis during pregnancy. Establishing a standardized infection model in the chorioamniotic membranes is imperative, followed by a treatment protocol for isolating both cellular and mycobacterial RNA. This will enable the expression analysis during the maternal-fetal interface interaction with M. tuberculosis. The proposed methodology might be invaluable for qRT-PCR, microarrays, and sequencing research.

Neonatal Sepsis Diagnosis Decision-Making Based on Artificial Neural Networks.

Neonatal sepsis remains difficult to diagnose due to its non-specific signs and symptoms. Traditional scoring systems help to discriminate between septic or not patients, but they do not consider every single patient particularity. Thus, the purpose of this study was to develop an early- and late-onset neonatal sepsis diagnosis model, based on clinical maternal and neonatal data from electronic records, at the time of clinical suspicion. A predictive model was obtained by training and validating an artificial Neural Networks (ANN) algorithm with a balanced dataset consisting of preterm and term non-septic or septic neonates (early- and late-onset), with negative and positive culture results, respectively, using 25 maternal and neonatal features. The outcome of the model was sepsis or not. The performance measures of the model, evaluated with an independent dataset, outperformed physician's diagnosis using the same features based on traditional scoring systems, with a 93.3% sensitivity, an 80.0% specificity, a 94.4% AUROC, and a regression coefficient of 0.974 between actual and simulated results. The model also performed well-relative to the state-of-the-art methods using similar maternal/neonatal variables. The top 10 factors estimating sepsis were maternal age, cervicovaginitis and neonatal: fever, apneas, platelet counts, gender, bradypnea, band cells, catheter use, and birth weight.