Angiotensin II type-1 receptor autoantibodies and effects in neonates of women with preeclampsia.

Background: Maternal agonistic autoantibodies against the angiotensin II type 1 receptor (AT1-AAs) have been implicated in the pathophysiology of preeclampsia, but their presence in their offsprings and their possible neonatal effects have not been specifically explored. This prospective study aimed to evaluate the presence of AT1-AAs and their potential clinical effects in neonates of AT1-AAs positive mothers.

Methods: Women with preeclampsia and their neonates were included.

The effects of maternal voice on pain during placement of peripherally inserted central catheter in neonates.

Background: Peripherally inserted central catheter (PICC) are a necessary procedure for preterm newborns care. Despite the use of analgesic treatments, its insertion can be painful. Our objective was to study the effect of maternal voice on pain during PICC insertion.

Vaginal screening for group B streptococcus using PCR in pregnant women with unknown colonization status: Impact on newborn monitoring for early-onset sepsis.

Background: Early-onset neonatal sepsis represents a diagnostic challenge, as it is a cause of neonatal mortality and morbidity. Guidelines for the prevention of group B streptococcus (GBS) infection recommend that all pregnant women must be screened for GBS carriage at the end of pregnancy, with intrapartum antibiotic prophylaxis being provided for GBS carriers. If vaginal culture is not available, GBS polymerase chain reaction (GBS-PCR) is an alternative option for this type of screening.

Application of machine-learning models to predict the ganciclovir and valganciclovir exposure in children using a limited sampling strategy.

Intravenous ganciclovir and oral valganciclovir display significant variability in ganciclovir pharmacokinetics, particularly in children. Therapeutic drug monitoring currently relies on the area under the concentration-time (AUC). Machine-learning (ML) algorithms represent an interesting alternative to Maximum-a-Posteriori Bayesian-estimators for AUC estimation.

A Machine Learning Algorithm to Predict the Starting Dose of Daptomycin.

Background And Objective: The dosage of daptomycin is usually based on body weight. However, it has been shown that this approach yields too high an exposure in obese patients. Pharmacokinetic and pharmacodynamic indexes (PK/PD) have been proposed for daptomycin's antibacterial effect (AUC/CMI >666) and toxicity (C0 > 24.