Reassessing acquired neonatal intestinal diseases using unsupervised machine learning.

Background: Acquired neonatal intestinal diseases have an array of overlapping presentations and are often labeled under the dichotomous classification of necrotizing enterocolitis (which is poorly defined) or spontaneous intestinal perforation, hindering more precise diagnosis and research. The objective of this study was to take a fresh look at neonatal intestinal disease classification using unsupervised machine learning.

Methods: Patients admitted to the University of Florida Shands Neonatal Intensive Care Unit January 2013-September 2019 diagnosed with an intestinal injury, or had imaging findings of portal venous gas, pneumatosis, abdominal free air, or had an abdominal drain placed or exploratory laparotomy during admission were included.

Detection of SARS-CoV-2 IgA and IgG in human milk and breastfeeding infant stool 6 months after maternal COVID-19 vaccination.

Objective: Assess presence, durability, and neutralization capacity of SARS-CoV-2-specific antibodies in breastfeeding infants' stool, mother's plasma and milk following maternal vaccination.

Design: Thirty-seven mothers and 25 infants were enrolled between December 2020 and November 2021 for this prospective observational study. All mothers were vaccinated during lactation except three, which were vaccinated during pregnancy.

Detection of SARS-CoV-2 IgA and IgG in human milk and breastfeeding infant stool 6 months after maternal COVID-19 vaccination.

Assess the presence, durability, and neutralization capacity of SARS-CoV-2-specific antibodies in breastfeeding infants' stools, mother's plasma, and human milk following maternal vaccination. Thirty-seven mothers and 25 infants were enrolled between December 2020 and November 2021 for this prospective observational study. Human milk, maternal plasma, and infants' stools were collected pre-vaccination and at periods up to 6 months following COVID-19 vaccine series initiation/completion.